Abstract
Genomics is one of core components of very basic biological processes of transcription, translation and peptide molecule synthesis, collectively known as the ‘central dogma of life’. This is the basis of multitude of biological sciences with the OMICS suffix, for example, transcriptomics, proteomics, metabolomics and others. Almost all OMIC sciences have direct or indirect bearing on many interrelated biological processes relevant to integrated molecular pathways and systems for all body organs and functioning tissues. The concept of the multi-OMICS medicine is now supported by recurrent outcomes of fundamental and applied life sciences research. The common purpose of multi-OMICS medicine is to deliver the medicine and integrated healthcare as precisely as possible targeted at specific molecular points in an individual patient thus satisfying the basic requirement of personalised medicine. There is growing interest and evidence to integrate multi-OMICS with the traditional ancient medicine systems like Ayurveda. This review provides factual and applied information on multi-OMICS in the context of the evidence-based, ethically sound, precision and personalised medicine.
Keywords
Introduction
The OMICS is an umbrella term to assign specific scientific relevance to a number of life sciences. Genomics is the key field centrally linked to many other biological fields each with the OMICS suffix; examples include transcriptomics, proteomics, metabolomics, phenomics, glycomics, lipdomics, microbiomics and many more. The current shape and philosophy of the practice of medicine and healthcare are changing rapidly with translations and novel applications of genomics and multi-OMICS.[1] The common purpose is now to deliver the medicine as precisely as possible targeted at specific points in an individual patient.[2] There is huge amount of data accumulated on genomic applications and translations in precision and personalised medicine.[3] This is not achievable by genomics alone; integration with many other OMICS fields is undeniably necessary. The unified aim is to develop the infrastructure and core strategic framework for integration of genomics and multi-OMICS to harness and deliver the most effective and efficient precision and personalised medicine.[4,5] This missionary approach is likely to transform the contemporary approaches for both communicable[6] and non-communicable diseases.[7] There is growing interest and cumulative evidence to integrate genomic/multi-OMIC medicine with the traditional medicine systems such as Ayurveda and Chinese medicine.[8,9] The future of clinical medicine and preventive healthcare is dependent on the successes of multi-OMICS-led precision medicine.[10] The review looks at current evidence and recent advances for developing the framework for integrating the multi-OMIC approaches translating to patient-specific precision clinical management and personalised healthcare.
The Multi-OMICS Life Sciences
Genome, proteome and metabolome are three major fields of biological sciences, each with the suffix-ome. The ome in molecular biology refers to conceptual reference to completeness or totality in the metaphoric context. It is an example of a neo-suffix formed by abstraction from various Greek terms in -ωμα, a sequence that does no form an identifiable suffix in Greek (
There are several ways to define and explain the term Genomics. In simple words, it denotes ‘the functions and interactions of all coding sequences, non-coding regions and all regulatory segments in the genome’. Functionality of the genome is the key of the Nobel Laureate Sir Francis Crick’s central dogma of life- ‘DNA transcribed to RNA (transcriptome) and translated to the assembly of peptide molecule (Proteome)’ [Figure 1].[12] The net outcome of the central dogma is the metabolome closely interacting and balancing with the environment or complex ecosystems. This is considered as the foundation of OMICS.
The central dogma: ‘DNA–RNA-Protein’ as the basis of metabolome interacting with the environment[12]
Genomics occupies the central position to all omics biological sciences. The individual segment of the core OMICS generates series of fundamental biomarkers that collectively form the discovery markers used in diverse applications from understanding the molecular basis of disease processes, outcomes of individual organ or system functions, developing diagnostic and prognostic bioprofiles and discovery and development of novel drugs and vaccines. Apart from the core OMICS, several others are now added [Figure 2]. The individual OMIC field deals with defined specific biomedical specialised area; however, with significant overlap between various fields. No individual OMIC field is independent since molecular processes and actions are closely related to the outcomes.
