Showing papers on "Metabolome published in 2021"
TL;DR: In this article, a longitudinal analysis of the impact of three divergent diets, vegan, omnivore, and a synthetic enteral nutrition (EEN) diet lacking fiber, on the human gut microbiome and its metabolome, including after a microbiota depletion intervention.
101 citations
TL;DR: This work investigated fecal microbiome and metabolome alterations in PD, and their clinical relevance, and concluded that gut microbiome alterations in Parkinson disease are likely to be influenced by environmental factors.
Abstract: Objective Gut microbiome alterations in Parkinson disease (PD) have been reported repeatedly, but their functional relevance remains unclear. Fecal metabolomics, which provide a functional readout of microbial activity, have scarcely been investigated. We investigated fecal microbiome and metabolome alterations in PD, and their clinical relevance. Methods Two hundred subjects (104 patients, 96 controls) underwent extensive clinical phenotyping. Stool samples were analyzed using 16S rRNA gene sequencing. Fecal metabolomics were performed using two platforms, nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry. Results Fecal microbiome and metabolome composition in PD was significantly different from controls, with the largest effect size seen in NMR-based metabolome. Microbiome and NMR-based metabolome compositional differences remained significant after comprehensive confounder analyses. Differentially abundant fecal metabolite features and predicted functional changes in PD versus controls included bioactive molecules with putative neuroprotective effects (eg, short chain fatty acids [SCFAs], ubiquinones, and salicylate) and other compounds increasingly implicated in neurodegeneration (eg, ceramides, sphingosine, and trimethylamine N-oxide). In the PD group, cognitive impairment, low body mass index (BMI), frailty, constipation, and low physical activity were associated with fecal metabolome compositional differences. Notably, low SCFAs in PD were significantly associated with poorer cognition and low BMI. Lower butyrate levels correlated with worse postural instability-gait disorder scores. Interpretation Gut microbial function is altered in PD, characterized by differentially abundant metabolic features that provide important biological insights into gut-brain pathophysiology. Their clinical relevance further supports a role for microbial metabolites as potential targets for the development of new biomarkers and therapies in PD. ANN NEUROL 2021;89:546-559.
84 citations
TL;DR: In this article, the authors tested whether glyphosate can inhibit the shikimate pathway of gastrointestinal microorganisms, with potential health implications, and found that it does not have any harmful effects.
Abstract: Background: There is intense debate on whether glyphosate can inhibit the shikimate pathway of gastrointestinal microorganisms, with potential health implications. Objectives: We tested whether gly...
84 citations
TL;DR: In this article, an automated high-throughput metabolite array technology that can rapidly and quantitatively determine 324 metabolites including fatty acids, amino acids, organic acids, carbohydrates, and bile acids is reported.
Abstract: The application of metabolomics in translational research suffers from several technological bottlenecks, such as data reproducibility issues and the lack of standardization of sample profiling procedures. Here, we report an automated high-throughput metabolite array technology that can rapidly and quantitatively determine 324 metabolites including fatty acids, amino acids, organic acids, carbohydrates, and bile acids. Metabolite identification and quantification is achieved using the Targeted Metabolome Batch Quantification (TMBQ) software, the first cross-vendor data processing pipeline. A test of this metabolite array was performed by analyzing serum samples from patients with chronic liver disease (N = 1234). With high detection efficiency and sensitivity in serum, urine, feces, cell lysates, and liver tissue samples and suitable for different mass spectrometry systems, this metabolite array technology holds great potential for biomarker discovery and high throughput clinical testing. Additionally, data generated from such standardized procedures can be used to generate a clinical metabolomics database suitable for precision medicine in next-generation healthcare.
81 citations
TL;DR: Improvement of alcoholic liver disease by targeting the intestinal microbiota involves the AhR pathway, which should be considered as a new therapeutic target.
