Analysis Of Metabolomics Data Research Articles

Bioinformatic Analysis of Metabolomic Data: From Raw Spectra to Biological Insight

Metabolites are at the end of the gene–transcript–protein–metabolism cascade. As such, metabolomics is the omics approach that offers the most direct correlation with phenotype. This allows, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics to possibly provide an answer. Complex phenotypes, which are determined by the influence of multiple small-effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased exponentially in recent years. As a newer discipline, metabolomic bioinformatic analysis pipelines are not as standardized as in the other omics approaches. In this review, we synthesized the different steps that need to be carried out to obtain biological insight from annotated metabolite abundance raw data. These steps were grouped into three different modules: preprocessing, statistical analysis, and metabolic pathway enrichment. We included within each one of them the different state-of-the-art procedures and tools that can be used depending on the characteristics of the study, providing details about each method’s characteristics and the issues the reader might encounter. Finally, we introduce genome-scale metabolic modeling as a tool for obtaining pseudo-metabolomic data in situations where their acquisition is difficult, enabling the analysis of the resulting data with the modules of the described workflow.

Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury

BackgroundThe equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions.MethodsTwenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches.ResultsPhenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa.ConclusionsResults of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology.8RW7XWUKiyvANo7PaYZA3nVideo

Transcriptomic and metabolomic analyses reveal the importance of ethylene networks in mulberry fruit ripening

Mulberry (Morus alba L.) is a climacteric and highly perishable fruit. Ethylene has been considered to be an important trigger of fruit ripening process. However, the role of ethylene in the mulberry fruit ripening process remains unclear. In this study, we performed a comprehensive analysis of metabolomic and transcriptomic data of mulberry fruit and the physiological changes accompanying the fruit ripening process. Our study revealed that changes in the accumulation of specific metabolites at different stages of fruit development and ripening were closely correlated to transcriptional changes as well as underlying physiological changes and the development of taste biomolecules. The ripening of mulberry fruits was highly associated with the production of endogenous ethylene, and further application of exogenous ethylene assisted the ripening process. Transcriptomic analysis revealed that differential expression of diverse ripening-related genes was involved in sugar metabolism, anthocyanin biosynthesis, and cell wall modification pathways. Network analysis of transcriptomics and metabolomics data revealed that many transcription factors and ripening-related genes were involved, among which ethylene-responsive transcription factor 3 (MaERF3) plays a crucial role in the ripening process. The role of MaERF3 in ripening was experimentally proven in a transient overexpression assay in apples. Our study indicates that ethylene plays a vital role in modulating mulberry fruit ripening. The results provide a basis for guiding the genetic manipulation of mulberry fruits towards sustainable agricultural practices and improve post-harvest management, potentially enhancing the quality and shelf life of mulberry fruits for sustainable agriculture and forestry.

Different profiles of soil phosphorous compounds depending on tree species and availability of soil phosphorus in a tropical rainforest in French Guiana

BackgroundThe availability of soil phosphorus (P) often limits the productivities of wet tropical lowland forests. Little is known, however, about the metabolomic profile of different chemical P compounds with potentially different uses and about the cycling of P and their variability across space under different tree species in highly diverse tropical rainforests.ResultsWe hypothesised that the different strategies of the competing tree species to retranslocate, mineralise, mobilise, and take up P from the soil would promote distinct soil 31P profiles. We tested this hypothesis by performing a metabolomic analysis of the soils in two rainforests in French Guiana using 31P nuclear magnetic resonance (NMR). We analysed 31P NMR chemical shifts in soil solutions of model P compounds, including inorganic phosphates, orthophosphate mono- and diesters, phosphonates, and organic polyphosphates. The identity of the tree species (growing above the soil samples) explained > 53% of the total variance of the 31P NMR metabolomic profiles of the soils, suggesting species-specific ecological niches and/or species-specific interactions with the soil microbiome and soil trophic web structure and functionality determining the use and production of P compounds. Differences at regional and topographic levels also explained some part of the the total variance of the 31P NMR profiles, although less than the influence of the tree species. Multivariate analyses of soil 31P NMR metabolomics data indicated higher soil concentrations of P biomolecules involved in the active use of P (nucleic acids and molecules involved with energy and anabolism) in soils with lower concentrations of total soil P and higher concentrations of P-storing biomolecules in soils with higher concentrations of total P.ConclusionsThe results strongly suggest “niches” of soil P profiles associated with physical gradients, mostly topographic position, and with the specific distribution of species along this gradient, which is associated with species-specific strategies of soil P mineralisation, mobilisation, use, and uptake.

