Samenvatting Functional Genomics
College 1:
Central dogma cell: self-assembly, catalysis, replication, mutation, selection regulatory & metabolic networks
→ Polymers (DNA, RNA, proteins, metabolites): initiate, elongate, terminate, fold, modify, localize, degrade
Cell function levels: genome (sequence) | transcription (gene activity; functional genomics) | proteins | metabolism |
systems biology | phenotype
Forward genetics = start with (altered) phenotype and work toward identifying (mutated) regulating genes
Reverse genetics = start with a perturbation to a gene (knockout/down, mutation) and see what the phenotypic effects are
Functional genomics = study of how genes and intergenic regions of genome contribute to different biological processes
- Study genes or regions on a genome-wide scale with hope of narrowing the down to a list of candidate genes/regions
to analyse in more detail
- Goal: determine how individual components of biological system work together to produce a particular phenotype
- Focusses on the dynamic expression of gene products in a specific context (vb. disease stage)
- Uses current knowledge of gene function to develop a model linking genotype to phenotype
Integration of omics data (gen, transcript, proteo, metabol) is expected to provide a model of the biological system
Func gen in drug discovery: which proteins can be targeted for anti-cancer strategy?
Func gen in personalized medicine: why do some rugs only work effectively in subset of patients with disease (= SNPs)?
Func gen in safety assessment: why some drugs induce organ toxicity/failure in some patients?
Modified proteins = proteins after post-translational modification
mRNA targeting is nieuw | DNA targeting is nieuw (CRISPR-CAS) | protein targeting is oud
Drug discovery pipeline: characterisation of disease process & identification of drug targets
→ Target: protein or mRNA which, when modified by drug, favourably affects outcome of a disease
→ Reason why drugs fail: does not significantly affect disease
Target selection: cellular & genetic targets | genomics | proteomics | bioinformatics
Lead discovery: assay development | high-throughput screening →→ medicinal chemistry (library, screen, synth)
In vitro studies: drug affinity & selectivitiy | cell disease models | MOA | lead candidate refinement
Functional genomics can provide info or evidence for the relationship between potential targets & their associated disease
- DNA level: SNP, copy number variations, epigenetics
- RNA level: gene expression microarrays, RNA-seq
- Protein level: DNA/RNA-protein interactions (ChIP-seq)
Bioinformatics is used to acquire, analyse and integrate huge amount of DNA, RNA & protein data to discover new target-
disease relationships
→ Open targets: open access tool for identification of novel associations between target-disease
→ Integrates data from many sources to calculate a ‘score’ for each potential target-disease association
Vb. expression atlas, GWAS, UniProt, ChEMBL, Reactome
Drug that targets more than 1 target (2/3) → less resistance, want rescue pathway niet mogelijk
Genes may influence variation in drug responses: (vb. resistance/less responsive)
- PK: drug metabolizing enzymes, transporters (how drug is handled by body; clearance & delivery)
- PD: drug targets, enzymes, receptors, ion channels (drugs effect on body; conc-effect relationship)
SNP = single nucleotide polymorphism → haplotype = combi of SNPs -> can be predictive for drug responsiveness
Main goals for genetic profiling: increases efficacy & reduces toxicity (tests created for certain diseases)
Functional genomics experiments measure changes in DNA ((epi)genome), RNA (transcriptome), DNA/RNA interactions,
proteins and metabolites that influence phenotype
Genotyping: identify differences in DNA sequence (genotype) which may explain difference In phenotype
▪ SNP analysis: differences in DNA sequence at single nucleotide level
▪ Copy number variations (CNVs): = increase of decrease in number of copies of a DNA segment
▪ Structural variations (order of magnitude larger than CNVs; megabases of DNA ipv (kilo)bases)
Epigenetic profiling: how biochemical modifications/physical interaction of DNA/chromatin affect gene regulation in cell
- DNA level: methylation of CpG dinucleotides (bisulfite-seq)
- Chromatin level: modifications of tails of histone proteins (immunoprecipitation)
DNA/RNA-protein interactions: TFs & ribosomes can bind to nucleic acid sequences and influence transcription &
translation of genes → immunoprecipitation to study protein binding sites on RNA
Transcription profiling: = expression profiling → quantification of gene expression at transcription (RNA) level
▪ Quantification: collect biological samples & extract DNA following treatment/at fixed time-points → snapshots
▪ Quantify transcription of all or subset of transcript, genes, coding exons, non-coding RNA, etc
Proteomics: western blot to compare (phosphorylated) protein levels | mass spectrometry
Metabolomics: LC | UV | MS | MS/MS | NMR
Genome – transcription – transcriptome – translation – proteome – reactions – metabolome – interactions – interactome –
integration – phenotype
, Bioinformatics: to understand & organize the info associated with molecules on a large scale
3 thing to consider when designing a functional genomics experiment:
- Scale & intent: number of samples & genes to be analysed
→ Number of samples = trade off → number of replicates for statistically robust results | ease of obtaining
samples | budget | influence on methods used in study
▪ rt-PCR analysis: to analyse small number of genes in small number of samples
▪ Microarray: for measuring larger numbers of genes, but reduced sensitivity compared with PCR
▪ RNA-seq & NGS: for in depth analyses | for discovery/to identify new transcripts, study non-coding RNAs/
map transcription start sites/characterize location of epigenetic modifications
→ More flexible than microarray because not restricted by prior knowledge or genetic sequence ($$$)
- Data analysis: only after wet-lab experi procedures | How analyse data? Special software? Extra info needed?
