biomarker development in translational research and...
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Singapore – 22nd & 23rd November 2012
Biomarker development in translational researchand commercialisation
Michael W. Pfaffl
Qiagen Seminar Tour
Singapore, November 2012
Michael W. PfafflPhysiology, TU München Weihenstephan, 85354 Freising, GermanyMichael.Pfaffl@wzw.tum.dewww.Gene-Quantification.info
Singapore – 22nd & 23rd November 2012
Quantification of specific mRNAs and microRNAs(cattle, sheep, pig, rat, horse, monkey, buffalo, h umans, …. etc.)
Molecular Physiology - Immunology - Endocrinology:• Immuno-modulation and immuno-stimulation of the gastro-intestinal tract of farm animals (cattle, pig & sheep)• Immunology in Mammary Gland (cattle & sheep)• Growth Physiology & Lactation Physiology (cattle & pig) • Transcriptional Biomarker
mRNA and microRNA quantification assays:• competitive RT-PCR (for mRNAs)• real-time RT-qPCR• absolute and relative quantification strategies• qPCR arrays (96-well and 384-well format)• validity testing for reference genes
RNA integrity (Experion, Bioanalyzer 2100, Nano Drop )• Improvement of RNA and microRNA extraction• Total-RNA , mRNA and microRNA integrity measurement• RIN / RQI algorithm development
Software application development:• Relative Expression Software Tool (REST)• BestKeeper• Efficiency calculation (algorithm development)• Kineret• GenEx – multi dimensional modelling (PCA and HCA)
qPCR Quality management:• MIQE Guidelines• QM management in the molecular biology laboratory
Next generation Sequencing• RNA Seq (mRNA and small-RNA) vs. High throughput RT-qPCR
Singapore – 22nd & 23rd November 2012
Analytical challenges for the future
• Sensitive assay – on level of single molecules, single-cell or pathogen
• Quick diagnosis – in hours or even less than an hour
• Early detection of pathogen or micro-organism
• Exact identification of the pathogen or micro-organism
• Wide quantification range
• Minimal amount of biological matrix necessary
• Highly standardized and reproducible
• Stable and valid biomarker(s) or biomarker pattern
⇒ qPCR (RT-qPCR) is the method of choice
⇒ Amplification, analysis, and quantification in one tube
⇒ Highly standardized (MIQE compliant)
⇒ Reliable biomarker pattern = Transcriptional biomar kers
Singapore – 22nd & 23rd November 2012
1. The central problems in RT-qPCR
2. Method standardisation (= MIQE guidelines)
3. mRNA & microRNA Expression profiling
4. Biomarker discovery using –omics technologies
5. Biomarker validation using PCA and HCA
Outline
Singapore – 22nd & 23rd November 2012
Central problem areas in qPCR
Insufficient information about:
1. Sampling technique & Sample quality
2. Extraction quality & extraction efficiency Pre-PCR steps
3. RNA quality (quantity, integrity, and purity)
4. Experimental design & Type of replicates (=> statistics)
5. qPCR protocol (=> unspecific products)
6. Assay validation (specificity, sensitivity, reproducibility, quantification range, LOD, LOQ, … )
7. Raw data handling (=> Background correction)
8. Cq data handling - Handling of outlier
9. Normalization strategy (one RG, multiple RGs, test for optimal RGs) Post-PCR steps
10. Result interpretation => significance of fold change
11. Statistical test => biological relevance
Singapore – 22nd & 23rd November 2012
Sources of variability in quantitative RT-PCR work flow
biological variance technical variability
work flow
varia
bilit
y
hu bt sc rn mm cell-culturesolitaire-cells
• genetic variability (n = 1)• inbreeding status• generation interval• environmental impact
Pre-PCR steps:• sampling technique• tissue storage• NA stabilization• NA extraction• long-time NA storage
RTqPCR
normalization
• tissue type• tissue complexity• heterogeneity <-> homogeneity
hardware
model system
Singapore – 22nd & 23rd November 2012
⇒ MIQE is a set of guidelines that describe the minim um information necessary for evaluating qPCR experiments
⇒ The goal is:• To provide guidelines for authors, reviewers and editors to measure the technical quality
of submitted manuscripts• To establish a clear framework to conduct quantitative RT-PCR experiments• To support experimental transparency• To increase reproducibility between laboratories worldwide• To promote more consistent, more comparable, and more reliable results• To standardize international qPCR nomenclature
⇒ To increase reliability of results to help to insur e the integrity of scientific work, with major focus on biological relevance
Singapore – 22nd & 23rd November 2012
The MIQE checklist:
• 9 headings
• 85 sub-titles
• 57 essential information
• 28 desirable information
• Experimental design
• Sampling
• NA extraction
• RT
• qPCR target information
• qPCR primers
• qPCR protocol
• qPCR assay validation
• Data analysis & statistics
Download iOS, Android and html5 MIQE APPs on MIQE.Gene-Quantification.info
Singapore – 22nd & 23rd November 2012
Definitions of „Biomarker“
• Biological markers (biomarkers) have been defined as “cellular, biochemical or molecular alterations that are measurable in biological media such as human tissues, cells, or body fluids” (Hulka, 1990)
• “A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes, or pharmacologic responses to a therapeutic / drug intervention”(definition by “Biomarkers definitions working group” of NIH, 2001)
• More recently the definition has been broadened to include more biological characteristics that can be objectively measured and evaluated as a biological indicator (Mayeux, 2004)
• The application of integrative functional informatics represents the novel direction in such biomarker technology integration (Ilyin et al., 2004)
• Integrative functional informatics = the interfacing and integration of different technologies for data collection and their analysis are pivotal to biomarker identification, characterization, validation, and successful usage.
