8. Omics techniques for target identification and development of biologics

Omics

Studies of the entire collection of a type of molecules. Omics means “a study of the totality of something”. The main four ones are genomics, transcriptomics, proteomics and metabolomics.

Genomics

The study of the set of genes contained in the chromosomes.

‎gene + ‎-omics

Transcriptomics

The study of the set of mRNA molecules being expressed at a given time under specified conditions.

Proteomics

The study of the set of proteins being expressed at a given time under specified conditions and their state of modification.

‎protein + ‎-omics

Metabolomics

The study of the set of small molecules at a given time under specified conditions.

‎metabolism + ‎-omics

Omics for target identification

• Find molecular differences between healthy and disease

• Samples: Patient biopsy samples (from tissue which is quite invasive or liquid biopsy, blood sample), patient-derived cell lines (collect cells from a tumour), etc.

• Compare groups of samples to distinguish variation due to disease from normal population variation

• Find out which differences are fundamnetal to pinpoint suitable targets

Omics for biologics development

• Interaction partners of biopharmaceutical.

• Effect of biologics can be followed using omics

• Omics also useful for finding systemic side-effects.

Techniques for genomics

• Whole genome Sequencing (WGS)

• Targeted resequencing using Next Generation Sequencing (NGS)

• Chip based variant detection and analyses using oligoneclotide probes (look for a small nucleic difference)

• Exome sequencing (NGS)

Techniques for transcriptomics

• RNA-sequencing (NGS)

• cDNA microarrays

• Single cell sequencing (NGS)

• Spatial transcriptomics where location of expression is captured by imaging together with transcript read out using probes or NGS

Microarrays and RNASeq are the most common.

Techniques for proteomics

•  Mass spectrometry (main method)

•  Affinity-based proteomics

What can genomics be used for?

• Find genome-level deviations that may be causing disease

  • can be point mutations, indels (insertion or deletion of bases), cross-overs, copy number variations in cancers

• Associations between genotype and phenotype

  • GWAS (Genome-wide association studies)

  • Large patient cohorts (people who share a characteristic, usually age) needed to obtain statistical power

What type of information does transcriptomics give?

•  Look at the genes that are actually expressed under a certain condition

• The information content of an organism is recorded in the DNA of its genome and expressed through transcription.

•  A transcriptome captures a snapshot in time of the total transcripts present in a cell.

Differential expression analyses – find differences between disease and control or subgroups of disese

Heatmapping is used to visually represent patterns and relationships in large datasets

• To find differences between disease and control or subgroups of disease

• To find out if a gene is differentially expressed when comparing samples from different conditions

Gene expression varies across tissues and conditions

• Have to consider where the gene is expressed

• Some genes are expressed in all tissues, while others only expressed in specific tissues/conditions.

• Tissue specific genes might be better targets to avoid secondary effects.

Network Analyses and Systems Biology: How genes work together?

• Genes/proteins do not work alone

• Often many genes/proteins change expression levels between conditions

• Networks (representation of complex systems (cells, tissues and organisms)) and pathways enable us to interpret global patterns in the data (map all these findings into networks and pathways)

Gene set enrichment analyses (GSEA)

• Look at pathways or collections of genes that are connected to a pathway, disease etc.

• Look at the differentially expressed genes and see how they map onto the pathways.

• Look for differences that are likely not random → likely associated with the observed changes.

Benefits of proteomics for drug purposes

Pinpoint which part of the protein is important

– Secreted proteins (secretome), be important for signilling or target 

– Cell surface proteins

– Phosphoproteome for signalling

Omics and precision (personalised) medicine

• Some diseases are heterogeneous in the way that they have different mechanisms (even in the same tissue, e.g. cancer) etc.

• People are different. Some respond to treatment, while others don’t

→ Use omics approaches to stratify disease and patients

• Can we find relevant biomarkers to measure in the clinic? Develop ’Companion diagnostics’ to identify responders, patient that risk side effects and/or to follow treatment

• Find relevant biomarkers to distinguish from different forms of the disease, to find out which patients that respond/don’t respond to treatment etc.

Techniques for metabolomics

Main techniques:

• Mass spectrometry

• NMR

Subclassification of diseases like cancer using expression data

• Cancers are heterogenous. Subgroups may respond very differently to treatment (very different mechanism)

• Clustering of cancers using gene expression data may provide new subgroups (genetic typing can be done for known driver mutations in some cancers, but multiple mutations may have similar phenotypic effects), that can get different treatments

Why do you proteomics instead of transcriptomics? Isn’t RNA sequencing enough?

• Protein translation modifications (PTMs) affect protein function for example phosphorylation

• Protein localisation (secreted / membrane etc), different location might have functions

• Protein-protein interactions, important for protein function

• Sometimes low correlation between protein and RNA levels

What do to with quantitative proteomics data?

• Similar to transcriptomics data (although RNA seq and MS proteomics data have different distributions)

• Differential abundance comparisons between sample groups

• Mapping to pathways etc

• Complicated by:

– Mapping peptides back to proteins and further to genes may be ambiguous

– Post-translational modifications, complicate analysis