omics-strategies and methods in the fight against doping

Forensic Science International 213 (2011) 20–34

OMICS-strategies and methods in the fight against doping§

Christian Reichel *

Doping Control Laboratory, AIT Seibersdorf Laboratories, A-2444 Seibersdorf, Austria

A R T I C L E I N F O

Article history:

Received 2 May 2011

Received in revised form 15 July 2011

Accepted 16 July 2011

Available online 20 August 2011

Keywords:

Doping control

Transcriptomics

Proteomics

Metabolomics

Bioinformatics

A B S T R A C T

During the past decade OMICS-methods not only continued to have their impact on research strategies in

life sciences and in particular molecular biology, but also started to be used for anti-doping control

purposes. Research activities were mainly reasoned by the fact that several substances and methods,

which were prohibited by the World Anti-Doping Agency (WADA), were or still are difficult to detect by

direct methods. Transcriptomics, proteomics, and metabolomics in theory offer ideal platforms for the

discovery of biomarkers for the indirect detection of the abuse of these substances and methods.

Traditionally, the main focus of transcriptomics and proteomics projects has been on the prolonged

detection of the misuse of human growth hormone (hGH), recombinant erythropoietin (rhEpo), and

autologous blood transfusion. An additional benefit of the indirect or marker approach would also be

that similarly acting substances might then be detected by a single method, without being forced to

develop new direct detection methods for new but comparable prohibited substances (as has been the

case, e.g. for the various forms of Epo analogs and biosimilars). While several non-OMICS-derived

parameters for the indirect detection of doping are currently in use, for example the blood parameters of

the hematological module of the athlete’s biological passport, the outcome of most non-targeted OMICS-

projects led to no direct application in routine doping control so far. The main reason is the inherent

complexity of human transcriptomes, proteomes, and metabolomes and their inter-individual

variability. The article reviews previous and recent research projects and their results and discusses

future strategies for a more efficient application of OMICS-methods in doping control.

� 2011 Elsevier Ireland Ltd. All rights reserved.

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1. Introduction

In 1994 Marc Wilkins introduced the word ‘‘proteome’’ to thescientific community as a counterpart to the word ‘‘genome’’(vide infra). A ‘‘genome’’ contains the entire set of geneticinformation of a cell or organism, while a ‘‘proteome’’ comprisesall expressed proteins of a cell or organism [1,2]. Subsequently,words with similar meaning were created with relation tometabolites (‘‘metabolome’’) or the RNA transcripts (‘‘transcrip-tome’’). The science related to these terms was namedaccordingly ‘‘genomics’’, ‘‘transcriptomics’’, ‘‘proteomics’’, and‘‘metabolomics’’ (Fig. 1) [3]. Within a few years an increasingnumber of OMICS-terms was created (e.g. glycomics, lipidomics,phosphoproteomics, neuroproteomics, degradomics, topo-nomics, interactomics) [4], which also was reasoned by thedevelopment of high-throughput methods for the parallelidentification, characterization, and quantitation of thousands

§ This paper is part of the special issue entitled: Fight Against Doping in 2011,

Guest-edited by Neil Robinson (Managing Guest Editor), Martial Saugy, Patrice

Mangin, Jean-Luc Veuthey, Serge Rudaz and Jiri Dvorak.

* Tel.: +43 50550 3572; fax: +43 50550 3590.

E-mail address: [email protected].

0379-0738/$ – see front matter � 2011 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.forsciint.2011.07.031

of biomolecules. OMICS-sciences have been leading to anincreased understanding of diseases on the molecular leveland stimulated the search for new and more specific biomarkersfor the early diagnosis of diseases, their progression andprognosis, as well as the monitoring of therapies. Since similaralterations on the molecular level have been assumed for theapplication of prohibited doping substances, OMICS-strategieshave also been applied to anti-doping research. While theultimate goal has been the development of indirect methods forthe detection of not or difficult to detect substances (e.g. hGH)and methods (autologous blood transfusion), the longitudinalmonitoring of changes in ‘‘OMICS-patterns’’ (e.g. the pattern ofendogenous steroids) added a possible new dimension to anti-doping control within the framework of the athlete’s biologicalpassport. However, currently no OMICS-derived biomarkers areused in doping testing. The main application so far has been theuse of methods and instruments usually applied in proteomics(two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE), or nano-liquid chromatography (LC) coupled to massspectrometry) for the direct (targeted) detection of misusedsubstances like peptides or hGH. In consequence, the focus of thisarticle is on research projects, which have been using a non-

targeted transcriptomic, proteomic, or metabolomic approach forthe discovery of biomarkers in anti-doping control.

http://dx.doi.org/10.1016/j.forsciint.2011.07.031

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http://dx.doi.org/10.1016/j.forsciint.2011.07.031

DNA

RNA

Protein

Metabol ite

Geno metransc riptional regulatio n

(e.g. DNA-m ethyla tio n, hi ston e-acetyla tion)

Transcr iptomepost-transc rip tional regulatio n

(e.g. al terna tive spli cing, miRN As)

Proteomepost-tran slational regul atio n

(e.g. pho spho rylation, glyc osy lation)

Metabolome

GENOMICS

TRANSC RIPTOMICS

PROTEO MICS

METABOLOMICS

Fig. 1. Flow of information from genome to transcriptome, proteome, and metabolome [3].

C. Reichel / Forensic Science International 213 (2011) 20–34 21

2. Genomics and transcriptomics

The biological information of all cellular life forms is stored indeoxyribonucleic acid (DNA) and the sequence of its nucleotides

(bases). Some viruses partly use ribonucleic acid (RNA) for data-storage [2]. The entirety of this hereditary nucleic acid storedinformation is called a genome. Genomics is the science which dealswith the exploration of genomes. The human genome consists oftwo parts, the nuclear genome (ca 3,200,000,000 DNA-nucleotidesin size and organized in 24 chromosomes of different length) andthe mitochondrial genome (ca 16,500 nucleotides, which are locatedin multiple copies in the mitochondria of a cell). The main object ofthe Human Genome Project, which in full intensity started in 1990,was the determination of the nucleotide sequence of the humannuclear genome [2]. A second and privately funded human genomeproject started in 1998. A first draft of this sequence was publishedin 2001. Genomes are organized in genes, which contain theinformation required for the synthesis of proteins. DNA has to befirst transcribed into RNA, specifically messenger RNA (mRNA), inorder to be further translated into proteins. The entire set of RNAtranscripts of a cell or organism is called a transcriptome, whichalso includes non-coding RNA (e.g. ribosomal RNA [rRNA], transferRNA [tRNA], small RNA [sRNA]). However, usually the meaning ofthe word ‘‘transcriptome’’ is confined to the entire set of expressedmRNA, and transcriptomics is the science, which studies theexpressed mRNA of a cell, tissue, or whole organism. Keytechnologies used for this so called ‘‘expression profiling’’ areDNA microarrays, SAGE (serial analysis of gene expression), and next-

generation sequencing (NGS) [5,6]. Since the degree of quantitativeinformation obtained from microarray or SAGE analyses is limited,the obtained results (i.e. differences in observed expression levels)are then gene-specifically evaluated with another technologynamed quantitative real-time polymerase chain reaction (qRT-PCR)[7,8]. In the context of anti-doping research the majority oftranscriptomics projects has been dedicated to the influence ofpeptide hormones (hGH, IGF-I, Epo), blood and gene doping, as wellas anabolic steroids on DNA-transcription (Table 1).

