Metabolomics, the large-scale study of the metabolic complement of the cell [1][2][3] , is a mature science that has been practiced for over 20 years 4 . Indeed, it is now a commonly used experimental systems biology tool with demonstrated utility in both fundamental and applied aspects of plant, microbial and mammalian research [5][6][7][8][9][10][11][12][13][14][15] . Among the many thousands of studies published in this area over the last 20 years, notable highlights [5][6][7][8]10,11,16 are briefly described in Supplementary Note 1.Despite the insight afforded by such studies, the nature of metabolites, particularly their diversity (in both chemical structure and dynamic range of abundance 9,12 ), remains a major challenge with regard to the ability to provide adequate coverage of the metabolome that can complement that achieved for the genome, transcriptome and proteome. Despite these comparative limitations, enormous advances have been made with regard to the number of analytes about which accurate quantitative information can be acquired, and a vast number of studies have yielded important biological information and biologically active metabolites across the kingdoms of life 14 . We have previously estimated that upwards of 1 million different metabolites occur across the tree of life, with between 1,000 and 40,000 estimated to occur in a single species 4 .

Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader–Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations. Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut. When transplanted into germ-free mice, the pre-intervention gut microbiota induced higher inflammation and larger adipocytes compared with the post-intervention microbiota from the same volunteer. Our multi-omics-based systems analysis indicates a significant etiological contribution of dysbiotic gut microbiota to both genetic and simple obesity in children, implicating a potentially effective target for alleviation.Research in contextPoorly managed diet and genetic mutations are the two primary driving forces behind the devastating epidemic of obesity-related diseases. Lack of understanding of the molecular chain of causation between the driving forces and the disease endpoints retards progress in prevention and treatment of the diseases. We found that children genetically obese with Prader–Willi syndrome shared a similar dysbiosis in their gut microbiota with those having diet-related obesity. A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

Big data analytics is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

Cancer cells are known to undergo metabolic reprogramming to sustain survival and rapid proliferation, however, it remains to be fully elucidated how oncogenic lesions coordinate the metabolic switch under various stressed conditions. Here we show that deprivation of glucose or glutamine, two major nutrition sources for cancer cells, dramatically activated serine biosynthesis pathway (SSP) that was accompanied by elevated cMyc expression. We further identified that cMyc stimulated SSP activation by transcriptionally upregulating expression of multiple SSP enzymes. Moreover, we demonstrated that SSP activation facilitated by cMyc led to elevated glutathione (GSH) production, cell cycle progression and nucleic acid synthesis, which are essential for cell survival and proliferation especially under nutrient-deprived conditions. We further uncovered that phosphoserine phosphatase (PSPH), the final rate-limiting enzyme of the SSP pathway, is critical for cMyc-driven cancer progression both in vitro and in vivo, and importantly, aberrant expression of PSPH is highly correlated with mortality in hepatocellular carcinoma (HCC) patients, suggesting a potential causal relation between this cMyc-regulated enzyme, or SSP activation in general, and cancer development. Taken together, our results reveal that aberrant expression of cMyc leads to the enhanced SSP activation, an essential part of metabolic switch, to facilitate cancer progression under nutrient-deprived conditions.