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  1. 01Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.Background Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition. Aim of review This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported. Key scientific concepts of review System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-la
  2. 02Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis.Background Higher circulating levels of the branched-chain amino acids (BCAAs; i.e., isoleucine, leucine, and valine) are strongly associated with higher type 2 diabetes risk, but it is not known whether this association is causal. We undertook large-scale human genetic analyses to address this question. Methods and findings Genome-wide studies of BCAA levels in 16,596 individuals revealed five genomic regions associated at genome-wide levels of significance (p < 5 × 10-8). The strongest signal was 21 kb upstream of the PPM1K gene (beta in standard deviations [SDs] of leucine per allele = 0.08, p = 3.9 × 10-25), encoding an activator of the mitochondrial branched-chain alpha-ketoacid dehydrogenase (BCKD) responsible for the rate-limiting step in BCAA catabolism. In another analysis, in up to 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of 1 SD in amino acid level was associated with an odds ratio for type 2 diabetes of 1.44 (95% CI 1.26-1.65,
  3. 03Fecal Microbiota, Fecal Metabolome, and Colorectal Cancer Interrelations.Background and aims Investigation of microbe-metabolite relationships in the gut is needed to understand and potentially reduce colorectal cancer (CRC) risk. Methods Microbiota and metabolomics profiling were performed on lyophilized feces from 42 CRC cases and 89 matched controls. Multivariable logistic regression was used to identify statistically independent associations with CRC. First principal coordinate-component pair (PCo1-PC1) and false discovery rate (0.05)-corrected P-values were calculated for 116,000 Pearson correlations between 530 metabolites and 220 microbes in a sex*case/control meta-analysis. Results Overall microbe-metabolite PCo1-PC1 was more strongly correlated in cases than in controls (Rho 0.606 vs 0.201, P = 0.01). CRC was independently associated with lower levels of Clostridia, Lachnospiraceae, p-aminobenzoate and conjugated linoleate, and with higher levels of Fusobacterium, Porphyromonas, p-hydroxy-benzaldehyde, and palmitoyl-sphingomyelin. Through postulate
  4. 04Livestock metabolomics and the livestock metabolome: A systematic review.Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular "omics" approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabol
  5. 05Metabolite Concentration Changes in Humans After a Bout of Exercise: a Systematic Review of Exercise Metabolomics Studies.Background Exercise changes the concentrations of many metabolites, which are small molecules (< 1.5 kDa) metabolized by the reactions of human metabolism. In recent years, especially mass spectrometry-based metabolomics methods have allowed researchers to measure up to hundreds of metabolites in a single sample in a non-biased fashion. To summarize human exercise metabolomics studies to date, we conducted a systematic review that reports the results of experiments that found metabolite concentrations changes after a bout of human endurance or resistance exercise. Methods We carried out a systematic review following PRISMA guidelines and searched for human metabolomics studies that report metabolite concentrations before and within 24 h after endurance or resistance exercise in blood, urine, or sweat. We then displayed metabolites that significantly changed their concentration in at least two experiments. Results Twenty-seven studies and 57 experiments matched our search criteria and w
  6. 06MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have
  7. 07Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies.Introduction Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO). Objectives We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress. Methods We reviewed the literature to identify studies inves
  8. 08HMDB 5.0: the Human Metabolome Database for 2022.The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicte
  9. 09A metabolome atlas of the aging mouse brain.The mammalian brain relies on neurochemistry to fulfill its functions. Yet, the complexity of the brain metabolome and its changes during diseases or aging remain poorly understood. Here, we generate a metabolome atlas of the aging wildtype mouse brain from 10 anatomical regions spanning from adolescence to old age. We combine data from three assays and structurally annotate 1,547 metabolites. Almost all metabolites significantly differ between brain regions or age groups, but not by sex. A shift in sphingolipid patterns during aging related to myelin remodeling is accompanied by large changes in other metabolic pathways. Functionally related brain regions (brain stem, cerebrum and cerebellum) are also metabolically similar. In cerebrum, metabolic correlations markedly weaken between adolescence and adulthood, whereas at old age, cross-region correlation patterns reflect decreased brain segregation. We show that metabolic changes can be mapped to existing gene and protein brain atlases
  10. 10Single-cell sequencing to multi-omics: technologies and applications.Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting t
  11. 11Nicotinamide Riboside Augments the Aged Human Skeletal Muscle NAD<sup>+</sup> Metabolome and Induces Transcriptomic and Anti-inflammatory Signatures.Nicotinamide adenine dinucleotide (NAD + ) is modulated by conditions of metabolic stress and has been reported to decline with aging in preclinical models, but human data are sparse. Nicotinamide riboside (NR) supplementation ameliorates metabolic dysfunction in rodents. We aimed to establish whether oral NR supplementation in aged participants can increase the skeletal muscle NAD + metabolome and if it can alter muscle mitochondrial bioenergetics. We supplemented 12 aged men with 1 g NR per day for 21 days in a placebo-controlled, randomized, double-blind, crossover trial. Targeted metabolomics showed that NR elevated the muscle NAD + metabolome, evident by increased nicotinic acid adenine dinucleotide and nicotinamide clearance products. Muscle RNA sequencing revealed NR-mediated downregulation of energy metabolism and mitochondria pathways, without altering mitochondrial bioenergetics. NR also depressed levels of circulating inflammatory cytokines. Our data establish that oral NR i
  12. 12Dietary supplementation with inulin-propionate ester or inulin improves insulin sensitivity in adults with overweight and obesity with distinct effects on the gut microbiota, plasma metabolome and systemic inflammatory responses: a randomised cross-over trial.Objective To investigate the underlying mechanisms behind changes in glucose homeostasis with delivery of propionate to the human colon by comprehensive and coordinated analysis of gut bacterial composition, plasma metabolome and immune responses. Design Twelve non-diabetic adults with overweight and obesity received 20 g/day of inulin-propionate ester (IPE), designed to selectively deliver propionate to the colon, a high-fermentable fibre control (inulin) and a low-fermentable fibre control (cellulose) in a randomised, double-blind, placebo-controlled, cross-over design. Outcome measurements of metabolic responses, inflammatory markers and gut bacterial composition were analysed at the end of each 42-day supplementation period. Results Both IPE and inulin supplementation improved insulin resistance compared with cellulose supplementation, measured by homeostatic model assessment 2 (mean±SEM 1.23±0.17 IPE vs 1.59±0.17 cellulose, p=0.001; 1.17±0.15 inulin vs 1.59±0.17 cellulose, p=0.009
  13. 13L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine.Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic a
  14. 14Multi-Omics Profiling for Health.The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseas
  15. 15A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data.Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, usi
  16. 16Guide to Metabolomics Analysis: A Bioinformatics Workflow.Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and
  17. 17Alzheimer's Disease: An Update and Insights Into Pathophysiology.Alzheimer's disease (AD) is an irreversible brain disorder associated with slow, progressive loss of brain functions mostly in older people. The disease processes start years before the symptoms are manifested at which point most therapies may not be as effective. In the hippocampus, the key proteins involved in the JAK2/STAT3 signaling pathway, such as p-JAK2-Tyr1007 and p-STAT3-Tyr705 were found to be elevated in various models of AD. In addition to neurons, glial cells such as astrocytes also play a crucial role in the progression of AD. Without having a significant effect on tau and amyloid pathologies, the JAK2/STAT3 pathway in reactive astrocytes exhibits a behavioral impact in the experimental models of AD. Cholinergic atrophy in AD has been traced to a trophic failure in the NGF metabolic pathway, which is essential for the survival and maintenance of basal forebrain cholinergic neurons (BFCN). In AD, there is an alteration in the conversion of the proNGF to mature NGF (mNGF),