Integration of Metabolomic and Transcriptomic Analyses Reveals the Molecular Mechanisms of Flower Color Formation in Prunus mume.

Flower color is an important trait that affects the economic value of Prunus mume, a famous ornamental plant in the Rosaceae family. P. mume with purple-red flowers is uniquely charming and highly favored in landscape applications. However, little is known about its flower coloring mechanism, which stands as a critical obstacle on the path to innovative breeding for P. mume flower color. In this study, transcriptomic and targeted metabolomic analyses of purple-red P. mume and white P. mume were performed to elucidate the mechanism of flower color formation. In addition, the expression patterns of key genes were analyzed using an RT-qPCR experiment. The results showed that the differential metabolites were significantly enriched in the flavonoid synthesis pathway. A total of 14 anthocyanins emerged as the pivotal metabolites responsible for the differences in flower color between the two P. mume cultivars, comprising seven cyanidin derivatives, five pelargonium derivatives, and two paeoniflorin derivatives. Moreover, the results clarified that the metabolic pathway determining flower color in purple-red P. mume encompasses two distinct branches: cyanidin and pelargonidin, excluding the delphinidin branch. Additionally, through the integrated analysis of transcriptomic and metabolomic data, we identified 18 key genes responsible for anthocyanin regulation, thereby constructing the gene regulatory network for P. mume anthocyanin synthesis. Among them, ten genes (PmCHI, PmGT2, PmGT5, PmGST3, PmMYB17, PmMYB22, PmMYB23, PmbHLH4, PmbHLH10, and PmbHLH20) related to anthocyanin synthesis were significantly positively correlated with anthocyanin contents, indicating that they may be the key contributors to anthocyanin accumulation. Our investigation contributes a novel perspective to understanding the mechanisms responsible for flower color formation in P. mume. The findings of this study introduce novel strategies for molecular design breeding aimed at manipulating flower color in P. mume.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design withmultiple factors orphenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization,and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Transcriptome and Metabolome Analyses Reveal Ascorbic Acid Ameliorates Cold Tolerance in Rice Seedling Plants

Ascorbic acid (AsA) plays a crucial role in the physiological processes of rice plants when they encounter various biotic and abiotic stresses. However, the specific mechanism by which AsA affects cold tolerance in rice seedlings remains unclear. Two rice genotypes, Zhongzao39 (ZZ39) and its recombinant inbred line RIL82, were exposed to cold stress, resulting in more damage observed in RIL82 compared to ZZ39. This damage included higher levels of relative electrolytic leakage (REC), malondialdehyde (MDA), H2O2, a lower Fv/Fm, and a lower survival rate. A comprehensive analysis of transcriptome and metabolome data indicated that AsA was involved in regulating cold tolerance in ZZ39 and RIL82 seedling plants. AsA content increased in ZZ39 while it decreased in RIL82 under cold stress. Additionally, analysis of carbohydrate contents highlighted their important role in the responses to cold stress of these two genotypes. Importantly, exogenous AsA and sucrose, either alone or in combination, enhanced the values of maximum fluorescence quantum yield (Fv/Fm) and effective quantum yield (YII) as well as decreased H2O2 and MDA levels to improve cold tolerance in both genotypes compared with plants treated with H2O. These findings highlight the potential significance of AsA in mitigating the effects of cold stress on rice seedling plants.

