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Christian Klose, PhD, Head of Research and Development, Lipotype

In 1999, genomics reached a huge milestone, passing the 1000 publications-per-year landmark for the first time. The ensuing explosion of research utilizing omics technologies has led to an accompanying explosion of expenses, with a global average of roughly $2.9 billion in public funds per year being spent in the early 2000s in genomics research alone. In addition to this spending, there are investments in other omics—transcriptomics, metabolomics, proteomics, phenomics, and more—reflecting the promise omics holds for groundbreaking clinical advancements. Twenty years into the omics explosion, however, we have yet to see omics credited with any transformative changes in medicine. They always seem to be just around the corner. What hinders us from remaking diagnostics and drug development?

One likely problem is reliance on single-omics approaches. Whether one follows a genomics-, proteomics-, or lipidomics-only approach, one will find limitations that are specific to that approach. If one combines multiple omics disciplines, however, one could could achieve more well-rounded coverage and avoid single-omic shortcomings. To determine which omics science is suited for a joint multiomics approach, one need only ask the simple question: “What is it that my omics is bad at?”

What genomics can’t do…

Genomics delivers genome mapping and editing data, illuminates structure and function relationships, and gives insights into evolutionary processes. The primary outcome is the DNA sequence, which will remain mostly unchanged throughout an organism’s life. Genomics data can be used to determine disease risk factors—which are valid throughout a patient’s life—and to develop corresponding personalized therapies. However, the hopes inspired by genomics data often amount to wishful thinking.

Genetic sequence, simply speaking, specifies only the eventual protein sequence of genes and provides some measure of regulatory information regarding where and when they are translated into proteins. It is these proteins, and not the genes themselves, that then regulate the organism’s metabolism and behavior. But the genome sequence alone provides little insight into a protein’s expression levels or its contribution to given phenotypes. Many environmental factors can also affect protein activity independent of genomic sequence.

The good news is that there is a tool that can overcome these shortcomings. This tool is lipidomics.

…lipidomics can!

Where genomics addresses the sum of all genes and their interrelationships, lipidomics is the study of lipids and lipid metabolites in biological systems. From storing energy and forming cell membranes to serving as hormones and signaling molecules, lipids have many different biological functions. They have been shown to have roles in many diseases, including cardiovascular disease, obesity, Alzheimer’s, asthma, diabetes, hypertension, and amyotrophic lateral sclerosis.

The task of lipidomics is to unveil the lipid molecules involved in different diseases and states. Whereas genomics sequences DNA, lipidomics employs technology to comprehensively analyze lipid composition in cells and body fluids. Lipidomics analysis provides what genomics cannot: detailed insights into the current state of a patient’s metabolism, in the form of a lipid phenotype. Combining genomic data with lipidomic phenotyping pushes genomics beyond its limitations. An immediate benefit of this combined approach is enhanced disease risk prediction performance through analysis of interconnected disease risk factors. What sounds like science fiction is just science.

A plea for clinical lipidomics panels

Lipids are by no means new to clinical diagnostics. Levels of and changes in blood plasma lipids have been used for decades to monitor and predict risk for cardiovascular diseases. The widely used, traditional lipid panel often presents four data points: total triglyceride, total cholesterol, LDL-cholesterol, and HDL-cholesterol levels. However, human blood plasma contains hundreds of chemically distinct lipids, constituting a full-scale lipidome.

Arguing that the traditional lipid panel provides only a sneak peek into the lipid cosmos, an international research team led by geneticists and medical scientists from the University of Helsinki took on the task of analyzing lipid-associated single nucleotide polymorphisms (SNPs).1 Using a multiomics approach, they assessed traditional lipid panel measures (Figure 1, upper panel) and lipidomics data (Figure 1, lower panel) for their respective abilities to identify associations of SNPs with specific lipid species. The results clearly showed that molecular lipidomic data have much stronger associations (lower P values/higher on the y-axis) with lipid-associated SNPs and provide greater statistical power to identify SNPs with direct roles in lipid metabolism.

Figure 1. Disease-associated single nucleotide polymorphisms identified by traditional lipid measures (upper panel) or by lipidomic data (lower panel). Compared with traditional lipid measures, individual lipid species usually had more statistical power. Noticeable associations have been annotated.1

Backed by the outcomes of this comparison, the researchers were able to identify individual lipids as potential independent disease risk factors. In many genes, an SNP was found that helped to confirm a gene-disease association. For example, SNP findings helped associate BLK with obesity and gallstones, GADS2 with thrombophlebitis, and SPTLC3 with gallbladder disease.

These newly discovered associations are a strong argument for genomics-lipidomics multiomics approaches in clinical diagnostics. In the future, patients with known lipid-associated genomic disease risk factors can be lipidomically monitored to identify disease early and maximize prevention interventions. Where traditional lipid panel measures fail, lipidomics profiles succeed in indentifying genomic predispositions.

In the meantime

While this is still far from realization, there are tasks a genomics-lipidomics multiomics approach can already take on. The research possibilities ahead of us are plentiful: identifying new interconnected disease risk factors, unraveling the missing links between genome and lipidome via pathway analysis, discovering new drug targets and developing new therapeutic approaches, paving the way for clinical screenings and diagnostics by improved protocol and method development.

Biology and medicine are ready to remake diagnostics. Multiomics is the fuel required for the take off.

 

Reference
1. Tabassum R et al. Genetics of human plasma lipidome: Understanding lipid metabolism and its link to diseases beyond traditional lipids. bioRxiv 2018

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