Mass spectrometry-based metabolomics towards understanding of gene functions with a diversity of biological contexts

Microbial Metabolomics: From Methods to Translational Applications

Most microbe-associated infectious diseases severely affect human health. However, clinical diagnosis of pathogenic diseases remains challenging due to the lack of specific and highly reliable methods. To better understand the diagnosis, pathogenesis, and treatment of these diseases, systems biology-driven metabolomics goes beyond the annotated phenotype and better targets the functions than conventional approaches. As a novel strategy for analysis of metabolomes in microbes, microbial metabolomics has been recently used to study many diseases, such as obesity, urinary tract infection (UTI), and hepatitis C. In this chapter, we attempt to introduce various microbial metabolomics methods to better interpret the microbial metabolism underlying a diversity of infectious diseases and inspire scientists to pay more attention to microbial metabolomics, enabling broadly and efficiently its translational applications to infectious diseases, from molecular diagnosis to therapeutic discovery.

Functional metabolomics innovates therapeutic discovery of traditional Chinese medicine derived functional compounds

Traditional Chinese medicines (TCMs) produce chemically diverse functional compounds that are importantly chemical resource for facilitating new drug discovery and development against a diversity of diseases. However, modern exploration of TCM derived functional compounds is significantly hindered by the inefficient elucidation of pharmacological functions over past decades, because conventional research methods are incapable of efficiently elucidating therapeutic potential of TCM conferred by multiple functional compounds. Functional metabolomics has the priority-capacity to characterize systems therapeutic actions of TCM by precisely capturing molecular interactions between disease response metabolite biomarkers (DRMB) and functional compounds (secondary metabolites), which underline pharmacological efficiency and associated therapeutic mechanisms. In this critical review, we innovatively summarize systems therapeutic feature of TCM derived functional compounds from a functional-metabolism perspective, then systems metabolic targets (SMT) identified by functional metabolomics method are strategically proposed to better understanding of therapeutic discovery of TCM derived functional compounds. In addition, we propose the perspective strategy as Spatial Temporal Operative Real Metabolomics (STORM) to considerably improve analytical capacity of functional metabolomics method by selectively incorporating the cutting edge technologies of mass spectrometry imaging, isotope-metabolic fluxomics, synthetic and biosynthetic chemistry, which could considerably enhance the precision and resolution of elucidating pharmacological efficiency and associated therapeutic mechanisms of TCM derived functional compounds. Collectively, such critical review is expected to provide novel perspective-strategy that could significantly improve modern exploration and exploitation of TCM derived functional compounds that further promote new drug discovery and development against the complex diseases.

FUNCTIONAL METABOLOMICS DECIPHER BIOCHEMICAL FUNCTIONS AND ASSOCIATED MECHANISMS UNDERLIE SMALL-MOLECULE METABOLISM

Metabolism is the collection of biochemical reactions enabled by chemically diverse metabolites, which facilitate different physiological processes to exchange substances and synthesize energy in diverse living organisms. Metabolomics has emerged as a cutting-edge method to qualify and quantify the metabolites in different biological matrixes, and it has the extraordinary capacity to interrogate the biological significance that underlies metabolic modification and modulation. Liquid chromatography combined with mass spectrometry (LC/MS), as a robust platform for metabolomics analysis, has increased in popularity over the past 10 years due to its excellent sensitivity, throughput, and versatility. However, metabolomics investigation currently provides us with only phenotype data without revealing the biochemical functions and associated mechanisms. This limitation indeed weakens the core value of metabolomics data in a broad spectrum of the life sciences. In recent years, the scientific community has actively explored the functional features of metabolomics and translated this cutting-edge approach to be used to solve key multifaceted questions, such as disease pathogenesis, the therapeutic discovery of drugs, nutritional issues, agricultural problems, environmental toxicology, and microbial evolution. Here, we are the first to briefly review the history and applicable progression of LC/MS-based metabolomics, with an emphasis on the applications of metabolic phenotyping. Furthermore, we specifically highlight the next era of LC/MS-based metabolomics to target functional metabolomes, through which we can answer phenotype-related questions to elucidate biochemical functions and associated mechanisms implicated in dysregulated metabolism. Finally, we propose many strategies to enhance the research capacity of functional metabolomics by enabling the combination of contemporary omics technologies and cutting-edge biochemical techniques. The main purpose of this review is to improve the understanding of LC/MS-based metabolomics, extending beyond the conventional metabolic phenotype toward biochemical functions and associated mechanisms, to enhance research capability and to enlarge the applicable scope of functional metabolomics in small-molecule metabolism in different living organisms.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research

