Biology subjects

Jones, D. P.

Publications and source records attributed to Jones, D. P..

15 recordsLinked to original sources

bioRxiv2026

Lactococcus lactis subsp. cremoris Reprograms the Gut-Liver Axis and Protects Against Liver Injury

Metabolic diseases are increasingly linked to dysregulation of the gut-liver axis, highlighting the therapeutic potential of probiotics. Lactococcus lactis subsp. cremoris (LLC) protects against experimental hepatic steatosis, but the mechanisms underlying its benefits remain poorly understood. Here, we integrated untargeted metabolomics, gnotobiotic mouse models, targeted bile acid profiling, and liver injury paradigms to systematically define LLC-mediated metabolic reprogramming. LLC extensively remodeled serum and hepatic metabolomes in Western diet-fed mice, enriching pathways associated with lipid metabolism, xenobiotic biotransformation, and redox homeostasis. LLC attenuated ethanol-induced steatosis and acetaminophen-induced liver injury, accompanied by activation of Nrf2-dependent antioxidant programs and FXR signaling. LLC monocolonization was sufficient to reprogram the hepatic metabolome, revealing direct host metabolic effects. Reduced intestinal bile acid conjugation emerged as a prominent LLC-associated phenotype. Together, these findings identify LLC as a probiotic that reprograms the gut-liver metabolic axis to enhance metabolic resilience and hepatoprotection.

microbiology↗

bioRxiv2026

Anti-psychotic therapeutics modify gut microbial metabolism and modulate susceptibility to gastrointestinal infection in mice

Antipsychotic medications are widely prescribed and associated with increased infection risk, but underlying mechanisms remain unclear. We examined if antipsychotic-induced microbiome and metabolome alterations impair colonization resistance. Using a murine model, we evaluated haloperidol, olanzapine, risperidone, and quetiapine through longitudinal microbiome profiling, untargeted metabolomic fingerprinting, behavioral testing, and enteric infection challenge. Antipsychotic exposure induced persistent behavioral changes and increased susceptibility to Citrobacter rodentium, with quetiapine and olanzapine producing the greatest weight loss. Microbiome sequencing revealed treatment-specific shifts in beta diversity without consistent alpha diversity changes, while untargeted metabolomic analysis demonstrated robust, drug-specific metabolic reprogramming, particularly in lipid and sterol pathways. Although microbial compositional changes did not fully account for functional outcomes, their integration with metabolomics data revealed disrupted bacterial taxa-metabolite networks and loss of homeostatic metabolic modules. These results provide a potential mechanistic link between the disruptive effects of antipsychotics on the microbiome and impaired colonization resistance.

microbiology↗

bioRxiv2026

MSMICA: computational metabolite identification in untargeted metabolomics by integrating MS, retention time, and biological evidence

Mass Spectrometry Metabolomics Identification Connection Algorithm (MSMICA) is an algorithm for automated metabolite identification in untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses. Limitations in metabolite identification can occur due to the availability and cost of standards and prevent recognition of metabolic factors impacting human health and disease. MSMICA performs mass-to-charge-ratio matching with chemical structures and clusters of LC-HRMS features for adduct and isotope forms. A local optimization is then used to integrate retention time prediction, metabolite precursor-product and transporter correlations, and biospecimen-specific abundance information for metabolite identification. Applying MSMICA to various internal and external mammalian datasets, validation results showed a 96.2 +- 5.1% correct rate of metabolite identification. When multiple LC-HRMS datasets were used, MSMICA enabled greater metabolite identifications, expanded metabolic pathway coverage, and data harmonization. Thus, MSMICA applies multiple pieces of evidence to substantially improve metabolite identification coverage and accuracy for known metabolites.

bioinformatics↗

bioRxiv2026

Development of a metabolomics-based index to monitor dietary effects on chronic inflammation: The Dietary Metabolomics Inflammation Index

