Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research

Editorial overview: recent innovations in the metabolomics revolution

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Systems biology in inflammatory bowel diseases

Purpose of review: Ulcerative colitis (UC) and Crohn’s Disease (CD) are the two predominant types of inflammatory bowel disease (IBD), affecting over 1.4 million individuals in the US. IBD results from complex interactions between pathogenic components, including genetic and epigenetic factors, the immune response and the microbiome through an unknown sequence of events. The purpose of this review is to describe a system biology approach to IBD as a novel and exciting methodology aiming at developing novel IBD therapeutics based on the integration of molecular and cellular "omics" data. Recent Findings: Recent evidence suggested the presence of genetic, epigenetic, transcriptomic, proteomic and metabolomic alterations in IBD patients. Furthermore, several studies have shown that different cell types, including fibroblasts, epithelial, immune and endothelial cells together with the intestinal microbiota are involved in IBD pathogenesis. Novel computational methodologies have been developed aiming to integrate high - throughput molecular data. Summary: A systems biology approach could potentially identify the central regulators (hubs) in the IBD interactome and improve our understanding of the molecular mechanisms involved in IBD pathogenesis. The future IBD therapeutics should be developed on the basis of targeting the central hubs in the IBD network

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu