Analysis Of Metabolomics Data Research Articles

Probiotics Bifidobacterium lactis M8 and Lactobacillus rhamnosus M9 prevent high blood pressure via modulating the gut microbiota composition and host metabolic products.

Elevated blood pressure affects 40% of the adult population, which accounts for high cardiovascular disease risk and further high mortality yearly. The global understanding of the gut microbiome for hypertension may provide important insights into the prevention. Bifidobacterium lactis M8 and Lactobacillus rhamnosus M9 originated from human breast milk, were able to decrease blood pressure, and modified metabolites in a high fructose-induced elevated blood pressure mouse model. Moreover, we found there was a close relationship between unexplored gut microbes and elevated blood pressure. Also, subsequently, the cross-link was explored among gut microbes, metabolites, and some metabolic pathways in gut microbial environment through introducing novel prediction methodology and bioinformatic analysis. It allowed us to hypothesize that probiotics can prevent elevated blood pressure via gut microbiota and related metabolism.Thus, utilization of dietary strategies (such as probiotics) to maintain the blood pressure level is of crucial importance.

Transcriptomic and metabolomic analyses to study the key role by which Ralstonia insidiosa induces Listeria monocytogenes to form suspended aggregates.

Ralstonia insidiosa can survive in a wide range of aqueous environments, including food processing areas, and is harmful to humans. It can induce Listeria monocytogenes to form suspended aggregates, resulting from the co-aggregation of two bacteria, which allows for more persistent survival and increases the risk of L. monocytogenes contamination. In our study, different groups of aggregates were analyzed and compared using Illumina RNA sequencing technology. These included R. insidiosa under normal and barren nutrient conditions and in the presence or absence of L. monocytogenes as a way to screen for differentially expressed genes (DEGs) in the process of aggregate formation. In addition, sterile supernatants of R. insidiosa were analyzed under different nutrient conditions using metabolomics to investigate the effect of nutrient-poor conditions on metabolite production by R. insidiosa. We also undertook a combined analysis of transcriptome and metabolome data to further investigate the induction effect of R. insidiosa on L. monocytogenes in a barren environment. The results of the functional annotation analysis on the surface of DEGs and qPCR showed that under nutrient-poor conditions, the acdx, puuE, and acs genes of R. insidiosa were significantly upregulated in biosynthetic processes such as carbon metabolism, metabolic pathways, and biosynthesis of secondary metabolites, with Log2FC reaching 4.39, 3.96, and 3.95 respectively. In contrast, the Log2FC of cydA, cyoB, and rpsJ in oxidative phosphorylation and ribosomal pathways reached 3.74, 3.87, and 4.25, respectively. Thirty-one key components were identified while screening for differential metabolites, which mainly included amino acids and their metabolites, enriched to the pathways of biosynthesis of amino acids, phenylalanine metabolism, and methionine metabolism. Of these, aminomalonic acid and Proximicin B were the special components of R. insidiosa that were metabolized under nutrient-poor conditions.

Integrated analysis of nutritional quality changes and molecular regulation in ‘Qingcui’ plum fruit treated with 1-MCP during storage

To elucidate the effect of 1-Methylcyclopropene (1-MCP) treatment on molecular and nutritional quality changes during ‘Qingcui’ plum fruit ripening, metabolomic profiling and transcriptome analysis were conducted to identify key metabolites and differentially expressed genes. Application of 1-MCP suppressed ‘Qingcui’ plum fruit softening and maintained higher soluble solids content. Metabolome profiling identified numerous metabolites that differentially accumulated during ripening and significantly contributed to the nutritional quality of plum fruit. These included sugars, organic acids, amino acids, and phenolic compounds. A special focus was given to twelve phenolic-related metabolites that were reduced by 1-MCP treatment during ripening. Transcriptome profiling revealed the enrichment of transcription factors and biosynthetic genes related to phenolic-related metabolites among differentially expressed genes, indicating that 1-MCP likely influences phenolic-related metabolite levels through transcriptional regulation of their biosynthetic pathways. This integrated analysis of metabolome and transcriptome data provides new insights into the complex mechanisms controlling the nutritional quality of ‘Qingcui’ plum fruit in response to 1-MCP. Understanding the regulatory networks influencing plum fruit ripening can be valuable for enhancing its nutritional value and facilitating future plum breeding efforts.