  18. 18Plant Metabolomics: An Overview of the Role of Primary and Secondary Metabolites against Different Environmental Stress Factors.Several environmental stresses, including biotic and abiotic factors, adversely affect the growth and development of crops, thereby lowering their yield. However, abiotic factors, e.g., drought, salinity, cold, heat, ultraviolet radiations (UVr), reactive oxygen species (ROS), trace metals (TM), and soil pH, are extremely destructive and decrease crop yield worldwide. It is expected that more than 50% of crop production losses are due to abiotic stresses. Moreover, these factors are responsible for physiological and biochemical changes in plants. The response of different plant species to such stresses is a complex phenomenon with individual features for several species. In addition, it has been shown that abiotic factors stimulate multi-gene responses by making modifications in the accumulation of the primary and secondary metabolites. Metabolomics is a promising way to interpret biotic and abiotic stress tolerance in plants. The study of metabolic profiling revealed different types o
  19. 19<i>APOE</i> genotype influences the gut microbiome structure and function in humans and mice: relevance for Alzheimer's disease pathophysiology.Apolipoprotein E ( APOE ) genotype is the strongest prevalent genetic risk factor for Alzheimer's disease (AD). Numerous studies have provided insights into the pathologic mechanisms. However, a comprehensive understanding of the impact of APOE genotype on microflora speciation and metabolism is completely lacking. In this study, we investigated the association between APOE genotype and the gut microbiome composition in human and APOE -targeted replacement (TR) transgenic mice. Fecal microbiota amplicon sequencing from matched individuals with different APOE genotypes revealed no significant differences in overall microbiota diversity in group-aggregated human APOE genotypes. However, several bacterial taxa showed significantly different relative abundance between APOE genotypes. Notably, we detected an association of Prevotellaceae and Ruminococcaceae and several butyrate-producing genera abundances with APOE genotypes. These findings were confirmed by comparing the gut microbiota of
  20. 20Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies.The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to
  21. 21The contribution of gut bacterial metabolites in the human immune signaling pathway of non-communicable diseases.The interaction disorder between gut microbiota and its host has been documented in different non-communicable diseases (NCDs) such as metabolic syndrome, neurodegenerative disease, and autoimmune disease. The majority of these altered interactions arise through metabolic cross-talk between gut microbiota and host immune system, inducing a low-grade chronic inflammation that characterizes all NCDs. In this review, we discuss the contribution of bacterial metabolites to immune signaling pathways involved in NCDs. We then review recent advances that aid to rationally design microbial therapeutics. A deeper understanding of these intersections between host and gut microbiota metabolism using metabolomics-based system biology platform promises to reveal the fundamental mechanisms that drive metabolic predispositions to disease and suggest new avenues to use microbial therapeutic opportunities for NCDs treatment and prevention. Abbreviations : NCDs: non-communicable disease, IBD: inflammato
  22. 22jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population.In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic reson
  23. 23Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics.With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods,
  24. 24Revolutionizing Personalized Medicine: Synergy with Multi-Omics Data Generation, Main Hurdles, and Future Perspectives.The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in te
  25. 25MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for be
  26. 26Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance.Background Recently, we reported that some dairy cows could produce high amounts of milk with high amounts of protein (defined as milk protein yield [MPY]) when a population was raised under the same nutritional and management condition, a potential new trait that can be used to increase high-quality milk production. It is unknown to what extent the rumen microbiome and its metabolites, as well as the host metabolism, contribute to MPY. Here, analysis of rumen metagenomics and metabolomics, together with serum metabolomics was performed to identify potential regulatory mechanisms of MPY at both the rumen microbiome and host levels. Results Metagenomics analysis revealed that several Prevotella species were significantly more abundant in the rumen of high-MPY cows, contributing to improved functions related to branched-chain amino acid biosynthesis. In addition, the rumen microbiome of high-MPY cows had lower relative abundances of organisms with methanogen and methanogenesis functions,
  27. 27MetaboLights: open data repository for metabolomics.MetaboLights is a global database for metabolomics studies including the raw experimental data and the associated metadata. The database is cross-species and cross-technique and covers metabolite structures and their reference spectra as well as their biological roles and locations where available. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the continued growth and diversity of submissions and the significant developments in recent years. In particular, we highlight MetaboLights Labs, our new Galaxy Project instance with repository-scale standardized workflows, and how data public on MetaboLights are being reused by the community. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at https://github.com/EBI-Metabolights.
  28. 28Urine steroid metabolomics for the differential diagnosis of adrenal incidentalomas in the EURINE-ACT study: a prospective test validation study.Background Cross-sectional imaging regularly results in incidental discovery of adrenal tumours, requiring exclusion of adrenocortical carcinoma (ACC). However, differentiation is hampered by poor specificity of imaging characteristics. We aimed to validate a urine steroid metabolomics approach, using steroid profiling as the diagnostic basis for ACC. Methods We did a prospective multicentre study in adult participants (age ≥18 years) with newly diagnosed adrenal masses. We assessed the accuracy of diagnostic imaging strategies based on maximum tumour diameter (≥4 cm vs <4 cm), imaging characteristics (positive vs negative), and urine steroid metabolomics (low, medium, or high risk of ACC), separately and in combination, using a reference standard of histopathology and follow-up investigations. With respect to imaging characteristics, we also assessed the diagnostic utility of increasing the unenhanced CT tumour attenuation threshold from the recommended 10 Hounsfield units (HU) to 20
  29. 29Applications of Multi-Omics Technologies for Crop Improvement.Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat ( Triticum aestivum L.), soybean ( Glycine max ), tomato ( Solanum lycopersicum ), barley ( Hordeum vulgare L.), maize ( Zea mays L.), millet ( Setaria italica L.), cotton ( Gossypium hirsutum L.), Medicago truncatula , and rice ( Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and p
  30. 30WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research.WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. F
  31. 31MetaboLights: a resource evolving in response to the needs of its scientific community.MetaboLights is a database for metabolomics studies, their raw experimental data and associated metadata. The database is cross-species and cross-technique and it covers metabolite structures and their reference spectra as well as their biological roles and locations. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the significant updates that we have made over the last two years to the resource to respond to the increasing amount and diversity of data being submitted by the metabolomics community. We refreshed the website and most importantly, our submission process was completely overhauled to enable us to deliver a far more user-friendly submission process and to facilitate the growing demand for reproducibility and integration with other 'omics. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use via the web at https
  32. 32Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics.The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile r
  33. 33Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach.Background Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining. Objectives We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion. Methods Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list. Results A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant
  34. 34The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches.Background Metagenomic studies carried out in the past decade have led to an enhanced understanding of the gut microbiome in human health; however, the Indian gut microbiome has not been well explored. We analyzed the gut microbiome of 110 healthy individuals from two distinct locations (North-Central and Southern) in India using multi-omics approaches, including 16S rRNA gene amplicon sequencing, whole-genome shotgun metagenomic sequencing, and metabolomic profiling of fecal and serum samples. Results The gene catalogue established in this study emphasizes the uniqueness of the Indian gut microbiome in comparison to other populations. The gut microbiome of the cohort from North-Central India, which was primarily consuming a plant-based diet, was found to be associated with Prevotella and also showed an enrichment of branched chain amino acid (BCAA) and lipopolysaccharide biosynthesis pathways. In contrast, the gut microbiome of the cohort from Southern India, which was consuming an om
  35. 35Spatial multimodal analysis of transcriptomes and metabolomes in tissues.We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson's disease.
