Analysis Of Metabolomics Data Research Articles

Integrated analysis of transcriptomic and metabolomic data reveals how slurry ice treatment affects sugar metabolism in sweet corn (Zea mays L. var saccharata) during cold storage

AbstractSweet corn rapidly loses its sweetness, along with other nutrients, once it is harvested, which reduces its economic value. The present study was conducted to elucidate the regulatory mechanisms by which a rapid cooling slurry ice (SI) treatment followed by storage at 0 ± 0.5°C extends the postharvest quality of sweet corn relative to slower direct cooling at 0 ± 0.5°C. In this study, we found that SI treatment can maintain the appearance quality of sweet corn and inhibit grain shrinkage, respiration, and ethylene release. SI treatment delayed the aging of corn by regulating the expression levels of genes and the levels of metabolites. SI treatment also maintains its carbohydrate content, which is mainly related to starch and sucrose metabolism, glycolysis, citric acid cycle, pentose phosphate pathway, and cutin biosynthesis. The results provide a possible target for improving the postharvest quality of sweet corn.

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance

BackgroundThe transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs).ResultsA total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites.ConclusionThis study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.

Joint transcriptomic and metabolomic analysis provides new insights into drought resistance in watermelon (Citrullus lanatus)

IntroductionWatermelon is an annual vine of the family Cucurbitaceae. Watermelon plants produce a fruit that people love and have important nutritional and economic value. With global warming and deterioration of the ecological environment, abiotic stresses, including drought, have become important factors that impact the yield and quality of watermelon plants. Previous research on watermelon drought resistance has included analyzing homologous genes based on known drought-responsive genes and pathways in other species.MethodsHowever, identifying key pathways and genes involved in watermelon drought resistance through high-throughput omics methods is particularly important. In this study, RNA-seq and metabolomic analysis were performed on watermelon plants at five time points (0 h, 1 h, 6 h, 12 h and 24 h) before and after drought stress.ResultsTranscriptomic analysis revealed 7829 differentially expressed genes (DEGs) at the five time points. The DEGs were grouped into five clusters using the k-means clustering algorithm. The functional category for each cluster was annotated based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; different clusters were associated with different time points after stress. A total of 949 metabolites were divided into 10 categories, with lipids and lipid-like molecules accounting for the most metabolites. Differential expression analysis revealed 22 differentially regulated metabolites (DRMs) among the five time points. Through joint analysis of RNA-seq and metabolome data, the 6-h period was identified as the critical period for watermelon drought resistance, and the starch and sucrose metabolism, plant hormone signal transduction and photosynthesis pathways were identified as important regulatory pathways involved in watermelon drought resistance. In addition, 15 candidate genes associated with watermelon drought resistance were identified through joint RNA-seq and metabolome analysis combined with weighted correlation network analysis (WGCNA). Four of these genes encode transcription factors, including bHLH (Cla97C03G068160), MYB (Cla97C01G002440), HSP (Cla97C02G033390) and GRF (Cla97C02G042620), one key gene in the ABA pathway, SnRK2-4 (Cla97C10G186750), and the GP-2 gene (Cla97C05G105810), which is involved in the starch and sucrose metabolism pathway.DiscussionIn summary, our study provides a theoretical basis for elucidating the molecular mechanisms underlying drought resistance in watermelon plants and provides new genetic resources for the study of drought resistance in this crop.

NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei.

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Transcriptome and metabolome integrated analysis reveals the wax biosynthesis mechanism of Allium cepa L.

Cuticle waxes play a crucial role in protecting plants from biotic and abiotic stresses, and their biosynthetic mechanisms are well understood in a variety of crops. However, the regulatory mechanism of cuticular wax synthesis in Allium cepa has not been fully understood. In this study, we conducted an integrated analysis of the transcriptome and metabolome to elucidate the wax biosynthesis mechanism of A. cepa using the wild type, designated as WT1, and glossy mutant type, known as glo1, plants of A. cepa. The results revealed that the total wax content of WT1 was higher than that of glo1, with 16-Hentriacontanone identified as the main component of the wax. Transcriptomics analysis showed 384 up-regulated genes and 412 down-regulated genes in glo1 compared with WT1. Through Kyoto Encyclopedia of Genes and Genomes analysis, weighted gene co-expression network analysis, and joint analysis of transcriptome and metabolome data, we identified 21 key genes related to wax synthesis and metabolism. This research provides a comprehensive landscape of the transcriptome and metabolome, shedding light on the molecular mechanisms of cuticular wax synthesis in A. cepa. Furthermore, it lays the foundation for the development of potential breeding strategies in the future.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

DIMet: An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data.

