ReviewAssessment of gut microbiota fecal metabolites by chromatographic targeted approaches
Graphical abstract
Introduction
The gut microbiota (GM), i.e., the dense, taxonomically diverse and individually specific microbial community (mainly including bacteria but also fungi, viruses and protists) populating the gastrointestinal tract, provides the host with several functions relevant to our physiology, especially to metabolic and immunological homeostasis. Almost all these contributions are made through the production of microbial metabolites and GM-derived bioactive molecules (i.e., the GM metabolome), acting locally in the gut or systemically in other organs [1]. Several factors, both endogenous and external (such as age, diet, lifestyle, etc.) are recognized to influence the GM structure, with oscillations in the GM metabolome. In particular, diet is held to be a pivotal determinant of the GM composition and function, capable of sustaining homeostasis or contributing to disease susceptibility through the modulation of the GM metabolite landscape and the downstream effects on the host physiology [2]. To date, severe imbalances in the GM complexity and stability, i.e., dysbioses, have been linked with a plethora of disorders, as gastroenterological (e.g., inflammatory bowel disease and colorectal cancer), metabolic (e.g., obesity, insulin resistance and type 2 diabetes), immunological, hepatic, respiratory, cardiovascular, neurological and even oncologic diseases [3]. The GM dysbiotic states are typically featured by loss of diversity, bloom of pathobionts, loss of beneficial microbes and shifts in GM metabolic activity with loss of “keystone” metabolites [4]. Thus, a deep understanding of the metabolically interactive host-GM axis is instrumental to the development and fine-tuning of personalized precision intervention strategies aimed at the prevention or treatment of disease states, to maintain long-term health [1,5,6]
Insights into the intricate GM-host relationships in health and disease require not only an understanding of the structure and function of the GM, made possible by next-generation sequencing-based approaches, but also the measurement of effector molecules acting along the GM-host axis [7,8]. The growing interest toward the study of the GM metabolome is evidenced by many recent review articles dealing with metabolomics methodologies, often non-targeted thus intended as the comprehensive analysis of all the molecules in a sample including chemical unknowns. Non-targeted metabolomics is performed to have an overall view of the products by different metabolic pathways and their variations due to pathological states or to particular conditions and lifestyles [[5], [6], [7], [8], [9], [10], [11], [12], [13]]. Once chemical entities are identified as markers of a specific state, these can be studied at a quantitative level with specific approaches, which refers to analysis of defined classes of compounds. All of the most common biological samples can be considered as a useful source for GM metabolomics, such as urine, plasma/serum, saliva, exhaled breaths, cerebrospinal fluid and tissues from target organs. However, feces has attracted a major interest as a non-invasive sample that more readily responds to the need for directly probing the complexity of GM [9,14,15].
Advanced chromatographic techniques hyphenated to mass spectrometry play a pivotal role in non-targeted metabolomics; in Table SM1 a summary of the recent studies based on GC, LC and CE combined to TOFMS in non-targeted metabolomics on fecal samples, has been given.
In the present review article, it is proposed an overview on the applications of chromatographic techniques focused on the targeted determination of specific GM metabolites in feces. The considered classes of compounds were selected according to the examination of recent literature and mainly following a previous article by us, where we discussed on the biological role of the main GM-delivered molecules, from fermentation end-products to other metabolites, including those deriving from GM-host co-metabolism, in human health and disease [1]. Furthermore, we briefly describe the main computational approaches that are currently used for the integrative analysis of GM and metabolome data. Coupling metabolomics with microbiomics has the great potential to advance the knowledge on the functions of GM, its interactions with the host and contribution to human (patho)physiology [1,8]. Not least, unraveling the vast repertoire of GM-derived molecules has an extraordinary, still untapped medicinal chemistry potential, paving the way for innovative GM-based strategies aimed at blocking/enhancing signaling pathways relevant to human health. A schematic workflow of chromatographic targeted determination of GM metabolites in fecal samples is given in Fig. 1.
Section snippets
Fecal sample handling
Plasma, serum, urine and bile are the typical biofluids of interest in life sciences. On the other hand, due to the raising popularity of the investigations on the complex interactions between the GM and the host metabolism, the fecal sample has gained a major role being the most accessible biological matrix which can directly probe the connection between intestinal bacteria and the physiology of the holobiont. Comprehensive reviews on human fecal metabolomics have been published in recent
Lipids
The lipid chemical complexity has given rise to the need for official classification by the LIPID MAPS consortium, thus in the LIPID MAPS Structure Database (LMSD), eight lipid categories are reported: fatty acyls, glycerolipids, glycerophospholipids, polyketides, prenol lipids, saccharolipids, sphingolipids and sterol lipids [[81], [82], [83]]. Each category has numerous classes and subclasses, among which tens of thousands of compounds have been identified as biologically significant species.
Bioinformatic and statistical tools for integrating the host metabolome and intestinal microbiome
Integration of metabolite data with the GM structure has the potential to provide valuable functional insights into the GM influence in health and disease, by uncovering the GM-contributed molecules that are more likely to mediate GM-host interactions [8]. A number of bioinformatics and statistical tools are available to integrate metabolomics data in the context of microbiome studies [202,203]. Multi-omics datasets consist of two or more matrices that share the same sample IDs but contain
Conclusion
In the present review addressed to the targeted approaches in assessment of gut microbiota metabolites, the role of enhanced chromatographic techniques has powerfully emerged. However, it should be stressed that a generic sample preparation as well as the uncritical application of analytical strategies even if advanced (both in the approach and instrumentation) could lead to artifacts production, biased results as well as data misinterpretations. The importance of absolute quantitative
Acknowlegment
This work was supported by 2017 (RFO) Ricerca Fondamentale Orientata – University of Bologna, Italy.
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