Metabolomics India –
Entering the next level of population health

Inborn Errors of Metabolism (IEM) are genetic disorders due enzyme deficiencies or defective transporters.

Classically, diagnosis is based on increased and/or decreased levels of metabolites in body fluids. This is a cheap and fast high-throughput tool used for example in population-based newborn mass screening as a strategy of prevention. It may be applied to IEM with a good metabolic biomarker.

In recent years considerable improvement in techniques of genomics have been made allowing to diagnose IEM based on variants in respective genes. These techniques are particularly useful in IEMs where there is no good metabolic biomarker, e.g. in mitochondriopathies. Genetic testing may yield variants of unknown significance or ‘benign’ mutations. Functional testing by metabolomics and/or enzymology may lead to better classification of some variants. These methods are also faster high-throughput diagnostic tools and often cheaper. Genomics offers the possibility of prenatal testing in affected families with an index case.

For the diagnostic work-up of IEM genomics, metabolomics and enzymology are complementary methods all having its place in diagnosing IEM.

The sirtuins and histone deacetylases are the best-characterized members of the lysine deacetylase (KDAC) enzyme family. Recently, we annotated the “orphan” enzyme ABHD14B (α/β-hydrolase domain-containing protein # 14B) as a novel KDAC and showed this enzyme’s ability to transfer an acetyl-group from protein lysine residue(s) to coenzyme-A to yield acetyl-coenzyme-A, thereby, expanding the repertoire of this enzyme family. However, the role of ABHD14B in metabolic processes is not fully elucidated. Here, we investigated the role of this enzyme using mammalian cell knockdowns in a combined transcriptomics and metabolomics analysis. We found from these complementary experiments in vivo that the loss of ABHD14B results in significantly altered glucose metabolism, specifically the decreased flux of glucose through glycolysis and the citric acid cycle. Further, we show that depleting hepatic ABHD14B in mice also results in defective systemic glucose metabolism, particularly during fasting. Taken together, our findings illuminate the important metabolic functions that the KDAC ABHD14B plays in mammalian physiology and pose new questions regarding the role of this hitherto cryptic metabolism-regulating enzyme.

COVID-19 has become a global impediment by bringing everything at halt starting from January 2020. India underwent the lockdown starting from 22nd March 2020 with the sudden spike in number of COVID-19 patients in major cities and states. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). This study focused on how metabolites play a crucial role in SARS-CoV-2 prognosis. We performed metabolome profiling of 106 plasma samples and 24 swab samples from symptomatic patients in Indian population of Mumbai region. COVID-19 positive samples were further segregated under non-severe COVID-19 (Nsc) patient cohort and severe COVID-19 (Sc) patient cohort for both plasma and swab. 11 and 35 significantly altered metabolites were found in COVID-19 positive as compared to COVID-19 negative plasma and swab samples, respectively. Also, 9 and 23 significantly altered metabolites were found in severe COVID-19 positive to non-severe COVID-19 positive plasma and swab samples respectively. The majorly effected pathways in COVID-19 patients were found to be amino acid metabolism pathway, sphingosine metabolism pathway and bile salt metabolism pathway. Mapping the non-redundant metabolites-based formulae to the blood exposome database revealed that 4 exposomes were significantly altered in COVID-19 severe patients. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

Metabolomics studies have facilitated many advances in the area of infectious disease biology. Much of the early studies were focused on understanding parasite biology with respect to their core carbon and energy metabolic function. Insights gained from such studies are now becoming useful for unraveling the mechanism of action of novel drugs as well as in understanding host response to pathogens and how this might affect disease outcome. In this talk, I will be discussing these aspects with respect to malaria disease and presenting our research findings. Malaria still remains endemic to many tropical countries, although malaria elimination programs, supported by WHO, have greatly reduced the overall number of malaria cases and deaths globally. One of the reasons for the persistence of malaria is the ability of the parasite (Plasmodium Sp) to become resistant to existing drugs in clinical use. This necessitates the discovery of novel antimalarial drugs. We have identified potent novel antimalarial compounds which were found to act by disrupting the mitochondrial respiratory functions by mass spectrometry metabolomics and genetics. In another study, we have used the model rodent malaria parasite P. berghei, to track the extent of metabolic dysregulation in the host by performing both targeted and untargeted profiling of serum metabolites. This study has revealed that a distinct set of metabolites are found to be altered during malaria progression. Such metabolites, in the context of human malaria, can be further studied as biomarkers for delineating between severe and benign malaria outcomes.

Targeted metabolomics data is available in multiple prospective cohorts in Germany and offers a great chance for pan-cohort metabolomics. In two German cohorts (EPIC-Potsdam and EPIC-Heidelberg), we studied associations between serum metabolites including acylcarnitines, amino acids, hexose and phospholipids, and risk of cardiovascular diseases.

For this purpose, we used samples from two case-cohort studies in EPIC-Potsdam and EPIC-Heidelberg with random subcohorts of n=2214 and n=770 each, and including n=204 and n=228 incident cases of myocardial infarction as well as n=147 and n=121 incident cases of stroke, respectively. Using a meta-analytical approach, we found that higher concentrations of several sphingomyelins, acyl-alkyl- and diacyl-phosphatidylcholines were linked to a higher risk of myocardial infarction in both cohorts. No consistent associations were observed between serum metabolites and stroke risk. Furthermore, in subsamples of four German cohorts namely KORA (n=3029), EPIC-Potsdam (n=2458), CARLA (n=1427) and EPIC-Heidelberg (n=812), we aimed to better understand metabolic differences between diverse cohorts.

