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ROLE OF REGENERATING PROTEIN mReg2 IN PANCREATIC
ISLET CELL PRESERVATION AND REG Iα AS A POSSIBLE
PREDICTIVE MARKER OF DIABETES
By
Dr. SADAF SALEEM UPPAL
(2011-NUST-Tfr Ph.D-Med-33)
NATIONAL UNIVERSITY OF SCIENCE & TECHNOLOGY
ISLAMABAD, PAKISTAN
2016
ROLE OF REGENERATING PROTEIN mReg2 IN PANCREATIC ISLET
CELL PRESERVATION AND REG Iα AS A POSSIBLE PREDICTIVE
MARKER OF DIABETES
A thesis submitted to National University of Science and Technology in partial
fulfillment of the requirement for the degree of
Doctor of Philosophy
in
Biochemistry
By
Dr. Sadaf Saleem Uppal
(2011-NUST-Tfr Ph.D-Med-33)
National University of Science &Technology (NUST)
Islamabad, Pakistan
(2016)
DECLARATION
I hereby declare that the material contained in this thesis is my original work. I have not
previously presented any part of this work elsewhere for any other degree.
Dr. Sadaf Saleem Uppal
CERTIFICATE
Certified that the contents and form of thesis titled “Role of regenerating protein mReg2
in pancreatic islet cell preservation and REG Iα as a possible predictive marker of
diabetes” submitted by Dr. Sadaf Saleem Uppal have been found satisfactory for the
requirement of the PhD degree.
Dated.___________
Supervisor:
Professor
Dr. Abdul Khaliq Naveed, Ph.D.
Biochemistry & Molecular Biology
Co-Supervisor:
Assistant Professor
Dr. Palvasha Waheed, PhD.
Biochemistry &Molecular Biology
Co-Supervisor:
Professor
Dr. Saeeda Baig, Ph.D.
Department of Biochemistry,
Zia-ud-din University, Karachi
CERTIFICATE
Certified that the contents and form of thesis titled “Role of regenerating protein mReg2
in pancreatic islet cell preservation and REG Iα as a possible predictive marker of
diabetes” submitted by Dr. Sadaf Saleem Uppal have been found satisfactory for the
requirement of the PhD degree.
Dated.___________
Supervisor:
Professor
Dr. Abdul Khaliq Naveed, PhD
Biochemistry & Molecular Biology
Co-Supervisor:
Assistant Professor
Dr. Palvasha Waheed, PhD
Biochemistry &Molecular Biology
Co-Supervisor:
Professor
Dr. Saeeda Baig, PhD
Deptartment of Biochemistry,
Zia-ud-din University, Karachi
Member (1):
Professor
Dr. Mohammad Saeed, PhD Dean University of Faisalabad
Member (2):
Associate Professor
Dr. Amir Rashid, PhD Biochemistry & Molecular Biology
Member (3):
Assistant Professor
Dr. Asifa Majeed, PhD
Biochemistry & Molecular Biology
Local Evaluator:
Professor Dr Mudassir Ahmed Khan, PhD
Department of Biochemistry,
Khyber Medical College, Peshawar
i
ACKNOWLEDGEMENTS
In the name of Allah, Most Gracious, Most Merciful. “All praise and thanks for Almighty
Allah who is the entire source of knowledge and wisdom, delivered to mankind” (Al-
Quran)
My work would be incomplete without acknowledging the contributions made by my
teachers, friends, patients and family who provided invaluable guidance and support
through the process of conducting the study and writing this thesis.
First of all I would like to express my heartfelt gratitude and sincere thanks to my
supervisor, Professor Dr Abdul Khaliq Naveed, for his constant guidance, valuable
suggestions, and motivation throughout my study period and I feel lucky to have worked
with him.
I would like to extend my humble gratitude to Professor Dr. Coimbatore B. Srikanth and
Associate Professor Dr. Jun Li Liu, McGill University Health Centre Montreal, Canada
for their kind supervision and for imparting me skills that had helped shape my career
and professional life.
I would also like to extend my sincere thanks to Professor Saeeda Baig and Assistant
Professor Palvasha Waheed who took time out from their busy schedule to co-supervise
me. I would like to thank them for their guidance, nice attitude and special interest
throughout my research. I would also like to extend my gratitude to Dr. Bushra Chaudhry
from Aga Khan University for her unconditional guidance regarding the statistical
analysis of my work at the genomic level.
Special thanks to my GEC members, Professor Dr. Muhammad Saeed, Assistant
Professors Dr. Amir Rashid and Dr. Asifa Majeed, who provided encouraging and
productive feedback. I am grateful for their thoughtful comments while reviewing my
thesis along with their warm attitude during the course of my study.
ii
To my dearest friends Dr. Sarah Sadiq, Dr. Fatima Qaiser, Dr. Zehra Naz, Dr Gulshan
Trali, Dr. Qudsia Bashir and Huma Shafique who always stood by me through thick and
thin throughout my research. They were always there with words of wisdom and
encouragement.
I am thankful to Cdr. Asif Farooq, Cdre. Javed Khattak, Cdre. Shahid Aziz and Cdre.
Javed Iqbal medical specialist PNS Shifa hospital for providing me with the patients as
well as for their unconditional support. I am especially thankful to Cdre. Jalil,
Pathologist, PNS Shifa hospital for permitting me utilize his lab for my research and for
his full support and guidance.
I am thankful to Mr. Farman and his team from the pathology lab, PNS Shifa and Mr.
Harris from Biochemistry Research lab, Zia ud din University, Mr Zia Farooqi from
CREAM lab, AMC for providing invaluable help during practical work.
I would also like to thank NUST and HEC for making this possible for me by providing
financial support under “Mega S & T merit Scholarship and International research
support initiative program” respectively.
In the end I would like to extend my heartfelt thanks to my family especially my father
(may Allah rest his soul in peace), my mother, my sisters, my brothers and my in-laws
whose love, support, encouragement and prayers kept me going through the toughest time
of my research work.
My husband Cdr. Mazhar Shafiq and my children Neha Hajira, Muhammad Ali and
Muhammad Umar have been my best of friends along this long and tedious path, great
guide, and a constant source of encouragement. Their confidence and faith in me was the
main support that made this dream become a reality.
In the end I thank Allah Almighty for enabling me to complete my research.
iii
DEDICATION
I dedicate my work to my parents, husband and children
whose love, cooperation, guidance and prayers have
always been with me.
iv
TABLE OF CONTENTS
Title Pg. No.
Acknowledgements i
Dedication iii
List of Tables vii
List of Figures and Graphs x
List of Abbreviations xv
List of Appendices xxi
Abstract xxii
Chapter 1: INTRODUCTION AND REVIEW OF LITERATURE
1.1 Regulation of Glucose Homeostasis in the Body 1
1.2 Diabetes and its Statistics 1
1.3 Classification of Diabetes 2
1.4 Pathogenesis of Diabetes 3
1.5 β-cell Preservation/Regeneration 7
1.6 Pancreatic β-cell Apoptosis 10
1.7 Apoptotic Signaling Pathways 10
1.8 ER stress induced β-cell Apoptosis 11
1.9 Caspase Independent Apoptotic Pathway: Apoptosis Inducing
Factor (AIF)
13
1.10 Scythe/BAT3 18
1.11 Heat Shock Protein 70 (hsp 70) 21
1.12 Doxorubicin (Dox) 23
1.13 Reg Family Proteins 23
Rationale of the Study 39
Objectives 40
v
Chapter 2: MATERIALS AND METHODS
2.1 Setting of the Study 41
2.2 Study Design 41
2.3 Ethical Approval 42
2.4 Duration of Study 42
2.5 Sample Size 42
2.6 Inclusion Criteria 43
2.7 Exclusion Criteria 43
2.8 Sampling Technique 43
2.9 Sample Collection 44
2.10 Study 1: Cell Culture 44
2.11 Study 2: Biochemical Tests: 58
2.11.1 Fasting Blood Glucose (FBG) 58
2.11.2 Glycosylated Haemoglobin (HbA1c) 60
2.12 Regenerating Islet Derived Protein 1 Alpha (REG Iα) Bioassay 63
2.13.1 Extraction of Genomic DNA from whole blood 69
2.13.2 Polymerase Chain Reaction (PCR) 70
2.13.3 Horizontal Gel Electrophoresis 73
2.13.4 DNA Sequencing 74
2.13.5 Nucleotide Polymorphisms (SNPs) 74
Statistical Analysis 75
Chapter 3: RESULTS
3.1 Western Blot Analysis to see the effect of mReg2 on AIF and
Scythe after treatment with doxorubicin using MIN6-VC and
MIN6-mReg2 cells
76
3.1.1 Induction of time-dependent increase in AIF in the nucleus of
MIN6-VC cells
76
3.1.2 mReg2 inhibits nuclear translocation of AIF and promotes nuclear 77
vi
retention of Scythe
3.1.3 Expression of hsp70 in the presence of mReg2 78
3.2 ELISA analysis of serum REG Iα levels 89
3.2.1 Clinical and demographic characteristics of the study groups 89
3.2.2 Serum REG Iα levels in type I and type II diabetics 90
3.2.3 Correlation between serum REG Iα levels and duration of disease 90
3.2.4 Correlation between serum REG Iα levels and age of the patient 91
3.2.5 Serum REG Iα levels correlates positively with age at disease onset
in type I diabetics
91
3.2.6 Correlation between serum REG Iα levels and metabolic factors 92
3.2.7 Serum REG Iα level and the risk factors for diabetes 92
3.2.8 Serum REG Iα level are higher in patient with diabetic complications 93
3.3 Identification of Polymorphisms in the REG Iα gene 107
3.3.1 Clinical and demographic characteristics of the study groups 107
3.3.2 Optimization of PCR amplication using different primer sets 107
3.3.3 Polymorphisms identified in REG Iα gene in Pakistani subjects 108
3.3.4 Genotypes, alleles frequencies and chi square (γ2) using Hardy
Weinberg Equilibrium (HWE) to show significance of variables
108
3.3.5 Odd ratio (95% CI) calculations for allele-1 and allele-2 111
3.3.6 Comparison between effects of genetic variants and clustering of
risk factors on hyperglycemia /Fasting Blood Glucose
113
Chapter 4: DISCUSSION 141
CONCLUSIONS 156
LIMITATION AND RECOMMENDATIONS 157
References 158
Appendices 180
vii
LIST OF TABLES
Table No. Title Pg. No.
1.1 Reg./.REG family members in the Mouse, Rat and Human 27
2.1 Plating of MIN6-VC and MIN6-mReg2 cells with and without
treatment with Doxorubicin
48
2.2 Composition of solutions for cell fractionation 49
2.3 Preparation of diluted BSA standards 52
2.4 Solution Preparation for SDS-PAGE (per gel) 54
2.5 Solution Preparation for Western Blotting 56
2.6 Primary and Secondary Antibodies used with their dilutions
prepared
56
2.7 Reconstitution of Standard Double Dilution Series from Standard
Stock Solution and Standard Diluent
65
2.8 Primer Sequence for REG Iα Gene 71
2.9 Optimized reaction conditions u s ed for each exon 72
3.1 Time dependent increase in AIF in Nuclear Fraction of MIN6-VC
cells
79
3.2 Effect of 12h treatment with doxorubicin (1 µM) on AIF and
Scythe in Nuclear Fraction of MIN6-VC and MIN6-mReg2 cells
81
3.3 Effect of 24h treatment with doxorubicin (1 µM) on AIF and
Scythe in Nuclear Fraction of MIN6-VC and MIN6-mReg2 cells
84
3.4 Effect of mReg2 on the expression of inducible hsp70 protein 87
3.5 Clinical and Demographic Characteristics of the Study Groups 94
3.6 Figure showing correlation coefficient between REG Iα and
Biochemical/Clinical Parameters in type I (group I) and type II
96
viii
(group II) diabetics
3.7 A) Frequency and percentage of controls, diabetics without disease
complications and with disease complications
106
3.7 B) Frequency and percentage of diabetics with different type of
disease complications
106
3.8 Clinical Characteristics of the Study Subjects by using
Independent Sample t-test (n=51)
117
3.9 Single Nucleotide Polymorphisms (SNPs) identified in REG Iα
gene
122
3.10 A) Genotypes, allele frequencies and Chi-sq to show significance of
variables with Hardy Weinberg Equilibrium (HWE)
125
3.10 B) Genotypes, allele frequencies and Chi-sq to show significance of
variables with Hardy Weinberg Equilibrium (HWE)
126
3.10 C) Genotypes, allele frequencies and Chi-sq to show significance of
variables with Hardy Weinberg Equilibrium (HWE)
127
3.10 D) Genotypes, allele frequencies and Chi-sq to show significance of
variables with Hardy Weinberg Equilibrium (HWE)
128
3.11 A) Logistic Regression determinants of main variables with
frequencies of allele-T and Allele-C
129
3.11 B) Logistic Regression determinants of main variables with
frequencies of allele-T and Allele-G
130
3.11 C) Logistic Regression determinants of main variables with
frequencies of allele-G and Allele-T
131
3.11 D) Logistic Regression determinants of main variables with
frequencies of allele-G and Allele-A
132
3.12 A1) Models for cluster analysis using different variables to find their
association with genotype (SNP1 -385T>C)
133
3.12 A2) Models for cluster analysis using different variables to find their 134
ix
association with genotype (SNP1 -385T>C)
3.12 B1) Models for cluster analysis using different variables to find their
association with genotype (SNP2 -243T>G)
135
3.12 B2) Models for cluster analysis using different variables to find their
association with genotype (SNP2 -243T>G)
136
3.12 C1) Models for cluster analysis using different variables to find their
association with genotype (SNP3 +209G>T)
137
3.12 C2) Models for cluster analysis using different variables to find their
association with genotype (SNP3 +209G>T)
138
3.12 D1) Models for cluster analysis using different variables to find their
association with genotype (SNP4 +2199G>A)
139
3.12 D2) Models for cluster analysis using different variables to find their
association with genotype (SNP4 +2199G>A)
140
x
LIST OF FIGURES AND GRAPHS
Figure No. Title
1.1
A) Figure showing the classical model for pathogenesis of type I
diabetes
B) A re-creation of the classical model of type 1 diabetes,
originally proposed in 1986
4
4
1.2 Islet β-cell failure and the natural history of type II diabetes 6
1.3 Possible mechanisms of insulin producing β-cell birth and
regeneration
9
1.4 Extrinsic and intrinsic caspase activation cascades and intrinsic
caspase independent activation of apoptosis
12
1.5 ER stress induced apoptotic pathways triggered by free fatty acids
and chronically high glucose in β-cells
14
1.6 Sub-cellular localization of apoptosis-inducing factor 16
1.7 Mechanism of AIF-mediated apoptosis 17
1.8 Apoptogenic AIF with its cytoplasmic partners 19
1.9 Apoptogenic AIF with its nuclear effects 20
1.10 Structure of doxorubicin 24
1.11 Proposed mechanism of DOX-induced apoptosis 24
1.12 Map of Reg1α gene on chromosome 2p12 (2.7kb) Gene ID:5967 28
1.13 Possible mechanism of Reg gene activation 31
1.14 Proposed Reg I protein-Reg I receptor system for ß-cell replication 34
1.15 Proposed model for Reg1 mechanism of action on ductal and β-
cells
34
1.16 Possible model of mReg2 attenuated pro-apoptotic changes
induced by Stz
37
xi
1.17 mReg2 inhibits ER stress driven UPR, a proposed model 38
2.1 MIN6 cells transfected with mReg2 45
3.1A) Induction of time-dependent increase in AIF in the nucleus of
MIN6-VC cells. A) Nuclear fractions of the MIN6-VC cells
treated with 1 uM doxorubicin for the varying times were
subjected to Western blot analysis for AIF. The blots were
reprobed with anti-USF-2 antibody (nuclear marker) to check the
loading efficiency
80
3.1 B) Graphical representation of the AIF levels in the nuclear fraction
of MIN6-VC cells
80
3.2A) Inhibition of the nuclear translocation of AIF and promotion of the
nuclear accumulation of Scythe by mReg2. Cells were incubated
with 1 µM doxorubicin for 12 h. Nuclear and cytosolic
distribution of AIF and Scythe following the removal of
mitochondrial fraction was assessed by immunoblot analysis.
Blots were reprobed with anti-USF-2 antibody (nuclear marker)
and anti-actin/ anti-tubulin antibodies (cytosolic marker). (A)
Treatment with Doxorubicin led to an increase in the nuclear
presence of AIF in MIN6-VC cells but not in the MIN6-mReg2
cells. In contrast, the level of Scythe in the nucleus was lower in
MIN6-VC cells but markedly higher in MIN6-mReg2 cells after
treatment
82
3.2B) (B)Left In response to treatment with Doxorubicin, increase level
of AIF and decrease level of Scythe were observed in nucleus in
MIN6-VC cells and opposite was observed in MIN6-mReg2 cells.
Right The cytosolic presence of Scythe increased AIF decreased in
MIN6-VC cells. In comparison, in MIN6-mReg2 cells decrease
levels of Scythe and increased level of AIF was observed in the
cytosol
82
xii
3.2C) (C) Graphic representation of increase levels of Scythe and
decrease levels of AIF in the MIN6-mReg2 cells with opposing
effects in MIN6-VC cells
83
3.3A) Inhibition of the nuclear translocation of AIF and promotion of the
nuclear accumulation of Scythe by mReg2. Cells were incubated
with 1 µM doxorubicin for 24 h. (A) Treatment with Doxorubicin
led to an increase in the nuclear presence of AIF in MIN6-VC
cells but not in the MIN6-mReg2 cells. In contrast, the level of
Scythe in the nucleus was lower in MIN6-VC cells but markedly
higher in MIN6-mReg2 cells treated with the apoptotic stimulus
85
3.3B) (B)Left In response to treatment with Doxorubicin, increase level
of AIF and decrease level of Scythe were observed in nucleus in
MIN6-VC cells and opposite was observed in MIN6-mReg2 cells.
Right The cytosolic presence of Scythe increased AIF decreased in
MIN6-VC cells. In comparison, in MIN6-mReg2 cells decrease
levels of Scythe and increased level of AIF was observed in the
cytosol
85
3.3C) (C) Graphic representation of increase levels of Scythe and
decrease levels of AIF in the MIN6-mReg2 cells with opposing
effects in MIN6-VC cells
86
3.4A) Effect of mReg2 on the expression of hsp70 in the absence and
presence of treatment.(A) Whole cell lysates subjected to western
blot analysis demonstrated the increased presence of hsp70 in
MIN6-mReg2 cells compared to that detected in MIN6-VC cells.
Loading consistency was verified by reprobing the blots for actin
88
3.4B) Graphical representation of expression of hsp70 in MIN6-VC and
MIN6-mReg2 cells
88
3.5 Serum REG Iα levels in controls, type I diabetics and type II
diabetics
95
xiii
3.6 Correlation between serum REG Iα levels and duration of disease
in type II diabetics
97
3.7 Correlation between serum REG Iα levels and duration of disease
in type I diabetics
97
3.8 Serum REG Iα levels and different duration of disease groups in
type II diabetics
98
3.9 Correlation between serum REG Iα levels and age of the patient
(years) in type II diabetics
99
3.10 Correlation between serum REG Iα levels and age of the patient
(years) in type I diabetics
99
3.11 Serum levels of REG Iα in type II diabetics depending on age at
disease onset
100
3.12 Correlation between serum REG Iα levels and FBG (mmol/l) in
type II diabetics
101
3.13 Correlation between serum REG Iα levels and FBG (mmol/l) in
type I diabetics
101
3.14 Correlation between serum REG Iα protein and HbA1c in type II
diabetics
102
3.15 Correlation between serum REG Iα protein and HbA1c in type I
diabetics
102
3.16 Serum REG Iα levels and smoking in type II diabetics 103
3.17 Correlation between serum REG Iα and BMI in type II diabetics 104
3.18 Correlation between serum REG Iα and BMI in type I diabetics 104
3.19 Serum levels of REG Iα in controls, both type I and type II
diabetics without disease complications and with disease
complications
105
3.20 Optimization of PCR amplication using different primer sets 118
xiv
3.21 A & B Figures are representing selected DNA samples for PCR
amplification of exonic regions
119-
120
3.22 Map of REG Iα gene on chromosome 2p12 (2.7kb) (Gene ID:
5967, exon 2-6 coding) with the identified SNPs
121
3.23 A Left (SNP: -385 T>C). The electro-chromatogram of study
subject showing substitution of T>C (as indicated by an arrow).
Right (SNP: -243 T>G) The electro-chromatogram of study
subject showing substitution of T>G (as indicated by an arrow)
123
3.23 B Left (SNP: -145 G>A) (all the study population had A). The
electro-chromatogram of study subject showing substitution of
G>A (as indicated by an arrow). Right (SNP: +142 A missing in
all study subjects). The electro-chromatogram of the study
subjects showing deletion of A (as indicated by an arrow)
123
3.23 C Left (SNP: +209 G>T). The electro-chromatogram of study
subject showing substitution of G>A (with reverse primer C>T)
(as indicated by an arrow). Right (SNP: +226 A>G). The electro-
chromatogram of study subject showing substitution of G>A (with
reverse primer T>C) (as indicated by an arrow)
124
3.23 D Left (SNP: +2199 G>A). The electro-chromatogram of study subject
showing substitution of G>A (as indicated by an arrow). Right
(SNP: +2360 A>G). The electro-chromatogram of study subject
showing substitution of A>G (as indicated by an arrow)
124
4.1 Proposed mechanism of mReg2 mediated inhibition of AIF-
dependent apoptosis
145
4.2 Proposed model for release of REG Iα in response to β-cells
damage and its role in preservation of β-cells
151
xv
LIST OF ABBREVIATIONS
Abbreviation Used For
ADA
AIF
Akt (PKB)
ANOVA
Apaf1
APS
ATF4
ATF6
ATP
BAD
BAK
BAX
BCA
Bcl-2
Bcl-xL
BID
BMI
BSA
Caspases
CDK4
CHO cells
CHOP
CRD
CREAM
CypA
dbSNP
DCCT
American Diabetic Association
Apoptosis-Inducing Factor
Protein Kinase B
Analysis Of Variance
Apoptosis Protease-Activating Factor 1
Ammonim Per Sulfate
Activating Transcription Factor 4
Activation Transcription Factor 6
Adenosine Triphosphate
Bcl-2-Associated Death Promoter
Bcl-2 Homologous Antagonist Killer
Bcl-2-Associated X Protein
Bicinchoninic Acid
B-cell lymphoma
B-cell Lymphoma-extra Large
BH3 Interacting-Domain
Body Mass Index
Bovine Serum Albumin
Cysteine-aspartate specific proteases
Cyclin-dependent Kinase 4
Chinese hamster ovary cells
CCAAT/-enhancer-binding-protein homologous protein
Carbohydrate Recognition Domains
Center for Research in Experimental and Applied Medicine
Cyclophilin A
Database of Single Nucleotide Polymorphisms
Diabetes Control and Complications Trial
xvi
Abbreviation
ddH2O
ddNTPs
dH2O
DISC
DMEM
dNTPs
Dox
DPBS
DTT
Dx
ECG
ECL
EDTA
EGTA
elF2
ELISA
EMG
Endo G
ER
EXT
EXTL3/EXTR1
FBG
FBS
FCPD
FDA
FH
G6PD
GADA
GDIP
Used For
Double distilled Water
Dideoxynucleotide Triphosphates
Distilled Water
Death Inducing Signalling Complex
Dulbecco´s Modified Eagle’s Medium
Deoxynucleotide Triphosphates
Doxorubicin
Dulbecco´s Phosphate Buffered Saline
Dithiothreitol
Dexamethasone
Electrocardiography
Enhanced' Chemiluminescence
Ethlene diamine tetra acetic acid
Ethylene Glycol Tetra acetic acid
Elongation factor 2
Enzyme Linked Immunosorbant Assay
Electromyography
Endonuclease G
Endoplasmic Reticulum
Exostoses
Exostoses-like 3/ Exostoses-related1
Fasting Blood Glucose
Fetal Bovine Serum
Fibrocalculous Pancreatic Diabetes
Food and Drug Association
Family History
Glucose-6-Phosphate Dehydrogenase
Glutamic Acid Decarboxylase Antibodies Glucose
Dependent Insulinotropic Polypeptide
xvii
Abbreviation
GIP
GIT
GLP-I
GRP78
HbA1c
HEPES
HK
Hsp70
HWE
IAA
IAP
ICA
IDDM
IDF
IDV
IFN-α
IGF-I
IGT
IHD
IL-22
IL-6
INGAP
INGAP-PP
IRE1α
JAK/STAT
LLD
MAP
MIN6 Cells
Used For
Glucagon Like Peptide
Gastrointestinal Tract
Glucagon Like Peptides I
Glucose Response Protein 78
Glycosylated Heamoglobin
[4-(2-hydroxyethyl)-1-piperazine]-ethane sulfonic acid
Hexokinase
Heat Shock Protein 70
Hardy Weiberg’s Equation
Insulin Autoantibodies
Inhibitor of Apoptosis
Islet Cell Antibodies
Insulin Dependent Diabetes Mellitus
International Diabetes Foundation
Integrated Density Values
Interferon- α
Insulin Like Growth Factor I
Impaired Glucose Tolerance
Ischemic heart disease
Interleukin-22
Interleukin-16
Islet Neogenesis-Associated Protein
Islet Neogenesis-Associated Protein-Pentadecapeptide
Inositol-Requiring Enzyme 1
Janus kinase/signal transducers and activators of
transcription
Lower Limit of Detection
(MAP) Kinase Phosphatase-1
Mouse Insulinoma Cells
xviii
Abbreviation
MIN6-mReg2
MIN6-VC
MKP-1
MLS
MMP
MODY
MOMP
mReg2
mTORC1
NADH
NCBI
NCS
NF-κB
NGSP
NIDDM
NLS
NOD
NP40
OD
OPD
OR
PARP-I
PBD
PBS
PCR
PDGF
PERK
PI3K
PP
Used For
Mouse Insulinoma Cells-mReg2
Mouse Insulinoma Cells-Vector
Mitogen-Activated Protein (MAP) Kinase Phosphatase 1
Mitochondrial Localization Signal
Mitochondrial Membrane Permeability
Maturity Onset Diabetes of the Young
Mitochondrial outer membrane permeabilization
Mouse Regenerating Islet Derived Protein 2
mammilian Target of Rapamycin Complex I
Nicotinamide Adenine Dinucleotide Hydrgen
National Centre for Biotechnology Information
Nerve Conduction Studies
Nuclear Factor Kappa B
National Glycohemoglobin Standardization Program
Non-Insulin Dependent Diabetes Mellitus
Nuclear Localization Signal
Non Obese Diabetic
Nonyl Phenoxypolyethoxylethanol40
Optical Density
Out Door Patient
Odd Ratio
Poly [ADP-ribose] polymerase 1
Peptide Binding Domain
Phosphate Buffered Saline
Polymerase Chain Reaction
Platelet-Derived Growth Factor
Pancreatic ER kinase-like ER kinase
Phosphoinositide 3-Kinase
Pancreatic polypeptide
xix
Abbreviation
PSP
PTP
Rb
Reg
Reg
REG
REG
REG Iα
REG Iβ
REG/Reg
Rel to Unt
RFTs
ROS
SDS
SDS-PAGE
SEM
SNPs
SPSS
STDEV/SD
T, C, G and A
TBE
TCP
TEMED
TINIA
TNF
TNFα
TRIS
Tt
Used For
Pancreatic Stone Protein
Pancreatic Thread Protein
Retinoblastoma
Animal Reg protein
Animal Reg gene
Human Reg protein
Human Reg gene
Regenerating Islet Derived Protein 1 Alpha
Regenerating Islet Derived Protein 1 Beta
Regenerating (Regenerating islet derived protein)
Relative to Untreated
Renal Function Tests
Reactive Oxygen Species
Sodium Dodecyl Sulphate
Sodium Dodecyl Sulphate-Polyacrylamide gel
electrophoresis
Standard Error Mean
Single Nucleotide Polymorphisms
Statistical Package for Social Sciences
Standard Deviation
Thymine, Cytosine, Guanine and Adenine
Tris borate EDTA
Trophic Calcific Pancreatitis
Tetramethylethylenediamine
Turbidimetric Inhibition Immunoassay
Tumor Necrosis Factor
Tissue Necrotic Factor Alpha
Tris(hydroxymethyl)-aminomethane
Treated
xx
TTAB
Abbreviation
TZD
UPR
USF2
UT/Unt
XIAP
Used For
Tetradecyltrimethylammonium
Thiazolidinediones
Unfolded Protein Response
Upstream stimulatory factor2
Untreated
X-Linked Inhibitor of Apoptosis
xxi
LIST OF APPENDICES
Appendix
No. Title
Page
No.
Ι Table showing demographic, anthropometric, clinical and
biochemical data of type II diabetics (n=60)
180
ΙΙ Table showing demographic, anthropometric, clinical and
biochemical data of type I diabetics (n=10)
183
ΙΙΙ Table showing demographic, anthropometric and biochemical
data of controls (n=20)
184
ΙV Standard calibration curve for calculation of REG Iα 185
V Correlation between serum REG Iα and systolic blood pressure
in type II diabetics
Correlation between serum REG Iα and diastolic blood
pressure in type II diabetics
186
VI Patients consent form 187
VII Performa for data collection 189
xxii
ABSTRACT
Reg (Regenerating) family proteins appear to be highly effective in preserving islet cell
biology. Reg2 the only member of Reg II family, found in mouse, has been found to play
a role in islet β-cell survival and/or regeneration. However, there is limited evidence on
the molecular mechanism underlying or contributing to the anti-apoptotic effect of
mReg2 on islet β-cells. Previously conducted studies reported that it prevents
mitochondrial membrane disruption, inhibits caspase-dependent apoptosis and also
attenuates ER stress induced UPR. In the present study by utilizing MIN6 cells over
expressing mReg2 subjected to doxorubicin treatment in a time dependent manner we
have found that mReg2 exerts protective effects downstream of UPR by inhibiting AIF-
mediated, caspase-independent, apoptosis by (i) stimulating the nuclear sequestration of
Scythe (ii) attenuating the cytosolic to nuclear translocation of AIF and (iii) up-regulating
the expression of inducible hsp70.
