Analysis Of Metabolomics Data Research Articles

The interaction mechanism of nickel ions with L929 cells based on integrative analysis of proteomics and metabolomics data.

The aim of this article was to study the toxicity mechanism of nickel ions (Ni2+) on L929 cells by combining proteomics and metabolomics. First, iTRAQ-based proteomics and LC/MS metabolomics analyses were used to determine the protein and metabolite expression profiles in L929 cells after treatment with 100 μM Ni2+ for 12, 24 and 48 h. A total of 177, 2191 and 2109 proteins and 40, 60 and 74 metabolites were found to be differentially expressed. Then, the metabolic pathways in which both differentially expressed proteins and metabolites were involved were identified, and three pathways with proteins and metabolites showing upstream and downstream relationships were affected at all three time points. Furthermore, the protein–metabolite–metabolic pathway network was constructed, and two important metabolic pathways involving 4 metabolites and 17 proteins were identified. Finally, the functions of the important screened metabolic pathways, metabolites and proteins were investigated and experimentally verified. Ni2+ mainly affected the expression of upstream proteins in the glutathione metabolic pathway and the arginine and proline metabolic pathway, which further regulated the synthesis of downstream metabolites, reduced the antioxidant capacity of cells, increased the level of superoxide anions and the ratio of GSSG to GSH, led to oxidative stress, affected energy metabolism and induced apoptosis.

GXP: Analyze and Plot Plant Omics Data in Web Browsers.

Next-generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever-growing number of life science research projects. Typically, established software pipelines analyze raw data and produce quantitative data informing about gene expression or concentrations of metabolites. These results need to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some of these tools already exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNAseq and Metabolomics data visualization and analysis tool entirely running in the user’s web browser, thus not needing any custom installation, manual programming or uploading of confidential data to third party servers. Consequently, upon receiving the bioinformatic raw data analysis of RNAseq or other omics results, GXP immediately enables the user to interact with the data according to biological questions by performing knowledge-driven, in-depth data analyses and candidate identification via visualization and data exploration. Thereby, GXP can support and accelerate complex interdisciplinary omics projects and downstream analyses. GXP offers an easy way to publish data, plots, and analysis results either as a simple exported file or as a custom website. GXP is freely available on GitHub (see introduction)

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk.

The etiology of colorectal cancer is not fully understood. Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer. This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation.

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

Commentary on “Integrative Transcriptomics, Proteomics, and Metabolomics Data Analysis Exploring the Injury Mechanism of Ricin on Human Lung Epithelial Cells”

Ricin toxin (RT) is classified as a potential bio-threat agent in assassinations and terrorism due to its extreme toxicity. Due to aerosol RT exposure is the most lethal route, it is urgent to study its injury mechanism.

Integrated Metabolomic and Transcriptomic Analyses to Understand the Effects of Hydrogen Water on the Roots of Ficus hirta Vahl.

Wuzhimaotao (Ficus hirta Vahl) is an important medicinal and edible plant in China. The extract from the roots of Ficus hirta Vahl contains phenylpropanoid compounds, such as coumarins and flavonoids, which are the main active components of this Chinese herbal medicine. In this study, we analyzed the transcriptomic and metabolomic data of the hydrogen-water-treated roots of Ficus hirta Vahl and a control group. The results showed that many genes and metabolites were regulated in the roots of Ficus hirta Vahl that were treated with hydrogen water. Compared with the control group, 173 genes were downregulated and 138 genes were upregulated in the hydrogen-rich water treatment group. Differential metabolite analysis through LC-MS showed that 168 and 109 metabolites had significant differences in positive and negative ion mode, respectively. In the upregulated metabolites, the main active components of Wuzhimaotao, such as the phenylpropane compounds naringin, bergaptol, hesperidin, and benzofuran, were found. Integrated transcriptomic and metabolomic data analysis showed that four and one of the most relevant pathways were over enriched in positive and negative ion mode, respectively. In the relationship between metabolites and DEGs, phenylpropanoid biosynthesis and metabolism play an important role. This indicates that phenylpropanoid biosynthesis and metabolism may be the main metabolic pathways regulated by hydrogen water. Our transcriptome analysis showed that most of the DEGs with |log2FC| ≥ 1 are transcription factor genes, and most of them are related to plant hormone signal transduction, stress resistance, and secondary metabolism, mainly phenylpropanoid biosynthesis and metabolism. This study provides important evidence and clues for revealing the botanical effect mechanism of hydrogen and a theoretical basis for the application of hydrogen agriculture in the cultivation of Chinese herbal medicine.

