Chronic infections induce CD8 T cell exhaustion, marked by impaired effector function. While intrinsic drivers are well studied, the role of the surrounding metabolic environment in shaping exhausted CD8 T cells (Tex) is less understood. Using untargeted metabolomics and the murine lymphocytic choriomeningitis virus infection model, we investigated systemic metabolite changes following acute vs. chronic viral infections. We identified distinct short-term and persistent metabolite shifts, with the most significant differences occurring transiently during the early phase of the sustained infection. This included nutrient changes that were partially associated with CD8 T cell-induced anorexia and lipolysis. One remarkable observation was the elevation of medium- and long-chain fatty acids (FA) and acylcarnitines during the first week after chronic infection. Consistently, virus-specific CD8 T cells from chronic infection exhibited increased lipid accumulation and uptake compared to their counterparts from acute infection, particularly the stem-like Tex (TexSTEM), which generates TexINT that directly limit viral replication. Notably, only TexSTEM increased oxidative metabolism upon ex vivo FA exposure, while short-term administration of FA during late chronic infection exclusively increased TexSTEM and their mitochondrial potential. The last-mentioned treatment also led to reduced TexINT and enhanced PD-1 across all Tex subsets, which coincided with compromised viral control. Our study offers a valuable resource for investigating the regulatory role of specific metabolites during acute and chronic viral infections and highlights the potential of FA to fine-tune Tex subsets during protracted infections.

BACKGROUND:Metabolomics reflects the molecular communications within biological systems. Urine is a noninvasive biofluid, rich in metabolites that serve as potential biomarkers for human health and disease. The impact of storage conditions and DNA stabilizers for urine samples in metabolomic studies remain unclear. OBJECTIVE:To evaluate the impact of common storage conditions and the presence of a DNA stabilizer, AssayAssure® (Thermo Scientific), on the metabolite content of voided human urine. METHODS:We assessed the urinary metabolite composition under different storage conditions and with the addition of AssayAssure® to determine its effect on metabolomic analysis. RESULTS:Urinary metabolite composition remained consistent across different storage conditions. However, the addition of AssayAssure® significantly altered the metabolic profile due to adduct formation. Despite these alterations, the identification of parent metabolites was not compromised, and biological differences were still distinguishable. CONCLUSION:These findings suggest that urine biobanked under the tested storage conditions is suitable for metabolomic analysis. The addition of AssayAssure® does not hinder the detection of parent metabolites, although it may affect the overall metabolic profile.

Alzheimer’s disease (AD) is a neurodegenerative disorder with significant environmental factors, including diet, that influence its onset and progression. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored the KD’s molecular impact for markers of AD therapeutic potential. The BEAM diet study simultaneously profiled the KD’s effect on the lipidome, blood and cerebrospinal metabolome, and microbiome of both cognitively impaired and cognitively normal individuals. The findings summarized here assess the biological impact of a Modified Mediterranean KD in the context of Alzheimer’s disease treatment and prevention. BEAM involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed HPLC-MS/MS lipidomics profiling in plasma, nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF, and metagenomics profiling on fecal samples before and after each diet to assess dietary-induced changes. The MMKD led to significant alterations in the blood, CSF, and microbiome. These changes included a global elevation across all plasmanyl and plasmenyl ether lipid species, improved modifiable risk factors, like increased HDL-C and reduced BMI, the reversal of serum metabolic disturbances linked to AD such as an increase in valine levels, and a reduction in systemic inflammation. Leveraging prior clinical studies on AD (n = 1,912), we found that MMKD was inversely associated with the peripheral lipidomic signature of prevalent and incident AD. In the CSF, the MMKD was linked to modified amino acid levels and the breakdown of branched-chain amino acids (BCAAs). Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. In addition, participants with MCI on the MMKD had lower levels of GABA-producing microbes and GABA, and higher levels of GABA-regulating microbes. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier. Funding : Alzheimer’s Gut Microbiome Project, NIA U19AG063744

