PubMed

mBio. 2024 Nov 22:e0334323. doi: 10.1128/mbio.03343-23. Online ahead of print.ABSTRACTIn a new respiratory virus pandemic, optimizing allocation of scarce medical resources becomes an urgent challenge. Infection prognosis takes on particular importance when allocating scarce antiviral antibodies and drugs, which are most effective when administered before the onset of severe disease. During arrival of the COVID-19 pandemic to the United States in 2020, we conducted a prognostic biomarker discovery and validation effort based upon metabolomic profiling with a liquid-chromatography-mass spectrometer (LC-MS) type used clinically for rapid toxicology. We obtained urine specimens from 163 patients presenting for evaluation. We obtained LC-MS profiles in the initial cohort and used machine learning methods to define a simplified urine metabolomic signature associated with respiratory failure or death by 90 days. This signature was composed of three metabotypes linked to intestinal microbiome metabolism and anticonvulsant use, with a receiver-operator characteristic area under the curve (ROC AUC) of 89.4%. Blinded application of this signature to the subsequent validation cohort yielded a ROC AUC of 81.2%. A model trained on the two baseline metabotypes present before intubation exhibited similar performance in the validation cohort. This study demonstrates the plausibility and promise of rapid metabolome-based prognostic discovery and validation in the opening wave of a pandemic. The approach used here could be used to inform therapeutic and resource allocation decisions early in a future epidemic.IMPORTANCEIn a new respiratory virus pandemic, the ability to identify patients at greatest risk for severe disease is essential to direct scarce medical resources to those most likely to benefit from them. Tools to predict disease severity are best developed early in a pandemic, but laboratory-based resources to develop these may be limited by available technology and by infection precautions. Here, we show that an accessible metabolic profiling approach could identify a prognostic signature of severe disease in the initial wave of COVID-19, when patients presenting for care often exceeded the available doses of convalescent plasma and remdesivir. In a future pandemic, this approach, alongside efforts to identify clinical disease severity predictors, could improve patient outcomes and facilitate therapeutic trials by identifying individuals at high risk for severe disease.PMID:39576111 | DOI:10.1128/mbio.03343-23

JCI Insight. 2024 Nov 22;9(22):e184279. doi: 10.1172/jci.insight.184279.ABSTRACTHuman studies linking metabolism with organism-wide physiologic function have been challenged by confounding, adherence, and precisionHere, we united physiologic and molecular phenotypes of metabolism during controlled dietary intervention to understand integrated metabolic-physiologic responses to nutrition. In an inpatient study of individuals who underwent serial 24-hour metabolic chamber experiments (indirect calorimetry) and metabolite profiling, we mapped a human metabolome onto substrate oxidation rates and energy expenditure across up to 7 dietary conditions (energy balance, fasting, multiple 200% caloric excess overfeeding of varying fat, protein, and carbohydrate composition). Diets exhibiting greater fat oxidation (e.g., fasting, high-fat) were associated with changes in metabolites within pathways of mitochondrial β-oxidation, ketogenesis, adipose tissue fatty acid liberation, and/or multiple anapleurotic substrates for tricarboxylic acid cycle flux, with inverse associations for diets with greater carbohydrate availability. Changes in each of these metabolite classes were strongly related to 24-hour respiratory quotient (RQ) and substrate oxidation rates (e.g., acylcarnitines related to lower 24-hour RQ and higher 24-hour lipid oxidation), underscoring links between substrate availability, physiology, and metabolism in humans. Physiologic responses to diet determined by gold-standard human metabolic chambers are strongly coordinated with biologically consistent, interconnected metabolic pathways encoded in the metabolome.PMID:39576013 | DOI:10.1172/jci.insight.184279

Anal Chem. 2024 Nov 22. doi: 10.1021/acs.analchem.4c04375. Online ahead of print.ABSTRACTLiquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.PMID:39575948 | DOI:10.1021/acs.analchem.4c04375

Stud Health Technol Inform. 2024 Nov 22;321:175-179. doi: 10.3233/SHTI241087.ABSTRACTA dynamic study of ceramide concentrations and their association with recurrent event risk could enhance our understanding of cardiovascular complications. To assess the prognostic value of ceramide concentrations (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:1), Cer(d18:1/24:0)) and their dynamics in combination with standard clinical and laboratory parameters and therapeutic interventions in ACS patients. Among 110 ACS patients, triple blood sampling was performed for targeted lipidomic analysis using high-performance liquid chromatography-tandem mass spectrometry. All ceramide concentrations peaked at admission and decreased by the 3rd day of hospitalization and at the 3-month follow-up. The difference between Cer(d18:1/18:0) concentration 3 months after hospital discharge and its baseline value on admission was strongly associated with recurrent events, independent of prior statin treatment. The association of the Cer(d18:1/18:0) change from 3rd day of hospitalization and its baseline concentration on admission with prognosis varied depending on the glycemic profile.PMID:39575803 | DOI:10.3233/SHTI241087