Enzymes play indispensable roles in producing biofuels, a sustainable and renewable source of transportation fuels. Lacking rational design rules, the development of industrially relevant enzyme catalysts relies heavily on high-throughput screening. However, few universal methods exist to rapidly characterize large-scale enzyme libraries. Therefore, assay development is necessary on an ad hoc basis to link enzyme properties to spectrophotometric signals and often requires the use of surrogate, optically active substrates. On the other hand, mass spectrometry (MS) performs label-free enzyme assays that utilize native substrates and is therefore generally applicable. But the analytical speed of MS is considered rate limiting, mainly due to the use of time-consuming chromatographic separation in traditional MS analysis. Thanks to new instrumentation and sample preparation methods, direct analyte introduction into a mass spectrometer without a prior chromatographic step can be achieved by laser, microfluidics, and acoustics, so that each sample can be analyzed within seconds. Here we review recent advances in MS platforms that improve the throughput of enzyme library screening and discuss how these advances can potentially facilitate biofuel research by providing high sensitivity, selectivity and quantitation that are difficult to obtain using traditional assays. We also highlight the limitations of current MS assays in studying biofuel-related enzymes and propose possible solutions.

Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Siderophore biosynthesis coordinately modulated the virulence-associated interactive metabolome of uropathogenic Escherichia coli and human urine

Uropathogenic Escherichia coli (UPEC) growth in women’s bladders during urinary tract infection (UTI) incurs substantial chemical exchange, termed the “interactive metabolome”, which primarily accounts for the metabolic costs (utilized metabolome) and metabolic donations (excreted metabolome) between UPEC and human urine. Here, we attempted to identify the individualized interactive metabolome between UPEC and human urine. We were able to distinguish UPEC from non-UPEC by employing a combination of metabolomics and genetics. Our results revealed that the interactive metabolome between UPEC and human urine was markedly different from that between non-UPEC and human urine, and that UPEC triggered much stronger perturbations in the interactive metabolome in human urine. Furthermore, siderophore biosynthesis coordinately modulated the individualized interactive metabolome, which we found to be a critical component of UPEC virulence. The individualized virulence-associated interactive metabolome contained 31 different metabolites and 17 central metabolic pathways that were annotated to host these different metabolites, including energetic metabolism, amino acid metabolism, and gut microbe metabolism. Changes in the activities of these pathways mechanistically pinpointed the virulent capability of siderophore biosynthesis. Together, our findings provide novel insights into UPEC virulence, and we propose that siderophores are potential targets for further discovery of drugs to treat UPEC-induced UTI.

A comparative LC-MS based profiling approach to analyze lipid composition in tissue culture systems

Although lipids participate in many cellular processes both as signaling and structural molecules, our understanding of the roles of individual lipids as well as global changes in lipid composition are limited. Here we describe an LC-MS based method to identify lipids that change in a biological process. This method describes the isolation of lipids from tissue culture cells, sample preparation for LC-MS, the LC-MS run, and the subsequent data processing steps to compare the global lipid profiles and identify species that are enhanced or depleted. Identifying lipids that change is the first step towards functional studies to unravel their roles.

Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system

Metabolomics is the qualitative and quantitative analysis of all of the small molecules in a biological sample at a specific time and influence. Technologies for metabolomics analysis have developed rapidly as new analytical tools for chemical separations, mass spectrometry, and NMR spectroscopy have emerged. Plants have one of the largest metabolomes, and it is estimated that the average plant leaf can contain upward of 30 000 phytochemicals. In the past decade, over 1200 papers on plant metabolomics have been published. A standard metabolomics data set contains vast amounts of information and can either investigate or generate hypotheses. The key factors in using plant metabolomics data most effectively are the experimental design, authentic standard availability, extract standardization, and statistical analysis. Using cranberry (Vaccinium macrocarpon) as a model system, this review will discuss and demonstrate strategies and tools for analysis and interpretation of metabolomics data sets including eliminating false discoveries and determining significance, metabolite clustering, and logical algorithms for discovery of new metabolites and pathways. Together these metabolomics tools represent an entirely new pipeline for phytochemical discovery.

Structure-function analysis of NEET proteins uncovers their role as key regulators of iron and ROS homeostasis in health and disease

A novel family of 2Fe-2S proteins, the NEET family, was discovered during the last decade in numerous organisms, including archea, bacteria, algae, plant and human; suggesting an evolutionary-conserved function, potentially mediated by their CDGSH Iron-Sulfur Domain. In human, three NEET members encoded by the CISD1-3 genes were identified. The structures of CISD1 (mitoNEET, mNT), CISD2 (NAF-1), and the plant At-NEET uncovered a homodimer with a unique "NEET fold", as well as two distinct domains: a beta-cap and a 2Fe-2S cluster-binding domain. The 2Fe-2S clusters of NEET proteins were found to be coordinated by a novel 3Cys:1His structure that is relatively labile compared to other 2Fe-2S proteins and is the reason of the NEETs' clusters could be transferred to apo-acceptor protein(s) or mitochondria. Positioned at the protein surface, the NEET's 2Fe-2S's coordinating His is exposed to protonation upon changes in its environment, potentially suggesting a sensing function for this residue. Studies in different model systems demonstrated a role for NAF-1 and mNT in the regulation of cellular iron, calcium and ROS homeostasis, and uncovered a key role for NEET proteins in critical processes, such as cancer cell proliferation and tumor growth, lipid and glucose homeostasis in obesity and diabetes, control of autophagy, longevity in mice, and senescence in plants. Abnormal regulation of NEET proteins was consequently found to result in multiple health conditions, and aberrant splicing of NAF-1 was found to be a causative of the neurological genetic disorder Wolfram Syndrome 2. Here we review the discovery of NEET proteins, their structural, biochemical and biophysical characterization, and their most recent structure-function analyses. We additionally highlight future avenues of research focused on NEET proteins and propose an essential role for NEETs in health and disease. This article is part of a Special Issue entitled: Fe/S proteins: Analysis, structure, function, biogenesis and diseases.