What is a biomarker ?
Singapore – 22nd & 23rd November 2012
DNA => pre-mRNA => mRNA => protein => function↑↑↑↑ ↑↑↑↑ ↑↑↑↑ ↑↑↑↑
methylation transcription splicing translation
↓↓↓↓ ↓↓↓↓ ↓↓↓↓ post-translatory modificationsDNA => pri-miR => pre-miR => miR
genome transcriptome proteome metabolomeepi-genome small-RNA transcriptome
splicome
Biomarkers on various “- ome” layers
Singapore – 22nd & 23rd November 2012
Food Quality & Food Safety ?
Food SafetyProtecting the food supply from microbial, chemical, and physical hazards or contamination that may occur during all stages of food production: growing, harvesting, processing, transporting, preparing, distributing, storing, and handling.The goal of food safety monitoring is to keep food wholesome, safe to eat and free of disease causing agents such as:
– infectious agents– toxic chemicals– additives: antibiotics, preservatives, drugs, hormones, …– foreign objects
Food QualityFood quality is the quality characteristics of food that is acceptable to consumers. This includes food appearance (size, shape, colour, gloss, and consistency),texture, flavour, factors such as federal grade standards, and ingredients (chemical, physical, microbial, secondary plant components, pre- or probiotics, synbiotics, …)
Z I E L - Research Center for Nutrition and Food Sc iences
Singapore – 22nd & 23rd November 2012
Gynecomastia: development of abnormally large mammary glands
Estrogens => development of primary and secondary sexual characteristics
Prolactin => breast development, milk production
Singapore – 22nd & 23rd November 2012
Classical analytical methods fordrug/hormone residue analysis
Use of specific anabolic agents in agriculture is licensed in Canada, USA, Australia, South Africa ... Since 1988 the use of anabolic agents is prohibited in the EU !
Anabolica misuse => sensitive screening and test sy stem are established in EU
– RIA
– ELISA (EIA)
– LC-MS/MS
– GC-MS
– GC-MS/MS
⇒ analysis of specific compounds
⇒ analysis of known anabolics
Singapore – 22nd & 23rd November 2012
Aim is the physiological way!
Establishment of a new hormone screening method
Screening of physiological response- in various hormone sensitive tissues- in easy accessible tissues, e.g. body liquids, blood, and hair roots
Regulative impact of hormones on transcriptome
Quantitative RT-PCR => “most sensitive” method(=> 1 molecule detection limit)
Evaluation and analysis of regulated genes mRNA & microRNA transcript candidates
Candidate verification - via statistical methods, e.g. HCA or PCA- via independent methods (NGS)
Specification of “Expressed Biomarkers”
Identification of “Gene Networks”The physiological way: monitoring RNA expression changes as new approach to combat illegal growth promoter application.Riedmaier , Pfaffl & Meyer Drug Test Analysis 2012
Singapore – 22nd & 23rd November 2012
Why use transcriptional biomarkers ?
Gene expression biomarker on mRNA or microRNA level :• Activated steroid hormone receptors act as transcription factors:
=> direct influence on gene expression activity• Expression changes are relatively fast – within hours• Various tissues are easily available for biomarker screening:
– body fluids: blood, urine, saliva, sputum, etc ...– non-invasive sampling: hair roots, buccal or nasal swabs & vaginal smear containing
epithelial cells, ...
• Very sensitive RT-qPCR is validated in our lab (single-cell RT-qPCR)
What is the candidate gene approach?• Selection of target genes by screening the actual literature for steroidal effects
in tissues– Published tissues: with high steroid receptor abundance, primary and secondary
sexual organs– Published candidate gene groups: steroid hormone receptors, growth factors,
apoptosis factors, inflammatory factors, transcription factors, ...