2.1. Human growth hormone and IGF-I

The administration of human growth hormone (hGH) isprohibited according to article S2 (‘‘Peptide hormones, growthfactors and related substances’’) of the ‘‘2011 Prohibited List’’ ofWADA [9]. In 2003 WADA funded a project by T. Friedmannentitled ‘‘Microarray detection methods for GH and IGF-I’’ [10].Muscle stem cells were treated in vitro with insulin-like growthfactor I (IGF-I). Subsequent microarray analysis showed that theexpression of several hundred genes was altered with the majorityof these genes being up-regulated. Pathway analysis revealed thatprimarily cholesterol, steroid, and fatty acid biosynthesis wasinfluenced. However, in vivo studies on mouse skeletal muscle afterIGF-I treatment reversed the picture, i.e. more of the genes weredown- than up-regulated, and in particular muscle growth factors.It was concluded that more data would be required for usingmicroarray methods for the development of anti-doping tests. Asimilar microarray-based project was carried out by Gmeiner et al.[11–13]. Cell cultures of specific human leukocytes (THP-1monocytes, IM-9 B-lymphocytes, H9 T-lymphocytes) and periph-erial blood mononuclear cells (PBMC) were treated in vitro withhGH. Whole genome arrays were used for transcriptomic profiling.All four cell model systems responded to the treatment with hGH.The T-lymphocyte cell line showed the highest response and theTHP-1 cells the lowest. In general, hGH led to a higher degree ofgene up- than down-regulations. For the H9 cell model the up-regulated genes belonged to pathways related to cell adhesion,fatty acid oxidation, DNA replication, and polyamine synthesis.Down-regulated genes were associated with non-apoptotic celldeath, and osmotic pressure regulation. The other cell linesfeatured partly similar gene expression patterns. PBMC showedincreased activity in steroid hormone synthesis, insulin receptorsignaling, as well as protein phosphorylation and glycosylationpathways. A follow-up project tested these findings in an in vivo

model. Eight test persons were treated with hGH for 3 weeks(0.8 mg hgH daily for 1 week, then 2 weeks 1.6 mg per day), whileanother group of eight persons received a placebo. Blood (50 mL)

Table 1List of selected anti-doping research projects and publications applying transcriptomics and genomics strategies for biomarker discovery and the confirmation of sample

identity.

Author Title Year Type Topic

T. Beiter, M. Zimmermann, A. Fragasso,

J. Hudemann, A.M. Niess, M. Bitzer,

U.M. Lauer, P. Simon

Direct and long-term detection of gene doping

in conventional blood samples

2011 Publication Gene doping

N. Leuenberger, M. Saugy, S. Pradervand Circulating microRNAs as stable biomarkers for

detection of erythropoiesis-stimulating agent

abuse

2010 Project Epo

B. Daniel Can genomic analysis be the answer to autologous

blood transfusion detection?

2010 Project Blood transfusion

J. Rupert Development of a genotype-based assay for

homologous blood doping


A. Baoutina, T. Coldham, G.S. Bains, K.R. Emslie Gene doping detection: evaluation of approach

for direct detection of gene transfer using

erythropoietin as a model system


L. Bailly-Chouriberry, F. Noguier, L. Manchon,

D. Piquemal, P. Garcia, M.A. Popot, Y. Bonnaire

Blood cells RNA biomarkers as a first long-term

detection strategy for EPO abuse in horseracing

2010 Publication Epo

T. Pottgiesser, Y.O. Schumacher, H. Funke,

K. Rennert, M.W. Baumstark, K. Neunuebel,

S. Mosig

Gene expression in the detection of autologous

blood transfusion in sports – a pilot study

2009 Publication Blood transfusion

E. Varlet-Marie, M. Audran, M. Ashenden,

M.T. Sicart, D. Piquemal

Modification of gene expression: help to detect

doping with erythropoiesis-stimulating agents


M. Ashenden, S. Easteal, R. Williams,

J. Henderson

Confirmation of differentially expressed genes

associated with autologous transfusion


C.J. Mitchell, A.E. Nelson, M.J. Cowley,

W. Kaplan, G. Stone, S.K. Sutton, A. Lau,

C.M. Lee, K.K. Ho

Detection of growth hormone doping by gene

expression profiling of peripheral blood

2009 Publication hGH

H. Thakkar, A.N. Butt, J. Powrie, R. Holt,

R. Swaminathan

Circulating nucleic acids in the assessment of

endogenous growth hormone production


T. Beiter, M. Zimmermann, A. Fragasso,

S. Armeanu, U.M. Lauer, M. Bitzer, H. Su,

W.L. Young, A.M. Niess, P. Simon

Establishing a novel single-copy primer-internal

intron-spanning PCR (spiPCR) procedure for the

direct detection of gene doping


P. Simon, U. Lauer, M. Bitzer, T. Beiter,

M. Zimmermann

Sensitivity and specificity of a gene doping test

detecting transgenic DNA on a single molecule

level in peripheral blood probes

2008 Project Gene doping

Y. Pitsiladis, H. Pawel, G. Gmeiner A gene microarray based approach to the detection

of recombinant human erythropoietin doping in

endurance athletes

2008 Project Epo

C. Gore, M. Ashenden, A. Hahn, J. Moerkeberg,

R. Damsgaard, B. Belhage

The effect of training, altitude exposure and

athlete’s sex on expression of genes know to

change following autologous blood transfusion

2008 Project blood transfusion

M. Thevis, H. Geyer, U. Mareck, G. Sigmund,

J. Henke, L. Henke, W. Schanzer

Detection of manipulation in doping control

urine sample collection: a multidisciplinary

approach to determine identical urine samples

2007 Publication DNA

V. Castella, M.L. Morerod, N. Robinson,

M. Saugy, P. Mangin

Successful DNA typing of ultrafiltered urines

used to detect EPO doping


J. Rupert Identification and Characterization of Transcriptional

Markers Diagnostic of Autologous Blood Doping


J. Rupert, G. Payne, M. Fedoruk Development of a highly-sensitive quantitative

assay to detect siRNA-mediated gene doping

2007 Project siRNA, gene doping

R.I.G. Holt, R. Swaminathan, D. Cowan,

P.H. Sonksen, E. Bassett

The use of Blood mRNA Technology to Detect

Abuse with GH and IFG-I in Sport

2007 Project hGH, IGF-I

N. Robinson, V. Castella, C. Saudan, P.E. Sottas,

C. Schweizer, N. Dimo-Simonin, P. Mangin,

M. Saugy

Elevated and similar urinary

testosterone/epitestosterone ratio in all samples

of a competition testing: suspicion of a manipulation


V. Castella, N. Dimo-Simonin, C. Brandt-Casadevall,

N. Robinson, M. Saugy, F. Taroni, P. Mangin

Forensic identification of urine samples: a comparison

between nuclear and mitochondrial DNA markers


R.O. Snyder, P. Moullier A pilot study to develop a reliable blood test for the

detection of gene doping after intramuscular injection

of naked plasmid DNA


P. Simon, U.M. Lauer, M. Bitzer Sensitivity and specificity of a gene doping test

detecting transgenic DNA on a single molecule

level in peripheral blood probes


J. Segura, J. Pascual, Z. Nikolovski, D. Andreu,

B.G. de la Torre, C. Fillat,

E. Martinez-Miralles; J. Llop, J. D. Gispert

IMAGENE: Non-invasive molecular imaging of gene

expression useful for doping control: Extension

study in animals after erythropoietin gene transfer


M. Schoenfelder, T. Schulz, K. Mueller,

R. Grucza

Comparative gene expression profiling in human

buccal epithelium and leukocytes after the abuse

of beta-2-agonists and anabolic steroids

2006 Project Beta-2-agonists,

anabolic steroids

H. Geyer, J. Henke, L. Henke PCR based DNA analyses and steroid profiling for

the detection of manipulation and individualisation

of urine samples in doping control

2006 Project DNA

M. Ashenden Investigation of indirect markers of autologous blood

transfusion in peripheral blood samples

2006 Project hGH

F. Labrie, V. Luu-The, E. Calvo, C. Martel,

J. Cloutier, S. Gauthier, P. Belleau,

J. Morissette, M.H. Levesque, C. Labrie

Tetrahydrogestrinone induces a genomic signature

typical of a potent anabolic steroid

2005 Publication Anabolic steroids

C. Reichel / Forensic Science International 213 (2011) 20–3422

Table 1 (Continued )