Dissecting of the AI-2/LuxS Mediated Growth Characteristics and Bacteriostatic Ability of Lactiplantibacillus plantarum SS-128 by Integration of Transcriptomics and Metabolomics.

Lactiplantibacillus plantarum could regulate certain physiological functions through the AI-2/LuxS-mediated quorum sensing (QS) system. To explore the regulation mechanism on the growth characteristics and bacteriostatic ability of L. plantarum SS-128, a luxS mutant was constructed by a two-step homologous recombination. Compared with ΔluxS/SS-128, the metabolites of SS-128 had stronger bacteriostatic ability. The combined analysis of transcriptomics and metabolomics data showed that SS-128 exhibited higher pyruvate metabolic efficiency and energy input, followed by higher LDH level and metabolite overflow compared to ΔluxS/SS-128, resulting in stronger bacteriostatic ability. The absence of luxS induces a regulatory pathway that burdens the cysteine cycle by quantitatively drawing off central metabolic intermediaries. To accommodate this mutations, ΔluxS/SS-128 exhibited lower metabolite overflow and abnormal proliferation. These results demonstrate that the growth characteristic and metabolism of L. plantarum SS-128 are mediated by the AI-2/LuxS QS system, which is a positive regulator involved in food safety. It would be helpful to investigate more bio-preservation control potential of L. plantarum, especially when applied in food industrial biotechnology.

Integrated Metabolomic and Transcriptomic Analysis of the Flavonoid Accumulation in the Leaves of Cyclocarya paliurus at Different Altitudes.

Cyclocarya paliurus is a medicinal plant containing flavonoids, triterpenoids, polyphenolics, polysaccharides, and other compounds with diverse biological functions. C. paliurus is distributed across altitudes ranging from 400 to 1,000 m. However, little is known about the effect of altitude on metabolite accumulation in C. paliurus. Also, the biosynthetic pathway involved in flavonoid accumulation in C. paliurus has not been fully elucidated. In this study, mature leaves of C. paliurus growing at low altitude (280 m) and high altitude (920 m) were sampled and subjected to metabolomic and transcriptomic analyses. The flavonoid content and composition were higher in the leaves of C. paliurus collected at high altitude than in those collected at low altitude. Most of the differentially accumulated metabolites (DAMs) were enriched in “flavone and flavonol biosynthesis.” The significant differentially expressed genes (DEGs) between low and high altitudes were mainly enriched in “biological process.” The most heavily enriched KEGG pathway was related to the subcategory “Oxidative phosphorylation,” indicating that complicated biological processes are involved in the response of C. paliurus to harsh environmental factors. High UV-light might be the main influencing factor among the harsh environmental factors found in high altitudes. Integrated analysis of metabolomic and transcriptomic data showed that 31 flavonoids were significantly correlated with 227 DEGs, resulting in 412 related pairs (283 positive and 129 negative) between the DEGs and flavonoids. The possible mechanisms underlying the differentially accumulation of flavonoids at different altitude might be due to variations in transport and relocation of flavonoids in C. paliurus leaves, but not different flavonoid biosynthesis pathways. The up-regulation of genes related to energy and protein synthesis might contribute to flavonoid accumulation at high altitudes. This study broadens our understanding of the effect of altitude on metabolite accumulation and biosynthesis in C. paliurus.

