advances in biomedical research selected topicsbc.wydawnictwo-tygiel.pl/public/assets/288/advances...

Advances in Biomedical Research

– selected topics

Advances in Biomedical Research

– selected topics

Editors:

Łukasz Biały

Izabela Młynarczuk-Biały

Lublin 2018

Reviewers:

dr hab. n. med. Elżbieta Rębas, prof. nadzw.

dr hab. Marcin Grąz

dr hab. Roman Paduch

dr n. o zdr. Mariola Janiszewska

dr Maciej Frant

dr n. med. Paweł Kiciński

dr n. o zdr. Magdalena Skórzewska

All published chapters received positive reviews.

Typesetting:

Monika Maciąg

Design:

Marcin Szklarczyk

© Copyright by Wydawnictwo Naukowe TYGIEL sp. z o.o.

ISBN 978-83-65932-55-6

Publisher:

Wydawnictwo Naukowe TYGIEL sp. z o.o.

ul. Głowackiego 35/341, 20-060 Lublin

www.wydawnictwo-tygiel.pl

Table of Contents

Introduction .......................................................................................................................... 7

Application of biosensors in cancer research ...................................................................... 9

Detection of newly synthesized proteins in cancer cells: A practical approach ............... 22

Direct visualization of undegraded protein sequestration within aggresomes

by proteasome inhibitors ................................................................................................... 36

Novel podophyllotoxin derivatives as Anticancer Agents: Design, Synthesis,

and Biological Screening ................................................................................................... 48

Immunohistochemical expression of erythropoietin in invasive breast carcinoma

with metastasis to lymph nodes ........................................................................................ 62

The inflammatory components in lung cancer .................................................................. 80

Normalisation of microRNA qPCR results in lung cancer – strategies and problems .... 92

Statistical comparison of deleteriousness prediction methods for single nucleotide

variants from next generation sequencing ....................................................................... 101

Amnion as a potential source of neural and glial cell precursors – from cell culture to

a practical application ...................................................................................................... 109

What makes the bacterial vaginosis difficult to diagnose: our experience in Silesia,

Poland .............................................................................................................................. 119

Detection of sleepiness in narcolepsy .............................................................................. 130

Increased incidence of CNS tumours in Parkinson Disease .......................................... 139

Role of vitamin D in etiology of mood disorders – literature review ............................ 147

Index of authors ............................................................................................................... 154

Introduction

The monograph you hold is the result of the meetings and hard work of young

scientists from various universities and institutes in Poland. These meetings have

been held in Warsaw from 9 years under the auspices of the Department of Histology

and Embryology of the Medical University of Warsaw and for the last two years also

the Warsaw Branch of the Polish Society for Histochemistry and Cytochemistry.

In the monograph we present the most important topics in biomedical research from

2017. All presented works have passed the peer-review process positively. These

works come from various fields of biomedicine from medical biology throughout

biophysics to clinical sciences.

Wishing you enjoyable and productive reading

Editors,

Łukasz Biały

Izabela Młynarczuk-Biały

9

Application of biosensors in cancer research

Anna Sobiepanek*, Tomasz Kobiela

Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664

Warsaw Poland

*Corresponding author: Anna Sobiepanek, Faculty of Chemistry, Warsaw University

of Technology, [email protected]

Abstract

Cancer research is based mainly on identifying many features of cells including mutations in DNA

or RNA, changes in proteins and protein levels as well as on comparing cells’ properties like

morphology, adhesion or elasticity.

The most interesting techniques for biological applications, among different microscopies, are those

using biosensors. As an analytical device, biosensor enables the binding of the selected analyte to the

examined biological material (e.g. tissue, cells, proteins, RNA, DNA). They can be classified according

to their transducer type or the biorecognition elements placed on the sensor. Most analysis performed on

biosensors require the labeling of the analyte with a specific marker. However, there are also many

techniques that allow a direct detection of analytes without prior labeling. The common “label-free”

biosensor technologies are the quartz crystal microbalance (QCM) and the surface plasmon resonance

(SPR).

In this review we would like to focus on the currently described examples of biosensors used

as diagnostic and prognostic tools for cancer development.

Introduction

The first person to use the term ‘biosensor’ was the Professor of Analytical

Chemistry Karl Cammann in 1977. However, the definition of biosensor was not

specified by the International Union of Pure and Applied Chemistry (IUPAC) until

1997. It was designed to combine three areas of science (chemistry, biology and

engineering) in one device to detect bioanalytical molecules from samples (Islam,

Uddin, 2017).

For the construction of a biosensor two components are unavoidable: the transducer

and the biorecognition element placed on the surface of the sensor. Transducer

transforms the biochemical response appearing on the sensor surface to a measurable

output signal. Some commonly used types of transducers are calorimetric (thermal),

electrochemical (amperometric, impedimetric, potentiometric), magnetic (electro-

magnetic, electrodynamic and piezomagnetic), mass sensitive (acoustic, piezo-

electric) and optical (colorimetric, photometric) based systems (Bora, Sett et al.

2013, Lenk, Ballas et al. 2011, Zhang, Yang et al.2013). In table 1 different

techniques using transducer types are gathered. On the other hand, the biorecognition

element (bioreceptor) placed on the sensor ensures capturing the matching analyte

from the solution sample. They may be classified as elements with biocatalytic

properties (enzymes/substrate) or component with specific affinities (antibodies/

antigens, nucleic acids, receptors/ligands, cells/tissues). The detection of some

metabolic or biological components is also possible (Islam, Uddin, 2017, Pihíková,

Kasák et al. 2015, Keshavarz, Behpour et al. 2015).

Anna Sobiepanek, Tomasz Kobiela

10

Table 1. A set of example methods used in bioanalytical techniques T

ech

niq

ues

Calorimetric thermal biosensors

enzyme thermistor (ET)-based

sensors

(Perumal,

Hashim, 2014)

metal oxide thermistors-based

sensors

ceramic semiconductor

thermopile–based sensors

Electrochemical

amperometric first-/second-/third-generation

biosensors

(Borgmann,

Schulte et al.

2011)

voltammetric

cyclic voltammetry (CV)

(Pihíková,

Kasák et al.

2015)

differential pulse voltammetry

(DPV)

square-wave voltammetry

(SWV)

potentiometric ion-selective electrodes (ISE) (Perumal,

Hashim, 2014) ion-sensitive field

Magnetic

magnetic

nanoparticles (MNPs);

magnetic beads

(MBs);

semiconductor

quantum dots (QDs)

magnetoresistance (MR)-based

biosensors

(Llandro,

Palfreyman et

al. 2010)

anisotropic magnetoresistance

(AMR)-based biosensors

giant magnetoresistance

(GMR)-based biosensors

Mass sensitive

bulk acoustic wave

(BAW) devices

shear horizontal acoustic plate

mode (SH-APM) sensor

(Durmuş, Lin

et al. 2008)

quartz crystal microbalance

(QCM)

quartz crystal microbalance in

dissipation mode (QCM-D)

surface acoustic wave

sensors (SAW)

capacitive micromachined

ultrasonic transducers

(CMUTs)

microcantilevers microelectronic mechanical

systems (MEMs)

(Prasad,

Shameem,

2016)

Optical interferometric

changes

resonant mirror (RM)

(Fang, 2006) resonant waveguide grating

(RWG)

surface plasmons resonance

(SPR)

(Zhang, Yang

et al. 2013)


11

surface plasmons resonance

imaging (SPRi)

fluorescence

fluorescence resonance energy

transfer (FRET)

fluorescent semiconductor-

based sensors

chemiluminescence /

luminescence

luminescent semiconductor-

based sensors

Biosensor measurement methodology determines the type of detection: label-free or

non-label free. Label-free detection is based on binding the original and unmodified

analyte molecule directly to the biorecognition element, whereas in some methods

(amperometric, voltammetric or fluorescent experiments) only analyte molecules

tagged with a label may be recognized by the biorecognition element to obtain an

electroactive signal (Fritz, 2008). Some commonly used labels are fluorophores

(especially for fluorescent microscopy), enzymes (often for Western blot analysis)

and several nanoparticles (essential for magnetic resonance imaging; MRI). For

medical applications the usage of magnetic nanoparticles (MNPs), magnetic beads

(MB), semiconductor quantum dots (QDs) with combined biosensor techniques is

increasingly justified. Metal nanoparticles (mainly gold or silver) have a significant

affinity for cancer cells, that is why they are frequently used in cancer research

(Llandro, Palfreyman et al. 2010, Keshavarz, Behpour et al. 2015). Specific markers

are often introduced into the tested compound using chemical synthesis or genetic

engineering methods. However, the labeling process may require additional sample

preparation or must be followed by a second molecule binding. Unfortunately, the

attachment of the label may significantly alter the properties of the tested molecule;

substances used as markers may attach to other molecules than the target, and when

using living cells, they may interfere with their metabolism. Considering all the

above, label-free methods gain much more attention (ErtuğruL, Uygun, 2013).

Nowadays, popular label-free methods are quartz crystal microbalance (QCM),

surface plasmon resonance (SPR) and microelectronic mechanical (MEM)

cantilevers, where cantilever sensors emerged from the atomic force microscopy

(AFM) (Fritz, 2008) (Fig. 1). These techniques allow tracking and determining the

kinetic/ thermodynamic analysis of the interaction process of two complemental

molecules in real time, where one molecule is immobilized on the surface and the

second one is in flow. Moreover, QCM and MEM-cantilever use changes in resonant

frequency to observe the mass shifts on the sensor (Sobiepanek, Milner-Krawczyk et

al. 2017, Wang, Wang et al. 2014), but SPR utilizes changes in the refractive index

of thin metal layers (like gold surfaces) to quantify the binding process of

biomolecule to the sensor surface (Ahmed, Wiley et al. 2010).


12

Figure 1. Schematic illustration of the three selected label-free methods: SPR, QCM and AFM

microcantilever (own elaboration)

The development of reliable and sensitive molecular electronic devices is a matter of

great importance in the field of biotechnology, medicine and pharmacy (Pihíková,

Kasák et al. 2015). Especially in case of diagnostics, biosensors may significantly

facilitate and accelerate early detection of several diseases like the Parkinson and

Alzheimer disease, diabetes or various types of cancer. The monitoring of clinical

treatment may also be performed via the usage of properly designed biosensors

(Parolo, Merkoçi, 2013). Body fluids such as urine, blood, saliva, tears or sweat may

be treated also as samples full of specific disease biomarkers for micro/

nanotechnology-based techniques (SPR, QCM, microcantilevers), even though their

concentration in these samples is often very low (Pasinszki, Krebsz et al. 2017,

Wang, Wang et al. 2014). These methods are non-destructive for samples in

comparison to others like Western blot, mass spectrometry (MS), matrix-assisted

laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) or

electrospray ionization mass spectrometry (ESI-MS) (Ahmed, Wiley et al. 2010). On

the other hand, isolation of the selected biomarkers from patient’s tissue or cells (Fig.

2) may simplify their utilization as analytes on a biomolecular-based biosensor

(DNA-, RNA-, aptamer-, protein-, antibody-based biosensors). Identifying mutations

in DNA/RNA or uncovering changes in proteins and protein levels are common

diagnostic strategies for cancers, though they require expensive and time-consuming

tests performed in well-equipped laboratories, as well as good samples quality and an

appropriate sample size. The most popular microarrays for these analyses are the

quantitative polymerase chain reaction (qPCR) and enzyme-linked immunosorbent

assay (ELISA). Nonetheless, experiments executed on whole cells or even tissues are

far more reliable because they reflect the real conditions prevailing in the human

body (Islam, Uddin, 2017). Studying cancerous cell properties with advanced


13

nanotechnological methods can bring many important biophysical information about

these cells and may improve the knowledge about metastasis. Optical and fluorescent

microscopies are often used for the examination of cell morphology, as well as the

topography imaging with atomic force microscopy. What is more, AFM may be

applied for studies concerning cell elasticity and adhesion, properties that change

during the cancer progression, and even molecular interactions (Sobiepanek, Milner-

Krawczyk et al. 2017). For the adhesion and molecular interaction QCM

investigations can also be adjusted, not only with the molecular-based biosensors, but

also with immobilized cells or even tissues on sensors. Yet, those cell-/tissue-based

biosensors have to be properly prepared (Perumal, Hashim, 2014).

Figure 2. Schematic illustration of various biomarkers as targets for cancer detection with the biosensors

methodology (own elaboration)

New insight

Biosensors may be applied successfully in the medical field, especially in cancer

research. Due to the rising number of cancer cases each year all over the world,

investigations concerning early cancer detection becomes a vital matter. Also, the

possibility of cancer treatment monitoring with biosensor techniques gives hope for

a personalized therapy. That is why providing a more sensitive and simplified

method at lower costs, which brings even more information about the basis of the

disease, is still desired (Abu-Salah, Zourob et al. 2015). For these advanced studies

some special molecular-based or cell-/tissue-based biosensors have already been

developed and will be briefly characterized in the following sections.

Nucleic acid-based (NABs) biosensors

Some typical NABs are deoxyribonucleic acids (DNA), ribonucleic acids (RNA),

peptide nucleic acids (PNA) and aptamers. During the investigation the nucleic acid

should be immobilized on the surface of the sensor by adsorption, biotin-avidin

interaction, covalent bonding, entrapment in a polymer matrix, ionic interaction or


14

self-assembly (Abu-Salah, Zourob et al. 2015). The most frequently used

immobilization method includes the application of thiolated-NABs for creating

a self-assembly monolayer on a gold sensor surface. The single-stranded DNA and

RNA sequences could anneal to the immobilized complementary sequences and the

occurring interaction depends on the molecules. For DNA and RNA sequences

binding, the Chargaff's rules of base pairing is fulfilled (DNA: A=T, C≡G; RNA:

A=U, C≡G). On these bases, related to cancer mutations in DNA or RNA may be

revealed (Bora, Sett et al. 2013). Very interesting molecules for cell research are

microRNAs (miRNAs), which are naturally existing small non-coding ribonucleic

acids (RNA). They play a significant role in cell development (proliferation, cell

cycle progression, apoptosis) and is related to a number of cancer cases. miRNA may

be extracted from cells or tissues, however, their amount in the cancer cells differs

from that of normal cells (Kilic, Topkaya et al. 2012, Zhang, Chua et al. 2009).

PNAs are synthetic DNA or RNA analogues (sugar-phosphate backbone is replaced

by pseudo-peptide backbone) that bind to their complementary strands with higher

specificity and strength. Aptamers may be classified into two groups: DNA- or RNA-

aptamers (short oligonucleotides) and peptide-aptamers (short peptide domains), but

their detection is more similar to antigen-antibody or receptor-ligand interactions.

They can be easily modified or integrated with a variety of nanomaterials (Bora, Sett

et al. 2013, Sohrabi, Valizadeh et al. 2016).

DNA-DNA binding: Breast cancer is associated with various gene mutations like

breast cancer 1 (BRCA1). Its detection in the concentration range of 10 and 100

μM is possible due to the designed electrochemical biosensor. Short

oligonucleotide DNA was immobilized onto zinc oxide nanowires that was

chemically synthesized onto gold electrode via the hydrothermal technique. The

hybridization of ssDNA was studied by the differential pulse voltammetry

(DPV) (Mansor, Zain et al. 2014).

RNA-RNA binding: The detection of mir21 from the total RNA breast cancer

samples was carried out on a selective and sensitive enzyme-based

electrochemical biosensor. mir21 was covalently attached onto the pencil

graphite electrode (PGE) by coupling agents and the hybridization was achieved

with a biotinylated complementary target. Next an avidin labeled alkaline

phosphatase was introduced to the system for obtaining the biotin-avidin

interaction. Through the enzymatic conversion of the reaction substrate alpha

naphtol phosphate to the reaction product alpha naphtol (α-NAP) the oxidation

signal was detected by Differential Pulse Voltammetry (DPV) (Kilic, Topkaya

et al. 2012).

PNA-RNA binding: In order to detect let-7b in the total RNA extracts from HeLa

cells (human epithelial cervical cancer) via base pairing, the silicon nanowire

field-effect transistors (SiNW-FETs) with immobilized complementary PNA

were used. With the optimized assay the detection limit of 1 fM could be

obtained (Zhang, Chua et al. 2009).


15

DNA aptamer-antigen binding: One of the well-known biomarkers for prostate

cancer is the prostate-specific antigen (PSA) present in blood samples. The

functionalization of a gold sensor with thiolated-DNA aptamer enabled the

detection of PSA with the quartz crystal microbalance in dissipation mode

(QCM-D) with the affinity constant equal 37 nM. These experiments provided

not only information about the amount of PSA bound to the sensor, but also

information about the aptamer conformation and layer hydration (Formisano,

Jolly et al. 2015).

DNA aptamer-cells-nanoparticles binding: Blood cancer is a very aggressive

type of cancer disease. Leukemia cells may be selectively captured by special

DNA aptamers immobilized on the QCM sensor and the signal may be

increased after the application of gold nanoparticles (AuNPs) on the already

attached cells (Shan, Pan et al. 2014).

The biomolecule-based biosensors

The noncovalent, purely physicochemical binding forces are involved in many

specific interactions like antigen (Ag)-antibody (Ab), enzyme-substrate, lectin-

carbohydrate and ligand-receptor (van Oss, 2000). These interactions may be based

on hydrogen bonds, van der Waals forces and hydrophobic interactions (Coelho,

Silva et al. 2017). To be more precise, some conventional biochemical methods are

the basis of these interactions, especially ELISA or immunofluorescence assay on

Ag-Ab specific binding (Zhou, Wang et al. 2011). However, these methods require

several steps of preparation (proper sample/antibody concentration, washing stages,

sample labeling). Label-free techniques based on biomolecular biosensors (for

example, antibody-/lectin-/protein-based) may not only accelerate the interaction

measurement, but also provide full analysis of the interaction with the affinity

calculation for the examined compound.

Antibody-antigen binding: Three liver cancer antigens: alpha-fetoprotein (AFP),

hepatocyte growth factor (HGF) and gamma-glutamyltransferase-2 (GGT-2);

were detected with high specificity as well as good precision by the novel

microcantilever with immobilized antibodies (Wang, Wang et al. 2014). Also,

the p53 antibody accumulates in human serum for many types of cancer

including breast, lung, prostate, ovarian and melanoma. A quantitative detection

of p53 antibody ranging from 20 ng/ml to 20 μg/ml was obtained for human

serum samples with p53 antigen-coated microcantilever (Zhou, Wang et al.

2011). Moreover, three independent SiNW-FET devices were designed, on

which different antibodies were immobilized for the detection in pg/ml scale of

PSA, carcinoembryonic antigen (CEA) and mucin-1 from blood samples (Chen,

Li et al. 2011).

Antibody-protein binding: Vimentin protein indicates the presence of

Osteosarcoma, a common type of bone cancer. Immobilized anti-vimentin

antibody on the surface of the MEM-cantilever was successfully used in the

early detection of this cancer (Balwir, Sahare et al. 2016). At the same time,

collagen type IV (COLIV) occurs in serum samples of patients with colorectal,


16

gastric, lung, liver and breast cancers. The immobilization of anti-COLIV

antibody to the Surface Plasmon Resonance imaging (SPRi) gold sensor gave

a dynamic response in molecular binding in the range between 10 and 300 ng/ml

for COLIV (Sankiewicz, Lukaszewski et al. 2016). The laminin-5 protein

functions as a motility factor or as an adhesive factor, depending on the

proteolytic processing state. During the interactions with several cell-surface

receptors it may promote tumor invasion. An antibody-based SPRi biosensor

was used to determine the laminin-5 concentration in blood plasma with the

detection limit 4 pg/ml (Sankiewicz, Romanowicz et al. 2016).

Lectin-carbohydrate binding: Binding kinetics of lectin-carbohydrate

interactions gains attention, due to the fact that cancer cells during their

progression change their glycosylation profile what could be a possible drug

target. Two mannose specific lectins (Lens culinaris and Concanavalin A, Con

A) were immobilized onto gold QCM-D sensors separately via thiol groups and

next carboxypeptidase Y was introduced in the buffer solution. The analysis of

the lectin to carbohydrate affinity may serve as a quick biomarker classification

assay in cancer research (Senkara-Barwijuk, Kobiela et al. 2012). Furthermore,

an interesting application of lectin-based sensors was achieved for cells in

suspension. The modification of QCM sensor with Con A induced the binding

of human leukemia cell line, which then was followed by the attachment of the

second lectin on top of the cells. This approach may lead to the development of

a novel label-free suspension cell-based biosensor (Li, Pei et al. 2013).

Protein-DNA binding: With the SPR method it was also possible to semi-

quantitatively detect the UV-irradiated DNA sequence obtained from human

cell extracts. The DNA sequence was biotinylated and captured onto

a streptavidin-coated sensor chip (Ahmed, Wiley et al. 2010). Protein-DNA

binding can also be investigated by the DNA functionalized (SiNW-FET)

biosensor. The estrogen receptor alpha (ERα, protein) regulates gene expression

by the direct attachment to estrogen receptor sequences (ERE, dsDNA)

immobilized on the sensor, which may be used for detecting these protein-DNA

interactions in nuclear extracts from breast cancer cells. The designed biosensor

was capable of detecting ERα in the 10 fM concentration (Zhang, Huang et al.

2011).

Cell-/tissue-based biosensors

Methods in which you could apply whole cells deliver information which is more

congenial than the techniques using samples isolated from cells or tissues (for

example, DNA/RNA, proteins). This is due to the isolation process, which might

damage or change the conformation of the biomolecules, influence the concentration

of the required analyte or the stability of the sample. What is more, measurements

performed on whole cell-based biosensors may simulate processes taking place in

physiological conditions. Cells seeded onto sensors may be assigned for experiments

with living cells (for compound absorption tests) or fixed cells (like compound

binding ability tests) (Min, Yea et al. 2009). Cells in vitro are typically cultured on


17

polystyrene or glass surfaces, often on precoated surfaces with proteins from the

extracellular matrix (ECM) like fibronectin, collagen, laminin, vitronectin; but rarely

on metals such as titanium, zirconium or gold (Depprich, Ommerborn et al. 2008,

Ryan, 2008). Bare gold sensors, common for QCM and SPR methods, are not quite

desirable surfaces for cell culture experiments due to non-specific adsorption of

many biological molecules like DNA and proteins that may be used as analytes (Tan,

Lin et al. 2014). That is why gold sensors are frequently coated with polystyrene or

silicon dioxide. Nevertheless, surface modification plays an important role in the up

or down regulation of physiological processes of cells such as adhesion, proliferation

and differentiation (Wang, Ren et al. 2013). Monitoring of the cell attachment and

spreading may be crucial for designing devices that control the behavior of living

cells. Simultaneously, it could bring a more detail knowledge about the metastasis

process. Adhesion of cells is a complex process, which begins with cell

sedimentation followed by the formation of nonspecific cell–substrate interactions

and next the establishment of specific molecular binding of the receptor–ligand type.

With time cell adhesion to the surface alters due to the ongoing physiological

processes. To measure the cell-type-specific interactions, QCM-D technique can be

applied (Iturri, García-Fernández et al. 2015, Wegener, Janshoff et al. 2001). Some

examples of cell-/tissue-based biosensor applications are:

Compound absorption tests on cells: The combination of whole cell sensing and

real-time label-free monitoring of nanoparticle uptake by cells can be obtained

by means of the SPR technique. The uptake kinetic of selected nanoparticles has

already been tested on HeLa cells in the μg/mL concentrations. However, this

process is temperature-dependent: for about 20 °C the uptake is higher, whereas

for 37 °C it is lower (Suutari, Silen et al. 2016).

Compound binding ability tests on cells: Two stages of human colorectal cancer

cells were derived from the same patient (primary and metastases), seeded onto

a gold QCM sensor coated with polystyrene and lectin–carbohydrate interaction

was measured with lectin Helix pomatia agglutinin (HPA). At the end a higher

affinity of HPA to metastatic cells was obtained (Peiris, Markiv et al. 2012).

Also, the glycosylation level of melanocytes and melanoma cells (cultured on

QCM-D gold sensors coated with polystyrene) was investigated by lectin Con

A. The study revealed that mannose and glucose types of oligosaccharides

present on metastatic melanoma cells consists of long and branched structures,

whereas primary tumor cells and normal cells have short and less ramified

oligosaccharides. Furthermore, the affinity of Con A to oligosaccharides on

metastatic melanoma cells was ten times higher than for primary tumor cells and

melanocytes (Sobiepanek, Milner-Krawczyk et al. 2017). Cell-based biosensors

may be also used for cancer drug tests. Antibody-conjugated drug Herceptin

detects the human epidermal growth factor receptor 2 (HER2) protein, which is

over-expressed in 25–30% of breast cancers. It induces a cytostatic effect

associated with the G1 phase cell cycle arrest, as well as antibody dependent

cell-mediated cytotoxity (Peiris, Spector et al. 2017). On the other hand, the G

protein-coupled receptors (GPCRs) are crucial drug targets that may be


18

activated by histamine, respectively. The SPR study has shown a triphasic

response of HeLa cells to histamine interaction: 1 – GPCRs triggered calcium

release, 2 – alternations of cell-matrix adhesion after the activation of Protein

Kinase C, 3 – dynamic mass redistribution in cells (Lu, Yang et al. 2016).

Surprisingly, up till now only few tissue-based biosensors have been described.

Tonsil, prostate and breast tumor specimens were obtained and immobilized on

the surface of gold QCM sensor. Next the interaction between the rVAR2

protein and placental-like chondroitin sulfate present on most cancer cells was

analyzed and the calculated affinity was in the nanomolar range (Clausen,

Pereira et al. 2016).

Cell adhesion tests: These strategies may be applied for the characterization of

cell membrane receptors activity in cancer cells and for the search of other cell-

specific ligands. For example, cell attachment to the surface is controlled mainly

by the cell transmembrane integrin receptor that binds to Arg-Gly-Asp (RDG)

sequence. The QCM-D sensor was modified with a photo-activatable RGD

peptide for determining the time point of presentation of adhesive ligand from

the human umbilical vein endothelial cells (HUVEC) (Iturri, García-Fernández

et al. 2015). Also, with the use of a novel high-throughput label-free resonant

waveguide grating (RWG) imager the HeLa cells spreading kinetics on the

ligand RGD tripeptide was determined (Orgovan, Peter et al. 2014). On the

other hand, vitronectin protein- as well as antibody (CA-125)-based QCM

biosensors were used for the binding of the suspended melanoma, cervix and

ovarian cancer cells (Ishay, Kapp-Barnea et al. 2015).

Review and discussion

Cancer could develop at a very rapid pace, therefore the simplicity of measurements,

quickness of the test and low costs are in request from the potential new methods that

are to be applied. For this reason, biosensor techniques, especially those with label-

free detection, have gained massive attention recently. Their main assumption is the

specific interaction occurring between the biorecognition element and the selected

analyte. What is important, some of these measurements (like those performed on

QCM-D device) can be made on living or fixed cells and may deliver kinetic and

thermodynamic analysis of the obtained interaction, as well as the information about

the affinity, conformation of the created complex and even viscoelastic properties of

the new appearing biomolecular surface. A wide variety of biosensors is available

among the transducer type and the biorecognition element alike. This is why

biosensors may have a versatile application.

Acknowledgements

This work was supported by the Faculty of Chemistry, Warsaw University of

Technology and by the National Science Centre (Poland), project no

2017/27/N/ST4/01389.


19

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22

Detection of newly synthesized proteins in cancer cells:

A practical approach

Łukasz P. Biały1#

, Łukasz Zaręba2#

, Izabela Młynarczuk-Biały1*

1 Department of Histology and Embryology, Medical University of Warsaw

2 HESA Histology and Embryology Students Association at the Department for

Histology and

Embryology Centre for Biostructure Research, Warsaw Medical University, Poland # Both authors contributed equally to this work

*Corresponding Author: Izabela Młynarczuk-Biały, [email protected]

Abstract

Up to 30% of newly synthesized proteins in cells are DRiPs/RDPs (Ribosomal Defective Products/

Rapidly Degraded Proteins). They are a special subset of nascent proteins that never attain their native

structure and are rapidly degraded after biosynthesis. The major degradation pathway of DRiPs/RDPs

involves classical ubiquitin-26S proteasome pathway but their smaller fraction is eliminated by 20S

proteasome without ubiquitin tagging.

The increasing interest on DRiPs/RDPs biological implications and their kinetic nature results in

invention of several detection methods. Here we provide characterizations and comparison between

three most common pulse-chase radiolabeling icnl. radioisotopes containing amino acids

35S-Methionine 3H-Leucine as well as novel non-radioactive technique using biorthogonal amino acid

tagging and click-IT chemistry. Moreover, we discuss cyto-physiological properties of DRiPs/RDPs as

the main source of MHC class I antigens and contribution of newly synthesized proteins to anticancer

action of proteasome inhibitors.

Finally, in practical approach we show that azidohomoalanine (AHA) metabolic labeling of murine

colon carcinoma cells C26 for 5h followed by ethanol-based fixation and Click-IT detection by Alexa

Fluor 555 alkyne methods can be used for in situ histochemical visualization of synthesized proteins by

means of Laser Scanning Confocal Microscopy. We visualized the forming aggresome in C26 cells

upon proteasome inhibition. This aggresome was composed of the inner dense core and the mantel of

labeled proteins. Thus this method can be a valuable tool in anticancer research on proteasome

inhibitors.

Introduction

Proteins are the main building blocks forming the structure of all living organisms.

They are composed of amino acids and are the products of complicated intracellular

machinery, in which DNA code is transcripted into mRNA and subsequently

translated into polypeptide chain consisted of covalently linked amino acids. This

step takes place on ribosome in ATP-dependent manner. The nascent protein must

undergo proper folding to obtain unique three-dimensional conformation with the

additional help of molecular chaperons (Hartl, Bracher et al. 2011). This multistep

process of protein production makes it susceptible to errors. Any mistake in synthesis

may result in misfolding and loss of macromolecule function. The errors potentially

generated are numerous: mutations, translation errors, age related errors, exposure to

environmental stress conditions such as heat, metal ions, oxidation are the most

prevalent (Berner, Reutter et al. 2018). Therefore, cells adopted various quality

control systems to prevent generation and accumulation of harmful misfolded

proteins in cells (Brandman, Hegde 2016). Two general systems acts parallel in

cellular proteolysis: lysosomal-based degradation incl. authophagy and Ubiquitin-

Detection of newly synthesized proteins in cancer cells: A practical approach

23

Proteasome System (UPS) (Varshavsky 2017). The majority of misfolded proteins

undergoes UPS-degradation (Spinnenhirn, Bitzer et al. 2017).

The special subset of proteins was distinguished in nascent proteins: Defective

Ribosomal Products (DRiPs). DRiPs are defined as proteins that never attain their

native structure and are rapidly degraded after biosynthesis (Qian, Princiotta et al.

2006; Schubert, Anton et al. 2000). The similar term Rapidly Degraded Polypeptides

(RDP) are also used in regard to this pool of short-lived proteins (Qian, Princiotta et al.

2006; Schubert, Anton et al. 2000).

The aim of this article is to discuss and compare the published methods of nascent

protein detection pointing a special attention to their degradation pathways. Moreover

we demonstrate a practical approach of newly synthesized protein visualization during

aggresome formation in C26 cancer cells by Laser Scanning Confocal Microscopy.

Methods

Search strategy and selection criteria

Our research strategy was aimed at evaluating studies for detection and visualization of

nascent protein degradation. The terms “nascent proteins”, ”newly synthesized

proteins", "Short-lived proteins" "Defective Ribosomal Products (DRiPs)", as well as

"protein degradation", "proteasome", "Ubiquitin-Proteasome System (UPS)”, were

searched in PubMed (NCBI) and Google Scholar. Scientific articles from 1971 to 2018

were searched.

Experimental Materials and Methods

Reagents

L-azidohomoalanine (Click-iT® AHA C10102 ThermoFisher) was dissolved with

DMSO to 50mM stock solution. Alexa Fluor® 555 alkyne (A20013 ThermoFisher)

was dissolved in Wather/gicerol (1:1 solution) to the recommended final concentration

of 2.5mM. The proteasome inhibitor MG-132 (Calbiochem/Merck) was dissolved on

DMSO to the final concentration of 10mM. All reagents were Aliquoted and stored at

–20°C until use.

Cell culture

Murine adenocarcinoma C26 cell line was obtained from ATCC. Cells were grown in

RPMI1640 (Biochrom, Germany) with stable glutamine and supplemented with 10%

FCS and standard antibiotics. Cells were cultured in RPMI-1410 medium with stable

glutamine (Biochrom, Germany) supplemented with 10% heat-inactivated FCS

(Biochrom, Germany), 1% Antibiotic–Antimycotic (Thermo Fisher/Life Technologies,

US) in 25 cm2 tissue flasks (Greiner, Germany) and kept at 37°C in a 5% CO2

humidified incubator and passaged every three days with EDTA-Tripsin solution

(Biochrom).

Metabolic labeling and cytochmistry

C26 cells grown onto multi-chamber slides (Becton Dickinson) and before metabolic

labeling were staved for 30 min in RPMI-1410 Methionin-free medium (Sigma). The

medium was replaced 2 times to eliminate the residual methionin before adding

Łukasz P. Biały, Łukasz Zaręba, Izabela Młynarczuk-Biały

24

L-azidohomoalanine. After starvation L-azidohomoalanine was added to the final

concentration of 0.25mM.

Cells were incubated at 37°C, 5% CO2 with L-azidohomoalanine (AHA) for 5h in

the presence of proteasome inhibitor MG-132 in the concentration of 10mM to

prevent degradation of newly synthesized proteins. After metabolic labeling were

fixed using the modified routine procedure for cytopathology with ice-cold buffered

70% ethanol (pH 7.5; phosphate buffer) for 3 minutes and directly rehydrated (pH

7.5; phosphate buffer). Washed once with 3% BSA in PBS. Subsequently, 1 µM

Alexa Fluor® 555 alkyne in Click-iT® cell reaction cocktail with Copper (II) sulfate

(C10269 ThermoFisher) was added or 30 minutes at room temperature in darkness.

Cells were washd three times with 1BSA in PBS and embedded in Vecta Shield

containing 4′,6-diamidino- 2-phenylindole (DAPI) (Vector Laboratories, US).