Gut microbiome as a biomarker for predicting early recurrence of HBV-related hepatocellular carcinoma.

To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV-related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV-associated HCC and 82 HBV-related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial-liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe-derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial-tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC.

Integrated metabolomic and transcriptomic analysis reveals perturbed glycerophospholipid metabolism in mouse neural stem cells exposed to cadmium

Cadmium (Cd) is a ubiquitous heavy metal with neurotoxicity. Our previous study reported that Cd could inhibit the proliferation of mouse neural stem cells (mNSCs). However, the underlying mechanisms are obscure. In recent years, the rapid growth of multi-omics techniques enables us to explore the cellular responses that occurred after toxicant exposure at the molecular level. In this study, we used a combination of metabolomics and transcriptomics approaches to investigate the effects of exposure to Cd on mNSCs. After treatment with Cd, the metabolites and transcripts in mNSCs changed significantly with 110 differentially expressed metabolites and 2135 differentially expressed genes identified, respectively. The altered metabolites were mainly involved in glycerophospholipid metabolism, arginine and proline metabolism, arginine biosynthesis, glyoxylate and dicarboxylate metabolism. Meanwhile, the transcriptomic data demonstrated perturbed membrane function and signal transduction. Furthermore, integrated analysis of metabolomic and transcriptomic data suggested that glycerophospholipid metabolism might be the major metabolic pathway affected by Cd in mNSCs. More interestingly, the supplementation of lysophosphatidylethanolamine (LPE) attenuated Cd-induced mitochondrial impairment and the inhibition of cell proliferation and differentiation in mNSCs, further supporting our analysis. Overall, the study provides new insights into the mechanisms of Cd-induced neurotoxicity.

Chemical characterization of dicaffeoyl polyamine derivatives to understand specific secondary metabolites in goji berry

Goji berries are among the most valuable edible and medicinal crops worldwide due to their richness in healthy compounds. Dicaffeoyl polyamine derivatives (DCPAds) contain a considerable number of isomers and are prominent specialized metabolites in goji berries. Very little has been achieved thus far in the evaluation of these compounds because of the lack of standards. The characterization and natural variation of DCPAds in goji berry remain unclear. The present study focused on the comprehensive characterization and quantitative analysis of DCPAds in goji berries using liquid chromatography- mass spectrometry. A strategy was developed to determine the fragmentation patterns of authentic standards. Three diagnostic ions were used as characteristic markers to quickly identify these compounds in goji berries. Furthermore, the natural variation and differential spatiotemporal accumulation patterns of seven DCPAds in 94 goji berry batches were revealed. In addition, principal component analysis of metabolomics data grouped the wild and cultivated samples, while green samples and those at other developmental stages could also be discriminated. These results improve our understanding of DCPAds in goji berries, which can provide essential data for chemical insight into further phytochemical studies, metabolite identification, and quality control of goji berries.

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts.

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Metabolomic evolution of the postpartum dairy cow uterus.

High rates of early pregnancy loss are a critical issue in dairy herds, particularly in seasonal, grazing systems. Components of the uterine luminal fluid (ULF), on which the early embryo depends for sustenance and growth, partly determine early pregnancy losses. Here, changes in ULF from early to mid-postpartum in crossbred dairy cows were explored, linking them with divergent embryo development. For this, the uteri of 87 cows at Day 7 of pregnancy at first and third estrus postpartum were flushed to collect ULF. Eighteen metabolites (chiefly organic acids and sugars) significantly varied in abundance across postpartum, indicating a molecular signature of physiological recovery consistent of the upregulation of pyrimidine metabolism and glycerophospholipid metabolism, and downregulation of pentose phosphate and taurine metabolism pathways. Joint pathway analysis of metabolomics data and a previously generated proteomics data set on the same ULF samples suggests key links between postpartum recovery and subsequent successful embryo development. These include upregulation of VEGFA and downregulation of metabolism, NRF2, T-cell receptor, which appear to improve the ULF's capacity of sustaining normal embryo development, and a putative osmo-protectant role of beta-alanine. These relationships should be further investigated to develop tools to detect and reduce early pregnancy loss in dairy cows.