Clinical Parameters and Metabolomic Biomarkers That Predict Inhospital Outcomes in Patients With ST-Segment Elevated Myocardial Infarctions.

BackgroundPostoperative risk stratification is challenging in patients with ST-segment elevation myocardial infarction (STEMI) who undergo percutaneous coronary intervention. This study aimed to characterize the metabolic fingerprints of patients with STEMI with different inhospital outcomes in the early stage of morbidity and to integrate the clinical baseline characteristics to develop a prognostic prediction model.MethodsPlasma samples were collected retrospectively from two propensity score-matched STEMI cohorts from May 6, 2020 to April 20, 2021. Cohort 1 consisted of 48 survivors and 48 non-survivors. Cohort 2 included 48 patients with unstable angina pectoris, 48 patients with STEMI, and 48 age- and sex-matched healthy controls. Metabolic profiling was generated based on ultra-performance liquid chromatography and a mass spectrometry platform. The comprehensive metabolomic data analysis was performed using MetaboAnalyst version 5.0. The hub metabolite biomarkers integrated into the model were tested using multivariate linear support vector machine (SVM) algorithms and a generalized estimating equation (GEE) model. Their predictive capabilities were evaluated using areas under the curve (AUCs) of receiver operating characteristic curves.ResultsMetabonomic analysis from the two cohorts showed that patients with STEMI with different outcomes had significantly different clusters. Seven differentially expressed metabolites were identified as potential candidates for predicting inhospital outcomes based on the two cohorts, and their joint discriminative capabilities were robust using SVM (AUC = 0.998, 95% CI 0.983–1) and the univariate GEE model (AUC = 0.981, 95% CI 0.969–0.994). After integrating another six clinical variants, the predictive performance of the updated model improved further (AUC = 0.99, 95% CI 0.981–0.998).ConclusionA survival prediction model integrating seven metabolites from non-targeted metabonomics and six clinical indicators may generate a powerful early survival prediction model for patients with STEMI. The validation of internal and external cohorts is required.

Integrated Transcriptomics and Metabolomics Analyses of Stress-Induced Murine Hair Follicle Growth Inhibition.

Psychological stress plays an important role in hair loss, but the underlying mechanisms are not well-understood, and the effective therapies available to regrow hair are rare. In this study, we established a chronic restraint stress (CRS)-induced hair growth inhibition mouse model and performed a comprehensive analysis of metabolomics and transcriptomics. Metabolomics data analysis showed that the primary and secondary metabolic pathways, such as carbohydrate metabolism, amino acid metabolism, and lipid metabolism were significantly altered in skin tissue of CRS group. Transcriptomics analysis also showed significant changes of genes expression profiles involved in regulation of metabolic processes including arachidonic acid metabolism, glutathione metabolism, glycolysis gluconeogenesis, nicotinate and nicotinamide metabolism, purine metabolism, retinol metabolism and cholesterol metabolism. Furthermore, RNA-Seq analyses also found that numerous genes associated with metabolism were significantly changed, such as Hk-1, in CRS-induced hair growth inhibition. Overall, our study supplied new insights into the hair growth inhibition induced by CRS from the perspective of integrated metabolomics and transcriptomics analyses.

Integrated Analysis of Metabolome and Transcriptome Data for Uncovering Flavonoid Components of Zanthoxylum bungeanum Maxim. Leaves Under Drought Stress.