The analysis and images were made using a confocal laser SP5 microscope equipped

with appropriate lasers (HeNe 633nm, DPSS diode 561nm, and Argon laser: line

488nm) and Las-AF software (Leica, Germany).

The state of Art

The Ubiquitin-Proteasome system in degradation of nascent proteins

UPS is an conservative proteolytic system for protein clearance in eukaryotic cells.

This is composed of ubiquitin conjugation machinery and the proteasome as an

proteolytic complex degrading the targeted protein into oligopeptides. Ubiquitin is

a 76-amino-acid polypeptide, which is covalently attached to the protein in a form of

a polyubiquitin chain serving as a degradation signal. The polyubiquitination is

achieved by cascade of enzymes: the ubiquitin-activating enzyme (E1), ubiquitin-

conjugating enzymes (E2s), and ubiquitin-protein ligases (E3s) The ubiquitin is

activated by E1 enzyme than transferred onto E2. Finally E3 enzyme catalyzes the

conjugation of ubiquitin to the target protein. The activation of ubiquitin catalyzed by

the E1 enzyme is ATP dependent and leads to formation of high-energy thioester

bond (Hershko, Ciechanover 1998).

The ubiquitin is attached to the lysine of the targeted protein but there has been found

that also serine, threonine and cysteine may be modified by ubiquitin (Ciechanover,

Stanhill 2014). Within ubiquitin polypeptide there are 7 internal own lysine residues,

each of which can be used for bonding another ubiquitin molecule and create

polymeric ubiquitin chain. Thus there can be formed various poly ubiquitin chains

but the chain linked by lysine 48 (K48 chains) predominantly serves as a signal for

proteasomal degradation (Chau, Tobias et al. 1989), (Finley, Sadis et al. 1994),

(Pickart, Fushman 2004).

The 26S proteasome is a multisubunit multicatalytic complex composed of 20S catalytic core and two 19S regulatory particles forming the 26S proteasome complex (Voges, Zwickl et al. 1999). The 20Sproteasome is a central cylinder formed from multiple subunits arranged into four heptameric rings with three main proteolytic activities: trypsin-like, chymotrypsin-like and caspase-like attributed to the β1, β2 and β5 subunits respectively (Kruger, Kuckelkorn et al. 2003). The 20S core proteasome displays itself the ability to degrade unfolded polypeptide chains without


25

prior polyubiquitination (Ciechanover 2005). The19S cup complexes contains six-subunit protein ring, which occupy each end of the cylinder. The cup structure is a link between arriving ubiquitinated protein and central proteolytic core. It regulates opening of a gate to 20S proteasome complex and unfold proteins in ATP-dependent manner. Each of 19S particles contains ubiquitin receptors, which can bind approaching proteins and a metalloprotease removing polyubiquitin chain before protein degradation (Tomko, Hochstrasser 2013), (Finley, Chen et al. 2016). In the case of defective protein that cannot be successfully folded by molecular chaperons the CHIP co-chaperone performs ubiquitinylation of targeted protein that is transferred to 26S proteasome for degradation (Wojcik 2002). However severe misfolded proteins can be degraded by 20S Proteasome co-translationally (Qian, Princiotta et al. 2006).

Inhibition of the proteasome leads to accumulation of newly synthesized proteins and UPS components in the aggregates, which can be observed within the cell as single round structures termed aggresomes, which initially assembles in the peri-centriolar regions of the cell (Wojcik, Schroeter et al. 1996), (Wojcik 1997), (Johnston, Ward et al. 1998).

Other extralysosomal proteolytic systems

The function of UPS is supplemented by several proteases. TPPII is the biggest known peptidase forming self-compartmentalizing, twisted double stranded cytosolic peptidase complex of 6MDa (Rockel, Peters et al. 2005). It displays both exo- and endo-proteolytic activities . Thus it provides support and even substitutes some of the proteasomal functions. It can supplement the downstream action of the proteasome in sequential protein degradation through trimming of tripeptides from oligopeptides released by the proteasome (Tomkinson, Lindas 2005), (Peters, Schonegge et al. 2011). The products released by 20/26S proteasomes are oligopeptides comprised of 4 to 25 AA (Voges, Zwickl et al. 1999), (Kruger, Kuckelkorn et al. 2003). In order for complete degradation TPPII digests substrates longer than 15 AA (Reits, Neijssen et al. 2004), (York, Bhutani et al. 2006). TPPII was described as being involved in several processes of cell regulation (Mlynarczuk-Bialy 2008), (Rockel, Peters et al. 2005). Recently, TPPII was shown to be recruited into aggresomes in colon cancer C26 cells upon proteasome inhibition (Bialy, Kuckelkorn et al. 2018) and semispecific TPPII inhibitor induces protein aggregation in leukemic U937 cells (Bialy, Fayet et al. 2018).

The UPS cooperates also with an ER-associated degradation (ERAD) in degradation of defective proteins form endoplasmic reticulum. There are 3 types of this pathway: ERAD-L for misfolded luminal Endoplasmic Reticulum proteins, ERAD-M for ER proteins with error in membrane domain and ERAD-C for ER proteins with unfunctional cytosolic domain ERAD systems contain enzymes machinery, which is able to recognize misfolding, prevent leaving invalid proteins from ER to excretory pathway, export it back to the cytosol by retrotranslocation and lead to degradation (Berner, Reutter et al. 2018).


26

The origin of newly synthesized proteins and their degradation pathways

From 70th XX century on the basis of degradation kinetics cellular proteins are

divided into two main groups of proteins: “short-lived” (with a fast degradation rate) and “long-lived” (with a much slower degradation rate) (Goldberg, Dice 1974), (Poole, Wibo 1973), (Pine 1972). Both kinetics of degradation were separated by a clear discontinuity and these two groups of proteins have been identified in all cells.

The studies revealed that 30% of newly synthesized proteins are eliminated with a half-life <10 minutes and this part of extremely short-lived proteins was named DRiPs (defective ribosomal products)/RDPs (rapidly degraded proteins) (Qian, Princiotta et al. 2006; Schubert, Anton et al. 2000). 75% of these proteins are eliminated by 26S proteasomes and remaining 25% by 20S proteasome complex independently of 19S regulators (Qian, Princiotta et al. 2006). DRiPs/RDPs can be divided into two classes based on their solubility. The soluble RDPs fraction is degraded by ubiquitin-depended manner, which is linked with 26S proteasome but insoluble fraction seems to be degraded in a different way without involvement of ubiquitin. Qian et al. bring proofs and conclude that 75% of DRiPs/RDPs are degraded by 26S proteasome Hsc70-depended pathway based on ubiquitination. The remaining 25% are degraded without ubiquitin-link by 20S proteasome without involvement of 19S regulatory subunit and are largely unaffected by Hsc70 modulatory activity (Qian, Princiotta et al. 2006). DRiPs/RDPs are also the largest fraction of proteins degraded by proteasomes and constitute 70-75% of all the protease complex substrates (Princiotta, Finzi et al. 2003; Yewdell 2011).

Figure 1. DRiPs/RDP and their degradation pathways

DRIPs/RDPs are degraded co-translationally. The larger fraction of them one is water soluble and undergoes

polyubiquitination prior to degradation by 26S proteasome. The minor fraction (ca 25%) is digested directly

within 20S core of proteasome without previous ubiquitinylation. Most generated oligopeptides is a source of

MHC class I epitopes (vial or cancer antigens). Original Figure.


27

DRIPs and immunity

At the beginning DRiPs were defined as ribosomal products that never attain their

mature structure because of defective mRNAs (Shastri, Nguyen et al. 1995; Wang,

Parkhurst et al. 1996), ribosomal frame shifting (Bullock, Eisenlohr 1996; Fetten,

Roy et al. 1991), tRNA- amino acid misacylation (Netzer, Goodenbour et al. 2009),

transcription errors (Gout, Thomas et al. 2013) or all other mistakes during

converting DNA information into protein (Anton, Yewdell 2014). This definition

implicates that RDPs/DRiPs are accidental products of protein synthesis but recent

studies have proven that they’re not only a junk biosynthesis consequence but also

have a crucial contribution to cellular processes. Thus, DRiPs/RDPs was considered

only as abnormal, misfolded proteins but now it is known that they are a main source

of cellular and viral antigens presented by histocompatibility complex class I

molecules: viral and cancer (Reits, Vos et al. 2000; Schubert, Anton et al. 2000).

Yewdell concluded form Dolans studies (Dolan, Li et al. 2011; Yewdell 2011) that

even 70% of MHC class I complexes can originate from DRiPs. This remark is also

supported by interesting observation of immunologic response for virus-infected

cells. CD8+ cytotoxic T-lymphocytes recognize specific antigens on the surface of

infected cell simultaneously to virus is entrance and long before source protein could

be detected within infected cell. Transition of viral protein into antigen should take

much longer and probably is not responsible for activation of early cytotoxic

T-lymphocytes. However antigens for MHC class I presentation can be also derived

from long lived proteins but DRiPs generation seems to be favored for rapid

recognition of infected cells by CD8+ T cells (Anton, Yewdell 2014). DRiPs were

also described as a major contributor to the antigenic presentation associated with

immunological tolerance (Arguello, Reverendo et al. 2016). Noticeable, the role of

rapidly degraded proteins indicates possibility for preconcerted RDP-biosynthesis,

which is not only an error effect. Referring to this hypothesis, scientists have indeed

found unique subset of ribosomes specialized for antigenic relevant DRiPs synthesis

named immunoribosomes (Yewdell 2002; Yewdell 2005). Proteins produced by

those complexes are believed to be rapidly degraded by (immuno)proteasomes into

oligopeptides dedicated to antigen generation. There exists hypothesis for close

relationship of both of them (Dolan, Bennink et al. 2011), which is supported by

discovery of several proteins capable to interact with the both complexes (Panasenko,

Landrieux et al. 2006; Sha, Brill et al. 2009) for example eIF1A (Chuang, Chen et al.

2005).

Newly synthesized proteins and Cancer

Current established therapeutic procedures include inhibition of the proteasome as

anticancer strategy (Teicher, Tomaszewski 2015). Inhibition of proteasome proteo-

lysis leads to misfolded protein response and accumulation of newly synthesized

proteins that induces apoptosis. Thus most sensitive to proteasome inhibition are

cells that perform intensive protein synthesis like plasmmocytoma cells.


28

Upon proteasome inhibition newly synthesized ubiquitinated proteins are transported

along microtubules to the pericentriolar proteolytic center of the cell and stored there

as an aggresome (Johnston, Ward et al. 1998; Wojcik 1997; Wojcik, Schroeter et al.

1996). The accumulation of misfolded proteins within the aggresome is one of the

postulated mechanisms of action of proteasome inhibitors as anticancer drugs.

Nonetheless, aggresome disruption by histone deacetylase inhibitors (Nawrocki,

Carew et al. 2006) and microtubule acting agents (Miyahara, Kazama et al. 2016) can

augment the anticancer activity of proteasome inhibitors. Thus, aggresome formation

in cancer cells likely protects them from proteasome inhibitor-induced unfolded

protein stress, and therefore can limit anticancer effects of proteasome inhibitors (see

next chapter).

Comparison of DRIP detection protocols

The biological features and function of DRiPs/RDPs becomes a substantial subject of

studies recently because of their serious impact on cell biology. As mentioned before,

DRiPs/RDPs are nascent proteins with a half-life < 10mins a and can constitute up to

30% of newly synthesized proteins (Qian, Princiotta et al. 2006), (Schubert, Anton et

al. 2000). Thus due to extremely short live time of DRiPs/RDPs, their experimental

detection is difficult, and several studies were focused on acquiring the best way of

their detection. Various protocols were used to achieve this aim.

The most common method is classic radioactive pulse-chase analysis (Qian,

Princiotta et al. 2006). The method involves labeling of newly synthesized proteins

with radioactive amino acids which allows quantitative analysis of the fate of given

proteins in the time-depended manner (Magadan 2014). The detection of radioactive

protein can be subsequently preformed by either liquid scintillation counter or

autoradiography. Alternatively non-radioactive amino acids surrogates such as

L-azidohomoalanine (AHA) can be used for metabolic labeling of proteins followed

by detetection by selective click-chemistry techniques.

Pulse-chase radiolabeling of newly synthesized proteins is a well-known method for

DRiPs/RDPs analysis. First step of the method pulsing of cells for a short time period

with special medium that contains radiolabeled amino acids. The most common

radioactive amino acid used in this method is 35SMethionine. It is used due to its

high specific activity (>800Ci/mmol) and quick detection. However, potential

disadvantage of this amino acids is its low abundance in proteins (~1.8% of the

average amino acid composition) (Bonifacino Juan, Gershlick David et al. 2016) thus

high radioacivity must be used up to 5 mCi/ml (Schubert, Anton et al. 2000).

Alternatively 3HLeucine can be used for radioactive protein labeling. Because of

DRiP high turnover rate pulse-labeling time has to be short enough for appropriate

detection. Qian et al. measured nascent proteins degradation by radiolabeling in

HeLa cells with 35s Met for 5 min (Qian, Princiotta et al. 2006). Furthermore,

Shubert with colleagues did it for only 30 seconds in their studies (Schubert, Anton

et al. 2000).


29

The second step of the technique contains chasing of radioactive palsed cells in

complete medium with unlabeled methionine for longer period of time to allow

degradation of newly synthesized proteins. Qian et al. chased cells for up to 4h and

Shubert et al. did it for up to 60 min.

At the end the detection of signal can be made by measuring radioactivity present in

trichloroacetic acid precipitates of whole cell lysates and culture media (Qian,

Princiotta et al. 2006). Measurement can be done by scintillation counter or

alternatively by exposing dried gel from SDS-PAGE to phosphor imager screen or

X-ray film (Qian, Bennink et al. 2005).

Alternatively 3HLeucine radioactive protein labeling was used by Knecht’s group for

pulse-chase analysis of newly synthesized (Fuertes, Villarroya et al. 2003). The

degradation of short-labeled proteins was examined in fibroblasts by release of

trichloroacetic acid-soluble radioactivity. Fuertes at al. estimated DRiP rate to about

30% of newly synthesized. However half-life observed longer up to 1 h probably due

to longer tilme of the radioacive pulse. They also calculated that proteasomes are

responsible for degradation go 60% newly synthesized proteins. The described

methodology has a high accuracy but working with radioactive materials is

potentially dangerous for experimenter and environment especially with long-lasting

3H nuclide.

Details and comparison of the mentioned protocols is displayed in Table 1.

Table 1. Comparison of most common DRiPs/RDPs detection protocols

Shubert et al. Qian et al. Fuertes et al.

Cell line HeLa HeLa Human fibroblasts

Pulse time 30s 5min 15min

Chase time 60min 4h Different chase

time up to 25h

Source of

radioactivity

[35S]methionine

5 mCi/ml

[35S]methionine [3H]leucine or

[3H]valine

1 mCi/ml

Detection SDS-PAGE and

radioactivity

measurement in dried

gel

TCA precipitation and scintillation

counting/ SDS-PAGE and

radioactivity measurement in dried

gel

TCA precipitation

and scintillation

counting

T1/2 10min 7.6min 1.1h

Click-IT chemistry in detection of newly synthesized proteins

Recently new method for detecting on synthesized proteins was described. It allows

to detect proteins of interests without using radioisotopes. Thus, it is safer for

environment, experimenters and furthermore enables in situ histochemical visua-

lization. Bioorthogonal noncanonical amino acid tagging – BONCAT is a novel


30

method based on application of amino acid surrogates modified with azide or alkyne

groups, which are incorporated into proteins during biosynthesis (Dieterich, Link et

al. 2006). It is possible because certain tRNA synthetases employed to the process

are unable to recognize natural amino acids and can recognize those with slightly

different structure (Link, Mock et al. 2003). Bioorthogonal molecules are characterized

as synthetic, not biologically synthesized and not interfering with native biochemical

processes. They are frequently analogs of native biomolecules with addition of

special groups required for click chemistry (Hatzenpichler, Scheller et al. 2014).

There have been described a series of biorthogonal amino acids which are able to

successfully compete with native amino acid molecules during translation but there

are only several molecules, which can be used by translational machinery without

genetic modifications of the host cell (Ngo, Tirrell 2011). One of the most efficient,

often described as a member of this subset is an amino acid substitute

L-azidohomoalanine (AHA) – an analog of L-methionine (Kiick, Saxon et al. 2002).

An azide group of AHA incorporated into proteins makes them distinct from other

polypeptides in cell and enables selective click chemistry-mediated detection

(Hatzenpichler, Scheller et al. 2014). The functional azide group reacts with alkyne

through 1,3-dipolar cycloaddition to yield triazole linkage. The reaction is catalyzed

by Cu(II) and here we came across the serious disadvantage – copper catalyst in

higher concentration is toxic to cells. To circumvent the problem several metal-free

azyde-alkyne cycloadditions have been developed (Lim, Lin 2010). The whole

process allows to visualize proteins of interest by many methods e.g. microscope in

vivo, SDS-PAGE due to azide- alkyne fluorescence dye linkage. The 1,2,3- triazole

linkage is extremely strong and resistant to hydrolysis, oxidation, reduction or

ionization in mass spectrometry analysis. Finally, proteins labeled with AHA can be

detected using subsequent analysis by flow cytometry, imaging or standard

biochemistry techniques such as gel electrophoresis. Detection sensitivity obtained in

this process is similar to that in radioactive 35S Met method and is compatible with

downstream LC-MS/MS and MALDI MS analysis ("Molecular probes the

handbook").

The Practical Approach of newly synthesized protein visualization in cells

We show localization of newly synthesized proteins using amino acid analogue AHA

visualized by Click chemistry with fluorochrome within murine colon

adenocarcinoma C26 cells.

C26 cells were chosen for this practical approach due to their properties. In our

laboratory we investigated anticancer properties of various proteasome inhibitors

against many cancer cell lines and among them C26 cells respond to proteasome

inhibition by formation of the giant aggresomes and can survive long-term

proteasome inhibition making them an ideal candidate for protein aggregation study.

The aggregate is formed in perinuclear region and fully mature easy to detect large

and round is formed within 12-24 hours of proteasome inhibition (unpublished labor

data).


31

Results

As it is shown in Fig 1 after 5h of proteasome inhibition by 10µM MG-132

synthesized proteins (red) accumulate in the cytoplasm of C26 cells. They

predominately accumulate in a one single round structure in perinuclear region

corresponding to forming aggresome (white arrows in panel B). The forming

aggregate is composed of a dense solid core (arrows in panel C) with a mantle

containing less dense material (arrowheads in Panel C). Moreover, the residual part

of cytoplasm is stained less intensively. There was no signal within nuclei observed.

Figure 2: Visualization of newly synthesized protein in C26 mouse adenocarcinoma

C26 cells were treated for 6h with 10uM Mg132 to inhibit protein degradation. Synthesized proteins were

detected by AHA incorporation (for 6h) and visualized by Alexa Fluor 555 alkyne after ethanol-based fixation

using confocal microscopy. Nuclei – Blue – nuclei stained with DAPI; Synthesized proteins – Red – proteins

with incorporated AHA; Merge; Scale bar: 7.5 µM.


32

Short Discussion and Conclusion

Non-radioactive by Click-IT a chemistry-based method of newly synthesized protein

detection gives an interesting alternative for their radioactive detecting. Moreover

Click-IT methods have an additional advantage because they can be used in situ

histochemical visualization of synthesized proteins. Moreover, our validation

endorsed the ethanol based fixation suitable for this application. We visualized the

forming aggresome in C26 cells upon proteasome inhibition. This aggresome was

composed of the inner dense core and the mantel of labeled proteins.

Concluding this method can be a valuable tool in anticancer research on proteasome

inhibitors incl. aggresome formation studies.

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36

Direct visualization of undegraded protein

sequestration within aggresomes

by proteasome inhibitors

Łukasz P. Biały1, Przemysław Szymański

2, Izabela Młynarczuk-Biały

1*

1. Department for Histology and Embryology Centre for Biostructure Research,

Warsaw Medical University

2. HESA Histology and Embryology Students Association at the Department for

Histology and Embryology Centre for Biostructure Research, Warsaw Medical

University

* Corresponding author: Izabela Młynarczuk-Biały, Chałubińskiego 5, 02-004

Warszawa, Poland, [email protected]

Abstract

The ubiquitin-proteasome system (UPS) plays a crucial role in maintenance of cellular homeostasis.

It controls cell cycle progression, signal response, apoptosis, transcription, NF-κB activation. Inhibition

of the UPS induces accumulation of proteasomal substrates in a form of cellular aggregates that are

localized mainly in the perinuclear region. Tumour cells with deregulated protein turnover are especially

sensitive to inhibition of the UPS, thus proteasome inhibitors are applied in treatment of selected

malignancies like multiple myeloma. However, proteasome-inhibitor resistance of tumour cells is

a serious treatment perturbation and research on overcoming the resistance can improve therapy

effectiveness. One of possibilities for this resistance is formation of big aggresomes, in which

undegraded proteins are separated from important cellular processes by sequestration. Disruption of the

big aggresome made resistant tumour cells sensitive to death induction by proteasome inhibitors.

Herein, we show quick and direct inhibitor-based fluorescent staining proteasomes that enables fast and

effective analysis of aggresome structure and formation dynamics. This method can improve studies on

the reasons of tumour cell insensitivity towards proteasome inhibitors and can be a valuable screening

for novel proteasome inhibitor effects. Moreover, within this work we discuss the function of aggresome

and its impact on tumor resistance towards proteasome inhibition. In addition we present clinical

application of proteasome inhibitors with data from clinical trials.

Keywords: proteasome inhibitors, aggresome, inhibitor-based staining, UPS, HDAC6,

Introduction

The ubiquitin-proteasome system (UPS) is indispensable for cellular homeostasis.

The UPS is directly responsible for most of cytoplasmic protein degradation and

participates in a wide array of biological functions such as regulation of the cell

cycle, gene transcription, signal transduction, antigen processing and activation of

NF-κB (Adams, Palombella et al. 2000; Wojcik 1999, 2002).

The ubiquitin-proteasome system- general aspects

Intracellular protein degradation and turnover is regulated through the action of

ubiquitinylation enzymes designating proteins for degradation by the proteasome.

The proteasome is a multicatalytic enzyme complex localized in the in the cytoplasm

and in the nucleus of all eukaryotic cells. The fully active 26S proteasome is

composed of 20S barrel-shaped core particle and two 19S regulatory subunits,

localized at both ends of the 20S core particle. Each 20S core consists of 4 rings: two

outer α rings and two inner β rings. Each ring contains 7 globular subunits, α1-α7

mailto:[email protected]



37

within α-ring and β1-β7 within β-ring, respectively. Within neighbouring β-rings

there are 3 subunits per each ring displaying proteolytic activity. The distinct

activities are: trypsin-like (within the β1 subunit), caspase-like (within the β2

subunit) and chymotrypsin-like (within the β5 subunit) of the 20S core (Adams 2003;

Tanaka 2009).

Substrates for degradation by 26S proteasome are tagged with polyubiquitin chain in

the process named ubiquitination (Haglund, Dikic 2005). The ubiquitin (Ub) is

a highly conserved polypeptide, 76 acids in length, which acts as a molecular label in

cellular protein trafficking. Within each Ub polypeptide there is seven lysine residues

localized. These lysine residues are involved in linking multiple Ub polypeptides

together to form polyubiquitin chain. Ubiquitination alters proteins in many ways: it

can mark them for proteasomal degradation, alter their cellular localization, affect

their activity and promote or prevent protein interactions (Miranda, Sorkin 2007).

Three types of ubiquitination are distinguished in the process of cellular protein

modification. Mono-ubiquitination – is an attachment of single Ub molecule to the

substrate that signals for endocytosis, endosomal sorting, histone regulation, DNA

repair, virus budding, nuclear export. Multi-ubiquitination occurs when several single

Ub molecules are attached to several lysine residues on the substrate that signals for

endocytosis, or finally polyubiquitination that means formation of long ubiquitin

chain that is bound to the substrate via first ubiquitin in the poly-chain. Ub chains,

which are formed via Lys48 (K48) of Ub, target the modified protein for proteasomal

degradation (Pickart, Fushman 2004). Chains linked via Lys63 (K63) are implicated

in DNA repair and activation of protein kinases or lysosomal pathway (Acconcia,

Sigismund et al. 2009; Haglund, Dikic 2005).

The ubiquitination process serving as proteasomal degradation signal is regulated by

a cascade of E1, E2, E3 enzymes (Adams 2003). The ubiquitin-activating E1 enzyme

binds to the ubiquitin protein and passes it to the ubiquitin-conjugating E2 enzyme.

Subsequently, E2 enzyme and ubiquitin-ligase E3 enzyme are responsible for linking

ubiquitin molecule with protein targeted to the proteasomal degradation. This

connection occurs due to binding C-terminal glycine residue of ubiquitin to lysine

residue of the ubiquitinated protein. Consecutive ubiquitin molecules are bound to

one of the lysine residues of the previously linked ubiquitin molecule (Frankland-

Searby, Bhaumik 2012). In this way, polyubiquitin chain is formed, attached to the

substrate protein. The polyubiquitinated proteins are recognized by the 19S

regulatory particle and fed to the 20S core particle where they are degraded through

an ATP-dependent proteolysis (see Fig 1).

The role of the UPS system in cancer

Proper activity of the UPS system is crucial in maintaining the homeostasis in cells.

This system is engaged in regulation of prominent cellular processes such as growth,

mitosis and angiogenesis. Hence, the imbalance in degradation of some proteins may

result in serious disturbance to proliferation.

One of the key proteins, which activity is regulated by the UPS, is the nuclear factor

kappa-light-chain-enhancer of activated B cells (NF-κB). It was discovered for the

Łukasz P. Biały, Przemysław Szymański, Izabela Młynarczuk-Biały

38

first time in murine B leukocytes (Sen, Baltimore 1986). In fact, NF-κB is a group of

transcriptional factors which regulate expression of genes encoding cytokines,

chemokines, immunomodulatory proteins and growth factors (Pahl 1999). In context

of tumor development, permanently activated transcription factor NF-κB, triggers

pro-survival and anti-apoptotic phenotype of cancer cells. These conditions make

tumor cells resistant to apoptosis-inducing chemotherapeutics and enable their

survival despite of difficulties like hypoxia, acidosis, malnutrition, lack of external

stimuli especially in myeloma multiplex cells (Tornatore, Sandomenico et al. 2014).

Figure 1: Schematic presentation of the ubiquitin-proteasome system, deubiquitination process and its key

enzymes participating the process of cytosolic degradation of proteins. (Original Figure)

Cellular effects of proteasome inhibition

Most of cytosolic proteins are ubiquitinated and subsequently degraded within the 26S proteasome. If the system is impaired, for instance under action of proteasome inhibitors, polyubiquitinated proteins accumulate and sequestrate in perinuclear region in a form of protein-rich aggregate named aggresome. (Wojcik, Schroeter et al. 1996). Undegraded protein particles accumulate locally around microtubules and by an unknown mechanism are transported along microtubules to the minus end of microtubules. In the pericentriolar region numerous of such particles form a huge structure of aggresome, which diameter can be similar to the diameter of the nucleus (Paweletz, Wojcik et al. 1996), (Johnston, Ward et al. 1998).

Ub

Ub

E3

Ub

UbUbUbUb

Ub

Ub

Deubiquitinating

enzymes

Ub

E1Ub

Activating enzyme

E2Ub

Conjugating

enzyme

E3

Ub

Ligase

20S {

Proteasome 26S

ββα

α

19S

19S



39

Healthy, non-tumour cells are relatively resistant to action of proteasome inhibitors, while tumour cells, which display intensive protein turnover, are impaired by the inhibition of proteasome, resulting in accumulation of undegraded substrates, NF-κB inhibition and subsequently cell death (Mlynarczuk-Bialy, Doeppner et al. 2014; Mlynarczuk-Bialy, Roeckmann et al. 2006; Pleban, Bury et al. 2001).

The knowledge of the special feature that tumour cells are selectively sensitive to action of proteasome inhibitors was an inspiration for design and development of these compounds as anticancer drugs. After approval of the first 20S inhibitor, Bortezomib, second generation inhibitors (Carfilzomib, Marizomib) were studied and subsequently involved in clinical use (Kubiczkova, Pour et al. 2014). Presently, proteasome inhibitors are used in the treatment of multiply myeloma, mantle cell lymphoma and gastrointestinal stromal tumors (Adams 2001; Adams, Kauffman 2004)

In contrast to classical chemotherapeutics (like cisplatin, doxorubicin, etoposide), clinically used proteasome inhibitors display less systemic toxicity (Mlynarczuk-Bialy, Doeppner et al. 2014), however development of tumor cell resistance is a serious limitation of this group of drugs represented by proteasome inhibitors (Adams, Palombella et al. 2000; Merin, Kelly 2015; Richardson, Zimmerman et al. 2016).

Proteasome inhibitors at clinical applications

Bortezomib is the first proteasome inhibitor that was studied in clinical trials, it has shown in vitro and in vivo activity against a variety of malignancies, including myeloma, chronic lymphocytic leukaemia, prostate cancer, pancreatic cancer, and colon cancer. Bortezomib is a dipeptide boronic acid analogue whose trade name is Velcade (formerly known as PS-341, LDP-341 and MLM341). The drug is rapidly cleared from the vascular compartment, however according to pharmacodynamics data bortezomib-mediated proteasome blockade is dose-dependent and reversible. Basing on phase I studies it was demonstrated that bortezomib has manageable toxicities in patients with advanced cancers (Papandreou, Daliani et al. 2004). According to clinical trials determining the dose-limiting toxicity and maximum-tolerated dose, a dose-related 20S proteasome inhibition was seen, with dose-limiting toxicity at 2.0 mg/m

2 (diarrhoea, hypotension) occurring at an average 1-hour post-

dose of >/= 75% 20S PI. Other side effects were fatigue, hypertension, constipation, nausea, and vomiting. No relationship was seen between body-surface area and bortezomib clearance in that study (Papandreou, Daliani et al. 2004). There was evidence of biologic activity (decline in serum prostate-specific antigen and interleukin-6 levels) at >/= 50% 20S inhibition (Papandreou, Daliani et al. 2004).

Carfilzomib (Kyprolis) is a proteasome inhibitor used in treatment of the relapsed or refractory multiple myeloma. Firstly, it was approved as monotherapy, but now it is indicated as a part of combined therapy with dexamethasone or with lenalidomide (Revlimid) and dexamethasone. Carfilzomib is a modified epoxyketone which selectively and irreversibly binds with the 20S proteasome causing its inhibition. The medication is administered via intravenous infusion. The recommended starting dose is 20 mg/m

2. Carfilzomib is metabolized extrahepatically, mainly through peptidase

cleavage and epoxide hydrolysis. It is quickly eliminated by biliary and renal


40

excretion. Clinical trials showed that the most often adverse effects involve anaemia, diarrhoea, neutropenia, fatigue, thrombocytopenia, pyrexia, hypokalaemia, hypertension, acute renal failure and cardiac failure (Perel, Bliss et al. 2016).

Marizomib (salinosporamide A or NPI-0052) is a novel second generation irreversible proteasome inhibitor which is currently clinically tested. This compound was discovered in Salinispora tropica, marine bacterium, which naturally synthesize it. Marizomib belongs to β-lactone-γ-lactam superfamily of proteasome inhibitors (Potts, Albitar et al. 2011). In comparison to bortezomib and carfilzomib which inhibit mainly β5 (chymotrypsin-like) subunit of the 20S proteasome, marizomib also significantly affects β1 and β2 subunits (trypsin-like and caspase-like, respectively). Thus, it is expected to overcome cancer cells’ resistance to other PIs due to its pan-subunit activity and irreversible binding. Moreover, marizomib showed less side effects than other PIs during phase I clinical trials. The most frequent of them are fatigue, headache, nausea, diarrhoea, dizziness, and vomiting. Additionally, significant peripheral neuropathy, which occurs during treatment by bortezomib, was not observed. In phase 1 clinical trials the patients received marizomib via IV injection and the maximum dose was 0.7 mg/m

2 or 0.5 mg/m

2, depending on the

treatment schedule (Richardson, Zimmerman et al. 2016). Marizomib has also more lipophilic structure than bortezomib and carfilzomib. Therefore, it is expected to penetrate blood-brain barrier and may be efficient in treatment of brain tumours (Di, Lloyd et al. 2016).

MG132 is a peptide-aldehyde proteasome inhibitor obtained from a chinese medicinal plant. Its use is limited to the laboratory research (Guo, Peng 2013).

BSc2118 is another proteasome inhibitor, designed as an analogue of MG132, with very similar inhibitory profile to bortezomib and with relatively low toxicity. Its activity involve inhibition of all three hydrolytic subunits of the 20S proteasome (Braun, Umbreen et al. 2005). The binding of BSc2118 is reversible (Doeppner, Mlynarczuk-Bialy et al. 2012). BSc2118 proved an effective anti-tumour agent against melanoma in vitro (Mlynarczuk-Bialy, Roeckmann et al. 2006) and in a mouse melanoma model (Mlynarczuk-Bialy, Doeppner et al. 2014) and also against multiple myeloma in vitro (Sterz, Jakob et al. 2010).

Proteasome inhibitor induced ER-stress

The inhibition of UPS affects general protein turnover producing proteotoxic conditions and may also influence protein folding in the ER. Conditions affecting protein folding in the ER, such as heat shock, acidosis, action of tunicamycin activate cell adaptive pathway known as the unfolded protein response (UPR) (Lindholm, Korhonen et al. 2017). The UPR is composed of two main elements: on one hand general protein synthesis is decreased to reduce nascent protein delivery to the ER, on the other hand the level of ER resident conformation guards like chaperones, folding enzymes is elevated by up-regulated transcription of adaptive proteins. If these regulatory responses fail to prevent accumulation of misfolded proteins in the ER, these proteins are recognized by ER quality control and restored in the ER, stopped in further maturation. Finally protein which is not properly refolded, is targeted for ER-associated protein degradation (ERAD) (Berner, Reutter et al. 2018). ERAD include retrograde delivery of misfolded proteins to the cytoplasm and



41

subsequent degradation by the ubiquitin-proteasome system. Thus the condition and activity of ubiquitin-proteasome system keeps the maintenance of proper ER function especially in stress conditions. In multiple myeloma (MM) cells it was found that treatment of MM cells with proteasome inhibitors (PIs) initiates the UPR by inhibiting the retrograde translocation of misfolded proteins from the ER and that MM cells are highly sensitive to these agents because they produce large amounts of protein, namely immunoglobulin, which must be processed within the ER. Interestingly, it was found that MM cells constitutively express high levels of UPR survival components, but that PI treatment leads to the rapid induction of proapoptotic UPR genes (Obeng, Carlson et al. 2006) .