  36. 36Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer.Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration a
  37. 37Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses.The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcrip
  38. 38MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under
  39. 39NMR Spectroscopy for Metabolomics Research.Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metab
  40. 40metaX: a flexible and comprehensive software for processing metabolomics data.Background Non-targeted metabolomics based on mass spectrometry enables high-throughput profiling of the metabolites in a biological sample. The large amount of data generated from mass spectrometry requires intensive computational processing for annotation of mass spectra and identification of metabolites. Computational analysis tools that are fully integrated with multiple functions and are easily operated by users who lack extensive knowledge in programing are needed in this research field. Results We herein developed an R package, metaX, that is capable of end-to-end metabolomics data analysis through a set of interchangeable modules. Specifically, metaX provides several functions, such as peak picking and annotation, data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics, power analysis and sample size estimation, receiver operating characteristic analysis, biomarker selection, pathway annotation, correlation network analysis,
  41. 41Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies.Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profil
  42. 42Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chrom
  43. 43MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-
  44. 44MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data.Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through join
  45. 45Gut microbiota impact on the peripheral immune response in non-alcoholic fatty liver disease related hepatocellular carcinoma.The gut microbiota is reported to modulate the immune response in hepatocellular carcinoma (HCC). Here, we employ metagenomic and metabolomic studies to characterise gut microbiota in patients with non-alcoholic fatty liver disease (NAFLD) related cirrhosis, with or without HCC, and evaluate its effect on the peripheral immune response in an ex vivo model. We find that dysbiosis characterises the microbiota of patients with NAFLD-cirrhosis, with compositional and functional shifts occurring with HCC development. Gene function of the microbiota in NAFLD-HCC supports short chain fatty acid production, and this is confirmed by metabolomic studies. Ex vivo studies show that bacterial extracts from the NAFLD-HCC microbiota, but not from the control groups, elicit a T cell immunosuppressive phenotype, characterised by expansion of regulatory T cells and attenuation of CD8 + T cells. Our study suggest that the gut microbiota in NAFLD-HCC is characterised by a distinctive microbiome/metabolomi
  46. 46Profiling the human intestinal environment under physiological conditions.The spatiotemporal structure of the human microbiome 1,2 , proteome 3 and metabolome 4,5 reflects and determines regional intestinal physiology and may have implications for disease 6 . Yet, little is known about the distribution of microorganisms, their environment and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals 7 . To address these deficiencies, we developed an ingestible device that collects samples from multiple regions of the human intestinal tract during normal digestion. Collection of 240 intestinal samples from 15 healthy individuals using the device and subsequent multi-omics analyses identified significant differences between bacteria, phages, host proteins and metabolites in the intestines versus stool. Certain microbial taxa were differentially enriched and prophage induction was more prevalent in the intestines than in stool. The host proteome
  47. 47Modulation of the metabiome by rifaximin in patients with cirrhosis and minimal hepatic encephalopathy.Unlabelled Hepatic encephalopathy (HE) represents a dysfunctional gut-liver-brain axis in cirrhosis which can negatively impact outcomes. This altered gut-brain relationship has been treated using gut-selective antibiotics such as rifaximin, that improve cognitive function in HE, especially its subclinical form, minimal HE (MHE). However, the precise mechanism of the action of rifaximin in MHE is unclear. We hypothesized that modulation of gut microbiota and their end-products by rifaximin would affect the gut-brain axis and improve cognitive performance in cirrhosis. Aim To perform a systems biology analysis of the microbiome, metabolome and cognitive change after rifaximin in MHE. Methods Twenty cirrhotics with MHE underwent cognitive testing, endotoxin analysis, urine/serum metabolomics (GC and LC-MS) and fecal microbiome assessment (multi-tagged pyrosequencing) at baseline and 8 weeks post-rifaximin 550 mg BID. Changes in cognition, endotoxin, serum/urine metabolites (and microbiom
  48. 48Microbiome-metabolome reveals the contribution of gut-kidney axis on kidney disease.Dysbiosis represents changes in composition and structure of the gut microbiome community (microbiome), which may dictate the physiological phenotype (health or disease). Recent technological advances and efforts in metagenomic and metabolomic analyses have led to a dramatical growth in our understanding of microbiome, but still, the mechanisms underlying gut microbiome-host interactions in healthy or diseased state remain elusive and their elucidation is in infancy. Disruption of the normal gut microbiota may lead to intestinal dysbiosis, intestinal barrier dysfunction, and bacterial translocation. Excessive uremic toxins are produced as a result of gut microbiota alteration, including indoxyl sulphate, p-cresyl sulphate, and trimethylamine-N-oxide, all implicated in the variant processes of kidney diseases development. This review focuses on the pathogenic association between gut microbiota and kidney diseases (the gut-kidney axis), covering CKD, IgA nephropathy, nephrolithiasis, hyp
  49. 49The Need for Multi-Omics Biomarker Signatures in Precision Medicine.Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses-which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas-have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to
  50. 50Gut microbial carbohydrate metabolism contributes to insulin resistance.Insulin resistance is the primary pathophysiology underlying metabolic syndrome and type 2 diabetes 1,2 . Previous metagenomic studies have described the characteristics of gut microbiota and their roles in metabolizing major nutrients in insulin resistance 3-9 . In particular, carbohydrate metabolism of commensals has been proposed to contribute up to 10% of the host's overall energy extraction 10 , thereby playing a role in the pathogenesis of obesity and prediabetes 3,4,6 . Nevertheless, the underlying mechanism remains unclear. Here we investigate this relationship using a comprehensive multi-omics strategy in humans. We combine unbiased faecal metabolomics with metagenomics, host metabolomics and transcriptomics data to profile the involvement of the microbiome in insulin resistance. These data reveal that faecal carbohydrates, particularly host-accessible monosaccharides, are increased in individuals with insulin resistance and are associated with microbial carbohydrate metabolis
  51. 51Human steroid biosynthesis, metabolism and excretion are differentially reflected by serum and urine steroid metabolomes: A comprehensive review.Advances in technology have allowed for the sensitive, specific, and simultaneous quantitative profiling of steroid precursors, bioactive steroids and inactive metabolites, facilitating comprehensive characterization of the serum and urine steroid metabolomes. The quantification of steroid panels is therefore gaining favor over quantification of single marker metabolites in the clinical and research laboratories. However, although the biochemical pathways for the biosynthesis and metabolism of steroid hormones are now well defined, a gulf still exists between this knowledge and its application to the measured steroid profiles. In this review, we present an overview of steroid hormone biosynthesis and metabolism by the liver and peripheral tissues, specifically highlighting the pathways linking and differentiating the serum and urine steroid metabolomes. A brief overview of the methodology used in steroid profiling is also provided.