Zanthoxylum bungeanum Maxim. leaves (ZBLs) are rich in flavonoids and have become popular in nutrition, foods and medicine. However, the flavonoid components in ZBLs and the mechanism of flavonoid biosynthesis under drought stress have received little attention. Here, we performed an integrative analysis of the metabolome and transcriptome of ZBLs from HJ (Z. bungeanum cv. “Hanjiao”) and FJ (Z. bungeanum cv. “Fengjiao”) at four drought stages. A total of 231 individual flavonoids divided into nine classes were identified and flavones and flavonols were considered the most abundant flavonoid components in ZBLs. The total flavonoid content of ZBLs was higher in FJ; it increased in FJ under drought stress but decreased in HJ. Nine-quadrant analysis identified five and eight differentially abundant flavonoids in FJ and HJ leaves, respectively, under drought stress. Weighted gene correlation network analysis (WGCNA) identified nine structural genes and eight transcription factor genes involved in the regulation of flavonoid biosynthesis. Moreover, qRT-PCR results verified the accuracy of the transcriptome data and the reliability of the candidate genes. Taken together, our results reveal the flavonoid components of ZBLs and document changes in flavonoid metabolism under drought stress, providing valuable information for nutrition value and food utilization of ZBLs.

FUNKI: interactive functional footprint-based analysis of omics data.

MotivationOmics data are broadly used to get a snapshot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signalling networks.ResultsWe have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of transcriptomics, phosphoproteomics and metabolomics data, either from bulk or single-cell experiments. FUNKI also features different options to visualize the results and run post-analyses, and is mirrored as a scripted version in R.Availability and implementationFUNKI is a free and open-source application built on R and Shiny, available at https://github.com/saezlab/ShinyFUNKI and https://saezlab.shinyapps.io/funki/.Supplementary information Supplementary data are available at Bioinformatics online.

Human Plasma Metabolomics Identify 9-cis-retinoic Acid and Dehydrophytosphingosine Levels as Novel biomarkers for Early Ventricular Fibrillation after ST-elevated Myocardial Infarction

ABSTRACT The relevant metabolite biomarkers for risk prediction of early onset of ventricular fibrillation (VF) after ST-segment elevation myocardial infarction (STEMI) remain unstudied. Here, we aimed to identify these imetabolites and the important metabolic pathways involved, and explore whether these metabolites could be used as predictors for the phenotype. Plasma samples were obtained retrospectively from a propensity-score matched cohort including 42 STEMI patients (21 consecutive VF and 21 non-VF). Ultra-performance liquid chromatography and mass spectrometry in combination with a comprehensive analysis of metabolomic data using Metaboanalyst 5.0 version were performed. As a result, the retinal metabolism pathway proved to be the most discriminative for the VF phenotype. Furthermore, 9-cis-Retinoic acid (9cRA) and dehydrophytosphingosine proved to be the most discriminative biomarkers. Biomarker analysis through receiver operating characteristic (ROC) curve showed the 2-metabolite biomarker panel yielding an area under the curve (AUC) of 0.836. The model based on Monte Carlo cross-validation found that 9cRA had the greatest probability of appearing in the predictive panel of biomarkers in the model. Validation of model efficiency based on an ROC curve showed that the combination model constructed by 9cRA and dehydrophytosphingosine had a good predictive value for early-onset VF after STEMI, and the AUC was 0.884 (95% CI 0.714–1). Conclusively, the retinol metabolism pathway was the most powerful pathway for differentiating the post-STEMI VF phenotype. 9cRA was the most important predictive biomarker of VF, and a plasma biomarker panel made up of two metabolites, may help to build a potent predictive model for VF.

CRISPR/Cas9-mediated mutagenesis of VvbZIP36 promotes anthocyanin accumulation in grapevine (Vitis vinifera).

