Review of emerging metabolomic tools and resources: 2015-2016

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources

The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

The Hitchhiker's Guide to Untargeted Lipidomics Analysis: Practical Guidelines

Lipidomics is a newly emerged discipline involving the identification and quantification of thousands of lipids. As a part of the omics field, lipidomics has shown rapid growth both in the number of studies and in the size of lipidome datasets, thus, requiring specific and efficient data analysis approaches. This paper aims to provide guidelines for analyzing and interpreting lipidome data obtained using untargeted methods that rely on liquid chromatography coupled with mass spectrometry (LC-MS) to detect and measure the intensities of lipid compounds. We present a state-of-the-art untargeted LC-MS workflow for lipidomics, from study design to annotation of lipid features, focusing on practical, rather than theoretical, approaches for data analysis, and we outline possible applications of untargeted lipidomics for biological studies. We provide a detailed R notebook designed specifically for untargeted lipidome LC-MS data analysis, which is based on xcms software.

MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

Loss of function of lysosomal acid lipase (LAL) profoundly impacts osteoblastogenesis and increases fracture risk in humans

Lysosomal acid lipase (LAL) is essential for cholesteryl ester (CE) and triacylglycerol (TAG) hydrolysis in the lysosome. Clinically, an autosomal recessive LIPA mutation causes LAL deficiency (LALD), previously described as Wolman Disease or Cholesteryl Ester Storage Disease (CESD). LAL-D is associated with ectopic lipid accumulation in the liver, small intestine, spleen, adrenal glands, and blood. Considering the importance of unesterified cholesterol and fatty acids in bone metabolism, we hypothesized that LAL is essential for bone formation, and ultimately, skeletal health. To investigate the role of LAL in skeletal homeostasis, we used LAL-deficient (^-/-) mice, in vitro osteoblast cultures, and novel clinical data from LAL-D patients. Both male and female LAL^-/- mice demonstarted lower trabecular and cortical bone parameters , which translated to reduced biomechanical properties. Further histological analyses revealed that LAL^-/- mice had fewer osteoblasts, with no change in osteoclast or marrow adipocyte numbers. In studying the cell-autonomous role of LAL, we observed impaired differentiation of LAL^-/- calvarial osteoblasts and in bone marrow stromal cells treated with the LAL inhibitor lalistat. Consistent with LAL's role in other tissues, lalistat resulted in profound lipid puncta accumulation and an altered intracellular lipid profile. Finally, we analyzed a large de-identified national insurance database (i.e. 2016/2017 Optum Clinformatics®) which revealed that adults (≥18 years) with CESD (n = 3076) had a higher odds ratio (OR = 1.21; 95% CI = 1.03-1.41) of all-cause fracture at any location compared to adults without CESD (n = 13.7 M) after adjusting for demographic variables and osteoporosis. These data demonstrate that alterations in LAL have significant clinical implications related to fracture risk and that LAL's modulation of lipid metabolism is a critical for osteoblast function.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.

From Metabolite to Metabolome: Metabolomics Applications in Plasmodium Research

Advances in research over the past few decades have greatly improved metabolomics-based approaches in studying parasite biology and disease etiology. This improves the investigation of varied metabolic requirements during life stages or when following transmission to their hosts, and fulfills the demand for improved diagnostics and precise therapeutics. Therefore, this review highlights the progress of metabolomics in malaria research, including metabolic mapping of Plasmodium vertebrate life cycle stages to investigate antimalarials mode of actions and underlying complex host-parasite interactions. Also, we discuss current limitations as well as make several practical suggestions for methodological improvements which could drive metabolomics progress for malaria from a comprehensive perspective.

Biophysical characterization of antibacterial compounds derived from pathogenic fungi Ganoderma boninense