Integrative proteomics and metabolomics data analysis exploring the mechanism of brain injury after cardiac surgery in chronic stress rats

ObjectivePreoperative chronic stress (CS) is associated with postoperative brain injury in patients undergoing open heart cardiac surgery. This research is to explore the potential molecular biological mechanisms of brain damage following cardiac surgery in preoperative CS rats by the analyses combining proteomics and metabolomics.MethodsWe constructed the chronic unpredictable stress (CUS) and cardiac surgery models in adult rats. We proved the brain injury in CUS cardiac surgery rats by Hematoxylin–Eosin (H&E) staining, followed by separating the hippocampal tissue and investigating the potential mechanisms of brain injury by the methods of data-independent acquisition proteomics and untargeted metabolomics.ResultsThe signaling pathways of glycoproteins and metabolism of amino acids were the main possible mechanisms of brain injury in CUS rats following cardiac surgery according to the proteomics and metabolomics. In addition, the pathways of animo acids metabolism such as the pathways of lysine degradation and β-alanine metabolism may be the main mechanism of cardiac surgery related brain injury in preoperative CUS rats.ConclusionsThe pathways of animo acids metabolism such as lysine degradation and β-alanine metabolism may be the potential mechanisms of brain injury in CUS rats following cardiac surgery. We should focus on the varieties of bioproteins and metabolites in these pathways, and related changes in other signaling pathways induced by the two pathways.

Comprehensive analysis of MAPK gene family in upland cotton (Gossypium hirsutum) and functional characterization of GhMPK31 in regulating defense response to insect infestation

Key messageThe transcriptomic, phenotypic and metabolomic analysis of transgenic plants overexpressing GhMPK31 in upland cotton revealed the regulation of H2O2 burst and the synthesis of defensive metabolites by GhMPK31.Mitogen-activated protein kinases (MAPKs) are a crucial class of protein kinases, which play an essential role in various biological processes in plants. Upland cotton (G. hirsutum) is the most widely cultivated cotton species with high economic value. To gain a better understanding of the role of the MAPK gene family, we conducted a comprehensive analysis of the MAPK gene family in cotton. In this study, a total of 55 GhMPK genes were identified from the whole genome of G. hirsutum. Through an investigation of the expression patterns under diverse stress conditions, we discovered that the majority of GhMPK family members demonstrated robust responses to abiotic stress, pathogen stress and pest stress. Furthermore, the overexpression of GhMPK31 in cotton leaves led to a hypersensitive response (HR)-like cell death phenotype and impaired the defense capability of cotton against herbivorous insects. Transcriptome and metabolomics data analysis showed that overexpression of GhMPK31 enhanced the expression of H2O2-related genes and reduced the accumulation of defensive related metabolites. The direct evidence of GhMPK31 interacting with GhRBOHB (H2O2-generating protein) were found by Y2H, BiFC, and LCI. Therefore, we propose that the increase of H2O2 content caused by overexpression of GhMPK31 resulted in HR-like cell death in cotton leaves while reducing the accumulation of defensive metabolites, ultimately leading to a decrease in the defense ability of cotton against herbivorous insects. This study provides valuable insights into the function of MAPK genes in plant resistance to herbivorous insects.

Analyzing postprandial metabolomics data using multiway models: a simulation study

BackgroundAnalysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data.ResultsWe introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups.ConclusionsOur experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

Mechanism of Peppermint Extract-Induced Delay of 'Packham's Triumph' Pear (Pyrus communis L.) Postharvest Ripening.

Postharvest ripening is correlated to the quality and shelf life of European pear fruit. In this study, the effects of peppermint extract on fruit phenotype, related physiological activities, and aroma components during postharvest ripening of the European pear variety 'Packham's Triumph' were examined. Fruit treated with 2.0 g L-1 peppermint extract for 12 h showed delayed softening by 4 d compared with that of the untreated control group. The peak values of ethylene and respiratory rate in fruit were reduced to a certain extent after peppermint extract treatment; however, the peppermint extract did not delay the occurrence of the respiratory climacteric peak. Peppermint extract treatment also did not significantly increase the content of the characteristic peppermint aroma in pear fruit. Further, widely targeted metabolome analysis revealed 298 significantly different metabolites, with flavonoids (40%) and lipid compounds (15%) accounting for the highest proportion on the first day after treatment. The Kyoto Encyclopedia of Genes and Genomes pathway result showed significant enrichment in the metabolic pathways of biosynthesis of flavonoid, isoflavonoid, flavone and flavonol, linoleic acid, and alpha-linolenic acid metabolism following peppermint extract treatment. The combined analysis of transcriptome and metabolome data showed significant enrichment in linoleic acid metabolism and alpha-linolenic acid metabolism on the first, third, and fifth days after peppermint extract treatment. This study indicates that peppermint extract mainly affects the pear fruit softening process in the early stage after treatment.

