This webinar series highlights the collaborative projects of metabolomics research investigators and their biomedical collaborators with the intention of engaging scientists within other NIH institutes, as well as presenting case studies to the consortium of successful metabolomics partnerships.
May 17, 2022 @ 11am EST
NIH Metabolomics Scientific Interest Group Webinar
RaMP-DB 2.0: a comprehensive, public database and analytical tools for extracting biological and chemical insight from metabolomic and multi-omic data
Ewy Mathé, PhD, Director of Informatics, Division of Preclinical Innovation, National Center for Advancing Translational Sciences
Abstract: Metabolomic and multi-omic data are increasingly being collected in basic, preclinical, and clinical research studies. Interpretation of these data though remains challenging. Common challenges include the difficulty in identifying metabolites and assigning unique identifiers, and the scarcity of resources that provide up-to-data comprehensive annotations and analysis tools on integrated genes/proteins and metabolites. To aid in interpreting these complex data, we developed RaMP-DB 2.0, a public resource that contains comprehensive biological, structural/chemical, disease, and ontology annotations for human metabolites and metabolic genes/proteins. The associated RaMP-DB 2.0 framework provides the ability to query those annotations and to perform pathway and chemical enrichment analysis on input multi-omic datasets. Since our first release, RaMP-DB 2.0 has been substantially upgraded and now includes an expanded breadth and depth of functional and chemical annotations, and a reproducible and semi-automated method for entity resolution of analytes across the different source databases pulled. The usability of the RaMP-DB 2.0 has also been improved through updates of pathway and chemical enrichment analysis methods, and a completely revamped web interface and associated public API for programmatic access. RaMP-DB 2.0 currently pulls information from HMDB, KEGG (through HMDB), Reactome, WikiPathways, Lipid-MAPS, and ChEBI and includes 254,860 chemical structures, of which 43,338 are lipids, 15,389 genes, 53,745 pathways, 807,362 metabolic enzyme/metabolite reactions, and 699 functional ontologies (biofluid, health condition, etc.). RaMP-DB 2.0 is available at https://rampdb.nih.gov/.
Bio: Dr. Ewy Mathé is the Director of Informatics in the Division of Preclinical Innovation at NCATS. She received a bachelor’s degree in Biochemistry (minor in Sociology) from Mount Saint Mary’s University, MD in 2000 and a PhD in Bioinformatics from George Mason University, VA in 2006. During her post-doctoral training with Dr. Curtis Harris (NCI/NIH), she discovered putative esophageal and lung cancer biomarkers using miRNA microarrays and metabolomics, leading to two patent applications. She then joined Dr. Rafael Casellas’ laboratory (NIAMS/NIH), where she aimed to better understand modalities of transcriptional regulation in B lymphocytes, using next-generation sequencing techniques. Since then, she has focused on developing methods and frameworks to guide analysis, integration, and interpretation of high-throughput sequencing and multi-omic data to uncover biological mechanisms and identify valid biomarkers and therapeutic targets for the diagnosis, prognosis, and treatment of various diseases. She is very active in the metabolomics community (Metabolomics of North America, Metabolomics Society, Consortium of Metabolomics Studies), and is a proponent of open-source software development and data.
As Director of Informatics, she leads a diverse team of experts in bioinformatics, cheminformatics, data science, and software development that empower translational scientists to make meaningful data-driven decisions in their research. Her team is currently developing computational resources, methods and tools that optimize the use of large scale molecular (high throughput screening, multi-omics, etc.) and knowledge-driven datasets (various sources of information on drugs, including mechanisms of action, regulatory status, etc., drug targets, diseases, biological functions, etc.).
March 10, 2022 @ 2pm EST
Tracking Hepatic Mitochondrial Metabolism In VIVO and its Role in NAFLD
Jamey Young (Vanderbilt University)
Shawn Burgess (UT Southwestern)
Nonalcoholic fatty liver disease (NAFLD) is a chronic condition affecting approximately 25% of the US population. Hepatic insulin resistance and NAFLD are associated with altered mitochondrial energy metabolism, a factor that may play a causative role in hepatocellular damage. The Young and Burgess labs have collaborated for several years to develop, test, and apply novel technologies for assessing in vivo metabolism using stable isotopes. This presentation will describe some of these efforts, including the development of strategies that integrate MS and NMR isotopomer measurements to track substrate flux within overlapping pathways of glucose, lipid and mitochondrial metabolism. These measurement platforms typically involve the use of multiple stable isotopes (e.g., 2H, 13C) and rigorous mathematical modeling to determine metabolic readouts that could not be assessed using static measurements of metabolite abundance or enzyme expression alone.