Clearance of aggresomes by aggrephagy

Another mechanism for clearance of misfolded proteins is aggresome-macro-autophagy/autophagy pathway called aggrephagy (Lamark, Johansen 2012). Unfolded proteins tend to form aggregates that are potentially harmful for the cell, like in the case of numerous neurodegenerative diseases (for instance Alzheimer disease, Parkinson Disease). To prevent such conditions cells developed resistance mechanisms preventing from harmful effects aggregation. Soluble proteins are mainly degraded by the UPS, while large insoluble aggregated proteins can be isolated via isolation membranes within autophagosome for further destruction. The distinguishing of both clearance pathways depends on different tagging of substrates by ubiquitin. Namely the same molecule but differentially attached to the substrate leads either to the UPS or to the lysosomal/autophagy pathway. Which mechanism lead to different ubiquitination of the substrate, remains unknown. It is known that Lys48-linked chains designate proteins for proteasomes whereas Lys63-linked poly-ubiquitin chains facilitate degradation by lysosome/autophagy. However, autophagy receptors show no obvious preference binding Lys63-linked poly-ubiquitin chains over Lys48-linked chains. Thus, how substrates are sorted to their designated degradation pathway remained unclear (Acconcia, Sigismund et al. 2009; Haglund, Dikic 2005; Miranda, Sorkin 2007).

Aggresome disruption as re-sensitization to proteasome inhibitor resistant cancer cells

Nawrocki et al. observed and described the phenomenon of protective role of big aggresomes from death induced by proteasome inhibitors. The spatial cytoplasmic sequestration of undegraded protein in tumour cells had pro-survival effect while aggresome disruption restored tumour cells sensitivity to the action of proteasome inhibitors (Nawrocki, Carew et al. 2006). Sequestration of potentially harmful proteins within aggresome can reduce unfolded protein response and ER-stress thus by incubation of cells with histone deacetylase inhibitors and thereby aggresome disruption turns off this protective effect (see Figure 2).

Histone deacetylases HDAC6 and aggresome

Histone deacetylases (HDACs) is a superfamily of enzymes, which name should be considered rather historically. They take part not only in deacetylation of histones, but their targets comprise plenty of non-histone proteins. Thus, they are also described as lysine deacetylases to show their function more accurately (Dokmanovic, Clarke et al. 2007). HDAC6 belongs to class II HDACs and it is predominantly localized in


42

the cytoplasm. One of its known functions is deacetylation of α-tubulin which improves cell mobility. Moreover, it displays ability to bind both polyubiquitinated proteins and dynein motors. Hence, HDAC6 plays a crucial role in transport of misfolded protein with use of dynein motors, which results in formation of protein-rich structures called aggresomes. (Kawaguchi, Kovacs et al. 2003) This process has an adverse effect to treatment by proteasome inhibitors. Through sequestration of misfolded proteins and reduced exposition of cell molecules to their harmful action, aggresomes make cancer cells less sensitive to cytotoxic activity of proteasome inhibitors (Rodriguez-Gonzalez, Lin et al. 2008). However, the cytoprotective process of aggresome formation may be disrupted by HDAC inhibitors or siRNA and, consequently, effectiveness of treatment with proteasome inhibitors could be enhanced (Figure 2). Therefore, HDAC inhibitors was tested in preclinical and clinical studies, e.g. in multiple myeloma and chronic myelogenous leukaemia (Garcia-Manero, Yang et al. 2008), (Blum, Advani et al. 2009). Different new selective HDAC6 inhibitors are being developed to increase efficiency of their activity (Boumber, Younes et al. 2011).

Figure 2: The proposed model of aggresome role in cell death and survival upon proteasome inhibition

Big aggresome displays cytoptotective action hence aggresome disruption promotes apoptosis.

The vital inhibitor-based staining procedure of proteasomes

In order to analyze aggresome formation dynamics we develop vital inhibitor-based staining using a novel inhibitor BSc2118 fluorescent derivative.

Material and Methods Proteasome inhibitor

The novel proteasome inhibitor BSc2118 was synthesized as described previously (Braun, Umbreen et al. 2005). The fluorescent variant of BSc2118 (further named as BSc2118-FL) was synthesized as described elsewhere (Mlynarczuk-Bialy, Doeppner

Cell

nucleus

Big

aggre

gate

Inhibitors of

HDAC

Incubation with

proteasome inhibitors

Apoptotic

bodies

No

apoptosis

Aggregate

disruption



43

et al. 2014). BSc2118 was dissolved in DMSO at 20 mM and fluorescent Bodipy-BSc2118 was dissolved in DMSO at 1 μg/μl and kept at -20°C prior to use.

Cell culture

NIH-3T3 Cells (ATCC) were grown in DMEM containing stable L-glutamine and supplemented with 10% FCS and antibiotics. Cells were grown in cell culture incubator in a humidified atmosphere of 5% CO2 and passaged every three days.

Confocal microscopy

NIH-3T3 cells were directly incubated with BSc2118-FL at the final concentration of 1μM for indicated time. Subsequently cells were fixed with ice-cold buffered 70% ethanol (pH 7.5; phosphate buffer) for 3 minutes and directly rehydrated (pH 7.5; phosphate buffer). Thereafter cell nuclei were stained with DAPI (Sigma). Specimens were embedded in Vecta shield medium (Vector Laboratories). The specimens were examined with a Leica confocal laser scanning microscope SP5 (Leica Microsystems) equipped with a krypton-argon laser. Sequential scans at a series of optical planes were performed with a 63x oil immersion objective lens through specimens.

Figure 3. The dynamics of aggresome formation in NIH3T3 cells

Cells were incubated with BSc2118-FL (1μM) for 5 min (A) of for 2 h (B), 6 h (C) 24 h (D).

Scale Bars 10μM


44

Results

The dynamics of aggresome formation in NIH3T3 cells show time-dependent kinetic

spectrum of alternations. In NIH3T3 cells stained for 5 minutes with BSc2118-FL

(FIG 3 A) the cytoplasm is homogenously traced with the fluorescent inhibitor. The

cellular processes are stained as well. The nuclei remain unstained at this time point.

In comparison to shortly stained group, cells incubated with BSc2118-FL for 2 hours

demonstrate increase in signal in perinuclear region. This enhancement of the signal

in perinuclear region corresponds with forming aggresomes as was shown elsewhere

(Mlynarczuk-Bialy, Doeppner et al. 2014) using counterstaining with antibody for

20S and polyubiquitin. This early aggregates are visible after 2 hours of 20S

inhibition. There is a decrease in signal intensity within cellular processes, however

the processes still remain visible. Within cell nucleus there are visible clusters which

are stained with BSc2118-FL at 2h time point (FIG 3 B). At 6 h the bright

fluorescent signal is localized within aggresomes at perinuclear region (FIG 3 C)

whereas relative decrease in fluorescence intensity is observed within NIH3T3

cytoplasmic processes in comparison to the previous group. Starting form 24 h

period of proteasome inhibition, the aggresomes in perinuclear region form bright

fluorescent rounded structures, in nuclei of some cells there is a reduction of

fluorescent clusters that might be a sign of beginning degeneration (FIG 3 D).

The degeneration includes: condensation or even defragmentation of the cell nucleus,

alternations of the cytoplasmic region: vacuolisation due to endoplasmic reticulum

stress, cell membrane blebbing as a sign of apoptosis induction or cell membrane

rupture with release of the aggresome.

Short discussion and conclusion

Fluorescent-inhibitor based staining of enzymes give and interesting alternative for

classical immunolabeling in both microscopy and flow cytometry by reducing of

possible antibody cross-reactivity esp. with multi-color staining (Darzynkiewicz,

Pozarowski et al. 2011), (Marchetti, Gallego et al. 2004) . Inhibitor based detection

of proteasomes in biosensors enables proteasome and immunoproteasome

quantitative measurement in the serum (Patent Pending PL: P.396170; P.396171;

P.417435). The vital inhibitor-based staining procedure of proteasomes (BSc2118-FL

prior to fixation) combined with subsequent ethanol based fixation enables efficient

proteasome staining (Mlynarczuk-Bialy, Doeppner et al. 2014), (Bialy, Kuckelkorn

et al. 2018).

We found this method as promising for fast and effective aggresome formation

studies. Studies on aggresome formation process and development of methods for

their visualisation can lead to invention of diagnostic tools enabling prediction if

proteasome inhibitors can be effective in particular types of malignancy. In times of

more and more personalized medicine and due to facts that proteasome inhibitors are

expensive and not always effective such diagnostic tool would be a great benefit for

patients and the public health system.



45

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48

Novel podophyllotoxin derivatives as Anticancer Agents: Design, Synthesis, and Biological Screening

Piotr Strus1, Kamil Lisiecki

2, Zbigniew Czarnocki

2, Izabela Młynarczuk-Biały

3*,

Łukasz P. Biały3

1. HESA Histology and Embryology Students Association at the Department for Histology and Embryology Centre for Biostructure Research, Warsaw Medical University, Poland

2. Faculty of Chemistry, University of Warsaw, Pasteura 1, Warsaw 02-093, Poland 3. Department for Histology and Embryology Centre for Biostructure Research,

Warsaw Medical University, Poland * Corresponding author: [email protected], Chalubinskiego 5, 02-004 Warszawa, Poland

Abstract

The systemic treatment of advanced cancer, where local surgery is ineffective, still offers numerous difficulties due to therapy resistance, side effects and relapse. Thus, novel therapeutics are ongoing. The scaffolds for novel drugs often derive from naturally occurring substances. One of such example is podophyllotoxin – a plant derived substance. Podophyllotoxin itself is indicated in local anticancer treatment and due to its’ substantial toxicity is not recommend for systemic therapy. However, its analogues like etoposide is an element of systemic anticancer therapy administered in many malignancies. Limiting for etoposide is bone marrow depression and secondary leukemia induced in susceptible individuals. Thus, less toxic and more effective substances are needed for cancer therapy. Due to complexity of podophyllotoxin molecule it’s scaffold is nowadays intensively studied as a source for novel therapeutic substances. Within this project our team aimed to modify the molecule of podophyllotoxin to obtain novel derivatives that were screened for anticancer potential. Furthermore, one of new compound was conjugate of podophyllotoxin and benzothiazole. Benzothiazole is widely used in research because of antitumor, antibacterial, anticonvulsant, anti-inflammatory and other activities of its derivatives. The obtained derivates turned out to be less toxic for normal fibroblasts in comparison to parental podophyllotoxin.

Keywords: podophyllotoxin, benzothiazole, cancer, cell viability,

Introduction

A number of studies and many clinical examples demonstrate that the treatment of cancer often encounters numerous difficulties. The lack of therapy response, relapse, or even toxic side effects are a few of such examples (Miller, Siegel et al. 2016; Siegel, Miller et al. 2016). Moreover, in developed countries, the prevalence of cancer shows increasing tendency and application of cancer medicals makes its treatment one of the most costly (Miller, Siegel et al. 2016; Siegel, Miller et al. 2016). In advanced stages of malignant disease the existing treatment remains often ineffective or relapse is diagnosed (Miller, Siegel et al. 2016; Siegel, Miller et al. 2016). Hence, to overcome these difficulties new drugs that are effective, specific, less toxic and relatively cheap are ongoing.

Thus, the ideal anti-cancer drug – would be one that combined two features: nontoxic for normal cells and effectiveness for tumor cells (Gewirtz, Holt et al. 2007; Mattson, Calabrese 2009). It also should be cost-effective and have uncomplicated application.

Over last years, natural products continued to play a highly significant role in the drug discovery and development process. Thus in the area of anticancer drugs, according to published data including 25-year period of drug development, 47% of



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all approved antitumor drugs worldwide were either natural products or directly derived therefrom (Newman, Cragg 2007).

Our team was inspired by the ideas above. Therefore, we designed and synthesized derivatives of podophyllotoxin (PTOX) which is the anti-cancer, plant-derived drug (Ardalani, Avan et al. 2017; Zhang, Rakesh et al. 2018). PTOX belongs to the class of aryltetralinlactone cyclolignans and it is purer and more stable form of podophyllin (Gordaliza, Garcia et al. 2004; Saraiva, Vega et al. 2007; Silva, Coimbra et al. 2007; Silva, Martins et al. 2009; Srivastava, Negi et al. 2005; Yousefzadi, Sharifi, Behmanesh et al. 2010; Yousefzadi, Sharifi, Chashmi et al. 2010).

The crude Podophyllum peltatum plant extract is named podophyllin. However, podophyllin as a mixture of different active substances contains little active compound and numerous harmful ingredients of high toxicity, thus does not comply with the WHO guidelines for plant derived treatments and is not recommended for clinical treatment protocols (von Krogh, Longstaff 2001).

The purified form of podophyllin-derived active compound is named podophyllotoxin. This polycyclic compound was first isolated in 1880 from the Podophyllum peltatum species. However, there are few other species, such as Berberidaceae, Apocynaceae, Polygalaeae, Apiaceae, Linaceae and other, which contain PTOX and analogs. PTOX contains five rings, which are methylenedioxy, two tetrahydronaphthalene, lactone and aryl rings (Scheme 1). PTOX is common and the most effective cure for anogenital warts; especially for condyloma acuminatum caused by human papilloma virus (HPV) (Gilson, Ross et al. 2009; Keri, Patil et al. 2015; Seth 2015).

The exact mechanism of antineoplastic action of PTOX is unknown. However, it has been shown that PTOX due to binding tubulin, a subunit of microtubules, prevents the polymerization of tubulin into microtubules. Affinity and site of binding is similar to colchicine, well-known plant-derived (Colchicum autumnale) drug with similar mechanism (Spasevska, Ayoub et al. 2017), although PTOX binds more rapidly and reversibly whereas colchicine bind irreversibly. Thereby, this mechanism could include the cell cycle arrest at mitosis and impede the formation of the mitotic-spindle (Ardalani, Avan et al. 2017). Interestingly, PTOX competitively inhibits the binding of colchicine (Lu, Chen et al. 2012). As mentioned above, PTOX alone is used in a local treatment of anogenital warts (Lewis, Goldmeier 1995; Longstaff, von Krogh 2001). Clinical results with systemic application of PTOX were disappointing due to severe gastrointestinal side effects (Imbert 1998), therefore PTOX won’t be approved for systemic treatment. However, continuous efforts concerning the synthesis of PTOX analogues led to the discovery of new anticancer drugs. For example its derivative, etoposide is currently used in the clinic for the treatment of a variety of malignancies including lung and testicular cancers, glioblastoma multiforme, lymphoma and nonlymphocytic leukemia (Bohlin, Rosen 1996). Teniposide another PTOX derivate, is applied for the treatment of childhood acute lymphocytic leukemia (Bohlin, Rosen 1996). In contrast to the parent podophyllotoxin, which binds to tubulin and inhibits microtubule assembly, etoposide has a distinct mechanism of action (Kumar, Kumar et al. 2011). In fact, molecules such as etoposide, amsacrine, and mitoxantrone are topoisomerase II inhibitors that induce cell death by enhancing topoisomerase II-mediated DNA cleavage through stabilization of the transient DNA/topoisomerase II cleavage complex (Kumar, Kumar et al. 2011; Wang 1996).

Piotr Strus, Kamil Lisiecki, Zbigniew Czarnocki, Izabela Młynarczuk-Biały, Łukasz P Biały

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Like numerous anticancer drugs, also etoposide is not free of toxic side effects. Bone marrow depression is a serious, dose-limiting side effect diagnosed in patients receiving etoposide (Leroy, Kajava et al. 2001). The use of effective doses of etoposide is also associated with an increased risk of secondary acute myelogenous leukemia. For this reason, there exist an urgent need for development of more potent analogues of podophyllotoxin characterized by less toxic side effects.

Within this study we aimed to modify the molecule of phodophyllotoxin by binding it with functional groups to obtain novel compounds with potential for systemic application.

Therefore, three new cyclolignans were synthesized using photocyclization or acid-catalyzed cyclization strategy

(Lisiecki, Krawczyk et al. 2017; Lisiecki, Krawczyk et

al. 2016). One of the new compounds, named KL3, is a conjugate of two molecules with anti-tumor activity, podophyllotoxin and benzothiazole. The analogues of benzothiazole and its derivates are widely used in pharmaceutical research, because of their biological and pharmacological properties. Benzothiazole is a class of heterocyclic compounds having 2 hetero atoms: sulphur and nitrogen (Seth 2015). Benzothiazole is a privileged bicyclic ring system with multiple applications. In the 1950s, 2-aminobenzothiazoles were intensively studied as central muscle relaxants. Several years later riluzole was found to interfere with glutamate neurotransmission (6-trifluoromethoxy-2-benzothiazolamine, PK-26124, RP-25279, Rilutek). After that benzothiazole derivatives have been also studied as anticancer agents (Youssef, Noaman 2007). The potential of benzothiazole and related compounds was examined in breast tumors, regardless of estrogen receptor status, and against ovarian, renal, lung, and colon cancer cells (Kashiyama, Hutchinson et al. 1999).

Derivatives of benzothiazole serve as scaffolds for experimental drug design without technical difficulties (Ahmed, Yellamelli Valli Venkata et al. 2012; Ali, Siddiqui 2013; Bhuva, Kini 2010; Bolelli, Musdal et al. 2017; Henriksen, Hauser et al. 2007; Kok, Gambari et al. 2008; Liu, Lin et al. 2008). This versatile and unique compounds have received remarkable attention because of antitumor activity against breast (both estrogen receptor-positive and estrogen receptor-negative cell lines), ovarian, colon lung and renal cell lines and their interesting pharmacological activities, including anticonvulsant, analgesic, anti-tumor, antibacterial, anti-microbial, skeletal muscle relaxant and other activities such as: antidiabetic, anti-inflammatory, anticonvulsant, antiviral, antioxidant, antitubercular, antimalarial, antiasthmatic, antihelmintic, photosensitizing and diuretic (Amnerkar, Bhusari 2010; Bondock, Fadaly et al. 2009, 2010; Bradshaw, Wrigley et al. 1998; Caleta, Grdisa et al. 2004; Charris, Monasterios et al. 2002; Duggan, Lewis et al. 2009; Gurupadayya, Gopal et al. 2008; Hutchinson, Jennings et al. 2002; Kashiyama, Hutchinson et al. 1999; Kumbhare, Kosurkar et al. 2012; Sharma, Sinhmar et al. 2013; Shi, Bradshaw et al. 1996; Yoshida, Hayakawa et al. 2005).

In 2016 we have developed methodology for the stereoselective synthesis of cyclolignans related to podophyllotoxin (Lisiecki, Krawczyk et al. 2016). It involves the use of L-prolinol as a chiral auxiliary and continuous flow irradiation of a chiral atropoisomeric 1,2-bisbenzylidenesuccinate amide ester. Based on this discovery, a formal synthesis of (-)-podophyllotoxin and total synthesis of (+)-epigalcatin were


51

completed (Lisiecki, Czarnocki 2018; Lisiecki, Krawczyk et al. 2017; Lisiecki, Krawczyk et al. 2016).

New compounds should be soluble and stable in water solution. They need to have acceptable bioavailability, easily pass through cancer cells membrane and they should provide the evidence of high selectivity against cancer cells. An unsophisticated and cheap way to fast screening of new compounds is their incubation with selected cell lines in a cell culture. We can observe the effects of novel compounds on proliferation of cells, cell morphology and identify signs for degeneration using microscopic imaging as well as we can study cytotoxic/cytostatic effects within viability assays (Hejchman, Taciak et al. 2015; Mlynarczuk-Bialy, Doeppner et al. 2014; Mlynarczuk-Bialy, Roeckmann et al. 2006).

An assessment of cell survival is essential to test if compounds have cytotoxic/cytostatic effects. Cytotoxic effect results from direct harmful effect of examined compound on cells and cytostatic effect is a consequence of decreased cell proliferation that secondly may also lead to cell death. One of the best established viability assays is crystal violet decolorization assay (CVDA). In this method staining of attached, living cells with crystal violet dye, which binds to proteins and DNA, allow to detect the amount of remaining alive cells. Dead cells lose their adherence and by washing with PBS are eliminated from the population of cells, reducing the amount of bound crystal violet in a culture in comparison to the control, vehicle-treated group. This is a quick and if validated, adequate screening method that can be used for the estimation of cytostatic/cytotoxic effects after treatment with potential anticancer compounds (Hejchman, Taciak et al. 2015; Mlynarczuk-Bialy, Doeppner et al. 2014; Mlynarczuk-Bialy, Roeckmann et al. 2006). Therefore, to estimate the number of living cells we used CVDA.

To improve our results and determine cytopathic effects of novel compounds we also used light microscope. Due to specification of our Juli-Stage (NanoEnTek) microscope we were able to verify proliferation and amount of cells and also take photographs of cells incubated with new compounds.

Results and discussion Design of PTOX derivates

As shown in the available literature, cyclolignans are compounds with remarkable potential. However, most of derivatives are obtained by modifying natural compound (mainly podophyllotoxin). This does not allow for accessing other stereoisomers because some stereochemical features in the main carbon skeleton of podophyllotoxin cannot be changed during synthesis. What is more, the structural complexity of podophyllotoxin, derived from the presence of four stereogenic carbons in ring C has limited most of the structural activity relationship obtained by derivatization of the parent natural product rather than by the total synthesis. These features make it necessary to search for derivatives of PTOX with simplified structures, which can be obtained via short synthetic pathway from simple starting materials. Such are needed, since the therapeutic potential of PTOX and its derivatives is often limited by problems of drug resistance (Gupta 1983), hydrophobicity and low selectivity (Gordaliza, Garcia et al. 2004). Although many methods of stereoselective synthesis of cyclolignans are known, their main drawback is the lack of generality and often


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high cost of synthesis. To meet this, we developed new methodology (Yu, Che et al. 2017) based on the use of cheap and readily available starting materials such as simple aromatic aldehydes, succinic acid ester, and L-prolinol as a chiral auxiliary. The key step of the synthesis is photocyclization. It has been shown that this process occurs at much higher yield when carried out under flow conditions. This approach has an additional advantage, it allows for unlimited scale of synthesis, which is an important issue in the multi-step syntheses. In addition, avoiding the use of sophisticated organocatalysts or catalysts containing heavy metals, will be extremely important because of the elimination of the possibility of contamination with toxic metals. Our method is therefore extremely advantageous in the context of the synthesis of compounds with potential pharmaceutical application.

In this study, we investigated anticancer properties of three new derivatives of PTOX - 2, 3, 4. Compound 2 is a derivative of 1-arylnaphthalene which poses anti-cancer activity (Hui, Zhao et al. 2011). Derivative 3 contains in its structure L-prolinol moiety which is a H-bond donor and its conformationally restricted main chain may improve receptor binding. Compound 4 is a conjugate of podophyllotoxin (red) and benzothiazole moiety (blue). In recent years, benzothiazoles have been recognized as promising pharmacophores with diverse biological properties including anti-cancer activity (Sharma, Sinhmar et al. 2013). All of those compounds are in good accordance with Lipinski's rule of five (Lipinski, Lombardo et al. 2001) – less than 5 hydrogen bond donors, less than 10 hydrogen bond acceptors and molecular mass less than 500 daltons or not much above. Compounds 2 and 3 were obtained by us previously

(Lisiecki, Krawczyk et al. 2017; Lisiecki, Krawczyk et al.

2016) and compound 4 was synthetized from podophyllic aldehyde (total synthesis of this compound was already reported by us (Lisiecki, Krawczyk et al. 2016)) and 2-aminothiophenol (Scheme 1). The synthesis of presented compounds is highly efficient and starts from cheap and readily available substrates.

Scheme 1. Chemical structures of podophyllotoxin (PTOX), compounds 2 -KL1 and 3 -KL2 (top) and synthesis of compound 4 -KL3 (bottom).


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Cytotoxicity assay

We performed crystal violet viability assay to examine the sensitivity of tumor and non-tumor cells towards novel derivates in comparison to the parental one PTOX.

Compound KL1 did not induce any significant cytotoxic/cytostatic effect against HeLa, MDA-MB, DU145 and CFPAC. Also, in comparison to PTOX – a model toxic drug for NIH-3T3 cells, the novel KL1 compound was 100 times less toxic. Similar results were obtained for KL2 compound. The values of IC50 for this substance on all but one cell line (MCF7) was good above 140 µM and ranged from 140 µM to 490 µM (Table 1). In addition, in PC3 cell line the IC50 for KL1 was 48 µM. Also, non-tumor NIH-3T3 cells turned out to be 90 times less susceptible to the action to KL2 in comparison to the parental PTOX.

These results showed that compounds KL1 and KL2 by chemical modifications of parental PTOX exhibited lower cytotoxicity than PTOX itself. Moreover, both compounds turned out to be highly effective in induction of cytotoxic/cytostatic effects on MCF7 cells and in comparison to non tumor mouse fibroblasts they were tumor cell specific with selectivity index (SI) of 7.9 and 4.5 for KL1 and KL2, respectively (Table 2).

Compound KL3 turned out to be most active in induction of cytotoxic/cytostatic effects from all of three novel compounds. The IC50 values for tumor cells were form 0.5 µM to 10 µM. Only MCF7 breast cancer cells remained relatively resistant to KL3 with IC50 of 116 µM.

Such a good anti tumor profile of KL3 was accompanied with less toxicity towards NIH-3T3 cells in comparison to the parental PTOX.

As suspected – combination of two anti tumor agents within one molecule (KL3) improved the antitumor activity, in comparison to KL1 and KL2, and what is also very desiderated this drug was more toxic for most cancer cell lines than to no-tumor fibroblasts.

Table 1 Compilation of IC50 value of novel compounds and of the parental POTX estimated in 7 cell lines. IC50 – Concentration of compound corresponding to 50% growth inhibition after 48-hour incubation.

Inhibitory concentration (IC 50) [μM]

Compound

Immortalized non tumorogenic cells

Tumor derived cells

Fibroblast Cervix cancer

Breast cancer Prostate cancer Pancreas cancer

NIH-3T3 HeLa MDA-MB MCF7 PC3 DU145 CFPAC

KL1 55 165 300 7 48 425 370

KL2 45 140 490 10 144 290 400

KL3 1 0.5 0.7 116 0.7 6.8 10

PTOX 0.5 0.5 125 123 10 1 100


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The standard error bars did not extend 10% and for more transparency are not

included in the table. Results are from 3 independent experiments. The selectivity index is helpful for comparison between different tumor cell lines.

However, when IC50 values are high, the comparison between substances does not

matter. Obtained substances KL1 and KL2 are less toxic. Thus, we have obtained

leading structures of lower cytotoxicity than parental PTOX. In turn, KL3 as

compound represented by covalently bound of two anti tumor substances turned out

to be the most effective with the highest anti tumor activity from all studied

substances. The IC50 value for KL3 was below 1 μM for three cancer cell lines.

Table 2 Tumor cell selectivity

Selectivity Index (SI)

Compound Tumor derived cells

Cervix cancer Breast cancer Prostate cancer Pancreas cancer

HeLa MDA-MB MCF7 PC3 DU145 CFPAC

KL1 0.3 0.2 7.9 1.1 0.1 0.1

KL2 0.3 0.1 4.5 0.3 0.2 0.1

KL3 2.0 1.4 0.009 1.4 0.1 0.1

PTOX 1.0 0.004 0.004 0.1 0.5 0.005

Microscopy analysis

A series of images was recorded and representative regions of particular groups is

shown at Fig 1. Microscopic experiments were performed on NIH-3T3 cells conducted

from disaggregated BALB/c mouse embryos. They are extremely sensitive to contact

inhibition and are highly susceptible to transformation by SV40 VIRUS and murine

sarcoma virus (Labruère, Gautier et al. 2010).

In comparison to the control group, demonstrating spindle-shaped morphology of

NIH-3T3 cells that are elongated with long processes and share 95% of confluence,

both treated groups vary in cell count and shape from the control group. Cells treated

with 10 µM or 100 µM PTOX demonstrate 60 and 50% of confluence, respectively.

In comparison to the controls, their processes are shortened and thickened, the

perinuclear region is enlarged, the total surface occupied by a single cell is enlarged.

We can observe also signs of cell degeneration like small rounded cells and pyknotic

cells that’s number increases by increasing concentration of PTOX.

Compound KL3 induced shortness of NIH-3T3 processes similarly to parental

PTOX, also the perinuclear region was enlarged. Moreover, this enlargement was

more pronounced than in PTX treated group. The ratio of degenerating cells was also

higher than in PTX treated cells. We observed numerous pyknotic cells in KL3 100


55

µM treated group and some amount of big rounded cells – suggesting mitotic

catastrophe as a reason of such perturbations (Figure 1). Even if such initial compounds might have diminished cytotoxic potencies compared

with the parent PTOX, the ease of preparation of carefully designed libraries of

analogues would lead to more informative studies and expeditious structure

optimization. In this regard we have obtained less toxic scaffolds represented by KL1

and KL2 and a highly effective KL3 that is twice less toxic for NIH-3T3 cells in

comparison to PTOX and turned out to be selective for tumor cells. To improve the

molecules, further modifications should be designed and performed combined with

studies on detailed mechanisms of action of these compounds.

Materials and methods

Synthesis

Methyl 4-(benzo[d][1,3]dioxol-5-yl)-5,6,7-trimethoxy-3-methyl-2-naphthoate (2) was

prepared in accordance with the procedure previously described.3

(5R,6R)-Methyl 7-((S)-2-(hydroxymethyl)pyrrolidine-1-carbonyl)-5-(3,4,5-trimethoxy-

phenyl)-5,6-dihydronaphtho[2,3-d][1,3]dioxole-6-carboxylate (3) was prepared in

accordance with the procedure previously described.4

(5R,6R)-Methyl 7-(benzo[d]thiazol-2-yl)-5-(3,4,5-trimethoxyphenyl)-5,6-dihydronaphtho

[2,3-d][1,3]dioxole-6-carboxylate (4). A mixture of podophyllic aldehyde (prepared

in accordance with the procedure previously described4) (64 mg, 0.15 mmol, 1

equiv.), 2-aminothiophenol (19.7 mg, 0.16 mmol, 1.05 equiv.) and Na2S2O5 (30 mg,

0.16 mmol, 1.05 equiv.) in 1 mL of DMSO was heated at 120˚C for 2 h. The reaction

mixture was allowed to cool to room temperature, excess water was added, and

yellow solid precipitate was collected by filtration. The precipitate was washed with

water, dried and purified on a silica gel column, using ethyl acetate in n-hexane

(gradient, from 0% to 10%) as an eluent. A yellow solid was obtained (52 mg, 0.098

mmol, 65%). M.p. 165–166°C. Rf (50% AcOEt/n-hexane) 0.54. [α]D25 = 281.0 (c

0.1, CHCl3). 1H NMR (CDCl3, 300 MHz): δ 7.95 (dd, J1 = 8.1 Hz, J2 = 0.6 Hz,

1H), 7.84 (dd, J1 = 7.8 Hz, J2 = 0.6 Hz, 1H), 7.43 (m, 2H), 7.35 (dt, J = 7.8, 1.2 Hz,

1H), 6.86 (s, 1H), 6.63 (s, 1H), 6.56 (s, 2H), 5.98 (d, J = 1.5 Hz, 1H), 5.96 (d, J = 1.5

Hz, 1H), 4.61 (d, J = 7.5 Hz, 1H), 4.44 (d, J = 7.8 Hz, 1H), 3.89 (s, 3H), 3.84 (s, 6H),

3.43 (s, 3H). 13C NMR (CDCl3, 75 MHz): δ 171.7, 167.1, 153.8, 153.3, 148.6,

146.5, 137.4, 134.8, 134.4, 133.4, 131.7, 128.5, 127.0, 126.2, 125.3, 123.1, 121.4,

108.8, 108.8, 106.6, 101.4, 77.4, 77.0, 76.6, 60.9, 56.1, 51.7, 49.1, 48.5. HRMS (ESI)

m/z: calcd for C29H25NO7SH [M+H]+, 532.1424; found, 532.1440.

Reagents

PROX (SIGMA) and Novel compounds were dissolved in DMSO (SIGMA) at 100

mM and kept in the fridge until examination.


56

Cell Culture

Mouse NIH-3T3 fibroblasts (ATCC, USA) were cultured in Dulbeco (Biochrom,

Berlin, Germany) supplemented with 10% heat-inactivated FCS, penicillin (100

U/ml), streptomycin 100 µg/ml.

For analysis of antitumor activity of examined compounds, following cell lines were

used: MDA-MB-431, HeLa, PC3 and DU145. All of cell lines was obtained from

ATCC. All cells were cultured in media recommended by suppliers. Then

supplemented with standard antibiotics and 10% FCS all from Sigma-Aldrich. Cells

were kept in 25 cm2 tissue flasks (Greiner, Berlin, Germany) and passaged every 2-3

days.

Cell Proliferation Assay

The cytotoxic/cytostatic effects of novel compounds on culture cells were examined

in vitro using the crystal violet assay, as previously described (Mlynarczuk-Bialy et

al., 2006). Briefly, cells (5 x 103 cells/well) were seeded in 96-well microtiter plates

(BD, Biosciences, San Jose, California, USA) and incubated with serial dilutions of

examined compounds. Inhibitors were added in quadruplicate to a final volume of

200 µL. Appropriate volumes of culture medium, supplemented with DMSO

(<0.1%) were added as controls. After an incubation period of 24, 48 or 72 hours,

cells were washed once with PBS, fixed with 70% ethanol for 30 min and finally

stained with 0,1% crystal violet in PBS for 30 min and washed carefully with water

to remove unbound dye. The remaining dye was eluted by 1% SDS in water and

determined at 550 nM. Cytostatic/cytotoxic effect was expressed as relative viability

of treated cells (% of control cells incubated with medium only) and was calculated

as follows: relative viability = (Ae-Ab) x 100/(Ac-Ab), where Ab is background

absorbance, Ae is experimental absorbance and Ac is the absorbance of untreated

controls.