J Am Heart Assoc. 2024 Nov 22:e036573. doi: 10.1161/JAHA.124.036573. Online ahead of print.ABSTRACTBACKGROUND: The underlying biological mechanisms linking blood pressure (BP) and cardiovascular diseases (CVD) are only partly understood. We aimed to identify metabolic signatures associated with systolic and diastolic BP and investigate their subsequent association with risk of CVD.METHODS AND RESULTS: The study included 201 742 UK Biobank participants with measurements on 249 metabolic biomarkers. A multistep adaptive elastic net penalized regression with 10-fold cross-validation was employed to identify metabolic signatures for systolic BP and diastolic BP. External validation was conducted on 848 participants from the EHS (Epirus Health Study). We further assessed the associations between BP metabolic signatures and incident composite CVD (N=6742), myocardial infarction (N=4192), and stroke (N=2757) in the UK Biobank, using multivariable Cox regression models. The metabolic signatures comprised 31 and 25 metabolites, robustly correlated with systolic BP and diastolic BP, respectively, in both the UK Biobank and the EHS. Following adjustments (including BP), the metabolic signature for systolic BP was positively associated with incident myocardial infarction (hazard ratio [HR], 1.11 [95% CI, 1.07-1.15]) and CVD (HR, 1.07 [95% CI, 1.04-1.10]). Similarly, the metabolic signature for diastolic BP was associated with a higher risk of myocardial infarction (HR, 1.16 [95% CI, 1.12-1.20]) and CVD (HR, 1.09 [95% CI, 1.05-1.12]). The associations between the signatures and stroke were not significant. The metabolic signatures partly mediated the total effect of the BP traits on the risk of myocardial infarction and CVD.CONCLUSIONS: Our findings may enhance our understanding of the biological mechanisms through which BP affects CVD.PMID:39575750 | DOI:10.1161/JAHA.124.036573

Vet Med Sci. 2025 Jan;11(1):e70128. doi: 10.1002/vms3.70128.ABSTRACTBACKGROUND: Insulin resistance during early lactation in goats has been a topic of interest for researchers, as addressing this issue can significantly improve their metabolic health.OBJECTIVES: To investigate the potential of butaphosphan and cyanocobalamin in controlling insulin resistance, we conducted a study with the hypothesis that this combination may mitigate insulin resistance in dairy goats.METHODS: Ten adult goats were divided equally into two groups: Ctrl and B+C. The Ctrl group received 6 mL of normal saline, while the second group was administered 6 mL of 10% butaphosphan and 0.005% cyanocobalamin on days 21, 20, 19, and 12, 11, 10, and 3, 2, 1 before parturition. On the 10th and 20th days after parturition, blood samples were gathered to analyze the levels of different metabolites and evaluate insulin resistance/sensitivity through an intravenous glucose tolerance test and surrogate indices. Body condition scores, milk production, and weight gain of the kids were also recorded during the study.RESULTS: Although the B+C group showed slightly higher insulin responsiveness than the Ctrl group in the intravenous glucose tolerance test, but the difference was insignificant. Comparably, no significant differences were noticed in the remaining metabolic indicators amidst the Ctrl and B+C groups.CONCLUSIONS: The lack of substantial differences can be attributed to the limited sample size and the prescribed drug dosage. Further investigations with higher doses exceeding 6 mL are warranted to explore potential effects. Additionally, species-specific differences in ruminants might exist, and caprine metabolism of the compound might differ from that of bovine and ovine. Consequently, we recommend conducting more studies in this field.PMID:39575526 | DOI:10.1002/vms3.70128