Examples of selected multi-OMICS fields
Rationale of the Multi-OMICS Medicine and Healthcare
Following the tremendous and unprecedented successes of the genomic medicine and healthcare, the scope and power of multi-OMICS convinced scientists, physicians and related healthcare professionals to harness multi-OMICS in the precision and personalised delivery of healthcare. The current practice of clinical genomics and genomic medicine is supported with conventional and genome diagnostic laboratory techniques [Table 1].
Current conventional, genetic and genomic laboratory diagnosis tools
The typical model for integrated multi-OMICS in medicine and healthcare includes systematic approach starting either from the phenotype or building on the outcomes of OMIC laboratory work [Figure 3].[13] The laboratory data or information is interpreted along with the outcomes of clinical imaging, biochemical, serological and other investigations. This is analysed using various statistical approaches including machine or deep learning algorithms. This integrated multi-OMICS approach is expected to reveal novel pathways and molecular networks applicable for the precision diagnosis, targeted treatment, prediction of the disease outcomes and eventually prevention of life threatening and disabling diseases [Table 2]. Central to this approach is the discovery and applications of novel sophisticated biomarkers for the phenotype prediction for precision diagnosis, treatment and prevention.[14,15]
Landscape of the integrated multi-OMICS medicine[6]
Major components of the multi-OMICS medicine
A sophisticated and high-speed computational and bioinformatic facility is central to the successful implementation of the integrated multi-OMICS approaches in clinical medicine and healthcare. In addition to the multi-OMIC data, other data and information include the patient-specific data, conventional epidemiological information and annotated data from the public domain. The bioinformatic expert or the computational biologist carries out the computational analysis using different artificial intelligence (AI) and machine learning (ML) approaches. This information is further analysed using the outcomes of clinical investigations, health surveillance and the existing knowledgebase. The final end-point is refined decision support systems including novel therapeutic modalities [Figure 4].[16]
Schematic plan for the multi-OMICS research[17]
Selected Examples of Multi-OMICS Medicine
Multi-OMICS and Platelet Dysfunction
Platelets are small anucleate cell fragments (2-4 µm in diameter) in the blood that are essential components in haemostasis and coagulation. Disorders of platelet numbers (quantitative) or function (qualitative) result in life-threatening thromboembolic and cardiovascular events. Platelets contribute to one of the leading global causes of death. In addition to uncommon monogenic disorders of platelets, many other complex diseases are encountered with immunological dysfunction, inflammation, metastatic spread and thrombosis. Understanding molecular mechanisms in platelets activation is crucial for novel drug discovery and development. The platelet activation is an extremely rapid process involving multiple post-translational mechanisms (PTMs), notably proteolysis and phosphorylation. These are acknowledged to have a major role in platelet activation and inhibition. Multi-omics approaches have emerged as promising tools for unravelling complexity of platelets function. Several OMICs technologies are available to study platelets function in health and disease. These include transcriptomics, global proteomics, phosphor-proteomics, N-terminomics, glycoproteomics and lipidomics.[18]
There are several platelet disorders where multi-OMIC techniques are shown to have a significant role. This approach is demonstrated to elucidate the abnormality of platelet function in the pathogenesis and evolution of blood stasis syndrome (BSS).[19] This disorder has major implications in cardiovascular diseases and treatment. This model platelet disorder has opened the way forward for the application of genomics, proteomics and metabonomics in platelet research collectively now referred to as ‘proteomics’. This concept is strengthened by applications of systems biology, an integral part of the multi-OMICs technology. Detailed description of the role of individual OMIC approach in platelet biology and function in health and disease is beyond the scope of this review. However, undoubtedly multi-OMICS approaches in platelet pathophysiology will continue to be established in clinical practice.