Light microscopy

In order to assess the influence of novel derivates on morphology of examined cells,

after 48 h of incubation period, cells form cell culture were directly imaged by

a phase contrast microscope (Juli, NanoEnTek) at magnification of 100x.

In comparison to the controls (A,B) we observe dose-dependent cytopathic effects

including shortness of cytoplasmic processes and thickness of perinuclear region. In

the higher drug concentration, we observe also cell rounding and detachment from

the bottom. In comparison to PTOX (C,D) the effects induced by KL3 (E,F) are

similar but more pronounced (advanced).


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Figure 1: Photomicrographs of NIH-3T3 cells:

Untreated cells (A, B smaller and bigger magnification, respectively), C,D: cells treated with PTOX 10 µM or

100 µM – respectively, or E,F groups treated with KL3 10 µM or 100 µM – respectively. Scale bars 50 µM

Summary and conclusion

We aimed to create novel compounds of anti-cancer drug – PTOX. We planned and

designed the synthesis of more effective as well as less toxic compounds.

Furthermore, we were also focused on economic aspects and would like to reduce

costs of synthesis. Due to these assumptions we planned to use cheap and easily

available substrates. Moreover, in the process of synthesizing we don’t need to use

heavy metal catalysts, and thus there is no risk of contamination of compounds

A B

C D

E F


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obtained with heavy metals (whose content in the drug must be at a very low level).

The next advantage form the used methodology, thanks to the use of flow conditions

in the photocyclic process, it was easy to conduct synthesis on a large scale. This

methodology also allowed obtaining numerous derivatives with differently decorated

rings, thanks to which it was possible to influence parameters such as: solubility,

bioavailability, stability. What is interesting, when it comes to the KL3 compound

itself, it was the first synthesis of the PTOX and benzothiazole congeners with the cis

configuration at the C1 and C2 carbon atoms.

For in vitro tests of new substances, we applied low-cost and well-standardized

viability assays in combination with bio-imaging of the tested cells. We showed that

the applied methods are able to verify in an unambiguous manner whether the

examined chemical structures exhibit biological effects.

Result of cytotoxicity assay and microscopic analysis proved, novel compounds are

less toxic and more effective in comparison to parental PTOX. That is a promising

result because the toxicity of PTOX excludes its systemic application. Anti cancer

KL3 compound with better effectiveness and lover toxicity can be useful in more

applications as the initial substances.

In summary, we obtained KL3 derivative, that is less toxic than the parental one, in

an innovative synthesis process that allows to synthesize large quantities of the

product in its pure form - this work is therefore a comprehensive description of the

compound in cell culture-based model. Its future testing in animal models can verify

pre-clinical potential of KL3 compound.

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62

Immunohistochemical expression of erythropoietin

in invasive breast carcinoma with metastasis

to lymph nodes

Maksimiuk M.2, Sobiborowicz A.

2, Sobieraj M.

2, Liszcz A.

2, Sobol M.

1, Patera J.

3,

Badowska-Kozakiewicz A.M.1

1 Department of Biophysics and Human Physiology, Medical University of Warsaw

Chałubińskiego 5, 02-004 Warsaw 2 Students' Scientific Organization at the Medical University of Warsaw

Oczki 1a, 02-007 Warsaw 3 Department of Pathomorphology, Military Institute of Health Services, Warsaw, Poland

corresponding author: Marta Maksimiuk

Department of Biophysics and Human Physiology,

Medical University of Warsaw

Chałubińskiego 5, 02-004 Warsaw

[email protected]

tel: 691-230-268

ABSTRACT

Introduction: Tumor characteristics, such as size, lymph node status, histological type of the neoplasm

and its grade, are well known prognostic factors in breast cancer. The ongoing search for new

prognostic factors include Bcl2, Bax, Cox-2 or HIF1-alpha, which plays a key role in phenomenon of

tumor hypoxia and might induce transcription of the EPO gene. Erythropoietin may influence lymph

node metastasis or stimulate tumor progression, thus it seemed interesting to determine its expression in

invasive breast cancers with lymph node metastases presenting different basic immunohistochemical

profiles (ER, PR, HER2).

Aim: To evaluate the relationship between histological grade, tumor size, lymph node status, expression

of ER, PR, HER2 and immunohistochemical expression of erythropoietin in invasive breast cancer with

metastasis to lymph nodes.

Materials and methods: The material consisted of histological preparations derived from patients

treated for invasive breast cancer with metastasis to lymph nodes. Examined samples were stained using

standard methods. Routine tests were additionally performed in order to determine immuno-

histochemical expression of basic profile of diagnostic markers, such as estrogen receptor (ER),

progesterone receptor (PR) and HER2. Expression of erythropoietin (EPO) was assessed through using

of an appropriate antibody against its antigen.

Results: Among the studied group of cancers we selected four subgroups with different basic

immunohistochemical profiles. Additionally we studied the relationship between histological type of

breast cancer and basic immunohistochemical profile and a statistically significant correlation was noted

in all cases. Expression of erythropoietin was determined in all histological breast cancer types and it

was most frequently identified independently in: invasive ductal carcinoma of no special type (IDC

– NST), cancers with the highest histological grade (G3) and cancers evaluated as pT2 and pN1. The

largest group expressing EPO consisted of cancers presenting the ER-/PR-/HER2+ basic

immunohistochemical profile, while no EPO expression was most often demonstrated in cancers with

ER+/PR+/HER2- immunohistochemical profile. Furthermore our study revealed EPO expression in

over 40% of cases in the group of TNBC (ER-/PR-/HER2-).

Conclusions: In our study we demonstrated a statistically significant correlation between markers included in the basic immunohistochemical profile (ER, PR, HER2) with histological type of invasive breast cancer. There was no relationship between markers of basic immunohistochemical profile and



in invasive breast carcinoma with metastasis to lymph nodes

63

expression of EPO as well as no statistically significant dependence was found between expression of EPO and basic clinical features. However, conducted studies allow for formulating important conclusions for pathomorphological diagnostics. Moreover, expression of EPO in almost 40% of cancers with ER-/PR-/HER2- immunohistochemical profile (TNBC) suggests that in TNBC erythropoietin might be a prognostic marker.

Introduction

Tumor characteristics, such as size, lymph node status and histological type of the neoplasm, are well known prognostic factors in breast cancer. Certain microscopic features of the neoplasm, such as histological grade of malignancy or presence of neoplastic cells in blood and lymphatic vessels, also deserve special attention [1, 2]. Immunohistochemical techniques constitute an excellent tool for determination of new factors characteristic for neoplasms that may be considered prognostic or predictive factors and therefore, enabling determination of immunohistochemical profiles of various histological types of breast cancer. Standardization of methods of immunohistochemical detection of estrogen receptor (ER), progesterone receptor (PR) and HER2 protein overexpression contributed to inclusion of ER, PR and HER2 status assessment into routine evaluation of prognostic and predictive factors in breast cancer. Assessment of ER, PR and HER2 is currently essential to the therapeutic process. There is a number of factors, such as: Bcl2, Bax, or Cox-2 playing a key role in the process of carcinogenesis and cancer progression, but due to lack of standardized assessment methods, as well as lack of evidence for their clinical value, they are not used in routine pathomorphological evaluation of breast cancer and remain subject of intense research [3]. Since achieving the best antineoplastic effect and providing the patient with optimal quality of life are primary goals of treatment of neoplastic diseases, including breast cancer, there is an ongoing search for prognostic and predictive factors based on morphological features and knowledge of other characteristics of neoplastic cells enabling proper assessment of disease course, malignancy risk and susceptibility to treatment. In cases of neoplasms without lymph node metastases the following factors were proven important for the prognosis: patient age, histological type of neoplasm, histological grade of malignancy, proliferative activity and DNA ploidy. Therefore, the factors currently taken into consideration in the assessment of breast cancers include: markers of proliferation (mitotic index, proportion of cells in the S phase of the cell cycle, expression of nuclear antigen Ki67 and PCNA), DNA ploidy, expression of estrogen and progesterone receptors as well as HER2 protein [4].

Recent years have brought about growing interest in the phenomenon of tumor hypoxia and augmented activity of HIF1-alpha, a marker of hypoxia, which is considered one of the most important factors responsible for activation of tumor angiogenesis. It is believed that HIF1-alpha might induce transcription of the EPO gene. Its product, erythropoietin, is a physiological regulator of erythropoiesis sensitive to hypoxia. Biological action of erythropoietin involves induction of erythropoiesis by stimulating proliferation and differentiation of red blood cell precursors into mature erythrocytes. However, the effects of erythropoiesis are not limited to the hematopoietic system. Numerous reports published in the recent years indicate that erythropoietin is a cytokine with autocrine and paracrine properties and affects many non-hematological tissues [5]. Local production of erythropoietin was

Maksimiuk M., Sobiborowicz A., Sobieraj M., Liszcz A., Sobol M., Patera J., Badowska-Kozakiewicz A.M.

64

demonstrated in nervous tissue, genital tract and placenta, while expression of erythropoietin receptor was identified, among other locations, in kidneys, lungs and muscle tissue [5, 6]. Erythropoietin receptors were also found in many neoplasms: colon adenocarcinoma, stomach cancer, lung cancer, brain cancer, neoplasms of the head and neck, renal tumors, and in prostate cancer [7]. Ability of erythropoietin to stimulate angiogenesis in both normal and neoplastic cells together with a finding of erythropoietin receptor expression on neoplastic cells and cells of vascular endothelium suggest that erythropoietin might directly influence tumor growth and inhibit apoptosis, stimulating tumor progression or metastasis. It could modify tumor cells’ sensitivity to chemo- and radiotherapy [5, 6, 7].

The influence of erythropoietin on lymph node metastasis is an interesting, yet scarcely investigated, issue. Therefore, it seemed interesting to determine erythropoietin expression in invasive breast cancers with lymph node metastases presenting different basic immunohistochemical profiles (ER, PR, HER2). From a time perspective, results of this research might become an important parameter in the assessment of the risk of metastasis to lymph nodes and other organs.

Aim

The aim of this study was to evaluate the relationship between histological grade, tumor size, lymph node status, expression of ER, PR, HER2 and immuno-histochemical expression of erythropoietin in invasive breast cancer with metastasis to lymph nodes.


The material consisted of histological preparations derived from patients treated for invasive breast cancer with metastasis to lymph nodes. Histological and immunohistochemical studies were performed at the Department of Pathology, Military Medical Institute in Warsaw. Material for the study came from biopsies, excisional biopsies and modified radical mastectomies. Samples of tumors were fixed in 10% buffered formalin phosphate. After 24-hour fixation, material was dehydrated using alcohol in gradually increasing concentrations and embedded in paraffin. Paraffin blocks were cut into serial sections 4µm in thickness. They were then stained using standard methods. The tumors were classified and graded according to the WHO and the Nottingham modification of the Scarff-Bloom-Richardson systems. In the sections stained with routine H&E method, the following determinations were carried out: type of neoplasm (WHO classification), tumor grade including tubule formation, and intensity of division as well as the degree of neoplastic cell differentiation and mitotic index defined as a mean number of mitotic figures in neoplastic cells counted in 10 fields of vision at a x400 magnification (surface field 0.17 mm

2).

Immunohistochemical Staining Steroid Receptor (ER, PgR) and HER2

Paraffin sections on slides covered with 2% saline solution in acetone at a tempe-rature of 42

oC were used for immunohistochemical examination. Routine tests were

also performed in order to determine immunohistochemical expression of basic profile of diagnostic markers, such as estrogen receptor (ER), progesterone receptor (PR) and HER2. Immunohistochemistry was performed using the EnVision

TM



65

+ HRP DakoCytomation (EnVision TM

Dual Link System-HRP, DAB+, Code: K4065). Monoclonal antibodies against receptors for estrogen (Monoclonal Mouse Anti-Human Estrogen Receptor alpha, 1:50 dilution, Clone: 1D5, Code: IR654, DAKO) and progesterone (Monoclonal Mouse Anti-Human Progesterone Receptor, 1:400 dilution, Clone: PgR636, Code: IR068, DAKO) were used in order to determine the expression of steroid receptors. The study was conducted as follows: sections were incubated at 60°C overnight and subsequently dewaxed. The next step involved revealing the epitope by heating the slides in a buffer for 40 minutes. Subsequently, preparations were left at a room temperature for 20 minutes. Preparations were rinsed in buffer and endogenous peroxidase was blocked by washing in 3% H2O2 for 10 minutes. In the next step, preparations were incubated with an appropriate antibody for 30 minutes. After incubation, preparations were rinsed in buffer for 10 minutes, and then incubated with the reagent (Visualization Reagent) for 30 minutes. After incubation with the reagent, preparations were washed in TBS (Tris - Buffered Saline, Code: S1968) with pH 7.6 for 10 minutes, and then incubated with 3,3’-diaminobenidine (DAB) (Substrate Chromogen Solution) for 10 minutes to visualize the color of the reaction. At the end of the procedure, preparations were stained with hematoxylin. Evaluation of the immunohistochemical markers was performed by two pathologists as follows: ER and PR were categorized as negative – (0%), low positive – (1-10%); nuclear staining in >10% of tumor cells was considered positive for ER and PR. HER2 expression was determined using HerceptTest

TM DAKO test (Code: K5204). It enabled

detection of HER2 expression using a polyclonal antibody against this protein (Rb A – Hu HER2 – Rabbit Anti-Human HER2 Protein). Antigen retrieval for HER2 using HerceptTest was performed by immersing and incubating the slides in 10-mmol/L citrate buffer in a calibrated water bath (95-99°C) for 40 minutes (+/- 1 minute). After decanting the epitope- retrieving solution, sections were rinsed in the wash buffer and later, soaked in the buffer for 5 to 20 minutes before staining. The slides were loaded onto the autostainer using the HercepTest program, as described in the manufacturer’s insert. In the autostainer, the slides were rinsed, placed in 200μL of peroxidase-blocking reagent for 5 minutes, rinsed, placed in 200μL of primary anti-HER2 protein (or negative control reagent) for 30 minutes, rinsed twice and immersed in 200μL of substrate chromogen solution – DAB for 10 minutes. The slides were counterstained with hematoxylin and finally coverslipped. HER2 results were determined based on the maximum area of staining intensity according to the instruction in the package insert and the ASCO/CAP guidelines as follows: strong, circumferential membranous, staining in >30% of invasive carcinoma cell was scored 3+, moderate, circumferential, membranous staining in ≥10% of invasive tumor cells or 3+ staining in ≤30% of cells was designated as 2+ staining, weak and incomplete membranous staining in invasive tumor cells was scored 1+ and no staining was scored 0. Tumors with 0 and 1+ staining were considered negative.

Immunohistochemical staining of EPO

Expression of erythropoietin (EPO) was also assessed in all studied invasive breast cancers through use of an appropriate antibody against EPO antigen (Polyclonal Rabbit Anti-Human EPO, 1:100 dilution, Clone: H-162, Santa Cruz Biotechnology ®, Inc.) and subsequent application of the ImmunoCruz

TM Rabbit ABC Staining


66

System for visualization (Santa Cruz Biotechnology ®, Inc.). EPO staining results were scored according to the percentage of cytoplasmic positive cells as follows: (-), <10%; (+), 10%-20%; (++), >20%. Moderate expression EPO was defined as >20% tumor cells with positive staining whereas low expression was <20% [8].

Statistical analysis

All statistical analyses were performed with SPSS software v12.0 for Windows. The frequency of HER2 expression according to joint ER/PR status and the distribution of the hormone receptor status (ER, PR, and joint ER/PR) according to HER2 were

also calculated. The Chi-square (χ2) was used to assess the relationship between EPO and expression of steroid receptors, HER2, histological type of tumor, degree of histological malignancy and clinical staging. The Fisher exact test was used when the expected cell counts were less than 5. Differences were considered statistically significant at P < 0.05.

Results

Mean age of studied women was 60.5±11.7 years (median 61 years, range: 43-78 years). Histopathological examination revealed the following percentages of tumors: 68.96% IDC-NST, 18.96% IDC, 3.44% ILC, 6.9% metaplastic carcinoma and 1.72% mixed ductal and lobular carcinoma. Among the studied group of cancers we selected four subgroups with different basic immunohistochemical profiles, such as

PR+/ER+/HER2+ (22.4%); PR-/ER-/HER2+ (32.76%); PR-/ER-HER2- (12.07%); PR+/ER+/HER2- (32.76%). In all four subgroups of cancers presenting various basic immunohistochemical profiles IDC-NST was most numerous (68.96%) (Table 1).

Table 1. Correlation between histological type of invasive breast cancer and the basic immunohistochemical profile (ER, PR, HER2)

Immunohisto-chemistry basal panel for diagnosis of breast cancer

Frequency

n=58

Histopathological type of invasive breast cancer P-value*

IDC-NST

IDC ILC Metaplastic carcinoma

Mixed

ductal and lobular

PR+/ER+/HER2+

13 9 3 0 1 0 0.00001132*

PR-/ER-/HER2+

19 10 7 0 1 1 0.00005*

PR-/ER-/HER2-

7 6 0 0 1 0 0.012051*

PR+/ER+/HER2-

19 15 1 2 1 0 <0.001*

*Statistically significant results (P < 0.05) IDC-NST – invasive ductal carcinoma of no special type IDC – invasive ductal carcinoma ILC – invasive lobular carcinoma



67

In our material we studied the relationship between histological type of breast cancer

and basic immunohistochemical profile and a statistically significant correlation was

noted in all cases (p<0.05) (Table 1). In our study basic immunohistochemical profile

was extended to include a new marker – erythropoietin (EPO) – its expression was

determined in all histological breast cancer types (Figure 1). Depending on

expression of EPO in invasive breast cancers we differentiated eight subgroups of

cancers presenting different immunohistochemical profiles taking into consideration

this novel marker – EPO: PR+/ER+/HER2+/EPO+ (10.35%); PR-/ER-/HER2+/EPO+

(12.06%); PR-/ER-/HER2-/EPO+ (5.17%); PR+/ER+/HER2-/EPO+ (8.62%);

PR+/ER+/HER2+/EPO- (12.07%); PR-/ER-/HER2+/EPO- (20.69%); PR-/ER-

/HER2-/EPO- (6.90%); PR+/ER+/HER2-/EPO- (24.14%) (Table 2). In our study,

among all breast cancers 36.2% exhibited EPO expression in neoplastic cells, while

in 63.8% EPO expression was not found. IDC-NST was the most numerous group

among all histological types of cancers positive for EPO expression (24.1%) (Table

2). No expression of EPO was found in 43.1% of IDC-NST. Among the remaining

cancers EPO expression was found in 3.5% of IDC and in 8.6% of metaplastic

carcinomas, while no expression of EPO was demonstrated in 15.5% of IDC, 2.5%

of ILC and 1.7% of mixed ductal and lobular carcinomas (Table 2). A correlation

between histological type of cancer and immunohistochemical profile that included

EPO expression was studied and no statistically significant relationships were found

(Table 2).

Table 2. Correlation between the histological type of invasive breast cancer and the basic profile of

immunohistochemical, including EPO expression

Immunohistochemistry

basal panel for diagnosis

of breast cancer and

expression of EPO

Frequen-

cy

n=58

Histopathological type of invasive breast

cancer

P-value*

IDC-

NST

IDC ILC Meta-

plastic

carci-

noma

Mixed

ductal

and

lobular

PR+/ER+/HER2+/EPO+ 6 5 0 0 1 0 0.948854

PR-/ER-/HER2+/EPO+ 7 5 1 0 1 0 0.95319379

PR-/ER-/HER2-/EPO+ 3 1 0 0 2 0 0.5119721

PR+/ER+/HER2-/EPO+ 5 3 1 0 1 0 0.9427068

PR+/ER+/HER2+/EPO- 7 4 3 0 0 0 0.764538

PR-/ER-/HER2+/EPO- 12 5 6 0 0 1 0.1861714

PR-/ER-/HER2-/EPO- 4 4 0 0 0 0 0.4695367

PR+/ER+/HER2-/EPO- 14 12 0 2 0 0 0.153851

*Statistically significant results (P < 0.05)


68

Fig.1. Immunohistochemical image of invasive breast cancer – positive immunohistochemical staining for

EPO (original magnification, x200; scale bar, 50µm)

In our study we also assessed the dependence between the degree of histological

malignancy (G1-G3), tumor size (pT) or presence of lymph node metastases (pN),

and basic immunohistochemical profile that included EPO expression, but no

statistically significant correlations were found (Tables 3-5). Among G3 cancers 47%

exhibited expression of EPO, while no EPO expression was found in 53% of cases.

In a group of G2 cancers 24% were positive for EPO, while 76% of them did not

exhibit EPO expression. Among all cancers, the largest group expressing EPO were

the G3 cancers (67%) (Table 3).

Table 3. Correlation between the basic profile of immunohistochemical, including EPO expression, and

histological grading of invasive breast cancer

Immunohistochemistry basal

panel for diagnosis of breast

cancer and expression of EPO

Frequency

n=58

Histological grade

G1 G2 G3 P-value*

PR+/ER+/HER2+/EPO+ 6 0 2 4 0.83946

PR-/ER-/HER2+/EPO+ 7 0 1 6 0.76498

PR-/ER-/HER2-/EPO+ 3 0 0 3 0.50667

PR+/ER+/HER2-/EPO+ 5 1 3 1 0.25208

PR+/ER+/HER2+/EPO- 7 0 2 5 0.45285

PR-/ER-/HER2+/EPO- 12 1 7 4 0.97190

PR-/ER-/HER2-/EPO- 4 0 1 3 0.89404

PR+/ER+/HER2-/EPO- 14 1 9 4 0.55822




69

We also evaluated the relationship between tumor size (pT) and basic immuno-

histochemical profile that included expression of EPO and it was demonstrated that

among cancers exhibiting EPO expression the pT2 cancers comprised the largest

group (52.3%) (Table 4). As much as 36.2% of cancers with metastases to lymph

nodes expressed EPO, while no EPO expression was found in 63.8% of cancers with

lymph node metastases (Table 5).

Table 4. Correlation between the basic profile of immunohistochemical, including EPO expression, and tumor

stage of invasive breast cancer




Frequency

n=58

Tumor stage

pT1 pT2 pT3 pT4 P-value*

PR+/ER+/HER2+/EPO+ 6 1 3 0 2 0.80270

PR-/ER-/HER2+/EPO+ 7 0 5 1 1 0.48037

PR-/ER-/HER2-/EPO+ 3 1 2 0 0 0.74510

PR+/ER+/HER2-/EPO+ 5 4 1 0 0 0.16947

PR+/ER+/HER2+/EPO- 7 5 1 0 1 0.45243

PR-/ER-/HER2+/EPO- 12 7 4 0 1 0.81794

PR-/ER-/HER2-/EPO- 4 1 2 1 0 0.55984

PR+/ER+/HER2-/EPO- 14 3 10 0 1 0.23777


Table 5. Correlation between the basic profile of immunohistochemical, including EPO expression, and nodal

stage of invasive breast cancer




Frequency

n=58

Nodal stage

pN1 pN2 pN3 P-value*

PR+/ER+/HER2+/EPO+ 6 2 2 2 0.91074

PR-/ER-/HER2+/EPO+ 7 2 3 2 0.91759

PR-/ER-/HER2-/EPO+ 3 2 1 0 0.91308

PR+/ER+/HER2-/EPO+ 5 3 2 0 0.78999

PR+/ER+/HER2+/EPO- 7 4 3 0 0.62719

PR-/ER-/HER2+/EPO- 12 6 3 3 0.81628

PR-/ER-/HER2-/EPO- 4 2 0 2 0.46813

PR+/ER+/HER2-/EPO- 14 10 3 1 0.70751



70

A relationship between expression of EPO and basic immunohistochemical profile

(ER, PR, HER2) was also examined in the studied group of cancers and it was shown

that the largest group expressing EPO consisted of cancers presenting the ER-/PR-

/HER2+ basic immunohistochemical profile (33.3%), while no EPO expression was

most often demonstrated in cancers with ER+/PR+/HER2- immuno-histochemical

profile (37.8%) (Table 6). Our study also showed that in the group of “triple-negative

cancers” (TNBC) (ER-/PR-/HER2-) EPO expression was found in over 40% of

cases.

Table 6. The relationship between the basic profile immunohistochemistry and expression of EPO



cancer

Frequency

n=58

Expression of EPO P-value*

Positive1 Negative2

PR+/ER+/HER2+ 13 6 7 0.3968007

PR-/ER-/HER2+ 19 7 12 0.943628

PR-/ER-/HER2- 7 3 4 0.97477

PR+/ER+/HER2- 19 5 14 0.421906

*Statistically significant results (P < 0.05) 1 (+) 10%-20%; (++) >20% 2 (-) <10%

Discussion

It is presently known that erythropoietin stimulates the process of angiogenesis, both

in normal and neoplastic cells, results of some studies suggesting that erythropoietin

might influence tumor size and inhibit apoptotic mechanisms [9], which can induce

metastasis [10,11]. Therefore, such an effect may change neoplastic cells’

susceptibility to chemo- and radiotherapy, although this suggestion requires numerous

further studies. Data regarding expression of erythropoietin in invasive breast cancers

with lymph node metastasis in the context of various histological types and basic

immunohistochemical profiles (ER, PR, HER2) is scarce. There is also little data on

the influence of erythropoietin on lymph node metastasis thus, it seemed interesting

to determine expression of erythropoietin in invasive breast cancers depending on the

histological type of cancer, presence of lymph node metastasis, expression of

estrogen and progesterone receptors, as well as expression of HER2 – markers

determined routinely in immunohistochemical diagnostics. Our study was conducted

on a group of 58 patients with various histological types of invasive breast cancer.

Expression of basic markers used in routine immunohistochemical diagnostics, such

as ER, PR and HER2, was assessed and a statistically significant relationship was

demonstrated between all histological types of invasive breast cancers and expression

of estrogen and progesterone receptors, as well as HER2 expression (p<0.05) (Table

1). Since the main goal of this research was to assess the expression of EPO in all

studied histological types of invasive breast cancers, it seemed important to distinct

four subgroups of cancers exhibiting different immunohistochemical profiles:

PR+/ER+/HER2+ (22.4%); PR-/ER-/HER2+ (32.76%); PR-/ER-HER2- (12.07%);



71

PR+/ER+/HER2- (32.76%). In all four subgroups of invasive cancers presenting

various basic immunohistochemical profiles expression of erythropoietin was

examined using immunohistochemical techniques. In invasive breast cancer, we

found weak-to-moderate, granular, cytoplasmic staining for EPO in 36.2% of

specimens. In their studies Acs, Zhang et al. (2002) found the presence of weak-to-

moderate, granular, cytoplasmic EPO immunostaining in benign mammary epithelial

cells in 91.8% and 95.6% of the specimens, respectively [12]. Taking into

consideration histological type of invasive breast cancer the largest EPO-positive

group in our material comprised the IDC-NST (24.1%). Among the remaining

cancers EPO expression was demonstrated in 3.5% of IDC and in 8.6% of

metaplastic carcinomas, while in 15.5% of IDC, 3.5% of ILC and in 1.7% of mixed

ductal and lobular carcinomas expression of EPO was not found. No statistically

significant relationship between histological type of invasive cancers and expression

of EPO was found (p>0.05). In studies Acs, Zhang et al. (2002) EPO

immunostaining was similar in invasive ductal and lobular carcinomas and in

carcinomas with mixed ductal and lobular features. Acs, Zhang et al. (2002) found no

differences in EPO immunostaining between DCIS and LCIS. Also, the authors

found no statistically significant relationship between the expression of EPO and

histological type of breast cancer [12]. In our study we assessed the dependence

between histological grade of malignancy (G1-G3) and basic immunohistochemical

profile, including EPO expression, but no statistically significant correlations were

found (p>0.05) (Table 3). Among all cancers the most numerous group exhibiting

EPO expression were the G3 cancers (67%) (Table 3). We also assessed the

relationship between tumor size (pT), presence of lymph node metastasis and

expression of EPO, finding that among all cancers expressing EPO, cancers assessed

as pT2 comprised the largest group (52.3%) (Table 4). As much as 36.2% of cancers

with lymph node metastasis exhibited EPO expression., while in 63.8% of cancers,

expression of EPO was not demonstrated (Table 5). We also assessed the relationship

between expression of EPO and basic immunohistochemical profile (ER, PR, HER2)

in studied cancers and found that the most numerous group exhibiting EPO

expression were the cancers presenting ER-/PR-/HER2+ basic immunohistochemical

profile (33.3%), while no EPO expression was most often demonstrated in cancers of

ER+/PR+/HER2- basic immunohistochemical profile (37.8%) (Table 6). No

statistically significant correlation was demonstrated between EPO expression and

tumor size (pT), lymph node metastasis (pN) or expression of estrogen and

progesterone receptors as well as HER2 (p>0.05). Similarly, Acs, Zhang et al. (2002)

found in their studies no correlation between EPO immunostaining and tumor size,

tumor grade, lymph node status, hormone receptor status, or HER2/neu

overexpression [12]. In a study by Acs, Zhang et al. (2002), when the analysis was

performed using the differential tumor score for EPO, we found a significant

correlation between increased EPO immunostaining in the tumors and the presence

of lymph node metastases [12]. From a clinical point of view it is important to

determine whether adverse effects of rhEPO therapy correlate with the expression of

EpoR in neoplastic cells, since rhEPO remains an important element of treatment

scheme for anemia of malignancy. EpoR protein has been proposed to be


72

nonfunctional in tumor cells due to a non-cell surface location, and therefore,

presumably, is not available for activation by rhEPO [13]. Todaro, Turdo et al.

(2013) found that breast cancer stem–like cells (BCSCs) isolated from patient tumors

express the EpoR. Among all types of breast cancer basal-like subtype demonstrated

the greatest expression of EpoR. This trial also showed that BCSCs respond to EPO

treatment with increased cell proliferation and self-renewal rate. Importantly, EPO

stimulation increased BCSCs resistance to chemotherapeutic agents and activated

cellular pathways responsible for survival and drug resistance [14]. Specifically, the

Akt and ERK pathways were activated in BCSCs at early time points following EPO

treatment, whereas Bcl-xL levels increased at later times. In vivo, EPO admini-

stration counteracted the effects of chemotherapeutic agents on BCSCs-derived

orthotopic tumor xenografts and promoted metastatic progression both in the

presence and in the absence of chemotherapy treatment [14]. All this data shows that

EPO acts directly on the breast cancer stem-like cells through activation of specific

signal transduction pathways responsible for tumor protection from chemotherapy

and accelerates disease progression. According to the latest results of research, the

population of neoplastic stem cells seems to be responsible for progression of

neoplastic disease, its recurrence and metastasis. In light of those discoveries it is

necessary to develop treatment that would target this population of cells. It has been

known for several years now that use of rhEPO negatively influences survival among

breast cancer patients. Studies by Todaro, Turdo et al. (2013) were the first to show

the effect of EPO and EpoR on breast cancer stem cells. These results confirmed that

EPO contributes to development of chemotherapy resistance and acceleration of

tumor growth [14]. Several years before, Phillips, Kim et al. (2007) investigated the

effect of erythropoietin on cancer stem cells in breast cancer cell lines. They found

that pharmacological concentrations of rhEPO increased the number of putative

breast cancer initiating cells (BCICs) in established breast cancer cell lines. This

increase was mediated by the activation of the Notch signaling pathway. Primarily,

the Notch pathway is important for cell-cell communication, which involves gene

regulation mechanisms that control multiple cell differentiation processes. The

increase in the number of BCICs observed after rhEPO treatment was significant and

the cells were not only viable but, what is more important, exhibited an increased

self-renewal capacity as demonstrated by primary in vitro sphere formation [15].

Phillips, McBride et al. (2006) have previously proved that activation of the Notch

signaling pathway is a part of the cellular stress response to clinical doses of ionizing

radiation [16]. This effect was mediated by increased expression of the Notch

receptor ligand Jagged-1 in the non-BCIC population that activated Notch signaling

in BCICs. As for radiation, Phillips, Kim et al. (2007) have demonstrated that rhEPO

treatment activated Notch signaling pathway in BCICs [15]. Research conducted for

the past several years enabled determining function of EPO and EpoR in various

types of cells, including demonstration of high activity within neoplastic cells. Over

a decade ago Acs, Chen et al. (2004) described autocrine regulation of apoptosis by

these proteins in breast cancer. MCF-7 breast cancer cell lines were incubated in

increasing concentrations of O2 and it was shown that increased expression of EPO

mRNA correlates tightly with increasing degree of hypoxia in cell culture [17].



73

Similarly, escalation of acute hypoxic conditions induced transcription of EpoR

mRNA. Verification with Western blot corroborated the differences in EPO and

EpoR protein expression in a manner corresponding to mRNA transcripts. The study

also confirmed that autocrine EPO signaling induced by moderate levels of hypoxia

inhibits hypoxia-induced apoptosis and promotes survival in MCF-7 human breast

cancer cells. Anti-apoptotic effect of EPO correlated with upregulation of Bcl-2 and

Bcl-XL, thus its mechanisms appear to be similar to those described in hematopoietic

cells. The above-described mechanism, colloquially known as “apoptosis escape of

tumors,” plays a key role in development and progression of neoplastic process.