Author Title Year Type Topic

J. Rupert, D. McKenzie Development of a prototype blood-based test for

exogenous erythropoietin activity based on

transcriptional profiling

2005 Project Epo

K. Ho, A. Nelson, W. Kaplan, K-C. Leung,

D. Handelsman

Detection of growth hormone doping by gene

expression profiling of peripheral blood cells in

humans

2005 Project hGH

G. Gmeiner, C. Nohammer, N. Bachl Application of microarray technology for the detection

of changes in gene expression after doping with

recombinant hGH - part 2

2005 Project hGH

E. Varlet-Marie, M. Audran, M. Lejeune,

B. Bonafoux, M.T. Sicart, J. Marti,

D. Piquemal, T. Commes

Analysis of human reticulocyte genes reveals altered

erythropoiesis: potential use to detect recombinant

human erythropoietin doping


G. Gmeiner, C. Nohammer, C. Reichel,

R. Pichler

Application of microarray technology for the detection

of changes in gene expression after doping with

recombinant human growth hormone

2003 Project hGH

T. Friedmann Microarray detection methods for GH and IGF-I 2003 Project hGH, IGF-I


was taken before the application and on days 7, 14, and 21 duringthe treatment phase. Three more samples were taken during thewashout phase. PBMC were isolated by density gradientcentrifugation, the mRNA extracted, reversely transcribed intocDNA, labelled with fluorescent dyes, and then hybridized onwhole genome microarrays for studying gene expression. Afterrigorous data analysis the effect as observed during the in vitro

studies was no longer detectable. It was concluded that hGHtreatment in vivo had no significant effect on the PBMCtranscriptome. Yet another similar project was conducted byHo et al. [14] and later published by Mitchell et al. [15]. Twentymen and women were treated with hGH for 8 weeks, peripheralblood cells (white cell fraction) were isolated and the expressionprofiles investigated with microarrays. Although many changes inthe expression patterns were observed, the detected effects wereminimal (maximally two-fold for up- and down-regulation). Theauthors concluded that ‘‘it is unlikely that gene expressionanalysis of peripheral blood leukocytes would be a viableapproach for the detection of GH doping.’’ In order to investigatethe detection possibilities of circulating mRNAs for GH andgrowth hormone releasing hormone (GHRH), Thakkar et al.quantified these nucleic acids after their isolation from blood withreal-time PCR [16]. A pilot study conducted with acromegalicpatients (i.e. people who produce increased amounts of hGH) andhealthy controls showed that the amount of GH- and GHRH-mRNA was significantly in- and decreased in arcomegalic persons,respectively. It was concluded that both markers might be usefulfor hGH and IGF-I doping control purposes [16,17].

2.2. Erythropoietin and erythropoiesis-stimulating agents

The misuse of erythropoiesis-stimulating agents (ESA) isprohibited according to article S2 (‘‘Peptide hormones, growthfactors and related substances’’) of the ‘‘2011 Prohibited List’’ ofWADA [9]. Aside from erythropoietin (Epo) and its analogsseveral other compounds are also able to increase the productionof red blood cells (RBC). Hematide, a PEGylated and dimerizedderivative of an Epo mimetic peptide (EMP), acts by binding tothe Epo-receptor (Epo-R) and thus induces the same signaltransduction cascade as Epo [18–21]. HIF (hypoxia-induciblefactor) stabilizers (e.g. the prolyl hydroxylase inhibitor FG-2216),GATA-2 transcription factor inhibitors (e.g. K-11706), andhematopoietic cell phosphatase (HCP) inhibitors directly inter-fere with the intracellular signaling pathway and thus circum-vent the necessity of being first recognized by the extracellularpart of the trans-membrane Epo-receptor [22,23]. Due to thefact that the detection windows of currently used methods(IEF-PAGE, SDS-PAGE) for the direct detection of Epo abuse are

usually rather short (typically a few days depending on theadministered dose) – an exemption is the long-acting PEGylatedepoetin beta named Mircera [24,25] – indirect strategies basedon OMICS-methods have been employed. In 2004, Varlet-Marieet al. investigated changes in the expression rate of reticulocytegenes after administration of recombinant human Epo (rhEpo)[26]. Six athletes received 50 IU/kg rhEpo for 4 weeks and then20 IU/kg for 3 weeks (3 times per week). Four athletes received aplacebo. Target genes were selected from a SAGE library ofhuman reticulocytes and subsequently monitored in whole bloodsamples of the athletes by real-time PCR. The expression profilesof the rhEpo-treated athlets differed from the placebo group andin particular the profile of ornithine decarboxylase antizyme(OAZ) gene indicated Epo-treatment well. The authors suggestedthe combination of this targeted transcriptomics approach withthe blood parameter based strategy of the indirect OFF models[27,28] for enhancing sensitivity. A follow-up study usingtranscriptome analysis of total human blood cells was publishedby Varlet-Marie et al. [29]. Three SAGE libraries were generatedfrom athletes before, during, and after treatment with darbe-poetin alpha (NESP). After in silico analysis expression profiles ofthe 95 most promising genes were monitored by quantitativereal-time PCR. Finally, data were filtered with a microarray-based significance analysis method. Thirty-three genes werefound to be differentially expressed at high NESP doses, and outof this group five genes were differentially expressed at both highand micro doses. The authors concluded that trancriptomicscould provide valuable supplementary data for the hematologicalpassport.

A mouse model was used for transcriptional profiling oferythropoiesis-stimulated blood cells, by either exogenous Epo orhypoxia [30]. After SAGE analysis of blood, potential candidate geneswere monitored with quantitative PCR. Expression profiles of theselected genes were very inconsistent. The authors concluded that‘‘differential gene expression in blood cells following red-cellexpansion is not a promising method for detecting Epo use’’.Another project by Pitsiladis et al. (2008) proposed the application ofmicroarray technology for the differentiation between Epo-dopingand ‘‘naturally elevated blood markers’’ due to training and/or livingat high altitudes. The project is still ongoing. A similar approach asthe one described by Varlet-Marie et al. (2009; vide supra) was usedby Bailly-Chouriberry for the detection of Epo horse-doping [31].Three SAGE libraries were generated from total blood cells before,during, and after treatment of horses with epoetin alpha. Afterstatistical analysis of the mRNA signatures, a set of 49 differentiallyexpressed candidate genes was defined. Quantitative real-timePCR was used for the subsequent evaluation, which resulted ineight potential biomarker genes. Selection criteria were rhEpo


responsiveness and low inter-individual variation. The markersallowed detection of the administration up to 60 days after the 6-daytreatment period with rhEpo.

A completely different approach based on circulating microRNA(miRNA) analysis was applied by Leuenberger et al. [32,33].MicroRNAs are a special group of small RNAs (sRNA). They are non-protein coding and with a typical chain length of 19–25nucleotides [34]. Their main function is the post-transcriptionalmodulation of gene-expression, which is achieved by inactivationof corresponding mRNAs. The role of microRNAs during hemato-poiesis was investigated by several authors [35–37]. Of particularimportance for the differentiation of hematopoietic cells weremiR-181, miR-223, and miR-142 [38]. Zhan and Song found thatduring erythroid differentiation erythroid cells expressed morethan one-hundred miRNAs, of which miR-451 showed the highestup-regulation [39]. Several authors demonstrated in 2008 thathighly stable miRNAs can also be found in serum and plasma andthat these circulating miRNAs might serve as biomarkers for thedetection of diseases [40–42]. Leuenberger et al. used miRNAmicroarrays and quantitative real-time PCR for investigatingchanges in circulating miRNAs before and after administrationof Mircera [33]. A significant increase in miR-144 was observedafter application of the hormone.

2.3. Autologous blood transfusion

Blood doping is a prohibited method according to article M1(‘‘Enhancement of oxygen transfer’’) of the WADA prohibited list[9]. While a method for the detection of homologous bloodtransfusion was developed (vide infra), no comparable methodscurrently exist for the direct detection of autologous blood doping.Ashenden et al. investigated changes in the gene expression profileof blood after reinfusion of a rather high volume of blood (threebags) [43]. Significant changes were observed 7 days afterreinfusion, which were still detectable after up to 28 days.However, the reinfusion of only one bag led to much smallerdifferences. The authors concluded that additional experimentswould be necessary for a usable doping test. The reinfusion oferythrocyte concentrate, which was stored for ca 35 days at 4 8Cwas studied by Pottgiesser et al. [44]. T-lymphocytes were isolatedbefore and after the transfusion and their expression profile wasanalyzed with whole genome microarrays in combination withsubsequent evaluation of differentially expressed genes byquantitative real-time PCR. Seventy-two and 96 h post-transfusionthe expression of 728 and 659 genes was altered, respectively andin particular genes coding proteins relevant for the endocytosis ofsurface receptors and the toll-like receptor (TLR) pathway. Theobtained results showed that after reinfusion T-lymphocytes getactivated due to an immune response. Since it was only a pilotstudy, further research was suggested by the authors. In 2010Kannan and Atreya observed by differential profiling of red bloodcell (RBC) miRNAs, that upon storage at 4 8C 4 out of 52 selectedmiRNAs changed (miR-96, miR-150, miR-196a, miR-197) [45]. Agrant was awarded to B. Daniel (King’s College London) in October2010 by the Partnership for Clean Competition (PCC) in order toinvestigate changes in RBC-RNAs in the context of autologousblood transfusion [46].

2.4. Further research topics

Aside from the reviewed projects, transcriptomic and targetedgenomic methods have also been applied to the detection of gene-doping and the effect of anabolic steroids on gene expression(Table 1) [47–50]. Genomic methods (DNA typing) were used byseveral authors for the identification of urine samples which weresuspected of manipulation [51–54].