Multi-OMICS and Metabolic Syndrome
The metabolic syndrome (MTS) is a complex heterogeneous group of closely related disorders that manifest with hyperglycaemia, obesity and hyperlipidaemia.[20]
It is commonly diagnosed in patients with type two diabetes mellitus (T2D), ischaemic heart disease (IHD) and hypertension. Patients are often overweight with morbid obesity. True prevalence is not known; however, it is estimated that approximately one-third of adults in the western nations and probably similar suffer from MTS.[21]
Multiple diagnostic criteria for MTS are proposed and used in clinical medicine [Figure 5]. Essentially, it is based on current criteria for T2D, hypertension and obesity [Table 3].[22] It is agreed that MTS is closely related with dysregulated glucose and lipid metabolisms. Hyperglycaemia, secondary to insulin resistance, is central to abnormal lipid metabolisms. Glucose and lipid metabolisms are closely correlated. However, it is unclear whether abnormal lipid metabolism can be both cause and consequence of impaired glucose metabolism. It is unclear how abnormal glucose and lipid metabolisms are linked to peptide molecules involved with transport. Quantitative endogenous peptidomic analysis in T2D and prediabetes patients is shown to be part of the complex molecular pathology.[23]
Diagnostic criteria for conditions of the metabolic syndrome group[22]
The systemic framework for metabolic syndrome[22]
Multi-OMICS and Cancer
The value and importance of precision diagnosis in cancer are undisputed. Integrated treatment and management plan would be dependent on correct diagnosis and efficient use of effective therapy regimen. The wealth of experimental data arising from series of multi-OMICs studies has provided strong ground for the model of integrated cancer care [Figure 6].[24]
Multi-OMICs analysis of interactions at different levels in cancer[25]
The multi-OMICs model is defined as a biological approach based on physiological and pathological phenomena that shape the phenotype of interest and characterise different biological systems at different levels.[25] Recognition of molecular networks based on multi-OMICs data has a major role in understanding the molecular mechanisms of cancer facilitated by bioinformatic applications.[26]
Essentially, the whole process of multi-OMICs analysis with computational biology is part of the systems biology. It allows detection and validation of cancer-distinctive mechanisms involved in the transition process of a normal cell to malignancy. There are several steps of phenotypic and molecular changes in several metabolic pathways such as uncontrolled proliferation by blocking growth suppressors, reprogramming of energy metabolism, evading immune destruction, resisting cell death, angiogenesis and metastasis.[27] This is promising field for further research aimed at developing novel anti-cancer drugs.[28]
Amongst different multi-OMICs approaches, epigenomic analysis deserves special reference. The epigenetic regulation of cancer involves three main parameters: DNA methylation, histone modification and the non-coding RNA [Figure 7].
Epigenetic regulations in cancer[25]
Alterations in epigenetic modifications in cancer regulate various cellular responses, including cell proliferation, apoptosis, invasion and senescence. Through DNA methylation, histone modification and noncoding RNA regulation, epigenetics plays an important role in tumorigenesis. These main aspects of epigenetics present reversible effects on gene silencing and activation via epigenetic enzymes and related proteins.[25] The somatic multi-OMICs analysis of tumour tissues reveals different tumour cells show various patterns of histone modification, genome-wide or in individual genes, demonstrating that epigenetic heterogeneity exists at a cellular level and suggesting that tumorigenesis is the consequence of the combined action of multiple epigenetic events.[29]
Multi-OMICS in Digital Health
One of the major aspects of personalised medicine is the self-control on information and outcomes by individual person and including other members of the family. Digital devices, such as personal computers (desk top and/or laptop), cell phones and tablets, provide an individual to have easy and instant access to personal health data and related information through browsing single or multiple digital resources, notably Google searches. The digital health is now normal and likely to expand much more widely and deeper. Investigations, for example, sophisticated cardiac electrophysiological study or MRI guided cardiac perfusion scans, can be instantly monitored and analysed digitally by an expert sitting far away from the comfort of personal consulting rooms. This approach is extremely important for new emerging specialist healthcare approaches in primary healthcare.[30,31] Many diagnostic and research genomic laboratories regularly use digital platforms for analysing and interpreting the next-generation genome sequencing (NGS) data with the central database repository and relevant experts anywhere in the world. The digital health has the potential for substantial growth and impact with novel and rapid advances in AI and ML approaches.[32]