Therefore, we used measurements of 100 serum metabolites that have been collected in all four studies with different kits and constructed metabolite networks with Gaussian Graphical Modelling for each cohort. We then compared the metabolite networks across cohorts and found moderate to high similarity. The highest similarity of metabolite networks was observed for EPIC-Potsdam with CARLA and KORA. Eventually we constructed a meta-analytic network combining targeted metabolomics data of the four original studies. Pan-cohort metabolomics has been applied to different German prospective cohorts; it should move to European and global level as it provides rich datasets which can be the basis for complex analyses addressing population health.

Metabolomics is a potent non-invasive tool, that can identify low-molecular-weight metabolites in cells, tissues, and biofluids, when combined with cutting-edge bioinformatics techniques. It is well recognised that certain diseases, particularly cancers etc., have altered cellular metabolism. Nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), and sensitive gas chromatography-mass spectrometry (GC-MS) are capable of detecting the subtle chemical shift brought on by disturbed metabolism in the diseases.  However, it has not been thoroughly researched, yet, whether the altered metabolite profile would signify a subclinical, early-stage disease. To assess the metabolomics impact on chronic pancreatitis, we evaluated blood metabolomics profiles of 186 CP patients and 78 Healthy controls.  We discovered substantial changes in amino acids, sphingolipids, cholines, etc. using a targeted meta-analytical approach. We also analysed microbiome (bacteriome/mycobiome) profiles for each participant, which deciphered unique crosstalk between the host’s metabolome and microbiome in relation to the host’s glycemic status. Our Pan-cohort metabolomic and microbiome data will be useful in identifying the salient features pertaining to host health as well as addressing the mechanistic variations in the disease phenotypes, hence opening a window for personalised treatment.

The Himalayan region is a mega hot spot for various biological biodiversity with diversified medicinal plant species having health beneficial properties and industrial significance. Multivariate analysis is employed in metabolomics to statistically analyze the huge amount of analytical chemistry data generated by high-throughput and simultaneous metabolite analysis. Metabolomics assesses medicinal plants not only on the limited number of pharmacologically relevant metabolites but also on the fingerprints of minor metabolites and bioactive chemicals. Mass spectrometry in combination with multivariate data analysis was employed to examine metabolite distribution and visualization at different levels. Changes in metabolite abundance characterize the chemical flux generated by diverse biochemical processes, molecular mechanisms, and biological pathways. The identification of biosynthetic pathways in medicinal plants, which involve a complicated network of interactions involving metabolites, remains a key difficulty. These biosynthesis pathways have now been confirmed and validated through the fate of detected metabolite peak fragments in metabolomics data. The present study recently explored the pathway analysis of flavone and flavonol biosynthesis, riboflavin metabolism, and phenylpropanoid biosynthesis, flavonoids, triperpenoin and steroidal saponins in Citrus medica, Dactylorhiza hatagirea, Polygonatum verticillatum, Trillium govanianum, Picrorhiza kurroa, and Camellia sinensis. Metabolomics studies will aid in its agronomic and biotechnology interventions for improved quality production of medicinal plants in near future.

Gestational Diabetes Mellitus (GDM), defined as diabetes diagnosed for the first-time during pregnancy, affects 5-25% of all pregnant women, depending on the population studied, the definitions, and the screening/diagnostic methods used. GDM is also associated with a nearly tenfold increased risk for future type 2 diabetes mellitus (T2DM) and a higher incidence of cardiovascular diseases. While GDM is typically diagnosed in the late stages of pregnancy (24-28 weeks), earlier detection of women at high risk for subsequent GDM, can enable the initiation of therapeutic/lifestyle change management. Efforts in the past to identify early trimester biomarkers and risk predictions for GDM diagnosis have yielded limited results. While these models have variable degrees of predictive power depending on the choice of clinical parameters or biochemical surrogates of adiposity, it is also an issue that none have explored first-trimester-based metabolites to identify women at risk of GDM. Therefore, detecting women at higher risk of GDM early in pregnancy by appropriate biomarkers is a demanding clinical need and is expected to prevent the onset of GDM and the future development of T2DM.

Metabolomics, the systematic study of small molecule products of biochemical pathways, has shown promise in identifying biomarkers predictive of metabolic diseases. Prior metabolomic analysis in a prediabetic population identified specific amino acid clusters as predictive of T2DM. There is an unmet need for an effective technology (such as metabolomic profiling) that has the potential to identify an early diagnosis of GDM. The majority of current metabolomic studies on GDM are conducted during the third trimester or after delivery, and lack of data on metabolomic profiling in the 1st trimester of pregnancy to predict women who would subsequently develop GDM. Thus, enhanced identification of maternal metabolites in the prediction of GDM necessitates further studies in larger, more racially/ethnically diverse populations for a better understanding of the biochemical and molecular basis of GDM in pregnant women. Thus, the outcome is expected to unravel novel mechanisms that could have profound clinical and translational implications.

Based on recent technological developments and studies, it is now becoming potentially possible that clinically useful antenatal screening test(s) can be developed using metabolomics. The development of such test(s) will provide data that better informs clinical decision-making and patient management for GDM that will not only directly benefit the immediate pregnancy, but will also help mitigate the longer-term ramifications of these conditions for both the mother and the offspring.

Metabolomics analyses facilitate understanding of mechanisms underlying homeostasis in health in disease. Metabolomics has been applied in frequent human diseases to generate new hypotheses or elucidate dysfunctionality in obesity, diabetes, cancer, immunological responses, neurodegeneration and even mental diseases.

To provide sustainable resources or allow replication metabolomics studies have to follow strict SOPs in sample collection and storage, quality control and quality assurance during and after measurements, further the meta description of large datasets.

Metabolomics in large cohorts requests some special arrangements in study design, sample randomization and imputing which are different to that in other omics disciplines. Metabolite coverage and sample throughput and data presentation are bottlenecks in metabolomics but there are some strategies how to overcome them.