Metabolomics Datasets Research Articles

Ultra-high-throughput screening of antimicrobial combination therapies using a two-stage transparent machine learning model.

Here, we present M2D2, a two-stage machine learning (ML) pipeline that identifies promising antimicrobial drug combinations, which are crucial for combating drug resistance. M2D2 addresses key challenges in drug combination discovery by predicting drug synergies using computationally generated drug-protein interaction data, thereby circumventing the need for expensive omics data. The model improves the accuracy of drug target identification using high-throughput experimental and computational methods via feedback between ML stages. M2D2's transparent framework provides mechanistic insights into drug interactions and was benchmarked against chemogenomics, transcriptomics, and metabolomics datasets. We experimentally validated M2D2 using high-throughput screening of 946 combinations of Food and Drug Administration (FDA)- approved drugs and antibiotics against Escherichia coli . We discovered synergy between a cerebrovascular drug and a widely used penicillin antibiotic and validated predicted mechanisms of action using genome-wide CRISPR inhibition screens. M2D2 offers a transparent ML tool for rapidly designing combination therapies and guides repurposing efforts while providing mechanistic insights.

Enhancing supervised analysis of imbalanced untargeted metabolomics datasets using a CWGAN-GP framework for data augmentation

Untargeted metabolomics is an extremely useful approach for the discrimination of biological systems and biomarker identification. However, data analysis workflows are complex and face many challenges. Two of these challenges are the demand of high sample size and the possibility of severe class imbalance, which is particularly common in clinical studies. The latter can make statistical models less generalizable, increase the risk of overfitting and skew the analysis in favour of the majority class. One possible approach to mitigate this problem is data augmentation. However, the use of artificial data requires adequate data augmentation methods and criteria for assessing the quality of the generated data.In this work, we used Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty (CWGAN-GPs) for data augmentation of metabolomics data. Using a set of benchmark datasets, we applied several criteria for the evaluation of the quality of generated data and assessed the performance of supervised predictive models trained with datasets that included such data. CWGAN-GP models generated realistic data with identical characteristics to real samples, mostly avoiding mode collapse. Furthermore, in cases of class imbalance, the performance of predictive models improved by supplementing the minority class with generated samples. This is evident for high quality datasets with well separated classes. Conversely, model improvements were quite modest for high class overlap datasets. This trend was confirmed by using synthetic datasets with different class separation levels. Data augmentation is a viable procedure to alleviate class imbalance problems but is not universally beneficial in metabolomics.

Parallel phosphoproteomics and metabolomics map the global metabolic tyrosine phosphoproteome

Tyrosine phosphorylation of metabolic enzymes is an evolutionarily conserved posttranslational modification that facilitates rapid and reversible modulation of enzyme activity, localization, or function. Despite the high abundance of tyrosine phosphorylation events detected on metabolic enzymes in high-throughput mass spectrometry-based studies, functional characterization of tyrosine phosphorylation sites has been limited to a subset of enzymes. Since tyrosine phosphorylation is dysregulated across human diseases, including cancer, understanding the consequences of metabolic enzyme tyrosine phosphorylation events is critical for informing disease biology and therapeutic interventions. To globally identify metabolic enzyme tyrosine phosphorylation events and simultaneously assign functional significance to these sites, we performed parallel phosphoproteomics and polar metabolomics in nontumorigenic mammary epithelial cells (MCF10A) stimulated with epidermal growth factor (EGF) in the absence or presence of the EGF receptor inhibitor erlotinib. We performed an integrated analysis of the phosphoproteomic and metabolomic datasets to identify tyrosine phosphorylation sites on metabolic enzymes with functional consequences. We identified two previously characterized (pyruvate kinase muscle isozyme, phosphoglycerate mutase 1) and two uncharacterized (glutathione S-transferase Pi 1, glutamate dehydrogenase 1) tyrosine phosphorylation sites on metabolic enzymes with purported functions based on metabolomic analyses. We validated these hits using a doxycycline-inducible CRISPR interference system in MCF10A cells, in which target metabolic enzymes were depleted with simultaneous reexpression of wild-type, phosphomutant, or phosphomimetic isoforms. Together, these data provide a framework for identification, prioritization, and characterization of tyrosine phosphorylation sites on metabolic enzymes with functional significance.

Kaempferol Remodels Liver Monocyte Populations and Treats Hepatic Fibrosis in Mice by Modulating Intestinal Flora and Metabolic Reprogramming.

Changes in gut flora are associated with liver fibrosis. The interactions of host with intestinal flora are still unknown, with little research investigating such interactions with comprehensive multi-omics data. The present work analyzed and integrated large-scale multi-omics transcriptomics, microbiome, metabolome, and single-cell RNA-sequencing datasets from Kaempferol-treated and untreated control groups by advanced bioinformatics methods. This study concludes that kaempferol dose-dependently improved serum markers (like AST, ALT, TBil, Alb, and PT) and suppressed fibrosis markers (including HA, PC III, LN, α-SMA, and Collagen I), while kaempferol also increased body weight. Mechanistically, kaempferol improved the metabolic levels of intestinal flora dysbiosis and associated lipids. This was achieved by increasing the abundance of g__Robinsoniella, g__Erysipelotrichaceae_UCG-003, g__Coriobacteriaceae_UCG-002, and 5-Methylcytidine, all-trans-5,6- Epoxyretinoic acid, LPI (18:0), LPI (20:4), etc. to achieve this. Kaemferol exerts anti-inflammatory and immune-enhancing effects by down-regulating the Th17/IL-17 signaling pathway in PDGF-induced LX2 cells. In addition, kaempferol administration remarkably elevated CD4 + T and CD8 + T cellular proportions, thereby activating immune cells for protecting the body and controlling inflammatory conditions. The combined interaction of multiple data may explain how Kaempferol modulates the intestinal flora thereby remodeling the hepatocyte population and alleviating liver fibrosis.

Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics.

Several database search methods have been employed in untargeted metabolomics utilizing high-resolution mass spectrometry to comprehensively annotate acquired product ion spectra. Recent technical advancements in in silico analyses have facilitated the sorting of the degree of coincidence between a query product ion spectrum, and the molecular structures in the database. However, certain search results may be false positives, necessitating a method for controlling the false discovery rate (FDR). This study proposes 4 simple methods for controlling the FDR in compound search results. Instead of preparing a decoy compound database, a decoy spectral dataset was created from the measured product-ion spectral dataset (target). Target and decoy product ion spectra were searched against an identical compound database to obtain target and decoy hits. FDR was estimated based on the number of target and decoy hits. In this study, 3 decoy generation methods, polarity switching, mirroring, and spectral sampling, were compared. Additionally, the second-rank method was examined using second-ranked hits in the target search results as decoy hits. The performances of these 4 methods were evaluated by annotating product ion spectra from the MassBank database using the SIRIUS 5 CSI:FingerID scoring method. The results indicate that the FDRs estimated using the second-rank method were the closest to the true FDR of 0.05. Using this method, a compound search was performed on 4 human metabolomic data-dependent acquisition datasets with an FDR of 0.05. The FDR-controlled compound search successfully identified several compounds not present in the Human Metabolome Database.

Identification of a Novel Biomarker Panel for Breast Cancer Screening.

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Multiomic comparison of GLP1R analogues interaction networks

Abstract Background GLP1R analogs are drugs increasingly used in clinical practice in patients with type 2 diabetes, and obesity. Subsequent clinical trials indicate their beneficial effects, including cardioprotection, but the exact mechanisms of those actions are still not known. Aim of the study: This study aims to understand how GLP1R analogs exert cardioprotective and therapeutic effects in patients with type 2 diabetes and obesity. Through analyzing GLP1R interaction networks and associated metabolomic and lipidomic data, we seek to uncover the molecular mechanisms underlying these effects and their potential therapeutic implications. Methods We performed an integrative analysis of four GLP1Ri interaction networks and associated metabolomic and lipidomic datasets. Interactomics revealed 130 common genes among semaglutide, exenatide, tirzapeptide, and retatrutide, primarily associated with diabetes-related processes, including obesity and hyperglycemia. Noteworthy findings encompassed ontological terms related to cardiovascular diseases (CVDs), such as hypertension, calcium regulation in cardiac cells, and amino acid accumulation-induced mTOR activation. Additionally, enrichment in gene sets linked to longevity, less recognized terms like fatty liver disease, chemokine signaling, and sirtuin interactome, were observed. Results Specific processes for tirzepatide included behavior-related terms and hydrochloric acid secretion, while retatrutide exhibited associations with fibrosarcoma, thinking and speaking disturbances, and adipogenesis signaling. Shared processes like primary ciliary dyskinesia differed in implicated genes. Metabolomics identified shared metabolites associated with diverse pathways, including galactose metabolism and nitric oxide-related pathways. Lipidomics highlighted enriched phenotypes and pathways, such as arachidonic acid metabolism and cholesterol biosynthesis. Conclusion Integration of interaction networks with metabolomic and lipidomic data revealed top interactors and impacted metabolites, shedding light on the intricate molecular landscape of GLP1Ri. These findings contribute valuable insights into the potential therapeutic implications and broader health considerations of GLP1R drugsfig1fig2