Promoter variations in DBR2-like affect artemisinin production in different chemotypes of Artemisia annua.

Artemisia annua is the only known plant source of the potent antimalarial artemisinin, which occurs as the low- and high-artemisinin producing (LAP and HAP) chemotypes. Nevertheless, the different mechanisms of artemisinin producing between these two chemotypes were still not fully understood. Here, we performed a comprehensive analysis of genome resequencing, metabolome, and transcriptome data to systematically compare the difference in the LAP chemotype JL and HAP chemotype HAN. Metabolites analysis revealed that 72.18% of sesquiterpenes was highly accumulated in HAN compared to JL. Integrated omics analysis found a DBR2-Like (DBR2L) gene may be involved in artemisinin biosynthesis. DBR2L was highly homologous with DBR2, belonged to ORR3 family, and had the DBR2 activity of catalyzing artemisinic aldehyde to dihydroartemisinic aldehyde. Genome resequencing and promoter cloning revealed that complicated variations existed in DBR2L promoters among different varieties of A. annua and were clustered into three variation types. The promoter activity of diverse variant types showed obvious differences. Furthermore, the core region (-625 to 0) of the DBR2L promoter was identified and candidate transcription factors involved in DBR2L regulation were screened. Thus, the result indicates that DBR2L is another key enzyme involved in artemisinin biosynthesis. The promoter variation in DBR2L affects its expression level, and thereby may result in the different yield of artemisinin in varieties of A. annua. It provides a novel insight into the mechanism of artemisinin-producing difference in LAP and HAP chemotypes of A. annua, and will assist in a high yield of artemisinin in A. annua.

Approach strategies and application of metabolomics to biotechnology in plants.

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder.

The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

IntroductionMetabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.ObjectivesThis study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.MethodsWe designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.ResultsMDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.ConclusionThese findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

Transcriptomic Analysis of the <i>Levilactobacillus brevis</i> 47f Strain under Oxidative Stress

Levilactobacillus brevis 47f is a heterofermentative aerotolerant lactic acid bacterium isolated from the microbiota of the gastrointestinal tract of a healthy person. Previously, the strain showed anti-inflammatory properties and protects the murine intestine from enteropathy induced by 5-fluorouracil as part of preclinical studies. At the same time, the molecular mechanisms that account for the properties of the strain and its response to the action of reactive oxygen species, remain unexplored. The aim of this work was to study the response of the strain to the action of oxidizing agents – hydrogen peroxide and oxygen – using the method of transcriptional RNAseq analysis. Both oxidants exhibited a strong effect on the strain, increasing or decreasing the expression of several hundred genes – both general and specific for each oxidant. The characteristics of proteins whose expression was increased the most (DE ≥ 5) are provided. The genes activated under the action of both oxidants encode proteins related to stress, antioxidant activity, protein and nucleotide repair, cell wall, carbohydrate transport and metabolism, and catabolic energy storage pathways. Peroxide mainly activated the transcription of defense proteins, namely, stress response and molecular chaperones, antioxidant activity, DNA repair, and proteins involved in the formation of the cell wall. Under aerobic conditions, the genes that encode proteins involved in energy conversion (the use of fatty acids, nucleosides, fructose in addition to glucose as an energy source; proteins of the phosphoketolase pathway) and the import of peptides, amino acids, and sugars, were activated to a large extent. The data obtained in this work will be used by us to conduct an integrated analysis of transcriptomic, proteomic, and metabolomic data derived from this strain. This will make a significant contribution to the creation of a pharmacobiotic based on L. brevis 47f for the treatment of various inflammatory diseases.