November 16, 2021 @ 2pm EST
Space- and Time-Resolved Metabolomics of a Mouse Model of High-Grade Serous Ovarian Cancer
Dr. Facundo Fernandez
Dr. Jaeyeon Kim
Indiana University School of Medicine
Ovarian cancer (OC) is the most lethal gynecological malignancy with patients experiencing the highest mortality rate. High-grade serous carcinoma (HGSC), also known as high-grade serous ovarian cancer, is the most common and deadliest subtype, accounting for 70-80% of OC deaths. The high mortality rate of OC can be largely attributed to the asymptomatic tumor growth, combined with the lack of effective screening methods for tumor detection at early stages. Mass-spectrometry-based metabolomics studies of OC have proven to be crucial in enabling biomarker discovery. Yet metabolomic reprograming representative of HGSC development remains largely unknown. Here we present a time-resolved UHPLC-MS metabolomics study of HGSC showing longitudinal metabolic changes across the entire disease spectrum, starting from the disease onset until death.
A triple-mutant (TKO) mouse model of HGSC was developed by inactivating the Dicer1 and Pten genes and adding a p53 mutation. Serum samples from TKO (n=15) and control mice (n=15) were sequentially collected starting at 8 weeks of age until endpoint. Samples were extracted with isopropanol, and with methanol to obtain both non-polar and polar metabolome information. Reversed-phase (RP) LC-MS analysis was conducted with an Accucore C30 column and a Q-Exactive HF instrument. HILIC LC-MS analysis used a BEH amide column and an Orbitrap ID-X Tribrid mass spectrometer. LC-MS was carried out in both positive and negative ESI modes. Spectral features were extracted with Compound Discoverer software. Metaboanalyst v4.0 and PLS_Toolbox v.8.1.1 were used for data analysis.
A total of 11,389, 3475, and 1,988 features were extracted from ESI+ RP UHPLC-MS, ESI- RP UHPLC-MS, and ESI- HILIC UHPLC-MS datasets, respectively. Metabolite annotations for all features obtained from RP UHPLC-MS dataset was attempted with in-house spectral databases, m/zCloud and Lipid Maps databases. From the total 14,864 lipid features, 1002 features were annotated, and 422 features were statistically different (q<0.05) between TKO and TKO control groups. Time-course lipidomic changes were investigated by lipid classes using these 422 features, and specific temporal trends for 17 lipid classes were revealed. In general, we observed subtle lipidomic changes in the initial stages of HGSC, followed by drastic changes in the advanced stages. Most ceramides, glycerolipids, and glycerophospholipids showed an increase in abundance over time, whereas sphingomyelins, lysoglycerophospholipids, and ether phospholipids showed a decreasing temporal trend. A set of 22 features that best discriminated between early TKO and controls was selected. This panel included 20-alpha dihydroprogesterone, glycerophospholipids, sphingolipids, fatty acids, and glycerolipids, and differentiated early disease stages from controls with 95% accuracy in an oPLS-DA model. Additionally, the potential of lipid ratios as biomarkers of HSGC was evaluated. Interestingly, the best performing ratios were seen for the ratios of ceramide(d34:1) abundances, and ether phosphatidylethanolamines with AUC >0.70 for early stages and AUC >0.80 for advanced stages. From the HILIC MS dataset, 46 spectral features were identified as statistically significant between early TKO and TKO control samples. Metabolite annotation was attempted for those 46 features using metabolomic databases such as Metlin and mzCloud. Among the identified features were amino acids and derivatives, TCA acids, fatty acids derivatives, and some phytochemicals. An oPLS-DA model, built using the identified features excluding the phytochemicals, discriminated early TKO samples from controls with 76% accuracy. Time-course plots of amino acids and TCA cycle metabolites showed specific temporal trends and provided evidence for the importance of amino acid and TCA acid metabolism in HGSC progression.