Integrated systems biology identifies disruptions in mitochondrial function and metabolism as key contributors to heart failure with preserved ejection fraction (HFpEF).

Heart failure with preserved ejection fraction (HFpEF) accounts for ~50% of HF cases, with no effective treatments. The ZSF1-obese rat model recapitulates numerous clinical features of HFpEF including hypertension, obesity, metabolic syndrome, exercise intolerance, and LV diastolic dysfunction. Here, we utilized a systems-biology approach to define the early metabolic and transcriptional signatures to gain mechanistic insight into the pathways contributing to HFpEF development. Male ZSF1-obese, ZSF1-lean hypertensive controls, and WKY (wild-type) controls were compared at 14w of age for extensive physiological phenotyping and LV tissue harvesting for unbiased metabolomics, RNA-sequencing, and assessment of mitochondrial morphology and function. Utilizing ZSF1-lean and WKY controls enabled a distinction between hypertension-driven molecular changes contributing to HFpEF pathology, versus hypertension + metabolic syndrome. ZSF1-obese rats displayed numerous clinical features of HFpEF. Comparison of ZSF1-lean vs WKY (i.e., hypertension-exclusive effects) revealed metabolic remodeling suggestive of increased aerobic glycolysis, decreased β-oxidation, and dysregulated purine and pyrimidine metabolism with few transcriptional changes. ZSF1-obese rats displayed worsened metabolic remodeling and robust transcriptional remodeling highlighted by the upregulation of inflammatory genes and downregulation of the mitochondrial structure/function and cellular metabolic processes. Integrated network analysis of metabolomic and RNAseq datasets revealed downregulation of nearly all catabolic pathways contributing to energy production, manifesting in a marked decrease in the energetic state (i.e., reduced ATP/ADP, PCr/ATP). Cardiomyocyte ultrastructure analysis revealed decreased mitochondrial area, size, and cristae density, as well as increased lipid droplet content in HFpEF hearts. Mitochondrial function was also impaired as demonstrated by decreased substrate-mediated respiration and dysregulated calcium handling. Collectively, the integrated omics approach applied here provides a framework to uncover novel genes, metabolites, and pathways underlying HFpEF, with an emphasis on mitochondrial energy metabolism as a potential target for intervention.

Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data

Multiple sclerosis (MS) remains a challenging neurological condition for diagnosis and management and is often detected in late stages, delaying treatment. Artificial intelligence (AI) is emerging as a promising approach to extracting MS information when applied to different patient datasets. Given the critical role of metabolites in MS profiling, metabolomics data may be an ideal platform for the application of AI to predict disease. In the present study, a machine-learning (ML) approach was used for a detailed analysis of metabolite profiles and related pathways in patients with MS and healthy controls (HC). This approach identified unique alterations in biochemical metabolites and their correlation with disease severity parameters. To enhance the efficiency of using metabolic profiles to determine disease severity or the presence of MS, we trained an AI model on a large volume of blood-based metabolomics datasets. We constructed this model using an artificial neural network (ANN) architecture with perceptrons. Data were divided into training, validation, and testing sets to determine model accuracy. After training, accuracy reached 87 %, sensitivity was 82.5 %, specificity was 89 %, and precision was 77.3 %. Thus, the developed model seems highly robust, generalizable with a wide scope and can handle large amounts of data, which could potentially assist neurologists. However, a large multicenter cohort study is necessary for further validation of large-scale datasets to allow the integration of AI in clinical settings for accurate diagnosis and improved MS management.

MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.

