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Summary of the lectures of functional genomics - minor DSDT
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summary of all lectures of the course functional genomics as part of the minor DSDT of BFW
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Functional Genomics (Intro)What is functional genomics
The study of how genes and intergenic regions of the genome contribute to different biological
processes to develop a model-linking genotype to phenotype
• They focus on the dynamic expression of gene products in a specific context
Central dogma & continuity of life
• Self-assembly, catalysis, replication, mutation,
selection regulatory & metabolic networks
• Polymers: initiate, elongate, terminate, fold, modify,
localize and degrade
To analyse what genes do you can uses the techniques forward genetics or reverse genetics
Forward Genetics
• Start with a (altered) phenotype and work toward identifying the (mutated) regulating genes
Reverse Genetics
• Start with a perturbation to a gene (knock-out, - down, mutation) and see what the
phenotype effects are
The approaches to analyse the phenotype are:
• Genomics & epigenomics
• Transcriptomics
• Metabolites
• Proteomics
Together they describe the metabolites, proteins and
transcripts of a biological system, and the integration of
these data is expected to provide a complete model of the
biological system under study
Functional genomics for drug discovery
The first step in the pipeline is characterization of the disease process and identification of drug
targets
• Target is defined as a protein or messenger RNA which, when modified by a drug, favourably
affects the outcome of a disease
• Today many drugs fails because they are not significantly affect the disease
Target selection -> Lead discovery -> Medicinal chemistry -> in Vitro -> in Vivo -> clinical trials &
therapeutics
Functional genomics can provide information or evidence for the relationship between potential
targets and their associated disease at the:
• DNA level: SNP, copy number variations and epigenetics
• RNA level: gene expression microarrays, RNA-seq
• Protein level: DNA/RNA -> protein reactions ChIP-seq
,Open targets, provides a tool for discovering potential therapeutic target ant the links between these
targets and human disease
• Reactome, pathway mapping
• chEMBL, chemical
Functional genomics for personalized medicine
Pharmacogenomics to precision medicine
Individuals show with the same prescription:
• Full response
• Partial response
• No response
• Severe adverse reaction
This variation in drug response may cause by genetic factors also gender, age, diet, co-medication
plays a role
Genes that may influence the variation in drug response
• Pharmacokinetic: Drug metabolizing enzymes and transporters
o Affects how the drug is handled by the body
o Plasma clearance, delivery of drug metabolite to target cells
ABCB1 variants are associated with resistance to the effect of certain drugs such as anti-epileptic
agents
• Pharmacodynamic: Drug targets enzyme, receptors, ion channels
o The drug effect on the body
o The relationship between the drug concentration & its therapeutic effect
CYP2C19 is a metabolic enzyme, variants are associated with decreased level of responsiveness of
drug that are used to treat gastric complains
,OMICS revolution in biomedical science
Common study types in functional genomics
Functional genomic experiments measure changes in the DNA (genome & epigenome), RNA (
transcriptome) or interaction between DNA/RNA, proteins and metabolites that influence the
phenotype of a sample
Common branches are:
• Genotyping
• Transcription profiling
• Epigenetic profiling
• Protein profiling
• Nucleic acid-protein interactions
• Metabolites profiling
• Meta-analysis
Genotyping
Genotyping identify differences in the DNA sequence of a sample
The genomic DNA samples are often obtained from two contrasting groups samples, with the aim of
identifying differences in the genotype which may explain the difference in phenotype (from
genotype to phenotype).
Genotyping to identify DNA sequence
• Single nucleotide polymorphisms, analysis focuses on differences in the DNA sequence at the
single nucleotide level
• Copy number variations (CNVs): refer to an increase or decrease in the number of copies of a
segment of DNA ( a gene, or a locus-specific DNA repeat element). Each 'copy' can be as
short as 50 bases or up to 100 Kb.
• Structural variations: they are an order of magnitude larger than CNVs and often cover mega-
bases of DNA, and can be caused by chromosomal rearrangement events.
Epigenetic
Study of how biochemical modifications or physical interactions of DNA/chromatin affect gene
regulation in a cell, where such modifications/interaction are not related to changes in the
underlying DNA sequence
DNA level: Methylation of CpG dinucleotides ( often located near gene promoters) can be detected
by first converting unmethylated cytosine’s into uracil using bisulphite, which allows methylated and
unmethylated cytosine’s to be distinguished
Chromatin level: modifications of the tails of histone proteins (methylation or acetylation) can be
mapped by immunoprecipitation, where chromatin and proteins are chemically cross-linked
reversibly. The genomic DNA associated with the modification/protein of interest in then pulled-
down (precipitated) with specific antibodies raised against the modification/protein. After
precipitation, the cross-linking is reversed to release the genomic DNA for further analysis
, Transcription profiling
Transcription factors, ribosomes and other DNA/RNA-binding proteins can bind to nucleic acid
sequences and influence the transcription and translation of genes
• Immunoprecipitation technique has also been applied to study protein binding sites on RNA
Expression profiling involves the quantification of gene expression of many genes in cells or tissue
samples at the transcription level (RNA)
• Qualification done by collecting biological samples and extracting RNA following a treatment
or at fixed time-points in a time-series, thereby creating snap-shots of expression patterns
• Quantifying transcription of genes, coding exons, non-coding RNA
Proteomics consist of DIGE, Mass spectrometry, Protein microarray and surface plasmon resonance
Metabolomics consist of LC, MS, MS/MS, NMR and UV
Bioinformatics
Bioinformatics is conceptualizing biology in terms of molecules and then apply informatics
techniques ( math, CS and statistics) -> to understand and organize the information associated with
these molecules on large-scale
Designing a functional genomics experiment
Three important things to consider when designing a functional genomics experiment are
• Scale and intent
• Data analysis
• Reproducibility
Scale and intent
The scale of an experiment encompasses both the number of samples and the number of genes to be
analysed, the number of samples is often a trade-off between:
• The number of replicates required to produce statistically robust results
• The ease of obtaining the samples
• The budget for the project
• The scale and intent of the experiment will influence the methods used in the study
❖ Real-time PCR analysis useful to analyse a small number of genes in a small number of
samples
❖ Microarrays useful for measuring large numbers of genes ( or whole transcriptome), but they
have reduced sensitivity compared with PCR
❖ RNA-seq & NGS suited for in depth analyses and for discovery based project to identify new
transcripts, study non-coding RNA’s, map transcription start sites of characterise the precise
location of epigenetic modifications
• Is more flexible than microarray, not restricted by prior knowledge of genetic
sequence
• Detect transcripts over a higher dynamic range
• But relatively expensive, computationally/statistically intensive
For each experiment there will be further considerations including:
Technology (Taqman vs Sybr green PCR), platform (Illumina) and methods used to prepare samples
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