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

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study’s aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts.

PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P

Abstract Gut-microbiota derived metabolites are important regulators of host biology and metabolism. To understand the impacts of the microbial metabolite 4-cresol sulfate (4-CS) on four chronic diseases [type 2 diabetes mellitus, metabolic syndrome (MetS), non-alcoholic fatty liver disease, and chronic kidney disease (CKD)], we conducted association analyses of plasma 4-CS quantified by liquid chromatography coupled to mass spectrometry (LC–MS) in 3641 participants of the Nagahama study. Our results validated the elevation of 4-CS in CKD and identified a reducing trend in MetS. To delineate the holistic effects of 4-CS, we performed a phenome-wide association analysis (PheWAS) with 937 intermediate biological and behavioral traits. We detected associations between 4-CS and 39 phenotypes related to blood pressure regulation, hepatic and renal functions, hematology, sleep quality, intraocular pressure, ion regulation, ketone and fatty acid metabolisms, disease history and dietary habits. Among them, 19 PheWAS significant traits, including fatty acids and 14 blood pressure indices, were correlated with MetS, suggesting that 4-CS is a potential biomarker for MetS. Consistent associations of this gut microbial-derived metabolite on multiple endophenotypes underlying distinct etiopathogenesis support its role in the overall host health, with prospects of probiotic-based therapeutic solutions in chronic diseases.

Abstract Background Spectral CT imaging parameters have been reported to be useful in the differentiation of pathological grades in different malignancies. This study aims to investigate the value of spectral CT in the quantitative assessment of esophageal squamous cell carcinoma (ESCC) with different degrees of differentiation. Methods There were 191 patients with proven ESCC who underwent enhanced spectral CT from June 2018 to March 2020 retrospectively enrolled. These patients were divided into three groups based on pathological results: well differentiated ESCC, moderately differentiated ESCC, and poorly differentiated ESCC. Virtual monoenergetic 40 keV-equivalent image (VMI40keV), iodine concentration (IC), water concentration (WC), effective atomic number (Eff-Z), and the slope of the spectral curve(λHU) of the arterial phase (AP) and venous phase (VP) were measured or calculated. The quantitative parameters of the three groups were compared by using one-way ANOVA and pairwise comparisons were performed with LSD. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of these parameters in poorly differentiated groups and non-poorly differentiated groups. Results There were significant differences in VMI40keV, IC, Eff-Z, and λHU in AP and VP among the three groups (all p 0.05). The VMI40keV, IC, Eff-Z, and λHU in the poorly differentiated group were significantly higher than those in the other groups both in AP and VP (all p

Chemotherapy-induced peripheral neuropathy (CIPN) remains a clinical challenge for up to 80% of breast cancer survivors. In an open-label study, participants underwent three interventions: standard care (duloxetine) for 1 month (Phase 1), oral cannabidiol (CBD) for 2 months (Phase 2), and CBD plus multi-modal exercise (MME) for another 2 months (Phase 3). Clinical outcomes and gut microbiota composition were assessed at baseline and after each phase. We present the case of a 52-year-old female with a history of triple-negative breast cancer in remission for over five years presenting with CIPN. She showed decreased monocyte counts, c-reactive protein, and systemic inflammatory index after each phase. Duloxetine provided moderate benefits and intolerable side effects (hyperhidrosis). She experienced the best improvement and least side effects with the combined (CBD plus MME) phase. Noteworthy were clinically meaningful improvements in CIPN symptoms, quality of life (QoL), and perceived physical function, as well as improvements in pain, mobility, hand/finger dexterity, and upper and lower body strength. CBD and MME altered gut microbiota, showing enrichment of genera that produce short-chain fatty acids. CBD and MME may improve CIPN symptoms, QoL, and physical function through anti-inflammatory and neuroprotective effects in cancer survivors suffering from long-standing CIPN.