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

IntroductionHuman metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.ObjectivesThis study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.MethodsAn interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).ResultsDifferent laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.ConclusionThis collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Chronic infections drive a CD8 T cell program termed T cell exhaustion, characterized by reduced effector functions. While cell-intrinsic mechanisms underlying CD8 T cell exhaustion have been extensively studied, the impact of the metabolic environment in which exhausted CD8 T cells (Tex) operate remains less clear. Using untargeted metabolomics and the murine lymphocytic choriomeningitis virus infection model we investigated systemic metabolite changes early and late following acute versus chronic viral infections. We identified distinct short-term and persistent metabolite shifts, with the most significant differences occurring transiently during the acute phase of the sustained infection. This included nutrient changes that were independent of viral loads and partially associated with CD8 T cell-induced anorexia and lipolysis. One remarkable observation was the elevation of medium- and long-chain fatty acid (FA) and acylcarnitines during the early phase after chronic infection. During this time, virus-specific CD8 T cells from chronically infected mice exhibited increased lipid accumulation and uptake compared to their counterparts from acute infection, particularly stem-like Tex (Tex STEM ), a subset that generates effector-like Tex INT which directly limit viral replication. Notably, only Tex STEM increased oxidative metabolism and ATP production upon FA exposure. Consistently, short-term reintroduction of FA during late chronic infection exclusively improved Tex STEM mitochondrial fitness, percentages and numbers. This treatment, however, also reduced Tex INT , resulting in compromised viral control. Our study offers a valuable resource for investigating the role of specific metabolites in regulating immune responses during acute and chronic viral infections and highlights the potential of long-chain FA to influence Tex STEM and viral control during a protracted infection. Significance:This study examines systemic metabolite changes during acute and chronic viral infections. Notably, we identified an early, transient nutrient shift in chronic infection, marked by an increase in medium- and long-chain fatty acid related species. Concomitantly, a virus-specific stem-like T cell population, essential for maintaining other T cells, displayed high lipid avidity and was capable of metabolizing exogenous fatty acids. Administering fatty acids late in chronic infection, when endogenous lipid levels had normalized, expanded this stem-like T cell population and enhanced their mitochondrial fitness. These findings highlight the potential role of fatty acids in regulating stem-like T cells in chronic settings and offer a valuable resource for studying other metabolic signatures in both acute and persistent infections.

The gastrointestinal (GI) tract is innervated by intrinsic neurons of the enteric nervous system (ENS) and extrinsic neurons of the central nervous system and peripheral ganglia. The GI tract also harbors a diverse microbiome, but interactions between the ENS and the microbiome remain poorly understood. Here, we activate choline acetyltransferase (ChAT)-expressing or tyrosine hydroxylase (TH)-expressing gut-associated neurons in mice to determine effects on intestinal microbial communities and their metabolites as well as on host physiology. The resulting multi-omics datasets support broad roles for discrete peripheral neuronal subtypes in shaping microbiome structure, including modulating bile acid profiles and fungal colonization. Physiologically, activation of either ChAT+ or TH+ neurons increases fecal output, while only ChAT+ activation results in increased colonic contractility and diarrhea-like fluid secretion. These findings suggest that specific subsets of peripherally activated neurons differentially regulate the gut microbiome and GI physiology in mice without involvement of signals from the brain.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Background: Cavernous angiomas (CAs) affect 0.5% of the population, predisposing to serious neurologic sequelae from brain bleeding. A leaky gut epithelium associated with a permissive gut microbiome, was identified in patients who develop CAs, favoring lipid polysaccharide producing bacterial species. Micro-ribonucleic acids along with plasma levels of proteins reflecting angiogenesis and inflammation were also previously correlated with CA and CA with symptomatic hemorrhage. Methods: The plasma metabolome of CA patients and CA patients with symptomatic hemorrhage was assessed using liquid-chromatography mass spectrometry. Differential metabolites were identified using partial least squares-discriminant analysis ( p < 0.05, FDR corrected). Interactions between these metabolites and the previously established CA transcriptome, microbiome, and differential proteins were queried for mechanistic relevance. Differential metabolites in CA patients with symptomatic hemorrhage were then validated in an independent, propensity matched cohort. A machine learning-implemented, Bayesian approach was used to integrate proteins, micro-RNAs and metabolites to develop a diagnostic model for CA patients with symptomatic hemorrhage. Results: Here we identify plasma metabolites, including cholic acid and hypoxanthine distinguishing CA patients, while arachidonic and linoleic acids distinguish those with symptomatic hemorrhage. Plasma metabolites are linked to the permissive microbiome genes, and to previously implicated disease mechanisms. The metabolites distinguishing CA with symptomatic hemorrhage are validated in an independent propensity-matched cohort, and their integration, along with levels of circulating miRNAs, enhance the performance of plasma protein biomarkers (up to 85% sensitivity and 80% specificity). Conclusions: Plasma metabolites reflect CAs and their hemorrhagic activity. A model of their multiomic integration is applicable to other pathologies.

