The OMICS Universe And Its Future
The OMICS acronym crawled into the hive mind of the life science community as the Marvel universe onto the movie screens: slowly but successfully. Genomics, transcriptomics, proteomics, metabolomics, methylomics or surfaceomics occupy a growing field of research in systems biology lately. It will be an even more significant strand in the future with practical ramifications, such as transcriptomic tests for disease risks. Thus, it is recommended to get familiar with the OMICS universe. Here’s a glimpse of it.
Systems biology and the messenger game
When I was in fourth grade, a fun game sometimes referred to as the ‘broken telephone game’ lighted up my school trips. We usually made long hiking trips in nature, which for me, an impersonator of Sheldon Cooper, seemed rather dull and an unnatural place of stay. We formed a line, and the one at the end of the row whispered a message in the ear of the closest individual. The words were passed down to the other end of the line, with the last person saying it out loud. It was amusing to see how the message was twisted and turned due to different receptors, environmental factors and how it ended up as an end-product of the interplay between many ears, brains, noise, etc. That’s how Katie’s got a big head could end up being the navy caught a pig dead.
In a nutshell and in plain language, this is the process that happens when information from our DNA, the ultimate source, is passed down to different molecules in our cells, i.e., during the expression of the genes – to RNAs, proteins, enzymes, metabolites, etc. amid environmental factors and noise. Systems biology analyses the interrelations between the networks of all these processes at the cellular level applying a holistic approach, while the various layers are examined at unprecedented spatial or temporal resolution, depth, and thoroughness with the help of the OMICS universe: genomics, transcriptomics, proteomics, metabolomics, methylomics, surfaceomics, etc.
Breaking down the OMICS universe
“OMICS is really seeing the entire collection of molecules that make up you. Basically, it’s your DNA, it’s your proteins, it’s all your metabolites, and such,” explained Michael Snyder, Professor, and Chair of Genetics at Stanford University. These all represent relatively new subfields of biology made possible by the discoveries around the genome boosted by the rapid advancements in biotechnology and computing. The increasing amount of data extracted faster and more accurately than ever before allows researchers to meaningfully analyze processes, and go beyond the DNA – although it is still more challenging to examine RNAs and proteins than the ultimate code of life.
That’s why the first-ever OMICS-field was genomics or the study of the entire book of our genes, the genome. As the anecdote goes, the term was first coined by Dr. Thomas H. Roderick, a geneticist at the Jackson Laboratory, Bar Harbor, ME, in a bar in 1986. Back then, scientists were widely discussing the feasibility of mapping the entire genome – but less than twenty years later, the Human Genome Project was completed. Humanity could peek into the innermost secrets of life, but far from sitting back being satisfied, scientists realized that an incredible amount of information is waiting to be uncovered – the OMICS universe.
The first line of the transcription of genetic information happens with the help of RNA molecules. A wide variety of RNAs act inside a cell, such as mRNAs, non-coding RNAs, and small RNAs, etc. – together they are called the transcriptome. The “messenger RNA” or mRNA molecule carries a portion of the DNA code to other parts of the cell for processing. Thus, they are important as they reflect the actively expressed genes. Generally, the goal of transcriptome analysis is to detect which genes are expressed in the examined sample. This could happen through the identification of genes differentially expressed among different conditions, leading to a new understanding of the genes or pathways associated with the conditions. Scientists’ methods for analysis range from microarrays to RNA-seq, the latter is widely used to sequence the entire transcriptome, more recently at the single cell resolution.
In the future, the subfield could prove to be indispensable in personalized medicine. For example, the Oncotype DX gene test already analyses the risk of breast cancer reoccurrence based on transcriptomic data. But another panel test is also under clinical trial to rule out acute cellular rejection of heart transplants. Also, transcriptomics helps researchers understand the reaction to infections and treatment response.
The next step of DNA information processing happens with the help of proteins – that receive the transcripted data from mRNA molecules and process it further. These proteins differ from cell to cell and also change over time.
The proteome is defined as the set of all expressed proteins in a cell, tissue or organism, and the study of the proteome aims to understand the functional relevance of proteins within the cell and how information flows within the body through protein networks and pathways. Scientists want to see the mechanisms of action of bioactive molecules, find (bio)markers for diseases, and characterize new classes of pharmaceuticals (for example, antibodies) through proteomics.
The metabolome is one of the final downstream products of gene transcription, and one of the closest to the actual phenotype of the organism. Changes in the around thousands of metabolites clearly show the shifts in the proteome or the transcriptome. It is more physically and chemically complex than the other ‘omes’.
As the metabolome is inherently sensitive to subtle alterations in biological pathways, it can provide insight into biomarker discovery and the mechanisms that underlie various physiological conditions within disease states/progression and drug modes of action. Through the metabolome, it is possible to determine both environmental and genetic influences on the disease or therapeutic response. Thus it will have a significant impact on drug development and precision medicine.
While the subset of OMICS fields is clearly not depleted – lipidomics focuses on researching the cellular lipids of an organism or catalomics concentrates on the enzymes (catalists) of a cell -, these three are the most significant ones beyond genomics.
OMICS in the future of personalized medicine
In the last decades, research in the life sciences started to show the way to move from the generalized, ‘one-size-fits-all’ approaches in treatment pathways and disease management towards precision medicine. Instead of putting conditions in the center and categorizing people according to diseases, the individual genetic make-up and other characteristics will prove to be decisive in the future when it comes to disease management.
Currently, this is limited solely to genetic analysis. However, epigenetic, transcriptional, proteomic, posttranslational modifications, metabolic, and environmental factors also influence a patient’s response to disease and treatment. As more and more analytical and diagnostic techniques are incorporated into medical practice, OMICS will support the transition to precision medicine by offering a holistic view of a patient’s condition. Although we cannot provide an estimated timeframe for the widespread use of OMICS methods, we might be less than a decade away from a comprehensive understanding of transcriptomics, proteomics or metabolomics.
The Grand Unified Theory of OMICS?
Nevertheless, research, and in parallel, the amount of available data and the ability to process it more efficiently in OMICS have increasingly accelerated in the last couple of years. In fact, the individual fields are so complex and massively data-driven that researchers could master certain parts but cannot (ironically) get a holistic overview – no matter how interconnected the subfields are.
Nevertheless, the future might hold the promise of one grand OMICS test – just as the Grand Unified Theory in physics – to show the interconnection of OMES about diseases and conditions. How mind-boggling would it be to come up with a technology which could demonstrate the entire lifecycle of a syndrome: first a genetic mutation, then a mistranscribed mRNA bringing excessive amounts of proteins to the wrong places, while the whole process is accelerated by an environmental factor, say humidity.
However, for such a diagnostic tool to get realized, the acceleration of another technology is necessary: artificial intelligence. For such an incredible analysis, processing and evaluation of terabytes of data are needed – and that cannot happen without A.I. Jun Wang, CEO of iCarbonX, a Chinese start-up specializing in smart algorithms, already launched an initiative for OMICS, biotechnology and A.I. companies to work together – and drive precision medicine and predictive health forward. The Medical Futurist believes iCarbonX definitely shows the way ahead.
The future will hold the complex, A.I-powered analysis of samples both in research and clinical practice. “Complexomics”, the genetic, transcriptomic, proteomic and metabolomic test of any specimen will have the genuine power to give answers about the evolution or treatment of diseases. As an incredible amount of data will be present at such an analysis, A.I. algorithms, data analysts and biostatisticians will work together hand in hand with OMICS researchers to bring complexomics into the lives of doctors and patients.
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