Integrated analysis of proteomics, epigenomics and metabolomics data revealed divergent pathway activation patterns in the recent versus chronic post-traumatic stress disorder

Metabolomics, proteomics and DNA methylome assays, when done in tandem from the same blood sample and analyzed together, offer an opportunity to evaluate the molecular basis of post-traumatic stress disorder (PTSD) course and pathogenesis. We performed separate metabolomics, proteomics, and DNA methylome assays on blood samples from two well-characterized cohorts of 159 active duty male participants with relatively recent onset PTSD (<1.5 years) and 300 male veterans with chronic PTSD (>7 years). Analyses of the multi-omics datasets from these two independent cohorts were used to identify convergent and distinct molecular profiles that might constitute potential signatures of severity and progression of PTSD and its comorbid conditions. Molecular signatures indicative of homeostatic processes such as signaling and metabolic pathways involved in cellular remodeling, neurogenesis, molecular safeguards against oxidative stress, metabolism of polyunsaturated fatty acids, regulation of normal immune response, post-transcriptional regulation, cellular maintenance and markers of longevity were significantly activated in the active duty participants with recent PTSD. In contrast, we observed significantly altered multimodal molecular signatures associated with chronic inflammation, neurodegeneration, cardiovascular and metabolic disorders, and cellular attritions in the veterans with chronic PTSD. Activation status of signaling and metabolic pathways at the early and late timepoints of PTSD demonstrated the differential molecular changes related to homeostatic processes at its recent and multi-system syndromes at its chronic phase. Molecular alterations in the recent PTSD seem to indicate some sort of recalibration or compensatory response, possibly directed in mitigating the pathological trajectory of the disorder.

Nutrient Metabolism Pathways Analysis and Key Candidate Genes Identification Corresponding to Cadmium Stress in Buckwheat through Multiomics Analysis.

Fagopylum tatarium (L.) Gaertn (buckwheat) can be used both as medicine and food and is also an important food crop in barren areas and has great economic value. Exploring the molecular mechanisms of the response to cadmium (Cd) stress can provide the theoretical reference for improving the buckwheat yield and quality. In this study, perennial tartary buckwheat DK19 was used as the experimental material, its key metabolic pathways in the response to Cd stress were identified and verified through transcriptomic and metabolomic data analysis. In this investigation, 1798 metabolites were identified through non-targeted metabolomic analysis containing 1091 up-regulated and 984down-regulated metabolites after treatment. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differential metabolites was significantly enriched in galactose metabolism, glycerol metabolism, phenylpropane biosynthesis, glutathione metabolism, starch and sucrose metabolism. Linkage analysis detected 11 differentially expressed genes (DEGs) in the galactose metabolism pathway, 8 candidate DEGs in the lipid metabolism pathway, and 20 candidate DEGs in the glutathione metabolism pathway. The results of our study provided useful clues for genetically improving the resistance to cadmium by analyzing the molecular mechanism of cadmium tolerance in buckwheat.

CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources.

The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA.

The intricate dependency structure of biological "omics" data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

Insight into the effect of a heavy metal mixture on neurological damage in rats through combined serum metabolomic and brain proteomic analyses

