Spatial metabolomics in tissues and single cells.

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Metabolite imaging mass spectrometry promises to localize small molecules, metabolites, and lipids in tissues, microbial and cell cultures, and to interpret them in the context of cellular heterogeneity. Just until recently the molecular interpretation of the big data generated by this technique was hampered by the lack of bioinformatics for metabolite identification. We developed a bioinformatics approach that allowed us to reveal images of localization of hundreds of metabolites in a variety of biological systems. First, I will present how our community big data analytics of data from hundreds of datasets paves an avenue to creating comprehensive metabolite atlases on the levels of tissues and organs. Second, I’ll present our recently developed approach to spatial single-cell metabolomics of adherent cell cultures that enabled us to associate fluorescent phenotype with intracellular metabolites and to discover metabolically-determined cell subpopulations.

NMR metabolomics in action: from real-time analyses to longitudinal snapshots in biological systems.

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Nuclear Magnetic Resonance (NMR) is a versatile technique that can contribute to biological investigations across time scales as a powerful metabolic phenotyping platform.

We first illustrate how real-time NMR investigation of metabolites kinetics can deliver a detailed picture of the energetic metabolism for hybrid cell-free protein synthesis (CFPS) systems composed of rabbit reticulocyte lysates (RRL) ribosome-free supernatant complemented with ribosomes from different mammalian cell-types. A counterintuitive strategy, based on reducing the ribosomal fraction in RRL, is rationalized using a real-time NMR metabolomics investigation. We show that persistent ribosome-associated metabolic activity consuming ATP is a major obstacle for maximal protein yield, and reveal the potential of real-time NMR for optimization of CFPS systems.(1)

Meanwhile, NMR metabolomics contributes, among a range of phenotyping studies, to de novo characterization of model biological systems. We report a 1H NMR metabolomic study that evaluates the impact of different mutations in the thyroid hormone receptor alpha (TRα1), encoded by the THRA gene, which are involved in Resistance to Thyroid Hormone (RTHα), a recently discovered genetic disease. Germline mutations in the mouse Thra gene were introduced using CRISPR/Cas9 genome editing, and evaluated from urine and plasma metabolomics in 3 and 6 months-old adult mice. We provide a proof-of-principle that NMR metabolomics could be used to diagnose RTHα.

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References

[1] Panthu B., Ohlmann T., Perrier J., Schlattner U., Jalinot P., Elena-Herrmann B., and Rautureau G. J. P., ACS Synth. Biol. Article ASAP, doi: 10.1021/acssynbio.7b00280 (2017).

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Cell metabolomics: stories and challenges.

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Cell metabolomic strategies have been increasingly exploited in studies spanning from drug testing and development, to assessment of materials performance and biotoxicity. In this presentation, examples are given of the use of Nuclear Magnetic Resonance (NMR)-based metabolomics to analyse cells and/or cell extracts in the contexts of 1) anti-cancer drug development and 2) nanoparticle (NP) function and biotoxicity assessment. In vitro NMR studies of newly synthesized metal-based drugs (with Pt or Pd centers) are shown to provide insight into the response of cancer and healthy cells to treatment, both in single-drug and combination protocols, unveiling interesting drug synergetic effects. In the context of materials, silver NPs action and biotoxicity are addressed for several cell types. Here, in vitro metabolomics helps understand the roles of particle size/composition in triggering cell response and biotoxicity. The advantages and shortcomings of in vitro metabolomic strategies are discussed and an outlook on the translation to in vivo systems is presented.

References

[1] Lamego, I., Marques, M.P.M., Duarte, I.F., Martins, A.S., Oliveira, H., Gil, A.M., Journal of Proteome Research 16, 1773 (2017).

[2] Carrola, J., Bastos, V., Jarak, I., Oliveira-Silva, R., Malheiro, E., Daniel da Silva, A.L., Oliveira, H., Santos, C., Gil, A.M., Duarte, I.F., Nanotoxicology 10, 1105 (2016).

What can metabolic profiling and the exposome tell us about chemical risks?

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The complexity and metabolic regulation of the human ecosystem is partially controlled by environmental factors such as diet, lifestyle, toxic exposures and the gut microbiome, which interacts with the mammalian system at the level of genes, proteins and metabolism. Metabolic profiling of biofluids such as urine, plasma or fecal water encompassing high-resolution spectroscopic methods (NMR spectroscopy, LC-MS, GC-MS etc) in combination with multivariate statistical modeling tools, can provide a window for investigating the impact of toxins on human health since these metabolic profiles carry information relating both to genetic and environmental influences, including contributions from the microbiome, diet and xenobiotics [1]. Examples of urinary or faecal metabolites that are products of the metabolism of toxins or toxic/detoxification products of microbiota, or microbiota-host interactions include phenols, indoles, bile acids, short chain fatty acids and choline derivatives, all of which can be quantitatively profiled using spectroscopic technology.