Evasion of apoptosis is also one of the most important features enabling deter-

mination of the degree of tumor malignancy. Acs, Zhang et al. (2003) previously

shown that EPO can inhibit chemotherapeutic drug-induced apoptosis and

cytotoxicity [18]. In the next study their results suggested that the increased EPO

signaling induced by tumor hypoxia can play a significant role in resistance to

therapy of hypoxic tumors [19]. Hypoxia is a common phenomenon accompanying

solid tumors, such as breast cancer. Inadequate oxygen distribution, which most often

affects central regions of rapidly growing lesions, leads to rearrangement of gene

expression in tumor tissues subjected to hypoxia. In effect, there is increased

synthesis of proteins directed at protecting the cell from adverse conditions. It is

achieved by, among other things, blocking cell’s ability to initiate apoptosis. This

phenomenon is advantageous in most cells, but very dangerous from a point of view

of neoplastic process. Characteristic proteins that exhibit increased expression under

hypoxic conditions include EPO, EpoR and HIF-1α. Increased intracellular

concentration of STAT3 (signal transducer and activator of transcription 3) may be

also one of the markers of hypoxia. This transcription factor is activated through

phosphorylation of tyrosine 705. Despite the fact that this protein becomes

overexpressed in neoplasms and was even considered a protooncogene, accumulation

of STAT3 can be detected under various non-neoplastic conditions of increased cell

turnover and enhanced biosynthesis of various proteins [20, 21]. Links between

expressions of the proteins suggests functional dependences between STAT, HIF-1α,

EPO and EpoR in cell-to-cell signaling in breast cancer [22]. Breast cancer also

involves upregulation of transcriptional agents like STAT3, STAT3 activator – EpoR

(erythropoietin receptor), and a HIF-1 downstream protein – EPO [23]. STAT3

contributes to increase of EPO expression, which is also HIF-1α dependent. Tyrosine

phosphorylation of STAT3 is triggered by EPO. HIF-1α overexpression is an

indicator of poor prognosis and significantly decreased survival among patients with

breast cancer [24]. HIF-1 upregulates transcription of angiogenic genes like EPO and

vascular endothelial growth factor (VEGF), which induce sprouting of new vessels

and result in increased risk of metastasis as they increase contact surface between

tumor cells and vasculature. HIF-1 is responsible mainly for cellular adaptation to

hypoxic conditions. Genes triggered by this factor are responsible mainly for the

improvement in oxygen supply, adaptation of cells to anaerobic metabolism

conditions as well as for other changes facilitating cell survival in insufficient oxygen

availability. HIF-1 induces transcription of cytoprotective proteins in malignant cells

in hypoxic conditions [25, 26]. HIF-1 and STAT3 actions have been associated


74

indirectly with each other. This relationship was highlighted mainly by interference

of STAT3 transcription with small-molecule inhibitor and resultant downregulation

of HIF-1 and VEGF that delayed tumor growth and angiogenesis [27].

EPO and EpoR are induced by hypoxia in breast cancer and could contribute to

increased survival rate of tumor cells via counteraction to hypoxic injury [28]. EPO

counteracts outflow of cytochrome c from mitochondrion by upregulation of Bcl-xL.

EPO prevents apaf-1 complex dependent activation of caspase 9 and 3 by inhibition of

binding cytochrome c to apaf-1 and cyt-c in cytoplasm [23]. Wincewicz, Sulkowska et

al. (2007) described the above correlations in ductal breast carcinoma. STAT3 was

detected in 50% of cancers, HIF-1α in 72% of cases, EPO in 89% of all the cancers and

EpoR in 72%. There were significant associations between expression of STAT3 and

HIF-1α. STAT3 was significantly associated with expressions of EPO and EpoR in

cancers of all patients. HIF-1α was correlated with EPO and EpoR in most of analyzed

groups. This data indicates tight interrelationships between all those proteins in breast

cancer. They all become overexpressed under hypoxic conditions and they all

significantly influence the biology of the tumor. Occurrence of HIF-1α was

significantly increased in chemotherapy spared tumors compared to chemotherapy

treated cancers because chemotherapy could destructively affect cancer cells via

inhibition of protein expression [23]. Correlations between STAT3 and EPO suggest

their action in accord to support survival of breast cancer cells in human tumors in the

same fashion as in cell lines [29].

Results of studies by Reinbothe, Larsson et al. (2014) somewhat revolutionized the

perception of the role of EPO and EpoR in breast cancer. According to these results,

rhEPO stimulation of cultured EpoR-expressing breast cancer cells did not result in

increased proliferation, overt activation of EpoR (receptor phosphorylation) or

a consistent activation of canonical EpoR signaling pathway mediators such as

JAK2, STAT3, STAT5, or AKT. However, EpoR knockdown experiments suggested

functional EPO receptors in estrogen receptor positive (ERα+) breast cancer cell

lines, as reduced EpoR expression resulted in decreased proliferation, but not cell

death. This effect on proliferation was not seen in estrogen receptor negative (ERα-)

cells. These observations suggest that decreased expression of EpoR reduced the

ERα-dependent proliferation in breast cancer [30]. EpoR expression seems to play

a role in proliferation control of ERα+

breast cancers while survival seems to be

unaffected by reduction of EpoR expression. Studies by Reinbothe, Larsson et al.

(2014) show that in neoplasms of the breast exhibiting EpoR expression proliferation

is induced in conjunction with this receptor, but by an EPO-independent mechanism

in ERα+ breast cancers. Molecular mechanisms affecting the interactions between

EpoR and ERα as well as their effect on the biology of tumor cells require further

studies [30]. It is currently unknown how these proteins cooperate to regulate cell

proliferation in breast cancer. Questions need to be addressed in order to find out

how EpoR should be targeted to modulate the potent ERα signaling pathways.

Answers to this query may reveal the new potent therapeutic methods in breast

cancer. Conclusions from the studies by Reinbothe, Larsson et al. (2014) that use of

rhEPO does not influence proliferative activity or survival in the five tested breast



75

cancer cell lines are consistent with the results of studies by LaMontagne, Butler et

al. (2006) [30,31]. On the other hand, contradictory findings were reported in a study

by Acs, Acs et al. (2001) [28]. There are also discrepancies in the published data that

demonstrate that rhEPO stimulation of breast cancer cells results in changes in cell

signaling mediators, such as AKT, ERK1/2 and STATs [32,33].

Despite the fact that initially EPO was only attributed a role in regulation of

erythropoiesis, this protein turned out to be an important link connecting numerous

signal transduction pathways, both in many normal as well as cancerous non-

hematopoietic tissues. Moreover, in the recent years there have been reports

describing functional autocrine/paracrine EPO/EpoR systems in human neoplastic

cells derived from breast cancer, cervical cancer, melanoma and prostate cancer. This

data suggests that EPO/EpoR axis may affect tumor growth, disease progression and

metastasis [17, 34].

Liang, Qiu et al. (2014) described the autocrine/paracrine functions of EPO produced

by breast cancer cells under both normoxic and hypoxic conditions. They found that

the level of EPO produced by these cells was higher in hypoxia than in normoxia.

This observation is consistent with the knowledge that EPO is a product of hypoxia-

inducible gene expression. Unfortunately, this study did not allow for determining

whether the effects of EPO are produced as a result of auto- or paracrine signaling.

However, it was determined with high certainty that both types of intercellular

communication influenced the observed effects in cultured cell lines. Results

presented in the publication demonstrated also that EPO/EpoR autocrine/paracrine

signaling could mediate migration and influence invasion potential of breast cancer

cells. It suggests that autocrine/paracrine EPO signaling could be one of the effector

mechanisms, through which HIF-1 factor influences the invasiveness of breast cancer

[35]. Targeting of HIF-1, which is being actively investigated as a potential strategy

for cancer therapy, could inhibit the effects of autocrine/paracrine EPO signaling on

cell migration and invasiveness. Inhibition of EPO autocrine/paracrine signaling

pathways in cancer cells could be one of the mechanisms explaining the anticancer

effects of several anti-HIF-1 agents reported in the literature [36]. Furthermore, we

found that autocrine/paracrine production of EPO also played a role in stimulating

tumorsphere growth of breast cancer cells.

In recent studies Zhou, Damrauer et al. (2015) corroborated the hypothesis that

exogenous EPO does not significantly influence cell proliferation and does not

protect them from chemotherapy-induced apoptosis in vitro. It is somewhat different

under in vivo conditions, where EPO distinctly promotes progression of breast

cancer. According to these results, researches hypothesized that EPO’s tumor

promoting effects are seen in vivo but not with in vitro assays because it affects

a limited fraction of cells, such as breast tumor initiating cells. Thus, its effects might

only be seen with a longer period of EPO administration, such as those achieved in

vivo [37].

Moreover, it was observed that treatment of breast tumor-initiating cells (TICs) with

EPO activated JAK/STAT signaling as well as promoted their self-renewal. Studies

also confirmed the protumorigenic role of endogenous EPO in breast tumorigenesis.


76

It turned out that both breast cancer cells, as well as tumor-associated endothelial

cells can produce and release EPO into the microenvironment of the tumor.

Endogenous EPO expression was hypoxia-inducible in breast cancer cell lines, but

not in human mammary epithelial cells. EPO overexpression in breast cancer cells

correlates negatively with progression-free survival [37].

Research conducted in the recent years ascribes EPO another, clinically important

role. Observations allow saying that EPO antagonizes treatment with the anti-HER2

antibody, trastuzumab, by activating EpoR/JAK2 downstream effectors, effectively

bypassing HER2 signaling [38]. Moreover, the antagonizing effect should be only

apparent in patients treated with anti-HER2 antibodies. The fact that adverse effects

of rhEPO were observed among various subtypes of breast cancer as well as patients

with anemia of malignancy who had not been treated with trastuzumab strongly

suggests that there are other pathways not related to HER2, through which EPO

promotes tumor progression. The above observations demonstrate an important role

of both exogenous and endogenous EPO in the course of breast cancer. Results of

studies by Liang, Esteva et al. (2010) show that JAK2 inhibition in combination with

chemotherapy is a promising therapeutic strategy rather than EPO/EpoR inhibition in

the treatment of breast cancer patients [33].

Conclusions

Literature data suggest that EPO might influence tumor growth, disease progression

and metastasis. However, numerous studies are conducted on cell lines, but there is

scarce data from studies conducted on tissues using immuno-histochemical methods

routinely performed in pathomorphological diagnostics. There is an ongoing search

for prognostic markers in the diagnostics of breast cancer in women and researchers

strive to expand the basic immunohistochemical profile in the diagnostics of breast

cancer. In our study we demonstrated a statistically significant correlation between

markers included in the basic immunohistochemical profile (ER, PR, HER2) with

histological type of invasive breast cancer. There was no relationship between

markers of basic immunohistochemical profile and expression of EPO as well as no

statistically significant dependence was found between expression of EPO and basic

clinical features, such as tumor size, grade of histological malignancy or lymph node

status.

However, conducted studies allow for formulating important conclusions for

pathomorphological diagnostics. Based on the analysis, it may be concluded that:

1. Expression of erythropoietin (EPO) in the cytoplasm of cancer cells, was most

frequently identified in invasive ductal carcinoma of no special type (IDC

– NST);

2. Expression of EPO was most frequently identified in invasive breast cancers

evaluated as pT2 (52.3%);

3. Expression of EPO was most frequently identified in invasive breast cancers

evaluated as pN1;



77

4. Expression of erythropoietin (EPO) in the cytoplasm of cancer cells was most

often identified among cancers with the highest histological grade (G3). Our

research results suggest that increased EPO signaling may represent a novel

mechanism modulating differentiation of cancer cells and is connected with low

degree of differentiation of tumor cells;

5. Cancers with ER-/PR-/HER2- immunohistochemical profile, the so-called

“triple-negative” (TNBC) are characterized by, among other things: greater

clinical advancement of the disease at the moment of diagnosis, poor

histological differentiation (acc. to Bloom-Richardson classification; G3). In our

study the G3 cancers most often expressed EPO; also, among the TNBC

expression of EPO was confirmed in over 40% of cases, suggesting that in

TNBC erythropoietin might be a prognostic marker.

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The inflammatory components in lung cancer

Bartosz Szmyd, Marcin Kaszkowiak, Dorota Pastuszak-Lewandoska, Ewa Brzeziańska-

Lasota

Department of Biomedicine and Genetics, Medical University of Lodz, Poland

The corresponding author:

[email protected]

Dorota Pastuszak-Lewandoska, PhD

Department of Biomedicine and Genetics Chair of Biology and Medical Parasitology,

Medical University of Lodz 251 Pomorska Street (C-5 building), 92-213 Lodz

Abstract

The immune system is one of the most complicated and least known part of the human body. According

to Virchow’s observations and the current data, its response can cause both promotion and suppression

of formation and development of the neoplasm. It is estimated that as many as 25% of all cancers are

caused by chronic inflammation. Better understanding of the delicate balance between these activities is

essential for development of proper preventive and treatment methods as well as new biological

markers. In our paper we focus on biochemical and molecular mechanisms of tumorigenic activity of

chronic inflammation in lung cancer - the most frequent malignant neoplasm characterized by high

mortality. Moreover, we describe mechanisms in which the immune system fights against tumours cells.

The last part is concerned with new strategies in lung cancer diagnosis and treatment: biomarkers and

immunotherapy.

Keywords: Lung cancer, NSCLC, inflammation, biomarkers, immunotherapy

Introduction

The immune system and its role in cancerogenesis

In 1863 Rudolf Virchow observed that:

Cells of the immune system occur in tumours

Cancerogenesis tends to appear at sites of the long-lasting inflammation

(Balkwill, Mantovani, 2001), (Schetter, Heegaard et al. 2009).

When we take into consideration all sources of inflammatory conditions (such as

viral infections, exposure to allergens, autoimmune disorders and even obesity), it

reveals that they are responsible for approximately 15 to 25% of tumours (Hussain,

Harris, 2007)(Balkwill, Mantovani, 2001). As Figure 1 shows, the response of the

immune system can result in both: anti- and pro-tumorigenic effect. The first case is

typical for efficient immune effector system (Ostrand-Rosenberg, 2009), the second

for chronic inflammation which predispose cells for oncogenic transformation

(Mantovani, Allavena et al. 2008). The range of mechanisms involved in both

responses is outlined in further chapters.




81

Fig. 1. The balance between both pro-tumorigenic and anti-tumorigenic effect of immune response.

Based on the diagram from Schetter, Heegaard et al. 2009.

The influence of the immune system on cancerogenesis has been described for many

organs, especially: intestines, liver, stomach and lungs. The examples of tumours

with corresponding inflammatory state are summarized in Table 1.

Table 1. The inflammatory states and their correlating tumours.

Source of inflammation Tumour References

Chronic viral hepatitis Liver cancer (Tu, Bühler et al. 2017), (Abbas,

Abbas et al. 2015), (Benali-Furet,

Chami et al. 2005)

Helicobacter pylori Gastric cancer (Sepulveda, 2013), (Wroblewski,

Peek et al. 2010)

Tumour-associated

macrophages (TAMs)

activation

Oral squamous cell

carcinoma (OSCC)

(Petruzzi, Cherubini et al. 2017),

(Fujii, Shomori et al. 2012)

Inflammatory Bowel

Disease

Colorectal cancer (Wang, Fang, 2014), (Kim, Chang,

2014)

Bartosz Szmyd, Marcin Kaszkowiak, Dorota Pastuszak-Lewandoska, Ewa Brzeziańska-Lasota

82

Crohn's disease1 Colorectal cancer (Santos, Barbosa, 2017), (Freeman,

2008), (Gillen, Andrews et al. 1994)

Ulcerative colitis Colorectal cancer (Yashiro, 2014), (Isbell, Levin, 1988)

(Ransohoff, Riddell et al. 1985)

Hashimoto’s thyroiditis Papillary thyroid cancer

(PTC)

(Resende de Paiva, Grønhøj et al.

2017), (Chen, Lin et al. 2013)

Asbestos Mesothelioma, lung

cancer

(Hodgson, Darnton, 2000), (Berry,

1991), (Whitwell, Scott et al. 1977)

Tobacco smoking Lung cancer (Peto, Darby et al. 2000), (Wingo,

Ries et al. 1999), (Hecht, 1999)

Lung cancer

Lung cancer is the most common malignant neoplasm (Rafiemanesh, Mehtarpour et

al. 2016) and the leading cause of death of tumour (Islami, Torre et al. 2015). Current

research show that ASIR (standardized incidence rate) is equal 23.1 and ASMR

(standardized mortality rate) – 19.7 (per 100 000) (Rafiemanesh et al., 2016). Both

environmental and genetic factors are associated with lung cancer (Kanwal, Ding et al.

2017). Moreover, environmental factors (such as smoking and exposure to asbestos),

which results in inflammation, play an important role in the etiopathogenesis of lung

cancer (Schetter, Heegaard et al. 2009) (Pope III, Burnett et al. 2002).

Lung cancer can be classified according to histological (Zheng, 2016) and clinical

(Politi, Herbst, 2015) parameters. Focusing on the clinical classification, we can

distinguish:

Small-cell lung cancer (SCLC);

Non-small-cellular lung cancer (NSCLC) which is also divided into:

Squamous cell lung carcinoma (SCC),

Adenocarcinoma (AC),

Large-cell lung carcinoma (LCC).

The fight between cancer and the immune system

The anti-tumorigenic response is possible thanks to tumour-associated antigens

(TAAs). Dendritic cells (DCs) present these antigens by MHC class II. Other antigen

presenting cells (APCs) perform this function to a lesser extent (Szyszka-Barth,

Ramlau et al. 2014). DCs/APCs migrate to lymph nodes. It results in interaction

between TAA and naïve T-cells. Due to this interaction the first signal is conducted

by the antigen receptor – T-cell receptor (TCR). The second signal, needed to T-cells

activation, is called co-stimulation (Frauwirth, Thompson, 2002). These signals

activate effector T-cells (both CD4+ and CD8+, called helper and cytotoxic T-cells

(CTLs), respectively). Activated CD4+ T-cells secrete cytokines, which take part in

1 In memorial of Polish surgeon Antoni Leśniowski, who was first to describe ileitis terminalis, also called

Leśniowski-Crohn’s disease (Lichtarowicz, Mayberry, 1988).


83

e.g. CTLs’ and B-cell activation, phagocytosis promotion and stimulation of natural

killer cells (NKs).

However, the response of the immune system is often insufficient (Swann, Smyth et

al. 2007). Tumour fights with patient’s immunological system through:

the generation of antigen-loss variants;

the immunological tolerance;

the lesser expression of MHC on tumour cells’ surface;

the secretion of immunoinhibitors.

The origination of antigen-loss variants depends on selection of tumour cell

subpopulations which do not express antigens causing anti-tumorigenic immune

response. It enables protection against immune targeting (Olson, McNeel, 2012). The

tolerance mechanisms against some antigens result in the lack of response to them

with relevant immune response against others antigens (Mapara, Sykes, 2004).

Moreover, host cells fighting against tumour are recognized as autoreactive and

eliminated. The defence strategy bases on decreased expression level of MHC on

tumour cell surface limits the presenting of TAAs. More importantly, due to this

mechanism tumour cells are the target for NK-cells (Topham, Hewitt, 2009). Finally,

malignant cells can secrete immunoinhibitors. One of them is transforming growth

factor beta (TGF-β) (Szyszka-Barth, Ramlau et al. 2014). Physiologically, TGF-β

regulates many life processes, such as: cell growth, differentiation, inflammation and

angiogenesis. However, the overexpression of this cytokine is correlated with tumor

progression, metastasis and poor prognostic outcome. (Fabregat, Fernando et al.

2014).

Inflammatory biomarkers in lung cancer

In fact, all of inflammatory genes, theirs products, circulating cytokines and

microRNAs can be considered as biomarkers in lung cancer (Schetter, Heegaard et

al. 2009). For the purpose of our article, we will only focus on microRNAs affecting

inflammatory genes and circulating cytokines.

Fig. 2. The possibilities of biological material selection for assessment of expression level of different

biomarkers (microRNA, genes, proteins).


84

Both of them tend to be suitable biomarkers for early cancer detection because of the

relative non-invasiveness and easiness in collection of biological material (Paranjape,

Slack et al. 2009). They can be assessed in the serum samples, while the

inflammatory genes expression should be evaluated in tumour tissue obtained during

pulmonectomy/lobectomy (Mitra, Lee et al. 2011) (see Figure 2). The examples of

circulating proteins and microRNAs which can be considered as inflammatory

biomarkers in lung cancer are summarized in Table 2.

Table 2. The examples of circulating proteins and microRNAs which can be considered as inflammatory

biomarkers in lung cancer (with relevant references).

Biomarker References

circulating proteins

C-reactive protein (CRP) (Zhou, Liu et al. 2012), (Alifano, Falcoz et al. 2011)

Interleukin 6 (IL-6) (Brichory, Misek et al. 2001), (Heikkilä, Harris et al. 2009)

Interleukin 8 (IL-8) (Pine, Mechanic et al. 2011), (Zhu, Webster et al. 2004)

microRNAs

microRNA-21 (Xue, Liu et al. 2016), (Ma, Liu et al. 2012)

microRNA-17-92 cluster (Ebi, Sato et al. 2009), (Hayashita, Osada et al. 2005)

MicroRNAs are the class of short (20-25 nucleotides), evolutionarily conserved, non-

coding RNA (Williams, Cheng et al. 2017). They down-regulate genes expression by

both: translational repression and mRNA degradation (Anglicheau, Muthukumar et al.

2010). More importantly, single microRNAs can target many genes (Hashimoto,

Akiyama et al. 2013). We will focus on miRNA-21 and microRNA-17-92 cluster.

miRNA-21 regulates the expression of IL-12 (Lu, Munitz et al. 2009). This interleukin

performs many roles e.g.: it is engaged in origination of Th1 cells (Hsieh, Macatonia et

al. 1993) and is the suppressor of angiogenesis (Sgadari, Angiolillo et al. 1996). The

upregulation of microRNA-21 observed in lung cancer (Xue, Liue et al. 2016) results in

the downregulation of its targets. Thus, important functions of IL-12 are impaired.

While, microRNA-17-92 cluster is the set of seven microRNAs: microRNA-17-3p,

microRNA-17-5p, microRNA-18a, microRNA-19a, microRNA-20a, microRNA-19b-1

and microRNA-92a (Inamura, Ishikawa, 2016). Its overexpression is often observed in

lung cancer (Ebi, Sato et al., 2009). This MicroRNA class results in e.g. cell

proliferation under normoxia, inhibition of hypoxia-induced apoptosis and regulation

of angiogenesis (Osada, Takahashi, 2011).

The second group of potential biomarkers are circulating proteins, especially

cytokines. These small proteins are secreted by cells and influence these or other

cells (Zhang, An, 2007). They play key role in communication between cells

(Motaln, Turnsek, 2015). There are distinguished pro- (e.g. IL-1β, IL-8, IL-12) and

anti-inflammatory (IL-4, IL-5, IL-10) cytokines (Enewold, Mechanic et al. 2009).


85

IL-8 is involved in wide range of processes, such as recruitment/activation of

neutrophil, angiogenesis and metastasis in lung cancer (especially NSCLC).

Oncogenic activity of IL-8 can be result of EGFR transactivation. (Luppi, Longo et

al. 2007). IL-10 is anti-inflammatory cytokine. It is responsible for limiting host

immune response to pathogens and homeostasis maintain (Iyer, Cheng, 2012). Its

role in the tumorigenesis is controversial: its expression levels were significantly

increased in the lungs (mice model of lung cancer), higher level of IL-10 correlates

with a poor prognosis in lung cancer patients (Hsu, Wang et al. 2016). Hsu et al.

convince that IL-10 and EGFR regulate each other through positive feedback, which

leads to lung cancer formation.

The C-reactive protein (CRP) is pentameric protein, which occurs in blood in

response to inflammation. The performed meta-analysis of the role of its in lung

cancer (Zhou, Liu et al. 2012) shows:

RR of lung cancer for one unit change in natural logarithm CRP was 1.28 (95%

CI 1.17–1.41);

CRP was significantly correlated with increased risk of lung cancer among men:

RR=1.18 (95% CI 1.09–1.28);

Any statistically significant difference among women wasn’t observed.

The immunotherapy of lung cancer

The immunotherapy in treatment of neoplasm has been used for the first time in 1890

by doctor William Coley. He noticed that facial sarcoma could regress after bacterial

infection (Coley, 1893). Since that moment research in tumours immunotherapy have

developed significantly (Szyszka-Barth, Ramlau et al. 2014). Two types of

immunotherapy are distinguished: active and passive (see Table 3).

Table 3. The definitions of active and passive immunotherapy with examples. Based on (Szyszka-Barth,

Ramlau et al. 2014).

Type Definition Examples

Active Stimulation of immunological

system to fight against neoplasm

Vaccines (containing tumours antigens and

adjuvants)

Nonspecific immunomodulators

Passive The application of

immunologically active factors

synthesized/formed outside

patient’s organism

Adoptive T-cells

Monoclonal antibodies

Active immunotherapy depends on stimulation of immunological system to fight

against tumour. It comprised of vaccination and administration of nonspecific

immunomodulators. The possibility of vaccinations usage is notably exploited

especially in combination therapy of NSCLC. Many clinical trials have been

performed based on that topic (e.g. MAGE-A3, PRAME, Lucanix, CimaVax) (De

Pas, Giovannini et al. 2012). In this paragraph, we focus on vaccines targeting MUC-


86

1 (L-BLP25 and TG4010). It is transmembrane protein, which increased expression

is observed in NSCLC (AC: 86%, others: 74%) (Bouillez, Adeegbe et al. 2017)

(Szyszka-Barth, Ramlau et al. 2014). Genetic abnormalities of MUC-1 cause:

resistance to chemotherapy;

protection against apoptosis;

immunosuppression (Zappa, Mousa, 2016).

Since overexpression of MUC-1 and decreased level of patients’ own antibodies

against MUC-1 result in poor prognosis, this direction of research seems to be

reasonable (Lakshmanan, Ponnusamy et al. 2015).

As shown in Table 3, passive immunotherapy can be defined as application of

immunologically active factors synthesized or formed outside patient’s body.

Adoptive cell transfer (ACT) is considered as its example. Its following steps are

shown on Figure 3.

Fig. 3. Following steps of adoptive cell transfer. The last step is infusion of patient’s own T-cells, which are

characterized by anti-tumour properties.

Thanks to this procedure infused T-cells have relatively high avidity, which can be

also increased by genetic modification. Clinical trials of ACT in lung cancer focus

on: lymphokine-activated killer (LAK), cytokine-induced killer (CIK) and natural

killer (NKT) cells (Yang, Wang et al. 2016).

Summary

Better understanding of the delicate balance between opposite activities is essential

to the preparation of proper prophylactic methods, finding new biological markers

and the development of new treatment methods. Circulating microRNAs isolated

from serum exosomes or serum samples are promising biomarkers for detection and

predication of the clinical curse of lung cancer. Moreover, the immunotherapy can be

considered as the possible treatment.


87

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Normalisation of microRNA qPCR results

in lung cancer – strategies and problems

Marcin Kaszkowiak, Bartosz Szmyd, Dorota Pastuszak-Lewandoska, Ewa Brzeziańska-

Lasota

The corresponding author: [email protected],

Dorota Pastuszak-Lewandoska, PhD

Department of Biomedicine and Genetics Chair of Biology and Medical Parasitology,

Medical University of Lodz 251 Pomorska Street (C-5 building), 92-213 Lodz

Abstract

Lung cancer (LC) is one of the most common and death-causing carcinomas in the world. There is an

urgent need for innovative diagnostic tools and treatment strategies. MicroRNAs have a great potential

for being reliable LC biomarkers and therapy targets. qPCR is one of the best methods for evaluating

their expression during research. However, it has to be performed in a very careful way. Any committed

mistakes, especially during normalization of output data may lead to misleading conclusions. Data

Normalisation (DS) is a crucial step, required for levelling different sample sizes, volumes of nucleic

acids and other varying factors, allowing us to compare results with one another. There is no universal

strategy for this process. Every known method (volume size, housekeeping genes, global mean, etc.) has

many advantages and drawbacks. This article will discuss different approaches, their pros and cons, and

suggest ways to minimize qPCR inaccuracy.

Keywords: qPCR, microRNA, normalization, quantile, global mean

Background

Lung cancer incidence and mortality has been steadily growing since 1930s (Mao,

Yang, et al., 2016). Nowadays, it has become a global problem. LC was responsible

for 13% of all cancer incidences worldwide and caused 160340 deaths in United

States (Mao et al., 2016) in 2012, what made it the most common carcinoma and

a leading cause of cancer deaths.

The prognosis of Lung cancer strongly depends on the moment of diagnosis. The

5-year survival rate ranges from 52% (local disease) to 4% (distant disease). It results

in relatively high mortality, since only about 15% of LCs are diagnosed at early

stages. (Liam, Andarini, et al., 2015) It raises an urgent need for reliable biomarkers,

that would make diagnosing process faster and more accurate.

MicroRNAs are small strands of ribonucleic acid (built by about 23 nucleotides),

regulating genes expression at the transcriptomic level. They affect mRNA by either

repressing translation or inducing degradation (Wahid, Shehzad, et al., 2010). Many

recent studies suggest their significant role in cancer etiopathogenesis (Leva,

Garofalo, et al., 2014) and therefore consider them as promising biomarkers and

therapeutic targets (Berindan-neagoe, Monroig, et al., 2015; Kong, Zhang, et al.,

2015; Yang, Tang, et al., 2014; Zhao, Li, et al., 2015). That makes an accurate

evaluation of microRNAs expression a crucial step for developing new drugs and

diagnostic procedures.

Normalisation of microRNA qPCR results in lung cancer – strategies and problems

93

Technical introduction

Real-time PCR is a commonly used and reliable technique for measuring microRNA

relative expression. Among its advantages are sensitivity, high throughput and

accuracy. However, as every method it is prone to errors. There are two types of

variation in the obtained experimental data: biological (describing real molecular

processes) and experimentally-induced (due to experiment handling), that needs to be

excluded from further analysis. It is the purpose of data normalization. Reducing the

influence of material’s quantity, integrity, purity, etc. makes it possible to compare

the results from different experiments (Vandesompele, 2012).

Classic Normalization methods

Normalization to sample size

The simplest, but also the least used method is to carefully obtain the same sample

volume (or mass, number of cells, etc.) form each patient. This approach may seem

obvious and straightforward, but is extremely prone to errors. It is impossible to

measure sample size precisely enough. Moreover, biological material often contains

a different percentage of microRNA, what makes accurate analysis impossible

(Huggett, Dheda, et al., 2005).

Normalization to total sample RNA

Formula 1. Normalization to total sample RNA. δ’ – expression after normalization of j-th microRNA in i-th

sample; δ - expression of j-th microRNA in i-th sample; r – total RNA in i-th sample

Another volume-orientated technique bases on quantification of all RNAs present in

a sample. This measurement can be easily performed prior to reverse transcription.

Unfortunately, it can be affected by poor material quality. Moreover, this method has

two serious disadvantages. Firstly, it is unable to normalize variation formed in

further steps of qPCR method. Secondly, it bases on the assumption that proportion if

different RNA types (mRNA, rRNA, miRNA, etc.) is constant within all cells, which

has been proven wrong (Huggett et al., 2005).

Normalization to artificial molecule

Formula 2. Normalization to exogenous RNA. δ’ – expression after normalization of j-th microRNA in i-th

sample; δ - expression of j-th microRNA in i-th sample; γ – exteral molecule’s expression in i-th sample.

This method uses an external molecule as a reference microRNA. Exogenous RNA

(usually C. elegans cel-miR-39) of known quantity is added to a sample before

performing Reverse Transcription. On the contrary to previously described

technique, this method allows to reduce variation caused by further qPCR steps, but

has no use in normalization of differences in samples’ volume and quality

Marcin Kaszkowiak, Bartosz Szmyd, Dorota Pastuszak-Lewandoska, Ewa Brzeziańska-Lasota

94

(Schwarzenbach, Calin, et al., 2016). Among its advantages there is also insensitivity

to in-cell biological fluctuations. However, this may be considered as a drawback,

since it raises a need for result validation (Huggett et al., 2005). Generation of

suitable, not always commercially available external particles may also be a serious

problem in smaller laboratories (Huggett et al., 2005).

Normalization to endogenous reference microRNAs

Formula 3. Normalization to “housekeeping microRNA”. δ’ – expression after normalization of j-th

microRNA in i-th sample; δ - expression of j-th microRNA in i-th sample; θ – housekeeping microRNA

expression in i-th sample

Endogenous control is the most popular and most universal tool for a qPCR data

normalization (Schwarzenbach, Da Silva, et al., 2015). Similarly to gene expression

analysis, this method is based on the assumption, that some microRNAs’ expression

(the so called “housekeeping microRNAs”) is close to constant in all samples and

experimental conditions (Mar, Kimura, et al., 2009). With this hypothesis, other

expressions are adjusted using given formula. It allows to reduce variation caused by

all stages of an experiment. Unfortunately, this technique has numerous limitations.

There are no ‘100% universal’ reference molecules. Many studies report that, on

contrary to our assumption, some housekeeping microRNAs / genes expression can

significantly vary depending on tissue type and experimental conditions (Dheda,

Huggett, et al., 2004; Schmittgen & Zakrajsek, 2000; Thellin, Zorzi, et al., 1999;

Tricarico, Pinzani, et al., 2002; Ullmannová & Haskovec, 2003; Xiang, Zeng, et al.,

2014). This makes proper selection of endogenous control a very complex problem

and should be solved for each project individually in preliminary research. This step

is extremely important, since it is necessary for correct interpretation of results

(Hellemans & Vandesompele, 2014; Saviozzi, Cordero, et al., 2006).

Many researchers suggest usage of more sophisticated version of this method

– considering combination (geometric mean) of a few housekeeping microRNAs

expression as stable and reliable control (Mar et al., 2009). Candidates for mean

arguments can be selected using bioinformatics methods.

Formula 4. Normalization to multiple housekeeping microRNAs. δ’ – expression after normalization of j-th

microRNA in i-th sample; δ – expression of j-th microRNA in i-th sample; θ – k-th housekeeping microRNA

expression in i-th sample, n – number of housekeeping microRNAs

The most commonly used molecules in endogenous microRNA expression normali-

zation are small coding RNAs – RNU6A and RNU6B. Despite being considered as

universal housekeeping genes (Schwarzenbach et al., 2016), many studies report

difficulties in their application – e.g., RNU6B is not applicable in circulating

microRNA analysis (Xiang et al., 2014). RNUs are not microRNAs, which


95

introduces bias due to their different expression profiles and biochemical character.

Usage of microRNAs and their combinations as endogenous control is advised

(Schwarzenbach et al., 2016).

Among different miRNAs studied, miR-16 is highly and invariantly expressed in

different tissues. Bioinformatic analysis has proven its high stability, especially in

combination with miR-93. They are followed by miR-191, miR-106a, miR-17-5p

and miR-25 (Schwarzenbach et al., 2016).