BackgroundThe Dietary Inflammatory Index (DII) is widely used to assess the inflammatory potential of diet, but it relies on self-reported dietary assessment and does not directly capture individual differences in metabolism as an intermediate connection to inflammation. High-resolution metabolomics provides objective measurements that complement dietary assessment to support precision nutrition to control inflammation. ObjectiveWe developed, tested, and applied a Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolites measured by liquid chromatography-high-resolution mass spectrometry. MethodsDII was calculated using dietaryindex R package with Block Food Frequency Questionnaire (FFQ) data. To develop the DMII, chronic inflammation-related dietary metabolites corresponding to the DII food parameters were found through a literature review. Dietary metabolites were identified and quantified by authentic standards by our established laboratory procedures. DMII uses the same inflammatory effect scores as the DII. Three DMII versions were developed: concentration-based, median-based, and quintile-based DMII. Mean and standard deviation of 29 dietary metabolites were calculated by using 3025 human plasma samples from 3 studies. DMII was tested in the Center for Health Discovery and Well-Being cohort (CHDWB) and the Atlanta African American Maternal and Child cohort (ATLAA) using chronic inflammation biomarkers, including high-sensitivity C-reactive protein (hs-CRP), CRP, and IL-6. The median-based DMII was further applied to four Alzheimers disease metabolomics datasets as a proof-of-concept application. ResultsIn the CHDWB study, concentration-based DMII had a weak positive correlation with Block FFQ-derived DII and strongly correlated with median-based and quintile-based DMII. In the same study, all three DMII versions had significant positive correlations with hs-CRP and IL-6. In the ATLAA study, only concentration-based DMII was positively associated with CRP and IL-6. Higher median-based DMII was associated with higher odds of Alzheimers disease. ConclusionsDMII provides a metabolomics-based framework for assessing diet-related chronic inflammation using metabolomics data. This metabolomics approach may complement self-reported dietary assessment to use diet and nutrition to help protect against chronic disease linked to inflammation.

biochemistry↗

bioRxiv2026

Microbiome-Derived Metabolites Shape CD4⁺ T-Cell Differentiations and Immune Aging in Chronic HIV-1 Infection

The role of aromatic gut-derived bacterial metabolites (GDBMs) in shaping immune cell metabolism and function remains poorly explored. Using ex vivo metabolomic profiling of paired plasma and CD4 T-cells from people living with HIV-1 (PLWH), we identified a network of aromatic GDBMs whose cell-associated abundance, rather than systemic levels, was linked to broad alterations in CD4 T-cell metabolic and functional states. Among these metabolites, p-cresol sulfate (PCS) emerged as a mechanistic prototype investigated in depth. Ex vivo flow cytometry and single-cell RNA sequencing of CD4 T-cells stratified by cell-associated PCS levels revealed dose-dependent enrichment of transcriptional programs associated with impaired differentiation capacity, regulatory-like identity, and cellular senescence. Consistently, in vitro transcriptomic and proteomic analyses of PCS-exposed CD4 T cells demonstrated induction of cell-cycle arrest, mitochondrial dysfunction, and senescence-associated programs, including upregulation of p16 and p21. Integration of these immunometabolic features with measurements of HIV-1 reservoir size in PLWH revealed that CD4 T-cell states defined by cell-associated GDBMs track with intact proviral DNA levels in vivo. Together, these findings define a microbiome-derived axis that reshapes CD4 T-cell metabolism and fate and promotes immune aging-associated states in PLWH. Our data suggest that cell-associated GDBMs may foster immunometabolic CD4 T-cell states previously linked to long-term HIV-1 reservoir persistence in vivo. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/699280v1_figa1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@faf44aorg.highwire.dtl.DTLVardef@1bc590aorg.highwire.dtl.DTLVardef@79c557org.highwire.dtl.DTLVardef@8b0a64_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract.C_FLOATNO PCS-driven metabolic reprogramming and senescence promoting CD4+ T-cell immune cell aging.Dietary proteins are metabolized by proteolytic gut microbiota into p-cresol, which is absorbed and converted in the liver to PCS. Circulating PCS accumulates in CD4 T-cells, where it activates the aryl hydrocarbon receptor (AhR). AhR signaling reduces glycolysis and mTOR activity, while enhancing TGF-, Wnt/-catenin, and TCF7 pathways, driving a regulatory-like and stem-like transcriptional program. These changes are associated with increased expression of p16 and p21, leading to cell cycle arrest and cellular senescence promoting CD4+ T-cell immune cell aging. C_FIG

systems biology↗

bioRxiv2025

Metabolic Trajectories During Surgical Stress in Patients Undergoing Cardiac Surgery