Endogenous insulin deficiency either relative or absolute due to destruction of β-cells is
the basis of all forms of diabetes. REG Iα the first member of Reg family was found to be
released in response to β-cell damage and promotes their regeneration, which, as a
consequence, compensates for the β-cell loss and delays the onset of diabetes. Present
study was also conducted to evaluate REG Iα levels in diabetic patients as a marker of β-
cell regeneration. Raised levels of REG Iα were reported in both type I and type II
diabetics. In type II diabetics the levels were more raised in the patients with short
duration of the disease and may play a role in the pathogenesis of diabetes. REG Iα levels
were also found to be raised in patients with diabetic complications where other sources
xxiii
of REG Iα may have contributed. Hence, REG Iα may act as a possible predictive marker
for diabetes.
Present study also reported that out of the eight SNPs identified in the REG Iα gene, one
of the variant g.209T was found to be significantly associated with the generalized
susceptibility to type II diabetes. Moreover, some of variants also showed modest link
with smoking in type II diabetes in the Pakistani subjects.
Chapter 1
1
INTRODUCTION AND REVIEW OF LITERATURE
1.1. REGULATION OF GLUCOSE HOMEOSTASIS IN THE BODY
Glucose homeostasis is essential for maintaining normal biological functions. It is regulated
mainly by the hormones from the pancreatic islet cells. The islet cells make up just around 1 to
2% of the total pancreatic mass and consist of five hormone secreting cell types: α (glucagon),
β (insulin), δ (somatostatin), ε (ghrelin) and PP (pancreatic polypeptide). Insulin produced by
the β-cells that occupy nearly 80% of total islet cell mass is the major regulator of glucose
homeostasis (Steiner et al., 2010).
1.2. DIABETES AND ITS STATISTICS
Endogenous insulin deficiency either relative or absolute due to decline in functional β-cells is
the basis of all forms of diabetes. By definition “Diabetes mellitus is a group of metabolic
diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin
action, or both” (American Diabetic Association, 2014). The persistent hyperglycemia of
diabetes is linked with continuing damage, impairment, and collapse of multiple organ system
that puts a huge burden on the patient and as a whole on the health system and the economy of
the country (Hu, 2011). If this trend continues, diabetes will be the leading cause of morbidity
and mortality in the near future.
According to International Diabetes Foundation (IDF) 2014 Atlas, 387 million people
worldwide have diabetes with the prevalence of 8.3%; by 2035 this will rise to 592 million. In
Pakistan 12.9 million people are diagnosed with diabetes with a prevalence of 10%. It is
estimated that presently “Pakistan is ranked 7th
as far as diabetes population is concerned and
it will rise to 4th
number by the year 2030 which is extremely upsetting” (WHO statement,
Chapter 1
2
2011). A study published in 2011 revealed that total prevalence of diabetes in Pakistan is more
than 13%. Impaired glucose tolerance (IGT) is found in 5.61% of the population (Zafar et al.,
2011). Therefore, to decrease this economic burden worldwide, it is essential to have better
understanding of its etiology and pathophysiology to focus on its therapeutics as well as early
diagnosis and prognosis.
1.3. CLASSIFICATION OF DIABETES
The development of diabetes involves numerous pathological processes. These vary from
immune-mediated damage of the islet β-cells with secondary insulin deficiency to anomalies
that result in insulin resistance. The majority of diabetic cases fall into two wide groups
designated as type I diabetes and type II diabetes. Type I diabetes which includes only 5–10%
of diabetic population, also called “insulin-dependent diabetes mellitus (IDDM) or juvenile
onset diabetes", is a chronic inflammatory disease that is the consequence of T-cell mediated
autoimmune destruction of the insulin producing-cells leading ultimately to lifelong
dependency on exogenous insulin (Eisenbarth, 1986, Atkinson, 2012). Type II diabetes also
known as “non-insulin-dependent diabetes mellitus (NIDDM) or adult onset diabetes” that
holds the major share of up to 90-95% of total diabetes cases, is a heterogenous group of
disorders associated with insulin resistance, insulin secretory defects and hyperglycemia.
Persistent insulin resistance in these patients leads to continual increased insulin secretory
demand on islets and this eventually leads to islet β-cell demise and low circulating insulin
level (Meier and Bonadonna, 2013, American Diabetes Association, 2014, Halban et al.,
2014). It is associated with environmental factors like excess body weight and sedentary life
styles. Many of the type II diabetics ultimately require insulin therapy to control
hyperglycemia. Nearly 5-10% of Type I diabetics are being diagnosed in the adulthood while
Chapter 1
3
due to increased obesity and physical inactivity type II diabetes is increasingly being
diagnosed in adolescents (Alberti et al., 2004, Chiang et al, 2014). Therefore the age at disease
onset between the two types of diabetes is no longer a limiting factor.
Type I diabetes is further classified into type IA and type IB. Type IA accounts for 70-90% of
the cases and is characterized by loss of β-cells due to autoimmunity and antibodies against
glutamic acid decarboxylase (GADA), islet cells (ICA) and/or insulin (IAA) can be detected
in their serum. Whereas, type IB, is called as idiopathic and includes the remainder of the
cases, has all the clinical characteristics of type IA, but the auto antibodies are not detected
(American Diabetes Association, 2014, Chiang et al., 2014).
1.4. PATHOGENESIS OF DIABETES
Type I diabetes is a disorder characterized by immune-mediated demise of the β-cells leading
to lifelong dependency on exogenous insulin therapy. The three factors considered
accountable for the type I diabetes includes; genetic susceptibility, environment and the
immune system. The classical model for the pathogenesis of type I diabetes proposes that
individuals with genetic predisposition to the disease when exposed to the environmental
triggers, leads to immune linked damage to β-cells. This process involves the formation of
islet specfic auto-antibodies, the development of activated auto-reactive T-cells having the
ability to destroy β-cells, leading to progressive and anticipated loss in insulin production.
This model proposes that, clinical manifestation of type I diabetes occurs when approximately
80-90% of the insulin producing cells have been damaged, indicating a long way between the
beginning of auto-immune process and the onset of clinical diabetes. The classical model and
the recreation of the model after 25 years are shown in figures 1.1A & 1.1B (Eisenbarth, 1986;
Atkinson, 2012; Atkinson et al., 2014).
Chapter 1
4
Figure 1.1A: Figure showing the classical model for the natural history of type I diabetes
(Eisenbarth, 1986).
Figure 1.1B: A re-creation of the classical model of type 1 diabetes, originally proposed in
1986 (Atkinson et al., 2014)
Chapter 1
5
Type II diabetes is a progressive metabolic disease characterized by hyperglycemia due to
interaction between several environmental and genetic factors that leads to either defective
insulin release or impaired insulin action on its target organs. The normal response of β-cells
to hyperglycemia and insulin resistance secondary to obesity is a compensatory up-
regulation of insulin secretion in order to achieve normoglycemia. Type II diabetes develops
only when β-cells are unable to maintain the compensatory response (figure: 1.2) (Prentki
and Nolan, 2006). The natural course of the disease involves progressive β-cell dysfunction,
with associated decline in β-cell mass secondary to apoptosis. It has been reported that upto
65% decline in the β-cell mass occurs in patients with type II diabetes when compared with
age and BMI matched controls subjects (Butler et al., 2003a). Various mechanisms proposed
for the β-cell loss due to apoptosis include mitochondrial dysfunction, formation of reactive
oxygen species (ROS), endoplasmic reticulum (ER) stress, and glucolipotoxicity. Additional
mechanisms associated with glucotoxicity and the diabetic environment like inflammation
of islets, O-linked glycosylation, and deposition of amyloid peptide, speed up β-cell death
by apoptosis (Meier and Bonadonna, 2013; Halban et al., 2014).
ER stress plays a key role in the pathogenesis of especially type II diabetes and contributes
to β-cell death. Raised insulin secretory demand in turn leads to increased insulin flux
through ER leading to build up of misfolded proteins that induce secretory pathway stress
and trigger unfolded protein response (UPR). UPR play a dual role in β-cells, under
physiological conditions it has a rescuing effect on β-cells while in conditions of chronic
stress it triggers proapoptotic pathway which in turn leads to beta-cell dysfunction and
apoptosis (Eizirik et al., 2008). Obesity is thought to elicit type II diabetes by increasing
circulatory free fatty acid levels and insulin resistance. This inflicts augmented burden on
Chapter 1
6
Figure 1.2: Figure showing Islet β-cell failure and the natural history of type II diabetes.
(Prentki and Nolan, 2006)
Chapter 1
7
the β-cells to meet the insulin secretory demand, activating the pathway of UPR. Moreover,
increase burden on the mitochondria leads to its hyperactivity with production of lethal
levels of reactive oxygen species (ROS) and studies have shown that β-cells consists of
rather low concentration of some major antioxidants (Robertson et al., 2004). Inflammation
is central to all the conditions that are associated with diabetes (Donath et al., 2008, Donath,
2014). A novel mechanism of β-cell failure has been reported in type II diabetes involving
dedifferentiation of mature β-cells to immature status (Talchai et al., 2012).
1.5. β-CELL PRESERVATION/REGENERATION
To maintain optimal β-cell mass both in health and disease a net balance of four mechanisms
is required: rate of replication, neogenesis, hypertrophy and apoptosis (Dheen et al., 1997,
Rhodes, 2005). The maximum replication of β-cells occurs during the late embryonic and in
the early postnatal life. Enhanced β-cell death also occurs during this period and plays a key
role in development and remodeling of endocrine pancreas. Thereafter, a tight regulation of
the rate of β-cell replication and apoptosis occurs in order to overcome the metabolic need
(Bell and Polonsky, 2001; Bouwens and Rooman, 2005). It is only under the combinatorial
influence of genetic and environmental factors this regulation fails with the onset of clinical
diabetes. A common feature of all forms of diabetes is loss of functional β-cell mass.
Restoration of β-cell mass and function is the underlying goal for better diabetes
management and can be achieved either by the triggering endogeneous β-cells regeneration
or β-cells transplantation using exogenous sources (Migliorini et al., 2014).
The current therapeutic measures are aimed at developing a lasting cure to the disease
focusing on both aspects. Successful pancreatic or islet transplant in diabetes patients has
Chapter 1
8
been demonstrated to eliminate the use of exogenous insulin and also decrease the risk of
acute and chronic disease associated complications. However, its use is limited due to lack
of organ donors and increased chance of immune rejection (American Diabetes Association,
2006, Jahansouz et al., 2011). To promote endogenous growth and survival of β-cell, several
growth factors have been found to play a role including insulin like growth factor I (IGF-I),
gastrin, hepatocyte growth factor and glucagon like peptide I (GLP-I) (George et al., 2002,
Tourrel et al., 2001, Xu et al., 2006, Suarez-Pinzon et al, 2008). However, only a few of
these growth factors have been found effective for therapeutic use (Liu, 2007).
Betatrophin, a hormone produced by liver and adipose tissue has been shown to expand β-
cell mass and improve glucose tolerance by promoting β-cell proliferation (Yi et al., 2013).
Increased circulatory levels of betatrophin do not protect against C-peptide loss in mice
models of type 1 diabetes (Espes et al., 2014). Moreover, human β-cell lines were found to
be non responsive to betatrophins questioning their role in diabetes therapy (Jiao et al.,
2014).
Thus, ongoing research in the field of diabetes is focusing on identifying novel molecular
markers and their downstream targets that hold the capability to increase the long term
survival and functional efficacy of islet β-cells. In the last two and a half decades upto 29
Reg family members have been recognized as regulators of β-cell differentiation,
proliferation, survival as well as neogenesis (Li et al., 2015). In the pancreas, the expression
of Reg family proteins is induced during islet development or by β-cell damage and diabetes
(Liu, 2008).
Chapter 1
9
Figure 1.3: Possible mechanisms of insulin producing β-cell birth and regeneration
(Banerjee et al., 2005)
Chapter 1
10
1.6. PANCREATIC β-CELL APOPTOSIS
Pancreatic β-cell apoptosis is the hallmark of type I diabetes and a key feature of type II
diabetes. Moreover, excessive β-cell death through apoptosis is a major disadvantage of
pancreas or islet cell transplantation. There are two basic signaling pathways through which
β-cell apoptosis occurs: The extrinsic/death receptor pathway and the intrinsic/mitochondria
dependent pathway (Elmore, 2007, Galluzzi et al., 2012).
1.7. APOPTOTIC SIGNALING PATHWAYS
Apoptosis, highly regulated programmed cell death, occurs either by the activation of a family
of caspases (cysteine-aspartate specific proteases) through either extrinsic or intrinsic
pathways or it occurs in a caspase independent manner. The structural changes that are a
hallmark of apoptosis includes chromatin condensation and nuclear DNA fragmentation
(Galluzzi et al., 2012).
In extrinsic apoptotic pathway ligands bind and activate the death receptors present on the
cell membrane. Within the cytoplasm these activated death receptors forms death inducing
signalling complex (DISC) which activates the initiator caspase-8, followed by activation of
cascade of executioner caspases 3, 6 and 7 and induction of cell death. Activated Caspase 8
can also activate BH3 interacting-domain death agonist (Bid) which increases MMP
(Mitochondrial Membrane Permeability) thus linking extrinsic and intrinsic apoptotic
signalling pathways (figure 1.4) (Lee and Pervaiz, 2007, Kroemer et al., 2007).
Cellular stresses, activate the mitochondrial apoptotic pathway by activating pro-apoptotic
members of the Bcl-2 family such as Bax (Bcl-2-associated X protein), Bak (Bcl-2
homologous antagonist killer), Bad (Bcl-2-associated death promoter) and Bid. The activated
Chapter 1
11
proteins are recruited from the cytoplasm to the outer mitochondrial membrane. On the other
hand, Bcl-2 family proteins such as Bcl-2 and Bcl-xL (B-cell lymphoma-extra-large), blocks
apoptosis by preventing the recruitment of pro-apoptotic proteins to the mitochondrial outer
membrane. Oligmerization of both Bax and Bak leads to the formation of pores in the
mitochondrial outer membrane with the liberation of cytochrome C and AIF. Cytochrome C
once in the cytoplasm forms apoptosome with apoptosis protease-activating factor 1 (Apaf1)
which recruits and activates initiator caspase 9, which in turn activates capases 3, 6 and 7 and
induces apoptotic cell death. Apoptosis-inducing factor (AIF) from cytoplasm directly enters
the nucleus and leads to chromatin condensation and DNA degradation (Green, 2005, Choi
and Woo, 2010, Kroemer et al., 2007).
1.8. ER STRESS INDUCED β-CELL APOPTOSIS
ER is the major sub-cellular organelle involved in the synthesis of lipids, folding/maturation of
proteins and storage of calcium. Islet β-cells possess well developed ER for protein folding as
well as processing especially pro insulin (Oyadomari et al., 2002). Therefore, the maintenance
of ER homeostasis is crucial. Disturbance of the Ca2+
homeostasis during hypoxic
conditions, or buildup of misfolded proteins, leads to the activation of the adaptive response
within the cells called unfolded protein response (UPR). ER localised stress sensors are
induced, protein synthesis is attenuated and a protein degrading system is activated.
However, under conditions of chronic ER stress, normal cellular functions cannot be
restored, and proapoptotic UPR is activated (Eizirik et al., 2008).
Chapter 1
12
Figure 1.4: Extrinsic and intrinsic caspase activation cascades and intrinsic caspase
independent activation of apoptosis (Kroemer et al., 2007)
Chapter 1
13
Three major pathways of ER stress induced apoptosis that play a major role in β-cells
apoptosis are known (van der Kallen et al., 2009). The PERK/eIF2-ATF4- CHOP
proapoptotic signaling pathway that is induced by long chain saturated fatty acids, chronic
hyperglycemia and oxidative stress (figure 1.5) (Song et al., 2008, Diakogiannaki et al.,
2008). CHOP was reported to down regulate Bcl-2, a member of anti apoptotic protein
family (McCullough et al., 2001). In case of β-cells, cytokines like IL1-β and IFN-γ induced
depletion of ER Ca2+
leads to apoptotic β-cell death through IRE1α-JNK pathway (Wang et
al., 2009, Brozzi et al., 2015). While, the activation of caspase 12, an ER resident cystiene
protease also leads to ER stress induced apoptotic signaling in cells (van der Kallen et al.,
2009). In autoimmune type I diabetes, loss of β-cells occurs primarily due to the extrinsic
apoptotic pathway, however, β-cell apoptosis induced by ER stress have also been
implicated (Silva et al., 2003). In case of type II diabetes, death in islet β-cells can be
triggered by glucolipotoxicity and oxidative stress though intrinsic apoptotic pathway and
ER stress induced pro-apoptotic pathways (Lupi and Del Prato, 2008, Laybutt et al., 2007)
and also by extrinsic apoptotic pathway (Maedler et al., 2001).
1.9. CASPASE INDEPENDENT APOPTOTIC PATHWAY: APOPTOSIS
INDUCING FACTOR (AIF)
AIF is a 613 amino acids containing approximately 67 kDa mitochondrial resident precursor
protein that contains a 35 amino acids mitochondrial localization signal (MLS) located in its
N-terminus. After the translation of this 16 exon gene located on X-chromosome, its cytosolic
to mitochondrial import occurs with the help of inner and outer mitochondrial membrane
translocases followed by the removal of MLS by mitochondrial peptidase and its anchorage to
Chapter 1
14
Figure 1.5: ER stress induced apoptotic pathways triggered by free fatty acids (FFAs) and
chronically high glucose in β-cells (Back and Kaufman, 2012)
Chapter 1
15
the inner mitochondrial membrane as a mature 62 kDa protein (figure 1.6 and figure 1.7)
(Hangen et al., 2010). AIF is a flavoprotein, that in healthy cells is restricted to mitochondria,
required for detoxification of reactive oxygen species (ROS) and for the activity of the
respiratory chain complex I and its diminution leads to an enhanced susceptibility to oxidative
stress (Vahsen et al., 2004; Modjtahedi et al., 2006; Elguindy et al., 2015).
However, in response to apoptotic signals activated BH3 only proteins are recruited to the
mitochondrial outer membrane, they cause mitochondrial outer membrane permeabilization
(MOMP), this causes the translocation of AIF from mitochondrial to cytosol after certain
proteases like calpains and cathepsins causes release of 57 kDa soluble AIF fragment from its
inner membrane anchor (Otera et al., 2005, Norberg et al., 2010). Translocation of AIF into
the nucleus occurs with the help of its nuclear localization signal (NLS). Inhibition of MOMP
prevents AIF translocation (Ferrand-Drake et al., 2003). Once inside the nucleus, AIF interacts
directly with DNA and induces condensation of chromatin and DNA fragmentation (Ye et al.,
2002). Systematic deletion mapping of AIF protein revealed that its C-terminal domain ahead
of residue 567 is necessary for its chromatin condensation activity (Gurbuxani et al., 2003,).
AIF has also been shown to be regulated by Poly (ADP-ribose) polymerase I (PARP-I) a
nuclear repair enzyme. In response to an insult to the DNA, PARP-I generate and transfer
ADP ribose polymers to nuclear proteins like histones and topoisomerases and regulates
DNA repair (Hong et al., 2004). However, its overactivation by DNA alkylating agents
triggers the release of AIF from mitochondrial inner membrane and its nuclear translocation
leading to chromatolysis and cell death (Yu et al., 2002). To carry out its function AIF also
needs to interact with certain other proteins/ enzymes, one such protein is Cyclophilin
(CypA). Pro-apoptotic form of AIF upon translocation to nucleus interacts with H2AX to
Chapter 1
16
Figure 1.6: Sub-cellular localization of apoptosis-inducing factor (Kroemer et al., 2007)
Chapter 1
17
Figure 1.7: Mechanism of AIF-mediated apoptosis (Modjtahedi et al., 2006)
Chapter 1
18
carry out chromatinolysis in collaboration with CypA and AIF nuclear translocation was
found to be inhibited in CypA-/- mice and neurons in cerebral hypoxic/ischemic
environment (Cande et al., 2004, Zhu et al., 2007, Tanaka et al., 2011, Artus et al., 2010).
AIF was also found to interact with another mitochondrial protein Endonuclease G (EndoG)
to promote DNA cleavage and cell death (Wang et al., 2002).
Scramblases (flippases) that belong to the family of transmembrane lipid transporters when
activated translocate phosphatidylserine from the inner leaflet of plasma membrane to its outer
leaflet, which is a hallmark in mammalian cell death. AIF promotes this process by a
mechanism still not known (figure 1.8 and figure 1.9) (Sevrioukova, 2011). X-linked inhibitor
of apoptosis (XIAP), is one of the eight members of the inhibitor of apoptosis (IAP) family
that has a protective role in both caspase-dependent and caspase- independent AIF mediated
cell death pathways. It was revealed that XIAP directly interacts with both the mature and
apoptotic forms of AIF and targets it for degradation and in this way alter its enzymatic or
apoptosis inducing property (Wilkinson et al., 2008, Lewis et al., 2011).
Many other proteins are involved in regulating AIF mediated apoptosis. Among these Scythe
(BAT3/HLA-B- associated transcript/Bag6), a member of the BCL-2-associated athanogene
family of proteins and heat shock protein 70 (hsp70) needs special consideration.
1.10. SCYTHE / BAT 3
Scythe, also known as BAT3 (HLA-B-associated transcript/ Bag 6), is a 120 kDa protein that
belongs to the BCL-2-associated athanogene family of proteins and regulate AIF mediated cell
death. Identified as a nuclear protein with a C-terminal NLS, it can shuttle between the
nucleus and cytosol (Desmots et al., 2008, Manchen and Hubberstey, 2001). It has been
Chapter 1
19
Figure 1.8: Figure showing cytoplasmic partners of apoptogenic AIF (Sevrioukova, 2011)
Chapter 1
20
Figure 1.9: Apoptogenic AIF with its nuclear effects (Sevrioukova, 2011)
Chapter 1
21
shown to have a ubiquitin like domain close to its N-terminus with the help of which it
interacts with Reaper, the chief regulator of apoptosis during the developmental stages in the
fly Drosophila melanogaster. This interaction between Scythe and Reaper leads to the
release of cytochrome C from the mitochondrial inter membranous space, activation of
caspase cascade followed by DNA fragmentation and cell death in Xenopus laevis egg
extracts. Immune mediated depletion of Scythe prevented reaper-mediated apoptosis without
affecting programmed cell death initiated by caspase activation (Thress et al., 1998, Thress
et al., 1999). Scythe inactivation in mice was found to be associated with defective
organogenesis, while scythe-/-
cells were resistant to ER stress induced apoptosis (Desmots
et al., 2005).
Scythe has been observed to interact with AIF and regulates its stability in response to cell
death signals triggered by ER stress inducers. The stability of AIF is noticeably reduced in
cells lacking Scythe, and was resistant to endoplasmic reticulum stress. Addition of Scythe
or over expression of AIF in scythe deficient cells restores their apoptotic activity; further
supporting the role of Scythe in the regulation of AIF mediated apoptosis. Studies have
shown that the cytosolic Scythe has the ability to regulate both the caspase-dependent and
the AIF-mediated cell death signaling (Desmots et al., 2008, Preta and Fadeel, 2012b).
Stabilization of AIF by cytosolic Scythe is required for AIF induced scramblase activation
involved in the translocation of phosphatidylserine on the apoptotic cell surface (Preta and
Fadeel, 2012a).
1.11. HEAT SHOCK PROTEIN 70 (hsp 70)
Hsp70 as the name indicates is a 70kDa protein that belongs to the preserved family of
molecular chaperones and plays a crucial role in cell continued existence under stressful
Chapter 1
22
conditions. Its chaperone machinery assists the folding and assembly of the nascent proteins,
either refolding or degradation of misfolded proteins and polypeptides, prevent protein
aggregation, assists translocation of secretory and organellar proteins across membranes and
regulate the activity of signaling proteins (Mayer and Bukau, 2005). Role of hsp70 in the
regulation of both caspase dependent and independent pathways have been reported. In the
caspase independent cytotoxicity cytoplasmic hsp70 directly interacts with AIF and inhibits
its nuclear import. It was reported that hsp70 peptide binding domain (PBD) at its C-
terminal is required for its association with AIF and not its ATP binding domain, while in
case of AIF, a region between residue 150-228 binds hsp70. Deletion of the residues 150-
228 region from AIF completely abolished the interaction between hsp70 and AIF, resulting
in translocation of free AIF to the nucleus leading to increased chromatin condensation and
DNA degradation. (Gurbuxani et al., 2003, Lui and Kong, 2007, Matsumori et al., 2005,
Ravagnan et al., 2001). Moreover, knockdown of hsp70 also increase the nuclear
accumulation of AIF (Choudhury et al., 2011).
An elevated level of hsp70 neutralizes AIF by preventing its nuclear translocation,
chromatin condensation and DNA fragmentation and thus plays a protective role against the
hyperoxia induced disruption of lung endothelial barrier (Kondrikov et al., 2015, Wang et
al., 2015). Protein folding/refolding and trafficking mediated by hsp70 has been shown to be
reversibly inhibited by Scythe by binding to the ATPase region of hsp70 (Thress et al.,
2001). Therefore cyotosolic Scythe inhibits hsp70/AIF interaction resulting in enhanced
nuclear import of AIF and its apoptotic response.
Chapter 1
23
1.12. DOXORUBICIN (Dox)
Doxorubicin, a derivative of daunorubicin also known as 14-hydroxydaunomycin or
Adriamycin belongs to the family of anthracyclines, act as a potent anti-cancer agent that
controls cell proliferation and survival by exerting multiple effects (figure 1.10). Its main
action is to inhibit DNA synthesis by poisoning topoisomerase II or by intercalating into
DNA. It can also generate free radicals (ROS) and the accumulation of these ROS contribute
to DNA and cell membrane damage (Tacar et al., 2013, Thakur, 2011, Thorn et al., 2011).).
Doxorubicin causes cell death in cardiac cells through ER-stress induced apoptotic signaling
both in the caspase-dependent and independent mechanism, the later involving AIF release
from mitochondria (Zhang et al., 2009, Shi et al., 2011). Treatment with Doxorubicin
induces ROS generation followed by increase activation of cathepsin B, AIF cleavage from
IMM (Inner Mitochondrial Membrane), its translocation to nucleus and DNA fragmentation,
while AIF knockdown decreases its toxic effects (figure 1.11) (Moreira et al., 2014).
Doxorubicin has been shown to play a role in the inactivation of glucose response protein 78
(GRP78), a major molecular chaperone that act as the key regulator of the UPR. Its effect on
ER stress transducers like PERK, IRE1α and ATF6α is not known. However, it is known to
activate ATF4 and CHOP downstream effectors in ER stress induced apoptotic signaling
(Wu et al., 2002, Lu et al., 2011).
1.13. REG FAMILY PROTEINS
Reg family proteins constitute a small family within C-type Lectins superfamily. Lectins are
carbohydrate binding proteins and C-type lectins usually contain around 120 amino acids
carbohydrate recognition domains (CRD) and are found in the serum, extracellular matrix or
cell membranes. Reg proteins are small proteins of approximately 16 kDa and unlike other
Chapter 1
24
Figure 1.10: Structure of Doxorubicin
Figure 1.11: Proposed mechanism of Dox-induced apoptosis (Moreira et al., 2014)
Chapter 1
25
CRD-containing proteins are not known to bind carbohydrates (Laurine et al., 2005,
Lasserre et al., 1999). Based on the amino acids sequence, the Reg family members are
highly conserved between members and from human to rodent. All members of Reg family
protein has N-terminal organization starting with a 21-25 amino acids, putative signal
peptide followed by a single CRD. They are soluble, secretory proteins and have diverse
functions (Liu et al., 2008).
The first REG protein was identified in human pancreatic stones in 1979, it was thought to
precipitate pancreatic stone formation and was named Pancreatic Stone Protein (PSP) (De
Caro et al., 1979). Later it was found to be a strong inhibitor of calcium carbonate crystal
formation in pancreatic juice (Multigner et al., 1983). It was called pancreatic thread protein
(PTP) as it could undergo cleavage by trypsin to form insoluble fibrils (Gross et al., 1985).
PSP was also named as lithostathine due to its possible role in inhibiting pancreatic calculi
formation (Bimmler et al., 1997). The protein was re-discovered independently during the
screening of cDNA library derived from regenerating and hyperplastic islets of 90%
depancreatized and nicotinamide treated rats and from humans.
The term Regenerating Protein was coined as it was found in regenerating islets only and
was thought to play an important role in the regeneration of pancreatic islets and in the
treatment of experimental diabetes (Terazono et al., 1988). Later, it was concluded through
nucleotide sequence comparison study that PSP (Lithiostathine), PTP and Reg Protein were
all identical and the product of the same Reg gene (Watanabe et al., 1990). Several isoforms
of the Reg family have been identified since then.
Chapter 1
26
1.13.1 Classification of REG Family Genes/Proteins
A total of seven Reg family members have been identified in mice, all of them are located
on chromosome 6C with the exception of Reg IV. While, in rats and humans five members
have been identified in each. Reg proteins can be divided into four sub groups: Reg I, Reg
II, Reg III, and Reg IV based on the sequence homology studies and phylogenetic analysis
conducted on the data retrieved from NCBI using blast method (Okamoto, 1999, Li et al.,
2015) (table 1.1). REG I gene in humans is further subdivided into REG Iα and REG Iβ and
encodes 166 amino acid proteins which differ in 22 amino acid residues (Moriizumi et al.,
1994) with 87% homology. All human REG genes encoding for their respective proteins are
~3 kb in size, consist of six exons and five introns and are located on the same chromosomal
locus (2p12) except for the REG IV gene (1p11-3) (figure 1.12). Exon I and part of exon II
encodes the 5’-UTR (Untranslated Region) (Banchuin et al., 2002), while the remaining part
of exon II includes the start codon, and the begining of protein coding sequence. Exons III to
VI encodes the major part of the proteins while the 3’-UTR is located at the end of exon VI.
Based on the high degrees of domain and sequences homology, the REG family genes are
probably derived from the common ancestor gene by numerous gene duplication events, and
acquired divergency in both their expression and function in the course of genetic evolution
(Okamoto, 1999, Nata et al., 2004).