  52. 52Metabolomic profiles predict individual multidisease outcomes.Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or
  53. 53The Role of the Canine Gut Microbiome and Metabolome in Health and Gastrointestinal Disease.The gut microbiome contributes to host metabolism, protects against pathogens, educates the immune system, and, through these basic functions, affects directly or indirectly most physiologic functions of its host. Molecular techniques have allowed us to expand our knowledge by unveiling a wide range of unculturable bacteria that were previously unknown. Most bacterial sequences identified in the canine gastrointestinal (GI) tract fall into five phyla: Firmicutes, Fusobacteria, Bacteroidetes, Proteobacteria, and Actinobacteria. While there are variations in the microbiome composition along the GI tract, most clinical studies concentrate on fecal microbiota. Age, diet, and many other environmental factors may play a significant role in the maintenance of a healthy microbiome, however, the alterations they cause pale in comparison with the alterations found in diseased animals. GI dysfunctions are the most obvious association with gut dysbiosis. In dogs, intestinal inflammation, whether c
  54. 54The Origin and Evolution of Plant Flavonoid Metabolism.During their evolution, plants have acquired the ability to produce a huge variety of compounds. Unlike the specialized metabolites that accumulate in limited numbers of species, flavonoids are widely distributed in the plant kingdom. Therefore, a detailed analysis of flavonoid metabolism in genomics and metabolomics is an ideal way to investigate how plants have developed their unique metabolic pathways during the process of evolution. More comprehensive and precise metabolite profiling integrated with genomic information are helpful to emerge unexpected gene functions and/or pathways. The distribution of flavonoids and their biosynthetic genes in the plant kingdom suggests that flavonoid biosynthetic pathways evolved through a series of steps. The enzymes that form the flavonoid scaffold structures probably first appeared by recruitment of enzymes from primary metabolic pathways, and later, enzymes that belong to superfamilies such as 2-oxoglutarate-dependent dioxygenase, cytochrome
  55. 55Microglia: Agents of the CNS Pro-Inflammatory Response.The pro-inflammatory immune response driven by microglia is a key contributor to the pathogenesis of several neurodegenerative diseases. Though the research of microglia spans over a century, the last two decades have increased our understanding exponentially. Here, we discuss the phenotypic transformation from homeostatic microglia towards reactive microglia, initiated by specific ligand binding to pattern recognition receptors including toll-like receptor-4 (TLR4) or triggering receptors expressed on myeloid cells-2 (TREM2), as well as pro-inflammatory signaling pathways triggered such as the caspase-mediated immune response. Additionally, new research disciplines such as epigenetics and immunometabolism have provided us with a more holistic view of how changes in DNA methylation, microRNAs, and the metabolome may influence the pro-inflammatory response. This review aimed to discuss our current knowledge of pro-inflammatory microglia from different angles, including recent research h
  56. 56Abiotic Stress Responses and Microbe-Mediated Mitigation in Plants: The Omics Strategies.Abiotic stresses are the foremost limiting factors for agricultural productivity. Crop plants need to cope up adverse external pressure created by environmental and edaphic conditions with their intrinsic biological mechanisms, failing which their growth, development, and productivity suffer. Microorganisms, the most natural inhabitants of diverse environments exhibit enormous metabolic capabilities to mitigate abiotic stresses. Since microbial interactions with plants are an integral part of the living ecosystem, they are believed to be the natural partners that modulate local and systemic mechanisms in plants to offer defense under adverse external conditions. Plant-microbe interactions comprise complex mechanisms within the plant cellular system. Biochemical, molecular and physiological studies are paving the way in understanding the complex but integrated cellular processes. Under the continuous pressure of increasing climatic alterations, it now becomes more imperative to define a
  57. 57Precision Nutrition: A Review of Personalized Nutritional Approaches for the Prevention and Management of Metabolic Syndrome.The translation of the growing increase of findings emerging from basic nutritional science into meaningful and clinically relevant dietary advices represents nowadays one of the main challenges of clinical nutrition. From nutrigenomics to deep phenotyping, many factors need to be taken into account in designing personalized and unbiased nutritional solutions for individuals or population sub-groups. Likewise, a concerted effort among basic, clinical scientists and health professionals will be needed to establish a comprehensive framework allowing the implementation of these new findings at the population level. In a world characterized by an overwhelming increase in the prevalence of obesity and associated metabolic disturbances, such as type 2 diabetes and cardiovascular diseases, tailored nutrition prescription represents a promising approach for both the prevention and management of metabolic syndrome. This review aims to discuss recent works in the field of precision nutrition ana
  58. 58Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health.The gut microbiota is composed of a huge number of different bacteria, that produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activities are affected by environmental stimuli leading to the generation of a wide number of compounds, that influence the host metabolome and human health. Indeed, metabolite profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the pr
  59. 59Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine.In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised
  60. 60The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities.The totality of microbial genomes in the gut exceeds the size of the human genome, having around 500-fold more genes that importantly complement our coding potential. Microbial genes are essential for key metabolic processes, such as the breakdown of indigestible dietary fibres to short-chain fatty acids, biosynthesis of amino acids and vitamins, and production of neurotransmitters and hormones. During the last decade, evidence has accumulated to support a role for gut microbiota (analysed from faecal samples) in glycaemic control and type 2 diabetes. Mechanistic studies in mice support a causal role for gut microbiota in metabolic diseases, although human data favouring causality is insufficient. As it may be challenging to sort the human evidence from the large number of animal studies in the field, there is a need to provide a review of human studies. Thus, the aim of this review is to cover the current and future possibilities and challenges of using the gut microbiota, with its ca
  61. 61Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation.Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the M
  62. 62Microbiome and metabolome features of the cardiometabolic disease spectrum.Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy in
  63. 63Machine Learning Applications for Mass Spectrometry-Based Metabolomics.The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitati
  64. 64Comprehensive metabolomics expands precision medicine for triple-negative breast cancer.Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal
  65. 65Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment.Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding too
  66. 66Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome.The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual's genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of Eubacterium rectale in decreasi