Abstract: Objective Chronic alcohol consumption is an important cause of liver-related deaths. Specific intestinal microbiota profiles are associated with susceptibility or resistance to alcoholic liver disease in both mice and humans. We aimed to identify the mechanisms by which targeting intestinal microbiota can improve alcohol-induced liver lesions. Design We used human associated mice, a mouse model of alcoholic liver disease transplanted with the intestinal microbiota of alcoholic patients and used the prebiotic, pectin, to modulate the intestinal microbiota. Based on metabolomic analyses, we focused on microbiota tryptophan metabolites, which are ligands of the aryl hydrocarbon receptor (AhR). Involvement of the AhR pathway was assessed using both a pharmacological approach and AhR-deficient mice. Results Pectin treatment modified the microbiome and metabolome in human microbiota-associated alcohol-fed mice, leading to a specific faecal signature. High production of bacterial tryptophan metabolites was associated with an improvement of liver injury. The AhR agonist Ficz (6-formylindolo (3,2-b) carbazole) reduced liver lesions, similarly to prebiotic treatment. Conversely, inactivation of the ahr gene in alcohol-fed AhR knock-out mice abrogated the beneficial effects of the prebiotic. Importantly, patients with severe alcoholic hepatitis have low levels of bacterial tryptophan derivatives that are AhR agonists. Conclusions Improvement of alcoholic liver disease by targeting the intestinal microbiota involves the AhR pathway, which should be considered as a new therapeutic target.
80 citations
TL;DR: In this paper, the authors report alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care).
Abstract: The circulating metabolome provides a snapshot of the physiological state of the organism responding to pathogenic challenges. Here we report alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care). This analysis revealed major disease- and stage-associated shifts in the metabolome, meaning that at least 77 metabolites including amino acids, lipids, polyamines and sugars, as well as their derivatives, were altered in critical COVID-19 patient's plasma as compared to mild COVID-19 patients. Among a uniformly moderate cohort of patients who received tocilizumab, only 10 metabolites were different among individuals with a favorable evolution as compared to those who required transfer into the intensive care unit. The elevation of one single metabolite, anthranilic acid, had a poor prognostic value, correlating with the maintenance of high interleukin-10 and -18 levels. Given that products of the kynurenine pathway including anthranilic acid have immunosuppressive properties, we speculate on the therapeutic utility to inhibit the rate-limiting enzymes of this pathway including indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase.
80 citations
TL;DR: In this article, a review of UHPLC-HRMS-based metabolomics is presented, with a focus on expanding metabolome coverage, and the authors discuss current common strategies for UHP LC-HMS based metabolomics, and discuss the challenges of complexity and lack of comprehensive coverage of the metabolome.
Abstract: Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC–HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC–HRMS-based metabolomics, with a focus on expanding metabolome coverage. This Review surveys ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC–HRMS), a highly sensitive, high-throughput technique that is used for analyzing a broad range of metabolites.
78 citations
TL;DR: These findings support a connection between metabolism, gastrointestinal physiology, and complex behavioral traits and may advance discovery and development of molecular biomarkers for ASD.
76 citations
TL;DR: In this article, a prospective cohort study of COVID-19 patients with mild or severe symptoms on admission, patients who progressed from mild to severe symptoms, and patients who were followed from hospital admission to discharge was conducted.
Abstract: Background Metabolism is critical for sustaining life, immunity and infection, but its role in COVID-19 is not fully understood. Methods Seventy-nine COVID-19 patients, 78 healthy controls (HCs) and 30 COVID-19-like patients were recruited in a prospective cohort study. Samples were collected from COVID-19 patients with mild or severe symptoms on admission, patients who progressed from mild to severe symptoms, and patients who were followed from hospital admission to discharge. The metabolome was assayed using gas chromatography–mass spectrometry. Results Serum butyric acid, 2-hydroxybutyric acid, l -glutamic acid, l -phenylalanine, l -serine, l -lactic acid, and cholesterol were enriched in COVID-19 and COVID-19-like patients versus HCs. Notably, d -fructose and succinic acid were enriched, and citric acid and 2-palmitoyl-glycerol were depleted in COVID-19 patients compared to COVID-19-like patients and HCs, and these four metabolites were not differentially distributed in non-COVID-19 groups. COVID-19 patients had enriched 4-deoxythreonic acid and depleted 1,5-anhydroglucitol compared to HCs and enriched oxalic acid and depleted phosphoric acid compared to COVID-19-like patients. A combination of d -fructose, citric acid and 2-palmitoyl-glycerol distinguished COVID-19 patients from HCs and COVID-19-like patients, with an area under the curve (AUC) > 0.92 after validation. The combination of 2-hydroxy-3-methylbutyric acid, 3-hydroxybutyric acid, cholesterol, succinic acid, L-ornithine, oleic acid and palmitelaidic acid predicted patients who progressed from mild to severe COVID-19, with an AUC of 0.969. After discharge, nearly one-third of metabolites were recovered in COVID-19 patients. Conclusions The serum metabolome of COVID-19 patients is distinctive and has important value in investigating pathogenesis, determining a diagnosis, predicting severe cases, and improving treatment.