Integrated transcriptomic and metabolomic analyses elucidate the mechanism of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress

Seed propagation is the main method of mulberry expansion in China, an important economic forest species. However, seed germination is the most sensitive stage to various abiotic stresses, especially salinity stress. To reveal the molecular regulatory mechanism of mulberry seed germination under salt stress, flavonoid metabolomics and transcriptomics analyses were performed on mulberry seeds germinated under 50 and 100 mmol/L NaCl stress. Analysis of the flavonoid metabolome revealed that a total of 145 differential flavonoid metabolites (DFMs) were classified into 9 groups, 40 flavonols, 32 flavones, 16 chalcones and 14 flavanones. Among them, 61.4% (89) of the DFMs accumulated continuously with increasing salt concentration, reaching the highest level at a 100 mmol/L salt concentration; these DFMs included quercetin-3-O-glucoside (isoquercitrin), kaempferol (3,5,7,4'-tetrahydroxyflavone), quercetin-7-O-glucoside, taxifolin (dihydroquercetin) and apigenin (4',5,7-trihydroxyflavone), indicating that these flavonoids may be key metabolites involved in the response to salt stress. Transcriptional analysis identified a total of 3055 differentially expressed genes (DEGs), most of which were enriched in flavonoid biosynthesis (ko00941), phenylpropanoid biosynthesis (ko00940) and biosynthesis of secondary metabolites (ko01110). Combined analysis of flavonoid metabolomic and transcriptomic data indicated that phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), chalcone synthase (CHS), flavonol synthase (FLS), bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR) and anthocyanidin reductase (ANR) were the key genes involved in flavonoid accumulation during mulberry seed germination under 50 and 100 mmol/L NaCl stress. In addition, three transcription factors, MYB, bHLH and NAC, were involved in the regulation of flavonoid accumulation under salt stress. The results of quantitative real-time PCR (qRT‒PCR) validation showed that the expression levels of 11 DEGs, including 7 genes involved in flavonoid biosynthesis, under different salt concentrations were consistent with the transcriptomic data, and parallel reaction monitoring (PRM) results showed that the expression levels of 6 key enzymes (proteins) involved in flavonoid synthesis were consistent with the accumulation of flavonoids. This study provides a new perspective for investigating the regulatory role of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress at different concentrations.

Metabolic profiling of leaves of four <i>Ranunculus</i> species

BACKGROUND: Plant ability to survive oxygen deficiency is associated with the presence of various adaptations, majority of which are mediated by significant changes of metabolism. These alterations allow resistant wetland plants to grow even in an oxygen-depleted environment.
 AIM: To compare metabolic profiles of the leaves of the wetland species Ranunculus lingua, R. repens and R. sceleratus, and the mesophyte species R. acris growing in their natural habitat in order to identify the most characteristic metabolic traits of hypoxia-resistant plants.
 MATERIALS AND METHODS: Metabolite profiling was performed by GC-MS. Statistical analysis of metabolomics data was processed using R 4.3.1 Beagle Scouts.
 RESULTS: The resulting profile included 360 compounds. 74 of these were identified and 114 compounds were determined to a class. Sugars (114) were the most widely represented in the obtained profiles. 10 amino and 23 carboxylic acids, lipids and phenolic compounds have been identified. Significant differences were revealed between the profiles of leaf metabolomes of all tested species, which were clustered according to phylogenetic relation. The hydrophytic R. sceleratus, growing under submergence, showed the most unique metabolome, in which the level of sugars was reduced and intermediates of anaerobic metabolism, nitrogen metabolism, and alternative pathways of NAD(P)H reoxidation were accumulated. The profile of mesophytic R. acris was markedly different by decreased levels of amino acids, fatty acids and sterols. The metabolite profiles of waterlogged hydrophytes R. lingua and R. repens occupied an intermediate position.
 CONCLUSIONS: The identified differences of metabolomes of Ranunculus species are due to genetic determinants, ecological niche and direct impact of a stressor.