1.13.2 REG / Reg Gene Expression and its Regulation
The expression of REG genes normally occur during embryogenesis and are induced in
various pathological conditions including diabetes and cancer. REG I gene expression has
been observed at an early stage during human pancreatic development and corresponds with
the islet cell expansion (Mally et al., 1994). The expression of Reg I and Reg II gene have
Chapter 1
27
Table 1.1: Reg./.REG family members in the Mouse, Rat and Human
Reg
Gene/Protein
Orthology Amino
Acids
cDNA/Protein Numbers Chromosome
Location
Mouse
mReg1 Reg, PTP, PSP,
Lithiostathine,
Reg I
165 NM_009042/NP_033068,
P43137
6 (33.5 cM)
mReg2 PTP 2, PSP 2,
lithostathine 2,
Reg II
173 NM_009043/NP_036773,
Q08731
6 (C)
mReg3α PAP 2, PAP II,
Reg III alpha
175 NM_011259/NP_035389,
O09037
6 (33.5 cM)
mReg3β PAP, PAP1,
HIP, Reg III
beta
175 NM_011036/NP_0035166,
P35230
6 (C)
mReg3γ PAP 3, PAP III,
Reg III gamma
174 NM_011260/NP_035390,
O09049
6 (C)
mReg3δ INGAP-rp,
INGAP, Reg3d,
Reg III delta
175 NM_013893/NP_038921,
Q9QUS9
6 (syntenic)
mReg4 RELP, Reg IV 157 NM_026328/NP_080604.2,
Q9D8G5
3 (F3)
Rat
rReg1 Reg1α 165 NM_012641/NP_036773,
P10758
4 (q33-q34)
rReg3α REG3A, Reg
III, PAP II
174 NM_001145846,
NM_172077, L10229/
NP_001139318, P35231,
AAA02980
4 (q34)
rReg3β PAP, PAP I,
Reg-2, Reg 2,
Peptide 23
175/180 NM_053289, M98049,
S43715/NP_445741, P25031,
AAA16341, AAB23103
4 (q33-q34)
rReg3γ PAP III 174 NM_173097/NP_775120,
P42854
4 (q33-q34)
rReg4 Reg IV 157 NM_001004096/
NP_001004096, Q68AX7.1
2 (q34)
Chapter 1
28
Reg
Gene/Protein
Orthology Amino
Acids
cDNA/Protein Numbers Chromosome
Location
Human
hREG Iα REG1A, PSP,
lithostathine,
PTP
166 NM_002909/NP_002900,
P05451
2 (p12)
hREG Iβ REG1B, Reg L,
PSP 2
166 NM_006507/NP_006498,
P48304
2 (p12)
hREG IIIβ REG3A, PAP,
HIP, PAP I,
Reg-2, PTP,
INGAP
175 NM_002580, NM_138937,
NM_138938, BC036776,
M84337/NP_002571,
Q06141, NP_620354,
NP_620355, AAH36776,
AAA36415
2 (p12)
hREG IIIγ REG3G, REG
3γ, PAP IB,
PAP II, PAP III
175 AB161037, BAD51394;
NM_198448, AY428734
NM_001008387/NP_940850,
Q6UW15, NP_001008388,
BAD51394, AAR88147
2 (p12)
hREG IV REG 4 158 NM_032044,
NM_001159352/
NP_114433. Q9BYZ8,
NM_001152824
1 (p13.1-p12)
Adapted from (Liu et al., 2008, Li et al., 2015)
Figure 1.12: Map of REG Iα gene on chromosome 2p12 (2.7kb) Gene ID: 5967 (NCBI)
Chapter 1
29
been observed in the mouse embryos at about the same time as the expression of insulin I and II
genes (Perfetti et al., 1996b). In the postnatal period, the mRNA levels of Reg I in the pancreas
of mice models showed a progressive age-related decline over 30 months, while Reg II mRNA
levels did not change, demonstrating differential age-related regulation of the two genes (Perfetti
et al., 1996a). The expression of Reg II, Reg IIIα and Reg IIIβ, hardly detectable at the first
week after birth increase significantly at 30 days and decline by 90 days in mice pancreas. This
expression was mainly observed in the acinar cells, however, INGAP was expressed in α-cells
and its expression improved progressively during the first 90 days after birth (Wang et al.,
2011). The mReg2 has also been observed to be expressed in islet cells (Huszarik et al., 2010).
Expression of Reg I gene was significantly increased in isolated rat islets by nutrients like
glucose, amino acids, serum, growth factors such as insulin, growth hormone and platelet-
derived growth factor (PDGF) (Francis et al., 1992, Bonner et al., 2010). Reg gene expression
was also reported to be induced by inflammatory triggers. In the acinar cells, up-regulated
expression was observed with cytokines like interleukin (IL)-6, tumor necrosis factor (TNF)-γ
and/or interferon (IFN)-α, while the expression was down regulated by dexamethasone (Dx).
Incubation with IL-6 or Dx alone had no effect on the Reg gene expression from the acinar
cells, however, treatment with both agents together lead to a significant upregulation in Reg
mRNA level (Dusetti et al., 1996a, Dusetti et al., 1996b). Prominent activation of both Reg II
and Reg IIIβ genes by IL-6 and glucocorticoids was demonstrated in cells of both the exocrine
and endocrine pancreas (Luo et al., 2013). Addition of IL-6 and Dx together in human -cell
lines induced expression of both REG Iα and REG Iβ (Yamauchi et al., 2015). In rat models of
obesity induced diabetes both Reg I and Reg IIIβ expression was found to be upregulated
along with enhanced expression of IL-6 (Calderari et al., 2014). An IL-6 response element has
Chapter 1
30
been recognized in the promoter region of Reg genes and the local IL-6 levels plays a
significant role in influencing Reg gene expression (Dusetti et al., 1996b, Okamoto, 1999).
Increased expression of Reg I by IL-6 and Dx in rat β-cells (Akiyama et al., 2001) was
associated with the presence of a promoter cis-element (T−81
GCCCCTCCCAT−70
) a binding
site for PARP and PARP inhibitors like nicotinamide induce regeneration of β-cells in
depancreatized rats with upregulated Reg gene expression and secretion (figure 1.13). Another
cytokine, IL-22 stimulates Reg I and Reg II gene expression in isolated islets and contribute to
the regeneration of β-cells (Hill et al., 2013).
1.13.3 REG/Reg Protein Receptor
A putative receptor for Reg I protein was isolated from rat islets cDNA library. It was
suggested to be a large cell surface protein of 919-amino-acid with a long extracellular domain
of 868 amino acid residues at its C-terminus, a single transmembrane domain with residues
29–51, and a short intracellular region at its N-terminus (Kobayashi et al., 2000). The Reg
protein receptor had 97% homology to human multiple exostoses-like/related
(EXTL3/EXTR1) gene, a member of the EXT gene family, which may act as enzymes
involved in heparin sulphate synthesis. EXTL3 was observed to stimulate TNF-α mediated
NF-κB signaling (Mizuno et al., 2001). Both the rat and human Reg I bind with high affinity
to the EXTL3 receptor expressed in CHO cells. The receptor is detected in normal and
regenerating pancreatic islets, ductal cells, GIT, brain, pituitary and adrenal grands with
highest expression in pancreas (Kobayashi et al., 2000). A significant Reg dependent growth
was seen in RINm5F cells over-expressing Reg I receptor. No significant difference in the
expression of Reg receptor was observed in the regenerating islets when compared with the
normal one, suggesting that the regeneration and proliferation of islet β-cells are principally
Chapter 1
31
Figure 1.13: Possible mechanism of Reg gene activation (Okamoto, 1999)
Chapter 1
32
regulated by Reg protein expression (Kobayashi et al., 2000). Islet fibrosis due to
activation of islet stellate cells (ISCs) is thought to be involved in the pathogenesis of type II
diabetes. A recent study reported concomitant increase expression of Reg I and its putative
receptor in ISCs in diabetic mice playing a potential role in preventing ISCs activation (Xu et
al., 2015).
1.13.4 Role of REG/Reg Proteins in β-Cell Preservation
Reg family members have been shown to act as a trophic factor and stimulate cell growth,
replication, neogenesis, along with tissue regeneration in several different organs. Some of the
Reg family members have also been demonstrated to play a role in cell survival. Since the
discovery of Reg I, the potential therapeutic approach of these proteins regarding the
preservation of β-cells and management of diabetes is under immense consideration
(Okamoto, 1999).
1.13.4.1 REG I Protein
The probable role for Reg I in β-cell replication, growth, and maturation was suggested, when
its expression was found in regenerating and hyperplastic pancreatic islets but not in the
healthy non dividing islet cells (Terazono et al., 1988). Moreover, it was found that Reg
protein colocalizes with insulin in β-cells insulin secretory granules. The Reg I protein,
normally a product of acinar cells of exocrine pancreas has mitogenic effect on β-cells of
endocrine pancreas and has been shown to improve experimental diabetes in rats (Watanabe
et al., 1994, Zenilman et al., 1996, Levine et al., 2000). Reg Iα protein causes replication of
DNA in β-cells either in an autocrine or paracrine fashion and a putative receptor for Reg I
protein was identified that transfer the growth signal for regeneration of β-cells (Kobayashi et
al., 2000, Okamoto and Takasawa, 2002). Islets isolated from Reg knockout mice were much
Chapter 1
33
smaller in size and showed a decreased 3(H) thymidine incorporation, while β-cell specific
over expression of Reg I in NOD mice showed increased proliferation in isolated islets and
delayed the onset of diabetes (Unno et al., 2002), indicating its probable role in growth and
regeneration of insulin secreting cells.
Reduction of Reg I was linked with the pathological process involved in impaired glucose
tolerance and diabetes (Bluth et al., 2008), while administration of Reg I protein improved the
insulin secretory capability of β-cells in rat models of diabetes, indicating its role in the
managment of diabetes. REG I protein levels were found to be significantly raised in the islets
from a diabetic patient, and antibodies against Reg protein were identified which hinder β-cell
proliferation in the experimental models of diabetes (Gurr et al., 2002) and also in diabetic
patients (Shervani et al., 2004, Astorri et al., 2010), suggesting its role in the pathological
process involved in the human diabetes.
The mechanism by which Reg I protein stimulates β-cell replication and increase β-cell mass
may involve phosphorylation of transcriptional factor ATF-2 by phosphoinositide 3-kinase
(PI3K) and activation of cyclin D1 gene as inhibitors of PI3K attenuated this effect. Pospho-
ATF-2 support progression of cell cycle by phosporylation and inhibition of retinoblastoma
(Rb) protein and thus stimulates replication (Takasawa et al., 2006). In Reg knockout (R-/-
)
mouse no expression of Reg I was detected in the pancreas, while in isolated islet from the
Reg deficient mouse, rate of β-cell proliferation was reduced along with the decrease phospho-
ATF-2, cyclin D1 and phospho-Rb (figure 1.14). Pancreatic ductal and β-cells exposed to
exogenous Reg I grow by stimulation of mitogen-activated protein (MAP) kinase
phosphatases (MKP) and cyclins, and simultaneous induction of MKP-1. Reg I effects
occurred in a dose-dependent manner as high intracellular Reg I lead to decline in growth,
Chapter 1
34
Figure 1.14: Proposed Reg I protein-Reg I receptor system for ß-cell replication
(Liu et al., 2008)
Figure 1.15: Proposed model for Reg1 mechanism of action on ductal and β-cells (Mueller
et al., 2008)
Chapter 1
35
probably by binding to and inactivating MKP-1. With over expression of Reg I in rat insulinoma
cell lines, more differentiated state was observed (figure 1.15). It was therefore,
considered that relatively low levels Reg I, bind to the cell surface Reg receptor and induce
MAPK-cyclin D1 signal transduction pathway, while high levels may attenuate growth by
inactivating MKP-1, induction of apoptosis or their differentiation to other cell types (Mueller et
al., 2008). In human -cells the expression of both REG I and REG I were up regulated under
inflammatory conditions via JAK/STAT pathway (Yamauchi et al., 2015). Reg I have also been
shown to participates in islet β-cells neogenesis (Tezel et al., 2004).
1.13.4.2 Reg 2/Reg II Protein
Reg 2 protein is the member of the type II, subclass of Reg family and found only in the mice
(Unno et al., 1993), it exhibits 76% sequence homology with mReg1 and 63% with both hREG
Iα and hREG Iβ. It has been shown to be expressed in islet β-cells (Gurr et al., 2007) as well as
exocrine pancreas (Zhong et al., 2007, Luo et al., 2013). During β-cells regeneration, after partial
pancreatectomy, increased expression of Reg2, Reg3β, and Reg3 was observed in the pancreas
of both juvenile and elderly mice models (Rankin and Kushner, 2010). Increased expression of
Reg2 was also observed during the first three months of embryogenesis (Wang et al., 2011) and
after adjuvant immunotherapy in type I diabetes mice models that correlates with β-cells
regeneration, decline in insulitis and increase in insulin secretion (Huszarik et al., 2010). Both
Reg2 and Reg3β genes demonstrated a significant stimulation by glucocorticoids, signifying
their role in inflammation such as in pancreatitis (Luo et al., 2013).
Studies have revealed that mReg2 and mReg3β proteins protect insulin producing β-cells from
apoptosis. mReg2 protects β-cells against mitochondrial membrane disruption and apoptotic cell
death by inhibiting the mitochondrial caspase-dependent apoptotic signalling pathway (figure
Chapter 1
36
1.16) (Liu et al., 2010). The mReg2 also attenuates UPR induced by stress inducers by
upregulating the basal expression of the GRP78 an ER chaperone protein through Akt-mTORC1
(mammilian target of rapamycin complex I) signaling pathway (figure 1.17) (Liu et al., 2014).
1.13.4.3 Reg3/Reg III Protein
The mReg3α has been reported to be the product of α-cells of the endocrine pancreas (Gurr et
al., 2007) and stimulate islet β-cell proliferation, by activating Akt kinase/cyclin D1/CDK4 axis
(Cui et al., 2009). Pancreatic β-cell specific over expression of mReg3β protects mice from Stz-
induced β-cell damage and hyperglycemia (Xiong et al., 2011) thus having a pro survival effect
on β-cells in conditions of stress and inflammation. The role of hReg3α in regeneration of β-
cells is still under debate. mReg3δ or INGAP was reported to stimulate islet β-cell neogenesis
from ductal cells (Rafaeloff et al., 1997). An INGAP related pentadecapeptide (INGAP-PP)
similiar to amino acid 104-118 of INGAP sequence significantly increased release of insulin in
response to stimuli like glucose and amino acids (Borelli et al., 2005). Moreover, INGAP over-
expression in exocrine pancreas inhibited Stz induced β-cell death (Taylor-Fishwick et al.,
2006). Studies have shown that recombinant INGAP and INGAP-PP stimulated β-cell
proliferation by stimulating the Ras/Raf/Erk (Petropavlovskaia et al., 2012) and PI3K/Akt
signaling pathways (Jamal et al., 2005).
1.13.4.4 Reg4/Reg IV Protein
It is a separate isoform of Reg family and its expression has been documented in many tissues
including pancreas, stomach, small intestine, colon, brain and kidney (Azman et al., 2011).
Enhanced expression in gastrointestinal as well as pancreatic tumours makes REG 4 a screening
serum marker for cancers as well as inflammatory diseases (Oue et al., 2005, Takayama et
al., 2010).
Chapter 1
37
Figure 1.16: mReg2 attenuated pro-apoptotic changes induced by Stz (Liu et al., 2010)
Chapter 1
38
Figure 1.17: mReg2 inhibits ER stress driven UPR, a proposed model (Liu et al.,
Chapter 1
39
RATIONALE FOR THE STUDY
It was reported that mReg2 attenuates ER stress induced UPR and that it protects β-
cells against streptozotocin-induced mitochondrial membrane disruption and
apoptosis by inhibiting caspase mediated apoptotic signaling in β-cells (Liu et al.,
2010, Liu et al., 2014b). It was hypothesized that mReg2 may have an effect on
caspase-independent AIF-mediated cell death, it can protect against AIF-mediated
cell death by regulating Scythe and /or hsp70 independent of its ability to attenuate
UPR and inhibit caspase-signaled apoptosis.
The role of Reg Iα in β-cell proliferation and amelioration of experimental induced
diabetes have been reported in previous studies (Unno et al., 2002). This evidence
led to the present hypothesis that REG Iα proteins expression is enhanced during
both type I and type II diabetes in their effort to regenerate islet β-cells destroyed due
to autoimmunity, glucolipotoxicity and increased metabolic demand.
Chapter 1
40
OBJECTIVES OF THE STUDY
To assess if mReg2 regulate Scythe and/or hsp70 to protect against AIF-mediated
cell death.
To find out circulatory REG Iα levels in both type I and type II diabetics and
correlate them with the clinical/biochemical parameters and disease associated
complications.
To identify REG Iα gene polymorphisms and their association with type II diabetes
and its related risk factors.
Chapter 2
41
MATERIAL AND METHODS
2.1. SETTING OF THE STUDY
This study was conducted at the Department of Biochemistry and Molecular Biology and
Center for Research in Experimental and Applied Medicine (CREAM), Army Medical
College Rawalpindi, National University of Science and Technology, Islamabad; Pathology
Laboratory PNS Shifa Hospital, Karachi; Biochemistry Laboratory Zia-ud-din University,
Karachi and Fraser Laboratories, Department of Medicine, McGill University Health
Science Centre and Royal Victoria Hospital, Montreal, Quebec, Canada.
2.2. STUDY DESIGN
The study protocol was subdivided into two parts:
Study 1 was in vitro study involving tissue culture techniques using pancreatic β-cell lines.
Study 2 was a cross sectional comparative study utilizing human blood samples.
2.2.1. Study 1
The regulation of Scythe and hsp70 by mReg2 to protect against AIF-mediated cell death, in
MIN6 cell lines, was compared by treating MIN6-VC and MIN6-mReg2 transfected cells
with doxorubicin. This study was carried out at Fraser Laboratories, Department of
Medicine, McGill University Health Science Centre and Royal Victoria Hospital, Montreal,
Quebec, Canada.
2.2.2. Study 2
a) The measurement of serum REG Iα levels in diabetic patients and control subjects as a
Chapter 2
42
biomarker for β-cell damage and correlating them with their biochemical parameters. This
part of study was carried out in Center for Research in Experimental and Applied Medicine,
Army Medical College Rawalpindi.
b) To detect polymorphisms in REG Iα gene and their association with diabetes. This part of
the study was carried out in Pathology Laboratory, PNS Shifa Hospital and Biochemistry
Laboratory, Zia-ud-din University, Karachi.
2.3. ETHICAL APPROVAL
The Ethical Committee of Army Medical College, National University of Science and
Technology, Islamabad approved the study protocol. The study was conducted according to
the Good Clinical Practices as approved by FDA, 1996 (FDA, 1996) and Declaration of
Helsinki (WMA, 2000).
2.4. DURATION OF STUDY
The duration of study was 4 years.
2.5. SAMPLE SIZE
1. MIN6 cell lines transfected with empty vector (MIN6-VC) and mReg2 (MIN6-mReg2)
were used in Canada.
2. Type 1 diabetic patients (n=10) and type 2 diabetic patients (n=60) were recruited from
medical OPD of PNS Shifa Hospital, diagnosed by classified medical specialist
according to ADA criteria and healthy individuals (controls) (n=20) were selected for
comparison from among friends and relatives with no family history of the disease.
Chapter 2
43
2.6 INCLUSION CRITERIA
Patients of both type I and II diabetes diagnosed under ADA criteria were included. Among
the selected diabetics, patients with known diabetic complications were also included in the
study. Macro vascular complications included ischemic heart disease (IHD) and diabetic
foot. While micro vascular complication included diabetic nephropathy, retinopathy, and
neuropathy. Diabetic patients with cataract and skin manifestations like carbuncle were also
included in the study. Diabetic patients with IHD were diagnosed on the basis of clinical
findings and investigations like electrocardiography (ECG), angiography and thallium scan.
Diabetic foot and neuropathy were diagnosed on the basis of clinical examination,
electromyography (EMG) and nerve conduction studies (NCS). Patients with nephropathy
had more than double the normal levels of 24 h urinary protein, decreased creatinine
clearance and deranged renal function tests (RFTs). For diagnosis of diabetic retinopathy,
clinical examination and fundoscopy was done. Cataract was diagnosed on the basis of
ophthalmological examination by slit lamp. Patient with carbuncle was diagnosed on the
basis of dermatological examination.
2.7. EXCLUSION CRITERIA
Patients with significant co-morbidities like chronic liver disease, GIT disease, Alzhiemer’s
disease and cancers were excluded. Pregnant women were also excluded from the study.
2.8. SAMPLING TECHNIQUE
Non-probability convenience sampling was carried out after informing the study subjects
about the purpose of the study and a written consent was taken prior to sampling.
Chapter 2
44
2.9. SAMPLE COLLECTION
Under aseptic techniques 10 mL venous blood sample of diagnosed diabetes mellitus type I
and type II patients and healthy controls was collected after an overnight fast. A total of 4
mL blood was transferred to clot activator tubes and serum was separated, and stored at -
80°C in aliquots for ELISA. Rest of the blood was transferred to test tubes containing EDTA
for measurement of FBG, HbA1c and for DNA extraction either immediately or stored at 2
to 8°C for use within a week. All the tubes were labeled with the given ID of the patient and
the date of sampling.
STUDY 1
2.10. CELL CULTURE
Materials
MIN6-VC and MIN6-mReg2 cells, Dulbecco´s Modified Eagle’s Medium (DMEM) (Gibco,
Invitrogen), 10% Fetal Bovine Serum (FBS) heat inactivated, β-Mercaptoethanol, 1%
Antibiotic–Antimycotic (A/A), IX Dulbecco´s Phosphate Buffered Saline (DPBS) pH 7.4,
IX Trypsin + EDTA (Gibco, Invitrogen), hot water bath, centrifuge, incubator with 5% CO2,
microscope, pipettes, micropipettes, 70% ethanol and 10cm culture/petri dishes.
2.10.1 MIN6-VC and MIN6-mReg2 Transfected Cells
Pancreatic β-cell lines (MIN6 cells) procured from Dr Jun-ichi Miyazaki (Osaka University,
Japan) (Miyazaki et al., 1990) were transfected with mReg2 vector and empty vector using
lipofectamine 2000 by Liu et al (Invitrogen, Carlsbad, CA). RT-PCR and immunoblot
analysis were done to assess the overexpression of Reg2 in (MIN6-mReg2) transfected cells
compared to cells transfected with empty vector (MIN6-VC) (Liu et al., 2010) (figure 2.1).
Chapter 2
45
Figure 2.1: MIN6 cells transfected with pc DNA3.1 expression vector containing mReg2
Chapter 2
46
The cells were kept in DMEM containing 10% FBS, 60 mM β-mercaptoethanol, and 1%
A/A at 37°C, 5% CO2 in a humidified atmosphere or stored in cryovials in liquid nitrogen
tank.
2.10.2 Thawing of MIN6-VC and MIN6-mReg2 Cells
Method
MIN6-VC and MIN6-mReg2 transfected cells kept in cryovials were taken out from liquid
nitrogen tank and the base of cryovials was dipped in the hot water bath, at 37˚C, till fluid.
The vials were dried up with tissue paper and then cleaned with 70% ethanol before placing
in the hood to prevent contamination. In the tissue culture hood, 1 mL of thawed cells was
added to 9 mL pre-warmed culture media in 15 mL tubes. The tubes were centrifuged at
speed of 1000 rpm for 5 min at RT. Supernatant was removed and the cells were re-
suspended in 1 mL complete media [DMEM (500 mL) containing 10% FBS (50 mL), 60
mM β-mercaptoethanol (1.1 µL), and 1% A/A (5.5 mL)]. This mixture was added to the 10
cm petri dish containing 10 mL complete media. The cells were mixed slowly by to and fro
movement of the dish. After confirming the even distribution of the cells under the
microscope they were placed in the humidified incubator at 37˚C and 5% CO2.
2.10.3 Changing the Media
After every 48 h (hours), media was removed by suction and cells were washed twice with
10 mL PBS. A 10 mL pre-warmed fresh media was added to the cells in the petri-dish and
placed back in the incubator.
2.10.4 Subculturing of MIN6-VC and MIN6-mReg2 Cells
Both types of cells were sub-cultured separately. On about the 5th
-6th
day when the cells had
Chapter 2
47
reached 70-80% confluency, they were taken out from the incubator. After looking at the
cell morphology under the microscope the cells were sub-cultured as follows:
The media was removed by suction. The cells were washed twice with IX PBS (10
mL/plate). The PBS was removed by suction and the cells were covered with IX trypsin (2.5
mL/plate) and incubated at 37°C for 5 min. New plates were labeled and prepared with fresh
media (10 mL/plate). The cells with trypsin (& media) were washed and combined into 15
mL tubes and centrifuged at speed of 1000 rpm for 5 min. Supernatant was discarded by
suction and the pellet was carefully re-suspended in 2 mL to 5 mL fresh media (mixing
should be thorough to avoid clumps formation). Cells were plated at a density of 2 x 104
cells/cm2
and were spread evenly by to and fro movement and verified under the
microscope. Sub-cultured plates were returned to 37°C incubator. A total of 16 plates were
prepared, 8 with MIN6-VC and the 8 with MIN6 mReg2 cells.
2.10.5 Changing the Media
The media was changed as described above.
2.10.6 Treatment with Doxorubicin
Treatment for 6 h, 12 h and 24 h was done followed by nuclear and cytoplasmic
fractionation.
Material
Doxorubicin (Cat. No. 324380, Calbiochem USA)
Preparation of 1uM Doxorubicin
Stock Solution =10 mg/mL, Mol. wt. of Doxorubicin = 543.5 g/L or 543.5 mg/mL
(10 mg/mL) / (543.5 mg/mL) = 0.0184 M or 18.4 mM
Chapter 2
48
For 1 mM working stock solution
1 part (0.1 mL) stock solution in 17 parts (1.7 mL) fresh media = 1.8 mL (1 mM)
For 1 µM working solution
Using C1V1 = C2V2 (1 mM = 1000 µM)
V1 = (1 µM x 25 mL) / (1000 µM) = 0.025 ml = 25 µL
25 µL of 1 mM working stock solution and 25 mL media (1 µM)
Or 50 µL of 1mM working stock solution and 50 mL media (1 µM)
Method
1 µM doxorubicin was filtered and used.
Plating of cells
Inside the Cell Culture Hood, cells were plated for 60-70% confluency in 10 cm plates as
shown in table 2.1.
Table 2.1: Plating of MIN6-VC and MIN6-mReg2 cells with and without treatment with
doxorubicin
Type of Cells Untreated
(No. of plates)
Treatment with doxorubicin (1 µM) (No. of plates)
24 h 12 h 06 h
MIN6-VC 2 2 2 2
MIN6-mReg2 2 2 2 2
Chapter 2
49
2.10.7. Cell Fractionation
Cytosolic to nuclear fractionation was carried out using protocol from Dr Heather P.
Harding (from Dr David Ron lab, Cambridge Institute for Medical Research).
Material
Cell Scraper, 15 mL tubes, 1.5 mL micro-centrifuge tubes, IX DPBS from Life Technologies,
centrifuge machine for micro-centrifuge tubes (eppendorf), micropipette, vortex.
Table 2.2: Composition of Solutions for Cell Fractionation
A. Harvest Buffer For 50 mL For 20 mL For 10 mL
10 mM HEPES pH
7.9
0.5 mL 1 M HEPES 0.2 mL 1 M
HEPES
0.1 mL 1 M
HEPES
50 mM NaCl 0.5 mL 5 M NaCl 0.2 mL 5 M NaCl 0.1 mL 5 M NaCl
0.5 M Sucrose 8.56 g Sucrose 3.42 g Sucrose 1.71 g Sucrose
0.1 mM EDTA 10 mL 0.5 M EDTA 4 mL 0.5 M
EDTA
2 mL 0.5 M EDTA
0.5% Triton-X100 2.5 mL 10% Triton-
X100
1 mL 10%
Triton-X100
0.5 mL 10%
Triton-X100
*Following solutions were freshly added to the harvest buffer immediately before use:
*1 mM DTT 50 µL 20 µL 10 µL
*17.5 mM β-
Glycerophosphate
0.189 g 0.0756 g 0.0378 g
*1X Protease
Inhibitor Cocktail
(Roche Diagnostics,
Indianapolis, IN)
500 µL 200 µL 100 µL
B. Buffer A For 50 mL For 20 mL For 10 mL
10 mM HEPES pH
7.9
0.5 mL 1 M HEPES 0.2 mL 1 M
HEPES
0.1 mL 1 M
HEPES
10 mM KCL 500 µL 1 M KCL 200 µL 1 M KCL 100 µL 1 M KCL
0.1 mM EDTA
10 µL 0.5 M EDTA 4 µL 0.5 M
EDTA
2 µL 0.5 M EDTA
0.1 mM EGTA
20 µL 0.25 M
EGTA
8 µL 0.25 M
EGTA
4 µL 0.25 M
EGTA
Chapter 2
50
*Following solutions were freshly added to the harvest buffer immediately before use:
*1 mM DTT 50 µL 20 µL 10 µL
*1X Protease
Inhibitor Cocktail
500 µL 200 µL 100 µL
C. Buffer C For 50 mL IX For 20 mL IX For 10 mL IX
10 mM HEPES pH
7.9
0.5 mL 1 M HEPES 0.2 mL 1 M
HEPES
0.1 mL 1 M
HEPES
500 mM NaCl 5 mL 5 M NaCl 2 mL 5 M NaCl 1 mL 5 M NaCl
0.1 mM EDTA 10 µL 0.5 M EDTA 4 µL 0.5 M
EDTA
2 µL 0.5 M EDTA
0.1 mM EGTA 20 µL 0.25 M
EGTA
4 µL 0.25 M
EGTA
2 µL 0.25 M
EGTA
0.1% NP40
500 mL 10% NP40 200 mL 10%
NP40
100 mL 10% NP40
*Following solutions were freshly added to the harvest buffer immediately before use:
*1 mM DTT 50 µL
20 µL 10 µL
*1X Protease
Inhibitor Cocktail
500 µL 200 µL 100 µL
D. Ice cold PBS and
PBS-1mM EDTA
Method
MIN6-VC and MIN6-mReg2 were sub-cultured in 10 cm plates at a density of 2 x 104
cells/cm2 and allowed to reach 70% confluency. Both cell types were treated with 1 µM
doxorubicin for the times indicated. Two 10 cm plates for each treatment were taken for cell
fractionation. The whole procedure was carried out on ice unless indicated otherwise. Cells
were scraped with cell scraper and collected in a 15 mL tube along with the media, for each
treatment. Centrifuged, at the speed of 1000 rpm for 5 min and supernatant was discarded by
suction. Pellet in each case was re-suspended in 1 mL PBS and transferred to 1.5 mL micro-
centrifuge tube and was washed two times with 1 mL chilled PBS at 3000 rpm for
approximately 5 min at 4°C. Pellets were re-suspended in 450 µL Harvest Buffer. Incubated
Chapter 2
51
on ice for 5 min and centrifuged at 1000 rpm in swinging bucket rotor for 10 mins at 4°C to
pellet nuclei. Supernatant was then transferred to fresh labeled 1.5 mL micro centrifuge
tubes and centrifuged at 13,000 rpm for 15 min. The clear supernatant was transferred to
fresh labeled 1.5 mL micro-centrifuge tubes, this contains the cytoplasmic fraction.