The scope and power of digital technology, specifically AI, in diverse multi-OMICS applications in medicine and healthcare are likely to be massive and probably unimaginable.[33] In a typical scenario, the individual patient data such as biometrics, lifestyle, family history, occupation, past medical history, drugs, current symptoms, imaging reports, biomarkers and, if relevant, genomic data would be analysed using one of the advanced computation analysis platforms. The data generated would undergo data fusion and integrated by AI tools and stored for instant retrieval and usage [Figure 8]. The personalised multi-OMICS data could then be used through multi-linear approaches in the context of outcomes of patient investigations and the available knowledgebase. The clinician or any other healthcare professional would be able to evaluate and decide using the most effective decision support system.[34]
Model for integrated multi-OMICS patient-specific healthcare model using artificial intelligence and multi-linear approaches[34]
Organisation and Delivery of the Multi-OMICS Medicine and Healthcare
Introduction and enhancing awareness for the multi-OMICS approaches to precision medicine and healthcare is a major challenge. There are major hurdles in the implementation and integration of multi-OMICS in the future, particularly in emerging low- and middle-income countries such as India. First, the infrastructure to cater the needs for diagnostic workup offered by specialised laboratory network linked to dedicated data centres managed by experts in bioinformatics. Second, the economic affordability of huge costs incurred. Finally, availability of skilled and trained workforce to meet demands of clinical, laboratory analysis, computational experts and other supporting teams. Currently, there is very limited information available on the national level strategy and policy framework. The National Rare Diseases Policy of the Government of India provides a model for managing limited number of rare diseases including management through approved ‘Centres of Excellence’ and dedicated financial support.[35] This is a very positive and sound strategic development that could form the basis for future policy changes supporting future applications and implementation of the multi-OMICS driven integrative healthcare in India and other low- and middle-income emerging economies.[36,37]
An important initial focus shall be to introduce the concept of multi-OMICS medicine to wider medical and healthcare community including general public.[38] The aim is to raise awareness and recruit different level of faculty and postgraduate trainees into the project. This will be achieved through series of introductory lectures and seminars dealing with core and applied aspects of multi-OMICS precision medicine and healthcare. It is anticipated that this approach would help in building the essential team for specific research within the umbrella of the multi-OMICS medicine. The future of modern medicine would be closely linked with the emerging socio-cultural revolution to harness richness and deep rooted concepts of traditional medicine systems such as Ayurveda and Chinese.[8,9] Recent phenomenal studies and series of articles on the exciting work on Ayurgenomics illustrate the value of integrated genomic and multi-OMIC medicine with the traditional Ayurvedic medicine system of medicine.[39] Further, strategic initiatives, infrastructure development and evidence-based applications in multi-OMICS would enable harnessing societal and national benefits with deeper understanding and understanding of India’s novel Ayushman healthcare strategy.[40,41]
Successful and sustainable organisation and delivery of the multi-OMICs-based precision medicine would require an integrated data and knowledge platform. One of the major requirements would be the population-specific indigenous databases systematically curated genomic and related biomarkers datasets.[42] Most of the current available genomic and related bio-databases, such as the UK Biobank, include extremely small number of ethnically diverse people and are thus not truly representative of these peoples.[43] Genomics and multi-OMICS approach features in the context of traditional, complementary and integrative global healthcare in the WHO Science Council charter and the new WHO Traditional Medicine Strategy.[44]
There are only few studies carried out on the outcomes of multi-OMICS in real-time clinical conditions. The model of the Heart Failure Integrated Platform (HFIP) is promising and offers an excellent example for organising the integrated disease-specific multi-OMICS medicine delivery system.[45] This concept is central to data exploration, fusion analysis and visualisation by collecting and curating existing multi-OMICs data and knowledge from various public sources and also provided an auto-updating mechanism for future integration. [Figure 9]. There is urgent need to plan and conduct similar pilot projects in other common medical diseases.