Glioblastoma (GBM) is a highly malignant and devastating brain cancer characterized by its ability to rapidly and aggressively grow, infiltrating brain tissue, with nearly universal recurrence after the standard of care (SOC), which comprises maximal safe resection followed by chemoirradiation (CRT). The metabolic triggers leading to the reprogramming of tumor behavior and resistance are an area increasingly studied in relation to the tumor molecular features associated with outcome. There are currently no metabolomic biomarkers for GBM. Studying the metabolomic alterations in GBM patients undergoing CRT could uncover the biochemical pathways involved in tumor response and resistance, leading to the identification of novel biomarkers and the optimization of the treatment response. The feature selection process identifies key factors to improve the model's accuracy and interpretability. This study utilizes a combined feature selection approach, incorporating both Least Absolute Shrinkage and Selection Operator (LASSO) and Minimum Redundancy-Maximum Relevance (mRMR), alongside a rank-based weighting method (i.e., MetaWise) to link metabolomic biomarkers to CRT and the 12-month and 20-month overall survival (OS) status in patients with GBM. Our method shows promising results, reducing feature dimensionality when employed on serum-based large-scale metabolomic datasets (University of Florida) for all our analyses. The proposed method successfully identified a set of eleven serum biomarkers shared among three datasets. The computational results show that the utilized method achieves 96.711%, 92.093%, and 86.910% accuracy rates with 48, 46, and 33 selected features for the CRT, 12-month, and 20-month OS-based metabolomic datasets, respectively. This discovery has implications for developing personalized treatment plans and improving patient outcomes.

Integrative multi-omic analysis identifies genes associated with cuticular wax biogenesis in adult maize leaves.

Studying the genetic basis of leaf wax composition and its correlation with leaf cuticular conductance (gc) is crucial for improving crop productivity. The leaf cuticle, which comprises a cutin matrix and various waxes, functions as an extracellular hydrophobic layer, protecting against water loss upon stomatal closure. To address the limited understanding of genes associated with the natural variation of adult leaf cuticular waxes and their connection to gc, we conducted statistical genetic analyses using leaf transcriptomic, metabolomic, and physiological data sets collected from a maize (Zea mays L.) panel of ∼300 inbred lines. Through a random forest analysis with 60 cuticular wax traits, it was shown that high molecular weight wax esters play an important role in predicting gc. Integrating results from genome-wide and transcriptome-wide studies (GWAS and TWAS) via a Fisher's combined test revealed 231 candidate genes detected by all three association tests. Among these, 11 genes exhibit known or predicted roles in cuticle-related processes. Throughout the genome, multiple hotspots consisting of GWAS signals for several traits from one or more wax classes were discovered, identifying four additional plausible candidate genes and providing insights into the genetic basis of correlated wax traits. Establishing a partially shared genetic architecture, we identified 35 genes for both gc and at least one wax trait, with four considered plausible candidates. Our study enhances the understanding of how adult leaf cuticle wax composition relates to gc and implicates both known and novel candidate genes as potential targets for optimizing productivity in maize.

A Comprehensive Spatially Resolved Metabolomics Dataset for Lampreys

As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench. This dataset serves as a valuable resource for researchers using lampreys as a model organism. Additionally, it acts as a benchmark metabolomics dataset for evaluating new algorithms and software tools and comparing them with previously published results. A lamprey spatial metabolomics database is also provided to support studies utilizing lampreys as an animal model, and to complement and validate other spatial metabolomics studies on lampreys conducted with mass spectrometry imaging or other techniques.

A comprehensive IDA and SWATH-DIA Lipidomics and Metabolomics dataset: SARS-CoV-2 case control study

A significant hurdle in untargeted lipid/metabolomics research lies in the absence of reliable, cross-validated spectral libraries, leading to a considerable portion of LC-MS features being labeled as unknowns. Despite continuous advancement in annotation tools and libraries, it is important to safeguard, publish and share acquired data through public repositories. Embracing this trend of data sharing not only promotes efficient resource utilization but also paves the way for future repurposing and in-depth analysis; ultimately advancing our comprehension of Covid-19 and other diseases. In this work, we generated an extensive MS-dataset of 39 Covid-19 infected patients versus age- and gender-matched 39 healthy controls. We implemented state of the art acquisition techniques including IDA and SWATH-DIA to ensure a thorough insight in the lipidome and metabolome, ensuring a repurposable dataset.