  67. 67Metabolomics Reveal Nanoplastic-Induced Mitochondrial Damage in Human Liver and Lung Cells.Plastic debris in the global biosphere is an increasing concern, and nanoplastic (NPs) toxicity in humans is far from being understood. Studies have indicated that NPs can affect mitochondria, but the underlying mechanisms remain unclear. The liver and lungs have important metabolic functions and are vulnerable to NP exposure. In this study, we investigated the effects of 80 nm NPs on mitochondrial functions and metabolic pathways in normal human hepatic (L02) cells and lung (BEAS-2B) cells. NP exposure did not induce mass cell death; however, transmission electron microscopy analysis showed that the NPs could enter the cells and cause mitochondrial damage, as evidenced by overproduction of mitochondrial reactive oxygen species, alterations in the mitochondrial membrane potential, and suppression of mitochondrial respiration. These alterations were observed at NP concentrations as low as 0.0125 mg/mL, which might be comparable to the environmental levels. Nontarget metabolomics confirm
  68. 68Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling.Introduction The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner. Objectives Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow. Methods We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples. Results Our studies have shown that once data are filtered, missing values are not correlated with m/z , retention time or response. Following an exhaustive
  69. 69Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes.Insulin is as a major postprandial hormone with profound effects on carbohydrate, fat, and protein metabolism. In the absence of exogenous insulin, patients with type 1 diabetes exhibit a variety of metabolic abnormalities including hyperglycemia, glycosurea, accelerated ketogenesis, and muscle wasting due to increased proteolysis. We analyzed plasma from type 1 diabetic (T1D) humans during insulin treatment (I+) and acute insulin deprivation (I-) and non-diabetic participants (ND) by (1)H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. The aim was to determine if this combination of analytical methods could provide information on metabolic pathways known to be altered by insulin deficiency. Multivariate statistics differentiated proton spectra from I- and I+ based on several derived plasma metabolites that were elevated during insulin deprivation (lactate, acetate, allantoin, ketones). Mass spectrometry revealed significant perturbations in
  70. 70High-confidence structural annotation of metabolites absent from spectral libraries.Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human
  71. 71Interplay between epigenetics and metabolism in oncogenesis: mechanisms and therapeutic approaches.Epigenetic and metabolic alterations in cancer cells are highly intertwined. Oncogene-driven metabolic rewiring modifies the epigenetic landscape via modulating the activities of DNA and histone modification enzymes at the metabolite level. Conversely, epigenetic mechanisms regulate the expression of metabolic genes, thereby altering the metabolome. Epigenetic-metabolomic interplay has a critical role in tumourigenesis by coordinately sustaining cell proliferation, metastasis and pluripotency. Understanding the link between epigenetics and metabolism could unravel novel molecular targets, whose intervention may lead to improvements in cancer treatment. In this review, we summarized the recent discoveries linking epigenetics and metabolism and their underlying roles in tumorigenesis; and highlighted the promising molecular targets, with an update on the development of small molecule or biologic inhibitors against these abnormalities in cancer.
  72. 72Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers.Background Gut microbiota contributes to colorectal cancer (CRC) pathogenesis through microbes and their metabolites. The importance of microbiota-associated metabolites in colorectal carcinogenesis highlights the need to investigate the gut metabolome along the adenoma-carcinoma sequence to determine their mechanistic implications in the pathogenesis of CRC. To date, how and which microbes and metabolites interactively promote early events of CRC development are still largely unclear. We aim to determine gut microbiota-associated metabolites and their linkage to colorectal carcinogenesis. Results We performed metabolomics and metagenomics profiling on fecal samples from 386 subjects including 118 CRC patients, 140 colorectal adenomas (CRA) patients and 128 healthy subjects as normal controls (NC). We identified differences in the gut metabolite profiles among NC, CRA and CRC groups by partial least squares-discriminant and principal component analyses. Among the altered metabolites, n
  73. 73Depression and anxiety in patients with active ulcerative colitis: crosstalk of gut microbiota, metabolomics and proteomics.Patients with ulcerative colitis (UC) have a high prevalence of mental disorders, such as depression and anxiety. Gut microbiota imbalance and disturbed metabolism have been suggested to play an important role in either UC or mental disorders. However, little is known about their detailed multi-omics characteristics in patients with UC and depression/anxiety. In this prospective observational study, 240 Chinese patients were enrolled, including 129 patients with active UC (69 in Phase 1 and 60 in Phase 2; divided into depression/non-depression or anxiety/non-anxiety groups), 49 patients with depression and anxiety (non-UC), and 62 healthy people. The gut microbiota of all subjects was analyzed using 16S rRNA sequencing. The serum metabolome and proteome of patients with UC in Phase 2 were analyzed using liquid chromatography/mass spectrometry. Associations between multi-omics were evaluated by correlation analysis. The prophylactic effect of candidate metabolites on the depressive-like
  74. 74Development, characterization and comparisons of targeted and non-targeted metabolomics methods.The potential of a metabolomics method to detect statistically significant perturbations in the metabolome of an organism is enhanced by excellent analytical precision, unequivocal identification, and broad metabolomic coverage. While the former two metrics are usually associated with targeted metabolomics and the latter with non-targeted metabolomics, a systematic comparison of the performance of both approaches has not yet been carried out. The present work reports on the development and performance evaluation of separate targeted and non-targeted metabolomics methods. The targeted approach facilitated determination of 181 metabolites (quantitative analysis of 18 amino acids, 11 biogenic amines, 5 neurotransmitters, 5 nucleobases and semi-quantitative analysis of 50 carnitines, 83 phosphatidylcholines, and 9 sphingomyelins) using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and flow injection-tandem mass spectrometry (FI-MS/MS). Method accuracy and/or
  75. 75The Chemistry of Plant-Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance.Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant-plant and plant-microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within th
  76. 76Application of Metabolomics in Alzheimer's Disease.Progress toward the development of efficacious therapies for Alzheimer's disease (AD) is halted by a lack of understanding early underlying pathological mechanisms. Systems biology encompasses several techniques including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Metabolomics is the newest omics platform that offers great potential for the diagnosis and prognosis of neurodegenerative diseases as an individual's metabolome reflects alterations in genetic, transcript, and protein profiles and influences from the environment. Advancements in the field of metabolomics have demonstrated the complexity of dynamic changes associated with AD progression underscoring challenges with the development of efficacious therapeutic interventions. Defining systems-level alterations in AD could provide insights into disease mechanisms, reveal sex-specific changes, advance the development of biomarker panels, and aid in monitoring therapeutic efficacy, which should advance ind