Now the nuclei pellet that was collected previously was washed and re-suspended in 500 µL
Buffer A and centrifuged at 1000 rpm for 5-10 min. Supernatant was again discarded, and
the nuclei pellet re-suspended in 4 vol of Buffer C and vortex for 15-30 min at 4°C in cooled
room using high speed in the beginning followed by medium speed. The tubes were
centrifuged at 13,000 rpm for 10 min at 4°C. Supernatant now containing the nuclear
fraction was transferred to fresh labeled 1.5 mL micro-centrifuge tubes and stored at -20°C.
Protein concentrations of cytoplasmic and nuclear extracts were calculated using BCA
Assay.
2.10.8 BCA Assay
Protein quantification was done using protocol given in the Pierce™ BCA Protein Assay
Kit (Brown et al., 1989, Smith et al., 1985).
Material
PierceTM
Microplate BCA Protein Assay Kit by life sciences (Catalog No: 23227), 96-Well
Microplates, Enspire 2300 Multilabel Plate Reader by Perkin Elmer, 15 mL tubes, pipettes,
micropipettes and aluminium foil.
Reagent A, (250 mL)
Sodium carbonate (Na2CO3), sodium bicarbonate (NaHCO3), bicinchoninic acid (BCA) and
sodium tartarate in 0.1M sodium hydroxide (Oue et al., 2005).
Chapter 2
52
Reagent B, (25 mL)
Four percent cupric sulfate (CuSO4)
Reconstitution Buffer, (15 mL)
Albumin Standard, (2 mg/mL, 10 × 1 mL ampoules)
Bovine serum albumin (BSA) in 0.9% NaCl and 0.05% sodium azide (NaN3)
Method
Cytosol and nuclear lysates were quantitated, using BCA Assay. Standard dilutions were
prepared as shown in table 2.3. Every sample was run in duplicate in 96-well microplate.
Table 2.3: Preparation of diluted BSA standards
Vial Volume of Diluent (µL) Volume of BSA (µL) Final BSA
Concentration (µg/mL)
A 0 300 of stock 2000
B 125 375 of stock 1500
C 325 325 of stock 1000
D 175 175 of B 750
E 325 325 of C 500
F 325 325 of E 250
G 325 325 of F 125
H 400 100 of G 25
I 400 0 0
BSA working reagent was prepared as follows (Vol. of working reagent = (# of standards +
# of unknowns) x (# of replicates) x 200 µL/sample). Reagent A and Reagent B were mixed
by vortexing (Vol. of A: Vol. of B = 50: 1). A total of 10 µL of standards and unknown
protein samples (cytoplasmic and nuclear fraction) were added to 96 well plates in
duplicates (Vol. of sample: Vol. of working reagent = 1: 20). Working reagent was then
Chapter 2
53
added to each well containing unknown sample (200 µL) and mixed thoroughly by placing
the plate on plate shaker for 30 sec. Plate was covered with aluminum foil and placed in
small lab incubator at 37◦C for 30 min and then brought to RT and absorbance was
measured by using plate reader at 562 nm. To obtain the standard curve the average blank
corrected value at 560 nm for each BSA standard was plotted against the unknown sample
concentration (μg/mL). Amount of unknown sample needs to be loaded for SDS-PAGE was
calculated using the standard curve obtained (>5 ng of protein per well; <50 µL).
2.10.9 SDS PAGE: Sample Preparation
Material
Hot water bath, vortex, 1.5 mL micro-centrifuge tubes, micropipettes and floater.
Laemmli Buffer Solution
Laemmli buffer solution was prepared by adding 2X laemmli buffer (Bio-Rad, Catalog No:
161-0737) to β-mercaptoethanol (5%).
Method
Samples stored at -20°C freezer, were taken out, thawed and mixed with Laemelli buffer
solution (1:1 or 1:2), vortexed for 5 sec, heated by placing the floaters with the sample in hot
water bath at 95°C for 5-10 min and spinned.
2.10.10 SDS Page
Materials
Resolving Gel Buffer (Bio-Rad, Catalog No: 161-0798), Stacking Gel Buffer (Bio-Rad,
Catalog No: 161-0799), 30% Acrylamide Bis, 10% Sodium Dodecyl Sulphate (SDS)
(freshly prepared), 10% Ammonim Per Sulfate (APS) 0.1 g (freshly prepared), TEMED
(Tetramethylethylenediamine) (UltraPure, Invitrogen, Catalog No: 15524-010), Glycine,
Chapter 2
54
Tris Base, ddH2O, Well Combs 1.5 mm, Gel Cassette, Casting Stand, Spacer Plate,
Absorbent papers, Nitrocellulose Gel blot paper (Bio-Rad), Electrode Assembly, Prestained
Protein Ladder, 10-250 kDa (Thermo Scientific, life sciences, Catalog No: 26619)
Table 2.4: Solution Preparation for SDS-PAGE (per gel)
Resolving Gel (10%) Resolving Gel (7.5%) Stacking Gel
4.0 mL ddH2O 4.85 mL ddH2O *6.1 mL ddH2O
2.5 mL 1.5M Tris HCl pH 8.8 2.5 mL 1.5M Tris HCl pH 8.8 *2.5 mL 0.5M Tris HCl pH
6.8
3.3 mL 30% Acrylamide Bis 2.5 mL 30% Acrylamide Bis 1.3 mL 30% Acrylamide Bis
100 µL 10% SDS 100 µL 10% SDS 100 µL 10% SDS
50 µL 10% APS 50 µL 10% APS 50 µL 10% APS
10 µL TEMED 10 µL TEMED 10 µL TEMED
Preparation of 5X Running Buffer: Stock Solution (Freshly prepared running buffer was
used for inner chamber)
15g Tris base
72g Glycine
5g SDS
Added ddH2O to prepare final volume 1000 mL
To prepare IX Running Buffer: Working Solution
100 mL 5X was diluted with 400 mL ddH2O
Method
SDS-PAGE was carried out according to protocol by Lamellae (Laemmli et al., 1970), using
10% and 7.5% resolving gels. After setting up the gel apparatus, freshly prepared resolving
gel was poured into the setup (APS and TEMED were added last as they initiate
Chapter 2
55
polymerization). Distilled water (ddH2O) was slowly added to the interface of the resolving
gel to avoid contact of air with gel and allowed to polymerize for 20-30 min. After
polymerization, water layer was removed by suction or absorbent paper. Freshly prepared
stacking gel was immediately poured on top of the resolving gel and a 10 well containing
1.5 mm comb was quickly inserted into the stacking gel. After 15-20 min, comb was gently
removed and the 1X running buffer was added. Using prepared samples (30 µL) and protein
ladder (10 µL), loading was done. The gel was electrophoresed at constant volts default
setting of 90 volts for 1h 30 min to 2 h till the dye reached the very bottom of the gel.
2.10.11 SDS PAGE Transfer and Western Blot
Material
Transfer apparatus, red and black plastic case, cardboard pads, blotting pads, nitrocellulose
membrane, power supply.
Solution Preparation
Transfer Buffer
A total of 6.05 g Tris base, 28.5 g Glycine, 300 mL methanol (added last of all), ddH2O
added to make the final volume 2000 mL (2L)
Method
The transfer apparatus was set in the ice and gel with nitrocellulose membrane, was moisten
in the transfer buffer and set in sandwich form in the red and black plastic case as follows
(blotting pads, cardboard pads, gel, nitrocellulose membrane, cardboard pads, blotting pads),
while assembling transfer apparatus, special care was taken to get rid of air bubbles. The
Chapter 2
56
apparatus was filled with fresh transfer buffer and was run transfer, submerged in ice at 0.2
A (200 mA), for 1 h 30 min.
Incubation of membrane with antibodies
Materials
Alpha InnotechFluorChem 8800 Imager (San Leandro, CA), shaker, ECL Select (GE
Healthcare, RPN2235)
Table 2.5: Solution Preparation for western blotting
TBST (1L) Blocking Buffer Stripping buffer (100 mL)
10 g NaCl 10 mL TBST buffer 10 mL 0.5M Tris pH 6.8
20 mM 1.0M Tris-HCl (pH 7.5) 0.5 g milk powder (5% milk) /
0.2 g milk powder (2% milk) 20 mL 10% SDS
0.12% Tween 20 750 µL β-mercaptoethanol
dH2O was added to make the
final volume 1L 69 mL ddH2O
Table 2.6: Primary and Secondary Antibodies used with their dilutions prepared
Pri
mary
An
tib
od
ies
Antibody Cat No. Company Mol. Weight Dilution
AIF 04-430 Millipore 67 kDa 1:2000
Scythe/BAT3 AB-10517 Millipore 120 kDa 1:10,000
Hsp70 sc32239 SantaCruz 70 kDa 1:5000
USF2 (C-20) sc-862/12308 SantaCruz 44 kDa 1:200
Actin MM-0164-P Medimabs 42 kDa 1:1000
Tubulin MA5-11732 Thermo scientific 55 kDa 0.5-1.0
µg/mL
Sec
on
dary
An
tib
od
ies
Antibody Cat No. Company Dilution
Goat Anti-Mouse Antibody, HRP
conjugate (Mouse IgG (H+L)
Polyclonal Secondary Antibody)
32430 Thermo scientific 1:1000
Goat Anti-Rabbit Antibody HRP
conjugate (Rabbit IgG (H+L)
Polyclonal Secondary Antibody)
32460 Thermo scientific 1:1000
Chapter 2
57
Method
The nitrocellulose membrane was washed twice with TBST and then put in 10 mL blocking
buffer for 60 min, on a shaker. The blocking buffer was discarded and the membrane was
washed quickly with TBST. A 10 mL primary antibody solution (prepared in fresh milk)
was added, kept on shaker at RT for 2 h or at 4˚C overnight. After removing the primary
antibody solution, the membrane was washed with TBST 3 times for 10 min. This was
followed by treatment with secondary antibody solution (prepared in fresh milk), for 1 h and
30 min, at RT on shaker. After removing the secondary antibody solution, washing step was
repeated. Light-sensitive 300 µL of peroxide solution and 300 µL of luminol solution (ECL
Select) were mixed in a 1.5 mL micro-centrifuge tube covered with aluminum foil. The
membrane was covered near the expected band with the freshly prepared ECL solution and
exposed for different time periods as per requirement (in the dark).
Protein bands were imaged under ultravoilet (UV) light and signals captured using Alpha
InnotechFluorChem 8800 Imager. After capturing the signals, the membrane was quickly
washed with TBST, kept in stripping buffer for 15 min in water bath at 50°C, shaking
continuously. After removing the stripping buffer the membrane was washed again with
TBST 3 times for 10 min each, blocked and blotted with actin/tubulin using above
mentioned protocol.
Chapter 2
58
STUDY 2
2.11. BIOCHEMICAL TESTS
2.11.1 Fasting Blood Glucose
Principle
In the hexokinase (HK) method, hexokinase phosphorylates glucose to glucose-6-phosphate
by using adenosine triphosphate (ATP). Glucose-6-phosphate formed is then oxidized to
gluconate-6-phosphate by using glucose-6-phosphate dehydrogenase (G6PD) in the
presence of NADP to form NADPH. The rate of NADPH formation during the given
reaction is equivalent to the glucose concentration in the sample and is measured
photometrically (Schmidt., 1961)
Glucose + ATP Glucose-6-phosphate + ADP
Glucose-6-phosphate + NADP+ Gluconate-6-phosphate + NADPH + H
+
Reagent Composition
R1 Monoreagent: TRISa
buffer: 100 mmol/L, ph 7.8; Mg2+
: 4 mmol/L; ATP > mmol/L;
NADP > 1.0 mmol/L and preservative.
R2 Monoreagent: HEPESb
buffer: 30 mmol/L, ph 7.0; Mg2+
: 4 mmol/L; HK (yeast) > 8.3
U/mL; G-6-PDH (E.coli) > 15 U/mL and preservative.
a. TRIS = Tris(hydroxymethyl)-aminomethane
b. HEPES = 2-[4-(2-hydroxyethyl)-1-piperazine]-ethane sulfonic acid
HK
G6PD
Chapter 2
59
CAL Glucose Standard:
Glucose: 100 mg/dL (5.55 mmol/L)
Reagent Preparation
The reagents and the standard were ready to use.
Samples
Blood samples collected in heparinized or EDTA tubes after an overnight fast.
Materials Required
Roche/Hitachi Modular P-800 Analyzer (Cobas®), centrifuge, micropipettes and other
laboratory equipments.
Procedure
(Glucose MR Enzymatic Colorimetric Kit Method (End Point) by Linear Chemicals)
The reagents and samples were brought to the RT before use. The samples were prepared
according to kit manual instructions and run on auto-analyzer (Modular P-800, USA).
Absorbance for the samples and standards was set at 505 nm against the reagent blank.
Calculation of the Results
(A sample /A standard x C standard (100)) = mg/dL of glucose
Conversion Factor:
Glucose mg/dL x 0.05551 = Glucose mmol/L
The glucose was measured in mmol/L by Modular P auto-analyzer.
Chapter 2
60
Reference Values
Adults: 4.11 – 5.89 mmol/L or 74 – 106 mg/ dL (Tietz, 1995)
2.11.2 Glycosylated Haemoglobin (HbA1c)
PRINCIPLE (Turbidimetric Inhibition Immunoassay (TINIA) for hemolyzed whole blood)
This test uses Tetradecyltrimethylammonium bromide (TTAB) as the detergent in the
hemolyzing reagent in order to abolish any interference from leukocytes/WBCs as TTAB
prevent lyses of WBCs (Karl et al., 1993).
Hemoglobin A1c
The estimation of HbA1c is based on the turbidimetric inhibition immunoassay (TINIA) as
is carried out on hemolyzed whole blood.
Addition of R1 (buffer/antibody) to the Sample:
HbA1c contained in the sample reacts with anti-HbA1c antibody to form soluble antigen-
antibody complexes. Since the HbA1c molecule contains only one specific HbA1c antibody
site, formation of insoluble complexes does not take place.
Addition of R2 (buffer/polyhapten):
The polyhaptens added react with excess anti-HbA1c antibodies to form insoluble
complexes of antibodies with polyhaptens which are estimated turbidimetrically.
Reagent Composition
HbA1c
Chapter 2
61
R1 (Antibody Reagent)
MES buffer: 0.025 mol/L; TRIS buffer: 0.015 mol/L, pH 6.2; HbA1c antibody (ovine
serum): > 0.5 mg/mL; detergent; stabilizers and preservatives.
R2 (Polyhapten Reagent)
MES buffer: 0.025 mol/L; TRIS buffer: 0.015 mol/L, pH 6.2; HbA1c polyhapten: > 8
ug/mL; detergent; stabilizers and preservatives.
Hemoglobin
R3
Phosphate buffer: 0.02 mol/L, pH 7.4 and stabilizers.
Preparation of Reagents
The monoreagents and the standard were ready to use.
Samples
Whole blood collected in EDTA tubes under aseptic techniques.
Material Required
Roche/Hitachi Modular P-800 Analyzer (Cobas®), centrifuge, micropipettes and other
laboratory equipments.
Procedure
Hemolysate Preparation
The blood specimen and hemolyzing reagent for HbA1c were equilibrated at room
temperature prior to use. Each sample was mixed moderately, before pipetting to ensure a
Chapter 2
62
uniform mixture of RBCs and to avoid the formation of foam. The samples were diluted in
the ratio 1:101 (1 part to 100 parts) with the hemolyzing reagent (1+100). Mixing was done
either by gentle swirling or by using vibration mixer. After approximately 1-2 min the
hemolysate changed color from red to brownish-green and was ready to be used.
The samples were prepared according to kit manual instructions and run on auto-analyzer
(Modular P-800, USA). Absorbance for the samples and standards was set at two
wavelengths 340 and 405 nm against the reagent blank.
Calculations
% HbA1c according to NGSP*/DCCT*:
Correction formula was applied to calculate HbA1c concentration in percent.
HbA1c (%) = (9.15 x ) + 2.15
*Glycohemoglobin Standardization Program (NGSP) and standardized or traceable to the
*Diabetes Control and Complications Trial (DCCT)
Reference Values (Cohen et al., 2010)
Non diabetic individuals: HbA1c: 5.7-6.4 percent (pre diabetics), HbA1c: > 6.5 percent
(diagnostic criteria for diabetes)
Diabetic individuals: HbA1c: 5.5-6.7 percent (good control), HbA1c: 6.8-7.7 percent (fair
control), HbA1c: Above 7.7 percent (poor control)
Hb [mg/dL]
[mmol/L]
[mmol/L]
HbA1c [mmol/L]
Chapter 2
63
2.12 REGENERATING ISLET DERIVED PROTEIN 1 ALPHA (REG Iα)
BIOASSAY
Principle
The assay of REG Iα was performed using Regenerating Islet Derived Protein 1 Alpha
(REG Iα) BioAssay ELISA kit (Human) by US Biological, Life Sciences (catalog number:
027821), intended for research use only. It is based on quantitative sandwich enzyme
immunoassay technique. The wells of the microtiter strips provided in this kit were
precoated with REG Iα specific antibody. Samples and standards along with biotin-
conjugated antibodies specific to REG Iα were added to the appropriate wells. Next, each
well was incubated with avidin conjugated horseradish peroxidase (HRP). With the addition
of 3,3',5,5'-tetramethylbenzidine (TMB) substrate solution a change in color was observed in
only those wells that contain REG Iα. Stop solution containing sulphuric acid was added to
terminate the enzyme-substrate reaction and the change in color was measured at a
wavelength of 450nm + 10nm by spectrophotometer. The concentration of REG Iα in the
sample was then determined by comparing the optical density (O.D) of the sample to the
standard curve.
Material Provided/Kit Components
REG Iα Antibody Precoated Microtiter Strips (1 x 96 wells), Standard (2 x 1 vials), Standard
Diluent (1 x 20 mL), Detection Reagent A (green) (1 x 120 uL), Detection Reagent B (red)
(1 x 120 uL), Assay Diluent A (1 x 12 mL), Assay Diluent B (1 x 12 mL), TMB Substrate
(1 x 9 mL), Stop Solution (1 x 6 mL), Wash Buffer, 30X (1 x 20 mL).
Chapter 2
64
Material Required But Not Provided
Deionized or distilled water (ddH2O), PBS 0.01M (pH 7.0 - 7.2), microplate reader having
filter 450 + 10 nm, Accurately measuring single or multi-channel pipettes with disposable
tips, 1.5 mL microcentrifuge tubes for diluting samples, aluminium foil, absorbent paper for
blotting the microtiter plate, container for wash solution and other laboratory equipments.
Sample Collection and Storage
Serum
Blood sample collected in the clot activator tubes was allowed to clot for two hours at RT
before centrifugation for 20 min at approximately 1000 x g. Serum was transferred to 1.5
mL micro-centrifuge tubes.
Storage and Stability
Samples were stored in aliquots at -20°C or -80°C until the test was performed.
Reagent Preparation
All components of the kit and serum samples were brought to RT prior to use.
Standard
The standard was reconstituted with 1.0 mL of standard diluent and left standing for approx.
10 min at RT; shaken gently to avoid bubble formation. The stock solution thus formed has
the concentration 40,000 pg/mL. The stock solution was diluted to 4000 pg/mL and the
diluted standard was then used to prepare a double dilution series as shown in the table 2.7
given below. Thorough mixing was done before the next transfer. A total of 7 points were
set up for the standard curve: 4000 pg/mL, 2000 pg/mL, 1000 pg/mL, 500 pg/mL, 250
Chapter 2
65
pg/mL, 125 pg/mL, 62.5 pg/mL and the last tube was the blank with standard diluents at 0
pg/mL.
Table 2.7: Reconstitution of Standard Double Dilution Series from Standard Stock Solution
and Standard Diluent
Tube #
Stock
Vol. Standard
40,000pg (lyo.)
Vol. Std. Diluent
1000 µL
Final Concentration
40,000 pg/mL
1 100 µL from Stock 900 µL 4000 pg/mL
2 500 µL from Tube 1 500 µL 2000 pg/mL
3 500 µL from Tube 2 500 µL 1000 pg/mL
4 500 µL from Tube 3 500 µL 500 pg/mL
5 500 µL from Tube 4 500 µL 250 pg/mL
6 500 µL from Tube 5 500 µL 125 pg/mL
7 500 µL from Tube 6 500 µL 62.5 pg/mL
8 0 µL 500 µL 0 pg/mL
Detection Reagent A (green) and Detection Reagent B (red)
Stock detection A and detection B were subjected to centrifugation before use. They were
diluted to the working concentration (1:100) with assay diluent A and assay diluent B,
respectively.
Wash Buffer, (30X)
To prepare 600 mL of 1X Wash Buffer 20 mL of 30X Wash Buffer was diluted with 580
mL of ddH2O.
TMB Substrate
The required amount of the solution was aspirated with sterilized tips and special care was
taken not to put the remaining solution into the vial again.
Chapter 2
66
Sample Preparation
Serum samples required about a 500-fold dilution. The 500-fold dilution scheme used is as
follows: A total of 20 µL sample was diluted with 180 µL PBS to yield a 1:10 dilution. 10
µL of the resulting dilution + 490 µL PBS yielded a 1:500 dilution. Thorough mixing was
done at each stage. Samples were diluted with 0.01M PBS (pH 7.0-7.2).
Assay Procedure
A total of 100 μL each of standards, blank and samples were pipetted in duplicates into the
wells of the microtiter strips coated with REG Iα specific antibody. The plate with micotiter
strips was covered with plate sealer before incubation at 37°C for 2 h. After incubation, the
liquid from each well was removed by inverting, taking care to prevent the sample from one
well to flow into another; washing was not done at this stage. Each well was then incubated
with 100 µL detection reagent A working solution (Biotin-conjugated antibody specific to
REG Iα) for 1 h at 37 °C after casing it with the plate sealer. After this the solution was
aspirated and each well was washed 3-times with 1X wash solution (0.35 mL per well) using
a multi-channel pipette. The microwell plate was inverted between each wash to empty the
fluid. During the third wash after inverting the plate to remove the fluid, the remaining
liquid was removed by snapping the plate onto the absorbent paper. After washing, detection
reagent B working solution (Avidin-conjugated with HRP) (100 µL) was added to each well,
covered with the plate sealer and incubated for 30 min at 37 °C. Aspiration and wash
process were repeated as mentioned above. This time washing was done 5-times with wash
solution.
TMB substrate (90 µL) was then added to each well and after covering it with the plate
sealer it was incubated for 15 min at 37 °C. Aluminum foil was used to protect the wells
Chapter 2
67
from light as addition of substrate solution turned the liquid blue. Stop solution (acid
solution) (50µl) was added to each well to stop the enzyme reaction. The acid was added to
the wells in the same order as the substrate solution was added to the wells. The liquid
turned yellow on addition of the stop solution, the liquid was mixed by gently tapping the
side of the plate to ensure uniform mixing. After removing any drop of water or fingerprints
at the bottom of the plate and bubbles from the surface of the solution, reading was done at
wavelength of 450 nm with a microplate reader/spectrophotometer. For best results the O.D
values were measured immediately after the stop solution was added. Calibration was done
with the help of calibration curve.
Notes
Only freshly prepared samples and standards were used. The pipette tips were replaced by
fresh ones between addition of standards, samples and reagents in order to avoid cross
contamination. During incubations it was ensured to properly cover the plate with plate
sealers. During washing, complete removal of wash buffer at each step was ensured for
high-quality performance.
Calculation of Results
The mean O.D. values were calculated for the duplicates of the standards and the samples,
and then subtracted from mean zero standard (blank) O.D value. A standard curve was
obtained by plotting the mean O.D. value of the standard on x-axis and known
concentrations of standards on y-axis. They may be plotted on a linear, semi-log or log-log
graph. A line was drawn to connect the points. Using the standard curve the patients’
relative values were determined. The results were reported as the REG Iα (pg/mL)
concentration in samples. The exact amount of REG Iα in the serum sample was estimated
Chapter 2
68
by multiplying the test result by dilution factor 500 (e. g., 62.5 pg/ ml X 500 = 31250
pg/ml). Readings were converted to ng/ml after multiplying by 0.001 (31250 X 0.001 =
31.25). Sensitivity of the assay was assessed by the limit of detection, which is defined as
the lowest concentration of the analyte that can be reliably detected with a given analytical
method. The antibodies used in this ELISA were specific for human REG Iα with no
detectable cross reactivities to recombinant human Reg1 β, PAP, and Reg IV.
Detection Range:
According to the kit the detection range was 62.5-4000 pg/mL.
Sensitivity:
The minimum detectable dose of REG Iα is usually less than 27.2 pg/mL. The sensitivity of
this assay, or Lower Limit of Detection (LLD) was defined as the lowest protein
concentration that could be differentiated from zero.
Specificity
This assay detects REG Iα levels with very high sensitivity and excellent specificity. No
major crossreactivity between REG Iα and analogs were observed.
2.13.1 Extraction of Genomic DNA from Whole Blood
Sample Preparation
Nuclear DNA was extracted from whole blood taken in EDTA tubes of patients and controls
as described by Sambrook and Russell 2001 with some modifications (Sambrook and
Russel, 2001).
Chapter 2
69
Material Required
Spectrafuge centrifuge 16M (Lab net International Inc), vortex, micropipette with tips 1
mL, 500 µl, 100 µL, 20 µL, incubator, DNA vacuum concentrator/Lamp.
Solution Composition
RBC Lysis Buffer:
A total of 109.5 gm Sucrose, 1.58 gm Tris (pH 7.6), 476 mg MgCl2, 10 mL Triton X, 200
mg sodium azide(preservative) and dH2O upto 1L.
WBC’s lysis buffer:
Contains 7.85 g Tris (pH 8), 20 g SDS, 6.68 g Disodium acetate and dH2O upto 1L
Proteinase K. (20µL), Ammonium acetate (7.5M), Isopropanol, Ethanol (70%)
DNA dissolving buffer:
T.E (10mM Tris-Cl pH 8, 1mM EDTA pH 8) or distilled water (dH2O).
PROCEDURE
A total of 300 µL of blood was taken in a 1.5 mL micro-centrifuge tube and mixed with
900 µL of R B C s lysis buffer by inverting several times. Centrifugation was performed at
13000 rpm for 1 min and supernatant was discarded. This step was repeated again if the cell
pellet contains too many RBCs. The clear pellet with WBCs was resuspended in 300 µL of
WBC’s lysis buffer and 2 0 µL proteinase K was added and mixed by gentle vortexing.
The tubes were incubated at 37°C overnight or 56°C for 2 h. After incubation, 100 µL of
7.5M ammonium acetate was added to each tube, and were kept at -20 °C for 15 min,
vortexed and centrifuged at 13000 rpm for 1 min. The upper aqueous layer was transferred
to a separate autoclaved micro-centrifuged tube and 300 uL of isopropanol was added. The
Chapter 2
70
tubes were inverted gently 3-4 times and a whitish hairball like precipitate of DNA could be
seen with the naked eye.
Centrifugation was done at 13000 rpm for 1 min. DNA pellet was washed with 300 µL of
70% ethanol after removing supernatant and dried by keeping the tubes in inverted position
for 15-20 min on a clean tissue paper or in DNA vacuum concentrator at 37 °C till the
complete precipitation of residual ethanol. DNA was dissolved in DNA dissolving buffer or
DNase free water or distilled water and stored at 4°C for a few weeks or -20°C for several
months. The quality and quantity of DNA was checked on 0.8% agarose gel through
electrophoresis.
2.13.2 Polymerase Chain Reaction (PCR)
Sample
DNA extracted from whole blood as described above.
Materials Required
Bioer’s XP PCR Thermal cycler, Primers,
Taq PCR Master Mix (2X, red dye) by Bio Basic, Canada:
It is a ready to use solution containing Taq DNA polymerase, dNTPs, 1.5mM MgCl2, PCR
buffer and PCR stabilizers.
DNA Ladder 100bps, DNase free water, PCR tubes, micropipettes and tips, other lab
equipment.
Primer Designing
The Primers were designed using Primer BLAST software ( table2 .8)
Chapter 2
71
Preparation of the PCR Reaction Mixture (50 µL)
Master Mix: 25 µL (1X), DNA Template: 2 µL (0.1-10 ng), Forward Primer: 2 µL (0.4
µM), Reverse Primer: 2 µL (0.4 µM), DNase free water: 1 µL.
Table 2.8: Primer Sequence for REG Iα Gene
EXON Upstream Primers (5* to 3*) EXON Downstream Primers (5* to 3*) Size
R1-F TCCCAAAACTCACCGCTTGC R1-R CTCTGAGACACCCACACCTTC 322
R2-F AGGTAATAGGTGCTTTGCTCTCC R2-R TCCCCAAATCCACCATCACG 449
R3-F CCTTTTCCTTACCCTGAGAGCC R3-R CATTGCAGCCACTGAACACA 251
R4-F TTTTCTGACCCGTCCTCTTGG R4-R GAGACCAGAACTTGAACCTCCT 294
R5-F GGCCCAGTGATTCCATGTAT R5-R GGAGACCCGAAAGAGTATGACC 341
R6-F TCAAGCACAGGTGAGAGGCA R6-R GTTGAGTTGGAGAGATGGTCCG 358
Procedure
The PCR for each exon was optimized through polymerase chain reaction technique on
Bioer’s XP PCR Thermal cycler machine. The following parameters were optimized through
series of reactions;
Denaturation (to yield single-stranded DNA molecules).
Annealing temperature and time (to hybridize or anneal primers to their complementary
Chapter 2
72
sequences on target DNA).
Extension temperature and time (for chain elongation of target).
Final Elongation (72 °C).
The exons 1, 2, 3, 4, 5 and 6 of REG Iα gene were studied through PCR and sequencing.