Singapore – 22nd & 23rd November 2012
Cellular steroid hormone action
Riedmaier I, Becker C, Pfaffl MW, Meyer HHD. - ReviewThe use of -omic technologies for biomarker development to trace functions of anabolic agents. In Journal of Chromatography A, 2009
mRNA
microRNA
cell
Singapore – 22nd & 23rd November 2012
Functional target gene groups
• steroid receptors
• steroid hormone metabolism
• proliferation / apoptosis
• angiogenesis
• tissue remodelling
• immune relevant factors
Singapore – 22nd & 23rd November 2012
RNA extraction RNA integrity control RT-qPCR
∆∆ Cq
data analysisstatistical evaluation
Work flow (2)
gene regulation
Singapore – 22nd & 23rd November 2012
mRNA expression profiling resultsfrom heifers ovarian samples
Becker C. et al., HMBCI 2010
Singapore – 22nd & 23rd November 2012
microRNA expression profiling resultsmicroRNA qPCR array – 2x 384 miR set
miRNA regulation in bovine liver under the influence of TBA+E2up-regulated genes
miR 378: promotes cell survival, tumor growth, angiogenesis
miR 412: regulates uterine leiomyomas growth, benign uterine smooth muscle tumor
miR 15a: tumorgenesis, unarrested cell cycle
Singapore – 22nd & 23rd November 2012
microRNA expression profiling resultsmicroRNA qPCR array – 2x 384 miR set
miRNA regulation in bovine liver under the influence of TBA+E2
down-regulated genes
miR 130a: regulatory functions in the endometriuminduced by steroids
miR 34a: functions as a potential tumor suppressor by inducing apoptosis
Singapore – 22nd & 23rd November 2012
� �
15a 0.49 0.023 *
20a 0.49 0.024 *
27b 0.97 0.58 n.s.
29c 1.30 0.066 n.s.
34a 0.67 0.017 *
103 1.30 0.040 *
106a 1.40 0.057 n.s.
138 1.74 0.2 n.s.
181c 0.75 0.038 *
320d 0.72 0.065 n.s.
433 1.15 0.89 n.s.
miR- x-fold regulation (2-∆∆Cq) p-value significance
microRNA expression profilingsingle assays (Qiagen) vs. microRNA qPCR array (Exiqon)
P < 0.05
P < 0.1 (trend of regulation)
single assay PCR
0 1 2 3 4 5 6
PC
R a
rray
0
1
2
3
4
5
correlated regulation single microRNA assay vs. qPCR array
r = 0.662p < 0.001
Singapore – 22nd & 23rd November 2012
WBC expression data analysismRNA data
Significantly regulated genes in heiferblood (16 days after treatment)
PCA & HCA
Singapore – 22nd & 23rd November 2012
Combined WBC & vaginal epithelial cell data analysi s
PCA & HCA
=> integrative functional informatics
Singapore – 22nd & 23rd November 2012
Group I control animals (n=2)
Group II treated animals (n=3)
sequencing
data analysis
RNA isolation
TU München
EMBL Gene-Core
Genomatix
Alteration of gene regulation in bovine liver by treatment with anabolic steroids
* alignment / mapping * differential expression analysis
Confirmational study Finding new biomarker candidates
Experimental setup
NGS =>
Singapore – 22nd & 23rd November 2012
NGS results - sequence statistics
• number of sequences: > 25 mio.
• sequence length: 76 bases
• successfully mapped bovine genes: 16,947
• successfully mapped transcripts: 45,832
• significantly regulated genes: ~ 9,000 (control vs. treatment group)
• gene regulation at least 2-fold: ~ 300
⇒ screening for significantly regulated genes
⇒ with high relative expression change, more than 2-fold
⇒ and high expression levels
Singapore – 22nd & 23rd November 2012
Verification of 20 regulated genes via RT-qPCR (out of 40 selected)
no. acc. no. gene name x-fold NGS sign NGS x-fold qPCR p- value PCR
1 NM_174530 CYP2E1 0,02 1 0,38 0,015
2 NM_001083444 FADS2 0,09 1 0,37 0,004
3 NM_001046551 CXCL10 0,13 1 0,53 0,028
4 NM_001046269 PON1 0,15 1 0,28 0,001
5 NM_001082610 SOD3 0,16 1 0,40 0,007
6 NM_001080354 GSTM1 0,22 1 0,57 0,022
7 NM_173941 MX2 0,28 1 0,47 0,019
8 NM_001037480 APOA4 0,28 1 0,19 0,008
9 NM_174366 ISG15 0,33 1 0,54 0,058
10 NM_001014391 CLDN4 6,08 1 4,52 0,007
11 NM_001102326 SECTM1 6,42 1 3,66 0,037
12 NM_001077933 AK3L1 6,62 1 13,66 0,003
13 NM_173917 HBB 7,04 1 5,57 0,023
14 NM_001104967 PRODH2 7,79 1 3,59 0,000
15 NM_001078026 TNC 8,06 1 8,87 0,000
16 NM_001076400 GDPD1 8,39 1 4,18 0,001
17 NM_174097 HOPX 10,77 1 8,18 0,038
18 NM_001102054 SERPINI2 26,18 1 82,36 0,012
19 XM_602816 CUX2 26,76 1 6,33 0,000
20 NM_174307 EPYC 0,12 1 6,69 0,036
Up-regulated Down-regulateddown-regulated up-regulated