A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches

Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).

Metabolic differences in ripening of Solanum lycopersicum 'Ailsa Craig' and three monogenic mutants

Application of mass spectrometry enables the detection of metabolic differences between groups of related organisms. Differences in the metabolic fingerprints of wild-type Solanum lycopersicum and three monogenic mutants, ripening inhibitor (rin), non-ripening (nor) and Colourless non-ripening (Cnr), of tomato are captured with regard to ripening behaviour. A high-resolution tandem mass spectrometry system coupled to liquid chromatography produced a time series of the ripening behaviour at discrete intervals with a focus on changes post-anthesis. Internal standards and quality controls were used to ensure system stability. The raw data of the samples and reference compounds including study protocols have been deposited in the open metabolomics database MetaboLights via the metadata annotation tool Isatab to enable efficient re-use of the datasets, such as in metabolomics cross-study comparisons or data fusion exercises.

MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts

In vivo small molecules as necessary intermediates are involved in numerous critical metabolic pathways and biological processes associated with many essential biological functions and events. There is growing evidence that MS-based metabolomics is emerging as a powerful tool to facilitate the discovery of functional small molecules that can better our understanding of development, infection, nutrition, disease, toxicity, drug therapeutics, gene modifications and host-pathogen interaction from metabolic perspectives. However, further progress must still be made in MS-based metabolomics because of the shortcomings in the current technologies and knowledge. This technique-driven review aims to explore the discovery of in vivo functional small molecules facilitated by MS-based metabolomics and to highlight the analytic capabilities and promising applications of this discovery strategy. Moreover, the biological significance of the discovery of in vivo functional small molecules with different biological contexts is also interrogated at a metabolic perspective.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Cancer metabolism and mass spectrometry-based proteomics

Cancer metabolism has been extensively investigated by various tools, and the fact of diverse metabolic reprogramming in cancer cells has been gradually unveiled. In this review, we discuss some contributions in cancer metabolism by general proteomic analysis and post-translational modification analysis using mass spectrometry (MS) technique. Instead of following one or several metabolic enzymes/pathways, the current MS approach can quickly identify a large number of proteins and compare their expression levels in different samples, providing a potentially comprehensive picture of cancer metabolism. The MS analyses from pancreatic cancer cells support a hypothesis that hypoxia promotes cells in solid tumor to reprogram metabolic pathways in order to minimize the oxygen consumption. The oxidative stress in pancreatic cancer cells is lower than that in normal duct cells, and the cancer cells adaptively express less antioxidant proteins, contrary to claims that oxidative stress is higher in cancer cells. Separately, the MS analyses confirm that pyruvate kinase isoform 2 (PKM2) can be detected in both cancer and normal cells, disagreeing with report that tumor cells express exclusively PKM2. In addition, MS analyses from pancreatic cancer cells demonstrate that lactate dehydrogenase-B is significantly upregulated in pancreatic cancer cells, whereas previous reports show that lactate dehydrogenase-A is overexpressed and is responsible for lactate production in cancer cells. Lastly, the result from MS analysis suggests that the glutaminolysis in pancreatic cancer cells is different from that observed in glioblastoma cells.

13C-isotope-based protocol for prenyl lipid metabolic analysis in zebrafish embryos

Metabolism has a decisive role in many fundamental biological processes, including organism development and tissue homeostasis. Here we describe a protocol for fast and reliable (13)C-isotope-based in vivo metabolic profiling. This protocol covers the loading of isotope precursor; extraction, preparation and quantification of the labeled lipid metabolites (e.g., the prenyl lipid CoQ10) by the means of HPLC-MS; and its analysis in zebrafish embryos. This protocol can be applied to different types of experimental settings, including tissue-specific metabolic analyses or dynamic metabolic changes that occur during vertebrate embryogenesis. The protocol takes 5-7 d to complete, requiring minimal equipment and analytical expertise, and it represents a unique alternative to the existing ex vivo (e.g., cell lines) isotope-based metabolic methods. This procedure represents a valuable approach for researchers interested in studying the effect of gene manipulation on lipid metabolism in zebrafish and in understanding the genetic conditions that result in metabolism dysfunction.