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Psychiatric disorders are the leading cause of disability worldwide while the pathogenesis remains unclear. Genome-wide association studies (GWASs) have made great achievements in detecting disease-related genetic variants. However, functional information on the underlying biological processes is often lacking. Current reports propose the use of metabolic traits as functional intermediate phenotypes (the so-called genetically determined metabotypes or GDMs) to reveal the biological mechanisms of genetics in human diseases. Here we conducted a two-sample Mendelian randomization analysis that uses GDMs to assess the causal effects of 486 human serum metabolites on 5 major psychiatric disorders, which respectively were schizophrenia (SCZ), major depression (MDD), bipolar disorder (BIP), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Using genetic variants as proxies, our study has identified 137 metabolites linked to the risk of psychiatric disorders, including 2-methoxyacetaminophen sulfate, which affects SCZ (P = 1.7 × 10–5) and 1-docosahexaenoylglycerophosphocholine, which affects ADHD (P = 5.6 × 10–5). Fourteen significant metabolic pathways involved in the 5 psychiatric disorders assessed were also detected, such as glycine, serine, and threonine metabolism for SCZ (P = .0238), Aminoacyl-tRNA biosynthesis for both MDD (P = .0144) and ADHD (P = .0029). Our study provided novel insights into integrating metabolomics with genomics in order to understand the mechanisms underlying the pathogenesis of human diseases.

Metabolism, including glycolysis, oxidative phosphorylation, fatty acid oxidation, and other metabolic pathways, impacts the phenotypes and functions of immune cells. The metabolic regulation of the immune system is important in the pathogenesis and progression of numerous diseases, such as cancers, autoimmune diseases and metabolic diseases. The concept of immunometabolism was introduced over a decade ago to elucidate the intricate interplay between metabolism and immunity. The definition of immunometabolism has expanded from chronic low-grade inflammation in metabolic diseases to metabolic reprogramming of immune cells in various diseases. With immunometabolism being proposed and developed, the metabolic regulation of the immune system can be gradually summarized and becomes more and more clearer. In the context of many diseases including cancer, autoimmune diseases, metabolic diseases, and many other disease, metabolic reprogramming occurs in immune cells inducing proinflammatory or anti-inflammatory effects. The phenotypic and functional changes of immune cells caused by metabolic regulation further affect and development of diseases. Based on experimental results, targeting cellular metabolism of immune cells becomes a promising therapy. In this review, we focus on immune cells to introduce their metabolic pathways and metabolic reprogramming, and summarize how these metabolic pathways affect immune effects in the context of diseases. We thoroughly explore targets and treatments based on immunometabolism in existing studies. The challenges of translating experimental results into clinical applications in the field of immunometabolism are also summarized. We believe that a better understanding of immune regulation in health and diseases will improve the management of most diseases.

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism’s genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Metabolic reprogramming fulfils increased nutrient demands and regulates numerous oncogenic processes in tumors, leading to tumor malignancy. Branched-chain amino acids (BCAAs, i.e., valine, leucine, and isoleucine) function as nitrogen donors to generate macromolecules such as nucleotides and are indispensable for human cancer cell growth. The cell-autonomous and non-autonomous roles of altered BCAA metabolism have been implicated in cancer progression and the key proteins in the BCAA metabolic pathway serve as possible prognostic and diagnostic biomarkers in human cancers. Here we summarize how BCAA metabolic reprogramming is regulated in cancer cells and how it influences cancer progression.

Phthalic acid esters (PAEs) have long been known as the most widely used plasticizer with a broad range of industrial application. PAEs are ubiquitous in different environments and our daily life due to their large and widespread application. Recent PAEs research mainly focused on their environmental fate (including leaching, migration, transformation) and toxicology and risk assessment. With the comprehensive recognition of their potential hazard, the elimination of PAEs has attracted worldwide concerns. Although many factors may contribute to the degradation of PAEs, the dominant role of biodegradation was widely reported. Many PAEs-degrading bacteria were isolated, metabolites and metabolic pathways were proposed, and enzymes involved in the degradation were identified. The current paper presents an overview of available reports about PAEs-degrading bacteria and related molecular mechanisms. The metabolic pathways deduced from the identified intermediates were presented. The upstream and downstream pathways of PAEs metabolism were summarized, including the aerobic and anaerobic pathways of phthalic acid (PA) degradation. Known enzymes involved in the hydrolysis of ester bonds were characterized according to their properties. Based on phylogenetic analysis, all these enzymes were distributed in four families of esterases and one unknown family. For these five families, conserved sequence motifs were identified and the biological properties of these motifs were characterized. Challenges and emerging opportunities are also discussed.