3. Proteomics

The word ‘‘proteomics’’ was derived from the term ‘‘proteome’’ inthe late 1990s (vide supra) [55,56]. Marc Wilkins introduced theword ‘‘proteome’’ to the scientific community in 1994 at the Siena2D-electrophoresis meeting [57,58]. In its original conception‘‘proteome’’ meant ‘‘the PROTEins expressed by a genOME ortissue’’ [57] and proteome studies were primarily dedicated to the‘‘identification and characterization of all proteins expressed by anorganism or tissue’’ [59–61]. At that time the analytical methodsemployed were mostly two-dimensional (2D)-electrophoresis forprotein separation and matrix-assisted laser desorption ionization(MALDI) time-of-flight (TOF) mass spectrometry for proteinidentification – the so-called ‘‘peptide mass fingerprinting’’(PMF) technique. However, due to the complexity of, e.g. thehuman proteome, additional techniques had to be developed andproteomes sub-fractionated before analysis. Aside from two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) keytechniques used in proteomics today are immobilized pH-gradientisoelectric focusing (IPG-IEF), reversed-phase (RP) high perfor-mance liquid chromatography (HPLC), 2D- or multidimensional LC,HPLC depletion of high abundant proteins (e.g. by multi-immunoaffinity columns), offline nano-HPLC combined withMALDI-TOF/TOF mass spectrometry (‘‘LC-MALDI’’), online nano-HPLC coupled to an ion-trap, triple quadrupole, or hybrid massspectrometer via a nano-ESI ion source, and high resolution highaccuracy mass spectrometry. In order to identify changes inprotein synthesis between two or more states of proteomes (e.g.diseased/non-diseased, drug treated/non-treated), quantitative

proteomics methods were developed. Four main strategies havebeen used for relative and absolute quantitation purposes. The first

strategy, termed two-dimensional fluorescence difference gel elec-

trophoresis (2D-DIGE), is a special application of 2D-PAGE, whereequal amounts of proteins in two proteomes (e.g. drug treated/non-treated) are labelled with different fluorescent dyes (e.g. thecyanine dyes Cy2, Cy3, or Cy5) prior to electrophoretic separation(dyes 1 and 2) for the purpose of relative comparison [62].Typically, an internal standard is used in order to normalize forchanges in fluorescent intensities due to protein losses duringsample preparation (dye 3). The internal standard can be preparedby mixing equal amounts of the two samples before the labellingreaction with dye 3. After mixing the labelled proteins together,they are separated by a single 2D-PAGE experiment and are thenvisualized by laser-excitation at three different wavelengthscorresponding to the three dyes. The advantage of this type ofstrategy over regular 2D-PAGE is that slight performancedifferences between otherwise identical 2D-gels need not becorrected due to the separation of three samples in one gel. Since itis a 2D-PAGE method proteins are separated by charge (isoelectricpoint) and molecular mass, which also allows the observation ofchanges in isoform distributions. As for all techniques usingrelative quantitation the main disadvantage is that due to the‘‘multiplexing’’ approach (i.e. the simultaneous separation of twoor more samples in one experiment) the maximum protein amountwhich can be loaded on the gel (e.g. 1–2 mg for a 24 cm IPG-strip)has to be divided by the number of samples investigated, whichfurther reduces the chance of discovering changes in low abundantproteins.

The second strategy is based on labelling with compoundscontaining stable isotopes (e.g. the ‘‘light’’ and ‘‘heavy’’ isotopes1H/2H, 12C/13C, 14N/15N), either on protein or peptide level. Similarto DIGE experiments, the labelling reaction has to take placeindividually for each sample and before the analytical separation ina single multiplexed experiment. However, instead of a rather easyto operate fluorescent scanner a mass spectrometer is required asdetector for revealing the differences in stable isotope ratios


between samples. Labelling on protein level has the advantage thatseparation of protein isoforms is still possible, e.g. by 2D-PAGE, butthe disadvantage that – in case the primary amino groups of aminoacids are used as targets for the labelling reaction – only a limitedamount of labels can be attached to each protein (i.e. on the N-terminus and the e-amino group of lysines). Contrary to that,labelling on peptide level (i.e. after enzymatic digestion of theproteins with, e.g. trypsin) additionally tags the newly generatedN-termini of each peptide. Thus, the significance of subsequentstatistical evaluation of the mass spectrometric data is enhanced,since in differential-expression experiments the ratio of eachstable isotope labelled peptide of a protein has to be in theory thesame. Frequently used reagents targeting primary amino groupsare called, e.g. iTRAQ (‘‘isobaric tags for relative and absolutequantitation’’) [63], TMT (‘‘tandem mass tags’’) [64], or ICPL(‘‘isotope coded protein labelling’’) [65]. Both iTRAQ and TMT are‘‘isobaric mass tags’’, which means that the molecular masses ofthe ‘‘light’’ and ‘‘heavy’’ labelling compounds are the same. This isachieved by combining a so called ‘‘balance group’’ with the‘‘reporter group’’, which compensates mass differences caused bythe stable isotopes of the reporter group (Fig. 2). Thus, both theheavy- and light-labelled peptides appear at same m/z in the MS-spectrum and upon fragmentation by collision-induced dissocia-tion (CID) the reporter group is released and allows relativequantitation in the MS/MS-spectrum. The disadvantage is that onlyions which were selected for fragmentation will be quantified. Dueto the enzymatic cleavage of the protein mixture or proteomebefore the analytical separation, the obtained peptide mixture(‘‘digest’’) is too complex and has to be fractionated prior to massspectrometry. Usually, 2D-HPLC separations are used, whichcombine strong cation-exchange (SCX) with C18 reversed-phase(RP) chromatography [63].

Fig. 2. Stable isotope labelling of peptides for quantitative proteomics. The chemical st

labelled on their amino groups (B). Due to the balance group of the reagent identical pept

mode of the mass spectrometer. Upon fragmentation in the MS/MS-mode the reporter

sequence information.

Ross et al. [63] (permission granted).

Absolute quantitation (AQUA) is the third strategy. It uses stableisotope labelled peptides or proteins as internal standards, which aremixed to the sample at an early stage of sample preparation [66].Thus, all losses of the target peptide or protein occurring duringsubsequent steps are taken accurately into account. Due to the factthat the stable isotope labelled peptide or protein has to be firstsynthesized, AQUA-technology is only useful for targeted proteomics.Typically, AQUA starts with an enzymatic (e.g. tryptic) digest of thetarget protein and then those peptides, which generate the mostintense fragment ion transitions in the MS/MS spectra are selectedfor peptide synthesis. In case the target protein is not accessible an‘‘in-silico’’ digest is performed and then the most promising peptidesare selected for synthesis. Selection criteria are chain length,presence of instable amino acids (e.g. cysteine, methionine), anduniqueness of the sequence in the proteome investigated. Frequent-ly used ‘‘heavy’’ amino acids in AQUA-peptides are leucine, proline,or valine but also asparagine, phenylalanine, or tyrosine. Due to e.g. 613C and 1 15N atoms ‘‘heavy’’ leucine adds seven mass units to thecorresponding ‘‘light’’ peptide, the other ‘‘heavy’’ amino acids six(proline, valine, asparagine) or ten (phenylalanine, tyrosine) massunits. An additional advantage of AQUA-technology is that also post-translationally modified peptides can be accurately quantified aslong as the investigated post-translational modification (PTM) canbe chemically synthesized (e.g. phosphorylation, sulfonation,acetylation, farnesylation, or simple glycosylation of amino acidsin contrast to modifications involving more extended glycanstructures).