Data-driven Normalization methods

Normalization to global mean

Formula 5. Normalization to global mean. δ’ – expression after normalization of j-th microRNA in i-th

sample; δ - expression of j-th microRNA in i-th sample; φ – k-th microRNA expression in i-th sample,

n – number of microRNAs

Gold-standard workflow in qPCR data analysis consists of a pilot experiment in

search for molecules with stable expression, selecting a few of them and using their

expression combination for normalizing the results in further research. This approach

often presents many problems and difficulties. Suitable endogenous controls are

often either very hard, or even impossible to find (Pieter Mestdagh, Van Vlierberghe,

et al., 2009).

In response to those difficulties, relatively new method has been developed

– global mean normalization. It is based on the observation that in a large and

unbiased group of microRNAs, the average expression can be used for normalization

that would reduce variation from all steps of the experiment.(Vandesompele, 2012).

Studies suggest, that this method either outperforms or is as good as endogenous

control in terms of stability. It also allows us to observe real and significant

biological changes, that could be blurred when using other normalization techniques

(P Mestdagh, Van Vlierberghe, et al., 2009).

For usage in projects with analysis of smaller microRNA sets (i.e. 4 microRNAs),

with help of bioinformatic tools, a few microRNAs or small RNAs controls can be

selected that reassemble the mean expression value (P Mestdagh et al., 2009).

Quantile Normalization algorithm

It is simple, easy-applicable method that bases on simple statistical parameter

– quantile. This technique is mainly used in normalization of microarray data

(Hellemans & Vandesompele, 2014), but there is no obstacle for usage in qPCR

results. It bases on an assumption, that average distribution of gene expression levels

in the cell remains constant (Mar et al., 2009).

The algorithm treats sample as n data points (number of microRNAs), sorts them in

ascending/descending order and transforms i-th row in all samples with its mean


96

value. Those steps change expression levels while preserving rank-order (it is an

origin of method’s name – quantiles remain constant for each sample) (Mar et al.,

2009). The detailed step-by-step description is presented on Figure 1.

Create a matrix M with k rows (number of microRNAs in the study) and p

columns (number of samples in a study). Fill this array with your experimental

data (microRNA name and its expression).

Create a matrix M’ by sorting each column in ascending order. It corresponds to a

quantile distribution of each sample.

Iterate over rows and replace each column with the row’s mean value. After this

process normalized microRNAs expressions can be rearranged in the starting

order.

Figure 1. Quantile normalization

There may occur situations, where all investigated microRNAs for each sample

won’t fit in one plate. Technical variation occurring between those plates can (and

should) be reduced using same algorithm, considering plates as different samples.

However, it is very important that examined microRNAs should be distributed

randomly across plates. Afterwards, normal procedure may be performed (Mar et al.,

2009).

Rank-Invariant Set Normalization algorithm

In “housekeeping” classic expression normalization method, there has to be taken an

a priori hypothesis, that one (or more) microRNA /gene expression is constant across

all samples. This approach was often proven wrong (Dheda et al., 2004; Schmittgen

& Zakrajsek, 2000; Thellin et al., 1999; Tricarico et al., 2002; Ullmannová &

Haskovec, 2003; Xiang et al., 2014). In a dataset large enough, suitable microRNAs

can be selected from the data itself, after the experiment (Mar et al., 2009).

Bioinformatic analysis allows us to find rank-invariant (having the same rank across

all datasets) microRNAs across all samples, which are verified to be suitable for

normalization purpose (Tseng, 2001). The detailed algorithm description is presented

on Figure 2.


97

Create a matrix M with k rows (number of microRNAs in the study) and p

columns (number of samples in a study). Fill this array with your experimental

data (microRNA name and its expression).

Create a matrix M’ by sorting each column in ascending order.

Select one microRNA set as a reference, regarding experiment’s purpose and

methodology. This can be one special patient, commercially available set, global

mean or average, etc.

Compare each of p samples with R and find microRNAs that have the same rank

in both datasets. Create a set of genes S, by intersecting the results of all pairwise

comparisons.

Let αi be the average expression of rank invariant microRNAs (stored

in S) in i-th sample. Calculate

ratio.

Multiplicate i-th column of M by βi . Dataset contains normalized

microRNA expression values.

Figure 2. Rank-Invariant Set Normalization


98

Conclusions

There is no universal, perfect normalization method. All techniques have many

advantages and also drawbacks. While Data-Driven methods are more accurate,

cheaper, universal, and easy to use in large, genom-wide experiments, they may be

inaccurate, or even not-applicable in smaller projects. Classic methods, especially

“Housekeeping microRNA normalization” are considered gold-standard for

normalizing qPCR results. Among its advantages are low-price and easy usage even

in single-gene research. They are, however, extremely prone to errors and may cause

a serious misinterpretation of expression data.

The perfect normalization method has yet to be discovered. Nowadays, suitable

technique should be chosen individually for each experiment, after cautious pros and

cons consideration, literature study and public databases analysis. Careful selection is

crucial for research to succeed.

Table 1. List of key features of each normalization method. Shading: Classic Normalization Methods, Data-

Driven Normalization Methods

normalization method + -

sample size Cheap

Straightforward

Extremely prone to errors

Impractical

total sample RNA Easy to apply

Reduces Reverse Transcription

Bias

Prone to mechanical errors

Based on wrong assumptions

artificial molecule Insensitive to biological

fluctuations

Precise

Expensive

Does not reduce variation created

before Reverse Transcription step

housekeeping microRNAs Reduces variation from all steps

of experiment

Popular

Easily applicable and replicable

Prone to errors

Not universal

Based on wrong assumptions

global mean Reduces variation form all steps

of experiment

Universal

Easy to use

Accurate

Based on reasonable assumptions

Accuracy dependent on study size

quantile normalization Reduces variation from all steps

of the experiment

Accurate

Universal

Straightforward


Requires random microRNA

distribution in a dataset


99

Based on reasonable assumptions

Normalizes cross-plate variation

Rank-invariant set Combines advantages of

housekeeping microRNAs

method and data-driven

techniques

Applicable post-hoc

Allows to identify suitable

endogenous control


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Statistical comparison of deleteriousness prediction

methods for single nucleotide variants from next

generation sequencing

Dominik Wawrzuta, Students Research Association for Genetics of Medical University

of Warsaw

Contact author: Dominik Wawrzuta, Students Research Association for Genetics of

Medical University of Warsaw, Żwirki i Wigury 61, 00-001 Warszawa

[email protected]

Abstract

In the analysis of data from whole exome sequencing, algorithms are commonly used to calculate the

deleteriousness of single nucleotide variants. The commonly used deleteriousness prediction methods

are SIFT, Polyphen2, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM and MetaLR.

The aim of this study is a statistical analysis of the results of deleteriousness obtained by algorithms. In

the analysis whole exome sequencing data are used, the deleteriousness of the variants in exomes is

assessed by the examined methods. Then deleteriousness is categorized as 1 for variants marked as

harmful, 0 for unmarked and -1 for tolerable ones. Analysis of interrelations between the tested

algorithms is made using Spearman correlation, as well as k-means method and Multiple

Correspondence Analysis. The applied statistical methods indicate the occurrence of high correlation

and homogeneity between the prediction results made by the MetaSVM and MetaLR algorithms, which

similarly indicate the deleteriousness of the same variants. Different results were obtained for the SIFT,

Polyphen2, LRT, MutationTaster, MutationAssessor and FATHMM algorithms. This observation

suggests that the results obtained using these methods should not be analyzed at the same time, as they

multiply the information carried.

Introduction

A non-synonymous single nuclear polymorphism (nsSNP) is a variation at a single

position in a DNA sequence, which cause change in encoded amino acid and in

corresponding protein product. Some of the nsSNPs may affect the phenotype and be

associated with disorders. Variation is called nsSNP if less than 1% of a population

carry nucleotide at a specific position in the DNA sequence (Butler 2012). Due to

potential pathogenicity of nsSNPs, there is a significant need to characterize the

effect of nsSNPs on the corresponding protein function. In human genetics, it is

important to determine which nsSNPs negatively affect the protein product and

consequently the phenotype. In the analysis of data from whole exome sequencing,

algorithms are commonly used to calculate the deleteriousness of nsSNPs. The most

used deleteriousness prediction methods are Sorting Tolerant From Intolerant (SIFT),

PolyPhen-2, Likelihood Ratio Test (LRT), MutationTaster, MutationAssessor,

Functional Analysis Through Hidden Markov Models (FATHMM), MetaSVM and

MetaLR.

SIFT (Ng, Henikoff 2003) assumes that important amino acids in protein do not

change during evolution, otherwise the substitution affects protein function. SIFT

uses PSI-BLAST algorithm (Altschul, Madden et al. 1997) to searching protein

database for functionally related proteins. Then creates a dataset of found proteins

and aligns them with query sequence. In the next step the algorithm calculates

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Dominik Wawrzuta

102

conservation value and scale for each position the probability that the variant is

tolerated. (Kumar, Henikoff et al. 2009)

PolyPhen2 (Adzhubei, Schmidt et al. 2010) uses 11 features for predicting damaging

effects. Eight of them are sequence-based and three are structure-based. The most of

these features compare a properties of wild allele with a properties of analyzed allele.

The deleteriousness of the nsSNP is predicted from this individual features by Naive

Bayes classifier approach coupled with entropy-based discretization (Fayyad, Irani

1993).

LRT treats rare variants as random effects in linear mixed effects models. Likelihood

ratio test compare the goodness of fit of the models and provide probability of

deleteriousness of variant. (Zeng, Zhao et al. 2014) (Qian, Shao 2013)

MutationTaster predicts not only functional effects of non-synonymous variants but

also intronic and synonymous mutations, short insertion and deletion mutations.

Homozygous alterations found more than four times in HapMap Project or in 1000

Genomes Project are automatically predicted as neutral. Mutations described as

pathogenic in ClinVar are predicted as deleterious. (Schwarz, Cooper et al. 2014)

(Schwarz, Rödelsperger et al. 2010)

MutationAssessor calculates functional impact score (FIS) for amino acids changes.

FIS is based on evolutionary conservation patterns. To evaluate FIS score multiple

homologous sequences of analyzed sequence are clustered and aligned, then

difference of the entropy caused by mutation is estimated. (Reva, Antipin et al. 2011)

FATHMM uses Hidden Markov Models (HMM) to estimate deleteriousness of

nsSNPs. Probabilities of substituted amino acids are calculated by HMM, reduction

of probability comparing to wild type indicates negative impact upon protein

function. (Shihab, Gough et al. 2013)

MetaSVM and MetaLR use Support Vector Machine (SVM) and Logistic Regression

(LR) respectively. They are based on nine scoring methods (SIFT, PolyPhen-2

HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor,

FATHMM, LRT, SiPhy, PhyloP) and the maximum minor allele frequency (MMAF)

observed in the 1000 Genomes project population. (Dong, Wei et al. 2015)

Despite many analyzes comparing sensitivity and specificity of these algorithms

(Gnad, Baucom et al. 2013) (Wu, Kuang et al. 2016), it is not clear which method is

best to use and provides best results.

The purpose of this paper is to compare a results established by methods SIFT,

PolyPhen-2, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM and

MetaLR using a statistical clustering techniques.

Methods

In the analysis I use dataset named dbnsfp33a containing all known variants, which

deleteriousness is assessed by SIFT, PolyPhen-2, LRT, MutationTaster,

MutationAssessor, FATHMM, MetaSVM and MetaLR simultanously. The dataset is

provided by software ANNOVAR (Wang, Li et al. 2010). Then deleteriousness is

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Statistical comparison of deleteriousness prediction methods for single nucleotide variants

from next generation sequencing

103

categorized as 1 for variants marked as harmful, 0 for unmarked and -1 for tolerable

ones. As a result, for each analyzed method is created vector filled with values -1, 0,

1. Analysis of interrelations between the tested algorithms is made using Spearman

correlation, as well as k-means clustering and Multiple Correspondence Analysis. To

make calculations are used R packages factoextra (Kassambara, Mundt 2017) and

cluster (Maechler, Rousseeuw et al. 2017).

Spearman correlation The Spearman correlation calculates correlation between

ranks of vectors x and y.

Where

k-means clustering K-means clustering (MacQueen 1967) (Hartigan, Wong 1979) is

unsupervised machine learning algorithm which divides data into k groups.

Parameter k is determined by the analyst. For each cluster the centroid, which is the

mean of objects within cluster, is calculated. Finally a variation of objects within the

same cluster is as low as possible and it is calculated as a sum of squared Euclidean

distances between objects and centroid.

where

is number of clusters is object belonging to cluster is centroid of cluster

Multiple Correspondence Analysis Multiple correspondence analysis (MCA)

(Michailidis, de Leeuw 1998) is a technique for detecting and representing

underlying structures in nominal categorical data. Consider data of n objects on J =

{1, 2, …, J} categorical variables, where every category j J can take mj possible

values. Let X be a matrix containing the coordinates of the objects. Let Yj denote

matrix which contains the coordinates of every category. The data for every category

is encoded in matrix Gj , which is an n by mj matrix of category j where entry is 1 if

object belongs to category value and 0 otherwise. The scores are then obtained by

minimizing the loss-function which simultaneously minimizes for every category the

distances between the object score and the scores of that object on that category for

all objects.

The loss function is given by:

Dominik Wawrzuta

104

Results

Table 1 Results of Spearman correlation

Poly

Phen-2

Mutation

Taster

Mutation

Assessor

LRT FATHMM SIFT MetaLR MetaSVM

PolyPhen-2 1 -0.046 -0.085 -0.317 -0.471 0.200 -0.384 -0.327

Mutation

Taster

-0.046 1 0.278 -0.211 -0.404 -0.201 -0.430 -0.391

Mutation

Assessor

-0.085 0.278 1 -0.142 -0.334 -0.175 -0.412 -0.379

LRT -0.317 -0.211 -0.142 1 0.268 -0.239 0.032 -0.001

FATHMM -0.471 -0.404 -0.334 0.268 1 -0.353 0.610 0.498

SIFT 0.200 -0.201 -0.175 -0.239 -0.353 1 -0.334 -0.286

MetaLR -0.384 -0.430 -0.412 0.032 0.610 -0.334 1 0.820

MetaSVM -0.327 -0.391 -0.379 -0.001 0.498 -0.286 0.820 1

Table 1 shows the results of Spearman correlation between algorithms. The highest correlation occurs between the MetaSVM and MetaLR methods. The FATHMM algorithm also correlates with these two methods. We can distinguish two other groups, however weakly correlated, MutationTaster with MutationAssessor and SIFT with PolyPhen2.

Figure 1 graphs the results of the second applied method, which is k-means clustering. There is a clear division into three groups. One of them consists of the methods MetaSVM, MetaLR and FATHMM, which were also shown as highly correlated. In the second group the methods MutationTaster, MutationAssessor, SIFT and PolyPhen2 are clustered. The LRT algorithm creates a separate group. It should be noted that the variance explained by the axes is 25.9 and 39.2 percent.

The last method is Multiple Correspondence Analysis. The results are shown in the Figure 2. For every analyzed algorithm three binary vectors were created according to the convention “Name_Type” where Name is the name of an algorithm and Type is the letter denoting d for deleterious, u for unknown and t for tolerable values. The two directions explain almost 50% of the variation of the data. With respect to the first axis a definite split is found. We can notice division into two groups. The first group includes the MetaSVM and MetaLR algorithms. What is important, they are related in each of the statistical methods under study. The algorithms PolyPhen-2, LRT, FATHMM, SIFT, MutationAssessor and MutationTaster are in the second group. The cause of the division may be the type of data used by the algorithms. MetaSVM and MetaLR use results of other methods, while the other algorithms are based on molecular data about the tested proteins.



105

Figure 1 Results of k-means clustering

Figure 2 Results of Multiple Correspondence Analysis

Dominik Wawrzuta

106

Conclusions

The analyzed algorithms use various methods and data to assess the pathogenicity of

variants, but the multivariate techniques applied in the paper do not reveal

differences in the results of their calculations. The only distinctive group, shown by

clustering methods, are the MetaSVM and MetaLR methods, which differ from the

others in the way that they calculate the results. They use the results of

deleteriousness generated by other algorithms and do not calculate them on the basis

of biological parameters alone.

The results show that it is recommended to be very careful using the PolyPhen2,

SIFT, LRT, MutationAssessor, MutationTaster, FATHMM methods simultaneously

during genetic investigations. The multivariate analysis based on k-means method,

Multiple Correspondence Analysis and Spearman correlation reveals similar patterns

between different methods used to do pathogenicity assessment. It confirms that

applying multiple methods, which are based on similar assumptions implies little

gain in information and it should not be used to verify robustness of results of genetic

investigations.

Discussion

The evaluation of the impact of point mutations on protein activity is extremely

important during genetic research (Best, Wou et al. 2018) (Yang, Chen et al. 2017).

The creation of tools that would be able to make such an assessment with high

sensitivity and specificity would revolutionize the speed and cost of genetic testing.

Despite the creation of many deleteriousness assessment tools, there are no

significant differences in the results of their calculations. The MetaSVM and MetaLR

algorithms are the exception, which differ from other methods both in terms of

operation and calculated results. (Liu, Wu et al. 2016) showed also, that MetaSVM

and MetaLR have higher sensitivity and specificity than other methods. It can be

noticed that creating solutions based on existing algorithms may result in

significantly better methods than already existing.

An important source of data that can help to increase the efficiency of

deleteriousness assessment methods are the ClinVar (Landrum, Lee et al. 2018) and

HGMD (Stenson et al. 2009) databases. They contain information on pathogenic

mutations that have been confirmed by scientific research. At the moment

MutationTaster algorithm uses ClinVar data to calculate predictions of

deleteriousness.

Other data sources that carry important information about SNPs, and are rarely used

in deleteriousness assessment algorithms, are the ExAC, gnomAD (Lek, Karczewski

et al. 2016) and 1000 genomes (The 1000 Genomes Project Consortium 2015)

databases. They contain information on the incidence of SNPs in the population,

which is important information in the analysis, because the frequent occurrence of

the SNP in population probably does not negatively affects the phenotype.

In the future, the development of machine learning methods, especially based on

deep neural networks (LeCun, Bengio et al. 2015) may increase the accuracy of the



107

estimations. One of the first such tools is DeepVariant, which is created by the

Genomics team in Google Brain. Its results were positively evaluated by the 2016

PrecisionFDA Truth Challenge.

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109

Amnion as a potential source of neural

and glial cell precursors – from cell culture

to a practical application

Mateusz Król1, Łukasz Limanówka

2, Patrycja Szmytkowska

3, Piotr Czekaj

4

[email protected], Students Scientific Society, School of Medicine in Katowice,

Medical University of Silesia, Medyków 18, 40-758 Katowice, Poland [email protected], Students Scientific Society, School of Medicine in Katowice,

Medical University of Silesia, Medyków 18, 40-758 Katowice, Poland [email protected], Department of Cytophysiology, Chair of Histology and

Embryology, School of Medicine in Katowice, Medical University of Silesia, Medyków

18, 40-758 Katowice, Poland [email protected], Department of Cytophysiology, Chair of Histology and Embryology,

School of Medicine in Katowice, Medical University of Silesia, Medyków 18, 40-758

Katowice, Poland

Abstract

Chronic disease and acute lesions of the central and peripheral nervous system are the difficulty for

contemporary medicine due to limited ability of the nervous tissue to regenerate. A promising

alternative to pharmacological treatments would be therapy with the use of neural and glial cell

precursors obtained by differentiation of amniotic cells. Human amniotic cells (hAC) are viable, do not

form cancerous foci, and display features of pluripotency. They can differentiate in vitro into cells of all

three germ layers, e.g. endodermal cells of the liver and pancreas, mesenchymal muscle and bone cells,

as well as ectodermal neurons, astrocytes and oligodendrocytes.

Cells with pluripotency characteristics, are usually isolated from the amnion by enzymatic methods.

Factors stimulating ex vivo differentiation of amniotic cells towards the cells of nervous tissue are:

ATRA (all-trans retinoic acid), FGF (fibroblast growth factor) and EGF (epidermal growth factor). To

provide stem cell differentiation into specific types of nerve cells, additional factors are used, like: NGF

(nerve growth factor), SHH (sonic hedgehog homolog), FGF-8 (fibroblast growth factor 8), melatonin

and ligands of Wnt-4 receptors.

Preclinical studies with the use of amnion cells and neuron-like cells derived from amnion culture have

revealed considerable effectiveness in recovery of neurological functions after TBI (traumatic brain

injury) or ischemic stroke. The increase of cell survival and migration rate, have been also reported.

However, only a few percent of grafted cells survive longer than one month, and the neuroprotective

effect is achieved by secretion of neurotrophic factors like BDNF, NGF, CNTF, GDNF and NT-3 at the

site of injury.

Therapies involving the transplantation of neural cell precursors may contribute to the reduction of

pathological changes resulting from traumatic brain injury and ischemic stroke, slowing down the

course of neurodegenerative disease and could be used in regenerative medicine.

Introduction

Chronic and acute nervous system diseases always result in progressive inflammation, massive loss of nervous tissue cells, diffuse areas of impairment and changes in concentrations of specific neurotransmitters. Classical therapeutic methods, like pharmacological treatment, are not able to effectively prevent progression of injured area in conditions like TBI (traumatic brain injury) and stroke. Moreover, there is not

Mateusz Król, Łukasz Limanówka, Patrycja Szmytkowska, Piotr Czekaj

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known any therapy that could effectively suppress development of neurodegenerative and demyelinating disease. Therefore, a reasonable approach to tackle this issue could be intensification of regenerative processes, suppression of the inflammation and replacement of impaired cells at the site of injury. Such the strategy of treatment could contribute to partial or even full regression of lost tissue functions.

In recent years, there has been an increasing interest in various sources of stem cells which could be used in cell transplantation (Mamede, Carvalho et al. 2012). One promising source of stem cells is the human placenta. The innermost layer of the placenta is amniotic membrane containing cells characterized by low immuno-genicity, no teratoma formation and features of pluripotency. Since the amniotic membrane together with placenta is ordinarily discarded after childbirth, collecting placentae do not pose moral or ethical constraints. Furthermore, amniotic membrane-derived cells can differentiate into cells of all three germ layers, including neuron and glial cell precursors. These above-mentioned features make the amniotic membrane as an alternative source of stem cells for bone-derived mesenchymal stromal cells or embryonic-like stem cells (Wolbank, Peterbauer et al. 2007).

In preclinical studies, transplantation of undifferentiated amnion cells inhibited inflammation, promoted angiogenesis and improved cells viability. However, the major problem with this kind of application was the ability to differentiate these cells into mature neuron-like cells. Previous research has demonstrated that only few of grafted, undifferentiated cells possess neural morphology (Okawa, Okuda et al. 2001). The appropriate method for obtaining higher percentage of neural-like cells would be in vitro stem cells differentiation, before transplantation. On the other hand, in vivo regeneration can be achieved with the use of neuroprotective substances secreted by the grafted cells (Kim KS, Kim HS et al. 2013). Moreover, there are indications that differentiated amniotic cells would have greater clinical output due to higher production of neurotransmitters and neurotrophins, and ability to survive in host tissue (Yan, Zhang et al. 2013).

The purpose of this mini-review is to summarize recent studies concerning the potential of amniotic cells to differentiate into neural and glial cell precursors, as well as the effects of application both, differentiated and undifferentiated cells in preclinical trials.

Human amnion as a source of stem cells and mesenchymal tissue

Human amniotic membrane contains cells (hAC; human amnion cells) of different embryological origin (Fig. 1). hAEC (human amnion epithelial cells) originate from epiblast and form continuous monolayer of cells situated at the border between the amniotic fluid and the basement membrane (Miki, Marongiu et al. 2010). Sparsely distributed hAM-MSC (human amnion membrane-mesenchymal stromal cells) are located in mesenchymal tissue underlying the basement membrane (Soncini, Vertua et al. 2007). Both types of amnion cells can be obtained by means of enzymatic digestion. The received cell populations consist of cells with variable ability to self-renewal and differentiation (Parolini, Alviano et al. 2008). Important feature of hAC is low expression level of human leukocyte antigen class I (HLA-A, B, C) and lack of class II antigens (McDonald, Payne et al. 2015).

Amnion as a potential source of neural and glial cell precursors

– from cell culture to a practical application

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Figure 1. Human amniotic membrane. Semithin section, stained with toluidine blue (Dept. of Cytophysiology,

MUS Katowice). Magnification 1000x.

Not only the cells from amniotic membrane, but also amniotic membrane itself has

been used in regenerative medicine. Several publications have documented the use of

amniotic membrane in the management of burns, creation of surgical dressings, as

well as reconstruction of ocular surface or even peripheral nerve regeneration

(Niknejad, Peirovi et al. 2008; Diaz-Prado, Muinos-López et al. 2011; Mohammad,

Shenaq et al. 2000). Especially, mechanical properties of amniotic membrane such as

permeability, flexibility and plasticity allow its application to tissue replacement with

suitable strength necessary to resist stress during the growth of tissue. Amniotic

membrane also have the ability to prevent inflammatory cells and fibroblasts

migration into nerve tissue. (Sadraie, Parivar et al. 2016). Furthermore, the presence

of ECM (extracellular matrix) components, including collagen, laminin, fibronectin

and vitronectin, serving as ligands for surface transmembrane receptors called integrins,

is crucial for intracellular processes like cell transport (exocytosis and endocytosis),

proliferation, migration, differentiation and apoptosis. ECM components also

contribute to maintaining appropriate concentrations of growth factors and anti-

inflammatory cytokines, partly by binding them directly, as well as indirectly

– by absorption of water, in which these factors and cytokines are dissolved

(Niknejad, Peirovi et al. 2008). Thereby, ECM provides microenvironment that is

conducive to viability, proliferation and migration of grafted tissue.


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Extraction of human amnion cells

After obtaining written informed consent from the patient, a placenta is collected following term cesarean section. It must be shipped into laboratory immediately, so that the exposure time for hypoxia should be as short as possible. Subsequently, the amniotic membrane is mechanically peeled from the underlying chorion, and then it should be washed in PBS (phosphate-buffered saline) carefully and thoroughly to nearly ensure complete removal of blood, thus providing appropriate conditions for enzyme activities (Burton, Sebire et al. 2013).

Once the amniotic membrane is separated and washed, the isolation of amnion cells can be carried out. To release hAC cells from the membrane, the amnion tissue ought to be subjected to incubation with various enzymes. For example, hAEC can be obtained by incubation with dispase II or trypsin-EDTA solutions. The above described method allows to achieve from 12 to 14 million epithelial cells per gram of tissue (Gramignoli, Srinivasan et al. 2016).

Likewise, the isolation of hAM-MSC can be performed by enzymatic digestion, though with the use of various enzymes and slightly variant time periods of incubation. Collagenase and DNase (deoxyribonuclease) are also used. As a result, from 14 to 34 million of mesenchymal cells per one placenta can be obtained (Soncini, Vertua et al. 2007).

Characteristics of main cell populations derived from amnion

For the determination of amnion cell populations obtained after isolation, expression of specific cell markers should be assessed. In general, some of hAC cells show the expression of pluripotency markers specific for embryonic stem cells, e.g. OCT-4 (Octamer-binding protein 4), SOX-2 (SRY-related HMG-box gene 2), SSEA-3 (Stage Specific Embryonic Antigen-3), SSEA-4 (Stage Specific Embryonic Antigen-4), TRA (Tumor Rejection Antigen)-1-60 and TRA-1-81. These markers are typically present in cells having the capacity to self-renewal and differentiate into cells of three germ layers. However, hAC population contains both, pluripotent and multipotent stem cells. Moreover, hAC exhibit limited proliferation capacity in vitro (Diaz-Prado, Muinos-López et al. 2011), and the number of cells initially obtained from one amnion membrane is crucial for preparing appropriately dense cell suspension suitable for transplantation.

Presence of specific markers allows prompt distinction between hAEC and hAM-MSC, which is important due to variant differentiation capacity towards neuro-ectodermal lineages between both populations. Classical antigens of amniotic epithelial cells are cytokeratins 1-8, 10, 13-16, 19, integrins CD9 and CD24, CD324 (E-cadherin). On the other hand, cell markers such as SSEA-1, CD34, CD133 and CD117 (c-kit) are not present on hAEC or their expression was found to be very low (Miki, Lehman et al. 2005). In turn, hAM-MSC express classical mesenchymal markers, such as: CD29, CD44, CD49e, CD90, CD105 and CD166, though these cells are negative for hematopoietic and monocyte markers (CD34, CD45 and CD14) (Antoniadou, David et al. 2015). Unique characteristic feature, which merits attention, is absence of telomerase in amnion cells, which distinguishes these cells from embryonic stem cells and decreases risk of mutations (Tahan C., Tahan V. et al. 2014 and Kwon, Kim et al. 2016).



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Human amniotic cells culture and differentiation

Usually, hAEC and hAM-MSC isolated enzymatically with the use of enzymes such

as dispase, collagenase or trypsin, are resuspended in DMEM (Dulbecco’s modified

Eagle medium) mainly with addition of FBS (fetal bovine serum) and antibiotics and

seeded into plastic flasks. After achieving an appropriate confluence, the cells are

passaged and cultured (Sanluis-Verdes A., Sanluis-Verdes N. et al. 2017). Cell

culture can be supplemented with EGF (epidermal growth factor) to maintain

proliferation (Miki, Marongiu et al. 2010). After several passages, hAEC and hAM-

MSC cell cultures can be directly transplanted, to ameliorate regenerative processes

of nervous tissue. The grafted cells may produce growth factors and cytokines at the

site of injury whereby it can enhance the survival of injured neurons and glial cells.

Transplanted cells may also differentiate into appropriate neurons and glial cells at

the site of application and thus substitute injured cells. It was shown that only

a fraction of grafted cells undergoes differentiation into functional neurons and glial

cells in vivo. In order to transplant more differentiated cells, it would be important to

achieve the cells that are committed to a neuronal fate by induction of neural

differentiation in vitro (Wennersten, Meijer et al. 2004). In general, hAEC and hAM-

MSC can be subjected to differentiation after several passages. Then the medium

should be changed to induce neurospheres (Amendola, Nardella et al. 2014) and/or

terminal neural differentiation directly (Fig. 2).

Figure 2. In vitro methods for obtaining neural and glial cell precursors from amniotic cells. Terminal

differentiation can be achieved directly by placing hAEC or hAM-MSC in the specific differentiation medium

(A) or indirectly by placing the cells in the inductory medium (B) followed by differentiation medium (C).


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Neurospheres are complex structures containing cells of different phenotypes and

expressing various neural lineage-specific markers. Cytoarchitecture of these

structures is most likely caused by cell adaptation to the conditions prevailing in in

vitro culture. The inner part of neurosphere is composed of poorly proliferating cells

with low level of gene transcription. It is caused by a greater difficulty in nutrient

exchanging. On the other hand, the outer layer is rich in proliferating cells, and

actively produces proteins, such as neurotrophins and cytokines that can be

accumulated in the inner part and/or released into the culture medium (Bez, Corsini

et al. 2003 and Amendola, Nardella et al. 2014).

One of the standard medium that has been used to make the cells committed to

a neuronal fate is DMEM supplemented with, among others, FBS, RA (retinoic acid),

bFGF (basic fibroblast growth factor), EGF, L-glutamine and non-essential amino-

acids, 2-mercaptoethanol, sodium pyruvate, insulin-transferrin-selenium and ascorbic

acid. In this medium, hAC acquired some morphological neuron-like features, e.g.

presence of forming extensions, after 4 days of culture. At the end of culture in the

differentiating medium, these features were much more distinct and synapses or Nissl

bodies, were seen. In addition, the presence of NF (neurofilaments) and markers such

as GFAP (glial fibrillary acidic protein), vimentin, NGFR (nerve growth factor

receptor), and S100β (S-100β protein) has been reported (Sanluis-Verdes A., Sanluis-

Verdes N. et al. 2017). Different number of cells positive for individual markers

suggest the presence of different cell subpopulations in culture. Moreover, higher

expression of glial marker S100β and neural markers: NF and NGFR, has been

detected in hAEC in comparison with hAM-MSC (Sanluis-Verdes A., Sanluis-

Verdes N. et al. 2017). hAEC synthesize some neurotransmitters (dopamine and

other catecholamines) and enzymes, like tyrosine hydroxylase (TH), necessary for

the formation of these neurotransmitters (Kakishita, Elwan et al. 2000). Furthermore,

hAEC secrete neurotrophic factors, such as BDNF (brain-derived neurotrophic

factor), NT-3 (neurotrophin-3), insulin-like growth factors, EGF, prostaglandin E2

etc., which may be conducive to dopaminergic neurons survival after amniotic cells

transplantation in neurodegenerative disorders like PD (Parkinson’s disease) (Yang,

Song et al. 2010; Allen,Watson et al. 2013; Bothwell, 2014; Bennett, Lagopoulos,

2014; Ziegler, Levison et al. 2015; Oyagi, Hara, 2012). All these observations

indicate that potentially, hAEC can easier differentiate towards neural precursors.

Preclinical application of amnion tissue

Both, chronic and acute pathological conditions lead to massive neural cells degene-

ration resulting in progressive idiosyncratic clinical picture with characteristic signs

and symptoms. Nonstationary course of these pathological conditions inevitably

leads to deterioration of mental and neurological functions, and finally to dementia.

Unfortunately, none of currently used therapies is not able to effectively impede this

escalating neurodegenerative process. Since the cell therapy has proved its clinical

effectiveness of treatment of above-mentioned conditions, there are great

expectations of using these cells on a broad scale.

There are several studies concerning positive effects of undifferentiated hAC

transplantation for treating various diseases (Li, Guo et al. 2014; Kim KS, Kim HS et



115

al. 2013; Xue, Chen et al. 2012; Sheng, McShane et al. 1993; Yang, Song et al. 2010;

Liu, Vaghjiani et al. 2012; Liu, Chan et al. 2014; McDonald, Payne et al. 2015;

Chen, Lu et al. 2011; Liu, Wu et al. 2008). For example, in TBI, external forces act

on nervous tissue, resulting in short and long-term undesirable consequences, like

decreased level of consciousness, amnesia, neurologic deficits and ischemic

complications (Mckee, Daneshvar 2015). In the case of TBI, it has been shown that

for 2 weeks after ipsilateral intracerebroventricular hAC transplantation in rat model

of PBBI (penetrating ballistic-like brain injury), alleviation of axonal degeneration in

CC (corpus callosum), ipsilateral thalamus and around lesioned area were observed.