Physiol Plant. 2024 Nov-Dec;176(6):e14596. doi: 10.1111/ppl.14596.ABSTRACTThe multidimensional significance of metabolomics has gained increasing attention in oilseeds research and development. Sesame, peanut, soybean, sunflower, rapeseed, and perilla are the most important oilseed crops consumed as vegetable oils worldwide. However, multiple biotic and abiotic stressors affect metabolites essential for plant growth, development, and ecological adaptation, resulting in reduced productivity and quality. Stressors can result in dynamic changes in oilseed crops' overall performance, leading to changes in primary (ex: saccharides, lipids, organic acids, amino acids, vitamins, phytohormones, and nucleotides) and secondary (ex: flavonoids, alkaloids, phenolic acids, terpenoids, coumarins, and lignans) major metabolite classes. Those metabolites indicate plant physiological conditions and adaptation strategies to diverse biotic and abiotic stressors. Advancements in targeted and untargeted detection and quantification approaches and technologies aided metabolomics and crop improvement. This review seeks to clarify the metabolomics advancements, significant contributions of metabolites, and specific metabolites that accumulate in reaction to various stressors in oilseed crops. Considering the response of metabolites to multiple stress effects, we compiled comprehensive and combined metabolic biosynthesis pathways for six major classes. Understanding these essential metabolites and pathways can inform molecular breeding strategies to develop resilient oilseed cultivars. Hence, this review highlights metabolomics advancements and metabolites' potential roles in major oilseed crops' biotic and abiotic stress responses.PMID:39575499 | DOI:10.1111/ppl.14596

Neuro Oncol. 2024 Nov 22:noae255. doi: 10.1093/neuonc/noae255. Online ahead of print.ABSTRACTBACKGROUND: Diffuse Intrinsic Pontine Gliomas (DIPG) and other H3K27M-mutated diffuse midline gliomas (DMGs) are brain tumors that primarily affect children. Radiotherapy is the standard of care but only provides temporary symptomatic relief due to radioresistance. While hypoxia is a major driver of radioresistance in other tumors, there is no definitive evidence that DIPGs are hypoxic. DIPGs often contain histone mutations, which alter tumor metabolism and are also associated with radioresistance. Our objective was to identify the metabolic profiles of DIPG cells, detect hypoxia signatures, and uncover metabolism-linked mechanisms of radioresistance to improve tumor radiosensitivity.METHOD: Using DIPG models combined with clinical datasets, we examined mitochondrial metabolism and signatures of hypoxia. We explored DIPG reliance on mitochondrial metabolism using extracellular flux assays and targeted metabolomics. In vitro and in vivo models were used to explore the mechanisms of targeting mitochondrial bioenergetics and hypoxia for radiosensitization. Treatment-induced transcriptomics and metabolomics were also investigated.RESULTS: Comprehensive analyses of DIPG cells show signatures of enhanced oxidative phosphorylation (OXPHOS). We also identified increased expression of specific OXPHOS related genes and signatures of hypoxia gene expression in datasets obtained from DIPG patients. We found the presence of hypoxia in orthotopic mouse models bearing DIPG tumors. These findings enabled us to develop a proof-of-concept treatment strategy to enhance radiosensitivity of DIPGs in vitro and in animal models.CONCLUSION: DIPG cells rely on mitochondrial metabolism for growth, and targeting mitochondria disrupts bioenergetics, alleviates hypoxia, and enhances radiosensitivity. These findings warrant further exploration of OXPHOS inhibition as a radiosensitizing strategy for DIPG treatment.PMID:39575457 | DOI:10.1093/neuonc/noae255

Front Pharmacol. 2024 Nov 7;15:1477619. doi: 10.3389/fphar.2024.1477619. eCollection 2024.ABSTRACTAIM: This study aimed to gain deeper insights into the hepatotoxicity mechanisms of valproic acid (VPA), as well as to identify potential risk markers for VPA-induced hepatotoxicity.METHODS: Twenty-two children with epilepsy treated with VPA monotherapy were divided into a normal liver function (NLF) group, a mild abnormal liver function (ANLF1) group, and a serious abnormal liver function (ANLF2) group based on their liver function indicator levels. The full quantitative targeted metabolomics technique was used to systematically investigate how the differential endogenous metabolic components change with the development of liver injury.RESULTS: A total of 195 metabolic components were quantitatively analyzed. Nineteen identified metabolites, including five organic acids, four short-chain fatty acids, four amino acids, three fatty acids, and three benzenoids, differed significantly among the three groups, showing a strong association with VPA-induced hepatotoxicity. Only three bile acid metabolites, taurodeoxycholic acid, taurochenodeoxycholic acid, and deoxycholic acid, were significantly different between the ANLF1 and ANLF2 groups, increasing at first and then decreasing with the aggravation of liver injury. The mechanistic evaluation showed that SRT1720 activation could alleviate the severity of liver function abnormalities induced by VPA. Immunocoprecipitation indicated that VPA significantly increased the acetylation level of FXR, and the application of agonist SRT1720 can antagonize the acetylation of FXR by VPA.CONCLUSION: Nineteen identified metabolites showed a strong association with hepatotoxicity and three bile acid metabolites changed with the development of liver injury. The SIRT1-FXR pathway was firstly proposed to participate in VPA-induced hepatotoxicity.PMID:39575388 | PMC:PMC11578826 | DOI:10.3389/fphar.2024.1477619