72 citations
Wageningen University and Research Centre1, University of Wisconsin-Madison2, University of Montana3, University of California, San Diego4, Leibniz Association5, University of Tübingen6, Jenderal Soedirman University7, University of São Paulo8, University of Potsdam9, University of California, Merced10, University of Málaga11, University of California, Riverside12, Pasteur Institute13, Université Paris-Saclay14, Carnegie Mellon University15, Leiden University16, University of Helsinki17, University of Porto18, Yale University19, Great Lakes Bioenergy Research Center20, Institute of Tropical Medicine Antwerp21, University of Antwerp22, Technical University of Berlin23, J. Craig Venter Institute24, Georgia Institute of Technology25, Charité26, Sunchon National University27, Sookmyung Women's University28, Northwestern University29, Sungkyunkwan University30, University of Bonn31, Princeton University32, University of Copenhagen33, University of Illinois at Chicago34, Point Loma Nazarene University35, University of Illinois at Urbana–Champaign36, University of Nottingham37, Purdue University38, ETH Zurich39, Santa Catarina Federal Institute of Education, Science and Technology40, Memorial University of Newfoundland41, Federal University of Ceará42, Strathclyde Institute of Pharmacy and Biomedical Sciences43, University of Glasgow44
TL;DR: The Paired Omics Data Platform as mentioned in this paper is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.
Abstract: Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.
68 citations
TL;DR: In this article, ultra-performance liquid chromatography and gas chromatography-tandem mass spectrometry (GCMS) were used to analyze a widely targeted metabolome during green tea processing, and a total of 153 nonvolatile metabolites were screened out, and amino acids and esters were found to be closely associated with volatile metabolite formation.
TL;DR: JMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface, which will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.
Abstract: 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 resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.
TL;DR: In this paper, Gut microbiome reprogramming in patients with colorectal cancer is associated with alterations of the serum metabolome, and GMSMs have potential applications for CRC and adenoma detection.
Abstract: Objective To profile gut microbiome-associated metabolites in serum and investigate whether these metabolites could distinguish individuals with colorectal cancer (CRC) or adenoma from normal healthy individuals. Design Integrated analysis of untargeted serum metabolomics by liquid chromatography-mass spectrometry and metagenome sequencing of paired faecal samples was applied to identify gut microbiome-associated metabolites with significantly altered abundance in patients with CRC and adenoma. The ability of these metabolites to discriminate between CRC and colorectal adenoma was tested by targeted metabolomic analysis. A model based on gut microbiome-associated metabolites was established and evaluated in an independent validation cohort. Results In total, 885 serum metabolites were significantly altered in both CRC and adenoma, including eight gut microbiome-associated serum metabolites (GMSM panel) that were reproducibly detected by both targeted and untargeted metabolomics analysis and accurately discriminated CRC and adenoma from normal samples. A GMSM panel-based model to predict CRC and colorectal adenoma yielded an area under the curve (AUC) of 0.98 (95% CI 0.94 to 1.00) in the modelling cohort and an AUC of 0.92 (83.5% sensitivity, 84.9% specificity) in the validation cohort. The GMSM model was significantly superior to the clinical marker carcinoembryonic antigen among samples within the validation cohort (AUC 0.92 vs 0.72) and also showed promising diagnostic accuracy for adenomas (AUC=0.84) and early-stage CRC (AUC=0.93). Conclusion Gut microbiome reprogramming in patients with CRC is associated with alterations of the serum metabolome, and GMSMs have potential applications for CRC and adenoma detection.
TL;DR: In this article, the variation of metabolites between healthy control and COVID-19 via the untargeted metabolomics method was determined in terms of purine, glutamine, leukotriene D4 (LTD4), and glutathione metabolisms.