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of Stable Isotope-Resolved Metabolomics data, there is currently no available resource providing a comprehensive toolbox. In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786. Supplementary data are available at Bioinformatics online.

Integrative metabolome and transcriptome profiling reveal key metabolic regulatory networks in Ziziphus jujuba cv. Dongzao pulp

‘Dongzao’ (Ziziphus jujuba ‘Dongzao’) is an economically important fresh fruit well known for its distinctive flavor and significant nutritional benefits. However, dynamic alterations in the genetic and metabolic networks that regulate the development of ‘Dongzao’ pulp are not fully elucidated. In this study, first, a metabolic regulatory network was constructed. Next, the metabolic landscape of the ‘Dongzao’ jujube fruit pulp was established during development and ripening by using a joint analysis of metabolomic and transcriptomic data. This dataset was used to establish a global map of changes in the major metabolites throughout ‘Dongzao’ pulp development. The potential regulatory networks were then dissected, and the key structural genes and transcription factors associated with modulating the metabolism of important jujube fruit ingredients were identified. Further bioinformatics analyses and transient gene overexpression revealed that ZjSHINE3 and ZjNAC29 act as key transcription factors involved in sucrose and malic acid accumulation, respectively. In summary, this study provides novel insights into the metabolic regulatory networks underlying jujube fruit pulp development and generates a useful resource for genetic improvement and breeding studies of jujube.

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.

Podophyllotoxin via SIRT1/PPAR /NF-κB axis induced cardiac injury in rats based on the toxicological evidence chain (TEC) concept

BackgroundThe study of cardiotoxicity of drugs has become an important part of clinical safety evaluation of drugs. Podophyllotoxin (PPT), along with its various derivatives and congeners are widely recognized as broad-spectrum pharmacologically active compounds. Clinical cardiotoxicity of PPT and its derivatives are concerning, basic research on the mechanism of cardiotoxicity remained to be insufficient. PurposeIn the current study, our team's innovative concept of toxicological evidence chain (TEC) was applied to reveal the cardiac toxicity mechanism of podophyllotoxin (PPT) by targeted metabolomics, TMT-based quantitative proteomics and western blot. MethodsThe injury phenotype evidence (IPE) acquired from the toxicity manifestations including weight and behavior observation of Sprague-Dawley rat. The rat hearts were assessed for damage through histopathological examination, the levels of myocardial enzymes were detected, which were defined as Adverse Outcomes Evidence (AOE). Overall measurements of targeted metabolomics based on energy metabolism and TMT- based quantitative proteomics were obtained after exposure to PPT to acquire to Toxic Event Evidence (TEE). The mechanism of cardiac toxicity based on the integration analysis of proteomics and metabolomics data was speculated, which was verified by Western blot. ResultsThe results indicate that exposure to PPT may result in pathological alterations and significant elevation of myocardial enzymes in rat hearts. In addition, we found that PPT causes disorders in cardiac energy metabolism, characterized by the energy metabolism fuels down. TMT-based quantitative proteomics reveal that the PPAR (Peroxisome proliferators-activated receptor) signaling pathway deserves further study. It is worth noting that PPT may suppress the expression of SIRT1, subsequently inducing AMPK inhibition, decrease of PGC-1ɑ and PPARɑ and PPARγ protein, resulting in disorders of glucose oxidation, glycolysis and ketone body oxidation metabolism, which together with the increase of p-IKK and p-IҡBɑ expression leading to the nuclear translocation of NF-ҡB p65 from the cytosol lead to inflammation. ConclusionThis study comprehensively evaluated cardiac toxicity of PPT and initially revealed the mechanism of cardiotoxicity that PPT may lead to disorders of mitochondrial oxidation metabolism and inflammation via SIRT1/PPAR /NF-κB axis, resulting in cardiac injury.

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.