Gary Siuzdak is Sr. Director, Scripps Center for Metabolomics and Professor of Chemistry, Molecular and Computational Biology at Scripps Research.
Bruno Conti is Professor at San Diego BioMed and Professor Emeritus, Molecular Medicine/Neuroscience at Scripps Research.
Gary and Bruno have collaborated in applying metabolomics to study cell-cell communication.
you have provided many important contributions to metabolomics, including the development of XCMS and METLIN, research tools that have had thousands of users. Could you introduce metabolomics techniques and discuss prospects for the study of metabolites in cell-cell signaling?
The more mundane (albeit true) description of metabolomics is that it is the systematic study of the metabolites present in a biological system, such as a cell, tissue, or organism. Metabolites are small molecules produced by cellular metabolism that serve various functions, including energy generation, structural building blocks, and signaling molecules. The study of metabolomics has been facilitated by the development of analytical techniques that can identify and quantify metabolites in biological samples.
My preferred description (1) is that the metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other ‘omics’ levels, including the genome, epigenome, transcriptome, and proteome. In fact, the most interesting recent progress in using metabolomics is how to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites — which we term activity metabolomics (Figure 1) — is already having a broad impact on biology.
Figure 1. Activity metabolomics within the context of the central dogma of biology.
There are several analytical techniques used in metabolomics, including nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and chromatography. NMR spectroscopy is used to identify and quantify metabolites based on their characteristic chemical shifts, while MS and chromatography are used to separate and identify metabolites based on their molecular mass and polarity, respectively. NMR is a powerful tool that has utility for specific metabolites that are not amenable to MS, however the broad dynamic range and sensitivity of LC/MS has made it the method of choice in metabolomics.
One exciting area of research in metabolomics is the study of cell-cell signaling to identify active metabolites (2) (Figure 1) that drive these signals. Metabolomics future will be heavily involved in the discovery of new signaling molecules and pathways.
Another future prospect for metabolomics is the development of new analytical techniques that can provide more detailed information about metabolites, such as their subcellular localization and dynamic changes in their abundance. These advances will allow researchers to gain a better understanding of the complex signaling networks that exist within and between cells, and how they contribute to (and modulate) cellular function and disease.
Could you comment on how the approaches might be scaled up, and if it might be possible to get close to a complete picture of the role of metabolites in cell-cell communication?
The question of scaling up is a major current issue in metabolomics. One of the biggest obstacles is our inability to characterize all the endogenous and exogenous molecules we are observing. We are addressing this by scaling up METLIN tandem mass spectrometry database (3). Currently hosting data on over 900,000 molecules in 350 different chemical classes (Figure 2). Data that has been generated in both positive and negative ionization modes and at four different collision energies.
Figure 2. The METLIN tandem mass spectrometry database growth history over the last two decades.
METLIN is 30 times larger than the next biggest one, and orders of magnitude larger than the rest. Our goal is to map more comprehensively the active signaling metabolites (Figure 1), and our efforts have been focused on expanding METLIN to enable this.
Your main research interests are in aging, neuroscience and the neuroimmunology of cytokines. You have also investigated the role of metabolites as signaling molecules. Could you provide a biological perspective, including a discussion how the biological functions of the signaling metabolites can be validated, following up on metabolomics finding?
My laboratory investigates the biology of aging using the mouse as a preferred experimental model. Specifically, we investigate how diet and changes in temperature affect metabolism, healthy aging and lifespan. The analytical power of metabolomics is a formidable tool towards the identification of analytes that can serve as either markers or lead to the identification of biological processes that regulate lifespan that could represent targets for its regulation. One of the great advantages of using metabolomics in such an endeavor is its suitability for longitudinal studies. By providing a comprehensive qualitative and quantitative profile of changes on a relatively small number of samples, metabolomic can provide individual dynamic map of changes over time in tissues that can be collected periodically and non-invasively. This is fundamental when investigating the biology of aging since individuals age differently and metabolomic profiles can help distinguish chronological from biological age.
However, no matter how exciting, for biologists this the beginning of a journey for the validation of the selected changes that require time and resources not always available and are not necessarily successful. It is of paramount importance to minimize the possibility of following “false” leads. To achieve this several steps can be taken starting with the experimental design under investigation that should be established, controlled, and carried out carefully avoiding the introduction of unnecessary confounding variables (i.e. time of day tissue is collected, tissue processing and storage). It is also important to work with a sufficiently large number of samples to ensure statistical significance of the analysis of metabolomic profiles that should be carried out integrating educated biased and unbiased approaches including AI. Even so, a fundamental component for the success of the entire approach is the communication and the collaborative work of the biologists designing and executing the experiments and the chemists performing the metabolomics and identifying the metabolites. In my opinion, this should initiate with the experimental design, typically the domain of the biologists alone.
Once metabolites or pathways have been identified and considered interesting, they must be validated. Priority must be established, and this can be done considering the robustness of the findings, the availability of existing genetic and/or pharmacological tools or of specific experimental models (i.e. other organisms, cell cultures). With Gary Siuzdak and his group, we used this approach successfully to determine the relative contribution that diet and core body temperature have on the metabolomic changes occurring during calorie restriction, a dietary regimen promoting health span and life span (4). The approach also led to the identification of a pathway that regulates energy homeostasis relevant and can be an attractive target for body weight management (4,5).
1) Rinschen, M.M., Ivanisevic, J., Giera, M., Siuzdak, G., Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol 20, 353–367 (2019).
2) Giera, M., Yanes, O., Siuzdak, G., Metabolite discovery: Biochemistry’s scientific driver. Cell Metabolism 34, 21-34 (2022)
3) Xue, J., Guijas, C., Benton, H.P. , Warth, B. & Siuzdak G. METLIN MS2 molecular standards database: a broad chemical and biological resource. Nat Methods 17, 953–954 (2020).
4) Guijas C, Montenegro-Burke JR, Cintron-Colon R, Domingo-Almenara X, Sanchez-Alavez M, Aguirre CA, Shankar K, Majumder EL, Billings E, Conti B & Siuzdak, G. Metabolic adaptation to calorie restriction. Science Signaling, 648:eabb2490. (2020).
5) Cintron-Colon, R., Johnson, C. W., Montenegro-Burke, J. R., Guijas, C., Faulhaber, L., Sanchez-Alavez, M., Aguirre, C. A., Shankar, K., Singh, M., Galmozzi, A., Siuzdak, G., Saez, E. & Conti, B. Activation of Kappa Opioid Receptor Regulates the Hypothermic Response to Calorie Restriction and Limits Body Weight Loss. Curr Biol 29, 4291-4299 (2019).