Cureus. 2024 Nov 21;16(11):e74161. doi: 10.7759/cureus.74161. eCollection 2024 Nov.ABSTRACTDetermining postmortem interval (PMI) during forensic investigations is essential and challenging. The traditional methods used to predict PMI, such as algor mortis, rigor mortis, livor mortis, and decomposition changes, involve large margins of error, particularly when the person's death has occurred more than 48 hours ago. Organs and tissues experience profound biochemical and metabolomic changes after death. As such, new approaches are required to enhance the prediction of PMI. Novel developments in forensic sciences are focusing on identifying and analyzing postmortem metabolomics, which are biomarkers found in different body fluids and tissues serving as a "fingerprint" of continuous processes affected by both external and internal factors. This variability complicates the dataset, making examination challenging. Hence, the application of machine learning technology offers the capability to navigate through the complexities of metabolomic data, uncover hidden correlations, and enhance the accuracy of PMI prediction in forensic science. This article explores and assesses the new methodology that has recently been used to enhance the prediction of PMI by analyzing postmortem metabolomics' changes and applying these data to machine learning models. This development provides a significantly more reliable process that could potentially decrease the margin of error compared to the traditional methods used for PMI prediction.PMID:39575351 | PMC:PMC11580817 | DOI:10.7759/cureus.74161

Front Immunol. 2024 Nov 7;15:1419257. doi: 10.3389/fimmu.2024.1419257. eCollection 2024.ABSTRACTINTRODUCTION: Preclinical evidence suggests that host obesity is associated with tumor progression due to immuno-metabolic dysfunction, but the impact of obesity on immunity and clinical outcomes in patients is poorly understood, with some studies suggesting an obesity paradox. We recently reported that high-adiposity and low-muscle body composition phenotypes are associated with striking increases in epithelial ovarian cancer (EOC) mortality and we observed no evidence of an obesity paradox. However, whether at-risk versus optimal body composition phenotypes are associated with distinct immuno-metabolic milieus remains a fundamental gap in knowledge. Herein, we defined differentially abundant circulating immuno-metabolic biomarkers according to body composition phenotypes in EOC.METHODS: Muscle and adiposity cross-sectional area (cm2) was assessed using CT images from 200 EOC patients in The Body Composition and Epithelial Ovarian Cancer Survival Study at Roswell Park. Adiposity was dichotomized as low versus high; patients with skeletal muscle index (SMI) <38.5 (muscle cm2/height m2) were classified as low SMI (sarcopenia). Joint-exposure phenotypes were categorized as: Fit (normal SMI/low-adiposity), Overweight/Obese (normal SMI/high-adiposity), Sarcopenia/Obese (low SMI/high adiposity), and Sarcopenia/Cachexia (low SMI/low-adiposity). Treatment-naïve serum samples were assessed using Biocrates MxP Quant 500 for targeted metabolomics and commercially available Luminex kits for adipokines and Th1/Th2 cytokines. Limma moderated T-tests were used to identify differentially abundant metabolites and cytokines according to body composition phenotypes.RESULTS: Patients with 'risk' phenotypes had significantly increased abundance of metabolites and cytokines that were unique according to body composition phenotype. Specifically, the metabolites and cytokines in increased abundance in the at-risk phenotypes are implicated in immune suppression and tumor progression. Conversely, increased abundance of lauric acid, IL-1β, and IL-2 in the Fit phenotype was observed, which have been previously implicated in tumor suppression and anti-tumor immunity.CONCLUSION: In this pilot study, we identified several significantly differentially abundant metabolites according to body composition phenotypes, confirming that clinically significant joint-exposure body composition phenotypes are also biologically distinct. Although we observed evidence that at-risk phenotypes were associated with increased abundance of immuno-metabolic biomarkers indicated in immune suppression, additional confirmatory studies focused on defining the link between body composition and immune cell composition and spatial relationships in the EOC tumor microenvironment are warranted.PMID:39575261 | PMC:PMC11578747 | DOI:10.3389/fimmu.2024.1419257