- Reproducibility: include controls & replicates in design of experi | statistical tests | keep back-up copies of raw
data | document procedures & parameters in detail | learn & follow guidelines | submit experi to public database
Fair data = traceable (organized) and findable/readable (format)
Static: genomics dynamic: epigenetics, transcriptomics, proteomics, phosphor-proteomics, metabolomics
Spatio temporal info: vele tijdspunten meten → dynamics
Image-based systems biology (omics) & image-based monitoring of signal transduction → dynamics
Image-based transcriptomics: fluorescence in situ hybridization → detect → quantify
Image-based proteomics: biochemical perturbations → signal transduction → gene expression → protein expression
→ CRISPR-CAS
Image-based metabolomics & image-based monitoring of signal transduction: FRET
Omics data integration:
- Protein atlas: expression level in organs & where located in cell (transcriptomics & proteomics)
- Allen Brain Atlas: how cells develop different sites in brain (single cell transcriptomics of brain)
- Mouse Phenotype: large scale gene function discovery (phenotype when gene KO) | = mouse database
SV: Functional genomics = study of how genes, intergenic regions of genome, proteins and metabolites work together to
produce a particular phenotype → DNA level, RNA level, protein level, metabolite level
Integration of data from these approaches is expected to provide a complete model of the biological system
College 2:
Genome = all of hereditary info encoded in DNA (or RNA)
Transcriptome = set of all mRNAs (“transcripts”) produced from a genome
→ Transcript = complete set of transcript OR specific subset of transcripts
→ Transcriptome varies, because it reflects genes that are actively expressed at any given time
Gene expression is influenced by environmental factors (vb. drug treatment, disease, age)
Gene components: DNA & RNA – 5’UTR, exons, introns, 3’UTR | mRNA – 5’UTR, exons, 3’UTR (cds = exon)
Human genome variation large scale: translocations, insertions, deletions, amplifications
Detection of DNA alterations:
▪ Cytogenetics: large indels (insertions/deletions), amplification, translocations
▪ In situ hybridization: large indels, amplification, translocations
Human genome variation medium scale: copy number variations (CNVs) ----------------------->
→ Deletion, duplication, segmental duplication, inversion
→ Psoriasis – β-defensin = small secreted antimicrobial peptides → β-defensin genes are CNVs (>copies = >psoria risk)
Human genome variation medium scale: repeat expansions
- VNTRs: variable number tandem repeats | 14-100 bases | clusters of tandem repeats | 4-40x repeated/occurrence
- STRs: short tandem repeats | 2-10 bases | clusters of tandem repeats | often occur in introns
- SSRs/microsatellites: short simple repeats | 1-4 bases long
Human genome variation micro scale: SNPs = 1 puntmutatie
▪ Coding regions: missense, nonsense, silent
▪ Other regions: splice sites, UTRs, promoters/enhancers, intergenic regions, miRNA
DNA alterations: big – indels, amplification, translocation | small – point mutation, indels, repeat alteration
Haplotype = set of closely linked genetic markers present on one chromosome which tend to be inherited together
Array CGH (comparative genomic hybridization): to detect deletion/amplification of
individual genes & whole chromosomes
SNPType genotyping with Fluidigm Nanoliter q-PCR machine
Gene expression central dogma: genome transcription → transcriptome → translation
Gene splicing: if gene is activated, only exons will be transcribed (no introns)
often multiple mRNAs coding for different proteins are derived frome 1 gene