The forth strategy used in quantitative proteomics is the so-called ‘‘label-free quantification’’. Common feature of this type ofquantitation is that it requires no chemical modification of theprotein or peptide by a fluorescent or stable isotope tag. Bothlabelling techniques are rather cost-intense and thus limit the

ructure and quantitation principle of iTRAQ reagents is shown in (A). Peptides are

ides of e.g. four different stages of a proteome show identical m/z values in the MS-

group is released and allows then relative quantification in addition to the peptide


amount of investigated sample material as well as the number ofconducted experiments. Label-free methods are based on peptideHPLC separation, ionization, and subsequent mass spectrometricanalysis. They use either ion currents or spectral counting forquantification [67]. Quantitation by ion currents applies theobservation that ion suppression is greatly reduced under theconditions of proteomics LC–MS experiments, primarily due to theusage of nanoelectrospray ionization and enhanced peptideseparation. Extracted ion chromatograms (XIC) are prepared fromLC–MS or LC–MS/MS runs of target peptides and then the ratios ofthe peak areas at the pre-defined retention times are comparedacross samples for relative quantitation. It was further observedthat for the three most intense peptide ions of a protein the peakareas are identical regardless of the protein investigated.Consequently, the addition of a single internal standard couldbe used for estimating the absolute concentration of the proteinspresent in a run [67]. Spectral counting relies on the principle ofdata-dependent acquisition (DDA) of MS/MS spectra during an on-line LC–MS run. In this type of experiment the mass spectrometeris operated in a predefined and automatic way what the selectionof precursor ions for fragmentation is concerned. In a typicalexperiment the mass spectrometer is programmed to select, e.g.the three most intense ions in the full scan spectrum forfragmentation. After acquisition of the three MS/MS spectra thenext full scan spectrum is recorded and the selection andfragmentation of the three most ions starts again. Consequently,the frequency of peptide selection of a certain protein by DDA forMS/MS fragmentation is proportional to the concentration of theprotein in the sample. The method suffers from the fact that (a) thenumber of peptides generated from each protein is dependent onthe size of the protein and (b) the fragmentation behavior (which isused for identifying the peptide sequence) is determined by theamino acid sequence of the peptide. Hence, protein size-basednormalization is mandatory for spectral counting experiments[67].

In doping control both targeted and non-targeted proteomic

methods have been used. Targeted proteomic LC–MS/MS methods

Fig. 3. The vast dynamic range of the human serum and plasma proteome makes disc

Anderson and Anderson [89] (permission granted).

are currently and mainly used for the detection and quantitation ofpeptide hormones like insulins [68–70], insulin-like growth factors(IGFs) [71–74], gonadotropin-releasing hormone (GnRH) [75],growth-hormone-releasing peptides (GHRP) [76,77], andSynacthen [78–80]. Targeted 2D-PAGE methods were used forthe separation of endogenous and recombinant erythropoietins[81,82] and the development of a test for the detection of dopingwith human growth hormone [83]. However, these publicationswill not be subject of this review, which focuses on the applicationof non-targeted methods, i.e. the study of changes of humanproteomes due to the administration of prohibited substances withthe ultimate goal of discovering biomarkers for prolongeddetection of the abuse of these substances.

Proteome research in doping control has traditionally beenusing matrices, which have also been routinely used for thedetection of non-protein-based drugs, i.e. urine and blood (serum/plasma). Unfortunately, both matrices are not well suited forproteome studies. While urine can be non-invasively sampled, itsprotein content varies depending on the liquid intake and type ofexercise [84]. Usually, the total protein concentration of urine iswithin the range of 10–100 mg/L [85]. Most soluble urinaryproteins can be attributed to three sources, namely plasmaproteins after glomerular filtration, secreted epithelial cell proteins(e.g. Tamm-Horsfall protein), and proteins released by cells due toinjury (e.g. inflammation) [86]. Due to the varying degree of urinedilution, protein concentration is typically normalized according tothe urinary creatinine concentration. However, since the amountof creatinine excretion is influenced by age, gender, diseases, andtubular excretion some authors recommended normalizationbased on ‘‘housekeeping’’ urinary proteins [86]. The total proteincontent of blood serum and plasma is relatively constant, typically60–80 mg/mL [87]. Unfortunately, serum and plasma proteomestudies are hampered by the fact that (1) about 90% of theproteome is occupied by few high abundant proteins (e.g. serumalbumin, which represents ca 55%), and (2) the dynamic range ofindividual proteins spans at least ten orders of magnitude, i.e. itranges from mg/mL (e.g. albumin) to pg/mL (hGH, Epo) (Fig. 3)

overy of meaningful biomarkers difficult.


[88,89]. Consequently, many non-targeted proteome experimentsended up in measuring high to medium abundant proteins, but notin the detection of low abundant regulatory proteins.

The majority of doping-related proteomics projects could beassigned to the category of biomarker detection for the misuseof recombinant human growth hormone. Other topics wereblood transfusion, erythropoietin, anabolic steroids, proteases,non-specific antibody binding, insulin, and endurance exercise(Table 2).

Table 2List selected anti-doping research projects and publications applying non-targeted prot

Author Title

B. Christensen, L. Sackmann-Sala,

D. Cruz-Topete, J.O. Jørgensen,

N. Jessen, C. Lundby, J.J. Kopchick.

Novel serum biomarkers for erythr

humans: a proteomic approach

E. Imperlini, A. Mancini, S. Spaziani,

D. Martone, A. Alfieri, M. Gemei,

L. del Vecchio, P. Buono, S. Orru

Androgen receptor signaling induc

supraphysiological doses of dihydr

human peripheral blood lymphocy

M. Kohler, K. Walpurgis, A. Thomas,

M. de Maree, J. Mester, W. Schanzer,

M. Thevis

Effects of endurance exercise on th

analyzed by 2-D PAGE and Orbitra

B. Christensen, J.O. Lunde Jorgensen,

K. Vissing, J.J. Kopchick

Identification of novel serum biom

abuse: a proteomic approach

Z. Nikolovski, J. Segura, R. Ventura,

O. Fornas, M. Lopez

Test for blood transfusion (autolog

based on observed changes of eryt

proteome

R.G. Kay, C. Barton, C.P. Velloso,

P.R. Brown, C. Bartlett, A.J. Blazevich,

R.J. Godfrey, G. Goldspink, R. Rees,

G.R. Ball, D.A. Cowan, S.D. Harridge,

J. Roberts, P. Teale, C.S. Creaser

High-throughput ultra-high-perfor

chromatography/tandem mass spe

of insulin-like growth factor-I and

alpha-2-glycoprotein in serum as b

human growth

hormone administration

L. Sackmann-Sala, J. Ding, L.A. Frohman,

J.J. Kopchick

Activation of the GH/IGF-1 axis by

GHRH analog, results in serum pro

normal adult subjects

J. Ding, E.O. List, S. Okada, J.J. Kopchick Perspective: proteomic approach to

human growth hormone

M. Kohler, S. Franz, A. Regeniter, A. Ikonen,

K. Walpurgis, A. Thomas, W. Schanzer,

M. Thevis

Comparison of the urinary protein

2D-gel electrophoresis and mass sp

study

M. Kohler, A. Thomas, H. Geyer, L. Horta,

W. Schanzer, M. Thevis

Detection of the protease bacilloly

urine samples

C. Barton, P. Beck, R. Kay, P. Teale, J. Roberts Multiplexed LC–MS/MS analysis of

study doping in sport

L. Chung, A.E. Nelson, K.K. Ho, R.C. Baxter Proteomic profiling of growth horm

in human peripheral blood leukocy

L. Chung, R.C. Baxter Detection of growth hormone resp

SELDI-TOF mass spectrometry

J. Boateng, R. Kay, L. Lancashire, P. Brown,

C. Velloso, P. Bouloux, P. Teale, J. Roberts,

R. Rees, G. Ball, S. Harridge, G. Goldspink,

C. Creaser

A proteomic approach combining M

analysis for the detection and iden

of administration of exogenous hu

hormone in humans

C. Malm, L. Frangsmyr, S. Holmberg Detection of autologous blood tran

screening to find unique biomarke

C. Malm, L. Frangsmyr, A. Eriksson Skeletal muscle proteome alteratio

anabolic steroid abuse

C. Reichel Identification of zinc-alpha-2-glyco

AE7A5 antihuman EPO antibody by

and high-resolution high-mass acc

C. Malm Detection of autologous blood tran

screening to find unique biomarke

C. Reichel, G. Gmeiner, V. Jordan,

M. Watzinger, R. Kulovics

Quantitative proteomics of rhGH-d

stable isotope labelling and MALDI

spectrometry

J. Segura, Z. Nikolovski, D. Andreu,

R. Ventura, E. Borras, M. Rodriguez

Autologous blood transfusion dete

membrane proteome changes after

L. Chung, D. Clifford, M. Buckley,

R.C. Baxter

Novel biomarkers of human growt

serum proteomic profiling using pr

spectrometry

J.O. Jorgensen, J.J. Kopchick Proteomic analysis of serum expos

for detection of GH doping

J. Roberts, P. Teale, R. Kay, C. Creaser,

C. Rees, G. Ball, S. Mian, G. Goldspink,

S. Harridge

The application of cellular chemist

approaches to the detection of gen

3.1. Human growth hormone and IGF-I

Traditionally, proteomics projects mainly focused on thediscovery of biomarkers for doping substances otherwise difficultto detect. Growth hormone is synthesized by the anterior pituitaryand released in the bloodstream in a pulsatile way [90]. Theprocess is under control of hormones of the hypothalamus, namelygrowth hormone releasing hormone (GHRH), somatostatin, andghrelin and is additionally influenced by factors like gender, age,

eomics strategies for biomarker discovery.