The PCR reaction used was as listed previously. The annealing (Tm) temperature was
calculated through Primer BLAST software and reconfirmed by following formula:
Tm= 4(G+C) + 2(A+T)
All samples were amplified through PCR using all six exon primer sets on their
respective optimized program. This procedure was repeated three times for each primer to
get a total reaction mixture of 150 µL. The optimized reaction conditions u s ed for each
exon were as given in table 2.9.
Table 2.9: Optimized reaction conditions u s ed for each exon
Exon 1, 5, 6 Exon 2, 3, 4
Predenaturation 94 °C 4 min 94 °C 4 min
Denaturation 94 °C 30 sec
35 cycles
94 °C 30 sec
35 cycles Annealing 55 °C 40 sec 53 °C 40 sec
Extension 72 °C 1kb/min 72 °C 1kb/min
Postextension 70 °C 10 min 70 °C 10 min
Hold 4 °C 4 °C
(kb: kilobase/ kiloase pair)
Chapter 2
73
2.13.3. Horizontal Gel Electrophoresis
Sample
Amplified PCR product.
Materials Required
Agarose powder, 1X TBE (0.89 Tris-Borate, 0.032M EDTA pH= 8.3), microwave oven,
Horizontal Gel Electrophoresis System (Bio-rad), Gel Doc (Herolab), micropipette and tips
and other lab instruments.
Procedure
Amplified PCR products were visualized on 2% agarose gel. One gram agarose was
added to 50 mL of 1X TBE and dissolved by heating in a microwave oven for 1 min.
Cooled slightly and 0.4 µg/mL ethidium bromide was added and mixed with the agarose
gel. Gel apparatus was set, agarose gel was poured in gel plate and combs were inserted.
Gel was allowed to solidify (gel polymerization). DNA ladder and samples were loaded
(10 µL) into the wells. Electrophoresis was performed at 100 volts in 1X TBE buffer
for 30 min. The gel was visualized on both UV tec Transilluminator and Gel
Documentation (Herolab, B-1393-3U7N, Germany) using EasyWin 32 program and images
were saved.
PCR Product Purification
PCR products, purified through PCR cleanup kit using Spin Column Method by First
Base Sequencing Services.
Chapter 2
74
PCR Product Quantification
PCR product quantification was done by First Base Sequencing Services by
Spectrophotometer Reading using UV absorbance at 260 nm.
2.13.4. DNA Sequencing
DNA sequencing was performed by Sanger Dideoxy Method (Sanger et al., 1977).
Sequencing PCR
The pre-requisites (primers and DNA) were provided to First BASE Laboratories Sdn Bhd
[First Base Sequencing Services (Malaysia)]. Sequencing was carried out by Sanger
Dideoxy Method using BigDye® Terminator v3.1 cycle sequencing kit on Applied
Biosystems® Genetic Analyzer.
The chromatograms were analyzed by the Sequence Scanner 2 software and alignment
between sequences was done using software, Clustal X. Alignment of the REG Iα gene
sequence of the patients and controls was also done with the normal/known sequence form
NCBI (NC_000002.12) Accession: NC_000002 REGION: 79120458..79123419 GPC
000001294, (GRCh38.p2 (GCF_000001405.281)) using Nucleotide-BLAST (Basic Local
Alignment Search Tool).
2.13.5. Single Nucleotide Polymorphisms (SNPs)
SNPs were detected by alignment of the type II diabetes patients and control REG Iα gene
sequence with the known sequence given in NCBI. SNPs detected were then compared with
the known SNPs from REG Iα Gene Card and dbSNP NCBI Nucleotide BLAST.
Chapter 2
75
STATISTICAL ANALYSIS
Statistical analysis was carried out using Statistical Package for Social Sciences (SPSS
statistical software) version 16. Descriptive statistics were calculated. Shapiro Wilk test was
applied to check the normality of the data. Mean ± SD or Median (Inter Quartile Range)
were calculated. The Mann-Whitney U-test and the Kruskal-Wallis test were used to
compare quantitative variables. Spearman’s rank correlation test was used to investigate
correlations between variables. The p-values of less than 0.05 and less than 0.001 were
considered significant and highly significant respectively.
The Chi-square (γ) test was used to determine whether individual polymorphisms were in
Hardy-Weinberg equilibrium (p > 0.05). Logistic regression analysis was used for
calculating odds ratios (OR), 95% confidence interval and corresponding p values. Linear
regression was used to prepare models for cluster analysis using different variables to find
the true association with the variables. Cohen effect was applied to see the effect size.
MIN6-VC and MIN6-mReg2 cell lines were employed for the study of various parameters,
and each experiment was performed three to four times. The values were expressed as mean
± SEM. Statistical analyses were performed using one-way analysis of variance (ANOVA)
followed by Bonnferroni/ Dunnetts t-tests.
Chapter 3
76
RESULTS
3.1 WESTERN BLOT ANALYSIS TO SEE THE EFFECT OF mReg2
ON AIF AND SCYTHE AFTER TREATMENT WITH DOXORUBICIN
USING MIN6-VC AND MIN6-mReg2 CELLS
These experiments were carried out to study the effect of mReg2 protein on the
mitochondrial caspase-independent apoptotic signaling pathway in the presence of apoptotic
inducer doxorubicin in a time dependent manner.
3.1.1 Induction of Time-Dependent Increase in AIF in the Nucleus of MIN6-VC Cells
Incubation of MIN6-VC cells with doxorubicin (1 µM) for up to 24 h induced a time-
dependent increase in the nuclear import of AIF with significant, >1.5, > 2.5 and >4-fold
increase at 6, 12 and 24 h respectively when means were compared (figure 3.1A & B, table
3.1). The difference between the four groups (0, 6, 12 and 24 h) was found to be highly
significant with p<0.001 (p=0.000). The difference between individual groups was found to
be significant or highly significant as follows: between MIN6-VC untreated and MIN6-VC
treated for 6 h p=0.033, between MIN6-VC untreated and MIN6-VC treated for 12 h
p=0.000, between MIN6-VC untreated and MIN6-VC treated for 24 h p=0.000, between
MIN6-VC treated 6 h and MIN6-VC treated for 12 h p=0.001, MIN6-VC treated 6 h and
MIN6-VC treated for 24 h p=0.000, MIN6-VC treated 12 h and MIN6-VC treated for 24 h
p=0.000.
USF-2 was used as the nuclear marker and means are the results for three independent
experiments.
Chapter 3
77
3.1.2 mReg2 Inhibits Nuclear Translocation of AIF and Promotes Nuclear Retention
of Scythe
While assessing the effect of mReg2 on caspase-independent apoptotic signaling
comparison of the sub-cellular distribution of AIF in MIN6-VC cells and MIN6-mReg2
over-expressing cells was done in a time dependent manner. Treatment with doxorubicin led
to an increase in the nuclear presence of AIF in MIN6-VC cells represented by mean (IDV:
integrated density values) + SEM at 12 h, (2.60 + 0.12, >2.5 fold) and 24 h (4.48 + 0.25,
=4.5 fold) whereas nuclear accumulation of AIF did not occur in MIN6-mReg2 cells at 12 h
(0.75 + 0.08) and 24 h (0.98 + 0.14) with p <0.001 (p=0.000) (figure. 3.2 A, figure. 3.3 A,
table 3.2 and table 3.3). Highly significant difference was found between the AIF levels in
the MIN6-VC and MIN6-mReg2 cells after 12 h treatment with p<0.001 (p=0.000).
Similarly, after 24 treatment with doxorubicin highly significant difference was found
between MIN6-VC and MIN6-mReg2 cells with p<0.001 (p=0.000). However, no
significant difference was found between MIN6-mReg2 cells before or after treatment with
doxorubicin for 12 h and 24 h. On the contrary, an opposite effect was observed in the
cytosol with decrease presence of AIF in MIN6-VC cells and increase presence of AIF in
MIN6-mReg2 cells (figure 3.2B and 3.3B (right panel).
On the other hand, as shown in the figures 3.2A, 3.3A and table 3.2 and 3.3 increased level
of Scythe were observed in the nucleus of MIN6-mReg2 cells after 12 h (1.76 + 0.15, >1.5
fold) and 24 h (2.45 + 0.15, nearly 2.5 fold) treatment, while decreased levels were seen in
the nucleus of MIN6-VC cells after both 12 h (0.74 + 0.06) and 24 h (0.67 + 0.04) treatment
with the apoptotic stimuli. The difference was found to be highly significant in the MIN6-
VC and MIN6-mReg2 cells after 12 h treatment with p<0.001 (p=0.000) and 24 treatment
Chapter 3
78
with p<0.001 (p=0.000). Significant difference was also found in Scythe levels in MIN6-
mReg2 cells before or after treatment with doxorubicin for 12 h and 24 h with p<0.001 in
both cases. However, decreased presence of Scythe was observed in the cytosol of MIN6-
mReg2 cells while its cytosolic presence was increased in MIN6-VC cells.
Thus a reverse sub-cellular pattern of these proteins was observed in MIN6-mReg2 cells as
compared to MIN6-VC cells revealing increase levels of Scythe in the nucleus and of AIF in
the cytosol.
3.1.3 Expression of HSP70 in the Presence of mReg2
In whole cell lysate, a higher hsp70 expression at the protein levels was found in both
untreated MIN6-mReg2 cells (1.80 + 0.13) and treated MIN6-mReg2 cells (1.88 + 0.17) as
compared to that in untreated MIN6-VC cells (1.00 + 0.00) taken as control and treated
MIN6-VC cells (0.98 + 0.19) (figure. 3.4A and 3.4B and table 3.4). The difference between
the four groups (MIN6-VC untreated, MIN6-VC treated, MIN6-mReg2 untreated and
MIN6-mReg2 treated) was found to be highly significant with p<0.001 (p=0.000). Between
individual groups the difference was found to be significant or non-significant as follows:
between MIN6-VC untreated and MIN6-VC treated p=1.000, between MIN6-VC untreated
and MIN6-mReg2 untreated p=0.001, between MIN6-VC untreated and MIN6-mReg2
treated p=0.000, between MIN6-VC treated and MIN6-mReg2 untreated p=0.001, between
MIN6-VC treated and MIN6-mReg2 treated p=0.000 and between MIN6-mReg2 untreated
and MIN6-mReg2 treated p=1.000.
Chapter 3
79
Table 3.1: Time dependent increase in AIF in Nuclear Fraction of MIN6-VC Cells
All values are represented as mean + SEM. Mean is the representative of three independent
experiments
AIF Untreated Treated with Doxorubicin (1µM)
6 h 12 h 24 h
Rel to Unt 1 1.490 2.545 4.000
Rel to Unt 1 1.518 2.553 4.346
Rel to Unt 1 1.750 2.917 4.563
Mean 1 1.586 2.672 4.304
STDEV 0 0. 143 0. 213 0. 213
SEM 0 0. 082 0. 123 0. 163
AIF: Apoptosis Inducing Factor, Rel to Unt: Relative to Untreated, STDEV: Standard
Deviation, SEM: Standard Error Mean. Relative protein quantitation was performed to
obtain integrated density values according to published guidelines (Ferreira and Rasband.,
2012)
Chapter 3
80
Figure 3.1: Induction of time-dependent increase in AIF in the nucleus of MIN6-VC
cells. A) Nuclear fractions of the MIN6-VC cells treated with 1 uM doxorubicin for the
varying times were subjected to Western blot analysis for AIF. The blots were reprobed with
anti-USF-2 antibody (nuclear marker) to check the loading efficiency (**p<0.001, n=3). B)
AIF levels in the nuclear fraction of MIN6-VC cells in a time dependent manner after
treatment with doxorubicin
0
1
2
3
4
5
0 6 12 24
Nu
clea
r A
IF (
fold
incr
ease
)
Time (hours)
*
*
*
3.1B
3.1A
Chapter 3
81
Table 3.2: Effect of 12h treatment with doxorubicin (1 µM) on AIF and Scythe in Nuclear
Fraction of MIN6-VC and MIN6-mReg2 Cells
All values are represented as mean + SEM. Mean is the representative of three independent
experiments
AIF Scythe
MIN6-VC MIN6-mReg2 MIN6-VC MIN6-mReg2
UT Tt UT Tt UT Tt UT Tt
Rel to
Unt 1 2.355 1.050 0.791
Rel to
Unt 1 0.787 0.720 2.010
Rel to
Unt 1 2.710 1.352 0.871
Rel to
Unt 1 0.812 0.650 1.505
Rel to
Unt 1 2.751 0.970 0.590
Rel to
Unt 1 0.613 0.754 1.753
Mean 1 2.605 1.124 0.750 Mean 1 0.737 0.708 1.756
STDEV 0 0.217 0.201 0.145 STDEV
0 0.108 0.053 0.252
SEM 0 0.125 0.116 0.084 SEM 0 0.062 0.030 0.145
UT: Untreated, T: Treated, Rel to Unt: Relative to Untreated, STDEV: Standard Deviation,
SEM: Standard Error Mean. Relative protein quantitation was performed to obtain
integrated density values according to published guidelines (Ferreira and Rasband., 2012).
Chapter 3
82
Figure 3.2: Inhibition of the nuclear translocation of AIF and promotion of the nuclear
accumulation of Scythe by mReg2. Cells were incubated with 1 µM doxorubicin for 12 h.
Nuclear and cytosolic distribution of AIF and Scythe following the removal of
mitochondrial fraction was assessed by immunoblot analysis. Blots were reprobed with anti-
USF-2 antibody (nuclear marker) and anti-actin/ anti-tubulin antibodies (cytosolic marker).
(A) Treatment with doxorubicin led to an increase in the nuclear presence of AIF in MIN6-
VC cells but not in the MIN6-mReg2 cells. In contrast, the level of Scythe in the nucleus
was lower in MIN6-VC cells but markedly higher in MIN6-mReg2 cells after treatment.
(B)Left In response to treatment with doxorubicin, increase level of AIF and decrease level
of Scythe were observed in nucleus in MIN6-VC cells and opposite was observed in MIN6-
mReg2 cells. Right The cytosolic presence of Scythe increased AIF decreased in MIN6-VC
cells. In comparison, in MIN6-mReg2 cells decrease levels of Scythe and increased level of
AIF was observed in the cytosol
3.2A
3.2B
Chapter 3
83
Figure 3.2C: Figure showing increase levels of Scythe and decrease levels of AIF in the
nuclear fraction of MIN6-mReg2 cells with opposing effects in MIN6-VC cells
0.00
1.00
2.00
3.00
4.00
5.00
6.00
- + - +
Nu
cle
ar F
ract
ion
(fo
ld in
cre
ase
)
MIN6-VC MIN6-mReg2
3.2C
AIF
Scythe
**
** ##
##
Chapter 3
84
Table 3.3: Effect of 24h treatment with doxorubicin (1 µM) on AIF and Scythe in nuclear
fraction of MIN6-VC and MIN6-mReg2 Cells
All values are represented as mean + SEM. Mean is the representative of three independent
experiments
AIF Scythe
MIN6-VC MIN6-mReg2 MIN6-VC MIN6-mReg2
UT Tt UT Tt UT Tt UT Tt
Rel to
Unt 1 4.453 1.057 0.922
Rel to
Unt 1 0.580 0.902 2.25
Rel to
Unt 1 4.750 0.952 0.760
Rel to
Unt 1 0.677 0.780 2.451
Rel to
Unt 1 4.251 0.971 1.251
Rel to
Unt 1 0.712 0.653 2.757
Mean 1 4.484 0.993 0.977 Mean 1 0.656 0.778 2.486
STDEV 0 0.251 0.055 0.250 STDEV 0 0.068 0.124 0.255
SEM 0 0.250 0.032 0.144 SEM 0 0.0394 0.071 0.147
UT: Untreated, T: Treated, Rel to Unt: Relative to Untreated, STDEV: Standard Deviation,
SEM: Standard Error Mean. Relative protein quantitation was performed to obtain
integrated density values according to published guidelines (Ferreira and Rasband., 2012)
Chapter 3
85
Figure 3.3: Inhibition of the nuclear translocation of AIF and promotion of the nuclear
accumulation of Scythe by mReg2. Cells were incubated with 1 µM doxorubicin for 24
hrs. (A) Treatment with doxorubicin led to an increase in the nuclear presence of AIF in
MIN6-VC cells but not in the MIN6-mReg2 cells. In contrast, the level of Scythe in the
nucleus was lower in MIN6-VC cells but markedly higher in MIN6-mReg2 cells treated
with the apoptotic stimulus. (B) Left In response to treatment with doxorubicin, increase
level of AIF and decrease level of Scythe were observed in nucleus in MIN6-VC cells and
opposite was observed in MIN6-mReg2 cells. Right The cytosolic presence of Scythe
increased AIF decreased in MIN6-VC cells. In comparison, in MIN6-mReg2 cells decrease
levels of Scythe and increased level of AIF was observed in the cytosol
3.3A
3.3B
Chapter 3
86
Figure 3.3C: Figure showing increase levels of Scythe and decrease levels of AIF in the
MIN6-mReg2 cells with opposing effects in MIN6-VC cells
0
1
2
3
4
5
6
- + - +
Nu
cle
ar F
ract
ion
(fo
ld in
cre
ase
)
MIN6-VC MIN6-mReg2
AIF
Scythe
**
##
3.3C
**
*
##
Chapter 3
87
Table 3.4: Effect of mReg2 on the expression of inducible hsp70 protein
All values are represented as mean + SEM. Mean is the result of three independent
experiments
Untreated Treated Untreated Treated
hsp70
(MIN6-VC)
hsp70
(MIN6-VC)
hsp70
(MIN6-Reg2)
hsp70
(MIN6-Reg2)
Rel to Unt 1 1.202 1.850 2.051
Rel to Unt 1 0.850 1.901 1.902
Rel to Unt 1 0.900 1.650 1.700
Mean 1 0.983 1.800 1.883
STDEV 0 0.189 0.132 0.175
SEM 0 0.109 0.076 0.101
Rel to Unt: Relative to Untreated, STDEV: Standard Deviation, SEM: Standard Error Mean.
Relative protein quantitation was performed to obtain integrated density values according to
published guidelines (Ferreira and Rasband., 2012)
Chapter 3
88
Figure 3.4: Effect of mReg2 on the expression of hsp70 in the absence and presence of
treatment. (A) Whole cell lysates subjected to western blot analysis demonstrated the
increased presence of hsp70 in MIN6-mReg2 cells compared to that detected in MIN6-VC
cells. Loading consistency was verified by reprobing the blots for actin. (B) Expression of
hsp70 in MIN6-VC and MIN6-mReg2 cells. *p<0.01(p=0.001) and **p<0.001 (p=0.000)
0.00
0.50
1.00
1.50
2.00
2.50
- + - +
hsp
70
(fo
ld in
cre
ase
)
MIN6-VC MIN6-mReg2
hsp70
3.4B
** *
3.4A
Chapter 3
89
3.2 ELISA ANALYSIS OF SERUM REG Iα LEVELS
3.2.1 Clinical and Demographic Characteristics of the Study Groups
The clinical and demographic data of the study groups are shown in table 4.5. There were 12
males and 8 females in controls while type I diabetics included 7 males and 3 females and
type 2 diabetics included 44 males and 16 females. The (mean + SD) age of controls was 50
± 3, while in type I and type II diabetics it was 34 ± 16 and 55 ± 9 respectively. The (mean +
SD) age at the onset of the disease and duration of disease in type I diabetics were 24.2 ±
14.2 and 9.8 ± 6.04 respectively and in type II diabetics they were 47.4 ± 9.5 and 7.2 ± 5.6
respectively.
The BMI was significantly raised in type II diabetics as compared to the controls and type I
diabetics with p<0.05 (p=0.026). Significant difference between the systolic and diastolic
blood pressure were found in controls, type I diabetics and type II diabetics with p<0.001
(p=0.000). Type II diabetics had the highest levels followed by type I diabetics and controls
respectively (table 3.5).
The FBG levels were found to be significantly raised in type I diabetic (Median (IQR):
10.6(7.4-17)) and type II diabetics (8.0(6.7-9.9)) as compared to the control subjects
(3.7(3.5-4.4)) with p<0.001 (p=0.000). Type I diabetics had much higher levels as compared
to type II diabetics. Compared with the control subjects (Median (IQR): 5.5 (5.4-5.6))
HbA1c levels were also found to be significantly raised in type I diabetics (10.3(8.8-12))
and type II diabetics (8.2(7.2-9.3)) with p<0.001 (p=0.000). Type I diabetics had much
higher levels of HbA1c as compared to type II diabetics. Type II diabetics had higher TC
levels as compared to controls and type I diabetics with p<0.001 (p=0.000) as shown in table
Chapter 3
90
3.5. The TG although raised in type II and type I diabetics were not statistically significant
while Hb levels were decreased in type I and type II diabetics but were not statistically
significant.
3.2.2 Serum REG Iα Levels in Type I and Type II Diabetics
Raised levels of protein were observed in type I and type II diabetics of different age groups
and disease duration compared to age and sex matched controls (p<0.001, p=0.000). Raised
REG Iα protein levels were also observed in type I diabetics compared to normal subjects
(p=0.01). Type II diabetics also demonstrated raised protein levels compared to normal
subjects (p<0.001, p=0.000). REG Iα protein levels in type II diabetics were higher as
compared to the REG Iα protein levels in type I diabetics although the difference was not
statistically significant (figure 3.5). The (mean + SD) for REG Iα in controls, type I diabetics
and type II diabetics were 730.46 + 178.43 ng/mL, 1162.09 + 378.26 ng/mL and 1436.55 +
427.12 ng/mL respectively. The mean rank for controls, type I and type II diabetics were
18.60, 40.40 and 55.32 respectively.
3.2.3 Correlation between Serum REG Iα Levels and Duration of Disease
In type II diabetics, a significant negative correlation between disease duration and serum
REG Iα protein levels was observed with p=0.005 and Spearman r = -0.355 (figure 3.6 and
table 3.6). Although the protein levels declined with increase in the disease duration but they
remained significantly higher when compared with the controls. No significant correlation
between disease duration and serum protein levels was found in case of type I diabetics in
the present study (figure 3.7).
Type II diabetics were further divided into four groups according to the disease duration.
Chapter 3
91
Group I (n=13) patients with the disease duration of less than 2 years. Group II (n=13)
patients with disease duration between 2 to 5 years. Group III (n=16) patients with disease
duration between 5 to 10 years and Group IV (n=18) patients with disease duration of more
than 10 years. A significant difference was observed between the four groups with p value
of less than 0.05. Between any of the two groups p value was calculated to be either
significant or non significant between (group I and II p=0.83), (group I and III p=0.27),
(group I and IV p=0.02), (group II and III p=0.79), (group II and IV p=0.20) and (group III
and IV p=0.69) (figure 3.8).
The (mean + SD) for REG Iα in each group were group I=1686.68 + 224.75, group
II=1564.58 + 337.55, group III= 1380.50 + 415.48 and group IV= 1223.48 + 500.66. The
mean rank for each group was calculated to be group I =40.92, group II=32.12, group
III=28.66 and group IV=23.44.
3.2.4 Correlation between Serum REG Iα Levels and Age of the Patient
Serum REG Iα levels also showed a significant decline in their levels with increasing age of
patient in type II diabetics with p = 0.019 and Spearman r = -0.309 (figure 3.9 and table 3.6).
A positive significant correlation with p = 0.022 and Spearman r = 0.709 was seen in case of
type I diabetics (figure 3.10 and table 3.6).
3.2.5 Serum REG Iα Levels Correlates Positively with Age at Disease Onset in Type I
Diabetics
Type I diabetics showed lower circulatory protein levels with age at the disease onset less than
26 years as compared to those with age at the disease onset more than 26 years (p<0.05) (figure
3.11).
Chapter 3
92
3.2.6 Correlation between Serum REG Iα Levels and Metabolic Factors
Although the patients were on anti-hyperglycemic agents, a significant positive correlation
was found between REG Iα serum levels and FBG in type II diabetics with p =0.001 and
Spearman r=0.307 (figure 3.12 and table 3.6). A significant positive correlation was also
seen between protein levels and HbA1c with p value less than 0.001 and Spearman r=0.444
(figure 3.14 and table 3.6). However, a negative although insignificant correlation was seen
in case of type I diabetics for both FBG and HbA1c with the circulatory protein levels
(figure 3.13 and figure 3.15).
No correlation was found between protein levels and TC and TG levels in both type I and
type II diabetics.
3.2.7 Serum REG Iα Level and the Risk Factors for Diabetes
Smokers had higher levels of the protein as compared to the non-smokers but the data was
not statistically significant (p=0.12) (figure 3.16) in type II diabetics. There was only one
known smoker in patients with type I diabetes with the REG Iα levels of 1582 ng/mL.
Patients with increase BMI had significantly raised levels of the protein with p=0.001 and
Spearman r=0.411 in case of type II diabetics (figure 3.17 and table 3.6). While type I
diabetics also showed positive correlation with the circulatory protein levels but it was not
statistically significant (figure 3.18).
Family history of diabetes did not show any significant correlation with the REG Iα serum
levels in both type I and type 2 diabetics.
Chapter 3
93
Systolic blood pressure (p=0.08) and diastolic blood pressure (p=0.17) were found directly
proportional to increase in age, duration of the disease and decrease in REG Iα protein
levels, although the data was not statistically significant in type II diabetics (Appendix V).
3.2.8 Serum REG Iα Level are higher in Patient with Diabetic Complications
Serum levels of REG Iα were measured in controls, diabetics without complications and
diabetics with complications. A significant difference was found between the three groups
with p<0.001 (mean rank: control=15.26, diabetics without complications=30.90 and
diabetics with complications=48.32) Serum REG Iα levels were significantly raised in
diabetics without complications as compared to normal subjects (p=0.001), diabetic with
complications as compared to controls (p<0.001) and diabetics with complications as
compared to diabetics without complications (p<0.001) (figure 3.19). A total of 26 out of 70
diabetics presented with the disease complications, 3/10 patients were of type I diabetes and
23/60 were of type II diabetes (already diagnosed with one or more of the disease
complications). Frequency and percentage of controls, diabetics without disease
complications and with disease complications are shown in table 3.7A. The table 3.7B
depicts the frequency and percentage of diabetics with different types of disease
complications.
Chapter 3
94
Table 3.5: Clinical and demographic characteristics of the study groups
Parameters Control
(n=20)
Type I
(n=10)
Type II
(n=60) p value
Sex, M/F 12/8 7/3 44/16 ----------
Age, years 50±3 34±16 55±9 ----------
Age at onset of
disease, years ---------- 24.2±14.2 47.4±9.5 ----------
Duration of Disease,
years ---------- 9.8±6.04 7.2±5.6 ----------
BMI, kg/m2 24.1±4 22.3±5.2 25.7±3.8 <0.05*
Systolic Blood
Pressure, mmHg 120(110-120) 120(115-125) 130(120-140) <0.001**
Diastolic Blood
Pressure, mmHg 80(70-80) 80(78-82) 80(80-90) <0.001**
FBG, mmol/L 3.7(3.5-4.4) 10.6(7.4-17) 8(6.7-9.9) <0.001**
HbA1c, % 5.5(5.4-5.6) 10.3(8.8-12) 8.2(7.2-9.3) <0.001**
TC, mmol/L 3.7(3.2-4.7) 3.6(3.5-4.6) 5.0(4.5-5.6) <0.001**
TG, mmol/L 1.15(0.9-1.3) 1.5(0.9-1.7) 1.7(1.2-2.3) =0.055
Hb (mg/dL) 13.2(12.6-
14.2) 11.4(11-12) 12.0(11.6-12.9) =0.080
Data is represented as Mean + SD for normally distributed variables, and otherwise as
Median (Inter Quartile range, IQR). p values for differences between control group, type I
and type II diabetes patients (* significant, ** highly significant).
Chapter 3
95
Figure 3.5: Serum REG Iα levels in controls, type I diabetics and type II diabetics
Human REG Iα ELISA assay (USBiological, Life Sciences). Quantitative measurement of
human REG Iα protein in serum. Increased levels of REG Iα protein were observed in type I
and II diabetes patients compared to controls (p < 0.001).
Chapter 3
96
Table 3.6: Showing correlation coefficient between REG Iα and clinical / biochemical
parameters in type I (group I) and type II (group II) diabetics
Groups Variable Coefficient /
Significance
Clinical Parameters
FBG HbA1c
I REG Iα
r -0.224 -0.467
p 0.533 0.174
II REG Iα
r 0.407 0.444
p *0.001 **0.000
Groups Variable Coefficient /
Significance
Disease
Duration Age
I REG Iα
r 0.255 0.709
p 0.476 *0.022
II REG Iα
r -0.355 -0.309
p *0.005 *0.019
Groups Variable Coefficient /
Significance BMI
I REG Iα
r 0.612
p 0.060
II REG Iα
r 0.411
p *0.001
r = Spearman correlation coefficient, *p<0.05=significant, **p<0.001= highly significant.