Front Immunol. 2024 Nov 7;15:1437688. doi: 10.3389/fimmu.2024.1437688. eCollection 2024.ABSTRACTBACKGROUND: Autoimmune diseases (ADs) are a category of conditions characterized by misrecognition of autologous tissues and organs by the immune system, leading to severe impairment of patients' health and quality of life. Increasing evidence suggests a connection between fluctuations in plasma metabolites and ADs. However, the existence of a causal relationship behind these associations remains uncertain.METHODS: Applying the two-sample mendelian randomization (MR) method, the reciprocal causality between plasma metabolites and ADs was analyzed. We took the intersection of two metabolite genome-wide association study (GWAS) datasets for GWAS-meta and obtained 1,009 metabolites' GWAS data using METAL software. We accessed GWAS summary statistics for 5 common ADs, inflammatory bowel disease (IBD), multiple sclerosis (MS), type 1 diabetes (T1D), systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) from published GWAS data. MR analyses were performed in discovery and replication stage simultaneously. Meanwhile, the reverse MR analysis was conducted to investigate the possibility of reverse causal association. Furthermore, a series of sensitivity analyses were conducted to validate the robustness of the results. These statistical analyses were conducted using R software. Finally, the web version of MetaboAnalyst 5.0. was applied to analyze metabolic pathways. Ultimately, we conducted ELISA assays on plasma samples from patients to validate the results.RESULTS: 4 metabolites were identified to have causal relationships with IBD, 2 metabolites with MS, 13 metabolites with RA, and 4 metabolites with T1D. In the reverse MR analysis, we recognized causality between SLE and 22 metabolites, IBD and 4 metabolites, RA and 22 metabolites, and T1D and 37 metabolites. Additionally, 4 significant metabolic pathways were identified in RA by metabolic pathway analysis in the forward MR analysis. Correspondingly, in the reverse, 11 significant metabolic pathways in RA, 8 in SLE, and 4 in T1D were obtained using identical approaches. Furthermore, the protective role of glutamate was confirmed through ELISA assays.CONCLUSIONS: Our research established a reciprocal causality between plasma metabolites and ADs. Furthermore, diverse metabolic pathways correlated with ADs were uncovered. Novel insights into the prediction and diagnosis were provided, as well as new targets for precise treatment of these conditions were discovered.PMID:39575250 | PMC:PMC11578997 | DOI:10.3389/fimmu.2024.1437688

Front Microbiol. 2024 Nov 7;15:1405566. doi: 10.3389/fmicb.2024.1405566. eCollection 2024.ABSTRACTOBJECTIVE: This study aims to clearly define the effects of Banxia-Yiyiren on the gut microbiota and its metabolites in a para-chlorophenylalanine-induced insomnia model and the possible underlying mechanisms involved.MATERIALS AND METHODS: We employed 16S ribosomal ribonucleic acid (rRNA) gene sequencing combined with metabonomic analysis to explore the mutual effects of the PCPA-induced insomnia model and the gut microbiota and the intrinsic regulatory mechanism of Banxia-Yiyiren on the gut microbiota and metabolites in the PCPA-induced insomnia model.RESULTS: Banxia-Yiyiren was identified by mass spectrometry to include amino acids, small peptides, nucleotides, organic acids, flavonoids, fatty acids, lipids, and other main compound components. The elevated plus maze (EPM) test results revealed that high-dose Banxia-Yiyiren may increase willingness to explore by improving anxiety-like symptoms caused by insomnia. Through 16S rRNA gene sequencing, at the phylum level, compared with those in G1, the relative abundances of Bacteroidota and Proteobacteria in G2 increased, whereas the relative abundance of Firmicutes decreased. At the genus level, compared with those in G1, the relative abundances of Prevotella_9, Prevotella, Ralstonia, Escherichia-Shigella, and UCG-005 in G2 increased, whereas the relative abundances of Lactobacillus, Ligilactobacillus, Alloprevotella, Blautia, and Prevotellaceae_NK3B31_group decreased. The metabolomics analysis results revealed 1,574 metabolites, 36.48% of which were classified as lipids and lipid-like molecules, 20.76% as organic acids and their derivatives, and 13.36% as organic heterocyclic compounds. The correlation between the top 20 differentially abundant metabolites in the G1-G2 groups was greater than that between the G3-G2 and G6-G2 groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the main differentially abundant metabolites in each group were significantly enriched in various pathways, such as amino acid metabolism, adenosine triphosphate (ATP)-binding cassette (ABC) transporters, protein digestion, and absorption. Additionally, there was a significant Pearson correlation between the genus-level differences in the gut microbiota and the differentially abundant metabolites among the G1-G2, G3-G2, and G6-G2 groups.CONCLUSION: This study preliminarily verified that the PCPA-induced insomnia model is closely related to gut microbial metabolism and microecological disorders, and for the first time, we confirmed that Banxia-Yiyiren can act on the gut microbiota of PCPA-induced insomnia model rats and alleviate insomnia and anxiety by regulating the species, structure, abundance, and metabolites of the gut microbiota.PMID:39575182 | PMC:PMC11578828 | DOI:10.3389/fmicb.2024.1405566