Abstract The increasing application of RNA sequencing to study non-model species demands easy-to-use and efficient bioinformatics tools to help researchers quickly uncover biological and functional insights. We developed ExpressAnalyst ( www.expressanalyst.ca ), a web-based platform for processing, analyzing, and interpreting RNA-sequencing data from any eukaryotic species. ExpressAnalyst contains a series of modules that cover from processing and annotation of FASTQ files to statistical and functional analysis of count tables or gene lists. All modules are integrated with EcoOmicsDB, an ortholog database that enables comprehensive analysis for species without a reference transcriptome. By coupling ultra-fast read mapping algorithms with high-resolution ortholog databases through a user-friendly web interface, ExpressAnalyst allows researchers to obtain global expression profiles and gene-level insights from raw RNA-sequencing reads within 24 h. Here, we present ExpressAnalyst and demonstrate its utility with a case study of RNA-sequencing data from multiple non-model salamander species, including two that do not have a reference transcriptome.

Abstract Background Staphylococcus aureus is one of the prevalent etiological agents of contagious bovine mastitis, causing a significant economic burden on the global dairy industry. Given the emergence of antibiotic resistance (ABR) and possible zoonotic spillovers, S aureus from mastitic cattle pose threat to both veterinary and public health. Therefore, assessment of their ABR status and pathogenic translation in human infection models is crucial. Results In this study, 43 S. aureus isolates associated with bovine mastitis obtained from four different Canadian provinces (Alberta, Ontario, Quebec, and Atlantic provinces) were tested for ABR and virulence through phenotypic and genotypic profiling. All 43 isolates exhibited crucial virulence characteristics such as hemolysis, and biofilm formation, and six isolates from ST151, ST352, and ST8 categories showed ABR. Genes associated with ABR (tetK, tetM, aac6’, norA, norB, lmrS, blaR, blaZ, etc.), toxin production (hla, hlab, lukD, etc.), adherence (fmbA, fnbB, clfA, clfB, icaABCD, etc.), and host immune invasion (spa, sbi, cap, adsA, etc.) were identified by analyzing whole-genome sequences. Although none of the isolates possessed human adaptation genes, both groups of ABR and antibiotic-susceptible isolates demonstrated intracellular invasion, colonization, infection, and death of human intestinal epithelial cells (Caco-2), and Caenorhabditis elegans. Notably, the susceptibilities of S. aureus towards antibiotics such as streptomycin, kanamycin, and ampicillin were altered when the bacteria were internalized in Caco-2 cells and C. elegans. Meanwhile, tetracycline, chloramphenicol, and ceftiofur were comparatively more effective with ≤ 2.5 log10 reductions of intracellular S. aureus. Conclusions This study demonstrated the potential of S. aureus isolated from mastitis cows to possess virulence characteristics enabling invasion of intestinal cells thus calling for developing therapeutics capable of targeting drug-resistant intracellular pathogens for effective disease management.

Dietary glycans are a major driver of the human gut microbiota composition. Here, the authors apply next-generation metabolic labeling coupled with fluorescence-activated cell sorting to identify and isolate gut bacteria consumers of dietary glycans in human stool samples, linking bacteria to the glycans they consume.

The unequal distribution of territorial space resources stands out as a leading cause of the human–land contradictions and environmental degradation. These issues are especially pronounced in the Minjiang River Basin, which exhibits significant regional disparities. In pursuit of solutions to these pressing problems and the identification of sustainable developmental pathways, this study presents an innovative territorial space double optimization simulation model. This model integrates quantity structure optimization and distribution pattern optimization, in order to comprehensively consider the optimization of territorial space allocation and build a new territorial space pattern for the Minjiang River Basin in 2030. On this basis, we employed the Patch-generating Land Use Simulation (PLUS) model and scenario analysis method to design the double optimization scenario and natural development scenario. By comparing these two scenarios, and calculating the ecological benefits (EB), economic benefits (ECB), carbon storage (CS), and comprehensive benefits (CB) achieved in different scenarios, the validity of the double optimization model was fully verified. The results indicated that: ① the loss of sub-ecological space (PeS) under the natural development scenario was significantly larger than that under the double optimization scenario, and the loss should be mainly attributed to the large expansion of production space (PS) and living space (LS); ② the area of ecological space (ES) has reduced since 2020, but less area was lost and the retention rate was higher under the double optimization scenario; ③ the natural development scenario made the research region gain more ECB, but it also resulted in the loss of more EB and CS, whereas the Minjiang River Basin under the double optimization scenario was able to effectively balance the relationship among the three, thus achieving the best CB. The research findings provide strong scientific support for alleviating the human–land contradictions, protecting the ecological security in the basin, and promoting the sustainable development of the region.