Chapter 3
97
Figure 3.6: Correlation between serum REG Iα levels and duration of disease in type
II diabetics
Figure 3.7: Correlation between serum REG Iα levels and duration of disease in
type I diabetics
y = -31.56x + 1663 R² = 0.173
0
500
1000
1500
2000
2500
0 5 10 15 20 25 30
REG
Iα (
ng/
mL)
Duration of Disease (years)
Scattered values
Linear ( Scattered values)
Spearman r=-0.355 p=0.005
y = 22.06x + 945.88 R² = 0.1166
0
500
1000
1500
2000
2500
0 5 10 15 20 25
REG
Iα (
ng/
ml)
Duration of Disease (years)
Scattered values
Linear (Scattered values)
Spearman r=0.255 p=0.476
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98
Figure 3.8: Serum REG Iα levels and different duration of disease groups in type II
diabetics
Significant relationship was found between the groups p<0.05
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99
Figure 3.9: Correlation between serum REG Iα levels and age of the patient (years) in type
II diabetics
Figure 3.10: Correlation between serum REG Iα levels and age of the patient (years) in type
I diabetics
y = -14.678x + 2240.7 R² = 0.0929
0
500
1000
1500
2000
2500
0 20 40 60 80
REG
Iα (
ng/
mL)
Age of patient (years)
Scattered values
Linear (Scattered values)
Spearman r=-0.309 p=0.019
y = 17.053x + 580.59 R² = 0.4953
0
500
1000
1500
2000
2500
0 10 20 30 40 50 60
REG
Iα (
ng/
mL)
Age of patient (years)
Scattered values
Linear (Scattered values)
Spearman r=0.709 p=0.022
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100
Figure 3.11: Serum levels of REG Iα in type I diabetics depending on age at
disease onset
Chapter 3
101
Figure 3.12: Correlation between serum REG Iα levels and FBG (mmol/l) in type II
diabetics
Figure 3.13: Correlation between serum REG Iα levels and FBG (mmol/l) in type I
diabetics
y = 55.88x + 959.1 R² = 0.120
0
500
1000
1500
2000
2500
0 5 10 15 20
REG
Iα (
ng/
mL)
Fasting Blood Glucose (mmol/L)
Scattered value
Linear (Scattered value)
Spearman r=0.407 p=0.001
y = -25.085x + 1454.8 R² = 0.1055
0
500
1000
1500
2000
2500
0 5 10 15 20
REG
Iα (
ng/
mL)
Fasting Blood Glucose (mmol/L)
Scattered values
Linear (Scattered values)
Spearman r=-0.224 p=0.533
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102
Figure 3.14: Correlation between serum REG Iα protein and HbA1c in type II diabetics
Figure 3.15: Correlation between serum REG Iα protein and HbA1c in type I diabetics
y = 106.25x + 540.47 R² = 0.1701
0
500
1000
1500
2000
2500
0 5 10 15 20
REG
Iα (
ng/
mL)
HbA1c (%)
Scattered values
Linear (Scattered values)
Spearman r=0.444 p<0.001
y = -78.621x + 1988.4 R² = 0.1753
0
500
1000
1500
2000
2500
0 5 10 15
REG
Iα (
ng/
ml)
HbA1c (%)
Scattered values
Linear (Scattered values)
Spearman r=-0.467 p=0.174
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103
Figure 3.16: Serum REG Iα levels and smoking in type II diabetics
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104
Figure 3.17: Correlation between serum REG Iα and BMI in type II diabetics
Figure 3.18: Correlation between serum REG Iα and BMI in type I diabetics
y = 44.446x + 294.72 R² = 0.1531
0
500
1000
1500
2000
2500
0 10 20 30 40
REG
Iα (
ng/
mL)
Body Mass Index
Scattered values
Linear (Scattered values)
Spearman r=0.411 p=0.001
y = 54.385x - 49.6 R² = 0.5153
0
500
1000
1500
2000
2500
0 10 20 30 40
REG
Iα (
ng/
mL)
Body Mass Index
Scattered values
Linear (Scattered values)
Spearman r=0.612 p=0.060
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105
Figure 3.19: Serum levels of REG Iα in controls, both type I and type II diabetics without
disease complications and with disease complications
Chapter 3
106
Table 3.7A) Frequency and percentage of controls, diabetics without disease complications
and with disease complications
Complications Frequency Percent Valid Percent Cumulative Percent
Valid YES 26 28.9 28.9 28.9
NO 44 48.9 48.9 77.8
CON 20 22.2 22.2 100.0
Total 90 100.0 100.0
Table 4.7B): Frequency and percentage of diabetics with different type of disease
complications
Patients with Complications Frequency Percent Valid Percent Cumulative
Percent
Valid IHD 7 7.8 26.9 26.9
Cataract 5 5.6 19.2 46.2
Diabetic Foot 1 1.1 3.8 50.0
Carbuncle 1 1.1 3.8 53.8
Neuropathy 9 10.0 34.6 88.5
Nephropathy 1 1.1 3.8 92.3
IHD/DF 1 1.1 3.8 96.2
Neuro/Retino 1 1.1 3.8 100.0
Total 26 28.9 100.0
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107
3.3 IDENTIFICATION OF POLYMORPHISMS IN THE REG Iα GENE
3.3.1 Clinical and Demographic Characteristics of the Study Groups
The clinical and demographic characteristics of the study groups are shown in table 3.8.
There were 9 males and 6 females in controls while type II diabetics included 25 males and
11 females. The (mean + SD) age of controls was 50 ± 3, while in type II diabetics it was 52
± 9. The (mean + SD) age at the onset of the disease and duration of disease in type II
daibetics were 46.2 ± 9 and 6 ± 5 respectively.
The difference in BMI between controls and diabetics was not significant with p= 0.247.
Significant difference between the systolic and diastolic blood pressure were found in
controls and type II diabetics with p=0.001 and p=0.007 respectively.
Compared with the control subjects, type II diabetics had significantly raised FBG and
HbA1c levels with p<0.001in each case. Type II diabetics had significantly higher TC levels
as compared to controls with p<0.05. The TG although raised in type II was not statistically
significant while Hb levels were decreased in type II diabetics but were not statistically
significant.
3.3.2 Optimization of PCR Amplification using different Primer Sets
After the isolation of genomic DNA, the optimization of the primers was carried out for
different parameters to obtain best PCR amplification. The optimization PCR amplification
for each (exons and its corresponding region) using six set of primers are shown in figures
3.20 and 3.21.
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108
3.3.3 Polymorphisms Identified in REG IΑ Gene in Pakistani Subjects
A total of eight single nucleotide polymorphisms (SNPs) were identified in gene for REG Iα
in study population (figures 3.22 and table 3.9). When compared with the diabetic patients
three of the above mentioned SNPs [SNPs (g.-145G>A, g.142A missing and g.226G>A)]
were present in all the 51 subjects including controls and diabetics. Among the eight SNPs
identified in our population seven SNPs were already reported which are as follows with
their reference sequence (rs): (g.-385T>C [rs10165462], g.-243T>G [rs 283890], g.142A
missing [rs11339710], g.209G>T [rs2070707]), g.226A>G [rs 192054590], g.2199G>A [rs
3739142] and g.2360A>G [rs 12228]. The SNP g.-145G>A was novel, not reported earlier
and was found in all of the controls as well as type II diabetics.
3.3.4 Genotypes, Alleles Frequencies and CHI Square (2) using Hardy Weinberg
Equilibrium (HWE)
The genotype and allelic frequencies for all the SNPs were calculated using HWE (tables
3.10A - 3.10D). The selected population did not follow the HWE. The p <0.001, <0.01 and
<0.05 were significant and indicated that the population was deviating from HWE and
appeared to be stratified. This may be due to small sample size or genetic alterations which
are associated with the causation of disease. Among the eight SNP’s identified four were
selected for further analysis.
3.3.4.1 SNPs (SNP1:g.-385T>C, SNP2:g.-243T>G, SNP3:g.209G>T, SNP4:g.2199G>A)
In case of SNP1 (g.-385T>C) significant differences were observed in the distribution of the
three genotypes (TT, TC and CC) according to the clinical phenotype as shown in table
3.9A. The genotype CC was found in majority of the subjects, being 77% in hypertensive
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109
subjects, more than 75% in patients with raised BMI, more than 85% in subjects with age <
46 years, 73% in subjects with family history of diabetes, 76% in male and female each and
33% in smokers. Distribution of the genotypes was also found to be significantly different in
the diabetics and controls (table 3.10A). The genotype CC was found maximum in each
group i.e. diabetics 72% and controls 86%, while TT genotype was moderate with 25% and
13% in diabetics and controls respectively and the TC genotype was found in 1 (2.8%)
subject (diabetic) only. This showed γ2 value of 31 and p<0.001 for diabetics and γ
2 value of
15 and p<0.001 for controls. All of the phenotypes did not fall in accordance with HWE and
includes males, females, age at onset of disease < 46 years, age at onset of disease > 46
years , BMI < 23, BMI > 23, hypertension, non-hypertension, smokers, non-smokers and
subjects with and without family history of diabetes with p <0.001 in each case except
smokers (p=0.002).
For SNP2 (g.-243T>G) also significant differences were observed in the distribution of the
three genotypes (TT, TG and GG) according to the clinical phenotype as shown in table
3.10B. The genotype TT was found in majority of the subjects, being more than 63% in
hypertensive subjects, more than 64% in patients with raised BMI, 64% in subjects with age
< 46 years, 60% in subjects with family history of diabetes, 62% in male and 70% in
females and 22% in smokers. Distribution of the genotypes was also found to be
significantly different in the diabetics and controls. The genotype TT was found maximum
in each group i.e. diabetics 61% and controls 73%, while GG genotype was moderate with
25% and 13% in diabetics and controls respectively and the TG genotype was minimum
with 14% in diabetics and 13% in control subjects. This showed γ2 of 16 and p<0.001 for
diabetics and γ2 of 5 and p=0.02 for controls. Majority of the phenotypes did not fall in
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110
accordance with HWE and includes males p<0.001, females p<0.001, age at onset of disease
> 46 years p<0.001, BMI < 23 p=0.01, BMI > 23 p<0.001, hypertensive p=0.001, non-
hypertension p<0.001, smokers p=0.03 (CC genotype), non-smokers p<0.001and subjects
with and without family history of diabetes with p <0.001 in each case. Subjects with age at
the onset of disease < 46 years had p=0.10 and falls in HWE.
For SNP3 (g.209G>T) significant differences were observed in the distribution of the three
genotypes (GG, GT and TT) according to the clinical phenotype as shown in table 3.10C.
The genotype GG was found in majority of the subjects, being more than 77% in
hypertensive subjects, more than 81% in patients with raised BMI, 86% in subjects with age
> 46 years, 80% in subjects with family history of diabetes, 88% in male and 76% in
females and 78% in smokers. Distribution of the genotypes was also found to be
significantly different in the diabetics and controls. The genotype GG was found maximum
in each group i.e. diabetics 78% and controls 100%, while TT genotype was found in 14%
of the subjects and all were diabetics and the GT genotype was present in 8% of subjects all
were diabetics. This showed γ2 value of 18 and p<0.001 for diabetics while γ
2 value and p
value for control subjects could not be calculated as they only had GG genotype as
mentioned earlier. Majority of the phenotypes did not fall in accordance with HWE and
includes males p<0.001, females p=0.01, age at onset of disease > 46 years p<0.001, BMI <
23 p<0.001, BMI > 23 p<0.001, hypertensive p<0.001, non-hypertension p=0.01, non-
smokers p<0.001and subjects with and without family history of diabetes with p<0.001 in
each case. Subjects with age at disease onset <46 with p=0.10 and smokers with p=0.07 fall
in accordance with HWE.
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111
Significant differences were observed in the distribution of the three genotypes (GG, GA
and AA) according to the clinical phenotype as shown in table 3.10D for SNP4
(g.2199G>A). The genotype AA was found in majority of the subjects, being more than
59% in hypertensive subjects, more than 62% in patients with raised BMI, 54% in subjects
with age > 46 years, 70% in subjects with family history of diabetes, 65% in male and
females each and 33% in smokers. Distribution of the genotypes was also found to be
significantly different in the diabetics and controls (table 3.9D). The genotype AA was
found maximum in each group i.e. diabetics 64% and controls 67%, while GG genotype was
found in 33% of the diabetics and control subjects each and the GT genotype was present in
3% of subjects i.e. only one subject (diabetics). This showed γ2 value of 31 and p<0.001 for
diabetics and γ2 value of 15 and p<0.001 for control subjects. All of the phenotypes did not
fall in accordance with HWE and includes males p<0.001, females p<0.001, age at onset of
disease > 46 years p<0.001, BMI < 23 p<0.001, BMI > 23 p<0.001, hypertensive p<0.001,
non-hypertension p<0.001, smokers p=0.002, non-smokers p<0.001and subjects with and
without family history of diabetes with p <0.001 in each case.
3.3.5 Odd Ratio Calculations for Allele-1 & Allele-2
In case of SNP1 (g.-385T>C), the odd ratio (OR) with 95% confidence limit calculated to
test the relevance of frequencies of allele T and allele C in controls and diabetics was
OR=2.33 (0.71-7.55) with p value of 0.15. The calculations were also made when diabetics
were further grouped into male and female OR=1.06 (p value of 0.91), age at disease onset <
46 and > 46 OR=0.32 (p = 0.06), BMI < 23 and > 23 OR=0.50 (p = 0.25), normotensive and
hypertensive OR=1.37 (p = 0.55) and no family history of disease verses family history of
the disease OR=0.89 (p = 0.85) (table 3.11A). The OR calculated for smokers and non-
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112
smokers among diabetics had OR=0.11 with p < 0.001 (p=0.0002) indicating that smokers
with allele C has 11% less chance of the disease as compared to the non-smokers.
For SNP2 (g.-243T>G), the OR calculated to test the relevance of frequencies of allele-T
and allele-G in both controls and diabetics was 0.51 with p = 0.22. Calculations made for
male and female diabetics (OR = 1.37, p = 0.57), age at disease onset < 46 and > 46 (OR =
0.58, p = 0.31), BMI < 23 and > 23 (OR = 0.51, p = 0.25), normotensive and hypertensive
cases (OR = 0.89, p = 0.83) and diabetics with and without family history of the disease (OR
= 0.90, p = 0.85) were found to be non-significant. However, the OR=8.06 calculated for
smokers and non-smokers had highly significant p < 0.001 (p=0.0003) indicating 8 fold
increase risk of the disease in smokers (table 3.11B).
In case of SNP3 (g.209G>T), diabetics had both allele G and allele T while controls had
only allele G and the OR when calculated for frequencies of allele-G and allele-T in controls
and diabetics (p = 0.01) was found to be significant. Calculations were also made for male
and female diabetics (OR = 2.30, p = 0.17), age at disease onset < 46 and > 46 (OR = 0.47, p
= 0.22), BMI < 23 and > 23 (OR = 1.71, p = 0.51), non-smokers and smokers (OR = 1.06, p
= 0.93), no family history verses family history of the disease (OR = 1.15, p = 0.82),
normotensive and hypertensive diabetics OR = 1.77 with (p = 0.37) and were found to be
non-significant (table 3.11C).
In case of SNP4 (g.2199G>A) OR calculated for relevance of frequencies of allele-G and
allele-A in controls and diabetics (OR = 0.94, p = 0.89) was found to be non-significant.
Calculations were also made for male and female (OR = 1.10, p = 0.84), age at disease onset
< 46 and > 46 (OR = 2.78, p = 0.05), BMI < 23 and > than 23 (OR = 1.16, p = 0.79),
normotensive and hypertensive (OR = 0.69, p = 0.47) and no family history of disease
Chapter 3
113
verses family history of the disease (OR = 2.69, p = 0.05) and were found to be non
significant. OR = 0.21 calculated for smokers and non-smokers was found to be significant
with p=0.008. (table 3.11D).
3.3.6 Comparison between effects of Genetic Variants and Clustering of Risk Factors
on Hyperglycemia/Fasting Blood Glucose (FBG >5.6 mmol/L)
Linear and logistic regression analyses were carried out on subjects with FBG > 5.6 mmol/L
unadjusted for genotype and analyzed through different models after adjusting with risk
factors (i.e., gender, age at disease onset, family history of the disease, BMI and HTN).
These models provide us the effect size of genetic factors alone and the effect size with
clustering of the risk factors. Result showed that the environmental risk factors have more
pronounced effect than the genetic effect alone. Among all the tested clustered variables no
significant effect came from any of the variables (gender, age at disease onset, family
history, BMI and hypertension) for risk allele associated with raised FBG levels. Our results
are in accordance with the Cohen’s rule showing that the clustering effect is increased from
weak to moderate with the addition of risk factors (table 3.12A-3.12D).
3.3.6.1 Effects of genetic variants and clustering of risk factors on hyperglycemia in SNP1
-385T>C
In case of SNP1 -385T>C the unadjusted genotype TT has a weak protective effect (β
Coefficient = -0.271) in diabetic patients. When adjusted for risk factors, such as gender, age
at disease onset, family history of the disease, BMI and hypertension, the sum cluster for all
the risk factors still have a protective effect of moderate degree (β Coefficient = -0.515) in
male patients according to Cohen effect (-51.5), females also had protective effect but it was
less than males (β Coefficient = -0.350) and cohen effect (-35.0). This protective effect was
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114
seen with age at disease onset < 46 yrs, however it was almost lost with the age at onset of
disease > 46 years (β Coefficient = -0.002). On addition of risk factor like hypertension
there was a slight decrease in the overall protective effect. BMI and family history of the
disease have slight effect (table 3.12A).
On the other hand, in patients with hyperglycemia the unadjusted genotype CC had no
protective effect (β Coefficient = 0.078). However, after adjustment for risk factors the
overall effect was weakly protective (β Coefficient -0.169) and cohen effect (-16.9). Female
gender was less protected (-01.9). The weak protective effect of patients with age at disease
onset < 46 years was lost in patients with age at disease onset > 46 years (19.9 in males and
34.9 in females). The change from T to C results in the loss of protective effect but it is not
giving any significant difference in our observations.
3.3.6.2 Effects of genetic variants and clustering of risk factors on hyperglycemia in
SNP2 -243T>G
In case of SNP2 -243T>G, the genotype TT in hyperglycemic patients have a weak non-
protective effect (β-coefficient=0.141) when unadjusted for risk factors. After adjusting for
risk factors a very weak protective effect (β-coefficient= -0.003) and cohen effect (-0.3) is
seen. Both male sex and age at disease onset add to this protective effect, BMI and FH also
had a slight protective effect. On the other hand hypertension has a weak non-protective
effect. While overall effect still shows a trend towards protective side. Genotype TG and GG
also have weak protective effect, TG (-17.5) and GG (-33.8). In females the protective effect
is lost with TT (8.4), TG (2.6) and weak protective effect is seen with GG (-17.3) as
compared to males (table 3.12B).
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115
In males with age onset > 46 years the protective effect is lost with genotype TT (23.3) and
TG (13.1) and GG genotype. While in females the risk is slightly more than males for each
genotype i.e. TT (34.6), TG (33.2) and GG (17.5) but still weak according to Cohen effect.
In our population TT genotype is more common and has a weak risk in patients with
hyperglycemia when adjusted for risk factors especially females and age > 46. The change
from T to G is not giving any significant difference in our observations.
3.3.6.3 Effects of genetic variants and clustering of risk factors on Hyperglycemia in
SNP3 +209G>T
In case of SNP +209G>T, the patients of hyperglycemia unadjusted for genotype GG has a
weak protective effect (β-coefficient = -0.100). Among the clustered variables males, age at
disease onset, FH have a protective effect, hypertension has a non-protective effect while
BMI has a negligible effect in this case. After adjustment for risk factors the overall effect
remained weakly protective in males (-29.7) with age < 46 years according to Cohen’s rule.
A weaker but still protective effect was also seen with genotype TT (-8.8) and GT (-18.7).
This is also true in case of females although less marked as compared to males GG (-17.1),
and TT (-3.4) except GT (2.2) which shows no protective effect (tables 3.12C).
In patients of hyperglycemia with age at disease onset > 46 years the protective effect was
lost with all three genotypes GG (0.3), TT (7.5) and GT (25.8) in males as well as females
GG (12.9), TT (22.8) and GT (36.8). The effect being more marked in females especially
with single allele change GT.
In our population genotype GG is more prevalent and has a slight protective effect in
patients with hyperglycemia when adjusted for risk factors especially males and age < 46.
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116
However, the change from G to T is associated with risk but does not give any significant
difference in our observations.
3.3.6.4 Effects of genetic variants and clustering of risk factors on hyperglycemia in
SNP4 +2199G>A
In case of SNP +2199G>A, the patients of hyperglycemia unadjusted for genotype GG has a
weak protective effect (β-coefficient = -0.273). Among the clustered variables males, age at
disease onset, FH have a protective effect while raised BMI and hypertension have a non
protective effect. After adjustment for risk factors the overall effect became moderately
protective in males (-57.6) and weakly protective in females (-37.2) according to Cohen’s
rule in patients with age at onset < 46 years. Weak protective effect was seen with genotype
GA (-10.7) and AA (-21.7) in males, while in female very weak protective effect was seen
with AA (-3) while this effect was lost with GA (5.8) (table 3.12D).
In patients of hyperglycemia with age at disease onset > 46 years genotype GG still have a
weak protective effect (-21.8) which was lost with genotype GA (13.1) and genotype AA
(16.2) in males. Females have less protective effect with genotype GG (-1.4) and the
genotypes GA (29.8) and AA (34.6) were associated with risk as compared to the males. In
our population the genotype AA is more common which is less protective.
However, the change from G to A especially in females, age at disease onset > 46 years and
BMI > 23 although are associated with the risk for the disease but were not giving any
significant difference in our observations. Therefore the change will be considered to have
no direct or significant role in hyperglycemia/diabetes.
Chapter 3
117
Table No. 3.8: Clinical Characteristics of the Study Subjects by using Independent Sample
t-test (n=51)
Characteristics Control
(n = 15)
Type II diabetics
(n = 36)
p value *
Sex M/F 9/6 25/11 --------
Age, yr 50+3 52+9 0.480
Age at disease onset, yrs ------- 46+9 -------
Duration of disease, yrs ------- 6+5 -------
Weight of patients, kg 68±8.7 71±11.4 0.314
Height of patients, inches 66±3.2 65±3.4 0.389
BMI, kg/m2 24.6+4 26+3 0.247
SBP, mm Hg 120(110 to 120) 130(120 to 140) 0.001**
DBP, mm Hg 80(70 to 80) 80(80 to 90) 0.007*
FBG, mmol/L 4.1(3.7 to 5.6) 8 (6.1 to 10.9) <0.001**
HbA1c, % 5.5(5.4 to 5.6) 8.3(7 to 9.3) <0.001**
Cholesterol, mmol/L 4(3.5 to 5) 5 (4.8 to 5.6) 0.014*
Triglyceride, mmol/L 1.2(0.9 to 1.5) 1.7(1.2 to 2.5) 0.066
Hemoglobin, mg/dl 13.2(12.6 to 14.2) 11.9(11 to 13) 0.101
SBP: Systolic blood pressure, DBP: Diastolic blood pressure
Data are expressed as means ± standard deviation or median (interquartile range) were the
data is non parametric. **p <0.001 and *p <0.05 for difference between control and diabetic
group was calculated by independent sample t-test
Chapter 3
118
Figure 3.20: Optimization of PCR amplication using different Primer sets
500bp
400bp
300bp
200bp
100bp
500bp
400bp
300bp
200bp
100bp
Primer Set I Primer Set 2 Primer Set 3
Primer Set 4 Primer Set 5 Primer Set 6
Chapter 3
119
Exon 1 (322 bp)
Exon 2 (449 bp)
Exon 3 (251 bp)
Figure 3.21: Figures are representing selected DNA samples for PCR amplification of
exonic regions (Bp: base pair)
500bp
400bp
300bp
200bp
100bp
500bp
400bp
300bp
200bp
100bp
500bp
400bp
300bp
200bp
100bp
1 2 3 4 5 L 6 7 8 9 10 L
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 L
1 2 3 4 5 6 7 8 9 10 11 L
Chapter 3
120
Exon 4 (294 bp)
Exon 5 (341 bp)
Exon 6 (358 bp)
Figure 3.21: Figures are representing selected DNA samples for PCR amplification of
exonic regions (bp: base pair)
500bp
400bp
300bp
200bp
100bp
500bp
400bp
300bp
200bp
100bp
500bp
400bp
300bp
200bp
100bp
1 2 3 4 5 L 6 7 8 9 10 L
1 2 3 4 5 L 6 7 8 9 10 L
1 2 3 4 5 L 6 7 8 9 10 L
Chapter 3
121
Figure 3.22: Map of REG Iα gene on chromosome 2p12 (2.7kb) (Gene ID: 5967, exon 2-6
coding) with the identified SNPs (kb: kilobase or kilobase pair)
Chapter 3
122
Table 3.9: Single Nucleotide Polymorphisms (SNPs) identified in REG Iα gene
Sr.
No SNPs Type
Reference
sequence
(rs)
Novelty
yes or no
Text
abbreviation
of SNPs
used
1 -385 T>C
(2: 79120477)
utr variant
5’ rs 10165462 No SNP1
2 -243 T>G
(2: 79120619 )
intron
variant rs 283890 No SNP2
3 -145 G>A
(2: 79120717)
intron
variant --------
Novel (all
cases)
4 +142 A missing
2:79121004
intron
variant rs11339710 No
5 +209 G>T
(2:79121070)
intron
variant rs 2070707 No SNP3
6 +226 A>G
(2:79121087)
intron
variant rs 192054590 No
7 +2199 G>A
(2: 79123060)
intron
variant rs 3739142 No SNP4
8 +2360 A>G
2:79123221
utr variant
3’ rs 12228 No
Chapter 3
123
Figure 3.23A): Left (SNP: -385 T>C). The electro-chromatogram of study subject showing
substitution of T>C (as indicated by an arrow). Right (SNP: -243 T>G) The electro-
chromatogram of study subject showing substitution of T>G (as indicated by an arrow)
Figure 3.23B): Left (SNP: -145 G>A) (all the study population had A). The electro-
chromatogram of study subject showing substitution of G>A (as indicated by an arrow).