  77. 77Comprehensive metabolic profiling of Parkinson's disease by liquid chromatography-mass spectrometry.Background Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use. Methods We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes r
  78. 78Nicotinamide provides neuroprotection in glaucoma by protecting against mitochondrial and metabolic dysfunction.Nicotinamide adenine dinucleotide (NAD) is a REDOX cofactor and metabolite essential for neuronal survival. Glaucoma is a common neurodegenerative disease in which neuronal levels of NAD decline. We assess the effects of nicotinamide (a precursor to NAD) on retinal ganglion cells (the affected neuron in glaucoma) in normal physiological conditions and across a range of glaucoma relevant insults including mitochondrial stress and axon degenerative insults. We demonstrate retinal ganglion cell somal, axonal, and dendritic neuroprotection by nicotinamide in rodent models which represent isolated ocular hypertensive, axon degenerative, and mitochondrial degenerative insults. We performed metabolomics enriched for small molecular weight metabolites for the retina, optic nerve, and superior colliculus which demonstrates that ocular hypertension induces widespread metabolic disruption, including consistent changes to α-ketoglutaric acid, creatine/creatinine, homocysteine, and glycerophosphoch
  79. 79Emerging role of lipid metabolism alterations in Cancer stem cells.Background Cancer stem cells (CSCs) or tumor-initiating cells (TICs) represent a small population of cancer cells with self-renewal and tumor-initiating properties. Unlike the bulk of tumor cells, CSCs or TICs are refractory to traditional therapy and are responsible for relapse or disease recurrence in cancer patients. Stem cells have distinct metabolic properties compared to differentiated cells, and metabolic rewiring contributes to self-renewal and stemness maintenance in CSCs. Main body Recent advances in metabolomic detection, particularly in hyperspectral-stimulated raman scattering microscopy, have expanded our knowledge of the contribution of lipid metabolism to the generation and maintenance of CSCs. Alterations in lipid uptake, de novo lipogenesis, lipid droplets, lipid desaturation, and fatty acid oxidation are all clearly implicated in CSCs regulation. Alterations on lipid metabolism not only satisfies the energy demands and biomass production of CSCs, but also contributes
  80. 80Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts.The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus, Gemmiger formicilis, and Erysipelatoclostridium ramosum, which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To fu
  81. 81Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome.In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil ge
  82. 82Gut microbiota changes in the extreme decades of human life: a focus on centenarians.The gut microbiota (GM) is a complex, evolutionarily molded ecological system, which contributes to a variety of physiological functions. The GM is highly dynamic, being sensitive to environmental stimuli, and its composition changes over the host's entire lifespan. However, the basic question of how much these changes may be ascribed to variables such as population, diet, genetics and gender, and/or to the aging process per se is still largely unanswered. We argue that comparison among studies on centenarians-the best model of healthy aging and longevity-recruited from different geographical areas/populations (different genetics and dietary habits) can help to disentangle the contribution of aging and non-aging-related variables to GM remodeling with age. The current review focuses on the role of population, gender and host genetics as possible drivers of GM modification along the human aging process. The feedback impact of age-associated GM variation on the GM-brain axis and GM metab
  83. 83Proof of concept of microbiome-metabolome analysis and delayed gluten exposure on celiac disease autoimmunity in genetically at-risk infants.Celiac disease (CD) is a unique autoimmune disorder in which the genetic factors (DQ2/DQ8) and the environmental trigger (gluten) are known and necessary but not sufficient for its development. Other environmental components contributing to CD are poorly understood. Studies suggest that aspects of gluten intake might influence the risk of CD occurrence and timing of its onset, i.e., the amount and quality of ingested gluten, together with the pattern of infant feeding and the age at which gluten is introduced in the diet. In this study, we hypothesize that the intestinal microbiota as a whole rather than specific infections dictates the switch from tolerance to immune response in genetically susceptible individuals. Using a sample of infants genetically at risk of CD, we characterized the longitudinal changes in the microbial communities that colonize infants from birth to 24 months and the impact of two patterns of gluten introduction (early vs. late) on the gut microbiota and metabol
  84. 84Immune effects of PI3K/Akt/HIF-1α-regulated glycolysis in polymorphonuclear neutrophils during sepsis.Background Effective removal of pathogenic bacteria is key to improving the prognosis of sepsis. Polymorphonuclear neutrophils (PMNs) are the most important components of innate cellular immunity and play vital roles in clearing pathogenic bacteria. However, the metabolic characteristics and immunomodulatory pathways of PMNs during sepsis have not been investigated. In the present study, we explored the immune metabolism characteristics of PMNs and the mechanism by which neutrophilic glycolysis is regulated during sepsis. Methods Metabolomics analysis was performed on PMNs isolated from 14 septic patients, 26 patients with acute appendicitis, and 19 healthy volunteers. Transcriptome analysis was performed on the PMNs isolated from the healthy volunteers and the patients with sepsis to assess glycolysis and investigate its mechanism. Lipopolysaccharide (LPS) was used to stimulate the neutrophils isolated from the healthy volunteers at different time intervals to build an LPS-tolerant mo
  85. 85Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics.Background Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. Results We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of mag
  86. 86Metabolomics of Type 1 and Type 2 Diabetes.Type 1 and type 2 diabetes mellitus (DM) are chronic diseases that affect nearly 425 million people worldwide, leading to poor health outcomes and high health care costs. High-throughput metabolomics screening can provide vital insight into the pathophysiological pathways of DM and help in managing its effects. The primary aim of this study was to contribute to the understanding and management of DM by providing reliable evidence of the relationships between metabolites and type 1 diabetes (T1D) and metabolites and type 2 diabetes (T2D). Information for the study was obtained from the PubMed, MEDLINE, and EMBASE databases, and leads to additional articles that were obtained from the reference lists of the studies examined. The results from the selected studies were used to assess the relationships between diabetes (T1D and/or T2D) and metabolite markers-such as glutamine, glycine, and aromatic amino acids-in patients. Seventy studies were selected from the three databases and from the
  87. 87Mass spectrometry imaging for plant biology: a review.Mass spectrometry imaging (MSI) is a developing technique to measure the spatio-temporal distribution of many biomolecules in tissues. Over the preceding decade, MSI has been adopted by plant biologists and applied in a broad range of areas, including primary metabolism, natural products, plant defense, plant responses to abiotic and biotic stress, plant lipids and the developing field of spatial metabolomics. This review covers recent advances in plant-based MSI, general aspects of instrumentation, analytical approaches, sample preparation and the current trends in respective plant research.
  88. 88Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards.
