Module 2: Sep 18

Introduction to Omics

Overview of Omics Fields

The field of omics consists of four major disciplines: genomics, which studies genes and their functions; transcriptomics, focusing on the analysis of gene expression; proteomics, the study of proteins; and metabolomics, which analyzes metabolites. Metabolomics typically identifies chemicals with molecular masses of less than 2,000 Da.

The Central Dogma of Molecular Biology

The central dogma describes the flow of genetic information from DNA to RNA to protein. This process involves transcription, where RNA is synthesized from a DNA template, illustrated by the DNA sequence TACTTCAAACCGATT, which transcribes to the mRNA sequence AUGAAGUUUGGCUAA. Following transcription, translation occurs, synthesizing a protein from the mRNA template.

Sequencing Approaches for Bacterial Samples

To investigate complex bacterial populations, methods such as 16S sequencing identify present bacteria, shotgun metagenomics understand the functional capacity of microorganisms, and RNA-Seq investigate actively expressed genes within the community. Metatranscriptomics and metabolomics provide further insights into gene activity and metabolic products, respectively.

Gene Structure: Eukaryotic and Prokaryotic

Eukaryotic genes are composed of introns and exons, with processing events like splicing. Key features include the poly-A tail found at the 3' end of mRNA, regulatory sequences affecting transcription, and open reading frames (ORFs) that can be translated into proteins. Conversely, prokaryotic genes, typical in bacteria, have simpler structures, often grouped in operons for coordinated expression, lacking introns and polyadenylation, and containing intergenic regions in their genomes.

Reverse Transcription PCR (RT-PCR)

RT-PCR refers to converting RNA into complementary DNA (cDNA) to analyze gene expression. This involves using random primers for amplification of mRNA, resulting in cDNA production from the mRNA template.

RNA-Seq Workflow

The RNA-Seq workflow includes isolating RNA from samples, enriching mRNA by removing rRNA, converting RNA to cDNA, ligating sequencing adapters, and sequencing the fragments. The sequencing reads are then mapped to a reference genome for analysis.

Data Analysis Post RNA-Seq

The analysis following RNA-Seq encompasses quality control, mapping reads to the genome, identifying differentially expressed genes (DEGs), and conducting pathway analysis. The Benjamini-Hochberg method is applied to adjust p-values in multiple testing scenarios, with volcano plots visualizing significant DEGs based on log2 fold change and p-value adjustments.

Pathway Analysis Programs

Various tools for pathway analysis include GAGE, ClusterProfiler, and MetaScape, which explore connections among differentially expressed genes.

Emerging Technologies in Transcriptomics

Recent advances focus on longer RNA sequencing reads to enhance mapping and analysis capabilities, utilizing newer platforms like Oxford Nanopore to detect splicing variants and unexpected transcripts. Long reads improve identification of splicing variants in eukaryotes and aid in the analysis of operon expression in bacteria, as well as facilitate the detection of unanticipated genetic features.

Benefits of Direct RNA Sequencing

Direct RNA sequencing is amplification-free, preventing PCR bias or reverse transcription errors. It ensures compatibility with long reads, crucial for studying complex RNA structures, allowing direct measurement of modifications such as m6A.

Single Cell RNA-Seq and Spatial Transcriptomics

Single Cell RNA-Seq (scRNA-Seq) provides insights into heterogeneity among cell types within tissues, offering gene expression profiles on a per-cell basis as compared to bulk RNA sequencing. Spatial transcriptomics examines the spatial organization of cells in tissue by sequencing individual grid sections.

General Workflow of Metabolomics

The metabolomics workflow includes sample preparation, mass spectrometry analysis, and data interpretation, employing different chromatography techniques according to metabolite properties. Examples of metabolomics tools and methods include MALDI-MS, LC-MS, and GC-MS, each tailored for specific applications in metabolite detection.

Applications of MALDI-TOF

MALDI-TOF is utilized for rapid microbial identification through molecular fingerprinting in clinical specimens.

Challenges in Metabolomics

Challenges in metabolomics involve overlapping metabolite spectra, underrepresentation of certain compounds in databases, and difficulties in detecting small metabolites. The distinction between untargeted and targeted metabolomics highlights the potential for discovery versus focused analysis with known compounds.

Linking Microbes to Metabolites

There are ongoing challenges in correlating specific microbes to their metabolites using diverse analytical methods, with studies showing that the ketogenic diet influences microbial metabolism, leading to shifts in microbial populations and metabolites that affect physiological responses.

Mechanisms and Cross-Feeding Interactions

Untargeted metabolomics uncovers mechanisms to understand physiological interactions among gut microbes. For instance, cross-feeding dynamics between Akkermansia and Parabacteroides, influenced by dietary inputs, have been shown to improve health outcomes.

Conclusion and Upcoming Topics

The notes conclude with a summary of RNA-Seq capabilities, emerging transcriptomics methods, and metabolomics workflows, with an indication that the next week's topics will focus on whole genome sequencing and advanced microbial analysis methods.