There have been relatively few studies which support a link between Ganoderma boninense, a phytopathogenic fungus that is particularly cytotoxic and pathogenic to plant tissues and roots, and antimicrobial compounds. We previously observed that liquid-liquid extraction (LLE) using chloroformmethanol-water at a ratio (1:1:1) was superior at detecting antibacterial activities and significant quantities of antibacterial compounds. Herein, we demonstrate that antibacterial secondary metabolites are produced from G. boninense mycelia. Antibacterial compounds were monitored in concurrent biochemical and biophysical experiments. The combined methods included high performance thin-layer chromatography (HPTLC), gas chromatography-mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), fourier transform infrared (FTIR), and nuclear magnetic resonance (NMR) spectroscopy. The antibacterial compounds derived from mycelia with chloroform-methanol extraction through LLE were isolated via a gradient solvent elution system using HPTLC. The antibacterial activity of the isolated compounds was observed to be the most potent against Staphylococcus aureus ATCC 25923 and multidrug-resistant S. aureus NCTC 11939. GC-MS, HPLC, and FTIR analysis confirmed two antibacterial compounds, which were identified as 4,4,14α-trimethylcholestane (m/z = 414.75; lanostane, C₃₀H₅₄) and ergosta-5,7,22-trien-3β-ol (m/z = 396.65; ergosterol, C₂₈H₄₄O). With the aid of spectroscopic evaluations, ganoboninketal (m/z = 498.66, C₃₀H₄₂O₆), which belongs to the 3,4-seco-27-norlanostane triterpene family, was additionally characterized by 2D-NMR analysis. Despite the lack of antibacterial potential exhibited by lanostane; both ergosterol and ganoboninketal displayed significant antibacterial activities against bacterial pathogens. Results provide evidence for the existence of bioactive compounds in the mycelia of the relatively unexplored phytopathogenic G. boninense, together with a robust method for estimating the corresponding potent antibacterial secondary metabolites.

Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions

PURPOSE OF REVIEW

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer.

RECENT FINDINGS

Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Towards Mass Spectrometry-Based Chemical Exposome: Current Approaches, Challenges, and Future Directions

The proposal of the “exposome” concept represents a shift of the research paradigm in studying exposure-disease relationships from an isolated and partial way to a systematic and agnostic approach. Nevertheless, exposome implementation is facing a variety of challenges including measurement techniques and data analysis. Here we focus on the chemical exposome, which refers to the mixtures of chemical pollutants people are exposed to from embryo onwards. We review the current chemical exposome measurement approaches with a focus on those based on the mass spectrometry. We further explore the strategies in implementing the concept of chemical exposome and discuss the available chemical exposome studies. Early progresses in the chemical exposome research are outlined, and major challenges are highlighted. In conclusion, efforts towards chemical exposome have only uncovered the tip of the iceberg, and further advancement in measurement techniques, computational tools, high-throughput data analysis, and standardization may allow more exciting discoveries concerning the role of exposome in human health and disease.

BLANKA: an Algorithm for Blank Subtraction in Mass Spectrometry of Complex Biological Samples

Multispecies microbiome systems are known to be closely linked to human, animal, and plant life processes. The growing field of metabolomics presents the opportunity to detect changes in overall metabolomic profiles of microbial species interactions. These metabolomic changes provide insight into function of metabolites as they correlate to different species presence and the observed phenotypic changes, but detection of subtle changes is often difficult in samples with complex backgrounds. Natural environments such as soil and food contain many molecules that convolute mass spectrometry-based analyses, and identification of microbial metabolites amongst environmental metabolites is an informatics problem we begin to address here. Our microbes are grown on solid or liquid cheese curd media. This medium, which is necessary for microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analyses using LC-MS/MS data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analyses so that we may prioritize metabolites for further structure elucidation. Graphical Abstract .

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture-based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.

Individualized metabolomics: opportunities and challenges

The goal of advancing science in health care is to provide high quality treatment and therapeutic opportunities to patients in need. This is especially true in precision medicine, wherein the ultimate goal is to link disease phenotypes to targeted treatments and novel therapeutics at the scale of an individual. With the advent of -omics technologies, such as genomics, proteomics, microbiome, among others, the metabolome is of wider and immediate interest for its important role in metabolic regulation. The metabolome, of course, comes with its own questions regarding technological challenges. In this opinion article, I attempt to interrogate some of the main challenges associated with individualized metabolomics, and available opportunities in the context of its clinical application. Some questions this article addresses and attempts to find answers for are: Can a personal metabolome (n = 1) be inexpensive, affordable and informative enough (i.e. provide predictive yet validated biomarkers) to represent the entirety of a population? How can a personal metabolome complement advances in other -omics areas and the use of monitoring devices, which occupy our personal space?

Impact of Cr(VI) on the oxidation of polyunsaturated fatty acids in Helianthus annuus roots studied by metabolomic tools

