Samenvatting Functional Genomics deel 1 - Minor CADSDT & DSDT
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Course
Functional Genomics
Institution
Universiteit Leiden (UL)
Samenvatting van de colleges van Functional Genomics van de BFW minor CADSDT en DSDT. Dit is alleen het eerste deel van de samenvatting, omdat het bestand te groot was. De rest van de samenvatting is te vinden onder 'Samenvatting Functional Genomics deel 2 - Minor CADSDT & DSDT'. De samenvatting is...
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
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