The construction framework of HFIP[45]
The need for data integration and comprehensive health and disease parameters could not be over emphasised or the successful implementation of the multi-OMICS-based precision medicine delivery program.[46] This is an exciting area of multi-OMICs-based precision and personalised medicine. The essential component of this approach is the profiling of detailed variables from a large cohort in different health and disease categories.[10] This approach would enable us to incorporate detailed health and disease data derived from the electronic health records with thousands of different OMICs variables to create a holistic phenotypic profile of the individual for faster and effective precision diagnosis [Figure 10].[1,2,47]
Multi-OMICS data profiling and deep phenotyping for precision OMICs medicine[1,2]
Finally, the importance of AI and ML in the organisation and delivery of the multi-OMICS precision and personalised medicine is a major determining factor. ML can be used to find patterns in complex datasets and interactions between different OMIC layers involved in creating disease states.[48,49] A good example is the DeepInsight-3 D approach that uses ML and multi-OMICs data to predict-specific anticancer drug responses.[28,50] It is highly likely that gaps and challenges in future multi-OMICs studies and applications would be met with AI managing huge data generated from single-cell high throughput genome sequencing and other molecular technologies.[51]
Conclusions
Since the completion of the human genome sequence, rapid advances in genome technologies revolutionised the remit and scope of genetic and genomic diagnosis. This development offered the firm technical and scientific base for precision and personalised diagnosis. However, it soon became apparent that genome sequencing technologies alone would be insufficient for effective genomics-led precision medicine. Genomics just happened to be one of the many scientific and technical fields under the big umbrella of OMICS. Extensive research supported that other multi-OMIC parameters including transcriptomics, proteomics, metabolomics, epigenomics and few others were probably more relevant for precision medical management and healthcare leading the way forward for the multi-OMICS medicine. The spectrum and scope of multi-OMICS medicine are massive that require highly sophisticated laboratories undertaking specific OMIC analyses and integration of the vast amount of the multi-OMIC data with comprehensive health and disease parameters. The whole process would be intricately dependent upon in-depth bioinformatic analysis combined with cross database searches. The scope and applications of AI and ML are just beginning to be appreciated and appear to be extremely important not only for making projections and predictions for the individual’s outcome of the disease and as well as assessing and predicting drug responses. The whole field of multi-OMIC precision and personalised medicine and healthcare is vast with full of astonishing potentials for future translational and clinical applied research. This new paradigm of future medicine is closely linked to anticipated developments in AI and ML.[52] There is urgent need for developing carefully planned and adequately funded multi-OMIC research projects. In addition, curricula for both undergraduate and postgraduate medical and healthcare professional education and training need to be revised in keeping with genomic and multi-OMIC-based precision diagnostic, therapeutic and preventive applications.
Footnotes
Author’s note
This review is based on author’s personal clinical genomics and genomic medicine experience spread over four decades. Reflections and interpretations are included with evidence extracted from several recent publications related to multi-OMICs. As far as possible literature references are cited and appropriately indicated in the text. Illustrations are modified from original publications including others where permissions are obtained. The author has not made any financial gains from this article.
Declaration of conflicting interests
The author declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Author Dhavendra Kumar is a member of the Editorial Board of Apollo Medicine. The authors did not take part in the peer review or decision-making process for this submission and have no further conflicts to declare.
Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work sponsored by the Genomic Medicine Foundation UK (
Institutional ethical committee approval number
Ethical approval not relevant since this article is a review based on author’s personal research without any human or laboratory involvement.
Credit author statement
Full contribution of the author.
Data availability
No objection to data availability; only published data cited in the article.
Use of artificial intelligence
No use of artificial intelligence in this review article.