Right (SNP: +142 A missing in all study subjects). The electro-chromatogram of the study
subjects showing deletion of A (as indicated by an arrow)
Chapter 3
124
Figure 3.23C): Left (SNP: +209 G>T). The electro-chromatogram of study subject
showing substitution of G>A (with reverse primer C>T) (as indicated by an arrow). Right
(SNP: +226 A>G). The electro-chromatogram of study subject showing substitution of G>A
(with reverse primer T>C) (as indicated by an arrow)
Figure 3.23D): Left (SNP: +2199 G>A). The electro-chromatogram of study subject showing
substitution of G>A (as indicated by an arrow). Right (SNP: +2360 A>G). The electro-
chromatogram of study subject showing substitution of A>G (as indicated by an arrow)
Chapter 3
125
Table 3.10A: (SNP 1: -385T>C) Genotypes, allele frequencies and Chi-square to show
significance of variables with Hardy Weinberg Equilibrium (HWE), (n=51)
Characteristics
Genotype Frequency Alleles Frequency Chi-sq
value p-value
TT, n (%) TC, n (%) CC, n (%) T, n (%) C, n (%)
Diabetic 9 (25) 1 (2.8) 26 (72.2) 19 (26.4) 53 (73.6) 31.03 0.000
Control 2 (13.3) 0 13 (86.7) 4 (13.3) 26 (86.7) 15 0.0001
Male 7 (20.6) 1 (2.9) 26 (76.5) 15 (22) 53 (78) 28.43 0.0000
Female 4 (23.5) 0 13 (76.5) 8 (23.5) 26 (76.5) 17 0.0000
Age onset <46 2 (14.3) 0 12 (85.7) 4 (14.3) 24 (85.7) 14 0.0001
Age onset >46 7 (31.9) 1 (4.5) 14 (63.6) 15 (34) 29 (66) 17.77 0.0000
BMI <23 3 (21.4) 0 11 (78.6) 6 (21.4) 22 (78.6) 14 0.0001
BMI>23 8 (21.6) 1 (2.7) 28 (75.7) 17 (23) 57 (77) 31.56 0.0000
Hypertensive 5 (22.7) 0 17 (77.3) 10 (22.7) 34 (77.3) 22 0.0000
Non-hypertensive 6 (20.7) 1(3.4) 22 (75.9) 13 (22.4) 45 (77.6) 23.53 0.0000
Smoker 6 (66.7) 0 3 (33.3) 12 (66.7) 6 (33.3) 9 0.002
Non smoker 5 (11.9) 1 (2.4) 36 (85.7) 11 (13) 73 (87) 33.67 0.0000
FH (Yes) 7 (23.3) 1 (3.3) 22 (73.3) 15 (25) 45 (75) 24.90 0.0000
FH (No) 4 (19) 0 17 (81) 8 (19) 34 (81) 21 0.0000
The p value of <0.05, <0.001 and 0.0000 indicates the population is deviated from HWE and
appears to be stratified
Ω
Falling in accordance with HWE
Chapter 3
126
Table No. 3.10B: (SNP2: -243 T>G) Genotypes, allele frequencies and Chi-sq to show
significance of variables with HWE, (n=51)
Characteristics
Genotype Frequency Alleles Frequency Chi-sq
value p-value
TT, n (%) TG, n (%) GG, n (%) T, n (%) G, n (%)
Diabetic 22 (61) 5 (14) 9 (25) 49 (68) 23 (32) 16.67 0.0000
Control 11 (73.4) 2 (13.3) 2(13.3) 24 (80) 6 (20) 5.10 0.02
Male 21 (62) 6 (18) 7 (20) 48 (71) 20 (29) 11.24 0.0007
Female 12 (70.5) 1 (6) 4 (23.5) 25 (74) 9 (26) 12.25 0.0004
Age onset <46 9 (64.3) 3 (21.4) 2 (14.3) 21 (75) 7 (25) 2.57 0.10Ω
Age onset >46 13 (59) 2 (9) 7 (32) 28 (64) 16 (36) 14.20 0.0001
BMI <23 9 (64.3) 2 (14.3) 3 (21.4) 20 (71) 8 (29) 5.91 0.01
BMI>23 24 (64.8) 5 (13.5) 8 (21.6) 53 (72) 21 (28) 16.48 0.000
Hypertensive 14 (63.6) 3 (13.6) 5 (22.7) 31 (70.5) 13 (29.5) 9.94 0.001
Non-hypertensive 19 (65.5) 4 (13.8) 6 (20.7) 42 (72) 16 (28) 12.43 0.0004
Smoker 2 (22) 1 (11) 6 (67) 5 (28) 13 (72) 4.70 0.03
Non smoker 31 (74) 6 (14) 5 (12) 68 (81) 16 (19) 12.10 0.0005
FH (Yes) 18 (60) 5 (17) 7 (23) 41 (68) 19 (32) 11.34 0.0007
FH (No) 15 (71) 2 (10) 4 (19) 32 (76) 10 (24) 11.42 0.0007
The p value of <0.05, <0.001 and 0.0000 indicates the population is deviated from HWE and
appears to be stratified
Ω
Falling in accordance with HWE
Chapter 3
127
Table 3.10C: (SNP3: +209G>T) Genotypes, allele frequencies and Chi-sq to show
significance of variables with HWE, (n=51)
Characteristics
Genotype Frequency Alleles Frequency Chi-sq
value p-value
GG, n (%) GT, n (%) TT, n (%) G, n (%) T, n (%)
Diabetic 28 (78) 3 (8) 5 (14) 59 (82) 13 (18) 18.57 0.0000
Control 15 (100) 0 0 30 (100) 0 0 0
Male 30 (88) 1 (3) 3 (9) 61 (90) 7 (10) 24.03 0.0000
Female 13 2 2 28 6 6.02 0.01
Age onset <46 9 (64.3) 3 (21.4) 2 (14.3) 21 (75) 7 (25) 2.57 0.10Ω
Age onset >46 19 (86) 0 3 (14) 38 (86) 6 (14) 22 0.0000
BMI <23 13 (93) 0 1 (7) 26 (93) 2 (7) 14 0.0001
BMI>23 30 (81) 3 (11) 4 (8) 63 (85) 11 (15) 17.09 0.0000
Hypertensive 17 (77.3) 1 (4.5) 4 (18.2) 35 (80) 9 (20) 16.28 0.0000
Non-hypertensive 26 (90) 2 (7) 1 (3) 54 (93) 4 (7) 6.21 0.01
Smoker 7 (78) 1 (11) 1 (11) 15 (83) 3 (17) 3.24 0.07Ω
Non smoker 36 (85.7) 2 (4.8) 4 (9.5) 74 (88) 10 (12) 25.09 0.0000
FH (Yes) 24 (80) 3 (10) 3 (10) 51 (85) 9 (15) 11.08 0.0008
FH (No) 19 (90.5) 0 2 (9.5) 38 (90.5) 4 (9.5) 21 0.0000
The p value of <0.05, <0.001 and 0.0000 indicates the population is deviated from HWE and
appears to be stratified
Ω
Falling in accordance with HWE
Chapter 3
128
Table 3.10D: (SNP4: +2199G>A) Genotypes, allele frequencies and Chi-sq to show
significance of variables with HWE, (n=51)
Characteristics
Genotype Frequency Alleles Frequency Chi-sq
value
p-value GG, n (%) GA, n (%) AA, n( %) G, n (%) A, n (%)
Diabetic 12 (33) 1 (03) 23 (64) 25 (35) 47 (65) 31.7 0.0000
Control 5 (33) 0 10 (67) 10 (33) 20 (67) 15 0.0001
Male 11 (32) 1 (3) 22 (65) 23 (34) 45 (66) 29.67 0.0000
Female 6 (35) 0 11 (65) 12 (35) 22 (65) 17 0.0000
Age onset <46 3 (21) 0 11 (79) 6 (21) 22 (79) 14 0.0001
Age onset >46 9 (41) 1 (4.5) 12 (54.5) 19 (43) 25 (57) 18.11 0.0000
BMI <23 4 (29) 0 10 (71) 8 (29) 20 (71) 14 0.0001
BMI>23 13 (35) 1 (3) 23 (62) 27 (36) 47 (64) 32.81 0.0000
Hypertensive 9 (41) 0 13 (59) 18 (41) 26 (59) 22 0.0000
Non-hypertensive 8 (28) 1 (3) 20 (69) 17 (29) 41 (71) 24.37 0.0000
Smoker 6 (67) 0 3 (33) 12 (67) 6 (33) 9 0.002
Non smoker 11 (26.2) 1 (2.4) 30 (71.4) 23 (27) 61 (73) 37.12 0.0000
FH (Yes) 8 (27) 1 (3) 21 (70) 17 (28) 43 (72) 25.27 0.0000
FH (No) 9 (43) 0 12 (57) 18 (43) 24 (57) 21 0.0000
The p value of <0.05, <0.001 and 0.0000 indicates the population is deviated from HWE and
appears to be stratified
Ω
Falling in accordance with HWE
Chapter 3
129
Table 3.11A: Logistic regression determinants of main variables with frequencies of allele-
T and Allele-C in SNP 1: -385T>C
Alleles Comparative Frequencies of Cases
Total OR
95 % CI
Chi-sq
value p-value
Control Diabetes
-385T>C
Allele-T 4 19 23 2.33
(0.71-7.55) 2.06 0.15
Allele-C 26 53 79
30 72 102
-385T>C Age at onset < 46 Age at onset > 46
Allele-T 4 15 19 0.32
(0.09-1.10) 3.45
0.06 Allele-C 24 29 53
28 44 72
-385T>C BMI<23 BMI>23
Allele-T 6 13 19 0.50
(0.15-1.65) 1.30
0.25 Allele-C 10 43 53
16 56 72
-385T>C Non Smoker Smoker
Allele-T 9 10 19 0.11
(0.03-0.39)
13.8
0.0002 Allele-C 47 6 53
56 16 72
-385T>C Normotensive Hypertensive
Allele-T 9 10 19 1.37
(0.47-3.94) 0.34
0.55 Allele-C 21 32 53
30 42 72
-385T>C FH (No) FH (Yes)
Allele-T 6 13 19 0.89
(0.29-2.75) 0.03
0.85 Allele-C 18 35 53
24 48 72
-385T>C Female Male
Allele-T 6 13 19 1.06
(0.34-3.30)
0.01
0.91 Allele-C 16 37 53
22 50 72
Distribution of clinical factors are shown as number (%). p-values were obtained by chi-
square test. p-value** is <0.001 and p-value* is <0.05 for difference between two group
Chapter 3
130
Table 3.11B: Logistic regression determinants of main variables with frequencies of allele-
T and Allele-G in SNP 2: -243T>G
-243T>G
Comparative Frequencies of Cases
Total OR
95 % CI
Chi-sq
value
p-value
Control Diabetic
Allele-T 24 49 73 0.53
(0.19-1.5) 1.48 0.22
Allele-G 6 23 29
30 72 102
-243T>G Age at onset < 46 Age at onset > 46
Allele-T 21 28 49 0.58
(0.20-1.6) 1.01 0.31
Allele-G 7 16 23
28 44 72
-243T>G BMI<23 BMI>23
Allele-T 9 40 49 0.51
(0.16-1.6) 1.31 0.25
Allele-G 7 16 23
16 56 72
-243T>G Non Smoker Smoker
Allele-T 44 5 49 8.06
(2.3-27.7) 12.8 0.0003
Allele-G 12 11 23
56 16 72
-243T>G Normotensive Hypertensive
Allele-T 16 29 49 0.89
(0.32-2.4) 0.04 0.83
Allele-G 8 13 23
30 42 72
-243T>G FH (No) FH (Yes)
Allele-T 16 33 49 0.90
(0.31-2.6) 0.03 0.85
Allele-G 8 15 23
24 48 72
-243T>G Female Male
Allele-T 16 33 49 1.37
(0.45-4.2) 0.31 0.57
Allele-G 6 17 23
22 50 72
Distribution of clinical factors are shown as number (%). p-values were obtained by chi-
square test. p-value** is >0.001 and p-value* is >0.05 for difference between two group
Chapter 3
131
Table 3.11C: Logistic Regression determinants of main variables with frequencies of allele-
G and Allele-T in SNP 3: +209G>T
+209G>T Comparative Frequencies of Cases
Total
OR
95 % CI
Chi-sq
value p-value
Control Diabetic
Allele-G 30 59 89 0 6.20 0.01
Allele-T 0 13 13
30 72 102
+209G>T Age at onset < 46 Age at onset > 46
Allele-G 21 38 59 0.47
(0.14-1.59) 1.49 0.22
Allele-T 7 6 13
28 44 72
+209G>T BMI<23 BMI>23
Allele-G 14 45 59 1.71
(0.33-8.66) 0.42 0.51
Allele-T 2 11 13
16 56 72
+209G>T Non Smoker Smoker
Allele-G 46 13 59 1. 06
(0.25-4.43) 0.006 0.93
Allele-T 10 3 13
56 16 72
+209G>T Normotensive Hypertensive
Allele-G 26 33 59 1.77
(0.49-6.40) 0.77 0.37
Allele-T 4 9 13
30 42 72
+209G>T FH (No) FH (Yes)
Allele-G 20 39 59 1.15
(0.31-4.21) 0.04 0.82
Allele-T 4 9 13
24 48 72
+209G>T Female Male
Allele-G 16 43 59 2.30
(0.67-7.89) 1.81 0.17
Allele-T 6 7 13
22 50 72
Distribution of clinical factors are shown as number (%). p-values were obtained by chi-
square test. p-value** is >0.001 and p-value* is >0.05 for difference between two group
Chapter 3
132
Table 3.11D: Logistic Regression determinants of main variables with frequencies of allele-
G and Allele-A in SNP 4: +2199G>A
+2199G>A Comparative Frequencies of Cases
Total OR
95 % CI
Chi-sq
value p-value
Control Diabetic
Allele-G 10 25 35 0.94
(0.38-2.31) 0.01 0.89
Allele-A 20 47 67
30 72 102
+2199G>A Age at onset < 46 Age at onset > 46
Allele-G 6 19 25 2.78
(0.94-8.22) 3.57
0.05
Allele-A 22 25 47
28 44 72
+2199G>A BMI<23 BMI>23
Allele-G 6 19 25 1.16
(0.36-3.70)
0.07
0.79 Allele-A 10 37 47
16 56 72
+2199G>A Non Smoker Smoker
Allele-G 15 10 25 0.21
(0.06-0.70)
7.0
0.008 Allele-A 41 6 47
56 16 72
+2199G>A Normotensive Hypertensive
Allele-G 9 16 25 0.69
(0.25-1.89)
0.50
0.47 Allele-A 21 26 47
30 42 72
+2199G>A FH (No) FH (Yes)
Allele-G 12 13 25 2.69
(0.96-7.48) 3.70 0.05
Allele-A 12 35 47
24 48 72
+2199G>A Female Male
Allele-G 8 17 25 1.10
(0.38-3.16) 0.03 0.84
Allele-A 14 33 47
22 50 72
Distribution of clinical factors are shown as number (%). p-values were obtained by chi-
square test. p-value** is >0.001 and p-value* is >0.05 for difference between two group
Chapter 3
133
Table 3.12A: Models for cluster analysis using different variables to find their association
with genotype (SNP1: -385T>C)
Variables with
Adjustments
Model for FBG > 5.6 mmol/L
TT CC
Beta Coefficient
Increase in FBG
due to risk
factors
Beta Coefficient
Increase in FBG
due to risk
factors
Genotype
Gender (M)
Age (<46)
Family H/O
BMI(<23)
HTN
Sum Cluster
-0.265
-0.110
-0.174
-0.077
-0.015
0.126
-0.515
-51.5 (M)
0.078
-0.100
-0.184
-0.065
-0.008
0.110
-0.169
-16.9 (W)
Genotype
Gender (F)
Age (<46)
FH
BMI (<23)
HTN
Sum Cluster
-0.265
0.055
-0.174
-0.077
-0.015
0.126
-0.350
-35 (W)
0.078
0.050
-0.184
-0.065
-0.008
0.110
-0.019
-01.9 (W)
Genotype
Gender (M)
Age (>46)
FH
BMI (>23)
HTN
Sum Cluster
-0.265
-0.110
0.174
-0.077
-0.015
0.126
-0.167
-16.7 (W)
0.078
-0.100
0.184
-0.065
-0.008
0.110
0.199
19.9 (W)
Genotype
Gender (F)
Age (>46)
FH
BMI (>23)
HTN
Sum Cluster
-0.265
0.055
0.174
-0.077
-0.015
0.126
-0.002
-00.2 (W)
0.078
0.050
0.184
-0.065
-0.008
0.110
0.349
34.9 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
134
Table 3.12A: Models for cluster analysis using different variables to find their association
with genotype (SNP1 -385T>C)
Variables with
Adjustments
Model for FBG > 5.6 mmol/L
TT CC
Beta Coefficient Increase in FBG
due to risk factors Beta Coefficient
Increase in FBG
due to risk
factors
Genotype
Gender (M)
Age (<46)
FH
BMI (<23)
HTN
Sum Cluster
-0.265
-0.110
-0.174
-0.077
0.030
0.126
-0.470
-47 (M)
0.078
-0.100
-0.184
-0.065
0.017
0.110
-0.144
-14.4 (W)
Genotype
Gender (F)
Age (<46)
FH
BMI (<23)
HTN
Sum Cluster
-0.265
0.055
-0.174
-0.077
0.030
0.126
-0.305
-30.5 (W)
0.078
0.050
-0.184
-0.065
0.017
0.110
0.006
0.6 (W)
Genotype
Gender M
Age (>46)
FH
BMI (<23)
HTN
Sum Cluster
-0.265
-0.110
0.174
-0.077
0.030
0.126
-0.122
-12.2 (W)
0.078
-0.100
0.184
-0.065
0.017
0.110
0.224
22.4 (W)
Genotype
Gender (F)
Age (>46)
FH
BMI (<23)
HTN
Sum Cluster
-0.265
0.055
0.174
-0.077
0.030
0.126
0.043
4.3 (W)
0.078
0.050
-0.184
-0.065
0.017
0.110
0.374
37.4 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
135
Table 3.12B: Models for cluster analysis using different variables to find their association
with genotype (SNP2 -243T>G)
Variables
with
Adjustment
Model for FBG > 5.6 mmol/L
TT TG GG
Beta
Coefficient
Increase
in FBG
due to
risk
factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Genotype 0.141 0.07 -0.088
Gender (M) -0.076 -0.134 -0.11
Age (<46) -0.131 -0.153 -0.174
FH -0.088 -0.093 -0.077
BMI (>23) -0.011 0.005 -0.015
HTN 0.135
0.13 0.126
Sum Cluster -0.03 -3 (W) -0.175 -17.5 (W) -0.338 -33.8 (W)
Genotype 0.141 0.07 -0.088
Gender (F) 0.038 0.067 0.055
Age (<46) -0.131 -0.153 -0.174
Family H/O -0.088 -0.093 -0.077
BMI (>23) -0.011 0.005 -0.015
HTN 0.135 0.13 0.126
Sum Cluster 0.084 8.4 (W) 0.026 2.6 (W) -0.173 -17.3 (W)
Genotype 0.141 0.07 -0.088
Gender (M) -0.076 -0.134 -0.11
Age (>46) 0.131 0.153 0.174
Family H/O -0.088 -0.093 -0.077
BMI (>23) -0.011 0.005 -0.015
HTN 0.136
0.13 0.126
Sum Cluster 0.233 23.3 (W) 0.131 13.1 (W) 0.01 1 (W)
Genotype 0.141 0.07 -0.088
Gender (F) 0.038 0.067 0.055
Age (>46) 0.131 0.153 0.174
Family H/O -0.088 -0.093 -0.077
BMI (>23) -0.011 0.005 -0.015
HTN 0.135 0.13 0.126
Sum Cluster 0.346 34.6 (W) 0.332 33.2 (W) 0.175 17.5 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
136
Table 3.12B: Models for cluster analysis using different variables to find their association
with genotype (SNP2 -243T>G)
Variables
with
Adjustments
Model for FBG > 5.6 mmol/L
TT TG GG
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Genotype 0.141
0.07
-0.088
Gender (M) -0.076
-0.134
-0.11
Age (<46) -0.131
-0.153
-0.174
Family H/O -0.088
-0.093
-0.077
BMI (<23) 0.022
-0.011
0.03
HTN 0.135
0.13
0.126
Sum Cluster 0.003 0.3 (W) -0.191 -19.1 (W) -0.293 -29.3 (W)
Genotype 0.141
0.07
-0.088
Gender (F) 0.038
0.067
0.055
Age (<46) -0.131
-0.153
-0.174
Family H/O -0.088
-0.093
-0.077
BMI (<23) 0.022
-0.011
0.03
HTN 0.135
0.13
0.126
Sum Cluster 0.117 11.7 (W) 0.01 1 (W) -0.128 -12.8 (W)
Genotype 0.141
0.07
-0.088
Gender (M) -0.076
-0.134
-0.11
Age (>46) 0.131
0.153
0.174
Family H/O -0.088
-0.093
-0.077
BMI (<23) 0.022
-0.011
0.03
HTN 0.135
0.13
0.126
Sum Cluster 0.265 26.5 (W) 0.115 11.5 (W) 0.055 5.5 (W)
Genotype 0.141
0.07
-0.088
Gender (F) 0.038
0.067
0.055
Age (>46) 0.131
0.153
0.174
Family H/O -0.088
-0.093
-0.077
BMI (<23) 0.022
-0.011
0.03
HTN 0.135
0.13
0.126
Sum Cluster 0.379 37.9 (W) 0.316 31.6 (W) 0.22 22 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
137
Table 3.12C: Models for cluster analysis using different variables to find their association
with genotype (SNP3 +209G>T)
Variables
with
Adjustments
Model for FBG > 5.6 mmol/L
GG GT TT
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Genotype -0.1
0.115
0.005
Gender (M) -0.084
-0.075
-0.102
Age (<46) -0.15
-0.173
-0.131
Family H/O -0.093
-0.11
-0.094
BMI (>23) -0.002
-0.003
0.002
HTN 0.132
0.158
0.133
Sum Cluster -0.297 -29.7 (W) -0.088 -8.8 (W) -0.187 -18.7 (W)
Genotype -0.1
0.115
0.005
Gender (F) 0.042
0.035
0.051
Age (<46) -0.15
-0.173
-0.131
Family H/O -0.093
-0.11
-0.094
BMI (>23) -0.002
-0.003
0.002
HTN 0.132
0.158
0.133
Sum Cluster -0.171 -17.1 (W) 0.022 2.2 (W) -0.034 -3.4 (W)
Genotype -0.1
0.115
0.005
Gender (M) -0.084
-0.075
-0.102
Age (>46) 0.15
0.173
0.131
Family H/O -0.093
-0.11
-0.094
BMI (>23) -0.002
-0.003
0.002
HTN 0.132
0.158
0.133
Sum Cluster 0.003 0.3 (W) 0.258 25.8 (W) 0.075 7.5 (W)
Genotype -0.1
0.115
0.005
Gender (F) 0.042
0.035
0.051
Age (>46) 0.15
0.173
0.131
Family H/O -0.093
-0.11
-0.094
BMI (>23) -0.002
-0.003
0.002
HTN 0.132
0.158
0.133
Sum Cluster 0.129 12.9 (W) 0.368 (36.8W) 0.228 22.8 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
138
Table 3.12C: Models for cluster analysis using different variables to find their association
with genotype (SNP3 +209G>T)
Variables
with
Adjustment
Model for FBG > 5.6 mmol/L
GG GT TT
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Genotype -0.1 0.115 0.005
Gender (M) -0.084 -0.075 -0.102
Age (<46) -0.15 -0.173 -0.131
Family H/O -0.093 -0.11 -0.094
BMI (<23) 0.005 0.007 -0.003
HTN 0.132
0.158 0.133
Sum Cluster -0.29 -29 (W) -0.078 -7.8 (W) -0.192 -19.2 (W)
Genotype -0.1 0.115 0.005
Gender (F) 0.042 0.035 0.051
Age (<46) -0.15 -0.173 -0.131
Family H/O -0.093 -0.11 -0.094
BMI (<23) 0.005 0.007 -0.003
HTN 0.132 0.158 0.133
Sum Cluster -0.164 -16.4 (W) 0.032 3.2 (W) -0.039 -3.9 (W)
Genotype -0.1 0.115 0.005
Gender (M) -0.084 -0.075 -0.102
Age (>46) 0.15 0.173 0.131
Family H/O -0.093 -0.11 -0.094
BMI (<23) 0.005 0.007 -0.003
HTN 0.132
0.158 0.133
Sum Cluster 0.01 1 (W) 0.268 26.8 (W) 0.07 7 (W)
Genotype -0.1 0.115 0.005
Gender (F) 0.042 0.035 0.051
Age (>46) 0.15 0.173 0.131
Family H/O -0.093 -0.11 -0.094
BMI (<23) 0.005 0.007 -0.003
HTN 0.132 0.158 0.133
Sum Cluster 0.136 13.6 (W) 0.378 37.8 (W) 0.223 22.3 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
139
Table 3.12D: Models for cluster analysis using different variables to find their association
with genotype (SNP4 +2199G>A)
Variables
with
Adjustment
Model for FBG > 5.6 mmol/L
GG GA AA
Beta
Coefficient
Increase in
FBG due
to risk
factors
Beta
Coefficient
Increase in
FBG due
to risk
factors
Beta
Coefficient
Increase in
FBG due
to risk
factors
Genotype -0.273
0.084
0.079
Gender (M) -0.136
-0.11
-0.123
Age (<46) -0.179
-0.12
-0.188
Family H/O -0.174
-0.106
-0.149
BMI (>23) 0.017
-0.002
0.02
HTN 0.169
0.147
0.147
Sum Cluster -0.576 -57.6 (M) -0.107 -10.7 (W) -0.214 -21.4 (W)
Genotype -0.273
0.084
0.079
Gender (F) 0.068
0.055
0.061
Age (<46) -0.179
-0.12
-0.188
Family H/O -0.174
-0.106
-0.149
BMI (>23) 0.017
-0.002
0.02
HTN 0.169
0.147
0.147
Sum Cluster -0.372 -37.2 (W) 0.058 5.8 (W) -0.03 -3 (W)
Genotype -0.273
0.082
0.079
Gender (M) -0.136
-0.11
-0.123
Age (>46) 0.179
0.12
0.188
Family H/O -0.174
-0.106
-0.149
BMI (>23) 0.017
-0.002
0.02
HTN 0.169
0.147
0.147
Sum Cluster -0.218 -21.8 (W) 0.131 13.1 (W) 0.162 16.2 (W)
Genotype -0.273
0.084
0.079
Gender (F) 0.068
0.055
0.061
Age (>46) 0.179
0.12
0.188
Family H/O -0.174
-0.106
-0.149
BMI (>23) 0.017
-0.002
0.02
HTN 0.169
0.147
0.147
Sum Cluster -0.014 -1.4 (W) 0.298 29.8 (W) 0.346 34.6 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
140
Table 3.12D: Models for cluster analysis using different variables to find their association
with genotype (SNP4 +2199G>A)
Variables with
Adjustments
Model for FBG > 5.6 mmol/L
GG GA AA
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Beta
Coefficient
Increase in
FBG due to
risk factors
Genotype -0.273
0.084
0.079
Gender (M) -0.136
-0.11
-0.123
Age (<46) -0.179
-0.12
-0.188
Family H/O -0.174
-0.106
-0.149
BMI (<23) -0.034
-0.002
-0.039
HTN 0.169
0.147
0.147
Sum Cluster -0.627 -62.7 (M) -0.107 -10.7 (W) -0.273 -27.3 (W)
Genotype -0.273
0.084
0.079
Gender (F) 0.068
0.055
0.061
Age (<46) -0.179
-0.12
-0.188
Family H/O -0.174
-0.106
-0.149
BMI (<23) -0.034
0.003
-0.039
HTN 0.169
0.147
0.147
Sum Cluster -0.423 -42.3 (W) 0.063 6.3 (W) -0.089 -8.9 (W)
Genotype -0.273
0.082
0.079
Gender (M) -0.136
-0.11
-0.123
Age (>46) 0.179
0.12
0.188
Family H/O -0.174
-0.106
-0.149
BMI (<23) -0.034
0.003
-0.039
HTN 0.169
0.147
0.147
Sum Cluster -0.269 -26.9 (W) 0.136 13.6 (W) 0.103 10.3 (W)
Genotype -0.273
0.084
0.079
Gender (F) 0.068
0.055
0.061
Age (>46) 0.179
0.12
0.188
Family H/O -0.174
-0.106
-0.149
BMI (<23) -0.034
0.003
-0.039
HTN 0.169
0.147
0.147
Sum Cluster -0.065 -6.5 (W) 0.303 30.3 (W) 0.287 28.7 (W)
Importance of effect of clustered factors is shown based on Cohen’s rule as (W=weak,
M=moderate, S=strongest effects). FBG: Fasting blood glucose, BMI: Body mass index,
HTN: hypertension, FH: Family history
Chapter 3
141
80,82,83,85,86,88,95,97-105,118-121,123-124
76-79,81,84,87,89-94,96,106-117,122,125-140
Chapter 4
141
Discussion
This study, demonstrated that mReg2 inhibits AIF-mediated apoptotic signaling triggered by
doxorubicin in a time dependent manner. It was reported earlier that mReg2 protects insulin
producing cells from Stz activated cell death by inhibiting its aptitude to disrupt
mitochondrial membrane potential (ΔΨm) and also from mitochondrial caspase-mediated
apoptotic signaling (Liu et al., 2010). Moreover, it also inhibits the ER stress induced UPR
by up-regulating the basal expression of GRP78 a chaperone protein of ER (Liu et al.,
2014b). mReg2 inhibits apoptosis induced by stress inducers both in the absence and in the
presence of caspase inhibitor, thus indicating that it inhibits cell death not only in caspase-
dependent but also in caspase-independent manner (Liu et al., 2015).
In the present study, we demonstrated that mReg2 inhibits caspase-independent AIF-
mediated apoptotic signaling triggered by doxorubicin in a time dependent manner.
Ectopically introduced mReg2 in MIN6 cells prevents the nuclear import of AIF in cells
exposed to the apoptotic stimuli. In the MIN6 cells transfected with empty vector when
exposed to apoptotic stimuli nuclear accumulation of AIF occurred in concomitant with the
exclusion of Scythe from the nuclear compartment. In contrast, we observed accumulation
of Scythe in the nucleus and the cytosolic retention of AIF in MIN6-mReg2 cells. Moreover,
we also observed that over-expression of mReg2 in MIN6 cells resulted in significant up-
regulation in the expression of inducible hsp70 that is known to complex with AIF and
prevents it’s cytosolic to nuclear translocation (Cande et al., 2002). Thus to our knowledge,
this study is first to demonstrate that mReg2 by inducing hsp70 promotes the cytosolic
retention of AIF and regulates the nuclear accretion of Scythe.
Chapter 4
142
When cells are exposed to apoptotic stimuli, the pro-apoptotic events in the mitochondria,
involves cleavage and release of AIF located behind the outer mitochondrial membrane
layer as a soluble 57 kDa protein into the cytosol which then enters the nucleus, where it
directly interact with DNA and initiate chromatin condensation and disintegration of DNA
leading to apoptotic cell death in a caspase-free mode (Cheung et al., 2006, Otera et al.,
2005, Joza et al., 2009). When MIN6-VC cells were treated with doxorubicin, an
anthracycline which acts primarily as a topoisomerase II inhibitor and also generates free
radicals to induce cell death, they exhibited increase presence of AIF in the nucleus and
reduce levels of Scythe in this organelle. However, over-expression of mReg2 in these cells
prevented the cytosolic to nuclear translocation of AIF in response to Dox induced apoptotic
stimuli. This corresponds with the nuclear retention of Scythe. The present study thus
present the first documentary evidence that mReg2 inhibits caspase-independent apoptotic
signaling axis by escalating the nuclear Scythe and attenuating the nuclear transport of AIF
without affecting the overall expression of these proteins.
The present finding regarding the role of mReg2 in inhibiting AIF-mediated apoptosis due to
nuclear presence of Scythe is in accord with the reported ability of Scythe to protect the
nuclear DNA from AIF-dependent apoptosis in human osteosarcoma triggered by
papillomavirus-binding factor (Tsukahara et al., 2009). In response to apoptotic stimuli
elevated nuclear export of Scythe stabilizes YWK-II protein/APLP2 and promotes cell
survival in human colorectal cancer thus further supporting the anti-apoptotic role of nuclear
Scythe (Wu et al., 2012). Moreover, the increase in cytosolic Scythe and nuclear transport of
AIF in MIN6-VC leading to enhanced apoptosis in our study is in accord with the earlier
reported study demonstrating the nuclear to cytosolic export of Scythe in cells undergoing
Chapter 4
143
apoptosis (Desmots et al., 2005, Desmots et al., 2008). All the above mentioned
observations strongly support the cell survival role of nuclear Scythe and contradicts the
previously reported finding that Scythe being a nuclear protein is not redistributed in
response to apoptotic stimuli (Manchen and Hubberstey, 2001). Moreover, our finding that
the increase cytosolic Scythe is equivalent to the nuclear accretion of AIF in MIN6-VC cells
disagree with the previous study that reported cytosolic Scythe-AIF interaction affects the
turnover of AIF in cells undergoing programmed cell death (Desmots et al., 2005, Desmots
et al., 2008, Preta and Fadeel, 2012a). At present, we do not know how mReg2 regulates
sub-cellular distribution of Scythe and AIF. Further studies are required to elucidate how
mReg2 effects redistribution of Scythe and AIF at the sub-cellular level in order to have
enhanced understanding of the mReg2 role in protecting β-cells against AIF-mediated
apoptosis.
Up-regulation of hsp70 by mReg2 is another factor that may play a part in attenuation of
AIF-mediated apoptosis. It has been reported that up-regulated expression of hsp70 retains
AIF in the cytosolic compartment and thereby interferes with AIF-mediated programmed
cell death (Lui and Kong, 2007). The molecular chaperon hsp70 form a complex with AIF in
the cytosol and retards its nuclear translocation thus attenuating its apoptotic effect
(Gurbuxani et al., 2003, Matsumori et al., 2005, Ruchalski et al., 2006, Kondrikov et al.,
2015) whereas deletion of AIF binding domain of hsp70 or knockdown of hsp70 increases
AIF-mediated apoptosis (Gurbuxani et al., 2003, Choudhury et al., 2011). Taken together,
all these studies and our own data regarding mReg2 induces hsp70 expression supports the
concept that it might promote cytosolic withholding of AIF in MIN6-mReg2 cells. On the
contrary, increase cytosolic AIF has been reported to activate the caspase 7 of the caspase-
Chapter 4
144
dependent apoptotic pathway and leads to apoptotic exposure of phosphatidylserine on
interaction with Scythe (Kim et al., 2006, Preta and Fadeel, 2012a). However, the present
study suggests that cytosolic AIF may not influence either caspase activation or caspase-
dependent apoptosis in pancreatic β-cells in the presence of mReg2.
Our results suggest that mReg2 protects β-cells against AIF-mediated caspase-independent
apoptosis. This AIF-dependent apoptosis correlates with the nuclear export of AIF with
concurrent depletion of nuclear Scythe (figure 4.1). Ectopically introduced mReg2, inhibits
the nuclear entry of AIF but retains nuclear Scythe which may be important for cell survival
in accord with the previous findings (Tsukahara et al., 2009). Similarly, export of Scythe
from nucleus to cytosol in MIN6-VC cells may be responsible for increase nuclear transfer
of AIF during ER stress-induced apoptosis as previously reported (Desmots et al., 2008).
The increase cytosolic presence of Scythe in MIN6 cells may hinder the ability of hsp70 to
keep AIF in the cytosol, leading to increased nuclear entry of AIF, its associated DNA
fragmentation and cell demise. On the contrary, the increased hsp70 expression and the
reduction of cytosolic Scythe in response to cellular stress may stabilize the interaction
between hsp70 and AIF, preventing it's nuclear translocation. In conclusion, we have shown
that mReg2 have an anti-apoptotic or protective effect on insulin secretory cells regulated by
important molecular mediators like Scythe and hsp70.