Transplanted cells accumulated in subventricular zone and corpus callosum where

they could survive up to 4 weeks. Moreover, hAC had ability to intensively migrate

toward lesioned area. Neuroprotective effect of amnion cells transplantation,

probably results from well-known ability to secrete soluble factors, which support

neural cells viability and induce endogenous NSC (neural stem cells) population to

differentiation (Chen, Tortella et al. 2009).

Another example of potential hAC application is ischemic stroke. After ischemic

incident it is enormously important to immediately initiate neuroprotective therapy,

usually by means of neuroprotective drugs like piracetam or tPA (tissue plasminogen

activator), albeit limited by short time window of efficacy (Yu, Soncini et al. 2009).

In such condition, with the aid of transplanted hAC, a reduction of ischemic area and

improvement of neurological function could be obtained. In the rat model of

ischemic stroke, cells after injection to CC survived up to 3 weeks and had the

capability to migrate toward the areas of injury. Moreover, the infarct area volume

decreased by 10% in comparison to control group, and applied cells expressed neural

progenitor cell markers like Nestin and MAP2 (Li, Miao et al. 2012).

Advanced depletion of dopaminergic neurons in PD could also be ameliorate by hAC

administration. After hAC injection into striatum of rats with PD, animals achieved

better scores in behavioral tests. Immunohistochemical staining for human nuclear

antibody showed extensive migration of transplanted cells. After cells’ administration

number of TH-positive dopaminergic neurons increased, what suggested activation

of endogenous NSC responsible for physiologic neurogenesis (Kong, Cai et al. 2008).

The above examples of hAC treatment show that grafted cells contribute to restriction of injured areas and improvement of neurological function. It is very interesting because it was also reported that only a few percent of applied cells differentiate into mature nervous cells (Okawa, Okuda et al. 2001). It is important to consider the differences of effectiveness between differentiated and undifferentiated cells, and what stems from it, whether hAC should be differentiated in vitro, before transplantation. There is little study about preclinical application of neural precursors derived from hAC. Detailed examination of differences between hAM-MSC and hAM-NP (hAM-MSC differentiated into neural precursors) by Yan et al. (2013) suggests that cells subjected to differentiation in vitro have greater therapeutic potential. Despite the absence of significant differences in cell survival between hAM-MSC and hAM-NP in the rat model of TBI, there was observed more reduced area of damage in case of hAM-NP, in comparison to hAM-MSC. Moreover, rats treated with hAM-NP exhibited better scores in tests evaluating neurological


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recovery, compared with rats treated with hAM-MSC. Quantitative real-time PCR showed significantly stronger expression of neurotrophins like NGF (nerve growth factor), CNTF (ciliary neurotrophic factor), GDNF (glial cell line-derived neurotrophic factor), BDNF and NT-3 genes by hAM-NP in comparison to hAM-MSC (Yan, Zhang et al. 2013).

In another study, Gao et al. (2016) found that application of previously differentiated bovine amnion epithelial cells into spinal cord in the animal model of injury, gave distinctly better rate of transplant survive in comparison to application of undifferentiated AEC. In this study, it was also reported that the recovery of motor functions was significantly better in the group treated with neural-like cells derived from amnion epithelial cells. These findings confirm the thesis that amnion cells with acquired neural fate commitment have superior potential in treatment of nervous system diseases in comparison to undifferentiated cells (Gao, Bai et al. 2016).

Summary

It can be concluded that human amnion membrane can be an attractive source of cells for therapy of nervous system diseases. Obtained amnion cells can effectively differentiate toward neural and glial cell precursors. Application of the cells with previously committed neural fate in vitro, yields better neural function recovery than the use of undifferentiated cells. Higher rate of survival and substantial secretion of neurotrophins in vivo are also features of neural precursors which attract attention in view of clinical therapy. However, more studies are required to fully understand processes of both, hAC differentiation and regeneration before their clinical application in humans.

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What makes the bacterial vaginosis difficult

to diagnose: our experience in Silesia, Poland

Patryk Madeja1,3

, Olga Smolarek1, Agnieszka Mosler

1, Małgorzata Romanik

2

1Student Research Group at the Department of Medical Microbiology, School

of Medicine in Katowice, Medical University of Silesia, Katowice, Poland 2Department of Medical Microbiology, ul. Medyków 18, 40-752 Katowice, School

of Medicine in Katowice, Medical University of Silesia, Katowice, Poland 3Corresponding author: Department of Medical Microbiology, ul. Medyków 18,

40-752 Katowice, School of Medicine in Katowice, Medical University of Silesia,

Katowice, Poland; [email protected]

Abstract

INTRODUCTION: Bacterial vaginosis (BV) is manifested mainly by vaginal discharge and is defined

as an imbalance of the bacteria that are normally present in the vagina.

During our last study we find out that some smears are hard to classify according to Nugent’s Criteria.

The aim of this study was to characterize patients with problematical clinical diagnosis.

MATERIALS AND METHODS: Vaginal smears were collected from 100 women of childbearing age

with vaginal discharge without cervicitis and antibiotic therapy. Classification of the vaginal microbiota

by Nugent Criteria was performed by laboratory staff and students. In 44 cases there was diagnostic

discrepancies between students and laboratory staff.

RESULTS: Among the selected 44 women vaginal pH ≥ 4.5 had been noted in 38.63%, while only

16.07% of the other group patients had incorrect vaginal pH. Statistically 31.82% of the first group was

in age range 36-40 and only 14.29% of the properly diagnosed was in the same age range. Vaginal

douching had been used by 45.45% of incorrectly diagnosed group while in the other group only by

33.93%. Results of chi-square test revealed statistically significance in pH (χ2 = 13.2665; p = 0.00027 at

p < 0.05), patient’s age (χ2 = 12.411; p = 0.014543 at p < 0.05).

CONCLUSIONS: Our study showed that in case of diagnostic difficulties the vaginal pH and patient’s

age must be considered for the diagnosis of bacterial vaginosis.

Introduction

At present, over 19.8 million women live in Poland (CSO, 2016). About 7.6 million

are in childbearing age (CSO, 2013). It is obvious that the most common cause of

vaginal symptoms among women in this age group should be considered as serious

health problem (CDC, 2018).

Bacterial vaginosis (BV) is defined as chronic imbalance of the morphotypes

normally present in the vagina. The mixed microflora consisting of Gardnerella

vaginalis, Bacteroides spp., Mobiluncus spp., Mycoplasma hominis and multiple

other bacteria replaces population of Lactobacillus spp. – the predominant species in

healthy vaginal microbiota (Antonio, Hawes et al. 1999). Because the aetiology of

BV is still vague and associated with multiple genital microorganisms there are some

clinical signs (adherent, homogenous, white vaginal discharge with a characteristic

fishy odour and vaginal pH ≥ 4.5) that has been defined for diagnosis of BV and

called the Amsel criteria (Amsel, Totten et al. 1983). On the other hand, there are

several microbiological criteria, which may be used to confirm BV established by


Patryk Madeja, Olga Smolarek, Agnieszka Mosler, Małgorzata Romanik

120

three other groups of authors (Ison, Hay, 2002, Spiegel, Amsel et al. 1983, Nugent,

Krohn et al. 1991).

Correct diagnosis of BV may be problematic, because there are couple of factors

interfering the assessment: multitude of forms Lactobacillus morphotypes take,

problematic counting of some Lactobacillus morphotypes forming a leptotricha or

assessment of smears lacking any morphotypes (because of imperfections in

diagnostic criteria). Many factors including smoking, vaginal douching, taking

antibiotics, and high-risk sexual behaviour are associated with diversity of the

vaginal microbiota and lack of Lactobacillus spp. (Hellberg, Nilsson et al. 2000,

Cotrell, Hansen, 2010, Olson, Boohaker, et. al. 2018, Wessels, Lajoie, et al. 2017).

Recent data shows that using of copper intrauterine device may increase colonization

by BV-associated microbiota (Achilles, Austin et al. 2018).

BV increases risk of acquisition and transmission of sexually transmitted infections

(STI) including Human Immunodeficiency Virus (HIV), Herpes Simplex Virus Type

2 (HSV-2), Chlamydia trachomatis (C. trachomatis), and Neisseria gonorrhoeae (N.

gonorrhoeae) (Bautista, Wurapa, et al. 2016).

Many studies indicate clinical importance of BV, showing that this condition is a risk

factor for adverse pregnancy outcomes, including preterm deliveries, low birth weight

infants, and enhances risk of miscarriage in the first trimester and postpartum

endometritis (Nelson, Hanlon et al. 2009, Chawla, Bhalla et al. 2013, Ralph, Rutherford

et al. 1999). It may also lead to infertility (Mania-Pramanik, Kerkar et al. 2009).

According to Centers for Disease Control and Prevention (CDC) treatment guidelines

women with BV symptoms should be treated orally or intravaginally with

metronidazole or clindamycin (CDC, 2015; access: 14.03.2018). Therapy is calculated

to relieve vaginal symptoms and signs of infection, and potentially to reduce the risk

for acquiring C. trachomatis, N. gonorrhoeae, Trichomonas vaginalis, HIV, and

HSV-2 (Brotman, Klebanoff et al. 2010, Cherpes, Wiesenfeld, et al. 2006,

Schwebke, Desmond 2007). There are also presumptions that oral and vaginal

administration of probiotics may result in the cure of BV or cause reduction relapses

of symptoms (Kim, Park 2017). Despite antimicrobial treatment, BV has tendency to

relapse: recurrence was noted in 80% of cases within a year after treatment and in

50% of the cases within 5-9 years after treatment (Romanik, Ekiel et al. 2007).

The aim

The aim of this study was to characterize patients with problematical clinical diagnosis

and expose potential factors that may make BV difficult to diagnose, considering the

reasons mentioned in the introduction, we believe that early diagnosis and treatment of

BV is very important especially for women of childbearing age.


Vaginal smears were collected by clinician from 100 women of childbearing age with vaginal discharge and without cervicitis (in clinical and cytological examination). None of the patients were undergoing antibiotic therapy at the time of the collection. Classification of the vaginal microbiota by laboratory staff (two laboratory

What makes the bacterial vaginosis difficult to diagnose: our experience in Silesia, Poland

121

diagnosticians) and students was performed using three scales based on microscopic analysis of Gram-stained vaginal smears: Nugent scoring system, Hay and Ison score and Spiegel score. Smears were microscopically evaluated under 1000x magnification with oil immersion. Then clinical data of the patients on anonymous written form were analysed.

Table 1. Hay and Ison Criteria and Spiegel Criteria

Hay and Ison Criteria Spiegel Criteria Diagnosis

Grade 1 Predominance of Lactobacillus morphotypes

Domination of morphotypes of Lactobacillus spp.

Vaginal physiological

microbiota

Grade 2 Mixed vaginal flora with approximately equal amounts of Lactobacillus spp. and Gardnerella vaginalis morphotypes

Equal amount of Lactobacillus spp. morphotypes and Gardnerella-like bacteria

Intermediate condition

Grade 3 Predominance of Gardnerella vaginalis morphotypes and Mobiluncus morphotypes and a few Lactobacillus morphotypes

A few or no Lactobacillus spp. and many Gram-negative and/or Gram-variable morphotypes (typical for G. vaginalis)

BV

There are three grades in scale established by Hay and Ison’s (Table 1).

Another scale, called Spiegel score, is very similar to Hay and Ison’s method (Table 1). According to Spiegel’s system bacteria are categorized into groups of morphotypes in terms of its length. Lactobacillus spp. Gram-positive morphotypes are classified as long bacteria, but on the contrary Gram-variable morphotypes typical for Gardnerella vaginalis, Bacteroides spp., Prevotella spp., or Mobiluncus spp. are classified as short bacteria.

However, the current ‘gold standard’ in BV diagnostics is Nugent scoring system. (Hoffman, Bellad, et al. 2017) (Table 2).

Table 2. Nugent scoring system

Lactobacillus

morphotypes

Gardnerella or Bacteroides

morphotypes

Curved variable rods

Score 0 for >30 Score 0 for 0 Score 0 for 0

Score 1 for 5-30 Score 1 for <1 Score 1 for <5

Score 2 for 1-4 Score 2 for 1-4 Score 2 for 5+

Score 3 for <1 Score 3 for 5-30

Score 4 for 0 Score 4 for >30


122

The average of all 20 visual fields is taken as final Nugent score. Nugent score 0-3 is

considered physiological microbiota, 4-6 is considered ‘intermediate’ state, while ≥7

confirms BV.

Statistical analysis was performed using Microsoft Excel (Office 2007) and Chi-

Square Test Calculator available on www.socscistatistics.com/tests/chisquare2/

Default2.aspx (access: 14.12.2017) and Mann-Whitney U Test Calculator available

on www.socscistatistics.com/tests/mannwhitney/ (access: 14.12.2017), p<0.05 was

considered statistically significant.

During this study, patients were divided into two subgroups according to inter-

observer (students’ vs laboratory staff) agreement. The first subgroup, consisted of

patients misdiagnosed by students and counted 44 women. For simplicity we called

them ‘incoherent’. Another subgroup of patients, correctly diagnosed by students,

consisted of 56 women was analogically called ‘coherent’. We analysed these two

groups in terms of different parameters: vaginal pH, age, parity, contraceptives,

vaginal douching, cigarette smoking and recurrence of the symptoms after treatment

with metronidazole according to CDC guidelines (CDC, 2015; access: 14.03.2018).

Results

The age of women in both groups (coherent and incoherent) was comparable. Median

age of incoherent group was 35.5 years and coherent was 29 years. In incoherent group

age average was 33.6 years and in the coherent group was 30.6 (Table 3).

Table 3. Age characteristics (in years) in women from incoherent and coherent group

Group Age U-MW* p-value**

Min-max Median Average

-1.90266 0.05744 Incoherent 22-45 35.5 33.6

Coherent 20-45 29 30.6

*Mann–Whitney U test; **result is statistically significant p <0.05

The most numerous group with the results of the BV assessment were different

between the students and the diagnosticians was group of patients with age ranged

36-40. In coherent group 8 of 56 (14.29%) patients were in this age range, while in

incoherent group 14 of 44 (31.82%) patients were in mentioned age range (Table 4).


123

Table 4. Percentage of age range distribution in incoherent and coherent groups

Age range Incoherent Coherent

22-25 18.18% 23.21%

26-30 22.73% 35.71%

31-35 9.09% 14.29%

36-40 31.82% 14.29%

41-45 18.18% 12.5%

Out of 44 patients of incoherent group, the proper diagnosis of physiological microbiota was noted in 52.27% of cases, intermediate state in 29.55% and BV in 18.18% of cases (Table 5).

Table 5. Distribution of different diagnoses among women from incoherent and coherent group

Groups Nugent score χ2 p-value*

0-3 Normal vaginal microbiota

4-6 Intermediate state

7-10 BV

14.2489 0.000805 Incoherent Number (%)

23/44 (52.27%)

13/44 (29.55%)

8/44 (18.18%)

Coherent Number (%)

43/56 (76.79%)

7/56 (12.5%)

6/56 (10.71%)

*result is statistically significant p <0.05

Out of 56 patients of coherent group, majority (76.79%) possessed physiological microbiota, 12.5% – intermediate state, and in 10.71% of women BV was diagnosed. This analysis shows that out of 66 women, who in fact did not suffer from BV, 43 was diagnosed by students properly and 23 was diagnosed improperly. There were 20 patients with intermediate state and only 7 of them were diagnosed by students properly, while 13 was misdiagnosed. Among 14 women suffering from BV, 6 was diagnosed correctly, while 8 was diagnosed incorrectly.

Analysing subsequent factors, such as parity (Table 6) and contraception and its type (Table 7), there were no statistically significant differences between groups, this would indicate a high homogeneity of the groups covered by this study and a lack of association of parity and contraception type with diagnostic difficulties using Nugent score.

Among the incoherent group, 52.27% of women had never been pregnant while 25% (11 women) were multiparous. Primiparous were 10/44 (22.73%) of incoherent and 19/56 (33.93%) of coherent group.


124

Table 6. Distribution of parity in group incoherent and coherent in the study

Parity Incoherent Coherent χ2 p-value*

Nulliparas

Number (%)

23/44

(52.27%)

24/56

(42.86%)

3.0588 0.216669 Primiparas

Number (%)

10/44

(22.73%)

19/56

(33.93%)

Multiparas

Number (%)

11/44

(25%)

13/56

(23.21%)

*result is not statistically significant at p <0.05

Table 7. Distribution of contraception type used by patients of evaluated groups and performing vaginal

douching

Contraception Incoherent Coherent χ2 p-value***

No contraception

Number (%)

31/44

(70.45%)

38/56

(67.86%)

0.0935 0.759772

OC *

Number (%)

10/44

(22.73%)

11/56

(19.64%)

IUD **

Number (%)

0/44

(0%)

1/56

(1.79%)

Contraceptive transdermal

patch

Number (%)

2/44

(4.55%)

3/56

(5.36%)

Contraceptive vaginal ring

Number (%)

1/44

(2.27%)

3/56

(5.36%)

Vaginal douching

Number (%)

20/44

(45.45%)

19/56

(33.93%)

*OC – oral contraception; **IUD – intrauterine device; ***result is not statistically significant at p <0.05


125

Vaginal douching had been used by 20/44 (45.45%) of incorrectly diagnosed women,

while in another group it has been performed only by 19/56 (33.93%) of patients.

Majority of women from both groups claimed not to smoke tobacco. Only one

woman (1.79%) of coherent and three women (6.82%) of incoherent group have been

smoking cigarettes. Relapses of BV after treatment occurred only in 6.82% women

(3/44) of incoherent and 1.79% woman (1/56) of coherent group.

Abnormal vaginal pH noted in physical examination was strongly associated with

results obtaining in incoherent group (table 8).

Table 8. Distribution of vaginal pH in incoherent and coherent group in the study

Vaginal pH range Incoherent

N (%)

Coherent

N (%)

χ2 p-value*

≥4.5 17/44

(38.63%)

47/56

(16.07%)

13.2665 0.00027

<4.5 27/44

(61.36%)

9/56

(83.93%)


Furthermore, the microbiological assessment according to Nugent score shows

higher percentage of intermediate state in incoherent group (Table 9).

Table 9. Distribution of correct and incorrect diagnoses among women with different state of microbiota

Groups Nugent Score

0-3 (Normal vaginal

microbiota)

4-6 (Intermediate state) 7-10 (BV)

Incoherent Number (%) 23/66

(34.85%)

13/20

(65%)

8/14

(57.14%)

Coherent Number (%) 43/66

(65.15%)

7/20

(35%)

6/14

(42.86%)

x2 19.3488.

p-value 0.000063.



126

Discussion

There are many factors altering the result of microscopic evaluation of Gram-stained

smears using Nugent scoring system. Current study is the first to associate clinical

features with difficulty of making correct BV diagnosis. From all characteristics, we

took into consideration, all can alter vaginal ecosystem (Achilles, Austin, et al. 2018,

Sabour, Arzanlou, et al. 2018, Aslan, Bechelaghem 2018), and can complicate

microscopic evaluation.

Our study showed that among many factors as: using contraception and especially

type of contraception, age, vaginal douching, vaginal pH and parity, incorrect vaginal

pH value (≥ 4.5) demonstrated a statistically significant difference between two

studied groups. Percentage of misdiagnoses was the highest in the group of patients

diagnosed with intermediate state, especially in women between 36-40 years old.

Correct diagnosis of BV still remains problematic as its aetiology is vague. Nugent

scoring system is a method considered the ‘gold standard’ as it is the most objective

laboratory tool for diagnosing BV, but its usage may be challenging for

inexperienced diagnosticians.

First problem we met during this study was wide range of forms that Lactobacillus

morphotypes take, like large and intermediate rods, spherical or very long forms, so

called leptotrichas. Often it is possible to mistake intermediate rods with Gardnerella

spp. morphotypes or spherical forms with Gram-positive cocci. When mistaken with

another bacteria, morphotype is not taken into account and it may change the result

of the assessment. Gram variable rods – Gardnerella vaginalis has a very thin cell

wall with a characteristic Gram-negative staining pattern but cell walls are

unequivocally Gram-positive in their ultrastructural characteristics and chemical

composition (Sadhu, Domingue et al. 1989).

Another problem is connected with counting of Lactobacillus morphotypes, forming

a leptotricha, as long as it is hard to point a place where one morphotype ends and

another one begins. Assessment of smears without any morphotypes at all is also

challenging: such smears get 4 points for absence of Lactobacillus spp. morphotypes

and 0 points, for absence of Gardnerella spp. and Mobiluncus spp. morphotypes,

what gives in the end 4 points and include the smear in intermediate state group

– this not have anything to do with reality.

The evaluation of intermediate state is the most frequent diagnostic problem (Taylor-

Robinson, Morgan, et al. 2003, Bradshaw, Morton et al. 2005). Some researchers

suggest that there is no need for distinguishing intermediate state and a new cut-off

should be established (Rodrigues, Peixoto et al. 2015). Although, this might be

difficult, as intermediate state shares both characteristics of normal microbiota

(Santos-Greatti, da Silva, et al. 2016) and BV (Guédou, Van Damme et al. 2016,

Guédou, Van Damme et al. 2014, Waqqar, Aziz et al. 2017).

Using Nugent scoring system factors such as microscopic image area size (Larsson,

Carlsson et al. 2004), experience of slide reader (Chawla, Bhalla, et al. 2013),


127

thickness of sample or subjectivity of assessment of different morphotypes can alter

the result (Forsum, Larsson et al. 2008).

We did not calculate the prevalence of BV, providing that the slides we evaluated

were not randomly selected, because we needed a representative number of smears

with different diagnoses. BV is the most common cause of abnormal vaginal

discharge in women of reproductive age, however, frequency of this condition is

variable between different ethnic and BV risk groups and depends on diagnostic

method used.

At present there are many molecular methods used to detect BV-associated bacteria

(Lannon, Waldorf et al. 2018). In our opinion for BV diagnosis more appropriate is

using clinical (Amsel) or microscopic criteria (Nugent, Hay’s), than PCR based

methods. Not only because of its expensiveness, but also because of too high

sensitivity of PCR methods, especially in cases when physiological microbiota is

present. Further analysis among women with different BV risk is needed to

determine their potential impact on proper BV diagnosis.

CONCLUSION

The borderline diagnoses of vaginal abnormal microbiota (intermediate state) should

be confronted with clinical symptoms and vaginal pH before making final clinical

diagnosis using Nugent scoring system.

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Detection of sleepiness in narcolepsy

Renata Plucińska1, Mirosław Łątka

2, Aleksandra Wierzbicka-Wichniak

3, Wojciech

Jernajczyk3, Grzegorz Domański

4

1Warsaw University of Technology, Faculty of Electronics and Information Technology,

Institute of Electronic Systems, Nowowiejska 15/19, 00-665 Warsaw, e-mail:

[email protected]. 2Wrocław University of Science and Technology, Faculty of Fundamental Problems of

Technology, Department of Biomedical Engineering, Wybrzeże Stanisława

Wyspiańskiego 27, 50-370 Wrocław. 3Institute of Psychiatry and Neurology, Department of Neurophysiology, Jana

Sobieskiego 9, 02-957 Warsaw. 4Warsaw University of Technology, Faculty of Electronics and Information Technology,

Institute of Radioelectronics and Multimedia Technology, Nowowiejska 15/19, 00-665

Warsaw.

Abstract

Narcolepsy is a neurological disorder that involves impairment of sleep-wake cycle regulation.

Intermittent sleepiness which may occur at any time is a cardinal symptom of this disease.

In this preliminary study we attempt to differentiate group of narcoleptic patients and healthy subjects,

using the spectral parameters of EEG signal.

Using routine 19-channel EEG recordings (10-20 standard) of 25 narcoleptic patients and 18 healthy

volunteers we found the statistically significant differences in the normalized FFT power of alpha, beta

and theta rhythms. We exploited these differences to correctly identify all patients using neural network

classifier.

Keywords: Digital Signal Processing; EEG; Narcolepsy; MSLT; FFT

Introduction

Sleep disorders with predominant excessive daytime sleepiness are classified

according to the International Classification of Sleep Disorders, 3rd edition (ICSD-3)

as Central disorders of hypersomnolence. One of the best described and known

hypersomnia is narcolepsy (American Academy of Sleep Medicine, 2014).

Narcolepsy is a chronic, neurological disease, caused by hypocretin deficiency in the

brain. Hypocretin (also known as orexin) is a neurotransmitter produced by neurons

localized in the hypothalamus. It is believed that loss of the hypocretin neurons is an

effect of autoimmune process (Savvidou, Knudsen et al. 2013) Narcolepsy is a rare

disease, affecting 0,03-0,16% of population, depending on the ethnic origin. Most of

the cases of narcolepsy are sporadic, only about 5% are familial (Guilleminault,

Mignot et al 1989). The onset is usually within the second decade of life but can be at

any age. During the first years after the onset of first symptoms, there may be

progression of the disease.

Narcolepsy is characterized by the presence of excessive daytime sleepiness with

sleep attacks and clinical manifestation of dissociated rapid eye movement sleep

including cataplexy, hypnagogic hallucinations and sleep paralysis. Other sleep

disturbances, as REM sleep behaviour disorder (RBD), sleep apnea and periodic limb



131

movements in sleep are also frequent in narcolepsy (Wierzbicka, Wichniak et al

2006). Cataplexy is one of the most characteristic symptom in narcolepsy,

manifested by sudden decrease of muscle tone, leading to drop of the jaw, head,

bending knees, even falling down and is triggered by emotions, such as laughing,

excitation, amazement. Sleep paralysis is described by the patients as impossibility to

move the limbs during falling asleep or awakening. At the same time dream-like

sensations known as hypnagogic hallucinations may occur.

According to diagnostic criteria of ICSD-3 the diagnosis of narcolepsy is based on

the clinical features and diagnostic examinations and tests. The disease is divided in

two subtypes: narcolepsy type 1 and type 2. Both types require the presence of

3 months of excessive daytime sleepiness (American Academy of Sleep Medicine,

2014). The standard diagnosis recommends performing neurophysiological

procedures, such as polysomnography (PSG) and Multiple Sleep Latency Test

(MSLT), consisting of five scheduled naps during the day. The mean sleep latency

shorter than 8 min. and presence at least two sleep onset REM periods (SOREMPs)

in MSLT meet criteria for narcolepsy (Carskadon, Dement et al 1986). SOREMP

recorded during the PSG is also included into diagnostic criteria. In type 1 there is

clear-cut cataplexy or a proven undetectable amount of hypocretin-1 in the

cerebrospinal fluid (below 110pg/ml). In type 2, there is no cataplexy and no proven

hypocretin deficiency, but the diagnosis is confirmed in a sleep laboratory with

polysomnography and MSLT.

Beside deficiency of hypocretin in cerebral spinal fluid, another biological marker is

frequent in narcolepsy. It is associated with specific alleles of immunological system,

HLA DQB1*0602. These both biological markers are strongly associated with the

occurrence of cataplexy (Krahn, Pankratz et al 2002).

Narcolepsy significantly affects educational skills, work and social life of the

patients, lowers their quality of life, leads to emotional problems and depression

(Wierzbicka, Wichniak et al 2007).

The treatment consists of behavioural methods and pharmacological compounds,

especially stimulating medications.

Routine electroencephalography (EEG) is a short-lasting examination of brain

activity and usually helpful to estimate the vigilance state of the patient. However,

sleepiness is frequently missed by only visual evaluation of the EEG recordings.

Therefore, a novel method of analysing the EEG signal may enable to detect

sleepiness more precisely.

Some studies of alpha wave power in EEG signal and its change related to the status

of the subject were conducted (Benca, Obermeyer et al 1999; Cantero, Atienza et al,

2002). Variety of methods (including classical and model based spectral methods) to

describe state of alertness level from full spectrum EEG recordings were tested

(Subasi, 2005). There are already some automatic methods of detecting drowsiness,

sleepiness and alertness using power spectral density of discrete wavelet transform

followed by neural network (Kiymik, Akin et al 2004) and using multimodal analysis

followed by neural network as well (Garcés Correa, Orosco, et al. 2014). Some

Renata Plucińska, Mirosław Łątka, Aleksandra Wierzbicka-Wichniak,

Wojciech Jernajczyk, Grzegorz Domański

132

studies with detection drowsy driving using Discrete Wavelet Transform and

unsupervised learning through K-means clustering also were carried (Gurudath,

Riley, 2014). Detection of differences between patients responding and not

responding to the treatment for depression using wavelets with a power spectrum

covering the alpha wave range has been studied (Jernajczyk, Gosek et al. 2017).

Recent studies show that spectral analysis of EEG signal in nocturnal poly-

somnography of sleeping patients and control subjects shows statistically significant

differences in theta and delta waves activity as well (Yub, Choi et al 2017).

Aim of the study was to attempt to distinguish narcolepsy patients with clinically

significant sleepiness during the day from the healthy volunteers, using power

analysis of 3 EEG waves (alpha, beta, theta) and then neural network to recognise the

sleepiness.

Subjects and Methods

Subjects

We performed retrospective analysis of EEG recordings of 25 patients with

narcolepsy (20 men and 5 women, mean age 30 ± 8 years). 16 of them were

diagnosed with type 1 narcolepsy the others with type 2. The control group consisted

of 18 healthy volunteers (4 men and 14 women, mean age 26 ± 9 years). The analysis

was approved by the Ethics Committee of Institute of Psychiatry and Neurology in

Warsaw (IPIN).

All EEG recordings were performed at the Department of Neurophysiology of IPIN.

Data were collected using the Braintronics EEG ISO-1032CE electroencephalograph

and Elmiko's EEGDigiTrack software. 19 electrodes were positioned according to 10-

20 standard with the ground electrode at Fpz. The sampling frequency was 250 Hz.

EEG measurements were performed between 2009 and 2016 (patients) and in 2017

(control group).

The MATLAB R2016a programming environment was used for all analyses.

EEG analysis

The block diagram of the EEG data processing algorithm used in this study is shown

in Fig. 1.

The artifacts-free parts were extracted from the 3 sections of the EEG recording: eyes

closed (which preceded hyperventilation and photostimulation tests), hyperventilation

(which preceded photostimulation test) and right after hyperventilation (but before

photostimulation or eyes open). All sections were analysed independently, resulting

in three different approaches. The results were presented in Tab. 1-9.

Every part of every section was divided into a set of EEG segments of length equal to

512 samples (approximately 2 s) for which was calculated the FFT (Lyons, 2010)

power of alpha (8-13 Hz), beta (13-30 Hz) and theta (4-8 Hz) rhythms in each of 19

channels (Augustyniak, 2001; Jus, Jus, 1967). For each subject the number of

extracted segments depends on the length of the artefact-free parts.


133

Fig. 1. The EEG data processing algorithm block diagram

For a given rhythm, the power was normalized in a single channel by the sum of FFT

power in all 19 channels. Data filtration was not used, because only Fourier power

spectrum was extracted from the signal.

Afterwards the neural network was used for obtained segments. The task of the

neural network was to determine to which of the two groups the subject belongs.

Depending on the length of artifact-free parts, during the examination of one patient,

several dozens to several hundred segments were obtained. For each segment three

normalized power values were obtained. Calculation of the ratio of the number of

segments classified to one group and segments classified to the second group,

determinates the probability that the subject belongs to a given group.

For each segment 57 values of normalized FFT power (3 values of rhythms X 19

channels) were used for classification of subjects as patients or healthy controls. The

neural network with 57 input neurons, one hidden layer and the output layer with two

neurons was used. The number of neurons in a hidden layer was chosen by trials and

errors (Basheer, Hajmeer, 2000) and for closed eyes it consisted of 11 neurons, for

hyperventilation – 15 and after hyperventilation – 9. The backpropagation algorithm

was used to train a network. The 70% of input vectors were randomly assigned to the

training set. The others were used for testing. Main parameters of neural network

were calculated like in (Kiymik, Akin et al 2004). The task of the neural network was

to detect excessive sleepiness in a given segment. After completion of the training,

the network was used to classify all of testing input vectors. Each subject was

classified as healthy depending on whether the percentage of “awake” segments was

greater than 50%. If this percentage was smaller than 50% the subject was classified

as narcoleptic.



134

Results

The presented tables (Plucińska, 2017) show the effectiveness of identifying two-second segments of EEG recordings (during eyes closed, hyperventilation and right after hyperventilation). For all sections data were averaged after obtaining results for 10 attempts for teaching neural networks.

Segments with closed eyes

Table. 1. Average parameters obtained from teaching neural networks 10 times for segments with closed eyes.

Name of the parameter

Sensitivity False positive rate

Selectivity Accuracy Specificity

Average value 0.95 0.08 0.95 0.93 0.92

Standard deviation 0.012 0.016 0.012 0.003 0.016

For each subject, the average errors of classifying the test data was calculated.

Table. 2. Average errors of the test data for neural networks. Indications of control subjects during closed eyes

Subject number 1 2 3 4 5 6 7 8 9

Average error [%] 2.41 3.39 7.89 19.36 3.62 2.37 32.34 3.81 3.65

Minimum error [%] 1.85 0.00 5.26 12.77 2.13 1.69 27.66 1.59 1.92

Maximum error [%] 3.70 11.86 10.53 31.91 6.38 5.08 40.43 9.52 5.77

Subject number 10 11 12 13 14 15 16 17 18

Average error [%] 0.00 1.67 1.32 21.40 4.57 8.42 6.67 5.20 17.37

Minimum error [%] 0.00 0.00 0.00 16.00 2.86 5.26 2.38 2.00 15.79

Maximum error [%] 0.00 16.67 3.95 26.00 5.71 13.16 9.52 10.00 26.32

Table. 3. Average errors of test data for neural networks. Indications of narcoleptic patients during closed eyed

Patient number 1 2 3 4 5 6 7 8 9

Average error [%] 4.48 2.38 6.43 22.86 10.56 1.62 5.79 3.59 13.93

Minimum error [%] 3.45 0.00 3.57 17.86 5.56 0.00 5.26 2.56 7.14

Maximum error [%] 6.90 7.50 7.14 32.14 19.44 5.41 10.53 7.69 17.86

Patient number 10 11 12 13 14 15 16 17 18

Average error [%] 25.11 7.78 10.36 0.00 0.00 0.00 1.18 1.30 6.50

Minimum error [%] 19.15 3.70 7.14 0.00 0.00 0.00 0.00 0.00 5.00

Maximum error [%] 34.04 14.81 17.86 0.00 0.00 0.00 8.82 1.85 10.00

Patient number 19 20 21 22 23 24 25

Average error [%] 23.96 5.71 7.13 8.98 3.14 1.37 10.98

Minimum error [%] 18.75 3.57 3.96 6.12 1.96 0.00 7.32

Maximum error [%] 27.08 7.14 10.89 16.33 3.92 3.92 14.63


135

Tab. 2-3 show that with the 50% threshold controls group and narcoleptic patients

were correctly assigned to their group. The average error for control subject was

8.08% and for narcoleptics 7.40%.