Aging is the system-wide loss of homeostasis, eventually leading to death. There is growing evidence that the microbiome not only evolves with its aging host, but also directly affects aging via the modulation of metabolites involved in important cellular functions. The widely used model organism C. elegans exhibits high selectivity towards its native microbiome members which confer a range of differential phenotypes and possess varying functional capacities. The ability of one such native microbiome species, Chryseobacterium sp. CHNTR56 MYb120, to improve the lifespan of C. elegans and to promote the production of Vitamin B6 in the co-colonizing member Comamonas sp. 12022 MYb131 are some of its beneficial effects on the worm host. We hypothesize that studying its metabolic influence on the different life stages of the worm could provide further insights into mutualistic interactions. The present work applied LC-MS untargeted metabolomics and isotope labeling to study the impact of the native microbiome member Chryseobacterium sp. CHNTR56 MYb120 on the metabolism of C. elegans. In addition to the upregulation of biosynthesis and detoxification pathway intermediates, we found that Chryseobacterium sp. CHNTR56 MYb120 upregulates the glyoxylate shunt in mid-adult worms which is linked to the upregulation of trehalose, an important metabolite for desiccation tolerance in older worms.

IntroductionTo investigate immunogenic changes after percutaneous microwave ablation (MWA) in pulmonary malignancies.MethodsTwenty-two consecutive patients with pulmonary malignancies who underwent percutaneous lung tumor MWA were prospectively enrolled in this study. Peripheral blood samples were collected on the day before (D0) and one month (M1) after MWA. Changes in immune cell subsets (CD3+, CD4+, and CD8+ T cells, and B, natural killer, regulatory T (Treg), and CD3-CD20+ cells) and cytokines (interleukin [IL]-2, 4, 6, 10, 17A, tumor necrosis factor [TNF]-α, and interferon-γ) were noted and compared. Progression-free survival (PFS) and potentially related factors were analyzed.ResultsThe proportion of CD8+ T cells increased from 22.95 ± 7.38% (D0) to 25.95 ± 9.16% (M1) (p = 0.031). The proportion of Treg cells decreased from 10.82 ± 4.52% (D0) to 8.77 ± 2.05% (M1) (p = 0.049). The IL-2 concentration was also decreased from 1.58 ± 0.46 pg/mL (D0) to 1.26 ± 0.60 pg/mL (M1) (p = 0.028). The reduction in Treg cells predicted PFS independently of clinical prognostic features in multivariate analysis (hazard ratio = 4.97, 95% confidence interval: 1.32–18.66, p = 0.018). A reduction in the proportion of Treg cells was observed in 15 patients (68.2%) and the average of the reduction was 2.05 ± 4.60%. Those patients with a reduction in the proportion of Treg cells that was more than average showed a significantly longer median PFS time than those with a reduction that was less than average (16 months vs. 8.5 months, p = 0.025).DiscussionPercutaneous MWA of pulmonary malignancies leads to immunogenic changes. The reduction in the proportion of Treg cells was independently associated with PFS.

Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites, genes and phenotypes for hypothesis generation. It has become clear that identifying potential causal relationships between metabolites and phenotypes, as well as providing deep functional insights, are crucial for further downstream applications. Here, we introduce mGWAS-Explorer 2.0 to support the causal analysis between >4000 metabolites and various phenotypes. The results can be interpreted within the context of semantic triples and molecular quantitative trait loci (QTL) data. The underlying R package is released for reproducible analysis. Using two case studies, we demonstrate that mGWAS-Explorer 2.0 is able to detect potential causal relationships between arachidonic acid and Crohn’s disease, as well as between glycine and coronary heart disease.