Singapore – 22nd & 23rd November 2012
Blue diamonds: control animals
Red triangles: treated animals
PCA - qPCR dataobtained from 11 significantly regulated genes chosen by literature
Becker C. et al, Horm Mol Biol Clin Invest, 2010
Singapore – 22nd & 23rd November 2012
PCA and HCA - qPCR dataobtained from 20 significantly regulated genes chosen by NGS
Blue diamonds = control animals
Red triangles = treated animals
Riedmaier et al. Anal Chem (2012)
Singapore – 22nd & 23rd November 2012
All 9 untreated control animals vs. one treated animal
no. 5
treated animal no. 5 treated animal no. 8
no. 8
Singapore – 22nd & 23rd November 2012
Blue triangles: control animals
Pink diamonds: 1x treatment
Red crosses: 3x treatment
Hormone treatment via „Pour-On Mix“ (collaboration with RIKILT)Estradiolbenzoate, Testosteronedecanoate, Testosteronecypionate
Calves treated with hormone cocktail
PCA with 4 significantly regulated genesCLDN4, CUX2,
SOD3, PON1
Singapore – 22nd & 23rd November 2012
Boars treated with Synovex Plus200mg Trenbolon Azetat plus 28mg Östradiol Benzoat
(sample from an EU animal trial from 1998)
Green = control animals
Red = treated animals
Singapore – 22nd & 23rd November 2012
BiblioSphere
Cocitation based network of genes regulated at least 2 fold between control and treatment. (and min NE of 0.5 in at least one condition)
4.7
-6.8
0.0
Genes from input list are colored based on ratio between control and treatment. Red – induced , blue –repressed with treatment.
White boxes represent transcription factors that are cocited with at least 10 input genes.Green lines indicate potential binding sites in the promoter region.
Gene network regulated by exogenous anabolics
Singapore – 22nd & 23rd November 2012
Conclusion
• Tissues that are directly influenced by steroid hormones with abundant receptor concentrations are more sensitive and show higher gene regulations => hence better for the biomarker identification
• transcriptomics analysis discovered promising expression changes– mRNA served as first biomarker candidates– additional candidates via microRNA profiling– verification of existing candidates via NGS RNA-Seq– new mRNA candidates via NGS RNA-Seq
Integrative biomarker discovery approach on transcr iptome level:– significantly regulated transcripts (mRNA and microRNA)
– in various tissues (WBC, liver, and vaginal smear)
– by different analysis methods (qRT-PCR, qPCR array, and NGS RNA-Seq)
– by various statistical algorithms (PCA and HCA)
⇒ The more integrative biomarker candidates are disco vered the better a prediction of hormone treatment is pos sible
Singapore – 22nd & 23rd November 2012
Perspectives
To verify our first biomarker candidates:• Further trials with more animals, different breeds, and various
physiological conditions are needed• Large number of untreated control animals = stable control pool• A panel of anabolic agents / cocktails is needed• Combination of transcriptomics with other “-omics“ technologies, like
proteomics or metabolomics => steroid biology and system biology
New studies in progress:• DFG
• small-RNA profiling
• BISP
Singapore – 22nd & 23rd November 2012
Next Generation Thinking in Molecular Diagnostics6th international qPCR & NGS Event - Symposium & Industrial Exhibition & Application Workshops
18th – 22nd March 2013, in Freising-Weihenstephan,Technical University of Munich, Physiology - Weihenstephan, Bavaria, Germany
Symposium Talk and Poster sessions:• Main Topic: Molecular diagnostics• Main Topic: Next Generation Sequencing (NGS)• Main Topic: Transcriptional Biomarkers• High throughput analysis in qPCR• Systems biology• Single-cells diagnostics• MIQE & QM strategies in qPCR • non-coding RNAs - microRNA, siRNA and long non-coding RNAs • Digital PCR & Nano-fluidics • Pre-analytical Steps • qPCR data analysis - BioStatistics & BioInformatics• NGS data analysis - BioStatistics & BioInformatics• Lunch Seminars:
• qBASEplus - data analysis lunch seminar• GenEx - data analysis lunch seminar• NGS data analysis lunch seminars• more to be announced........
symposium.qPCR-NGS-2013.net
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