Year Type Topic

opoietin use in 2011 Publication Epo

ed by

otestosterone in

tes

2010 Publication Anabolic steroides

e urinary proteome

p MS

2010 Publication Endurance exercise

arkers for erythropoietin 2010 Project Epo

ous/homologous)

hrocyte membrane


mance liquid

ctrometry quantitation

leucine-rich

iomarkers of recombinant


CJC-1295, a long-acting

tein profile changes in

2009 Publication hGH, IGF-I

detect biomarkers of 2009 Publication hGH

patterns of athletes by

ectrometry-a pilot

2009 Publication Endurance sport,

strength sport

sin in doping-control 2009 Publication Proteases

horse plasma proteins to 2009 Publication Anabolic steroids

one-responsive proteins

tes.


onsive proteins using 2009 Publication hGH

S and bioinformatic

tification of biomarkers

man growth


sfusion by proteomics:

rs


ns after long term 2008 Project Anabolic steroids

protein binding to clone

means of nano-HPLC

uracy ESI–MS/MS

2007 Publication Non-specific

antibody interaction

sfusion by proteomics:

rs


oping by multiplexed

–TOF/TOF mass

2007 Project hGH

ction through erythrocyte

blood storage


h hormone action from

otein chip mass


ed to GH: a future essay 2006 Project hGH

ry and proteomic

e doping



sleep, stress, nutrition, and exercise. Its action is mainly mediatedby IGF-I, which is synthesized by hepatocytes after binding of hGHto the corresponding membrane-bound receptor. Pituitary humangrowth hormone is a mixture of different isoforms, of which themost abundant one is the 22 kDa isoform (ca 70–75%) followed bythe 20 kDa isoform (ca 5–10%). Minor representatives are eitherpost-translationally modified forms (e.g. by acetylation, deamida-tion) or fragments (e.g. 5 kDa, 17 kDa) [91,92]. The therapeuticallyused preparation of human recombinant growth hormone consistsof only the 22 kDa isoform, although the recombinant 20 kDaisoform is available for non-clinical research purposes. IGF-I (a7.5 kDa protein) is mainly found in plasma in a complex with atleast two other proteins, namely IGF binding protein 3 (IGFBP3)and acid-labile subunit (ALS). IGF-I is also prohibited according toarticle S2 of the WADA list of prohibited substances [9]. Aside fromdirectly effecting the synthesis of IGF-I (‘‘GH/IGF-I axis’’) hGH alsoinfluences bone and collagen metabolism [93–95]. Based on theseprinciples direct and indirect doping tests were developed for hGH.Currently, the so-called ‘‘hGH isoform differential immunoassays’’is the only WADA-accredited test for hGH abuse. It is a direct test

and is based on the principle that the ratio between the 22 kDa andthe non-22 kDa isoforms is temporarily altered by the applicationof recombinant 22 kDa hGH pharmaceuticals [91,96–98]. Howev-er, due to the short serum/plasma half-life of hGH-isoforms, thedetection window of the isoform test is rather short (22–36 hmaximum) [99,100]. Another disadvantage of the test is that itcannot detect doping with GH secretagogues and pituitary GH.Hence, indirect tests using biomarkers for hGH-administration havebeen developed. The GH-2000 project led to the selection of thetwo most promising markers (IGF-I, procollagen type 3 N-terminalpeptide [P-III-NP]) out of a set of 12 preselected ones. The testedmarkers belonged either to proteins of the GH/IGF-I axis (e.g. IGF-I,IGFBP-2, IGFBP-3, ALS) or the bone and collagen metabolism (e.g.osteocalcin, P-III-NP, C-terminal propeptide of type 1 collagen[PICP], C-terminal cross-linked telopeptide of type 1 collagen[ICTP]) and were found in blood [93–95,101]. The combination ofthe two markers allowed the expected prolonged detection of hGHabuse (typically dose-dependent in the range of 2–4 weeks afterdiscontinuing treatment). Lower sensitivity was observed for hGH-treated women. The subsequent GH-2004 project addressedquestions regarding the effect of ethnicity, reference ranges forIGF-I and P-III-NP in athletes, and the effects of injury on themarkers [102,103]. Currently, the marker method is still in theevaluation phase. Another direct method was published in 2008and used 2D-PAGE and immunoblotting for the visualization andquantitation of growth hormone isoforms after immunoaffinitypurification of hGH from plasma, and electrophoretic separation[83]. Typical detection windows were below 24 h.

The number of non-targeted proteomics projects conducted so farfor the discovery of novel biomarkers of hGH abuse has been ratherlimited (Table 2). According to the employed methods they can bedivided in at least four groups: projects using (1) 2D-PAGE, (2)SELDI-TOF MS, (3) MALDI- and/or LC–MS, and (4) stable isotopetags for quantitation. In 2006 a project entitled ‘‘Proteomic analysisof serum exposed to GH: a future essay for detection of GH doping’’was funded by WADA [104]. Healthy male subjects received hGHfor 8 days (‘‘high dose’’) and then serum samples were investigatedby 2D-PAGE in order to detect possible changes in proteinexpression. The authors observed changes in the isoformdistribution of several proteins and recommended that anextended study should be performed investigating at least twocandidate proteins (apolipoprotein A1, alpha-1-antitrypsin). How-ever, the two proteins are high abundant proteins in human serumand plasma (vide supra). Sackmann-Sala et al. used a similarstrategy for the discovery of potential biomarkers for theapplication of a long-acting GHRH analog (CJC-1295) [105]. Serum

samples were albumin-depleted, then separated by 2D-PAGE, anddifferentially expressed proteins identified by MALDI-TOF andMALDI-TOF/TOF mass spectrometry. It was expected that CJC-1295should have activated the GH/IGF-I axis (vide supra). Nevertheless,only changes in high abundant proteins were observed again(apolipoprotein A1, transthyretin, beta-hemoglobin, and frag-ments of albumin and an immunoglobulin). However, no changesin medium to lower abundant proteins were observed (e.g. themarkers already defined and established by the GH-2000 project),which is due to the fact that the majority of high abundant proteins(except partly albumin) was not removed by depletion beforeanalysis. Nowadays, depletion of six to twenty high abundantproteins is usually recommended for serum and plasma proteo-mics. Another reason was the limited sample volume used for 2D-PAGE (25 mL serum). In order to be at least theoretically able todetect low abundant proteins (pg/mL) by gel-based massspectrometric approaches, mL-amounts of serum or plasma arenecessary.

Surface-enhanced laser desorption ionization time-of-flightmass spectrometry (SELDI-TOF MS) was employed by Chung et al.for serum proteome profiling with the idea of detecting hGH-related biomarkers [106]. SELDI-TOF MS uses selective surfaces(‘‘ProteinChips’’), which act simultaneously as sample pre-frac-tionation tools and mass spectrometric target plates. Differentsurface chemistries (e.g. reversed-phase, ion-exchange, immobi-lized metal affinity [IMAC]) allow simple proteome sub-fraction-ation before the surface-bound proteins are detected with aMALDI-TOF mass spectrometer, which is operated in linear mode.A protein mass profile is obtained for each sample, and the profilesare then compared and evaluated for potential biomarkers withspecial software (ProteinChip Biomarker Wizard). The disadvan-tage of the system is that structural analysis and identification ofpromising m/z values requires access to a second – MS/MS capable– mass spectrometer. In the study by Chung et al. 60 subjects wereinvolved, who received either a placebo or 0.1 or 0.2 IU/kgrecombinant hGH (rhGH) for 4 weeks. On days 0 and 21 serumprotein profiles were obtained with Cu2+-IMAC ProteinChips.Several GH-dependent mass peaks were observed and after cross-validation a 15.1 kDa peak was selected as biomarker. Additionalexperiments identified the peak as hemoglobin alpha-1 chain(HbA1). In a follow-up administration study Chung and Baxterused the same approach but were no longer able to confirm HbA1as biomarker [107]. The disappearance of the HbA1 signal wasascribed to the usage of different blood collection tubes, which thistime contained a separator gel. Hence, the authors questionedHbA1 as useful biomarker for hGH administration. In yet anotherstudy Chung et al. investigated the influence of hGH on humanperipheral blood leukocytes [108]. Twenty-two persons receivedrhGH (2 mg/day) for 8 weeks, and eight persons a placebo. SELDI-TOF MS in combination with weak cation-exchange chips was usedthis time. Several peaks within the m/z range of 3–22 kDa werefound as either significantly up- or down-regulated. Three down-regulated proteins were identified as calcium-binding calgranulins(S100A8, A100A9, S100A12), which were also found in a previousSELDI-TOF study as biomarkers for arthritis [109]. So far, no furtherapplication of these markers for the detection of growth hormonedoping was reported.