Abstract Background A healthy microbiome influences host physiology through a mutualistic relationship, which can be important for the host to cope with cellular stress by promoting fitness and survival. The mammalian microbiome is highly complex and attributing host phenotypes to a specific member of the microbiome can be difficult. The model organism Caenorhabditis elegans and its native microbiome, discovered recently, can serve as a more tractable, experimental model system to study host-microbiome interactions. In this study, we investigated whether certain members of C. elegans native microbiome would offer a benefit to their host and putative molecular mechanisms using a combination of phenotype screening, omics profiling and functional validation. Results A total of 16 members of C. elegans microbiome were screened under chemically-induced toxicity. Worms grown with Chryseobacterium sp. CHNTR56 MYb120 or Comamonas sp. 12022 MYb131, were most resistant to oxidative chemical stress (SiO2 nanoparticles and juglone), as measured by progeny output. Further investigation showed that Chryseobacterium sp. CHNTR56 positively influenced the worm’s lifespan, whereas the combination of both isolates had a synergistic effect. RNAseq analysis of young adult worms, grown with either isolate, revealed the enrichment of cellular detoxification mechanisms (glutathione metabolism, drug metabolism and metabolism of xenobiotics) and signaling pathways (TGF-beta and Wnt signaling pathways). Upregulation of cysteine synthases (cysl genes) in the worms, associated with glutathione metabolism, was also observed. Nanopore sequencing uncovered that the genomes of the two isolates have evolved to favor the specific route of the de novo synthesis pathway of vitamin B6 (cofactor of cysl enzymes) through serC or pdxA2 homologs. Finally, co-culture with vitamin B6 extended worm lifespan. Conclusions In summary, our study indicates that certain colonizing members of C. elegans have genomic diversity in vitamin B6 synthesis and promote host fitness and lifespan extension. The regulation of host cellular detoxification genes (i.e. gst) along with cysl genes at the transcriptome level and the bacterium-specific vitamin B6 synthesis mechanism at the genome level are in an agreement with enhanced host glutathione-based cellular detoxification due to this interspecies relationship. C. elegans is therefore a promising alternative model to study host-microbiome interactions in host fitness and lifespan.

Background: A pneumonia associated with the coronavirus disease 2019 (COVID-19) recently emerged in China. It was recognized as a global health hazard. Methods: 234 inpatients with COVID-19 were included. Detailed clinical data, chest HRCT basic performances and certain signs were recorded Ground-glass opacity (GGO), consolidation, fibrosis and air trapping were quantified. Both clinical types and CT stages were evaluated. Results: Most patients (approximately 90%) were classified as common type and with epidemiologic history. Fever and cough were main symptoms. Chest CT showed abnormal attenuation in bilateral multiple lung lobes, distributed in the lower and/or periphery of the lungs (94.98%), with multiple shapes. GGO and vascular enhancement sign were most frequent seen, followed by interlobular septal thickening and air bronchus sign as well as consolidation, fibrosis and air trapping. There were significant differences in most of CT signs between different stage groups. The SpO2 and OI were decreased in stage IV, and the CT score of consolidation, fibrosis and air trapping was significantly lower in stage I (P

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Three-dimensional fluorescence is currently studied by methods such as parallel factor analysis (PARAFAC), fluorescence regional integration (FRI), and principal component analysis (PCA). There are also many studies combining convolutional neural networks at present, but there is no one method recognized as the most effective among the methods combining convolutional neural networks and 3D fluorescence analysis. Based on this, we took some samples from the actual environment for measuring 3D fluorescence data and obtained a batch of public datasets from the internet species. Firstly, we preprocessed the data (including two steps of PARAFAC analysis and CNN dataset generation), and then we proposed a 3D fluorescence classification method and a components fitting method based on VGG16 and VGG11 convolutional neural networks. The VGG16 network is used for the classification of 3D fluorescence data with a training accuracy of 99.6% (as same as the PCA + SVM method (99.6%)). Among the component maps fitting networks, we comprehensively compared the improved LeNet network, the improved AlexNet network, and the improved VGG11 network, and finally selected the improved VGG11 network as the component maps fitting network. In the improved VGG11 network training, we used the MSE loss function and cosine similarity to judge the merit of the model, and the MSE loss of the network training reached 4.6 × 10−4 (characterizing the variability of the training results and the actual results), and we used the cosine similarity as the accuracy criterion, and the cosine similarity of the training results reached 0.99 (comparison of the training results and the actual results). The network performance is excellent. The experiments demonstrate that the convolutional neural network has a great application in 3D fluorescence analysis.