Glucose and xylose are the major components of lignocellulose. Effective utilization of both sugars can improve the efficiency of bioproduction. Here, we report a method termed parallel metabolic pathway engineering (PMPE) for producing shikimate pathway derivatives from glucose–xylose co-substrate. In this method, we seek to use glucose mainly for target chemical production, and xylose for supplying essential metabolites for cell growth. Glycolysis and the pentose phosphate pathway are completely separated from the tricarboxylic acid (TCA) cycle. To recover cell growth, we introduce a xylose catabolic pathway that directly flows into the TCA cycle. As a result, we can produce 4.09 g L −1 cis , cis -muconic acid using the PMPE Escherichia coli strain with high yield (0.31 g g −1 of glucose) and produce l -tyrosine with 64% of the theoretical yield. The PMPE strategy can contribute to the development of clean processes for producing various valuable chemicals from lignocellulosic resources. In lignocellulose biomass, microbes prefer consuming glucose over xylose, which affects target compound production. Here, the authors achieve simultaneous utilization of glucose and xylose for target chemical production and cell growth, respectively, and realize high-level production of shikimate pathway derivatives.

Plants produce diverse specialized metabolites (SMs), but the genes responsible for their production and regulation remain largely unknown, hindering efforts to tap plant pharmacopeia. Given that genes comprising SM pathways exhibit environmentally dependent coregulation, we hypothesized that genes within a SM pathway would form tight associations (modules) with each other in coexpression networks, facilitating their identification. To evaluate this hypothesis, we used 10 global coexpression data sets, each a meta-analysis of hundreds to thousands of experiments, across eight plant species to identify hundreds of coexpressed gene modules per data set. In support of our hypothesis, 15.3 to 52.6% of modules contained two or more known SM biosynthetic genes, and module genes were enriched in SM functions. Moreover, modules recovered many experimentally validated SM pathways, including all six known to form biosynthetic gene clusters (BGCs). In contrast, bioinformatically predicted BGCs (i.e., those lacking an associated metabolite) were no more coexpressed than the null distribution for neighboring genes. These results suggest that most predicted plant BGCs are not genuine SM pathways and argue that BGCs are not a hallmark of plant specialized metabolism. We submit that global gene coexpression is a rich, largely untapped resource for discovering the genetic basis and architecture of plant natural products.

Rice bran (RB) consumption has been shown to reduce colorectal cancer (CRC) growth in mice and modify the human stool microbiome. Changes in host and microbial metabolism induced by RB consumption was hypothesised to modulate the stool metabolite profile in favour of promoting gut health and inhibiting CRC growth. The objective was to integrate gut microbial metabolite profiles and identify metabolic pathway networks for CRC chemoprevention using non-targeted metabolomics. In all, nineteen CRC survivors participated in a parallel randomised controlled dietary intervention trial that included daily consumption of study-provided foods with heat-stabilised RB (30 g/d) or no additional ingredient (control). Stool samples were collected at baseline and 4 weeks and analysed using GC-MS and ultra-performance liquid chromatography-MS. Stool metabolomics revealed 93 significantly different metabolites in individuals consuming RB. A 264-fold increase in β-hydroxyisovaleroylcarnitine and 18-fold increase in β-hydroxyisovalerate exemplified changes in leucine, isoleucine and valine metabolism in the RB group. A total of thirty-nine stool metabolites were significantly different between RB and control groups, including increased hesperidin (28-fold) and narirutin (14-fold). Metabolic pathways impacted in the RB group over time included advanced glycation end products, steroids and bile acids. Fatty acid, leucine/valine and vitamin B6 metabolic pathways were increased in RB compared with control. There were 453 metabolites identified in the RB food metabolome, thirty-nine of which were identified in stool from RB consumers. RB consumption favourably modulated the stool metabolome of CRC survivors and these findings suggest the need for continued dietary CRC chemoprevention efforts.

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 its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

ObjectivesTo characterise renal tissue metabolic pathway gene expression in different forms of glomerulonephritis.MethodsPatients with nephrotic syndrome (NS), antineutrophil cytoplasmic antibody-associated vasculitis (AAV), systemic lupus erythematosus (SLE) and healthy living donors (LD) were studied. Clinically indicated renal biopsies were obtained at time of diagnosis and microdissected into glomerular and tubulointerstitial compartments. Microarray-derived differential gene expression of 88 genes representing critical enzymes of metabolic pathways and 25 genes related to immune cell markers was compared between disease groups. Correlation analyses measured relationships between metabolic pathways, kidney function and cytokine production.ResultsReduced steady state levels of mRNA species were enriched in pathways of oxidative phosphorylation and increased in the pentose phosphate pathway (PPP) with maximal perturbation in AAV and SLE followed by NS, and least in LD. Transcript regulation was isozymes specific with robust regulation in hexokinases, enolases and glucose transporters. Intercorrelation networks were observed between enzymes of the PPP (eg, transketolase) and macrophage markers (eg, CD68) (r=0.49, p<0.01). Increased PPP transcript levels were associated with reduced glomerular filtration rate in the glomerular (r=−0.49, p<0.01) and tubulointerstitial (r=−0.41, p<0.01) compartments. PPP expression and tumour necrosis factor activation were tightly co-expressed (r=0.70, p<0.01).ConclusionThis study demonstrated concordant alterations of the renal transcriptome consistent with metabolic reprogramming across different forms of glomerulonephritis. Activation of the PPP was tightly linked with intrarenal macrophage marker expression, reduced kidney function and increased production of cytokines. Modulation of glucose metabolism may offer novel immune-modulatory therapeutic approaches in rare kidney diseases.