Among the third group of projects (MALDI– and LC–MS methodsfor biomarker discovery) the study by Boateng et al. has to bementioned [110]. Four male subjects received 0.075 IU/kg rhGHsubcutaneously per day for 2 weeks, and another four malesubjects a placebo. Blood serum was taken and after a 4 weekswashout phase the experiment was repeated with the rhGH-groupnow receiving the placebo and the placebo-group rhGH. SerumIGF-I was measured by chemiluminescent ELISA. In order to detectpossible new biomarkers two strategies were applied: (a) after


1:10 dilution of serum with 0.1% TFA the solution was mixed 1:1with sinapinic acid and spotted on a MALDI target plate for MALDI-TOF MS-analysis, and (b) serum was depleted from high abundantproteins by acetonitrile precipitation [111], then digested withtrypsin, and subsequently analyzed with nano-LC–MS in full scanmode (m/z 400–1600, 2 spectra/s, then combined to a singlecomposite-spectrum). Both, the MALDI–MS and LC–MS spectra(representing intact peptides/proteins and tryptic peptides,respectively) were analyzed with an artificial neuronal net(ANN). Six ions were identified in each of the two data sets whichallowed correct classification of the subjects with reasonablesensitivity and specificity (91% and 100% for MALDI–MS, 90% and95% for LC–MS). The strongest discriminatory power had the ionsat m/z 17310 and m/z 741.2 in the MALDI– and LC–MS models,respectively. The m/z 741.2 ion was then identified via LC–MS/MSas a tryptic fragment of leucine-rich alpha-2-glycoprotein (LRG).The authors suggested the combined usage of IGF-I (ELISA) and LRG(LC–MS) data for enhanced classification characteristics. In afollow-up study Kay et al. used two different tryptic LRG peptidesas well as one tryptic peptide of IGF-I peptide together with theirstable isotope labelled analogs as internal standards for quantita-tion of LRG and IGF-I [112]. Data were again evaluated with an ANNand the obtained model performed with an accuracy of 97%,sensitivity of 100%, and specificity of 96%.

The application of a quantitative proteomics approach usingstable isotope tags (iTRAQ) was proposed in another WADA-fundedproject by Reichel et al. (2007; Table 2) [113]. A depletion strategyfor high abundant plasma proteins has been used with subsequentprotein sub-fractionation in order to increase the possibility todetect lower abundant proteins. The project is still ongoing.

3.2. Autologous blood transfusion

The WADA accredited homologous blood transfusion test is basedon the flow cytometric detection of the presence of more than onepopulation of red blood cells (RBC) in the blood sample of theathlete. It uses up to twelve antibodies which are directed againstselected RBC surface antigens (i.e. proteins) [114–116]. Recently, ascreening method was introduced which measures the concentra-tion of di-(2-ethylhexyl)-phthalate (DEHP) metabolites in urine.The test can be used for both homologous and autologous bloodtransfusion [117,118]. DEHP is a plasticizer found in bloodtransfusion bags but is also present in other PVC products.Significantly elevated levels of these metabolites were found inpatients receiving blood transfusions compared to non-treatedcontrol groups and for the investigated period of 48 h posttreatment. However, so far the application of these phthalates hasbeen only for supportive evidence. For the long-term detection ofautologous blood doping proteomics might offer a solution [119]. Itwas observed that storage of RBC leads to changes in membraneproteins, which were mainly due to oxidation reactions. Among theaffected proteins were beta-actin, glyceraldehyde-3-phosphatedehydrogenase, ankyrin, and alpha and beta spectrin [120]. Storageunder helium strongly reduced these effects. 2D-PAGE was usedfor this study as method. Between 2007 and 2010 three projectshave been funded by WADA which have been using proteomicmethods for the detection of autologous blood transfusion. Malmet al. (‘‘Detection of autologous blood transfusion by proteomics:screening to find unique biomarkers’’) first studied changesbetween fresh and stored RBC with 2D-DIGE [121]. After 6 weeksat +4 8C and �80 8C, 58 and 71 protein spots in the 2D-pattern weresignificantly altered. Subsequently, a set of 20 candidate proteinswas defined for the freeze-stored RBC. An autologous bloodtransfusion study was then performed with ten subjects receivingRBC, which were freeze-stored for 16 weeks. Differences in the 2D-pattern were visible after transfusion, which were also partly

confirmed by Western blot analysis. Currently, the method is stillunder development [122]. Segura et al. (‘‘Autologous bloodtransfusion detection through erythrocyte membrane proteomechanges after blood storage’’) employed a complementaryapproach (2D-PAGE, iTRAQ labelling) for investigating changesin the RBC membrane proteome during storage [123]. Severalcandidate proteins were identified, which were either transmem-brane, cytoskeletal, or other proteins. A follow-up study wasfunded in 2010 (Nikolovski et al. ‘‘Test for blood transfusion(autologous/homologous) based on observed changes of erythro-cyte membrane proteome’’) [124].

3.3. Anabolic androgenic steroids

Malm et al. studied the effect of long-term use of anabolicandrogenic steroids (AAS) on skeletal muscle proteome [125].2D-DIGE analysis with MALDI-TOF identification of differentiallyexpressed proteins was used. While no details about thediscovered biomarkers were published, the authors concludedthat doped and non-doped athletes could be differentiated basedon the obtained proteomic, hematological, and morphologicaldata. A targeted proteomic profiling approach was used by Bartonet al. for the detection of AAS abuse in horses [126]. Forty-nineplasma proteins were quantitatively monitored in a single run andat the level of their tryptic peptides by an LC–MS/MS basedmultiple reaction monitoring (MRM) assay. After intra-muscularadministration of a long-acting testosterone ester the majority ofthe preselected proteins did not change. Only clusterin andleucine-rich alpha-2-glycoprotein were significantly increasedduring administration. Imperlini et al. investigated the influenceof dihydrotestosterone (DHT) on the androgen receptor (AR)signaling pathway in human peripheral blood lymphocytes [127].Lymphocytes were isolated from human blood (buffy coat), grownin cell culture, and treated three times with DHT before they wereharvested after 7 days. 2D-DIGE experiments were performed and31 differentially expressed proteins were identified by LC–MS. Fordeciding whether these proteins were linked to the AR pathway,gene promotor region analysis was performed. At least five of theproteins were found to be associated with the androgen receptor.

3.4. Further research topics

In order to identify a non-specifically interacting protein withthe monoclonal antibody used in Epo doping control (cloneAE7A5), Reichel performed a non-targeted (‘‘shotgun’’) proteomicsapproach [128]. After isoelectric focusing (IEF-PAGE) and Westernblot analysis the non-specifically bound isoforms were excisedfrom the membrane and on-membrane digested with trypsin.Subsequent nano-HPLC high resolution high accuracy massspectrometric analysis of the digests and bioinformatic dataevaluation showed that all for isoforms contained zinc-alpha-2-glycoprotein (ZAG). The result was confirmed by electrophoreticseparation of recombinant ZAG and subsequent Western blotanalysis with the non-specifically interacting antibody.

Kohler et al. reported the detection of a protease in two urinesamples of athletes [129]. One millilitre of each urine wasconcentrated by solid-phase extraction and the eluate analyzedby LC–MS. After database search of the obtained spectra theprotease could be identified as bacillolysin.

The influence of endurance and strength sport on the urinaryproteome was studied by Kohler et al. [84]. 2D-PAGE in combinationwith Coomassie protein staining and LC–MS was used for theidentification of differentially expressed proteins. A significantincrease in proteins within the mass-range of 30–80 kDa wasobserved (e.g. transferrin, ZAG, prostaglandin H2 D-isomerase) forthe endurance sport samples, while strength sport samples showed


an increase in proteins below 30 kDa (e.g. transthyretin, CD59antigen). In a second project Kohler et al. investigated the effect ofendurance exercise (marathon run) on urinary proteins [130]. Thesame methodical approach was used as for the previous project.Among the increased proteins were hemopexin, albumin, oroso-mucoid 1, transferrin, and carbonic anhydrase 1, which could belinked to erythrocyte degradation and fat metabolism.