The roles of the healthy microbiome on the host and the relationships between members of the microbiome remain to be fully characterized. Due to the complexity of the interactions between the mammalian microbiome and its host, the use of model organisms such as the nematode worm Caenorhabditis elegans is a promising strategy to study host-microbiome interactions in vivo, as well as bacterial crosstalk within the host. Previously it was found that native bacterial isolates of the worm, Chryseobacterium sp. CHNTR56 MYb120 and Comamonas sp. 12022 MYb131, possess genomic diversity in the biosynthesis of the active form of vitamin B6, pyridoxal 5′-phosphate (PLP), and contribute to host fitness and lifespan extension. However, the relative contribution of PLP from each isolate, as well as the existence of interbacterial relationships within the worm gut remain to be characterized. In the present work, we investigated the presence and measured the abundance of PLP in the isolates and in the worms grown with the isolates using ultraperformance liquid chromatography tandem-mass spectrometry (UPLC-MS/MS). Our analyses confirmed the presence of PLP in vitro and in vivo. The elevated abundance of PLP in the isolates (which reached statistically significant levels when the two isolates were combined), and within worms grown with the combination of bacterial isolates, compared to control, indicated synergism between the isolates in the production of PLP. Isotope labeling revealed that Comamonas sp. 12022 MYb131 was the main provider of PLP in worms grown with the combination of bacterial isolates. The dominance of this isolate inside the worm was further confirmed by a colonization assay. An untargeted metabolomics analysis of the bacteria showed that the pathways related to cell growth, protein synthesis and lipid synthesis/energy production were regulated in the combination group in comparison with Comamonas sp. 12022 MYb131 alone. Furthermore, glutamine, involved in the de novo synthesis of purine and pyrimidines, was specifically abundant in this group, indicating the potential role of this metabolite in initiating and sustaining bacterial growth. This bacterial crosstalk is suggested to promote the growth of Comamonas sp. 12022 MYb131 in vivo, and synthesis of bacterial metabolites such as PLP in the worm gut.

Engineered nanoparticles are used at an increasing rate in both industry and medicine without fully understanding their impact on health and environment. The nematode Caenorhabditis elegans is a suitable model to study the toxic effects of nanoparticles as it is amenable to comprehensive phenotyping, such as locomotion, growth, neurotoxicity and reproduction. In this study, we systematically evaluated the effects of silver (Ag) and five metal oxide nanoparticles: SiO2, CeO2, CuO, Al2O3 and TiO2. The results showed that Ag and SiO2 exposures had the most toxic effects on locomotion velocity, growth and reproduction, whereas CeO2, Al2O3 and CuO exposures were mostly neurotoxic. We further performed RNAseq to compare the gene expression profiles underlying Ag and SiO2toxicities. Gene set enrichment analyses revealed that exposures to Ag and SiO2consistently downregulated several biological processes (regulations in locomotion, reproductive process and cell growth) and pathways (neuroactive ligand-receptor interaction, wnt and MAPK signaling, etc.), with opposite effects on genes involved in innate immunity. Our results contribute to mechanistic insights into toxicity of Ag and SiO2 nanoparticles and demonstrated that C. elegans as a valuable model for nanotoxicity assessment.

This proof-of-concept study characterizes the Japanese quail (Coturnix japonica) hepatic metabolome following exposure to benzo[a]pyrene, chlorpyrifos, ethinylestradiol, fluoxetine hydrochloride, hexabromocyclododecane, lead(II)nitrate, seleno-L-methionine, and trenbolone in embryos and adults. The analysis revealed effects on lipid metabolism following exposure to several chemicals at both life stages. The most pronounced effects were observed in embryos exposed to 41.1 μg/g chlorpyrifos. This work highlighted challenges and the need for further avian metabolomics studies.

Crosstalk between the gut microbiome and the host plays an important role in animal development and health. Small compounds are key mediators in this host–gut microbiome dialogue. For instance, tryptophan metabolites, generated by biotransformation of tryptophan through complex host–microbiome co-metabolism can trigger immune, metabolic, and neuronal effects at local and distant sites. However, the origin of tryptophan metabolites and the underlying tryptophan metabolic pathway(s) are not well characterized in the current literature. A large number of the microbial contributors of tryptophan metabolism remain unknown, and there is a growing interest in predicting tryptophan metabolites for a given microbiome. Here, we introduce TrpNet, a comprehensive database and analytics platform dedicated to tryptophan metabolism within the context of host (human and mouse) and gut microbiome interactions. TrpNet contains data on tryptophan metabolism involving 130 reactions, 108 metabolites and 91 enzymes across 1246 human gut bacterial species and 88 mouse gut bacterial species. Users can browse, search, and highlight the tryptophan metabolic pathway, as well as predict tryptophan metabolites on the basis of a given taxonomy profile using a Bayesian logistic regression model. We validated our approach using two gut microbiome metabolomics studies and demonstrated that TrpNet was able to better predict alterations in in indole derivatives compared to other established methods.