Anthocyanins are plant secondary metabolites that have a variety of biological functions, including pigmentation. The accumulation of anthocyanins is regulated by both transcriptional activators and repressors. Studies have shown that bZIP family members act primarily as positive regulators of anthocyanin biosynthesis, but there are few reports of negative regulation. Here, we report that a grapevine (Vitis vinifera) bZIP gene from group K, VvbZIP36, acts as a negative regulator of anthocyanin biosynthesis. Knocking out one allele of VvbZIP36 in grapevine with CRISPR/Cas9 promoted anthocyanin accumulation. Correlation analysis of transcriptome and metabolome data showed that a range of anthocyanin biosynthesis genes were activated in VvbZIP36 mutant plants relative to the wild type, resulting in the accumulation of related metabolites, including naringenin chalcone, naringenin, dihydroflavonols, and cyanidin-3-O-glucoside. The synthesis of stilbenes (α-viniferin), lignans, and some flavonols (including quercetin-3-O-rhamnoside, kaempferol-3-O-rhamnoside, and kaempferol-7-O-rhamnoside) was significantly inhibited, and several genes linked to their metabolism were downregulated in the mutant plants. In summary, our results demonstrate that VvbZIP36 is a negative regulator of anthocyanin biosynthesis that plays a role in balancing the synthesis of stilbenes (α-viniferin), lignans, flavonols, and anthocyanins.

Overexpression of VqWRKY31 enhances powdery mildew resistance in grapevine by promoting salicylic acid signaling and specific metabolite synthesis.

Powdery mildew (PM), caused by the fungal pathogen Erysiphe necator, is one of the most destructive diseases of grapevine (Vitis vinifera and other Vitis spp.). Resistance to PM is an important goal for cultivar improvement, and understanding the underlying molecular mechanisms conditioning resistance is critical. Here, we report that transgenic expression of the WRKY transcription factor gene VqWRKY31 from the PM-resistant species Vitis quinquangularis conferred resistance to PM in V. vinifera through promoting salicylic acid signaling and specific metabolite synthesis. VqWRKY31 belongs to the WRKY IIb subfamily, and expression of the VqWRKY31 gene was induced in response to E. necator inoculation. Transgenic V. vinifera plants expressing VqWRKY31 were substantially less susceptible to E. necator infection, and this was associated with increased levels of salicylic acid and reactive oxygen species. Correlation analysis of transcriptomic and metabolomic data revealed that VqWRKY31 promoted expression of genes in metabolic pathways and the accumulation of many disease resistance-related metabolites, including stilbenes, flavonoids, and proanthocyanidins. In addition, results indicated that VqWRKY31 can directly bind to the promoters of two structural genes in stilbene synthesis, STS9 and STS48, and activate their expression. Based on our results, we propose a model where VqWRKY31 enhances grapevine PM resistance through activation of salicylic acid defense signaling and promotion of specific disease resistance-related metabolite synthesis. These findings can be directly exploited for molecular breeding strategies to produce PM-resistant grapevine germplasm.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Molecular networking in infectious disease models.

Small molecule metabolites are the product of many enzymatic reactions. Metabolomics thus opens a window into enzyme activity and function, integrating effects at the post-translational, proteome, transcriptome and genome level. In addition, small molecules can themselves regulate enzyme activity, expression and function both via substrate availability mechanisms and through allosteric regulation. Metabolites are therefore at the nexus of infectious diseases, regulating nutrient availability to the pathogen, immune responses, tropism, and host disease tolerance and resilience. Analysis of metabolomics data is however complex, particularly in terms of metabolite annotation. An emerging valuable approach to extend metabolite annotations beyond existing compound libraries and to identify infection-induced chemical changes is molecular networking. In this chapter, we discuss the applications of molecular networking in the context of infectious diseases specifically, with a focus on considerations relevant to these biological systems.

Pseudomonas aeruginosa biofilm dispersion by the mouse antimicrobial peptide CRAMP