The microbiome is highly metabolically active and has been shown to be capable of modulating toxins to either enhance or ameliorate the host response to toxicity. A range of examples taken from pre-clinical and clinical studies will be explored and the use of various models of microbial modulation discussed in the context of understanding drug metabolism and toxicity. Additionally the wider role of metabolic profiling in the context of biomonitoring applications is discussed.

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References

[1] Nicholson JK, et al Xenobiotica. 1999; 29(11):1181-9. PubMed PMID: 10598751.

Perspectives for Metabolomics in Human Nutrition

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Nutrition is key in determining health status and predisposition to develop disease throughout lifespan. By looking at the concentration dynamics of metabolites, e.g. the endpoints of physiological regulatory processes, Metabolomics is well suited to investigate the molecular interplay between genetic and environmental factors including nutrition. As such, Metabolomics owns great premises to help the definition of individual-specific nutritional requirements and thus to enable personalized nutrition for health maintenance, disease prevention and therapeutic management in future.

Nowadays, recommendations for daily intakes of nutrients and micronutrients are mainly based on data generated from epidemiological research and sometimes depletion/repletion studies. These reference systems, not always harmonized between countries, are meant for population nutritional management. Although the current recommendation systems includes some degree of stratification of the needs according to age, gender and physiological status, moving towards personalized nutrition will require additional granularity on the individual-specific nutritional requirements and their variation over time. Achieving a comprehensive nutritional status analysis through the quantitation of circulating micronutrient concentrations and functional markers can help determining someone’s nutritional needs. However, there are still knowledge gaps and lack of scientific consensus on the best biomarkers to be used for several micronutrients. Furthermore, available methodologies for nutritional status are often specific to single biomarker and the actual ability to cover a comprehensive set of nutrient and micronutrient biomarkers is hampered by the lack of fast, robust and cost effective profiling methodologies. So far, Metabolomics, either through targeted or untargeted approaches, has mainly been deployed to assess metabolic modifications of dietary patterns or nutrient intakes, without paying much attention to developing novel biomarkers of nutrient/micronutrient status. Yet several works highlight the potential of metabolic profiling approaches to deliver such biomarkers. Recently new analytical methodologies were developed and validated for the quantitative profiling of elements (including trace elements) and liposoluble vitamins in human biofluids by tandem mass spectrometry. Such methodologies and their extension to other classes of biologically relevant nutrients and micronutrients are expected to provide a holistic analysis of nutritional status at the molecular levels. A key advantage of such profiling approaches, beyond analytical performance, relies in the ability to easily compute several combinations of nutrients and micronutrients to be tested as potential novel nutrition-related biomarkers.

This lecture will briefly review deployment of Metabolomics in the field of nutrition and will report recent analytical chromatography and mass spectrometry developments for the quantitative profiling of multiple nutrients, micronutrients and their metabolites in biological fluids. It will report on the development of a novel nutritional phenotyping analytical platform that is currently used to assess nutritional requirements in both patient groups and general population. Future technological perspectives will also be discussed as regards with nutritional status biomarker discovery, instrument sensitivity, and miniaturization of sample collection and analytics.

Searching for metabolic changes in human nutritional intervention studies.

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Metabolomics data from human nutritional intervention studies are often characterised by large variations between the subjects. This is different from most animal studies where metabolic variation between the test animals is usually less abundant. The large variation between human subjects can give rise to two problems in the analysis. The first is that small and subtle treatment effects (e.g. dietary responses) can easily be overlooked, especially when the effect is smaller than the intrinsic variation between the subjects. The second problem is that the response and the impact of the treatment effect may differ between the subjects. This implies that an average treatment effect may not be the most relevant in studies where subsets of subjects respond differently upon a dietary intervention. To deal with the large variation between individuals, specific experimental designs are used in which the same individuals are subjected to multiple or to all treatments. Therefore, each subject in the study population can act as his or her own control and the data is often called paired. Multivariate extensions of the paired t-test can be used to analyse such data. In this presentation examples the metabolic effects as a result of drinking tea, coffee and also of drinking a large amount of alcohol are presented. It is shown that exploiting the paired data structure underlying the studies improves the power and the interpretability of the estimated metabolic effect.