Transcriptomic and Metabolomic Analyses Reveal the Role of Phenylalanine Metabolism in the Maize Response to Stalk Rot Caused by Fusarium proliferatum.

Stalk rot is a prevalent disease of maize (Zea mays L.) that severely affects maize yield and quality worldwide. The ascomycete fungus Fusarium spp. is the most common pathogen of maize stalk rot. At present, the molecular mechanism of Fusarium proliferation during the maize stalk infection that causes maize stalk rot has rarely been reported. In this study, we investigated the response of maize to F. proliferatum infestation by analyzing the phenotypic, transcriptomic, and metabolomic data of inbred lines ZC17 (resistant) and CH72 (susceptible) with different levels of resistance to stalk rot. Physiological and phenotypic results showed that the infection CH72 was significantly more severe than ZC17 after inoculation. Transcriptome analysis showed that after inoculation, the number of differentially expressed genes (DEGs) was higher in CH72 than in ZC17. Nearly half of these DEGs showed the same expression trend in the two inbred lines. Functional annotation and enrichment analyses indicated that the major pathways enriched for DEGs and DEMs included the biosynthesis of plant secondary metabolites, phenylalanine metabolism, biosynthesis of plant hormones, and plant-pathogen interactions. The comprehensive analysis of transcriptome and metabolome data indicated that phenylalanine metabolism and the phenylalanine, tyrosine, and tryptophan biosynthesis pathways played a crucial role in maize resistance to F. proliferatum infection. In addition, a transcription factor (TF) analysis of the DEGs showed that several TF families, including MYB, bHLH, NAC, and WRKY, were significantly activated after inoculation, suggesting that these TFs play important roles in the molecular regulatory network of maize disease resistance. The findings of this study provide valuable insights into the molecular basis of the response of maize to Fusarium proliferatum infection and highlight the importance of combining multiple approaches, such as phenotyping, transcriptomics, and metabolomics, to gain a comprehensive understanding of plant-pathogen interactions.

New insights into the physiological effects of structural environmental enrichment on black sea bream (Acanthopagrus schlegelii) based on a combined analysis of transcriptomics and metabolomics

Environmental enrichment (EE) has been generally accepted as an effective and promising way to enhance the performance and improve the welfare of animals kept in captivity. Evidence for the effects of EE on fish has accumulated rapidly, but little is known about its underlying mechanisms. In this study, black sea bream (Acanthopagrus schlegelii) were reared under enriched and enrichment-free conditions for 30 (post-larval) and 60 (juvenile) days. Physiological, transcriptome, and metabolome data were compared to investigate potential rearing improvements, physiological responses to acute stress, and the underlying mechanisms. The results showed that fish reared under enriched conditions had significantly better growth performance, a higher survival rate, and lower basal cortisol levels. Meanwhile, the cortisol, lactic acid, malondialdehyde (MDA), and total antioxidant capacity (T-AOC) levels in fish subjected to acute stress suggested that EE-reared individuals presented greater stress resistance. However, the effect of EE may decrease with the extension of treatment time for larger fish. The transcriptome analysis showed that energy-related pathways (including oxidative phosphorylation, the citrate cycle, and glycolysis/gluconeogenesis) as well as protein folding, sorting, and degradation-related pathways (protein processing in the endoplasmic reticulum and proteasome pathways); replication and repair related pathways (including pathways involved in nucleotide excision repair, base excision repair, mismatch repair, and homologous recombination); and immune-related pathways (complement and coagulation cascades pathway and the IL-17 signaling pathway) were significantly altered in the EE-reared individuals. In addition, integrated analysis of metabolome and transcriptome data revealed marked enrichment of transcription-associated metabolites involved in cortisol synthesis and secretion, taste transduction, olfactory transduction, and protein digestion and absorption pathways. Our results suggested that EE may influence the growth, fitness, and stress resistance of A. schlegelii by regulating a series of biological processes including energy metabolism, cortisol synthesis, antioxidant enzyme activities, protein folding, DNA replication and repair, and complement and coagulation cascades.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Microbial colony sequencing combined with metabolomics revealed the effects of chronic hexavalent chromium and nickel combined exposure on intestinal inflammation in mice