Abstract: Coronavirus Disease 2019 (COVID-19) is an infectious respiratory disease caused by the new strain of the coronavirus. There is limited data on pathogenesis and the cellular responses of COVID-19. In this study, it is aimed to determine the variation of metabolites between healthy control and COVID-19 via the untargeted metabolomics method. Serum samples were obtained from 44 COVID-19 patients and 41 healthy controls. Untargeted metabolomics analyses were performed by LC/Q-TOF/MS method. Data acquisition, classification, and identification were achieved by the METLIN database and XCMS. Significant differences were determined between patients and healthy controls in terms of purine, glutamine, leukotriene D4 (LTD4) and glutathione metabolisms. Down regulations were determined in R-S lactoglutathione and glutamine. Up-regulations were detected in hypoxanthine, inosine, and LTD4. Identified metabolites indicate roles for purine, glutamine, LTD4, and glutathione metabolisms in the pathogenesis of the COVID-19. The use of selective leukotriene D4 receptor antagonists, targeting purinergic signaling as a therapeutic approach and glutamine supplementation may decrease the severity and mortality of COVID-19. This article is protected by copyright. All rights reserved.
TL;DR: In this paper, the authors presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in Parkinson's disease using liquid chromatography-high resolution mass spectrometry-based metabolomics approach.
Abstract: 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. 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 resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts. A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naive PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD. This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.
TL;DR: In this paper, the faecal metabolome of COVID-19 patients was analyzed using gas chromatography-mass spectrometry, and Spearman's correlation analyses of clinical features, the serum metabolome, and the Faecal micro- and mycobiota were conducted.
TL;DR: In this article, the authors summarized the complex crosstalk between short-chain fatty acids and colorectal cancer, which might inspire new approaches for the diagnosis, treatment and prevention of CRC on the basis of gut microbiota-derived metabolites SCFAs.
TL;DR: In this article, the effects of environmental contaminants (ECs) exposure on liver metabolism were investigated in a non-alcoholic fatty liver disease (NAFLD) cohort of 105 individuals, and the results showed that exposure to ECs, particularly perfluorinated alkyl substances (PFAS), impacts liver metabolism, specifically bile acid metabolism.
TL;DR: A metabolome atlas of the aging wildtype mouse brain from 10 anatomical regions spanning from adolescence to old age is presented in this paper. But the brain atlas is limited to three assays and structurally annotated 1,547 metabolites.
Abstract: 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. The brain metabolome atlas is publicly available ( https://mouse.atlas.metabolomics.us/ ) and serves as a foundation dataset for future metabolomic studies.
TL;DR: It is found that short-term high fat diet (HFD) feeding strongly stimulates intestinal Lcn2 expression and secretion into the gut lumen, and correlation analyses suggest that LCN2-targeted Dubosiella and Angelakisella have a novel role in regulating SCFAs production and obesity.
Abstract: Lipocalin 2 (Lcn2), as an antimicrobial peptide is expressed in intestine, and the upregulation of intestinal Lcn2 has been linked to inflammatory bowel disease. However, the role of Lcn2 in shaping gut microbiota during diet-induced obesity (DIO) remains unknown. We found that short-term high fat diet (HFD) feeding strongly stimulates intestinal Lcn2 expression and secretion into the gut lumen. As the HFD feeding prolongs, fecal Lcn2 levels turn to decrease. Lcn2 deficiency accelerates the development of HFD-induced intestinal inflammation and microbiota dysbiosis. Moreover, Lcn2 deficiency leads to the remodeling of microbiota-derived metabolome, including decreased production of short-chain fatty acids (SCFAs) and SCFA-producing microbes. Most importantly, we have identified Lcn2-targeted bacteria and microbiota-derived metabolites that potentially play roles in DIO and metabolic dysregulation. Correlation analyses suggest that Lcn2-targeted Dubosiella and Angelakisella have a novel role in regulating SCFAs production and obesity. Our results provide a novel mechanism involving Lcn2 as an antimicrobial host factor in the control of gut microbiota symbiosis during DIO.
TL;DR: In this article, the authors review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data, and reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.
Abstract: The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.
TL;DR: In this paper, a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe, was performed.
Abstract: COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.
TL;DR: In this article, the role of dysregulation in the adipose tissue metabolome on vascular redox signaling and cardiovascular outcomes was explored in human aortic endothelial cells, and their clinical value was tested against hard clinical endpoints.
19 Apr 2021
TL;DR: In this paper, a review of the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics is presented and discussed.
Abstract: Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.