The heavy metals lead (Pb), cadmium (Cd), and mercury (Hg) that cause neurocognitive impairment have been extensively studied. These elements typically do not exist alone in the environment; they are often found with other heavy metals and can enter the body through various routes, thereby impacting health. Our previous research showed that low Pb, Cd, and Hg levels cause neurobehavioral impairments in weaning and adult rats. However, little is known about the biomarkers and mechanisms underlying Pb, Cd, and Hg mixture-induced neurological impairments. A combined analysis of metabolomic and proteomic data may reveal heavy metal-induced alterations in metabolic and protein profiles, thereby improving our understanding of the molecular mechanisms underlying heavy metal-induced neurological impairments. Therefore, brain tissue and serum samples were collected from rats exposed to a Pb, Cd, and Hg mixture for proteomic and metabolomic analyses, respectively. The analysis revealed 363 differential proteins in the brain and 206 metabolites in serum uniquely altered in the Pb, Cd, and Hg mixture exposure group, compared to those of the control group. The main metabolic impacted pathways were unsaturated fatty acids biosynthesis, linoleic acid metabolism, phenylalanine metabolism, and tryptophan metabolism. We further identified that the levels of arachidonic acid (C20:4 n-3) and, adrenic acid (C22:4 n-3) were elevated and that kynurenic acid (KA) and quinolinic acid (QA) levels and the KA/QA ratio, were decreased in the group exposed to the Pb, Cd, and Hg mixture. A joint analysis of the proteome and metabolome showed that significantly altered proteins such as LPCAT3, SLC7A11, ASCL4, and KYAT1 may participate in the neurological impairments induced by the heavy metal mixture. Overall, we hypothesize that the dysregulation of ferroptosis and kynurenine pathways is associated with neurological damage due to chronic exposure to a heavy metal mixture.

Empowering drug off-target discovery with metabolic and structural analysis

Elucidating intracellular drug targets is a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we develop a hierarchic workflow to focus on specific targets based on analysis of metabolomics data and growth rescue experiments. We deploy this framework to understand the intracellular molecular interactions of the multi-valent dihydrofolate reductase-targeting antibiotic compound CD15-3. We analyse global metabolomics data utilizing machine learning, metabolic modelling, and protein structural similarity to prioritize candidate drug targets. Overexpression and in vitro activity assays confirm one of the predicted candidates, HPPK (folK), as a CD15-3 off-target. This study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows for discovering off-targets of a metabolic inhibitor.

Multi-omics approach for identification of molecular alterations of QiShenYiQi dripping pills in heart failure with preserved ejection fraction

Ethnopharmacological relevanceTraditional Chinese medicine theory believes that qi deficiency and blood stasis are the key pathogenesis of heart failure with preserved ejection fraction (HFpEF). As a representative prescription for replenishing qi and activating blood, QiShenYiQi dripping pills (QSYQ) has been used for treating heart diseases. However, the pharmacological mechanism of QSYQ in improving HFpEF is not well understood. Aim of the studyThe objective of the study is to investigate the cardioprotective effect and mechanism of QSYQ in HFpEF using the phenotypic dataset of HFpEF. Materials and methodsHFpEF mouse models established by feeding mice combined high-fat diet and Nω-nitro-L-arginine methyl ester drinking water were treated with QSYQ. To reveal causal genes, we performed a multi-omics study, including integrative analysis of transcriptomics, proteomics, and metabolomics data. Moreover, adeno-associated virus (AAV)-based PKG inhibition confirmed that QSYQ mediated myocardial remodeling through PKG. ResultsComputational systems pharmacological analysis based on human transcriptome data for HFpEF showed that QSYQ could potentially treat HFpEF through multiple signaling pathways. Subsequently, integrative analysis of transcriptome and proteome showed alterations in gene expression in HFpEF. QSYQ regulated genes involved in inflammation, energy metabolism, myocardial hypertrophy, myocardial fibrosis, and cGMP-PKG signaling pathway, confirming its function in the pathogenesis of HFpEF. Metabolomics analysis revealed fatty acid metabolism as the main mechanism by which QSYQ regulates HFpEF myocardial energy metabolism. Importantly, we found that the myocardial protective effect of QSYQ on HFpEF mice was attenuated after RNA interference-mediated knock-down of myocardial PKG. ConclusionThis study provides mechanistic insights into the pathogenesis of HFpEF and molecular mechanisms of QSYQ in HFpEF. We also identified the regulatory role of PKG in myocardial stiffness, making it an ideal therapeutic target for myocardial remodeling.