May 6, 2021 @ 2pm EST
Dissecting 2-aminoacrylate global stress outcomes in Salmonella enterica using 1H-NMR Metabolomics
University of Georgia
National Renewable Energy Laboratory
The reactive intermediate deaminase, RidA, is conserved across all domains of life and neutralizes reactive enamine species through deamination. When Salmonella enterica ridA mutants are grown in minimal medium, 2-aminoacrylate (2AA) accumulates, damages several pyridoxal 5′-phosphate (PLP)-dependent enzymes, and elicits an observable growth defect. Genetic studies suggested that damage to serine hydroxymethyltransferase (GlyA), and the resultant depletion of 5,10-methelenetetrahydrofolate (5,10-mTHF), was responsible for the observed growth defect. However, the global metabolic consequences resultant of 2AA stress and the downstream effects stemming from GlyA damage by 2AA remains relatively unexplored. Untargeted proton nuclear magnetic resonance (1H NMR) metabolomics helped determine the metabolic state of an S. enterica ridA mutant. The data supported the conclusion that metabolic changes in a ridA mutant were due to the IlvA-dependent generation of 2AA, and that the majority of these changes were a consequence of damage to GlyA. While many of the metabolic differences for a ridA mutant could be explained, changes in some metabolites were not easily modeled, suggesting that additional levels of metabolic complexity remain to be unraveled. This study demonstrates the utility in implementing nutrient supplementation and genetic perturbation into metabolomics workflows as a means to disentangle complex metabolic outcomes stemming from a general metabolic stress, connecting metabolic outputs to physiological phenomena and establishing causal relationships. Overall, these sorts of metabolomics experiments show great potential as a complement to classical reductionist approaches to cost-effectively accelerate the rate of progress in expanding our global understanding of metabolic network structure and cellular physiology.
April 1, 2021 @ 2pm EST
Using metabolomics data as insight into the biochemical signatures of chronic exhaustion (ME/CFS and similar diseases)
University of California, Davis
The pathogenesis of ME/CFS, a disease characterized by fatigue, cognitive dysfunction, sleep disturbances, orthostatic intolerance, fever, gut intestinal complications, and lymphadenopathy, is poorly understood. We have previously reported biomarker discovery and topological analysis of plasma metabolomic, fecal bacterial metagenomic, and clinical data from ME/CFS patients and matched healthy controls. We have now replicated and extended our initial findings and found signatures that may provide insights into the pathogenesis of ME/CFS and its subtypes, with possible implications for other diseases such as long-covid patients that present with similar symptoms.
March 4, 2021 @ 2pm EST
Understanding the chemical biology of the gut-liver axis with metabolomics
The vast number of microbes (10 to 100 trillion) that comprise the mammalian microbiota serves numerous beneficial functions for the host that includes stimulation of immune development and competitive exclusion of pathogenic microorganisms. Exogenously administered viable bacteria –probiotics- can dampen inflammation, improve barrier function and promote reparative responses in vitro, and have shown promise as therapy in inflammatory and developmental disorders of the intestinal tract. Abnormalities of the microbiota are associated with inflammatory bowel disease (IBD) and other allergic, systemic immune, and infectious disorders (e.g., asthma, juvenile-onset diabetes, multiple sclerosis) and metabolic disorders (adult-onset diabetes, nonalcoholic steatohepatitis, and obesity). Thus, there is an increasing need to understand the mechanistic basis for microbiota in human disease. High throughput sequencing platforms allow species-level identification of complex communities associated with clinical phenotypes but do not provide insight into mechanisms by which bacteria can influence physiological and pathological processes. Advances in metabolomic analyses have enabled considerable progress in the study of host-microbial interactions. In our collaborations, we identified a small molecule activator of the Nrf2 liver antioxidant system, 5-methoxyindoleacetic acid, is produced by human commensal bacteria and protects against oxidative stress (1). In an ongoing untargeted LC-MS analysis of hepatic tissue in germ-free and conventionalized animals, we identified delta-valerobetaine (VB) as the top microbial metabolite present in liver and liver mitochondria. VB was found to decrease cellular carnitine, inhibit mitochondrial fatty acid oxidation, and increased central adiposity in mice. VB was also found to be elevated with obesity in humans. These studies illustrate the considerable value of metabolomics to complement genetic and molecular methods to address mechanisms underlying human health and disease.
February 3, 2021 @ 2pm EST
A global lipid map defines a network essential for Zika virus replication
Pacific Northwest National Laboratory
Oregon Health and Science University
Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we perform comprehensive lipidomics using liquid chromatography tandem mass spectrometry to create a lipid network map during ZIKV infection. We find that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein results in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocks ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites, and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.