A Simple Introduction to Functional Genomics

Once upon a time, the main hurdle in biological research was adequate access to accurate biological data. With technology, that’s now a problem in the past. An ongoing challenge is understanding and interpreting the humungous amount of data in our hands to have a clear picture of the organism under study.

Functional genomics is the study of genomic products (genes, transcripts, etc) and their interactions within and between regions. In the context of medicine, it studies how genomic features contribute to processes like growth, health, aging, and illness. In this piece, I’ll simplify the different approaches employed in functional genomics and explain the significance of understanding how genomic products operate.

Importance and Applications of Functional Genomics

Functional genomics is often driven by cell type, stage of life, environmental factors, etc. It helps answer questions like:

• What drives susceptibility or resistance to a disease condition?
• Why does the phenotype of this disease look different in patients?
• What’s the difference in the expression level of this gene in diseased and healthy states?
• What is the ancestral or evolutionary history of this organism?
• How do we design better drugs with improved efficacy?
• And so on…

Key Components of Functional Genomics

There are different approaches to functional genomics, depending on the question of interest. Combining these different components gives a full model of the organism under study. However, in the real world, researchers often approach from one angle.

Genotyping (Variant analysis)

In the context of functional genomics, genotyping determines the genetic makeup of an individual based on its DNA sequence and identifies the differences that may explain phenotypic differences. In simple terms, it involves comparing two or more biological samples to find out what makes them different and why.

Genotyping studies can identify single nucleotide variations, changes in the number of copies of a DNA segment (copy number variation), or larger chromosomal changes (structural variants). This often starts with sequencing, followed by computational analysis and some statistical tests to infer relationships.

For instance, one can find what structural variants are present in breast cancer patients and absent in healthy individuals. That may suggest that breast cancer is somehow related to that variant and lead to more probing to answer questions, like which came first: the cancer or the variant?

Transcriptomics (Gene expression)

You may remember from the central dogma that RNA is a product of DNA transcription. This process is the first step in making a gene useful. Transcriptomics studies the total RNA molecules, or transcripts, expressed within a biological entity.

In the context of functional genomics, this branch studies gene expression regulation and transcript quantity in different cell types under different conditions. It allows researchers to identify the genes involved in select environmental changes or characterise genes based on the similarity in their expression mechanisms, which may imply they have similar functions.

Blotting, reverse transcription polymerase chain reaction (RT-PCR), and microarrays have been used for transcriptome analysis. Today, direct sequencing (RNA-Seq) is the norm because it’s less labour-intensive, universal to all species, and allows for in-depth analysis. It basically involves extracting the RNA from a sample and quantifying it or analysing it with computational tools. RNA-seq allows researchers to identify novel transcripts and explore the functions and activities of non-coding RNA molecules.

Cancer research is also a good example here. Transcriptomics can inform a researcher of which transcripts are present in a cell sample and how they differ from healthy cells.

Epigenomics (Chemical reactions)

Epigenomics studies the chemical modifications in the genome and how they affect gene regulation without changing the DNA sequence. The most common type of epigenetic change is DNA methylation, where a methyl group is attached to the Cytosine base in a Cytosine-Guanine pair (CG).

In the context of functional genomics, high or low methylation can cause an increase or decrease in gene expression, not respectively. This change in gene expression potentially affects the interaction and function of said genes, which could cause phenotypic differences in the organisms under study.

Epigenomics was relatively tricky to explore in the earlier years of molecular biology research. However, sequencing technologies, like Oxford Nanopore Technology, provide access to chemical modifications alongside the DNA sequences.

Proteomics (Cellular processes)

Beyond genes, proteins are major drivers of biological processes like metabolism. Proteomics studies the entire protein translated from mRNA (central dogma again) to understand the complexity and determine the quantity generated.

In the context of functional genomics, proteomics tells a researcher how much protein is expressed in a certain condition, how the folding of a protein affects its expression, and what drives interactions between different protein complexes. It’s also how drug discovery researchers identify relevant protein targets in a disease for potential drug candidates. It minimises side effects and maximises drug efficacy.

Gel electrophoresis and mass spectrometry are common technologies used for proteomics analysis. There’s ongoing research into nanopore-based protein sequencing and identification to further simplify the process.

Genome annotation (Labelling)

Genome annotation is the process of identifying the location and function of genes in a genome after whole genome sequencing. In the context of functional genomics, genome annotation is used to identify the functional elements of a genome, such as genes, regulatory regions, and non-coding RNAs.

The process of genome annotation involves using computational programs to predict the locations and structures of genes within the genome. Then, functional annotation attaches biological information to genomic elements, such as biochemical function, biological function, involved regulation and interactions, and expression.

The level and style of annotation often depend on the research question and the available resources. For instance, If I were working on a cancer genome, I’d want to annotate it using information from some cancer databases to locate what part of the genome from my particular has a known cancer gene or a structural variant.

Two Common Tools in Functional Genomics

*** CRISPR is a tool that allows precise gene editing to understand and manipulate gene function. For instance, if one isn’t sure what a gene does, we can use CRISPR to alter that gene and observe how it’s expressed in the cell or organism under study.

*** RNA interference (RNAi) is a highly specific gene silencing technique used to study certain body functions like fat regulation and longevity. It works in a similar manner to CRISPR where researchers can silence a gene of interest and observe the effect of its expression.

Conclusion

Emerging technologies, some still in their infancy, promise to push the boundaries of what we can achieve.

There’s a growing approach known as multiomics analysis. It combines multiple levels of omics information to generate more comprehensive answers to a research question. That means, instead of having one perspective to drive decisions, you have an integrated and more reliable angle. Let’s just say it’s the future of omics research.