Visual analysis of multi-omics data.

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool's interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different "visual channel" of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

Ophthalmic acid is a bloodborne metabolite that contributes to age-induced cardiomyocyte hypertrophy.

Cardiac aging involves the development of left ventricular hypertrophy alongside a decline in functional capacity. Here, we use neutral blood exchange to demonstrate that the acute removal of age-accumulated blood factors significantly regresses cardiac hypertrophy in aged mice. The reversal of hypertrophy was not attributed to age-associated hemodynamic effects, implicating a role of blood-derived factors. In addition, the overarching paradigm of systemic aging maintains that the age-related overabundance of plasma proteins are largely responsible for causing pathological phenotypes in tissues. Our results suggest that blood metabolites, not proteins, drive cardiac hypertrophy instead. Upon analyzing serum metabolomics data sets, we identified ophthalmic acid as a circulating metabolite whose levels increase with advanced age. Treatment of adult mouse and neonatal rat cardiomyocytes in culture with ophthalmic acid increased their relative surface areas. This study uncovers a non-protein metabolite that may contribute to cardiomyocyte hypertrophy during aging. Identifying a method to counteract ophthalmic acid's hypertrophic effects may reveal novel therapeutic opportunities for cardiac rejuvenation.

Integrated transcriptome and metabolism unravel critical roles of carbon metabolism and oxidoreductase in mushroom with Korshinsk peashrub substrates

Edible fungi cultivation serves as an efficient biological approach to transforming agroforestry byproducts, particularly Korshinsk peashrub (KP) branches into valuable mushroom (Lentinus edodes) products. Despite the widespread use of KP, the molecular mechanisms underlying its regulation of mushroom development remain largely unknown. In this study, we conducted a combined analysis of transcriptome and metabolism of mushroom fruiting bodies cultivated on KP substrates compared to those on apple wood sawdust (AWS) substrate. Our aim was to identify key metabolic pathways and genes that respond to the effects of KP substrates on mushrooms. The results revealed that KP induced at least a 1.5-fold increase in protein and fat content relative to AWS, with 15% increase in polysaccharide and total sugar content in mushroom fruiting bodies. There are 1196 differentially expressed genes (DEGs) between mushrooms treated with KP relative to AWS. Bioinformatic analysis show significant enrichments in amino acid metabolic process, oxidase activity, malic enzyme activity and carbon metabolism among the 698 up-regulated DEGs induced by KP against AWS. Additionally, pathways associated with organic acid transport and methane metabolism were significantly enriched among the 498 down-regulated DEGs. Metabolomic analysis identified 439 differentially abundant metabolites (DAMs) in mushrooms treated with KP compared to AWS. Consistent with the transcriptome data, KEGG analysis on metabolomic dataset suggested significant enrichments in carbon metabolism, alanine, aspartate and glutamate metabolism among the up-regulated DAMs by KP. In particular, some DAMs were enhanced by 1.5-fold, including D-glutamine, L-glutamate, glucose and pyruvate in mushroom samples treated with KP relative to AWS. Targeted metabolomic analysis confirmed the contents of DAMs related to glutamate metabolism and energy metabolism. In conclusion, our findings suggest that reprogrammed carbon metabolism and oxidoreductase pathways act critical roles in the enhanced response of mushroom to KP substrates.

Identification and functional analysis of long non-coding RNA (lncRNA) and metabolites response to mowing in hulless barley (Hordeum vulgare L. var. nudum hook. f.)