Abstract Background For dogs and cats, chemoprophylaxis with macrocyclic lactone (ML) preventives for heartworm disease is widely used in the United States and other countries. Since 2005, cases of loss of efficacy (LOE) of heartworm preventives have been reported in the U.S. More recently, ML-resistant D. immitis isolates were confirmed. Previous work identified 42 genetic markers that could predict ML response in individual samples. For field surveillance, it would be more appropriate to work on microfilarial pools from individual dogs with a smaller subset of genetic markers. Methods MiSeq technology was used to identify allele frequencies with the 42 genetic markers previously reported. Microfilaria from ten well-characterized new isolates called ZoeKY, ZoeMI, ZoeGCFL, ZoeAL, ZoeMP3, ZoeMO, ZoeAMAL, ZoeLA, ZoeJYD-34, and Metairie were extracted from fresh blood from dogs. DNA were extracted and sequenced with MiSeq technology. Allele frequencies were calculated and compared with the previously reported susceptible, LOE, and resistant D. immitis populations. Results The allele frequencies identified in the current resistant and susceptible isolates were in accordance with the allele frequencies previously reported in related phenotypes. The ZoeMO population, a subset of the ZoeJYD-34 population, showed a genetic profile that was consistent with some reversion towards susceptibility compared with the parental ZoeJYD-34 population. The Random Forest algorithm was used to create a predictive model using different SNPs. The model with a combination of three SNPs (NODE_42411_RC, NODE_21554_RC, and NODE_45689) appears to be suitable for future monitoring. Conclusions MiSeq technology provided a suitable methodology to work with the microfilarial samples. The list of SNPs that showed good predictability for ML resistance was narrowed. Additional phenotypically well characterized D. immitis isolates are required to finalize the best set of SNPs to be used for large scale ML resistance screening.

The use of microfilaricidal drugs for the control of onchocerciasis and lymphatic filariasis (LF) necessitates prolonged yearly dosing. Prospects for elimination or eradication of these diseases would be enhanced by the availability of a macrofilaricidal drug. Flubendazole (FLBZ), a benzimidazole anthelmintic, is an appealing candidate. FLBZ has demonstrated potent macrofilaricidal effects in a number of experimental rodent models and in one human trial. Unfortunately, FLBZ was deemed unsatisfactory for use in mass drug administration campaigns due to its limited oral bioavailability. A new formulation that enables sufficient bioavailability following oral administration could render FLBZ an effective treatment for onchocerciasis and LF. Identification of drug-derived effects is important in ascertaining a dosage regimen which is predicted to be lethal to the parasite in situ. In previous histological studies, exposure to FLBZ induced damage to tissues required for reproduction and survival at pharmacologically relevant concentrations. However, more precise and quantitative indices of drug effects are needed. This study assessed drug effects using a transcriptomic approach to confirm effects observed histologically and to identify genes which were differentially expressed in treated adult female Brugia malayi. Comparative analysis across different concentrations (1 μM and 5 μM) and durations (48 and 120 h) provided an overview of the processes which are affected by FLBZ exposure. Genes with dysregulated expression were consistent with the reproductive effects observed via histology in our previous studies. This study revealed transcriptional changes in genes involved in embryo development. Additionally, significant downregulation was observed in genes encoding cuticle components, which may reflect changes in developing embryos, the adult worm cuticle or both. These data support the hypothesis that FLBZ acts predominantly on rapidly dividing cells, and provides a basis for selecting molecular markers of drug-induced damage which may be of use in predicting efficacious FLBZ regimens.

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

Xeno-miRNAs are microRNAs originating from exogenous species detected in host biofluids. A growing number of studies have suggested that many of these xeno-miRNAs may be involved in cross-species interactions and manipulations. To date, hundreds of xeno-miRNAs have been reported in different hosts at various abundance levels. Based on computational predictions, many more miRNAs could be potentially transferred to human circulation system. There is a clear need for bioinformatics resources and tools dedicated to xeno-miRNA annotations and their potential functions. To address this need, we have systematically curated xeno-miRNAs from multiple sources, performed target predictions using well-established algorithms, and developed a user-friendly web-based tool—Xeno-miRNet—to allow researchers to search and explore xeno-miRNAs and their potential targets within different host species. Xeno-miRNet currently contains 1,702 (including both detected and predicted) xeno-miRNAs from 54 species and 98,053 potential gene targets in six hosts. The web application is freely available at http://xeno.mirnet.ca.