  89. 89Altered Gut Microbial Metabolites in Amnestic Mild Cognitive Impairment and Alzheimer's Disease: Signals in Host-Microbe Interplay.Intimate metabolic host-microbiome crosstalk regulates immune, metabolic, and neuronal response in health and disease, yet remains untapped for biomarkers or intervention for disease. Our recent study identified an altered microbiome in patients with pre-onset amnestic mild cognitive impairment (aMCI) and dementia Alzheimer's disease (AD). Thus, we aimed to characterize the gut microbial metabolites among AD, aMCI, and healthy controls (HC). Here, a cohort of 77 individuals (22 aMCI, 27 AD, and 28 HC) was recruited. With the use of liquid-chromatography/gas chromatography mass spectrometry metabolomics profiling, we identified significant differences between AD and HC for tryptophan metabolites, short-chain fatty acids (SCFAs), and lithocholic acid, the majority of which correlated with altered microbiota and cognitive impairment. Notably, tryptophan disorders presented in aMCI and SCFAs decreased progressively from aMCI to AD. Importantly, indole-3-pyruvic acid, a metabolite from tryp
  90. 90Validation of biomarkers of food intake-critical assessment of candidate biomarkers.Biomarkers of food intake (BFIs) are a promising tool for limiting misclassification in nutrition research where more subjective dietary assessment instruments are used. They may also be used to assess compliance to dietary guidelines or to a dietary intervention. Biomarkers therefore hold promise for direct and objective measurement of food intake. However, the number of comprehensively validated biomarkers of food intake is limited to just a few. Many new candidate biomarkers emerge from metabolic profiling studies and from advances in food chemistry. Furthermore, candidate food intake biomarkers may also be identified based on extensive literature reviews such as described in the guidelines for Biomarker of Food Intake Reviews (BFIRev). To systematically and critically assess the validity of candidate biomarkers of food intake, it is necessary to outline and streamline an optimal and reproducible validation process. A consensus-based procedure was used to provide and evaluate a set
  91. 91Recent advances and perspectives of metabolomics-based investigations in Parkinson's disease.Parkinson's disease (PD) is the second most prevalent neurodegenerative disease of the central nervous system (CNS), which affects mostly older adults. In recent years, the incidence of PD has been dramatically increasing with the aging population expanding. Due to the lack of effective biomarkers, the accurate diagnosis and precise treatment of PD are currently compromised. Notably, metabolites have been considered as the most direct reflection of the physiological and pathological conditions in individuals and represent attractive candidates to provide deep insights into disease phenotypes. By profiling the metabolites in biofluids (cerebrospinal fluid, blood, urine), feces and brain tissues, metabolomics has become a powerful and promising tool to identify novel biomarkers and provide valuable insights into the etiopathogenesis of neurological diseases. In this review, we will summarize the recent advancements of major analytical platforms implemented in metabolomics studies, dedica
  92. 92Metabolic profiles characterizing different phenotypes of polycystic ovary syndrome: plasma metabolomics analysis.Background Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder accompanied with an increased risk of developing type 2 diabetes mellitus and cardiovascular disease; despite being a common condition, the pathogenesis of PCOS remains unclear. Our aim was to investigate the potential metabolic profiles for different phenotypes of PCOS, as well as for the early prognosis of complications. Methods A total of 217 women with PCOS and 48 healthy women as normal controls were studied. Plasma samples of subjects were tested using two different analytical platforms of metabolomics: 1H nuclear magnetic resonance (NMR) and gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS). Results Our results showed that carbohydrate, lipid and amino acid metabolisms were influenced in PCOS. The levels of lactate, long-chain fatty acids, triglyceride and very low-density lipoprotein were elevated, while glucose, phosphatidylcholine and high-density lipoprotein (HDL) concentrations w
  93. 93Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose eff
  94. 94jMorp: Japanese Multi-Omics Reference Panel update report 2023.Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a publ
  95. 95Predicting network activity from high throughput metabolomics.The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.
  96. 96Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation respo
  97. 97Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity.Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of exis
  98. 98Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between mul
  99. 99Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis.Background Sepsis is defined as a systemic inflammatory response to microbial infections with multiple organ dysfunction. This study analysed untargeted metabolomics combined with proteomics of serum from patients with sepsis to reveal the underlying pathological mechanisms involved in sepsis. Methods A total of 63 patients with sepsis and 43 normal controls were enrolled from a prospective multicentre cohort. The biological functions of the metabolome were assessed by coexpression network analysis. A molecular network based on metabolomics and proteomics data was constructed to investigate the key molecules. Results Untargeted metabolomics analysis revealed widespread dysregulation of amino acid metabolism, which regulates inflammation and immunity, in patients with sepsis. Seventy-three differentially expressed metabolites (|log 2 fold change| > 1.5, adjusted P value 1.5) that could predict sepsis were identified. External validation of the hub metabolites was consistent with the der
  100. 100Multi-Omics of Single Cells: Strategies and Applications.Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.
  101. 101Use of Shotgun Metagenomics and Metabolomics to Evaluate the Impact of Glyphosate or Roundup MON 52276 on the Gut Microbiota and Serum Metabolome of Sprague-Dawley Rats.Background There is intense debate on whether glyphosate can inhibit the shikimate pathway of gastrointestinal microorganisms, with potential health implications. Objectives We tested whether glyphosate or its representative EU herbicide formulation Roundup MON 52276 affects the rat gut microbiome. Methods We combined cecal microbiome shotgun metagenomics with serum and cecum metabolomics to assess the effects of glyphosate [0.5, 50, 175mg/kg body weight (BW) per day] or MON 52276 at the same glyphosate-equivalent doses, in a 90-d toxicity test in rats. Results Glyphosate and MON 52276 treatment resulted in ceca accumulation of shikimic acid and 3-dehydroshikimic acid, suggesting inhibition of 5-enolpyruvylshikimate-3-phosphate synthase of the shikimate pathway in the gut microbiome. Cysteinylglycine, γ-glutamylglutamine, and valylglycine levels were elevated in the cecal microbiome following glyphosate and MON 52276 treatments. Altered cecum metabolites were not differentially express
  102. 102Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches.Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation
  103. 103Spatial Transcriptomic and Metabolomic Landscapes of Oral Submucous Fibrosis-Derived Oral Squamous Cell Carcinoma and its Tumor Microenvironment.In South and Southeast Asia, the habit of chewing betel nuts is prevalent, which leads to oral submucous fibrosis (OSF). OSF is a well-established precancerous lesion, and a portion of OSF cases eventually progress to oral squamous cell carcinoma (OSCC). However, the specific molecular mechanisms underlying the malignant transformation of OSCC from OSF are poorly understood. In this study, the leading-edge techniques of Spatial Transcriptomics (ST) and Spatial Metabolomics (SM) are integrated to obtain spatial location information of cancer cells, fibroblasts, and immune cells, as well as the transcriptomic and metabolomic landscapes in OSF-derived OSCC tissues. This work reveals for the first time that some OSF-derived OSCC cells undergo partial epithelial-mesenchymal transition (pEMT) within the in situ carcinoma (ISC) region, eventually acquiring fibroblast-like phenotypes and participating in collagen deposition. Complex interactions among epithelial cells, fibroblasts, and immune
  104. 104Enablers and challenges of spatial omics, a melting pot of technologies.Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
  105. 105Therapeutic and Toxic Effects of Valproic Acid Metabolites.Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington's disease, Parkinson's disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains th
  106. 106A multi-omics approach unravels metagenomic and metabolic alterations of a probiotic and synbiotic additive in rainbow trout (Oncorhynchus mykiss).Background Animal protein production is increasingly looking towards microbiome-associated services such as the design of new and better probiotic solutions to further improve gut health and production sustainability. Here, we investigate the functional effects of bacteria-based pro- and synbiotic feed additives on microbiome-associated functions in relation to growth performance in the commercially important rainbow trout (Oncorhynchus mykiss). We combine complementary insights from multiple omics datasets from gut content samples, including 16S bacterial profiling, whole metagenomes, and untargeted metabolomics, to investigate bacterial metagenome-assembled genomes (MAGs) and their molecular interactions with host metabolism. Results Our findings reveal that (I) feed additives changed the microbiome and that rainbow trout reared with feed additives had a significantly reduced relative abundance of the salmonid related Candidatus Mycoplasma salmoninae in both the mid and distal gut co