Inter-individual differences in the gut microbiome are linked to alterations in inflammation and blood–brain barrier permeability, which may increase the risk of depression in people with HIV (PWH). The microbiome profile of blood, which is considered by many to be typically sterile, remains largely unexplored. We aimed to characterize the blood plasma microbiome composition and assess its association with major depressive disorder (MDD) in PWH and people without HIV (PWoH). In this cross-sectional, observational cohort, we used shallow-shotgun metagenomic sequencing to characterize the plasma microbiome of 151 participants (84 PWH and 67 PWoH), all of whom underwent a comprehensive neuropsychiatric assessment. The microbial composition did not differ between PWH and PWoH or between participants with MDD and those without it. Using the songbird model, we computed the log ratio of the highest and lowest 30% of the ranked classes associated with HIV and MDD. We found that HIV infection and lifetime MDD were enriched in a set of differentially abundant inflammatory classes, such as Flavobacteria and Nitrospira. Our results suggest that the circulating plasma microbiome may increase the risk of MDD related to dysbiosis-induced inflammation in PWH. If confirmed, these findings may indicate new biological mechanisms that could be targeted to improve treatment of MDD in PWH.

INTRODUCTION:The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention. METHODS:Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals' microbiome and metabolome throughout the dietary interventions. RESULTS:Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility. DISCUSSION:Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or “suspects,” were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Clinical testing typically relies on invasive blood draws and biopsies. Alternative methods of sample collection are continually being developed to improve patient experience; swabbing the skin is one of the least invasive sampling methods possible. To show that skin swabs in combination with untargeted mass spectrometry (metabolomics) can be used for non-invasive monitoring of an oral drug, we report the kinetics and metabolism of diphenhydramine in healthy volunteers (n=10) over the course of 24 hours in blood and three regions of the skin. Diphenhydramine and its metabolites were observed on the skin after peak plasma levels, varying by compound and skin location, and is an illustrative example of how systemically administered molecules can be detected on the skin surface. The observation of diphenhydramine directly from the skin supports the hypothesis that both parent drug and metabolites can be qualitatively measured from a simple non-invasive swab of the skin surface. The mechanism of the drug and metabolites pathway to the skin’s surface remains unknown.

Introduction: Cavernous Angioma (CA) is a hemorrhagic neurovascular disease characterized by either a familial form with autosomal dominant germline mutations in one of three CCM genes or a sporadic form with somatic mutations of the same genes. Circulating proteins have been previously investigated as possible diagnostic and prognostic biomarkers of disease activity, with up to 86% and 88% sensitivity and specificity, respectively. We hypothesize that differentially expressed (DE) plasma microRNAs and metabolites in CA patients can be integrated with plasma proteins to increase the sensitivity and specificity of circulating CA biomarkers. Methods: Mechanistically relevant homologous DE miRNAs were identified between familial CA patients and preclinical murine models and validated in an independent cohort of patients using real time qPCR. In conjunction, DE metabolites were determined in CA patients using liquid-chromatography mass spectrometry. The interactions of these metabolites with the previously established CA transcriptome, proteome, and microbiome were queried to assess for mechanistic relevance. Optimal diagnostic models of proteins, DE miRNAs, and DE metabolites alone were next established. Plasma metabolites and miRNAs were then separately integrated with protein, using a machine learning-implemented, Bayesian approach to develop diagnostic CA biomarkers. Results: The optimal diagnostic biomarker model with only DE miRNAs performed at up to 68%, while proteins and metabolites achieved up to 68%, and 82% accuracy respectively. The optimal combination for proteins with miRNAs improved the diagnostic association of familial-CA disease to up to 94.7% sensitivity and 100% specificity. Integrating metabolites and proteins improved the diagnosis of CA disease and its clinical manifestations to 100% sensitivity and 100% specificity. Conclusion: Combining plasma proteins with miRNAs or metabolites can improve diagnostic accuracy of CA disease and its disease characteristics above any single molecular modality alone. Future studies should incorporate proteins, miRNAs, and metabolites to further increase diagnostic accuracy, and validate these in a larger cohort with control for demographic and disease features.