medRxiv [Preprint]. 2024 Oct 29:2024.10.28.24316293. doi: 10.1101/2024.10.28.24316293.ABSTRACTBACKGROUND: Microscopic colitis (MC) is a common cause of chronic diarrhea, predominantly among older adults. Emerging evidence suggests that perturbations of gut microbiome and metabolome may play an important role in MC pathogenesis.OBJECTIVE: To comprehensively characterize alterations of the gut microbial and metabolic composition in MC.DESIGN: We established a longitudinal cohort of adult patients with MC and two control groups of individuals - chronic diarrhea controls and age- and sex-matched controls without diarrhea. Using stool samples, gut microbiome was analyzed by whole-genome shotgun metagenomic sequencing, and gut metabolome was profiled by ultra-high performance liquid chromatography-mass spectrometry. Per-feature enrichment analyses of microbial species, metabolic pathways, and metabolites were done using multivariable linear models both cross-sectionally comparing MC to controls and longitudinally according to disease activity. Lastly, we performed multi-omics association analyses to assess the relationship between microbiome and metabolome data.RESULTS: We included 683 participants, 131 with active MC (66 with both active and remission samples), 159 with chronic diarrhea, and 393 age- and sex-matched controls without diarrhea. The stool microbiome in active MC was characterized by a lower alpha diversity as compared to controls and the remission phase of MC. Compared to controls, we identified eight enriched species in MC, most of which were pro-inflammatory oral-typical species, such as Veillonella dispar and Haemophilus parainfluenzae . In contrast, 11 species, including anti-inflammatory microbes such as Blautia glucerasea and Bacteroides stercoris, were depleted in MC. Similarly, pro-inflammatory metabolites, including lactosylceramides, ceramides, lysophospholipids, and lysoplasmalogens were enriched in active MC as compared to controls or MC cases in remission. Multi-omics association analyses revealed strong and concordant links between microbes, their metabolic pathways, and metabolomic profiles, supporting the tight interplay between disturbances in stool microbiome and metabolome in MC.CONCLUSION: We observed a significant shift in stool microbial and metabolomic composition in MC. Our findings could be used in the future for development of non-invasive biomarkers for diagnosing and monitoring MC and developing novel therapeutics.WHAT IS ALREADY KNOWN ON THIS TOPIC: Microbiome dysbiosis has been proposed to contribute to microscopic colitis (MC) pathogenesis.However, previous studies have been limited by small sample sizes, reliance on 16S rRNA sequencing technique, potential confounding by stool consistency, and lack of functional analyses of microbiome and longitudinal data. Moreover, the metabolomic composition of MC remain largely unknown.WHAT THIS STUDY ADDS: In this largest longitudinal MC cohort with two control groups - chronic diarrhea controls and controls without diarrhea, gut microbiome of MC is characterized by a lower alpha diversity, enriched pro-inflammatory oral-typical species and depleted anti-inflammatory beneficial species.Gut metabolome of MC shows significant enrichment of pro-inflammatory metabolites, including lactosylceramides, ceramides, lysophospholipids, and lysoplasmalogens. Multi-omics analyses demonstrate strong and concordant relationships between microbes, metabolic pathways, and metabolomic profiles.HOW THIS STUDY MIGHT AFFECT RESEARCH PRACTICE OR POLICY: Our findings could facilitate development of non-invasive biomarkers and novel therapeutics for MC.PMID:39574841 | PMC:PMC11581081 | DOI:10.1101/2024.10.28.24316293

bioRxiv [Preprint]. 2024 Nov 6:2024.11.05.622009. doi: 10.1101/2024.11.05.622009.ABSTRACTDiscovery of cancer immunogenic chemotherapeutics represents an emerging, highly promising direction for cancer treatment that uses a chemical drug to achieve the efficacy of both chemotherapy and immunotherapy. Herein we report a high-throughput screening platform and the subsequent discovery of a new class of cancer immunogenic chemotherapeutic leads. Our platform integrates informatics-based activity metabolomics for rapid identification of microbial natural products with both novel structures and potent activities. Additionally, we demonstrate the use of microcrystal electron diffraction (MicroED) for direct structure elucidation of the lead compounds from partially purified mixtures. Using this strategy to screen geographically and phylogenetically diverse microbial metabolites against pseudomyxoma peritonei, a rare and severe cancer, we discovered a new class of leads, aspercyclicins. The aspercyclicins feature an unprecedented tightly packed polycyclic polyketide scaffold that comprises continuous fused, bridged, and spiro rings. The biogenesis of aspercyclicins involves two distinct biosynthetic pathways, leading to formation of chimeric compounds that cannot be predicted by bottom-up approaches mining natural products biosynthetic genes. With comparable potency to some clinically used anticancer drugs, aspercyclicins are active against multiple cancer cell types by inducing immunogenic cell death (ICD), including the release of damage-associated molecular patterns and subsequent phagocytosis of cancer cells. The broad-spectrum ICD-inducing activity of aspercyclicins, combined with their low toxicity to normal cells, represents a new class of potential cancer immunogenic chemotherapeutics and particularly the first drug lead for pseudomyxoma peritonei treatment.PMID:39574732 | PMC:PMC11580922 | DOI:10.1101/2024.11.05.622009