TL;DR: In this article, a combined transcriptome and metabolome analysis was performed to identify the stress-responsive genes, metabolites, and metabolic pathways based on a combined approach to understand the cold-tolerant (C18) and cold-sensitive (C6) rapeseed varieties.
Abstract: Rapeseed (Brassica napus L.) is an important oilseed crop in the world. Its productivity is significantly influenced by numerous abiotic stresses, including cold stress (CS). Consequently, enhancement in CS tolerance is becoming an important area for agricultural investigation and crop improvement. Therefore, the current study aimed to identify the stress-responsive genes, metabolites, and metabolic pathways based on a combined transcriptome and metabolome analysis to understand the CS responses and tolerance mechanisms in the cold-tolerant (C18) and cold-sensitive (C6) rapeseed varieties. Based on the metabolome analysis, 31 differentially accumulated metabolites (DAMs) were identified between different comparisons of both varieties at the same time points. From the transcriptome analysis, 2,845, 3,358, and 2,819 differentially expressed genes (DEGs) were detected from the comparison of C6-0 vs. C18-0, C6-1 vs. C18-1, and C6-7 vs. C18-7. By combining the transcriptome and metabolome data sets, we found that numerous DAMs were strongly correlated with several differentially expressed genes (DEGs). A functional enrichment analysis of the DAMs and the correlated DEGs specified that most DEGs and DAMs were mainly enriched in diverse carbohydrates and amino acid metabolisms. Among them, starch and sucrose metabolism and phenylalanine metabolism were significantly enriched and played a vital role in the CS adaption of rapeseed. Six candidate genes were selected from the two pathways for controlling the adaption to low temperature. In a further validation, the T-DNA insertion mutants of their Arabidopsis homologous, including 4cl3, cel5, fruct4, ugp1, axs1, and bam2/9, were characterized and six lines differed significantly in levels of freezing tolerance. The outcome of the current study provided new prospects for the understanding of the molecular basis of CS responses and tolerance mechanisms in rapeseed and present a set of candidate genes for use in improving CS adaptability in the same plant.
TL;DR: In this paper, the primary and secondary metabolites from purple leaf buds and green mature leaves were investigated using ultra-high-performance liquid chromatography/tandem mass spectrometry.
TL;DR: In this article, the authors colonized mice with four anaerobic symbionts and showed that acute immune responses result in dramatic transcriptional reprogramming of these commensals with minimal changes in their relative abundance.
TL;DR: In this article, the authors investigated the profiles of the gut microbiota and metabolites that changed after a 60% fat diet for 8 weeks, 16S rRNA gene amplicon sequencing and gas chromatography-mass spectrometry (GC-MS)-based metabolomic analyses were performed.
Abstract: Obesity can be caused by microbes producing metabolites; it is thus important to determine the correlation between gut microbes and metabolites. This study aimed to identify gut microbiota-metabolomic signatures that change with a high-fat diet and understand the underlying mechanisms. To investigate the profiles of the gut microbiota and metabolites that changed after a 60% fat diet for 8 weeks, 16S rRNA gene amplicon sequencing and gas chromatography-mass spectrometry (GC-MS)-based metabolomic analyses were performed. Mice belonging to the HFD group showed a significant decrease in the relative abundance of Bacteroidetes but an increase in the relative abundance of Firmicutes compared to the control group. The relative abundance of Firmicutes, such as Lactococcus, Blautia, Lachnoclostridium, Oscillibacter, Ruminiclostridium, Harryflintia, Lactobacillus, Oscillospira, and Erysipelatoclostridium, was significantly higher in the HFD group than in the control group. The increased relative abundance of Firmicutes in the HFD group was positively correlated with fecal ribose, hypoxanthine, fructose, glycolic acid, ornithine, serum inositol, tyrosine, and glycine. Metabolic pathways affected by a high fat diet on serum were involved in aminoacyl-tRNA biosynthesis, glycine, serine and threonine metabolism, cysteine and methionine metabolism, glyoxylate and dicarboxylate metabolism, and phenylalanine, tyrosine, and trypto-phan biosynthesis. This study provides insight into the dysbiosis of gut microbiota and metabolites altered by HFD and may help to understand the mechanisms underlying obesity mediated by gut microbiota.
TL;DR: It is suggested that the downregulated expression of calcium proteins and cysteine-rich kinases, and the up regulated expression of SBP1 and SDI2, were important contributors to the Se tolerance of C. violifolia.