The impact of Cr(VI) in sunflower roots has been studied, focusing on the oxidation of polyunsaturated fatty acids. Plants were grown hydroponically in the presence of 0, 1.0, 5.0 and 25 mgCr L^-1. Methanolic root extracts were analyzed by capillary liquid chromatography coupled through negative electrospray ionization to a quadrupole-time of flight mass spectrometry (capHPLC-ESI-QTOF-MS). Using partial least squares algorithm, eighteen features strongly affected by Cr(VI) were detected and annotated as linoleic acid (LA), alpha-linolenic acid (ALA) and sixteen oxidation products containing hydroperoxy-, epoxy-, keto-, epoxyketo- or hydroxy-functionalities, all of them classified as oxylipins. Inspection of the MS/MS spectra acquired for features eluting at different retention times but assigned as a sole compound, confirmed isomers formation: three hydroperoxy-octadecadienoic acids (HpODE), two oxo-octadecadienoic acids (OxoODE) and four epoxyketo-octadecenoic acids (EKODE). Around 70% of metabolites in sunflower LA metabolic pathway were affected by Cr(VI) stress and additionally, four EKODE isomers not included in this pathway were found in the exposed roots. Among ALA-derived oxylipins, 13-epi-12-oxo-phytodienoic acid (OPDA) is of relevance, because of its participation in the activation of secondary metabolism. The abundances of all oxylipins were directly dependent on the Cr(VI) concentration in medium; furthermore, autooxidation of LA to HpODE isomers was observed after incubation with Cr(VI). These results point to the direct involvement of Cr(VI) in non-enzymatic oxidation of fatty acids; since oxylipins are signaling molecules important in plant defensive response, their synthesis under Cr(VI) exposure sustains the ability of sunflower to grow in Cr(VI)-contaminated environments.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

How Viral and Intracellular Bacterial Pathogens Reprogram the Metabolism of Host Cells to Allow Their Intracellular Replication

Viruses and intracellular bacterial pathogens (IBPs) have in common the need of suitable host cells for efficient replication and proliferation during infection. In human infections, the cell types which both groups of pathogens are using as hosts are indeed quite similar and include phagocytic immune cells, especially monocytes/macrophages (MOs/MPs) and dendritic cells (DCs), as well as nonprofessional phagocytes, like epithelial cells, fibroblasts and endothelial cells. These terminally differentiated cells are normally in a metabolically quiescent state when they are encountered by these pathogens during infection. This metabolic state of the host cells does not meet the extensive need for nutrients required for efficient intracellular replication of viruses and especially IBPs which, in contrast to the viral pathogens, have to perform their own specific intracellular metabolism to survive and efficiently replicate in their host cell niches. For this goal, viruses and IBPs have to reprogram the host cell metabolism in a pathogen-specific manner to increase the supply of nutrients, energy, and metabolites which have to be provided to the pathogen to allow its replication. In viral infections, this appears to be often achieved by the interaction of specific viral factors with central metabolic regulators, including oncogenes and tumor suppressors, or by the introduction of virus-specific oncogenes. Less is so far known on the mechanisms leading to metabolic reprogramming of the host cell by IBPs. However, the still scant data suggest that similar mechanisms may also determine the reprogramming of the host cell metabolism in IBP infections. In this review, we summarize and compare the present knowledge on this important, yet still poorly understood aspect of pathogenesis of human viral and especially IBP infections.

Untargeted Metabolomics Reveal Defensome-Related Metabolic Reprogramming in Sorghum bicolor against Infection by Burkholderia andropogonis

Burkholderia andropogonis is the causal agent of bacterial leaf stripe, one of the three major bacterial diseases affecting Sorghum bicolor. However, the biochemical aspects of the pathophysiological host responses are not well understood. An untargeted metabolomics approach was designed to understand molecular mechanisms underlying S. bicolor⁻B. andropogonis interactions. At the 4-leaf stage, two sorghum cultivars (NS 5511 and NS 5655) differing in disease tolerance, were infected with B. andropogonis and the metabolic changes monitored over time. The NS 5511 cultivar displayed delayed signs of wilting and lesion progression compared to the NS 5655 cultivar, indicative of enhanced resistance. The metabolomics results identified statistically significant metabolites as biomarkers associated with the sorghum defence. These include the phytohormones salicylic acid, jasmonic acid, and zeatin. Moreover, metabolic reprogramming in an array of chemically diverse metabolites that span a wide range of metabolic pathways was associated with the defence response. Signatory biomarkers included aromatic amino acids, shikimic acid, metabolites from the phenylpropanoid and flavonoid pathways, as well as fatty acids. Enhanced synthesis and accumulation of apigenin and derivatives thereof was a prominent feature of the altered metabolomes. The analyses revealed an intricate and dynamic network of the sorghum defence arsenal towards B. andropogonis in establishing an enhanced defensive capacity in support of resistance and disease suppression. The results pave the way for future analysis of the biosynthesis of signatory biomarkers and regulation of relevant metabolic pathways in sorghum.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Current and future perspectives of functional metabolomics in disease studies-A review

Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

Beyond genomics: understanding exposotypes through metabolomics

BACKGROUND

Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

MAIN TEXT

Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.

CONCLUSIONS

Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

New tools and resources in metabolomics: 2016-2017

Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.