bioRxiv [Preprint]. 2024 Nov 8:2024.11.07.622457. doi: 10.1101/2024.11.07.622457.ABSTRACTEpstein-Barr virus (EBV) is associated with multiple types of cancers, many of which express the key viral oncoprotein Latent Membrane Protein 1 (LMP1). LMP1 is the only EBV-encoded protein whose expression is sufficient to transform both epithelial and B-cells. Although metabolism reprogramming is a cancer hallmark, much remains to be learned about how LMP1 alters lymphocyte oncometabolism. To gain insights into key B-cell metabolic pathways subverted by LMP1, we performed systematic metabolomic analyses on B cells with conditional LMP1 expression. This approach highlighted that LMP highly induces de novo purine biosynthesis, with xanthosine-5-P (XMP) as one of the most highly LMP1-upregulated metabolites. Consequently, IMPDH inhibition by mycophenolic acid (MPA) triggered apoptosis of LMP1-expressing EBV-transformed lymphoblastoid cell lines (LCL), a key model for EBV-driven immunoblastic lymphomas. Whereas MPA instead caused growth arrest of Burkitt lymphoma cells with the EBV latency I program, conditional LMP1 expression triggered their apoptosis. Although both IMPDH isozymes are expressed in LCLs, only IMPDH2 was critical for LCL survival, whereas both contributed to proliferation of Burkitt cells with the EBV latency I program. Both LMP1 C-terminal cytoplasmic tail domains critical for primary human B-cell transformation were important for XMP production, and each contributed to LMP1-driven Burkitt cell sensitivity to MPA. MPA also de-repressed EBV lytic antigens including LMP1 in latency I Burkitt cells, highlighting crosstalk between the purine biosynthesis pathway and the EBV epigenome. These results suggest novel oncometabolism-based therapeutic approaches to LMP1-driven lymphomas.IMPORTANCE: Altered metabolism is a hallmark of cancer, yet much remains to be learned about how EBV rewires host cell metabolism to support multiple malignancies. While the oncogene LMP1 is the only EBV-encoded gene that is sufficient to transform murine B-cells and rodent fibroblasts, knowledge has remained incomplete about how LMP1 alters host cell oncometabolism to aberrantly drive infected B-cell growth and survival. Likewise, it has remained unknown whether LMP1 expression creates metabolic vulnerabilities that can be targeted by small molecule approaches to trigger EBV-transformed B-cell programmed cell death. We therefore used metabolomic profiling to define how LMP1 signaling remodels the B-cell metabolome. We found that LMP1 upregulated purine nucleotide biosynthesis, likely to meet increased demand. Consequently, LMP1 expression sensitized Burkitt B-cells to growth arrest upon inosine monophosphate dehydrogenase blockade. Thus, while LMP1 itself may not be a therapeutic target, its signaling induces dependence on downstream druggable host cell nucleotide metabolism enzymes, suggesting rational therapeutic approaches.PMID:39574729 | PMC:PMC11581047 | DOI:10.1101/2024.11.07.622457

bioRxiv [Preprint]. 2024 Nov 4:2024.11.04.621595. doi: 10.1101/2024.11.04.621595.ABSTRACTBACKGROUND: A water extract of the Ayurvedic plant Centella asiatica (CAW) improves cognitive function in mouse models of aging and Alzheimer's disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plant's therapeutic benefit.METHODS: Mouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses respectively.RESULTS: Extensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicate the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8) and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA, and possible compound interactions at this level.CONCLUSIONS: Four gene expression modules and two metabolite modules were altered by the four types of treatments applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons.PMID:39574684 | PMC:PMC11580974 | DOI:10.1101/2024.11.04.621595

