Systems Biology and Proteomics Quiz

Biopharmaceuticals (2024) - Omics Techniques for Target Identification and Biologics Development

• Omics techniques are used for target discovery
• Omics techniques include genomics, transcriptomics, proteomics, and metabolomics
• Analysis of omics data identifies targets for personalized medicine
• Proteomics focuses on protein applications, with examples used
• Omics helps find relevant targets and develop biologics
• Omics techniques involve a conceptual understanding of processes like sequencing

Lecture Overview

• Omics can be used for target discovery
• Omics techniques (genomics, transcriptomics, proteomics and metabolomics)
• Analysis of omics data identifies targets
• Omics is used for personalized medicine
• Proteomics has specific focus areas and examples of applications

Learning Objectives

• Omics helps in discovering relevant targets and developing biologics
• Conceptual understanding of omics techniques
• Omics and its concepts related to personalized medicine

Omics - Studies of the Entire Collection of a Type of Molecules

• Genomics studies the entire set of genes contained in chromosomes
• Transcriptomics studies the set of mRNA molecules expressed under specific conditions
• Proteomics studies the set of proteins expressed under specific conditions, including modifications
• Metabolomics studies the set of small molecules present under specific conditions
• The central dogma of biology relates DNA, RNA, and proteins

OMICS Glossary

• Genomics: Study of genes in chromosomes
• Transcriptomics: Study of mRNA molecules under specified conditions
• Proteomics: Study of proteins expressed under specified conditions and modifications
• Metabolomics: Study of small molecules at a given time

Omics for Target Identification (Target Selection)

• Identify molecular differences between healthy and diseased states
• Use samples like patient biopsy samples, patient-derived cell lines
• Compare sample groups to distinguish disease variation from normal variation
• Find causative differences to pinpoint suitable targets

Some Techniques for Omics

• Genomics: Whole genome sequencing (WGS), targeted resequencing using NGS, chip-based variant detection, exome sequencing.
• Transcriptomics: RNA-sequencing (NGS), cDNA microarrays, single cell sequencing (NGS).
• Proteomics: Mass spectrometry, affinity-based proteomics.
• Metabolomics: NMR, Mass Spectrometry, Metagenomics (NGS), single-cell omics, Epigenetics (NGS or microarrays), spatial omics.

NGS - Massively Parallel Sequencing

• The cost of sequencing a full human genome has decreased dramatically over time.

Sequencing: How Does It Work?

• Sanger sequencing involves chain termination PCR using fluorescent-labeled ddNTPs and separating fragments based on size
• Second-generation sequencing involves shearing genomic DNA, ligation of adaptors, hybridization to a solid surface, and amplification to form clusters
• Sequencing involves highly sensitive cameras to record the order of lights.

Brief About the Bioinformatics Workflow for NGS Data

• Paired-end reads from NGS data are mapped to a reference genome
• Overlapping reads create contigs, which combined with gaps form scaffolds.

Third-Generation Sequencing (Long-Read Sequencing)

• Longer reads are used (more substantial length than from second-generation sequencing)
• PacBio and Oxford nanopore are common techniques
• Facilitates mapping of reads and isoforms particularly in repeat regions

NGS Applications in Human Health

• Technologies and their analysis in genomics, transcriptomics, epigenomics, metagenomics
• Analysis methods like point mutations, small indels, copy number variations, lineage identification, differential expression, RNA editing, methylation
• Applications from functional effects of mutations to network and pathway analysis and integrative analysis

Genomics

• Genome-level deviations cause diseases (point mutations, indels, copy number variations)
• Associations identified between genotype and phenotype through Genome-wide Association Studies (GWAS).
• To find statistical power, large patient cohorts are needed

Transcriptomics

• Identifies genes that are expressed

Techniques in Transcriptomics

• Transcriptomics technologies (DNA microarrays, RNA-Seq, scRNASeq, spatial transcriptomics)

RNA-Sequencing vs Microarrays

• Technical approaches and processes in RNA-sequencing and microarrays
• Technical comparison and differences between RNA-sequencing and microarrays

Comparison of Contemporary Methods for Bulk Transcriptomics

• A table comparing RNA-Seq and Microarray techniques (throughput, input RNA amount, labor intensity, prior knowledge, quantitation accuracy, sequence resolution, sensitivity, dynamic range, technical reproducibility)

Data Processing of Analysis for Target Discovery Using RNA-Sequencing/Gene Expression Data

• Data quality control (QC) steps in RNA-sequencing, including filtering low-quality reads, alignment, sample & gene level quality control, statistical analysis, systems biology, and enrichment analysis, data visualization
• Need to reduce experimental noise to isolate desired types of variation (biologically relevant)

QC: PCA Analyses for Sample QC

• Principal component analysis (PCA) used to assess the quality of samples

QC: Gene Level Filtering: Gene Dispersion Estimates

• Gene level filtering uses gene dispersion estimates with normalization methods

Data Normalization

• Normalization techniques compensate for differences in sample amounts

Between Sample Normalisation

• Statistical tests and correction (across multiple types of testing) determine significant differences.
• Example use of volcano plot

