How Metabolomics Enhances Human Genomics Studies

Posted In: Blog, Omics

New insights from recent studies reveal the combined power of genomics and metabolomics. Combining information on both genotype and metabolomics creates a powerful analysis not only for research but also for clinical application. While genomics on its own is widely used across biology, metabolomics is an incredibly powerful addition for extracting insights out of that research.

On what genes should we focus?

A big issue facing biologists and clinicians is the uncertainty of gene association to function in human health. What does a gene variant do or not do? Additionally, we know that the presence of a genetic mutation is not necessarily synonymous with the development of a disease. A range of factors, including epigenetics, environmental exposures, microbiome, and lifestyle choices such as diet and exercise, influence phenotype. Genotype ≠ phenotype.

Currently, genomic information can only suggest which diseases we might be predisposed to, but it’s an incomplete picture of overall health. The data may give either a false sense of security about implied good health or at the other extreme, lead to some sleepless nights, which is why genetic test results typically come with consumer disclaimers. Nature published an article in 2016, “A radical revision of human genetics: Why many ‘deadly’ gene mutations are turning out to be harmless,” that brings light to the issue of understanding genomic information.

How Do We Identify Genetic Variants That Could Contribute to or Cause Disease?

One approach to identifying genetic variants associated with disease is the utilization of genome-wide association studies (GWAS). These studies have linked thousands of loci (regions on a chromosome) to human disease, but there are issues with exactly locating and identifying the specific gene and mutations linked to a trait. In addition, one needs to establish causality and not just correlation. On top of this, perhaps only a few thousand genes1 out of the 20,000+ that we carry have currently been associated with human disease. So, how can we improve the process of identifying gene variants associated with disease and establishing causality? The answer is quite straightforward—we need phenotypic data.

Metabolomics aims to identify and quantify all the metabolites in a sample such as a body fluid or tissue. Measurement of metabolites provides a molecular phenotype that can be used as a proxy or surrogate for a physical phenotype—a snapshot of current health status. Metabolomics can be used in combination with genetic information to improve medical interpretation of an individual’s disease risk.1

Similarly, metabolomics combined with genomic analysis has been used to identify significant associations of gene variants with metabolite concentrations in blood.2,3 In other words, the readily seen changes in metabolite levels help demonstrate what the gene, or mutation in the gene, is actually doing.

As genomics and metabolomics technology have matured, 246 gene variant associations to metabolism have been identified. The work published in 2017 by Long et al.3 used Metabolon’s Global Discovery Panel coupled with whole genome sequencing.

Deepening the Search with Whole Genome Sequencing Combined with Metabolomics

The concentration of metabolites in the blood can vary widely from individual to individual, with variation arising from both genetic and environmental factors. For this reason, genomics alone cannot tell the full biological story, and phenotypic data are required to strongly identify significance. Researchers from Human Longevity, Health Nucleus, King’s College, Baylor College of Medicine, and Metabolon demonstrated a significant level of heritability for a large number of metabolites using Metabolon’s Global Discovery Panel to analyze blood samples, with the median heritability being quite high at 48%.3 Genetic sequence variations at 101 loci were associated with the levels of 246 metabolites, of which 90 associations of gene variant to metabolite level for 85 metabolites in plasma had not been seen before. Of the novel variants, five had previously been associated with diseases, but not with metabolite levels.

Rare Variants Revealed through Metabolic Outliers

In another study, Long et al. focused on extreme population outliers in terms of metabolite levels to identify rare variants associated with those extremely high or low levels. The researchers identified 151 individuals who had one or more of 69 metabolites with levels consistently very different from the population mean. Additional individuals were identified using further methods to make a total of 175. The researchers then looked for rare functional gene variants in these ‘outlier individuals’ that might explain the extreme metabolite levels. After excluding some variants that had also been identified in individuals with normal metabolite levels, the researchers identified 14 rare variants in 10 genes. In addition, 14 rare variants from seven genes were identified by searching the genomes of the 1960 study participants for rare functional variants that were associated with statistically significant (abnormal but less extreme) differential levels of metabolites.