  107. 107Metabolic Perturbation Associated With COVID-19 Disease Severity and SARS-CoV-2 Replication.Viruses hijack host metabolic pathways for their replicative advantage. In this study, using patient-derived multiomics data and in vitro infection assays, we aimed to understand the role of key metabolic pathways that can regulate severe acute respiratory syndrome coronavirus-2 reproduction and their association with disease severity. We used multiomics platforms (targeted and untargeted proteomics and untargeted metabolomics) on patient samples and cell-line models along with immune phenotyping of metabolite transporters in patient blood cells to understand viral-induced metabolic modulations. We also modulated key metabolic pathways that were identified using multiomics data to regulate the viral reproduction in vitro. Coronavirus disease 2019 disease severity was characterized by increased plasma glucose and mannose levels. Immune phenotyping identified altered expression patterns of carbohydrate transporter, glucose transporter 1, in CD8 + T cells, intermediate and nonclassical mo
  108. 108Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy.Background Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine. Methods The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis. Results In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that
  109. 109Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats.Background The increased growth rate of young animals can lead to higher lactation performance in adult goats; however, the effects of the ruminal microbiome on the growth of young goats, and the contribution of the early-life rumen microbiome to lifelong growth and lactation performance in goats has not yet been well defined. Hence, this study assessed the rumen microbiome in young goats with different average daily gains (ADG) and evaluated its contribution to growth and lactation performance during the first lactation period. Results Based on monitoring of a cohort of 99 goats from youth to first lactation, the 15 highest ADG (HADG) goats and 15 lowest ADG (LADG) goats were subjected to rumen fluid microbiome and metabolome profiling. The comparison of the rumen metagenome of HADG and LADG goats revealed that ruminal carbohydrate metabolism and amino acid metabolism function were enhanced in HADG goats, suggesting that the rumen fluid microbiome of HADG goats has higher feed ferment
  110. 110Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases.Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequen
  111. 111Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes.Background Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes. Methods Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and
  112. 112MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data.Background Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. Results A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope p
  113. 113The return of metabolism: biochemistry and physiology of the pentose phosphate pathway.The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. The PPP is important to maintain carbon homoeostasis, to provide precursors for nucleotide and amino acid biosynthesis, to provide reducing molecules for anabolism, and to defeat oxidative stress. The PPP shares reactions with the Entner-Doudoroff pathway and Calvin cycle and divides into an oxidative and non-oxidative branch. The oxidative branch is highly active in most eukaryotes and converts glucose 6-phosphate into carbon dioxide, ribulose 5-phosphate and NADPH. The latter function is critical to maintain redox balance under stress situations, when cells proliferate rapidly, in ageing, and for the 'Warburg effect' of cancer cells. The non-oxidative branch instead is virtually ubiquitous, and metabolizes the glycolytic intermediates fructose 6-phosphate and glyceraldehyde 3-phosphate as well as sedoheptulose sugars, yielding ribose 5-phosphate for the synthesis of nucleic acids and sugar phosphat
  114. 114Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools.The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workb
  115. 115American Gut: an Open Platform for Citizen Science Microbiome Research.Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of
  116. 116Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability.Cancer cell metabolism is heavily influenced by microenvironmental factors, including nutrient availability. Therefore, knowledge of microenvironmental nutrient levels is essential to understand tumor metabolism. To measure the extracellular nutrient levels available to tumors, we utilized quantitative metabolomics methods to measure the absolute concentrations of >118 metabolites in plasma and tumor interstitial fluid, the extracellular fluid that perfuses tumors. Comparison of nutrient levels in tumor interstitial fluid and plasma revealed that the nutrients available to tumors differ from those present in circulation. Further, by comparing interstitial fluid nutrient levels between autochthonous and transplant models of murine pancreatic and lung adenocarcinoma, we found that tumor type, anatomical location and animal diet affect local nutrient availability. These data provide a comprehensive characterization of the nutrients present in the tumor microenvironment of widely used mode
  117. 117The use of metabolomics to dissect plant responses to abiotic stresses.Plant metabolism is perturbed by various abiotic stresses. As such the metabolic network of plants must be reconfigured under stress conditions in order to allow both the maintenance of metabolic homeostasis and the production of compounds that ameliorate the stress. The recent development and adoption of metabolomics and systems biology approaches enable us not only to gain a comprehensive overview, but also a detailed analysis of crucial components of the plant metabolic response to abiotic stresses. In this review we introduce the analytical methods used for plant metabolomics and describe their use in studies related to the metabolic response to water, temperature, light, nutrient limitation, ion and oxidative stresses. Both similarity and specificity of the metabolic responses against diverse abiotic stress are evaluated using data available in the literature. Classically discussed stress compounds such as proline, γ-amino butyrate and polyamines are reviewed, and the widespread i
  118. 118Heavy Metal Tolerance in Plants: Role of Transcriptomics, Proteomics, Metabolomics, and Ionomics.Heavy metal contamination of soil and water causing toxicity/stress has become one important constraint to crop productivity and quality. This situation has further worsened by the increasing population growth and inherent food demand. It has been reported in several studies that counterbalancing toxicity due to heavy metal requires complex mechanisms at molecular, biochemical, physiological, cellular, tissue, and whole plant level, which might manifest in terms of improved crop productivity. Recent advances in various disciplines of biological sciences such as metabolomics, transcriptomics, proteomics, etc., have assisted in the characterization of metabolites, transcription factors, and stress-inducible proteins involved in heavy metal tolerance, which in turn can be utilized for generating heavy metal-tolerant crops. This review summarizes various tolerance strategies of plants under heavy metal toxicity covering the role of metabolites (metabolomics), trace elements (ionomics), tra
  119. 119Dynamic Modulation of the Gut Microbiota and Metabolome by Bacteriophages in a Mouse Model.The human gut microbiome is comprised of densely colonizing microorganisms including bacteriophages, which are in dynamic interaction with each other and the mammalian host. To address how bacteriophages impact bacterial communities in the gut, we investigated the dynamic effects of phages on a model microbiome. Gnotobiotic mice were colonized with defined human gut commensal bacteria and subjected to predation by cognate lytic phages. We found that phage predation not only directly impacts susceptible bacteria but also leads to cascading effects on other bacterial species via interbacterial interactions. Metabolomic profiling revealed that shifts in the microbiome caused by phage predation have a direct consequence on the gut metabolome. Our work provides insight into the ecological importance of phages as modulators of bacterial colonization, and it additionally suggests the potential impact of gut phages on the mammalian host with implications for their therapeutic use to precisely
  120. 120Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data.Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student's t-test followed by correlation analysis was condu
  121. 121Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET Cohort.Background The gut is the most extensively studied niche of the human microbiome. The aim of this study was to characterise the initial gut microbiota development of a cohort of breastfed infants (n = 192 ) from 1 to 24 weeks of age. Methods V4-V5 region 16S rRNA amplicon Illumina sequencing and, in parallel, bacteriological culture. The metabolomic profile of infant urine at 4 weeks of age was also examined by LC-MS. Results Full-term (FT), spontaneous vaginally delivered (SVD) infants' microbiota remained stable at both phylum and genus levels during the 24-week period examined. FT Caesarean section (CS) infants displayed an increased faecal abundance of Firmicutes (p < 0.01) and lower abundance of Actinobacteria (p < 0.001) after the first week of life compared to FT-SVD infants. FT-CS infants gradually progressed to harbouring a microbiota closely resembling FT-SVD (which remained stable) by week 8 of life, which was maintained at week 24. The gut microbiota of preterm (PT) infants
  122. 122Metabolomics enables precision medicine: "A White Paper, Community Perspective".Introduction background to metabolomics Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry anal
  123. 123Analytical methods in untargeted metabolomics: state of the art in 2015.Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from
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