Fish muscle, the main edible parts with high protein level and low fat level, is consumed worldwide. Diet contributes greatly to fish growth performance and muscle quality. In order to elucidate the correlation between diet and muscle quality, the same batch of juvenile grass carp (Ctenopharyngodon idellus) were divided into two groups and fed with either grass (Lolium perenne, Euphrasia pectinata and Sorghum sudanense) or artificial feed, respectively. However, the different two diets didn't result in significant differences in all the detected water quality parameters (e.g., Tm, pH, DO, NH3/NH4+-N, NO3--N, NO2-, TN, TP, and TOC) between the two experimental groups. After a 4-month culture period, various indexes and expression of myogenic regulatory factor (MRFs) and their related genes were tested. The weight gain of the fish fed with artificial feed (AFG) was nearly 40% higher than the fish fed with grass (GFG). Significantly higher alkaline phosphatase, total cholestrol, high density cholestrol and total protein were detected in GFG as compared to AFG. GFG also showed increased hardness, resilience and shear force in texture profile analysis, with significantly bigger and compact muscle fibers in histologic slices. The fat accumulation was most serious in the abdomen muscle of AFG. Additionally, the expression levels of MyoG, MyoD, IGF-1, and MSTNs were higher, whereas Myf-5, MRF4, and IGF-2 were lower in most positional muscles of GFG as compared to AFG. Overall, these results suggested that feeding grass could promote muscle growth and development by stimulating muscle fiber hypertrophy, as well as significantly enhance the expression of CoL1As. Feeding C. idellus with grass could also improve flesh quality by improving muscle characteristics, enhancing the production of collagen, meanthile, reducing fat accumulation and moisture in muscle, but at the cost of a slower growth.

Background: Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods: We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings: All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

BACKGROUND:Lymphatic filariasis and onchocerciasis are disabling and disfiguring neglected tropical diseases of major importance in developing countries. Ivermectin is the drug of choice for mass drug administration programs for the control of onchocerciasis and lymphatic filariasis in areas where the diseases are co-endemic. Although ivermectin paralyzes somatic and pharyngeal muscles in many nematodes, these actions are poorly characterized in adult filariae. We hypothesize that paralysis of pharyngeal pumping by ivermectin in filariae could result in deprivation of essential nutrients, especially iron, inducing a wide range of responses evidenced by altered gene expression, changes in metabolic pathways, and altered developmental states in embryos. Previous studies have shown that ivermectin treatment significantly reduces microfilariae release from females within four days of exposure in vivo, while not markedly affecting adult worms. However, the mechanisms responsible for reduced production of microfilariae are poorly understood. METHODOLOGY/PRINCIPAL FINDINGS:We analyzed transcriptomic profiles from Brugia malayi adult females, an important model for other filariae, using RNAseq technology after exposure in culture to ivermectin at various concentrations (100 nM, 300 nM and 1 μM) and time points (24, 48, 72 h, and 5 days). Our analysis revealed drug-related changes in expression of genes involved in meiosis, as well as oxidative phosphorylation, which were significantly down-regulated as early as 24 h post-exposure. RNA interference phenotypes of the orthologs of these down-regulated genes in C. elegans include "maternal sterile", "embryonic lethal", "larval arrest", "larval lethal" and "sick". CONCLUSION/SIGNIFICANCE:These changes provide insight into the mechanisms involved in ivermectin-induced reduction in microfilaria output and impaired fertility, embryogenesis, and larval development.