Pseudomonas aeruginosa (P. aeruginosa) is a known bacterium that produces biofilms and causes severe infection. Furthermore, P. aeruginosa biofilms are extremely difficult to eradicate, leading to the development of chronic and antibiotic-resistant infections. Our previous study showed that a cathelicidin-related antimicrobial peptide (CRAMP) inhibits the formation of P. aeruginosa biofilms and markedly reduces the biomass of preformed biofilms, while the mechanism of eradicating bacterial biofilms remains elusive. Therefore, in this study, the potential mechanism by which CRAMP eradicates P. aeruginosa biofilms was investigated through an integrative analysis of transcriptomic, proteomic, and metabolomic data. The omics data revealed CRAMP functioned against P. aeruginosa biofilms by different pathways, including the Pseudomonas quinolone signal (PQS) system, cyclic dimeric guanosine monophosphate (c-di-GMP) signalling pathway, and synthesis pathways of exopolysaccharides and rhamnolipid. Moreover, a total of 2914 differential transcripts, 785 differential proteins, and 280 differential metabolites were identified. A series of phenotypic validation tests demonstrated that CRAMP reduced the c-di-GMP level with a decrease in exopolysaccharides, especially alginate, in P. aeruginosa PAO1 biofilm cells, improved bacterial flagellar motility, and increased the rhamnolipid content, contributing to the dispersion of biofilms. Our study provides new insight into the development of CRAMP as a potentially effective antibiofilm dispersant.

Integrated gut microbiome and metabolome analyses identified fecal biomarkers for bowel movement regulation by Bifidobacterium longum BB536 supplementation: A RCT

BackgroundBifidobacterium longum BB536 supplementation can be used to regulate bowel movements in various people, including healthy subjects and patients with irritable bowel syndrome (IBS); however, individuals vary in their responses to B. longum BB536 treatment. One putative factor is the gut microbiota; recent studies have reported that the gut microbiota mediates the effects of diet or drugs on the host. Here, we investigated intestinal features, such as the microbiome and metabolome, related to B. longum BB536 effectiveness in increasing bowel movement frequency. ResultsA randomized, double-blind controlled crossover trial was conducted with 24 adults who mainly tended to be constipated. The subjects received a two-week dietary intervention consisting of B. longum BB536 in acid-resistant seamless capsules or similarly encapsulated starch powder as the placebo control. Bowel movement frequency was recorded daily, and fecal samples were collected at several time points, and analyzed by metabologenomic approach that consists of an integrated analysis of metabolome data obtained using mass spectrometry and microbiome data obtained using high-throughput sequencing. There were differences among subjects in B. longum intake-induced bowel movement frequency. The responders were predicted by machine learning based on the microbiome and metabolome features of the fecal samples collected before B. longum intake. The abundances of eight bacterial genera were significantly different between responders and nonresponders. ConclusionsIntestinal microbiome and metabolome profiles might be utilized as potential markers of improved bowel movement after B. longum BB536 supplementation. These findings have implications for the development of personalized probiotic treatments.

Application of data collaboration analysis to distributed data with misaligned features

The types of metabolites measured in metabolomics studies vary depending on many factors, including differences in methods. Centralizing the distributed raw data is also often difficult due to confidentiality issues. These difficulties prevent the integrated analysis of metabolomic data from multiple studies. In this study, we extend the data collaboration analysis, an integrated data analysis method, by sharing dimensionality-reduced intermediate representations instead of the raw data to allow it to be applied to distributed data where the samples are completely different, and features are partially common. We then evaluated the improvement in performance using non-common features in the data collaboration analysis. To perform this evaluation, we created the four artificial datasets and the two datasets generated from metabolomics public data where samples are completely different and features are partially common. For each of these datasets, we compared the classification performance including area under the curve in the receiver operating characteristic curve (ROC-AUC) with the following three cases: (i) a case where only local data were used for training, (ii) the data collaboration analysis with only the common features of the distributed datasets, and (iii) the data collaboration analysis with all the features including non-common features. In most cases, the data collaboration analysis using all features demonstrated better results compared to the data collaboration analysis only using common features (by 1.3–4.8 points ROC-AUC for each dataset on average) or that trained on only one of the datasets (by 1.8–2.9 points ROC-AUC for each dataset on average). It was confirmed that the data collaboration analysis could integrate and analyze distributed data where samples are completely different and features are partially common, which can improve the classification accuracy in machine learning without sharing the raw data.