Christensen et al. tried to find biomarkers in the serum ofrhEpo-treated persons [131,132]. Eight subjects received 5000 IUof epoetin beta every second day and for 16 days. Before and ondays 8 and 16 during the treatment serum was collected. Afteralbumin- and IgG-depletion proteins (300 mg) were separated by2D-PAGE, stained, and differentially expressed proteins identifiedby MALDI-TOF mass spectrometry. Seven protein spots showedsignificant decrease after 16 days and were attributed to isoformsof haptoglobin (4) and transferrin (2) as wells as a mixture ofhemopexin and albumin. However, as observed in similar serumproteome studies (vide supra) only high abundant proteins weredetected.

4. Metabolomics

In analogy to transcriptome and proteome the term metabolome

can be defined as the complete set of metabolites found in a cell,organism, or biological system [133,134]. The term metabonomics

was defined by Jeremy Nicholson in 1999 as ‘‘the quantitativemeasurement of the time-related multiparametric metabolicresponse of living systems to pathophysiological stimuli or geneticmodification’’ [134,135]. In 2001 the term metabolomics wasdefined by Oliver Fiehn as ‘‘a comprehensive and quantitativeanalysis of all metabolites’’ of a system [135]. According to Lindonet al. the main difference between metabolomics and metabo-nomics is that metabonomics also takes into account dynamicchanges and fluctuations of metabolites, hence is not only confinedto the determination of static metabolite concentrations [134].Analytical techniques focusing on a more restricted measurementof metabolites were termed as metabolic fingerprinting (i.e.acquisition of NMR or MS spectra, which act as a fingerprint ofthe metabolites of the investigated biological system, but usuallywithout identification of the metabolites. The fingerprints are thencompared via multivariate statistical methods), metabolite profiling

(i.e. targeted and frequently quantitative analysis of a specificgroup or class of metabolites, e.g. amino acids), and metabolite

target analysis (i.e. targeted and quantitative analysis of one or fewselected metabolites, typically by addition of stable isotopelabelled internal standards for absolute quantitation) [133,136].Contrary to nucleic acids, proteins, and peptides the molecularmass of metabolites usually is below 1000 Da (‘‘small molecules’’)[133]. Metabolome analyses are frequently performed on 1H NMR(nuclear magnetic resonance) instruments and on mass spectro-meters coupled to either gas-chromatography (GC–MS) or liquidchromatography (LC–MS). For enhanced resolution two-dimen-sional methods (NMR, GC, LC) and high resolution high accuracymass spectrometers are used [137,138].

In the context of anti-doping research hardly any results of non-targeted metabolomic-based investigations were published so far.Many projects were dedicated to endogenous steroid profiling,which primarily used GC-MS and isotope ratio mass spectrometry(IRMS) [139–145]. The group of J.T. Brenna has been working on aurinary steroidomics project applying two dimensional gaschromatography-TOFMS (2D GC � GC-TOFMS) and IRMS [146].In 2006 a pilot project was funded by WADA to study the‘‘metabonomic signature in bike athletes’’ [147]. Appolonova et al.applied a targeted metabolite analysis approach for the detectionof rhEpo administration [148]. It was assumed that rhEpoinfluences the system consisting of asymmetrical dimethylargi-

nine (ADMA), dimethylarginine dimethylaminohydrolase (DDAH),and nitric oxide synthase (NOS). Four urinary metabolites (ADMA,symmetrical dimethylarginine (SDMA), arginine, citrulline) werequantitatively measured by LC–MS. It could be shown that a singledose intravenous injection of 2000 IU rhEpo caused an increase inall four metabolites in the two tested subjects. However, nodetailed results about possible detection windows of rhEpo-dopingwere shown for this method.

5. Conclusions and future perspectives

During the past couple of years a considerable number ofdoping-related OMICS-projects was conducted, which was mainlydriven by the poor detectability of several WADA-prohibitedsubstances (e.g. hGH, Epo) or methods (autologous blood transfu-sion). The fact that the majority of these projects either failed or notdirectly led to applications in routine doping control is mainly dueto the complexity of human transcriptomes, proteomes, andmetabolomes. Additionally, the correlation between analysisresults obtained on the level of mRNAs, proteins, and metabolitesis poor [5]. The reasons have been explained, e.g. by the incompletetranslation of mRNAs into proteins, the post-transcriptionalregulation of mRNAs by miRNAs, alternative RNA-splicing, andpost-translational modification of the expressed proteins leadingto protein isoforms of different biological activity (Fig. 1). Anotherreason is the lack of analytical procedures which allow coveringthe highly dynamic range of human proteomes, which frequentlyend up in the characterization of high abundant proteins. Althoughthe observed changes in these proteins might be due to the studieddoping substance, additional reasons have to be taken in to accountwhich might question the causality of the observations (e.g.changes in high abundant acute phase proteins like alpha-1 acidglycoprotein or alpha-1 antitrypsin are frequently the result ofcytokine-mediated inflammatory reactions of the organism).Another point to be considered is substance heterogeneity, whichusually requires the application of several different analyticalstrategies and thus multiplies the required resources in terms ofinstrumentation, analysis time, materials (in particular stableisotope labelled reagents and standards as well as fluorescent dyesfor labelling reactions), experts, and overall budget. Thus, non-targeted proteomics frequently needs sub-fractionation of thesample and multi-dimensional separation before meaningfulapplication of mass spectrometry is possible. An additional andfrequently overlooked fact is the actually required sample amountfor detecting possible changes in low level biomarkers. This has totake into account the analytical sensitivity of the detection system(which e.g. in case of nano-LC coupled to mass spectrometrytypically is in the low fmol to high attomol range) and possiblelosses during storage and sample preparation due to inadequatestabilization (e.g. severe losses of low abundant proteins duringpurification can be avoided by using detergents, but many of thesedetergents are incompatible with the subsequent mass spectro-metric analysis). Consequently, the amount of human plasmarequired for e.g. a non-targeted mass spectrometry-based plasmaproteome study, which aims to cover low abundant biomarkerproteins in the pg/mL range, easily goes up to 1–10 mL (i.e. totalprotein contents in the medium to high mg range (e.g. 60–600 mg)have to be analytically handled. The typical capacity of a nano-HPLC column is below 1 mg in order to not risk extensive washingcycles for column regeneration between sample runs). Anadditional requirement for non-targeted proteomics and metabo-lomics studies using mass spectrometry is access to high resolutionhigh accuracy mass spectrometers and in particular to scanninginstruments with fast MS/MS capability. This is reasoned by thetype of experiment usually performed in these studies, whichdirects the mass spectrometer to acquire MS/MS data based on the


intensity-sequence of the ions observed in the immediatelypreceding full scan spectrum (‘‘data dependant acquisition’’).Consequently, only a limited number of high intensity ions of eachspectrum gets selected for identification by MS/MS.

For fulfilling statistical requirements OMICS-studies typicallyrequire large and carefully matched collectives. Since theconsidered variables (e.g. genes in a microarray experiment,observed m/z and retention time pairs in an LC–MS run) frequentlyexceed the number of subjects included in the study and alsobecause multivariate data analysis procedures are in most casesperformed (e.g. cluster analysis, principal components analysis[PCA], discriminant analysis [DA], application of support vectormachines [SVM] or artificial neuronal nets [ANN]), over-fitting ofdata has to be taken into account, for instance by rigorous cross-validation and the usage of independent data sets for modellingand model evaluation.

Due to all these constraints the success of many non-targetedOMICS-projects with respect to the immediate application ofobtained results in routine doping testing has been limited.Prerequisite is the usage of quantitative methods, which allowaccurate quantitation of observed differences between studiedcollectives (e.g. quantitative real-time PCR for the evaluation ofmicroarray data for transcriptomics, stable isotope labelledstandards for absolute quantification in MS-based proteomicsand metabolomics studies). Currently, only targeted OMICS-related methods found their way into application in routinedoping control, for instance for the analysis of small peptidehormones by LC–MS (e.g. insulins). A compromise betweentargeted and truly non-targeted strategies will be the applicationof signal-transduction- and pathway-driven OMICS-studies. The so-called newborn screening is one the best examples of successfulapplication of metabolite profiling for the early discovery of inbornerrors of metabolism. Similar approaches might prove useful foranti-doping purposes, and in particular in combination withlongitudinal monitoring and the concept of the athlete’s biologicalpassport. All three OMICS-disciplines (transcriptomics, proteo-mics, and metabolomics) including their sub-disciplines (e.g.Glycomics, Lipidomics) are perfectly suited for this targeted

profiling approach and should be more intensely investigated inthe future.

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