Helminth infections affect more than a third of the world's population. Despite very broad phylogenetic differences among helminth parasite species, a systemic Th2 host immune response is typically associated with long-term helminth infections, also known as the "helminth effect". Many investigations have been carried out to study host gene expression profiles during helminth infections. The objective of this study is to determine if there is a common transcriptomic signature characteristic of the helminth effect across multiple helminth species and tissue types. To this end, we performed a comprehensive meta-analysis of publicly available gene expression datasets. After data processing and adjusting for study-specific effects, we identified ~700 differentially expressed genes that are changed consistently during helminth infections. Functional enrichment analyses indicate that upregulated genes are predominantly involved in various immune functions, including immunomodulation, immune signaling, inflammation, pathogen recognition and antigen presentation. Down-regulated genes are mainly involved in metabolic process, with only a few of them are involved in immune regulation. This common immune gene signature confirms previous observations and indicates that the helminth effect is robust across different parasite species as well as host tissue types. To the best of our knowledge, this study is the first comprehensive meta-analysis of host transcriptome profiles during helminth infections.

BACKGROUND:Filarial nematodes cause serious and debilitating infections in human populations of tropical countries, contributing to an entrenched cycle of poverty. Only one human filarial parasite, Brugia malayi, can be maintained in rodents in the laboratory setting. It has been a widely used model organism in experiments that employ culture systems, the impact of which on the worms is unknown. METHODOLOGY/PRINCIPAL FINDINGS:Using Illumina RNA sequencing, we characterized changes in gene expression upon in vitro maintenance of adult B. malayi female worms at four time points: immediately upon removal from the host, immediately after receipt following shipment, and after 48 h and 5 days in liquid culture media. The dramatic environmental change and the 24 h time lapse between removal from the host and establishment in culture caused a globally dysregulated gene expression profile. We found a maximum of 562 differentially expressed genes based on pairwise comparison between time points. After an initial shock upon removal from the host and shipping, a few stress fingerprints remained after 48 h in culture and until the experiment was stopped. This was best illustrated by a strong and persistent up-regulation of several genes encoding cuticle collagens, as well as serpins. CONCLUSIONS/SIGNIFICANCE:These findings suggest that B. malayi can be maintained in culture as a valid system for pharmacological and biological studies, at least for several days after removal from the host and adaptation to the new environment. However, genes encoding several stress indicators remained dysregulated until the experiment was stopped.

The study of the microbiome, the totality of all microbes inhabiting the host or an environmental niche, has experienced exponential growth over the past few years. The microbiome contributes functional genes and metabolites, and is an important factor for maintaining health. In this context, metabolomics is increasingly applied to complement sequencing-based approaches (marker genes or shotgun metagenomics) to enable resolution of microbiome-conferred functionalities associated with health. However, analyzing the resulting multi-omics data remains a significant challenge in current microbiome studies. In this review, we provide an overview of different computational approaches that have been used in recent years for integrative analysis of metabolome and microbiome data, ranging from statistical correlation analysis to metabolic network-based modeling approaches. Throughout the process, we strive to present a unified conceptual framework for multi-omics integration and interpretation, as well as point out potential future directions.

Arctic Mesorhizobium sp. N33 isolated from nodules of Oxytropis arctobia in Canada's eastern Arctic has a growth temperature range from 0 °C to 30 °C and is a well-known cold-adapted rhizobia. The key molecular mechanisms underlying cold adaptation in Arctic rhizobia remains totally unknown. Since the concentration and contents of metabolites are closely related to stress adaptation, we applied GC-MS and NMR to identify and quantify fatty acids and water soluble compounds possibly related to low temperature acclimation in strain N33. Bacterial cells were grown at three different growing temperatures (4 °C, 10 °C and 21 °C). Cells from 21 °C were also cold-exposed to 4°C for different times (2, 4, 8, 60 and 240 minutes). We identified that poly-unsaturated linoleic acids 18:2 (9, 12) & 18:2 (6, 9) were more abundant in cells growing at 4 or 10 °C, than in cells cultivated at 21 °C. The mono-unsaturated phospho/neutral fatty acids myristoleic acid 14:1(11) were the most significantly overexpressed (45-fold) after 1 hour of exposure to 4 °C. As reported in the literature, these fatty acids play important roles in cold adaptability by supplying cell membrane fluidity, and by providing energy to cells. Analysis of water-soluble compounds revealed that isobutyrate, sarcosine, threonine and valine were more accumulated during exposure to 4 °C. These metabolites might play a role in conferring cold acclimation to strain N33 at 4 °C, probably by acting as cryoprotectants. Isobutyrate was highly upregulated (19.4-fold) during growth at 4 °C, thus suggesting that this compound is a precursor for the cold-regulated fatty acids modification to low temperature adaptation.

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.