Segments during hyperventilation

The same calculations were made for hyperventilation (Tab. 4-6).

Table 4. Average parameters obtained from teaching neural networks 10 times for segments with

hyperventilation

Name of the

parameter Sensitivity

False positive

rate Selectivity Accuracy Specificity

Average value 0.94 0.09 0.94 0.92 0.91


Table. 5. Average errors of the test data for neural networks. Indications of control subjects during

hyperventilation


Average error [%] 10.59 0.00 6.30 4.00 0.00 0.00 48.46 2.19 0.00

Minimum error [%] 5.88 0.00 3.70 4.00 0.00 0.00 42.31 0.00 0.00

Maximum error [%] 17.65 0.00 11.11 4.00 0.00 0.00 53.85 6.25 0.00

Subject number 10 11 12 13 14 15 16 17 18

Average error [%] 0.00 0.00 11.92 36.25 8.08 2.92 0.00 7.69 1.18

Minimum error [%] 0.00 0.00 7.69 29.17 7.69 0.00 0.00 7.69 0.00

Maximum error [%] 0.00 0.00 19.23 41.67 11.54 4.17 0.00 7.69 5.88

Table. 6. Average errors of test data for neural networks. Indications of narcoleptic patients during

hyperventilation

Patient number 1 2 3 4 5 6 7 8

Average error [%] 14.17 0.45 5.56 8.00 0.00 6.25 0.00 0.00

Minimum error [%] 8.33 0.00 0.00 6.67 0.00 6.25 0.00 0.00

Maximum error [%] 16.67 4.55 11.11 13.33 0.00 6.25 0.00 0.00

Patient number 9 10 11 12 13 14 15 16

Average error [%] 0.00 16.00 50.53 0.45 0.00 6.00 0.00 2.86

Minimum error [%] 0.00 16.00 42.11 0.00 0.00 4.00 0.00 0.00

Maximum error [%] 0.00 16.00 57.89 4.55 0.00 8.00 0.00 14.29

Patient number 17 18 19 20 21 22 23 24

Average error [%] 11.33 33.53 14.40 3.50 6.27 0.63 4.55 5.88

Minimum error [%] 6.67 23.53 12.00 0.00 5.88 0.00 4.55 5.88

Maximum error [%] 13.33 41.18 16.00 5.00 7.84 6.25 4.55 5.88



136

The average error for control subjects was 7.75%, and for narcoleptics 7.93%.

Looking only on average error, one narcoleptic patient had more “awake” segments,

which would lead to incorrect classification. Looking only on maximum error, also

one healthy volunteer wouldn’t be assigned to a proper group.

Segments after hyperventilation

Tables (Tab. 7-9) shows that after hyperventilation worse results were obtained.

Table 7. Average parameters obtained from teaching neural networks 10 times for segments after

hyperventilation

Name of the

parameter Sensitivity

False

positive rate Selectivity Accuracy Specificity

Average value 0.92 0.13 0.92 0.89 0.87


Table. 8. Average errors of the test data for neural networks. Indications of control subjects after

hyperventilation


Average error [%] 18.52 0.00 3.75 6.00 6.30 3.23 51.07 6.25 16.55

Minimum error [%] 18.52 0.00 3.13 5.00 3.70 3.23 46.43 0.00 10.34

Maximum error [%] 18.52 0.00 6.25 10.00 11.11 3.23 53.57 18.75 24.14

Subject number 10 11 12 13 14 15 16 17 18

Average error [%] 0.00 0.00 23.60 9.09 3.75 0.77 7.22 16.80 16.80

Minimum error [%] 0.00 0.00 16.00 9.09 3.13 0.00 0.00 12.00 12.00

Maximum error [%] 0.00 0.00 28.00 9.09 6.25 7.69 11.11 24.00 20.00

Table. 9. Average errors of test data for neural networks. Indications of narcoleptic patients after

hyperventilation

Patient number 1 2 3 4 5 6 7 8

Average error [%] 15.63 1.05 0.00 6.32 15.00 3.33 0.00 5.56

Minimum error [%] 15.63 0.00 0.00 5.26 14.29 0.00 0.00 5.56

Maximum error [%] 15.63 5.26 0.00 10.53 21.43 5.56 0.00 5.56

Patient number 9 10 11 12 13 14 15 16

Average error [%] 0.00 51.90 20.00 0.00 7.50 14.00 13.64 2.78

Minimum error [%] 0.00 47.62 13.64 0.00 0.00 0.00 9.09 0.00

Maximum error [%] 0.00 57.14 27.27 0.00 8.33 30.00 27.27 5.56

Patient number 17 18 19 20 21 22 23 24

Average error [%] 19.03 20.00 6.67 23.08 0.87 0.00 1.43 16.50

Minimum error [%] 9.68 20.00 6.67 23.08 0.00 0.00 0.00 10.00

Maximum error [%] 22.58 20.00 6.67 23.08 4.35 0.00 14.29 20.00


137

The average error for control subjects was 10,54% and for narcoleptics 10,18%. For

both groups one narcoleptic patient and one control subject has the average error

greater than 50%.

Summary of results

The comparison between different sections analysed above was shown in the summary

table (Tab. 10). Better results were obtained during analysis of the eyes closed section.

Table. 10. Summary of results. Comparison between different sections of EEG recordings based on the

maximum error of classification. Positive means patients with narcolepsy

Eyes Closed Hyperventilation After Hyperventilation

True positive 25 23 23

False positive 0 1 1

True negative 18 17 17

False negative 0 1 1

Discussion

The best discrimination between patients and controls was obtained for closed eyes.

In the other conditions there were at least one false positive and one false negative.

The presented results demonstrate that the EEG Fourier power spectrum may be used

to detect pathological sleepiness in narcoleptic patients. Further studies are needed to

validate this approach. We plan to increase the size of both cohorts and explore the

possibility of reducing the number of electrodes used in classification.

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139

Increased incidence of CNS tumours

in Parkinson Disease

Marcin Cackowski, Aleksandra Rosiek, Jarosław Jóźwiak*

*Center for Biostructure Research, Department of Histology and Embryology,

Medical University of Warsaw, Chałubińskiego Street 5, 02-004 Warsaw, Poland

[email protected]

Abstract

Purpose: Parkin, DJ-1, PINK1, LRRK2 and UCHL1 are neuroprotective proteins which play a key role

in Parkinson’s disease (PD) etiology. Central nervous system (CNS) tumors are more frequent in

patients with PD, hence these proteins may also be involved in oncogenesis. There are several papers

indicating that Parkinson’s-related proteins are dysfunctional in different types of CNS tumors.

Materials and methods: In this paper we are investigating activity of Parkin, DJ-1, PINK1, LRRK2,

UCHL1 in CNS tumors. We review recent publications in the PubMed database using the following

phrases: “Parkin/DJ-1/PINK1/LRRK2/UCHL1 and CNS tumor” and “Parkinson’s disease and CNS

tumor”. We also study the role of these proteins in physiology, PD and oncogenesis, and their relation to

common cell signaling pathways.

Results: Parkin is a tumor suppressor gene (TSG) and its expression is lowered in various CNS tumors.

PINK-1 is induced by another TSG called PTEN, which levels are also decreased in brain lesions. DJ-1 is

an oncogene and its hyperactivity is associated with higher proliferation and lower differentiation of these

tumors. LRRK-2 gene encodes MAPKKK protein, and UCHL-1 is overexpressed in juvenile gliomas.

Conclusion: Parkin, DJ-1, PINK1, LRRK2, UCHL1 may be used as markers of PD, as well as

prognostic factors for CNS tumors. Patients with diagnosed PD should undergo CNS tumor screening

tests more often. Drugs targeting these proteins may be used against both CNS tumors and PD, what

might be beneficial pharmacoeconomically.

Keywords: Parkin, DJ-1, PINK1, LRRK2, UCHL1, CNS tumor, Parkinson disease

Introduction

Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder

(Elbaz, Carcaillon et al. 2016). Major symptoms, such as rigidity, tremor and

akinesia, are caused by the dysfunction of dopaminergic neurons of the substantia

nigra (Lang, Lozano 1998). Levy’s bodies, which are intracellular aggregates of

accumulated α-synuclein (α-SNCA) and ubiquitin, are the most common

pathological findings in PD neurons (Polymeropoulos, Lavedan et al. 1997;

Wakabayashi, Tanji et al. 2007). Overall worldwide prevalence of PD is 315 (95%

CI 113-873) per 100,000 and is correlated with increasing patient age (Pringsheim,

Jette et al. 2014). Central Nervous System tumours are relatively rare lesions – they

represent 1.8% of cancer cases and were responsible for 2.3% of cancer-related

deaths in 2012 worldwide (Ferlay, Soerjomataram et al. 2015). The correlation

between PD and various types of cancer seems to be complex. PD increases the

incidence of several malignancies (e.g. melanoma, thyroid and breast carcinoma),

however it also decreases the frequency of many other cancers (e.g. lung and

bladder) (Inzelberg, Jankovic 2007). Furthermore, CNS tumours are more frequent in

patients with the history of PD, as reported in the metanalysis by Rong Ye and

colleagues. Overall, OR in this study was 1.51 (95%CI 1.21-1.89, p<0.001,

n=329,276). The incidence of tumours detected prior to the diagnosis of Parkinson’s

Marcin Cackowski, Aleksandra Rosiek, Jarosław Jóźwiak

140

disease was not significant, but it was significantly higher after the diagnosis (Ye,

Shen et al. 2016). Neuroprotective proteins (PINK1, Parkin, DJ-1, UCHL1 and

LRRK2) are dysfunctional in both familial and sporadic types of PD and their

mutations are considered to be risk factors (Klein, Schlossmacher 2007).

Additionally, they can act as oncogenes, tumour suppressor genes(TSGs) or can be

involved in multiple cell signalling pathways (Devine, Plun-Favreau et al. 2011)

(Table 1.). This is one of the leading hypotheses explaining why malignancies are

significantly more frequent in PD patients.

Table 1. Summary of PD-related proteins and their contribution to PD and oncogenesis

Gene Physiology Type of PD Role in oncogenesis

Parkin Mitophagy Familial AR TSG

PINK1 Mitophagy Familial AR Induced by PTEN (TSG)

DJ-1 Chaperone Familial AR Oncogene, PTEN inhibitor

LRRK2 Cytoplasmic kinase Familial AD MAPKKK protein

UCHL1 Deubiquitinase Familial AD?a Oncogene/TSG degradationb

TSG – Tumour suppressor gene, AR – Autosomal recessive, AD – Autosomal dominant,

PD – Parkinson disease a function unclear – limited amount of cases

b depending on cancer type

Parkin

Physiologically, parkin (PARK2) acts as an E3 ubiquitin-ligase (Shimura, Hattori et

al. 2000) and promotes autophagy of mitochondria (Chan, Salazar et al. 2011).

So-called mitophagy protects the cell from oxidative stress by depleting damaged

mitochondria (Lemasters, 2005). Ubiquitination regulates proteasomal and lysosomal

protein degradation (Komander, Rape 2012), thus reduced activity of parkin may

result in higher level of reactive oxygen species (ROS) (Xiao, Goh et al. 2017) and

protein accumulation (Shimura, Schlossmacher et al 2001) (Figure 1.). Certain

mutations of parkin gene cause familial autosomal recessive PD with early onset

(Kitada, Asakawa et al. 1998). Furthermore, parkin is a TSG (Cesari, Martin et al.

2003) and its levels are lowered in various malignancies (Inzelberg, Jankovic 2007).

In glioblastoma multiforme cell lines, parkin concentration decreases during hypoxia

and it regulates the expression pattern of hypoxia inducible factors (HIF-1α and

HIF-3α) (Maugeri, D’Amico et al. 2016). HIFs are proteins inducing angiogenesis in

normal tissues and cancers (Semenza 2003). Yeo and colleagues deduce that the

dysfunction of parkin corelates with higher mortality; therefore it can be considered

as a prognostic marker for patients with gliomas (Yeo, Ng et al. 2012). It seems that

p53 cooperates with parkin and their disturbances might result in both CNS

tumorigenesis and development of PD (Checler, Alves da Costa 2014).

Increased incidence of CNS tumours in Parkinson Disease

141

PINK1

PINK1 (PTEN-induced putative kinase 1, also known as PARK6) is a kinase that

functions as a mitochondrial sensor. In normal mitochondria, PINK1 levels are

reduced by PARL protease. However, in damaged cells its concentration is elevated

and PINK1 promotes parkin activation and therefore mitophagy (Youle, van der

Bilek 2012). Nonsense or missense mutations of PINK1 are responsible for

autosomal recessive hereditary PD with early onset (Valente, Abou-Sleiman et al.

2004). Due to its function as parkin activator, the role of PINK1 in oncogenesis and

PD etiology seems to be similar to that of parkin. Nevertheless, PINK1’s contribution

is more complex. It is activated by PTEN, which is one of the most frequently

mutated TSGs (Di Crostofano, Pandolfi 2000). Thus, PTEN abnormalities may also

lead to oxidative stress (Figure 1.). In CNS malignancies, PINK1 seems to act as

a TSG. It decreases growth of glioblastoma and inhibits aerobic glycolysis

(Warburg’s Effect). Loss of PINK1 was associated with low patient survival and was

observed in numerous brain lesions (Agnihothri, Golbourn et al. 2016). Notch in

noncanonical signalling interacts with PINK1, which alters mitochondrial function

and activates mTORC2/Akt pathway in Drosophila and human brain cancer stem

cells (Lee, Wu et al. 2013).

DJ-1

DJ-1 (PARK7) is a mitochondrial chaperone protein which stops α-synuclein

accumulation caused by oxidative stress (Zhou, Zhu et al. 2006). Hence, its

disturbances may lead to PD. Some DJ-1 mutations may result in autosomal

recessive parkinsonism with early-onset (Bonifati, Rizzu et al. 2003). It also protects

cells from heavy-metal induced cytotoxicity (Björkblom, Adilbayeva et al. 2013).

Unlike above-mentioned PD-related proteins, DJ-1 is an oncogene and it is involved

in Ras-related signalling (Nagakubo, Taira et al. 1997). It functions as a negative

regulator of the previously mentioned PTEN protein (Kim, Peters et al., 2005). Both

Ras pathway’s hyperactivity and TSG suppression might result in tumorigenesis

(Figure 1.). DJ-1 expression may be considered a prognostic factor in

medulloblastoma (Lin, Pan et al. 2014), as well as survival marker in astrocytoma

and glioblastoma (Abd El Atti, Abou Gabal et al. 2013). Its nuclear levels correlate

with WHO grading of astrocytomas (Miyajima, Sato et al. 2010).

LRRK2

LRRK2 gene (leucin-rich repeat kinase2 or PARK8) encodes a protein called

dardarin. It is a cytoplasmic kinase which has an important function in vesicular

transport system, protein homeostasis (Bae, Lee 2015) and inflammation (Gardet,

Benita et al. 2010). α-synuclein accumulation and apoptosis are observed in mice

with mutated LRRK2 gene (Tong, Yamaguchi et al. 2010). Mutations of LRRK2 are

responsible for significant number of cases of autosomal dominant type of PD as

well as some sporadic cases. Primarily, dardarin disturbances cause benign late-onset

PD (Zimprich, Biskup et al. 2004). LRRK2 also functions as a MAPKKK protein,

which can explain its role in tumorigenesis (Gloeckner, Schumacher et al. 2009)

(Figure 1.). LRRK2 mutations (especially G2019S) are present in various


142

malignancies and they boost the risk of developing these lesions (Agalliu, San

Luciano et al. 2015). LRRK2 is a well-identified target for brain-blood barrier

penetrating inhibitors in innovative concepts of PD treatment (Gunosewoyo, Yu, et

al. 2017). The same substances might be viable candidates for use in brain tumour

therapy. The number of papers concerning CNS tumours and LRRK2 disturbances is

very limited and their role in brain malignancies needs to be investigated.

UCHL1

UCHL1 (ubiquitin carboxy-terminal hydrolase L1 or PARK5) functions as

a deubiquitinase (Larsen, Krantz et al. 1998) and is a brain-specific enzyme (Doran,

Jackson et al. 1983). In particular, it is responsible for α-synuclein degradation (Liu,

Fallon et al. 2002) (Figure 1.). mRNA expression studies show higher concentration

of UCHL-1 in substantia nigra than in other structures of the brain (Solano, Miller et

al. 2000). In one case, a missense UCHL1 mutation was found in a German family

with history of familial PD (Leroy, Boyer et al. 1998). On the other hand, Healy and

colleagues’ metanalysis argues that UCHL-1 mutations don’t cause PD in any

hereditary model (Healy, Abou-Sleiman et al. 2006). UCHL-1 contribution to

familial PD remains unclear and further studies are required. The role of UCHL-1 in

tumorigenesis seems to be complex, since deubiquitinases can be both oncogenes or

TSGs, depending on tissue type (Shi, Grossman 2010). In juvenile high-grade

gliomas UCHL-1 is overexpressed and associated with cancer stem cells population

(Sanchez-Diaz, Chang et al. 2017). The number of studies regarding the role of

UCHL1 in CNS tumours is also very limited and further research is required.

1 depending on cell type

Figure 1. PD-related proteins’ signalling and their role in neuroprotection and tumorigenesis. DJ-1, PINK1

and parkin are related with mitochondrial compartment and LRRK2 and UCHL1 are cytoplasmic proteins


143

Conclusion

PD-related proteins may be considered as potential markers in brain lesions, as well

as in sporadic or familial types of PD. Maugeri and colleagues propose parkin

isoforms as candidates for glioma markers (Maugeri, D’Amico et al. 2015). Drug

development targeting parkin, PINK-1, DJ-1, LRRK2 and UCHL1 might be possibly

beneficial for both PD and brain cancer patients, which could be encouraging for

pharmacological companies. Drapalo and colleagues suggest that Parkin, DJ-1 and

PINK1 should be considered as new targets in ganglioglioma treatment (Drapalo,

Jozwiak 2018). Tumour screening tests might be considered for PD (especially

hereditary types) patients more frequently.

Discussion

In this paper we focused on one of several hypotheses explaining why tumours are

more frequent in PD. While PD-related proteins might be dysfunctional in these

lesions, other explanations should be considered. PD patients undergo neurological

examination or radiological imaging scans more often than regular population, which

may explain the positive correlation between the occurrence of brain malignancies

and PD (detection bias). According to some studies, L-DOPA (first line treatment in

PD) may cause oxidative stress (Vatessery, Smith et al. 2006), hence cells utilizing

this molecule (e.g. melanocytes or glia) might be exposed to ROS, commonly

associated with carcinogenesis.

The number of studies concerning PD proteins and CNS tumours is limited and many

more would have to be performed to confirm our suggestions and conclusions.

Research concerning incidence of brain malignancies in families with history of

hereditary types of PD is especially required.

Acknowledgement

Authors declare no conflict of interest.

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Role of vitamin D in etiology of mood disorders

– literature review

Konrad Majewski, Jarosław Jóźwiak

Department of Histology and Embryology, Medical University of Warsaw

Chałubińskiego Street 5, 02-004, Warsaw

E-mail: [email protected]

Phone number: +48 692 588 107

Abstract

Vitamin D deficiency is widely described as one of the crucial factors impacting the development of

mood disorders, especially depressive disorders. This literature review shows association between

vitamin 25(OH)D concentration in human serum and mood disorders from different points of view,

clustering on several molecular mechanisms.

In order to be included in the review, articles from the PubMed archive had to fit the following criteria:

original articles associated with vitamin D and mood disorder/depression/depressive

disorder/bipolar disorder (in accordance with DSM IV and V)

written in English

published between 1 Nov 2007 and 1 Nov 2017

Several molecular mechanisms of vitamin D effect on mental health were identified. Vitamin D

deficiency during gestation could manifest itself in disproportion in the amount of neurotransmitters in

different parts of cerebrum, compared with control group - offspring of female rats living without UVB

radiation restriction. In an analysis of transcriptome from samples harvested post mortem from patients

suffering from bipolar disorder, a dissimilar molecular structure of the receptor for calcitriol was

observed. Moreover, human orthologue of Caenorhabditis elegans gene ANK3, which mutated copy

could be corelated with a nematodes’ lifespan extension, was reported to be correlated with mood

disorders, while its overexpression is inhibited by vitamin D.

Introduction

Vitamin D3 is fat-soluble isoprenoid which play several roles in regulation of

molecular pathways in human physiology. Receptors for calcitriol, active form of

vitamin D, are found in cell nucleus; complex of receptor and molecule of calcitriol

acts as a transcription factor (Rodwell, Bender et al., 2014). Synthesis of calcitriol

(scheme 1.) starts with degradation of its first provitamin, 7-dehydrocholesterol,

stored in skin, by UVB radiation (Berg, Tymoczko et al., 2014). Lack or restriction

in sunlight connected with four seasonal cycle is tantamount to less intensive UVB

radiation and lower rate of provitamin D photolysis. Next, cholecalciferol procured

during photolysis is transported to the liver and metabolized to 25-hydroxycalciferol

– a chemical compound commonly detected during haemoanalysis. Vitamin

25(OH)D is then transported to kidneys, where metabolic cascade ends up in

production of 1,25-dihydrocalciferol - calcitriol. Hydroxylation catalyzed by

1-hydroxylase is upregulated by parathyroid hormone and downregulated by

elevated level of Ca2+

. Calcitriol, an active form of vitamin D, interconnects with

vitamin D-binding protein in serum (Langer-Gould, Lucas et al., 2018).


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Scheme 1. A biosynthesis of calcitriol

Vitamin D3 triggers production of calbindin, a calcium binding protein associated

with absorption of calcium ions from small intestine and connected with enteric

neuron activity (Zetzmann, Strehl et al., 2018). Calbindin is also commonly used as

a marker of mature GABA-ergic neurons in central nervous system (Radic, Frieß et

al., 2017), especially Purkinje cells (Shawn, Palliser et al., 2017), deep layers of the

superior colliculus (Najdzion, 2017) and neurons of oculomotor nucleus (de la Cruz,

Pastor et al., 2017). Due to this fact, prolonged lack of vitamin D has an impact on

pathogenesis of osteoporosis and rachitis – low concentration of calcium ions

triggers osteomalacia. The first reports correlating depression with osteoporosis were

published as early as in mid-sixties.

The minimum optimal concentration of vitamin 25(OH)D in human serum is 30

ng/ml (Bischoff-Ferrari, 2008), while concentration bellow 20 ng/ml is known

as critical for correct bone tissue physiology, especially bone growth and fracture

prevention. The groups crucially susceptible to low level of vitamin D are neonates

(a diet based on mother’s milk), pregnant women (common unbalanced diet) and

elderly people, hospitalized or staying in their homes (lack of UVB radiation).

As supplementation, 1000 UI of calcitriol per day elevates 25-hydroxyvitamin D in

serum above 30 ng/ml in up to 50% of the population.

The association between the level of 25-hydroxyvitamin D and etiology of mood

disorders was extensively studied during the several recent years. In the current paper

we wanted to show a few interesting concepts on molecular effect of vitamin D3

concentration in human serum on emergence of psychiatric manifestations, not only

statistical correlations reported in other studies. According to WHO statistics, mood

disorders, specifically depressive disorders, in the following decades will become the

Role of vitamin D in etiology of mood disorders – literature review

149

one of the most common medical conditions in the world (Kessler, Birnbaum et. al.,

2010; Alonso, Angermeyer et. al., 2004). A fact worth mentioning is that young

people (between 18 and 25 years) are critically impacted – for this group suicide as

a complication of mood disorder is the second most common cause of death (Reddy,

2010).

New insight

In PubMed database, there are few papers showing anything else than only statistical

correlation between lower concentration of 25-hydroxyvitamin D in serum and

elevated risk of depressive disorder or bipolar disorder. During the last years, several

advanced studies based on disturbances of calcitriol-depended molecular pathways

appeared. Lack of vitamin D3 in mother’s organism during gestation could lead to

alteration in the amount of neurotransmitters in different parts of cerebrum (Kesby,

Turner et al., 2017). In later life, those disproportions could hypothetically manifest

themselves in mental illnesses, including mood disorders (Shih-Hsien, Lan-Ting et

al., 2014). Another paper reports correlation of bipolar disorder with different

structure and/or distribution of receptors for vitamin D3 (Chu, Liu et al., 2009).

Furthermore, studies based on genome-wide association (GWAS) between

nematodes’ and human transcriptome lead to the discovery of gene thought to be

strongly correlated with mood disorders. Last but not least, we show the connection

between calcitriol and development of GABA-ergic neurons in several regions of

central nervous system and its role in psychopathology (Oh, Son et al., 2012).

Review and discussion

The first of chosen extensive studies is based on four-years cohort observation on

elderly Chinese men including 629 patients (Chan R., Chan D. et al., 2011). The

importance of this study manifests itself in prolonged time of patients monitoring and

regular repetition of psychological testing. Findings show reversed correlation

between the level of 25-hydroxyvitamin D and depression – the higher level of

vitamin D was present in the patients’ serum, the lover risk of onset of depressive

disorder was observed.

Another study (Jaddou, Batieha et al., 2012), based on nearly ten times greater,

diversified population (n=5640) of Jordanian adults, connected measurement of level

of 25-hydroxyvitamin D with mood evaluation based on Depression Anxiety Stress

Scale 21. The standardized scale stands for greater repeatability of this experiment.

Results allow to approximate hypothetical effect of concentration of vitamin

25(OH)D for onset of depressive disorder – 42.3 ng/ml shows statistically significant

positive correlation with decreasing risk of depression morbidity.

Relatively new hypothesis debates impact of level of vitamin D3 on pathogenesis of

mental illnesses according to central nervous system developmental disorders. Based

on studies on rat population, deficiency in vitamin D concentration in motherly

serum during growth of embryos alters multiple neurotransmitter systems in the

neonatal cerebrum (Kesby, Turner et al., 2017). In this experiment, before

insemination, for six weeks prior to gestation female rats were cut off from UVB

radiation (inducing photolysis of provitamin D) and food rich in vitamin D, in order


150

to eliminate reserves of calcitriol in mother’s body. After confinement, younglings

were sacrificed and their brain tissue was analyzed with high performance liquid

chromatography to evaluate disruption in the level of neurotransmitters in different

parts of cerebrum. The most significant changes are shown in table 1.

Table 1 Changes of the greatest magnitude presented in findings of Keby, Turner et al. studies.

Neurotransmitter Brain region Character of change

in level of neurotransmitter

3-methoxytyramine (3-MT) Basal ganglia Increase

Noradrenaline Hippocampus Increase

Serotonin Caudate putamen Decrease

5-hydroxyindoleacetic acid Medial prefrontal cortex Decrease

Glutamine Thalamus Decrease

Serine Medial prefrontal cortex, basal

ganglia, midbrain, cerebellum Increase

As we can see, deficiency of calcitriol during gestation can manifest in disruption of

several neurotransmitters. The most crucial neurotransmitters for mood disorders are

serotonin (5-hydroxytriptamine) and noradrenaline; one of the most popular

conception stands for major role of serotonin in progression of major depressive

disorder (Cowen, 2008). Furthermore, pathological concentration of noradrenaline in

hippocampus could lead to disruption in physiology of limbic system, commonly

known to be responsible for emotional reactions. This hypothesis is associated with

GABA-ergic calbindin-depended disorders described later in this article.

Another concept of molecular impact of calcitriol on mood disorder is connected

with different structure of nuclear receptor for vitamin D3 (Chu, Liu et al., 2009). By

analyzing transcriptome samples harvested post mortem from brain tissue of suicidal

patients suffering from bipolar disorder, unique properties of those receptors and

calcium signaling pathway enzymes have been discovered. Unfortunately, there is no

information on direct impact of mentioned unusual structure of receptors on

psychopathology of mood disorders; comparatively small sample population (only 10

samples from bipolar patients) increases requirement for additional studies.

Uncommon research based on an experiment on Caenorhabditis elegans

transcriptome (Rangaraju, Levey et al., 2016) suggests that there is at least one gene

strongly associated with mood disorders. Differential gene expression was analyzed

for nematodes treated with mianserin, antagonist of 2-adrenergic receptor,

commonly used in the treatment of depression as atypical antidepressant (Carman,

Ahdieh et al., 2008). Next, genes ranked based on p-value distribution were classified

via Bonferroni correction to find the gene most affected by mianserin. In parallel,

samples of human transcriptome were taken from cohort of elderly patients who

suffer from depressive symptoms, including suicidal patients (45 of 737 patients).

Genome-wide association study allowed to find an orthologue of nematode gene in


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human genome based on single nucleotide polymorphism. ANK3, gene most strongly

affected by the use of mianserin, has different role in the human and nematode

organism. C. elegans with mutation of ank3 show no response to mianserin and their

lifespan is prolonged. In humans, cellular expression of ANK3 fluctuates during life

– concentration of ANK3 transcript shows negative correlation with aging.

Overexpression of ANK3 was found in samples from patients suffering from mood

disorders, usually major depressive disorder. Rangaraju et al. suggested that vitamin

D3 could be one of the factors that negatively affect expression of ANK3, which

could be correlated with decreased risk of occurrence of signs and symptoms

characteristic for mood disorders. Moreover, scientists predict that the product of

ANK3 could be a blood marker for mood disorders in the future.

In another paper, in dorsolateral prefrontal cortex harvested post mortem from

patients suffering from major depressive disorder, lower density of glial cells and

GABA-ergic neurons were observed (Oh, Son et al., 2012). One of the markers of

GABA-ergic neurons, along with calretinin and parvalbumin, is calbindin. In brains

of mouse embryos, the level of calbindin-positive neurons usually increases before

birth (Dávila, Real et al., 2005). Due to direct influence of vitamin D3 on expression

of gene for calbindin it is possible that calcitriol could affect central nervous system

by the development of GABA-ergic neurons; disturbed proportion of excitatory and

inhibitory nervous cells could lead to many psychiatric manifestations, including

mood disorders (Luscher, Shen et al., 2011).

It is interesting that differences between physiology of male and female amygdalae,

a part of limbic system responsible among others for emotional reactions, memory

correlated with emotions and sexual behavior, could also be associated with

differences in expression of calcium-binding proteins, including calbindin (Równiak,

2017), which could explain the hypothetical connection of calbindin (and indirectly

also vitamin D) with mood disorder. However, some studies show that expression of

calbindin in brain is vitamin D3-indepdendent (Hall, Norman, 1991).

At this point, we are able to distinguish three planes of hypothetical molecular impact

of vitamin D3 on the development of mood disorder:

I Depletion of vitamin D3 directly before or during gestation could disturb

proportion of neurotransmitters or neurons performing different roles in central

nervous system. Only additional data could show if those changes are

maintained during the adult life.

II For an unknown reason, calcitriol nucleus receptors in brain tissue of patients

suffering from mood disorders are not able to perform their role correctly or

result from disturbed proportion of calcitriol in the maternal organism

– additional studies could confirm one of these hypotheses in the future.

III One or many genes could be directly engaged in etiology of mood disorders.

Additional data have to confirm character of this molecular impact; at this

moment the role of connection between the newly discovered ANK3 gene with

psychiatric manifestations is based on statistically significant correlation.


152

An additional problem of studies on molecular character of calcitriol on human

central nervous system was reported recently. It is obvious that psychiatric

disturbances could be observed only in living patients; molecular pathway

disturbance analyzed only with samples taken post mortem exclude the possibility for

running an additional psychological examination, which is important from the point

of view of standardization.

Disturbed molecular pathways during gestation do not resolve the problem of

increased psychiatric disturbances characteristic for seasonal mood disorders – it is

not clear if there is any impact of quarterly disturbance of photolysis of

7-dehydrocholesterol rather than psychological effect of lack of light. Studies on

correlation of vitamin D3 with another nosological entity could be also helpful.

To sum up, all above hypotheses on molecular character of impact of calcitriol on

mood disorders necessitate further studies. Only additional data could answer the

question if calcitriol has indeed impact on several molecular mechanisms whose

malfunction leads to mood disorder, or maybe lower level of 25-hydroxyvitamin D

has only statistical correlation with increased risk of depressive disorder morbidity.

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Index of authors

Badowska-Kozakiewicz A. M. .......... 62

Biały Ł. P. ............................... 22, 36, 48

Brzeziańska-Lasota E. ........................ 92

Cackowski M. .................................. 139

Czarnocki Z. ....................................... 48

Czekaj P. ........................................... 109

Domański G. .................................... 130

Jernajczyk W. ................................... 130

Jóźwiak J. ................................. 139, 147

Kaszkowiak M. .................................. 92

Kobiela T. ............................................. 9

Król M. ............................................. 109

Limanówka Ł. .................................. 109

Lisiecki K. .......................................... 48

Liszcz A. ............................................. 62

Łątka M. ........................................... 130

Madeja P. .......................................... 119

Majewski K. ..................................... 147

Maksimiuk M. .................................... 62

Młynarczuk-Biały I. ................ 22, 36, 48

Mosler A. .......................................... 119

Pastuszak-Lewandoska D. .................. 92

Patera J. ............................................... 62

Plucińska R. ...................................... 130

Romanik M. ...................................... 119

Rosiek A. ........................................... 139

Smolarek O. ...................................... 119

Sobiborowicz A. ................................. 62

Sobiepanek A. ....................................... 9

Sobieraj M. .......................................... 62

Sobol M. .............................................. 62

Strus P. ................................................ 48

Szmyd B. ............................................. 92

Szmytkowska P. ................................ 109

Szymański P. ....................................... 36

Wawrzuta D. ..................................... 101

Wierzbicka-Wichniak A. .................. 130

Zaręba Ł. ............................................. 22

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