Differential Expression Analyses

• Differential expression analyses identify differences in gene expression between disease and control conditions
• Analyze genes that are higher or lower between samples from different conditions
• Differentiates between upregulated and downregulated genes

Gene Expression Varies Across Tissues and Conditions (Tissue-specific gene expression)

• Gene expression patterns vary across different tissues and conditions
• Analyze gene expression by looking at samples across tissues to understand more about tissuespecific variations.
• Includes visualization plots

Network Analyses and Systems Biology

• Networks and pathways to analyze overall patterns in data
• Network analysis as representations of complex systems
• Use of networks to study global properties of interacting components

Pathway Analyses, Visualization for Interpretation

• Analyze genes via pathways and interpret results based on integration and existing knowledge

Gene Set Enrichment Analyses

• Statistical methods to find pathways with higher differentially expressed genes than expected by random.
• Method called Gene Set Enrichment Analysis (GSEA)
• A list of significant pathways or gene sets generated.

Network Analyses for Signatures

Genome-Scale Models for Organisms and Tissue Metabolism

• The complexity and needs of large-scale models are discussed

Topological Analyses of Networks

• Describe how graph analyses are used to study interactions

Things to Consider

• The reproducibility and replicability of results are considered
• Important factors like batch effects, randomization, and validation are highlighted

Proteomics

• Proteomics is closer to the phenotype

Why Bother? (Isn't RNA Sequencing Enough?)

• Protein modifications, localization, interactions, and sometimes low correlation between protein and RNA levels need to be considered and studied

Omes and Complexity

• Transcription, alternative splicing, post-translational modifications, interactome, and molecular functions

Challenges

• Large number of genes in the genome, dynamic range of proteins

How to Measure Protein Levels?

• Mass spectrometry and affinity reagents/antibodies

Basic Principles of Mass Spectrometry

• Sample ionization, gas phase ions, ion sorting, detection, mass spectrum

A Typical (Bottom-Up) Mass Spectrometry Proteomic Workflow

• Workflow steps for typical bottom-up mass spectrometry proteomics

The Workhorse - Tandem Mass Spectrometry

• Digestion, ionization, isolation, fragmentation, mass analysis

MS/MS Peptide Fragmentation

• Fragmentation of peptides in MS/MS

MS/MS of Peptide

• Details of MS/MS results of peptide

Search Space Decreased as Trypsin Cleavage Pattern Is Known

• Trypsin cleavage pattern decreases the search space in protein analysis

Match Scoring

• Peptide candidate matches, algorithm assumptions, probabilistic scores

Quantification?

• Relative and absolute quantification in proteomics

What to Do with Quantitative Proteomics Data

• Comparative analysis with transcriptomics, mapping to pathways

Benefits

• Studying relevant proteomes for drug purposes

Omics and Precision Medicine

• Heterogeneity of diseases, omics for stratifying disease, companion diagnostics.

Subclassification of Disease Using Expression Data (Disease Subtypes)

• Analysis of gene expression data of cancers to create subgroups based on responses to treatment

Breast Cancer Example

• Gene expression profiling to classify breast cancer and using the PAM50 classification
• Uses a selected gene panel, and the results are used in treatment decisions

Unsupervised Clustering of Tumor Biopsies

• Clustering using PAM50 proteins and mRNA analysis for tumor biopsies

Dynamic Range Problem

• High natural dynamic range causing challenges for mass spectrometry-based proteomics

Reference Intervals for Protein Analytes in Plasma

• Normal reference ranges of different protein analytes

Sample Fractionation and New Instruments Help

• Proteomics sample fractionation and new instruments

Some Current Figures

• Recent extensive separation and state of the art mass spectrometry techniques used to measure proteins in tissue samples

Affinity Binder-Based Quantification

• Techniques like aptamer-based and antibody-based sequence methods

Recent UK Biobank Example

• Details about a recent UK Biobank quantitative plasma protein study

Pan-Cancer Plasma Proteomics

• Next-generation approaches to pan-cancer blood proteome profiling

Interaction Proteomics

• Interactions among proteins

Interaction Proteomics - SARS-CoV-2 Example

• An example of studying protein-protein interactions for drug repurposing

Metabolomics

• Reflecting the phenotype and analytically challenging to capture all metabolites through common techniques

Target Discovery Followed by Biologics Development

• Interaction partners of biopharmaceuticals; effects of biologics (using omics); omics for finding systemic side effects

Summary

• Omics for molecular differences between sick and healthy, or subgroups
• Critical data analysis
• Considerations for individual variation, co-variates, and complex network of biomolecules
• Choice of techniques depending on disease & availability
• Multi-omics studies to improve precision medicine

Test your knowledge on systems biology concepts, molecular biology, and mass spectrometry techniques. This quiz covers essential principles such as proteoforms, gene expression analysis, and analytical methods. Challenge yourself with various questions to deepen your understanding of the interconnectedness of biological systems.