Interesting Associations

Overall, approximately one in ten unrelated individuals had metabolite blood levels that were associated with rare genetic variants. Many gene-metabolite pairs were associated with inherited metabolic disorders (IMDs). Surprisingly, the data revealed that some of these were novel, and that some outlier metabolite levels in heterozygous individuals were associated with autosomal recessive IMDs or other pediatric diseases. Since these associations were observed in heterozygous individuals, one would expect the individuals to be clinically and phenotypically normal.

One individual, heterozygous for a rare variant in SLC6A3, suffers from adult-onset Parkinson’s disease. Variants in this gene have been shown to cause infantile parkinsonism dystonia when homozygous, with reduced dopamine reuptake observed. Elevated levels of dopamine sulfate detected in this individual by the Global Discovery Panel may have resulted from this defective gene.  The possibility that the heterozygous variant may translate into adult-onset clinical symptoms should be considered. Thus, there is a real possibility that late-onset phenotypes, previously thought to occur in childhood, could be present and result in adult disease.

Identifying Unknown, Unidentified Metabolites Using Associations to Genes of Known Function

A number of unidentified metabolites were associated with genes of known function. The authors attempted to identify these unknown metabolites using liquid chromatography-mass spectrometry (LC-MS) data from the metabolomics experiments in combination with the corresponding genetic information.

How do you identify these unknown and unnamed metabolites? Metabolon has a great deal of institutional expertise in and has developed proprietary methods for interpreting mass spectral data for metabolite identification. In addition, knowledge derived from our already existing, extensive metabolite data aids in forming a putative identification of an unknown metabolite. For example, an unknown compound might have similar but not identical MS/MS data to a known metabolite. An example of an unknown metabolite designated as X-12511 was associated with N-acetyltransferase 8 (NAT8). Analysis of the LC-MS data, combined with this gene to metabolite association, gave the confident structural assignment of an acetylation product of 2-aminooctanoic acid, N-acetyl-2-aminooctanoic acid.

Summary

Many gene variants with large effects were identified in this study. There was a wide variance in metabolite levels from the extreme of IMDs to abnormal outlier metabolite profiles in adults, supporting the role of rare gene variants in common diseases. More than one-third of unidentified metabolites were successfully mapped to genetic loci, with some of these unknowns being subsequently identified when analyzed using metabolomics LC-MS data. Lastly, the authors stated that, “Our data underscore the metabolic consequences of multiple rare variants and leaves open the possibility that they may translate into adult-onset clinical symptoms.”

Genomics alone cannot tell us the full state of our health, it can only tell us about the potential risk of developing a disease sometime in the future. Metabolomics gives us a snapshot of our current health status. Combining genomics with metabolomics provides powerful insight into one’s overall health. Because each person is unique, their metabolisms are also unique, and they can make us susceptible to disease just as easily as they can protect us from it. Metabolomics is a powerful tool to help us understand that.

Genomics alone cannot tell us the full state of our health, it can only tell us about the potential risk of developing a disease sometime in the future. Metabolomics gives us a snapshot of our current health status. Combining genomics with metabolomics provides powerful insight into one’s overall health. Because each person is unique, their metabolisms are also unique, and they can make us susceptible to disease just as easily as they can protect us from it. Metabolomics is a powerful tool to help us understand that.

One of the most compelling conclusions to draw from genomics studies that have also collected metabolomics data is that we should consider using metabolic screening more routinely to understand chemical uniqueness and its impact on individual health.

If you would like to learn more about metabolomics coupled with genomics, contact us today.

References

1. Guo L, Milburn MV, Ryals JA, et al. Plasma metabolomic profiles enhance precision medicine for volunteers of normal health. Proc Natl Acad Sci U S A. 2015;112(35):E4901-E4910. doi:10.1073/pnas.1508425112

2. Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543-550. doi:10.1038/ng.2982

3. Long T, Hicks M, Yu HC, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet. 2017;49(4):568-578. doi:10.1038/ng.3809

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