BackgroundHulless barley (Hordeum vulgare L. var. nudum Hook. f.) is a significant cereal crop and a substantial source of forage for livestock. Long non-coding RNAs (lncRNAs) and metabolites play crucial roles in the nutrient accumulation and regeneration of hulless barley plants following mowing. The study aimed to identify differentially expressed lncRNAs and metabolites in hulless barley plants by analyzing transcriptomic and metabolomic datasets at 2 h, 24 h, and 72 h following mowing.ResultsThe study revealed that 190, 90, and 438 lncRNA genes were differentially expressed at the 2 h, 24 h, and 72 h time points compared to the non-mowing control. We identified 14 lncRNA genes—11 downregulated and 3 upregulated—showing consistently significant differential expression across all time points after mowing. These differentially expressed lncRNAs target genes involved in critical processes such as cytokinin signaling, cell wall degradation, storage protein accumulation, and biomass increase. In addition, we identified ten differentially expressed metabolites targeting diverse metabolic pathways, including plant hormones, alkaloids, and flavonoids, before and after mowing at various time points. Endogenous hormone analysis revealed that cytokinin most likely played a crucial role in the regeneration of hulless barley after mowing.ConclusionsThis study created a comprehensive dataset of lncRNAs, metabolites, and hormones in hulless barley after mowing, revealing valuable insights into the functional characteristics of lncRNAs, metabolites, and hormones in regulating plant regeneration. The results indicated that cytokinin plays a significant role in facilitating the regeneration process of hulless barley after mowing. This comprehensive dataset is an invaluable resource for better understanding the complex mechanisms that underlie plant regeneration, with significant implications for crop improvement.

Plasma metabolomics identifies differing endotypes of recurrent wheezing in preschool children differentiated by symptoms and social disadvantage

Preschool children with recurrent wheezing are a heterogeneous population with many underlying biological pathways that contribute to clinical presentations. Although the morbidity of recurrent wheezing in preschool children is significant, biological studies in this population remain quite limited. To address this gap, this study performed untargeted plasma metabolomic analyses in 68 preschool children with recurrent wheezing to identify metabolomic endotypes of wheezing. K-means cluster analysis was performed on metabolomic dataset including a total of 1382 named and unnamed metabolites. We identified three metabolomic clusters which differed in symptom severity, exacerbation occurrence, and variables associated with social disadvantage. Metabolites that distinguished the clusters included those involved in fatty acid metabolism, fatty acids (long chain monounsaturated fatty acids, long chain polyunsaturated fatty acids, and long chain saturated fatty acids), lysophospholipids, phosphatidylcholines, and phosphatidylethanolamines. Pathway analyses identified pathways of interest in each cluster, including steroid metabolism, histidine metabolism, sphingomyelins, and sphingosines, among others. This study highlights the biologic complexity of recurrent wheezing in preschool children and offers novel metabolites and pathways that may be amenable to future study and intervention.

Metabolomics and Multi-Omics Determination of Potential Plasma Biomarkers in PRV-1-Infected Atlantic Salmon.

Metabolomic analysis has been explored to search for disease biomarkers in humans for some time. The application to animal species, including fish, however, is still at the beginning. In the present study, we have used targeted and untargeted metabolomics to identify metabolites in the plasma of Atlantic salmon (Salmo salar) challenged with Piscine orthoreovirus (PRV-1), aiming to find metabolites associated with the progression of PRV-1 infection into heart and skeletal muscle inflammation (HSMI). The metabolomes of control and PRV-1-infected salmon were compared at three time points during disease development by employing different biostatistical approaches. Targeted metabolomics resulted in the determination of affected metabolites and metabolic pathways, revealing a substantial impact of PRV-1 infection on lipid homeostasis, especially on several (lyso)phosphatidylcholines, ceramides, and triglycerides. Untargeted metabolomics showed a clear separation of the treatment groups at later study time points, mainly due to effects on lipid metabolism pathways. In a subsequent multi-omics approach, we combined both metabolomics datasets with previously reported proteomics data generated from the same salmon plasma samples. Data processing with DIABLO software resulted in the identification of significant metabolites and proteins that were representative of the HSMI development in the salmon.

PathGPS: discover shared genetic architecture using GWAS summary data.

The increasing availability and scale of biobanks and "omic" datasets bring new horizons for understanding biological mechanisms. PathGPS is an exploratory data analysis tool to discover genetic architectures using Genome Wide Association Studies (GWAS) summary data. PathGPS is based on a linear structural equation model where traits are regulated by both genetic and environmental pathways. PathGPS decouples the genetic and environmental components by contrasting the GWAS associations of "signal" genes with those of "noise" genes. From the estimated genetic component, PathGPS then extracts genetic pathways via principal component and factor analysis, leveraging the low-rank and sparse properties. In addition, we provide a bootstrap aggregating ("bagging") algorithm to improve stability under data perturbation and hyperparameter tuning. When applied to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggests multiple new hypotheses for future investigations.