Multiomic-based therapeutics: The future

Introduction

The past three decades have witnessed a revolution in the medical sciences. Contributing to this are advances in omics , a rapidly evolving, multidisciplinary and emerging field that encompasses the central dogma of biology, which describes the process of gene transcription and translation that produces proteins acting as biological catalysts and key gatekeepers of metabolic pathways ( Fig. 15.1 ).

Each omic layer individually may provide important information about associations between omic and phenotypic variants. For example, one can associate a high relative abundance of the phylum Proteobacteria in the intestine with the development of bowel necrosis in preterm neonates. However, associations alone may not provide strong enough causal or mechanistic evidence for development of predictive or diagnostic biomarkers, strategies for prevention, or therapeutic measures. Another well-known problem is that a genomic variant may be detected in several individuals but only a small number of individuals with that variant express the phenotype. Full or dampened expression may require environmental exposures, the results of which may be detectable by downstream omic evaluation, which may include transcriptomics (gene expression), proteomics (gene translation), epigenomics (gene amplification or silencing), and metabolomics (small molecules that often act as effectors of various metabolic processes). It is to be noted that such analyses require powerful computational methods. These artificial intelligence methods are evolving alongside the rapid development of multiomics.

This chapter provides a brief review of the current understanding of this rapidly emerging area with the caveat that it is a new field with many examples of successful applications and therapeutics still under intense investigation. This is a field that likely will change our current paradigms of medical practice into an approach that is more individualized, precise, not based on population statistics, and proactive rather than reactive.

Individual omics

Expression of genetic information is highly variable and depends on environmental exposures. Stress, antibiotic use, nutrition, and other modifiable factors play important roles and the effects of these cannot be evaluated by simply interpreting the sequence of the original DNA template. The Hindi parable of “The Six Blind Men from Indostan” ( Fig. 15.2 ) provides an apt caveat to our current evaluation of single omics rather than their integration. Each of the blind men was asked to feel a portion of a structure they did not know was an elephant. One felt the side and was convinced this was a wall. Another felt the tusk and was convinced this was a spear. Another felt the tail and stated this was a rope, and so on. They dogmatically argued what they felt and their conclusions but were not able to identify the elephant for lack of integration and agreement.

Similarly, in our scientific endeavors we have problems with each individual omic stratum and are beginning to recognize that focus on one represents simple associations rather than a holistic scheme. Integration of these using bioinformatic techniques can yield systems biology networks that provide mechanistic patterns that can be used to relate these different layers to causality and mechanisms. A summary of the individual omic layers (aka individual blind men) is provided in Table 15.1 .

An in-depth discussion of all these technologies is beyond the scope of this chapter. Instead, three will be emphasized in more detail: genomics, proteomics, and metabolomics.

Genomics

The technologies associated with genomics and the description of the Human Genome Project have resulted in DNA amplification and sequencing that is being applied to the various taxa of life, including microbes. Not only have these technologies emerged rapidly, but their associated costs have decreased dramatically. Despite the excitement generated by genomics, there are limitations. The presence of a gene does not necessarily indicate its influence on the phenotype. Transcription of RNA can be modified by epigenetic factors, degradation, splicing and silencing, protein synthesis and degradation, protein folding, and environmental influences that control enzymatic production of small molecules (metabolites).