bioRxiv [Preprint]. 2024 Nov 6:2024.11.05.622058. doi: 10.1101/2024.11.05.622058.ABSTRACTAging is a key contributor of morbidity and mortality during acute viral pneumonia. The potential role of age-associated dysbiosis on disease outcomes is still elusive. In the current study, we used high-resolution shotgun metagenomics and targeted metabolomics to characterize SARS-CoV-2-associated changes in the gut microbiota from young (2-month-old) and aged (22-month-old) hamsters, a valuable model of COVID-19. We show that age-related dysfunctions in the gut microbiota are linked to disease severity and long-term sequelae in older hamsters. Our data also reveal age-specific changes in the composition and metabolic activity of the gut microbiota during both the acute phase (day 7 post-infection, D7) and the recovery phase (D22) of infection. Aged hamsters exhibited the most notable shifts in gut microbiota composition and plasma metabolic profiles. Through an integrative analysis of metagenomics, metabolomics, and clinical data, we identified significant associations between bacterial taxa, metabolites and disease markers in the aged group. On D7 (high viral load and lung epithelial damage) and D22 (body weight loss and fibrosis), numerous amino acids, amino acid-related molecules, and indole derivatives were found to correlate with disease markers. In particular, a persistent decrease in phenylalanine, tryptophan, glutamic acid, and indoleacetic acid in aged animals positively correlated with poor recovery of body weight and/or lung fibrosis by D22. In younger hamsters, several bacterial taxa ( Eubacterium , Oscillospiraceae , Lawsonibacter ) and plasma metabolites (carnosine and cis-aconitic acid) were associated with mild disease outcomes. These findings support the need for age-specific microbiome-targeting strategies to more effectively manage acute viral pneumonia and long-term disease outcomes.PMID:39574606 | PMC:PMC11580917 | DOI:10.1101/2024.11.05.622058

Front Plant Sci. 2024 Nov 7;15:1344155. doi: 10.3389/fpls.2024.1344155. eCollection 2024.ABSTRACTINTRODUCTION: Chieh-qua (Benincasa hispida Cogn. var. Chieh-qua How) is a wax gourd variety that is generally susceptible to infection and damage by Fusarium oxysporum during its cultivation. Therefore, analyzing the adaption mechanism of chieh-qua to F. Oxysporum infection is of great significance for cultivating resistant varieties.METHODS: Through comparative transcriptome analysis, comparative metabolome analysis, integrated analysis of transcriptome and metabolome and between F. Oxysporum infected samples and control samples of susceptible lines.RESULTS: This study found that proteins such as NPR1, TGA and PR1 in plant hormone signal transduction pathway were up-regulated after infection, which may activate a series of plant secondary metabolic synthesis pathways. In addition, the expression of 27 genes in the flavonoid biosynthetic process in resistant lines after infection was significantly higher than that in susceptible lines, indicating that these genes may be involved in fungal resistance. This study also found that alternative splicing of genes may play an important role in responding to F. Oxysporum infection. For example, plant protein kinase genes such as EDR1, SRK2E and KIPK1 were not differentially expressed after F. Oxysporum infection, but the transcripts they produced differ at the transcription level. Finally, through comparative metabolome analysis, this study identified potentially functional substances such as oxalic acid that increased in content after F. Oxysporum infection. Through integrated analysis of transcriptome and metabolome, some differential expressed genes significantly related to differential metabolites were also identified.DISCUSSION: This study provides a basis for understanding and utilizing chieh-qua's infection mechanism of F. Oxysporum through analysis of the transcriptome and metabolome.PMID:39574453 | PMC:PMC11578706 | DOI:10.3389/fpls.2024.1344155

Front Plant Sci. 2024 Nov 7;15:1349635. doi: 10.3389/fpls.2024.1349635. eCollection 2024.ABSTRACTRed alder (Alnus rubra) has highly desirable wood, dye pigment, and (traditional) medicinal properties which have been capitalized on for thousands of years, including by Pacific West Coast Native Americans. A rapidly growing tree species native to North American western coastal and riparian regions, it undergoes symbiosis with actinobacterium Frankia via their nitrogen-fixing root nodules. Red alder's desirable properties are, however, largely attributed to its bioactive plant phenol metabolites, including for plant defense, for its attractive wood and bark coloration, and various beneficial medicinal properties. Integrated transcriptome and metabolome data analyses were carried out using buds, leaves, stems, roots, and root nodules from greenhouse grown red alder saplings with samples collected during different time-points (Spring, Summer, and Fall) of the growing season. Pollen and catkins were collected from field grown mature trees. Overall plant phenol biochemical pathways operative in red alder were determined, with a particular emphasis on potentially identifying candidates for the long unknown gateway entry points to the proanthocyanidin (PA) and ellagitannin metabolic classes, as well as in gaining better understanding of the biochemical basis of diarylheptanoid formation, i.e. that help define red alder's varied medicinal uses, and its extensive wood and dye usage.PMID:39574452 | PMC:PMC11578710 | DOI:10.3389/fpls.2024.1349635