The pollution and toxic effects of hexavalent chromium [Cr(VI)] and divalent nickel [Ni(II)] have become worldwide public health issues. However, the potential detailed effects of chronic combined Cr(VI) and Ni exposure on colonic inflammation in mice have not been reported. In this study, 16S rDNA sequencing, metabolomics data analysis, qPCR and other related experimental techniques were used to comprehensively explore the mechanism of toxic damage and the inflammatory response of the colon in mice under the co-toxicity of chronic hexavalent chromium and nickel. The results showed that long-term exposure to Cr(VI) and/or Ni resulted in an imbalance of trace elements in the colon of mice with significant inflammatory infiltration of tissues. Moreover, Cr(VI) and/or Ni poisoning upregulated the expression levels of IL-6, IL-18, IL-1β, TNF-α, IFN-γ, JAK2 and STAT3 mRNA, and downregulated IL-10 mRNA, which was highly consistent with the trend in protein expression. Combined with multiomics analysis, Cr(VI) and/or Ni could change the α diversity and β diversity of the gut microbiota and induce significant differential changes in metabolites such as Pyroglu-Glu-Lys, Val-Asp-Arg, stearidonic acid, and 20-hydroxyarachidonic acid. They are also associated with disorders of important metabolic pathways such as lipid metabolism and amino acid metabolism. Correlation analysis revealed that there was a significant correlation between gut microbes and metabolites (P < 0.05). In summary, based on the advantages of comprehensive analysis of high-throughput sequencing sets, these results suggest that chronic exposure to Cr(VI) and Ni in combination can cause microbial flora imbalances, induce metabolic disorders, and subsequently cause colonic damage in mice. These data provide new insights into the toxicology and molecular mechanisms of Cr(VI) and Ni.

Identification of novel biomarkers for frailty diagnosis via serum amino acids metabolomic analysis using UPLC-MS/MS.

This study was aimed to analyze serum amino acid metabolite profiles in frailty patients, gain a better understanding of the metabolic mechanisms in frailty, and assess the diagnostic value of metabolomics-based biomarkers of frailty. This study utilized the ultra-performance liquid chromatography tandem mass spectrometry to examine amino acids associated with frailty. Additionally, we employed multivariate statistical methods, metabolomic data analysis, receiver operating characteristic (ROC) curve analysis, and pathway enrichment analysis. Among the assayed amino acid metabolites, we identified biomarkers for frailty. ROC curve analysis for frailty diagnosis based on the modified Fried's frailty index showed that the areas under ROC curve of tryptophan, phenylalanine, aspartic acid, and combination were 0.775, 0.679, 0.667, and 0.807, respectively. ROC curve analysis for frailty diagnosis based on Frail Scale showed that the areas under ROC curve of cystine, phenylalanine, and combination of amino acids (cystine, L-Glutamine, citrulline, tyrosine, kynurenine, phenylalanine, glutamin acid) were 0.834, 0.708, and 0.854 respectively. In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty. Frailty is a clinical syndrome, as a consequence it is challenging to identify at early course of the disease, even based on the existing frailty scales. Early diagnosis and appropriate patient management are the key to improve the survival and limit disabilities in frailty patients. Proven by the extensive laboratory and clinical studies on frailty, comprehensive analysis of metabolic levels in frail patients, identification of biomarkers and study of pathogenic pathways of metabolites contribute to the prediction and early diagnosis of frailty. In this study, we explored the serum amino acid metabolite profiles in frailty patients. These present metabolic analyses may provide valuable information on the potential biomarkers and the possible pathogenic mechanisms of frailty.