Serum REG Iα levels in diabetic patients
To extend our study further we measured serum REG Iα levels in diabetic patients. Serum
REG Iα was selected as it exhibits 63% sequence homology with mReg2 and also because of
the fact that humans does not have mReg2. We found REG Iα levels to be significantly up-
regulated in both type I and type II diabetics compared to normal subjects. Amongst the
Chapter 4
145
Figure 4.1: Proposed mechanism of mReg2 mediated inhibition of AIF-dependent
apoptosis. mReg2 protects against caspase-independent AIF-induced, apoptosis by (i)
promoting the nuclear accumulation of Scythe, (ii) preventing the cytosolic to nuclear
transport of AIF and(iii) increasing the expression of inducible hsp70. A reduction of
cytosolic Scythe and induction of hsp70 by mReg2 may inhibit movement of AIF from
cytosol to the nucleus (Liu et al., 2015).
Chapter 4
146
diabetics, the levels were more up-regulated in type II diabetics. The raised levels were
observed in type II diabetics with short disease duration, and to a lesser degree, in patients
with longer disease duration. Raised circulatory REG Iα levels have been reported
previously in both type I and type II diabetics and from third decade onwards in patients
with maturity onset diabetes of the young (MODY) (Astorri et al., 2010, Bacon et al., 2012,
Yang et al., 2014). However, this study is first of its kind in our country, where there is an
alarming rise in the number of individuals suffering from this life threatening and
debilitating disease. This study also confirms the previous widespread work done in
experimental models of diabetes and of β-cell regeneration (Unno et al., 2002).
The up-regulated expression of Reg I gene have been observed after loss of β-cells due to
local infiltration of immune cells, in type I diabetes (Gurr et al., 2002). Its expression has
also been demonstrated to be enhanced at a very early stage in high lipid diet-induced mice
models, while there progression from obesity to type II diabetes (Qiu et al., 2005). The role
of inflammatory process in obesity and type II diabetes has been described by many studies
(Donath, 2014, Wang et al., 2013). In islets of rat models of type II diabetes, increased Reg I
gene expression was observed in association with peri-islet immune cell infiltration and
increase levels of cytokines/ chemokines including IL-6 and IL-22 (Calderari et al., 2014,
Hill et al., 2013). Treatment of -cells by IL-6 and Dexamethasone (Dx) together induced
REG I expression in human β-cell lines (Yamauchi et al., 2015). An IL-6 response element
has been recognized in the Reg I gene promoter region and the increased local IL-6 levels
plays a pivotal role in upregulating Reg gene expression (Akiyama et al., 2001). It was
recently demonstrated that β-cells undergoing apoptotic cell death stimulate REG I gene
expression in the adjacent cells, facilitating β-cell regeneration and their insulin secretory
Chapter 4
147
capacity, thereby establishing a link between β-cell death and their regeneration (Bonner et
al., 2010).
High extracellular glucose concentration potentiates Reg gene expression and insulin
secretion (Bonner et al., 2010). Moreover, increased serum REG Iα levels in type I and type
II diabetics in the present study is in agreement with the proposition that the insufficiency in
β-cell mass as a result of enhanced β-cell damage due to high metabolic demand and
inflammation leads to upregulation of REG Iα expression in regenerating β-cells and acinar
cells of exocrine pancreas.
A significant negative correlation between disease duration and REG Iα levels was observed
in type II diabetics. Higher protein levels were found in the patients with short disease
duration. An up-regulated Reg I expression in early stages of the disease was also observed
in animal models of both obesity and type II diabetes (Qiu et al., 2005). With the increase in
the disease duration the metabolic demand of the insulin producing cells increases while
their regeneration capacity decreases in humans due to their diminished capacity to replicate
(De Tata, 2014). Moreover a decline in the expression of REG I gene has been observed
during aging process and age-related islet β-cells dysfunction (Perfetti et al., 1996a). In
impaired glucose tolerance of aging and pancreatitis associated diabetes, depletion in acinar
cells, the most important source of REG Iα has been reported (Bluth et al., 2008). In type II
diabetes with the increase in the disease time span a decline in functional β-cell mass occurs
(Lencioni et al., 2008), thus quite a few patients with longer disease duration were found to
be dependent on exogenous insulin therapy. High Reg I levels within the cell lead to
attenuation of β-cell expansion and probable stimulation of their apoptosis or differentiation
into other cell types (Mueller et al., 2008). Thus, the relative decline in REG Iα levels with
Chapter 4
148
age and disease duration may be due to extensive β-cells damage during the later stages of
the disease, their reduced regeneration capability and a decrease in acinar cell mass.
Although, type I diabetics were of different age groups, the major contributors were children
and young adults. In our study, among the ten type I diabetics, half of the cases had
relatively low REG Iα levels irrespective of the disease duration. These patients were
diagnosed in their first decade of life with diabetes, two among them were siblings. They
had relatively higher FBG and HbA1c levels and were totally insulin dependent and did not
respond to oral hypoglycemic medications. These patients may have complete β-cell loss at
the time of clinical onset of the disease, or they may be the ones with REG Iα auto-
antibodies, as such antibodies have been demonstrated in roughly 50% of the type I diabetics
(Astorri et al., 2010, Shervani et al., 2004) representing cases with more hostile nature of the
disease. Strong family history of the disease was also present in the above mentioned cases.
However, type I diabetics with disease onset in adulthood had relatively higher REG Iα and
lower FBG and HbA1c levels indicating less hostile nature of the disease or phenotype
suggestive of some β-cell regeneration in these cases. No significant correlation between
protein levels and disease duration was found in case of type 1diabetes in our study. A
decline in REG Iα levels with disease duration was reported in case of type I diabetics
(Astorri et al., 2010). However, no correlation between age or disease onset and REG Iα
levels was found in a study conducted by Bacon and colleagues (Bacon et al; 2012).
Interestingly, in this study raised serum REG Iα levels were demonstrated in patients with
disease complications irrespective of the disease duration. Due to lack of awareness in
developing countries like Pakistan many cases of diabetes remains undiagnosed and may
present for the very first time with disease complications. A recent pilot study by Yang and
Chapter 4
149
colleagues, related increased levels of REG I with the preclinical and clinical stages of the
disease and disease associated complications (Yang et al., 2014). Under normal conditions,
acinar cells of the exocrine pancreas are the main source of REG Iα, however, under
inflammmatory conditions other sources like regenerating islets, stomach and intestine have
been reported. Increased protein levels have been associated with the severity of
inflammation in patients with post-trauma complications (Keel et al., 2009), with infection
in patients with cardiac surgery (Klein et al., 2015) and in patients with sepsis (Llewelyn et
al., 2013). Increased REG Iα levels have also been reported in such conditions as Celiac
disease (Vives-Pi et al., 2013), in patients with ventilator associated pneumonia (Boeck et
al., 2011) and also in other inflammatory conditions. In the present study, at the time of
sampling all such conditions other than diabetes which lead to raised serum REG Iα were
excluded to the best of our knowledge. The exact source of increase serum REG Iα levels in
these cases is unknown. Up-regulated REG I expression has been addressed as a defense
machinery to the inflammatory response. The other organs damaged / inflamed in these
cases may possibly have contributed. More studies are needed to locate the exact source of
raised REG Iα in these patients.
A significant association between circulatory REG Iα, FBG and HbA1c levels was observed
in type II diabetics; patients with raised REG Iα levels had higher FBG and HbA1c levels.
Studies have shown that elevated glucose levels potentiate Reg gene expression (Qiu et al.,
2005, Bonner et al., 2010). A recent pilot study conducted in type II diabetics also reported a
positive highly significant correlation between REG Iα and HbA1c levels (Yang et al.,
2014), while no such correlation was observed between REG Iα and HbA1c levels in type I
diabetics and in patients with MODY (Astorri et al., 2010, Bacon et al., 2012).
Chapter 4
150
The patients with autoimmune type I diabetes had lower BMI compared to type II diabetes
which is associated with obesity related insulin resistance. Patients with raised BMI had
higher serum REG Iα levels showing a positive correlation especially in type II diabetes.
Relative β-cell mass was reported to be more in obese as compared to lean, normal subjects
(Meier and Bonadonna, 2013, Butler et al., 2003b). Obesity itself is associated with low
level inflammation and when linked with diabetes (Esser et al., 2014) it may lead to
augmented REG Iα expression, as is seen in obese mice models where inflammatory
markers were elevated (Calderari et al., 2014). Moreover, obesity linked insulin resistance
and raised glycemia may also lead to upregulated REG Iα gene expression.
Patients with raised systolic and diastolic blood pressure had lower REG Iα levels although
not statistically significant. In patients with poorly controlled diabetes, hypertension is one
of the major risk factor associated with death and disability (Campbell et al., 2011). Raised
REG Iα levels in response to β-cell injury has been linked to β-cell regeneration and increase
insulin secretion (Watanabe et al., 1994), it may lead to better disease control and its
associated risks. However, TC, TG and Hb levels in this study showed no correlation with
serum REG Iα levels.
Interestingly, a positive correlation between REG Iα levels and smoking was observed in
this study. This is possibly due to nicotine in cigarettes, which is associated with apoptosis
of β-cells (Xie et al., 2009) thus releasing REG Iα, elevated REG Iα levels has been
observed in smokers with inflammatory lung disease (Scherr et al., 2013).
In summary, REG Iα levels were demonstrated to be up-regulated in both type I and type II
diabetics and may serve as a biological marker for detection of β-cell apoptosis and
regeneration (figure 4.2). A diverse expression pattern of REG Iα was observed in both
Chapter 4
151
Figure 4.2: Proposed model for release of REG Iα in response to β-cells damage and its role
in preservation of β-cells
Chapter 4
152
types of diabetes especially when compared with the clinical and biochemical parameters.
These differences may be due to the difference in the pathogenesis of the two types of
diabetes.
REG Iα levels were also found to be raised in patients with disease complications
irrespective of disease duration and may be used to identify patients with disease
complications and in these cases additional source of REG Iα may need to be identified.
Large scale studies in diabetic patients are required with preclinical and clinical stages of the
disease to further verify the significance of REG Iα protein as an indicator of β-cell
regeneration and / or β-cell apoptosis.
Polymorphism in REG Iα gene
REG I protein is thought to preserve β-cell by stimulating their growth, regeneration and
neogenesis (Levine et al., 2000, Unno et al., 2002). Increase REG Iα expression were found
in experimental models of IGT and diabetes as well as diabetic patients indicating that it
may play a role in the pathogenesis of diabetes. Raised levels of REG Iα in diabetic
population in this study compelled us to look for genetic variants in REG Iα that may be
linked to this enhanced expression.
To this end, we identified 8 SNPs in REG Iα gene and our result indicated that one of these
(SNP3, g.+209G>T) may be associated with the vulnerability towards the disease causation,
moreover, some significant association of the SNPs with the risk factors of the disease were
observed in this study. Three SNPs (g.-145G>A, g.142A missing and g.226G>A) were
present in all the study subjects including controls and diabetics, among them the g.-
145G>A was novel not reported before and may be the representative of Pakistani
population. Moreover, when model were made for clustered analysis of different risk factors
Chapter 4
153
for the association with the genotype some exciting observations were made although they
were not statistically significant which may be due to reduced sample size.
In previously done studies analyzing the coding region of REG Iα gene in type I diabetics
and in patients of fibrocalculous pancreatic diabetes (FCPD) no involvement of the variants
with the disease was found (Hawrami et al., 1997, Banchuin et al., 2002). Several
polymorphisms including variants in the promoter region were detected in a study involving
wide-ranging analysis of the gene in patients of trophic calcific pancreatitis (TCP) but the
results suggest that they were not associated with the disease (Mahurkar et al., 2007).
Moreover, in a study conducted in type II diabetics in the Korean population some of the
genetic variants detected in the REG Iα showed modest associations with early-onset type II
diabetes however, they were not found to be associated with overall susceptibility to the
disease. They observed that the variants g.-385C and g.2199A lowered the risk of early-
onset type II diabetes and g.1385G increased the risk of early-onset type II diabetes (Koo et
al., 2010).
In the present study we identified that the SNP3 g.209G>T may be significantly associated
with the generalized susceptibility to the disease. Interestingly, all of the control subjects in
our study had only allele G (homozygous for GG) while diabetics had both allele G and
allele T (GG, GT or TT). As T allele is present only in the diabetics and not in the controls,
this gives an indication that we should predict with caution that g.209T can be a risk factor
in diabetes. The variant g.209G>T was also identified by Koo and colleagues however, they
did not found any associated with the disease susceptibility (Koo et al., 2010). To our
knowledge, this is the first ever study at the national or the international level to report the
association of any of the SNP in REG Iα gene with the risk of type II diabetes.
Chapter 4
154
When the diabetic patients were further grouped according to the disease risk factors the
following observations were made. The variants g.-385C (SNP1) and g.2199A (SNP4)
decreased the risk of diabetes while g.-243G (SNP2) increased the risk of the disease in
smokers. Moreover, the genetic variant g.-385C was found to decrease the risk of the disease
in patients with age < 46 years. Here our results are in agreement with the study conducted
in Korean population, showing that g.-385C lowered the risk of early-onset type II diabetes
(Koo et al., 2010). However, in our study we found that unlike the Korean population
g.2199A increases the risk of the disease in patients with age < 46 yrs and in those with the
family history of the disease.
In patients with diabetes after adjustment with multiple risk phenotypes such as sex, BMI,
HTN, age at onset of disease < 46 or > 46 and family history of diabetes no polymorphism
was found to be significantly associated with the risk of type II diabetes. In our study
population g.-385C, g.-243T, g.+209G and g.+2199A were more common, and when the
clustered effect of risk factor on genotype was observed in diabetics only, the variants g.-
385C, g.-243T, and g.+2199A were found to be less protective and have potential for the
risk of disease more in females aged 46 years or above but the effects were weak and non-
significant. While in case of g.+209G>T the homozygous GG was more protective, with a
single allele change the heterozygous GT lost it protective effect and became lethal,
however, with the change of both the alleles the homozygous TT was also found to be lethal
but to a lesser extent. This indicates that our population is at a slightly more risk for the
disease as compared to the other populations especially females of increasing age. To our
surprise almost negligible effect of BMI on SNPs was observed in diabetics.
Chapter 4
155
In summary, the genetic variant g.209T was found to be significantly associated with the
risk of type II diabetes which has not been reported before in any population. Moreover
some of the variants also showed significant association with risk factors like smoking, age
at disease onset and family history of the disease. The extensive search for SNPs in the REG
Iα gene by sequencing and the estimation of their risk association in Pakistani subjects are
novel. Further studies are required with a larger and more diverse population to verify the
association between REG Iα gene polymorphisms, type II diabetes and its associated risk
factors.
Conclusions
156
CONCLUSIONS
mReg2 by preventing the nuclear import of AIF and enhancing the accumulation of Scythe
in nucleus may act as a suppressor of AIF-dependent apoptotic signaling in β-cells.
Recognizing the molecular mechanism behind the protective effect of mReg2 in β-cells may
aid in designing strategies for better prevention and management of diabetes.
Raised levels of REG Iα in patient of type I and type II diabetes may be used as a potential
biomarker of β-cell apoptosis and regeneration. It may also be used as a possible predictor of
disease complications in both type 1 and type II diabetes.
The variant g.209T identified in the REG Iα gene was found to be significantly associated
with the overall risk of type II diabetes for the first time in any population. Moreover, some
of the variants identified showed modest association with smoking in type II diabetics.
Limitations and Recommendations
157
LIMITATIONS OF PRESENT STUDY AND FUTURE
RECOMMENDATIONS
In present study the sample size is limited. To address REG Iα as the possible
predictive marker of diabetes it needs to be tested in larger more diverse population
and also at different preclinical and clinical stages of the disease. Moreover, large
scale studies at the genomic level are required to find true association between
genetic variants in REG Iα and the risk factors of diabetes.
Further studies are required to elucidate how mReg2 effects redistribution of Scythe
and AIF at the sub-cellular level in order to have enhanced understanding of the
mReg2 role in protecting β-cells against AIF-mediated apoptosis.
It also needs to be verified whether mReg2 is an intracellular apoptosis regulator or
is a secretory protein that act either in an autocrine or paracrine fashion via supposed
mReg2 receptor to prevent apoptosis.
A definite mReg2 receptor needs to be identified.
The molecular mechanism underlying or contributing to the protective effect of
mReg2 on β-cells needs to be verified further using transgenic mouse models (over-
expressing Reg2 or Reg knockdown).
Role of miRNA (micro RNA) in the regulation of mReg2 mediated apoptosis
requires consideration.
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158
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Appendix
180
APPENDIX І
Table showing demographic, anthropometric, clinical and biochemical data of type II
diabetics (n=60)
S.
No.
Pts
ID. Sex
Age
(yrs)
Ht
(ins)
Wt
(kg)
SBP
(mm
Hg)
DBP
(mm
Hg)
Age at
disease
onset (yrs)
Disease
Duration
(yrs)
FBG
(mmol/L)
HbA1c
(%)
REG Iα
(ng/mL)
1 2 M 46 64 63 130 90 32.0 14.0 5.9 6.9 1571
2 3 F 50 60 72 100 70 46.0 4.0 6.7 7.6 1754
3 5 M 56 70 75 120 80 46.0 10.0 10.0 8.5 1603
4 6 M 50 71 88 110 80 34.0 7.0 4.0 7.1 780
5 7 M 41 64 70 140 90 33.0 8.0 9.7 10.2 1562
6 9 M 60 66 70 140 80 51.0 9.0 11.7 9.2 1329
7 11 M 54 66 63 120 80 54.0 0.1 7.0 7.9 1753
8 12 M 45 64 68 140 90 34.0 11.0 12.9 9.9 1774
9 13 M 42 69 87 120 80 40.0 2.0 7.1 8.4 1708
10 14 M 59 62 60 180 100 59.0 0.2 9.0 10.1 1718
11 15 M 71 67 80 160 90 67.0 3.0 7.3 9.0 1494
12 19 M 60 66 68 130 90 48.0 12.0 9.6 10.9 1825
13 20 M 59 65.5 60 120 80 59.0 0.3 7.8 7.6 1825
14 22 M 58 67 70 170 110 47.0 11.0 5.8 7.1 588
15 24 M 60 64 64 140 90 48.0 12.0 9.0 7.4 707
16 26 M 62 73 88 130 80 52.0 10.0 8.1 7.9 1413
17 27 F 64 63 57 140 90 52.0 12.0 7.0 6.9 623
18 28 M 50 68 78 130 90 39.0 11.0 15.2 9.0 1752
19 29 M 49 67 99 120 80 47.5 1.5 6.9 7.1 1523
20 34 M 56 68.5 62 140 80 49.0 7.0 6.4 6.0 618
21 36 M 55 71 95 140 90 52.0 3.0 6.0 6.6 1571
22 37 M 55 68 87 150 90 51.0 4.0 6.7 7.8 1364
23 38 M 63 70 86 140 80 61.0 2.0 6.0 5.4 1488
24 39 M 55 64 68 140 95 51.0 4.0 6.6 8.1 1753
Appendix
181
APPENDIX I (cont.)
Table showing demographic, anthropometric, clinical and biochemical data of type II
diabetics (n=60)
S.
No.
Pts
ID.
Sex Age
(yrs)
Ht
(ins)
Wt
(kg)
SBP
(mm
Hg)
DBP
(mm
Hg)
Age at
disease
onset (yrs)
Disease
Duration
(yrs)
FBG
(mmol/L)
HbA1c
(%)
REG Iα
(ng/mL)
25 40 M 49 62 69 120 80 48.0 0.6 8.3 7.0 1701
26 41 M 59 67 62 170 110 35.0 24.0 10.6 8.0 597
27 43 M 57 68 60 130 80 47.0 10.0 9.5 12.7 1753
28 44 M 53 64 64 105 85 35.0 18.0 7.3 8.2 1272
29 45 M 65 71 68 120 80 52.0 13.0 9.0 9.9 1708
30 46 M 46 66 60 120 80 36.0 10.0 6.1 5.5 945
31 47 M 54 71.5 83 130 90 44.0 10.0 8.4 6.9 678
32 48 F 59 64 74 130 85 49.0 10.0 15.1 10.2 1798
33 49 M 71 66 70 120 80 51.0 20.0 5.2 7.9 1315
34 50 F 49 60 73 120 80 46.0 3.0 6.9 7.2 1771
35 51 F 75 60 74 130 90 73.0 2.0 7.8 7.9 1772
36 52 F 49 64 76 130 90 41.0 8.0 13.1 8.8 1772
37 55 M 57 71 62 140 90 52.0 5.0 7.0 8.3 1246
38 56 F 45 61 65 120 80 44.0 1.0 9.0 9.5 1880
39 57 M 52 64 67 150 100 50.0 1.5 6.6 7.3 1039
40 59 M 51 66 68 120 80 46.0 5.0 5.3 8.5 614
41 60 M 66 68 68 130 80 59.0 7.0 10.2 8.0 1153
42 61 M 41 65 80 120 80 40.0 0.6 8.0 7.2 1897
43 62 M 36 66 74 140 100 35.0 1.0 7.5 8.9 1772
44 63 F 52 58 55 130 80 48.0 4.0 8.0 7.8 1577
45 64 F 48 63 82 130 80 41.0 7.0 8.0 8.1 1771
46 65 M 46 70 70 120 80 46.0 0.3 10.6 8.2 1573
47 66 F 55 58 57 140 80 40.0 15.0 9.0 9.9 1766
48 67 F 70 65 66 140 90 61.0 9.0 6.0 7.0 535
49 70 M 60 65 65 120 80 54.0 6.0 10.7 10.5 1285
Appendix
182
APPENDIX I (cont.)
Table showing demographic, anthropometric, clinical and biochemical data of type II
diabetics (n=60)
S.
No.
Pts
ID. Sex
Age
(yrs)
Ht
(ins)
Wt
(kg)
SBP
(mm
Hg)
DBP
(mm
Hg)
Age at
disease
onset (yrs)
Disease
Duration
(yrs)
FBG
(mmol/L)
HbA1c
(%)
REG Iα
(ng/mL)
50 71 M 60 64 63 150 100 45.0 15.0 5.5 6.1 927
51 72 M 70 61 55 120 80 55.0 15.0 10.0 9.9 633
52 73 M 64 67 104 140 90 56.0 8.0 8.8 9.6 1785
53 75 M 48 66 64 120 80 47.0 0.6 8.3 8.7 1644
54 77 M 71 66 75 130 80 69.0 2.0 10.8 12.0 1913
55 81 F 49 61 51 140 90 49.0 0.4 7.8 8.8 1850
56 82 F 48 63 88 110 80 39.0 10.0 9.9 7.8 1772
57 8 F 47 64 75 150 90 35.0 11.0 6.5 8.5 1572
58 85 F 50 62 55 120 80 49.0 0.5 18.4 14.7 1753
59 91 F 60 60 45 130 90 52.0 8.0 8.7 9.3 1294
60 119 M 35 68 85 120 80 22.0 13.0 12.3 8.6 1771
Appendix
183
APPENDIX II
Table showing demographic, anthropometric, clinical and biochemical data of type I
diabetics (n=10)
S.
No.
Pts
ID.
Sex
Age
(yrs)
Ht
(ins)
Wt
(kg)
SBP
(mmHg)
DBP
(mm
Hg)
Age at
disease
onset (yrs)
Disease
Duration
(yrs)
FBG
(mmol/L)
HbA1c
(%)
REG Iα
(ng/mL)
1 1 M 26 67 50 120 80 26.0 0.1 18.3 13.6 798
2 4 M 55 70 76 120 80 45.0 10.0 10.2 8.9 1613
3 30 M 46 68 74 120 80 24.0 22.0 13.3 9.9 1582
4 53 F 24 64 50 120 80 12.0 12.0 19.0 11.4 1003
5 78 M 51 71 88 120 80 36.0 15.0 4.0 7.0 786
6 80 M 41 64 70 140 90 33.0 8.0 9.9 10.0 1552
7 84 F 47 64 78 150 90 35.0 11.0 6.7 8.6 1582
8 87 M 29 66 65 120 80 25.0 4.0 7.6 12.0 1230
9 89 F 10 48 23 100 70 4.0 6.0 11.1 10.5 774
10 90 M 12 58 30 100 70 2.0 10.0 16.6 13.2 701
Appendix
184
APPENDIX III
Table showing Demographic, anthropometric and biochemical data of controls (n=20)
S. No Pts ID. Sex Age
(yrs)
Ht
(ins)
Wt
(kg)
SBP
mmHg
DBP
mmHg
FBG
(mmol/L)
HbA1c
(%)
REG Iα
(ng/mL)
1 94 F 44 65 80 120 80 3.5 5.8 1067
2 95 F 47 66 78 130 80 3.7 5.4 853
3 96 F 50 63 80 130 90 3.2 5.0 853
4 97 F 50 60 60 100 60 3.1 5.5 587
5 98 F 49 61 65 120 80 3.2 5.5 852
6 99 F 56 64 60 120 80 3.1 5.5 596
7 100 F 54 62 57 100 70 3.6 5.4 698
8 101 M 50 66 52 110 70 3.5 5.5 610
9 102 M 49 67 58 120 80 4.4 5.6 530
10 103 M 45 71 65 110 70 3.6 5.6 752
11 104 M 52 70 55 100 70 5.1 5.5 667
12 105 M 48 67 72 120 80 4.8 5.6 1077
13 106 M 56 71 65 120 80 4.4 5.4 852
14 109 M 49 70 60 110 80 4.0 5.5 455
15 111 M 55 68 72 120 70 4.2 5.3 753
16 113 M 49 65 70 120 80 3.6 5.4 567
17 114 M 48 67 70 120 80 3.6 5.6 572
18 115 M 49 66 60 120 80 3.7 5.5 961
19 117 M 52 63 58 120 80 4.9 6.9 555
20 118 F 53 61 70 110 70 5.2 6.9 752
Appendix
185
APPENDIX IV
Standard Calibration Curve for calculation of REG Iα
y = 1.339x + 3.006 R² = 0.979
0
1
2
3
4
-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6
Co
nce
ntr
atio
n (
pg/
mL)
Absorbance
Standard Calibration Curve for REG Iα
REG Iα
Linear (REG Iα)
Appendix
186
APPENDIX V
Correlation between serum REG Iα and systolic blood pressure in type II diabetics.
Correlation between serum REG Iα and diastolic blood pressure in type II diabetics.
y = -9.345x + 2711.3 R² = 0.1171
0
500
1000
1500
2000
2500
0 50 100 150 200
REG
Iα (
ng/
mL)
Systolic Blood Pressure (mmHg)
Scattered values
Linear (Scattered values)
Spearman r=-0.223 p=0.08
y = -16.256x + 2870.2 R² = 0.0996
0
500
1000
1500
2000
2500
0 50 100 150
REG
Iα (
ng/
mL)
Diastolic Blood Pressure (mmHg)
Scattered values
Linear (Scattered values)
Spearman r=-0.178 p=0.17
Appendix
187
APPENDIX VI
DEPARTMENT OF BIOCHEMISTRY AND MOLECULAR BIOLOGY
ARMY MEDICAL COLLEGE, RAWALPINDI
CONSENT FORM (PATIENT/PARTICIPANT)
Serial No: ____________
Dated: ____________
PERSONAL INFORMATION
Name: ________________________________ Age/Sex: _____________________________
Height: ________________________________ Weight: ______________________________
Profession: _____________________________ CNIC No: _____________________________
Address: _________________________________________________________________________
Phone: _________________________________ Registration No: ______________________
WILLING TO PARTICIPATE IN THIS STUDY: YES NO
Role of Regenerating protein mReg2 in Pancreatic Islet Cell Preservation and REG Iα
as a possible predictive marker of Diabetes
I ------------------------------ Son/Daughter or Father/Mother of-----------------------------hereby
willfully give my consent for participation in study titled “Role of Regenerating protein
mReg2 in Pancreatic Islet Cell Preservation and REG Iα as a possible predictive marker of
Diabetes”. The study is being conducted by Dr Sadaf Saleem Uppal under the supervision of
Professor Dr Abdul Khaliq Naveed Head of Department of Biochemistry, Islamic
International Medical College, Riphah International University, Rawalpindi. I have been
explained the purpose and procedures of the study. I am willing to go through this study
procedure and I will give the blood sample of about 10 ml through syringe. The laboratory
will inform us about the results.
Appendix
188
Lab. Findings
Serum REG Iα: __________________________________________________
SIGNATURE:
Appendix
189
APPENDIX VII
PROFOMA FOR DATA COLLECTION
“ROLE OF REGENERATING PROTEIN mReg2 IN PANCREATIC ISLET CELL
PRESERVATION AND REG Iα AS A POSSIBLE PREDICTIVE MARKER FOR
DIABETES”
Department of Biochemistry and Molecular Biology
Army Medical College
SERIAL NO: DATE:______________
NAME: _____________________________________________________________
AGE: _______________________________________________________________
SEX: _______________________________________________________________
QUALIFICATION: ___________________________________________________
EMPLOYMENT STATUS: _____________________________________________
ADDRESS: __________________________________________________________
TELEPHONE NO: ____________________________________________________
MARITAL STATUS: __________________________________________________
CHILDREN: _________________________________________________________
WEIGHT:___________ HEIGHT:___________ BODY MASS INDEX:__________
BLOOD PRESSURE: __________ FASTING BLOOD SUGAR (F): ____________
HbA1c: ___________LIPID PROFILE: ____________________________________
ULTRASOUND:________________________________ _______________________
ANYOTHER: ________________________________________________________
TYPE OF DIABETES: _________________________________________________
AGE AT ONSET OF DISEASE: _________________________________________
DURATION OF DISEASE: _____________________________________________
MEDICAL HISTORY: _________________________________________________
____________________________________________________________________
DRUG HISTORY: ____________________________________________________
____________________________________________________________________
HISTORY OF SMOKING: ______________________________________________
FAMILY HISTORY OF DIABETES:_____________________ _______________
PARTICIPATION IN OTHER TRIALS: ___________________________________
SYSTEMIC COMPLICATION: __________________________________________
MACROVASCULAR: _________________________________________________
MICROVASCULAR: __________________________________________________