Stress hyperglycemia (SH) during acute illness is linked to adverse surgical outcomes, yet the accompanying metabolic perturbations are incompletely characterized. We profiled longitudinal metabolic changes in adults without diabetes undergoing cardiac surgery to identify pathways associated with perioperative SH (defined as point-of-care glucose [≥]140 mg/dL on [≥]3 readings or [≥]180 mg/dL once). Blood was collected at baseline before surgery (T0) and at 2 h (T1), 24-48 h (T2), and 72-96 h (T3) after surgical initiation. High-resolution metabolomics (LC-MS) was integrated with continuous glucose monitoring and inflammatory/cardiac biomarkers. At T0, several pathways were associated with subsequent SH, including bile acid metabolism, the carnitine shuttle, and fatty-acid oxidation, suggesting preoperative metabolic susceptibility. In longitudinal analyses, participants who developed SH showed coordinated postoperative changes with significant enrichment of pathways not evident at baseline, C21-steroid hormone biosynthesis, glycerophospholipid metabolism, and glycosphingolipid (ceramide) metabolism, consistent with lipid remodeling and inflammatory signaling during surgical stress. Individuals with SH also exhibited higher inflammatory biomarker levels (high-sensitivity C-reactive protein and soluble urokinase plasminogen activator receptor). A machine-learning model using early metabolomic features predicted SH with an area under the receiver operating characteristic curve of 0.86. These findings highlight distinct preoperative and perioperative metabolic trajectories associated with SH and implicate established dysglycemia-related pathways, as well as stress-induced pathways in perioperative metabolic dysregulation. Pathway enrichment analyses were exploratory and hypothesis-generating; validation in larger cohorts and assessment of implications for clinical outcomes are warranted.

biochemistry↗

bioRxiv2025

Genes govern metabolism-Enzymes define pathways and metabolic relationships

Gene-centric pathway mapping tools, widely used to interpret untargeted liquid chromatography mass spectrometry (LC-MS) metabolomics data, may underperform because a single metabolite can generate multiple spectral features, inflating false positive rates. Classic enzymology, which established metabolite flow before gene sequencing, offers experimentally validated precursor-product relationships that could overcome these ambiguities. We evaluated whether enzymology-defined precursor-product correlations are consistently detectable in human plasma LC-MS data. We detected amino acids, carnitine-related, TCA cycle, and pentose phosphate pathway metabolites in one individual sampled eight times over five years and in 50 adults sampled 6 to 8 times each. In the single participant repeated measures, strong positive correlations were observed for most direct precursor-product pairs. The longitudinal and cross-sectional analyses reproduced these patterns. Precursor-product proportionality, a fundamental principle of enzymology, is detectable in LC-MS datasets and remains consistent across studies. Applying these correlations to metabolomics workflows can improve pathway analysis, help metabolite identification, and reveal how genetic variations, diets, therapeutic drugs, and environmental exposures jointly impact metabolic pathways.

systems biology↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2022

High-throughput Production of Diverse Xenobiotic Metabolites with P450-transduced Huh7 Hepatoma Cell Lines

Precision medicine requires methods to assess drug metabolism and distribution, including the identification of known and undocumented drug and chemical exposures as well as their metabolites. Recent work demonstrated high-throughput generation of xenobiotic metabolites with human liver S-9 fractions and detection in human plasma and urine. Here, we developed a panel of lentivirally transduced human hepatoma cell lines (Huh7) that stably express individual cytochrome P450 (P450) enzymes and generate P450-specific xenobiotic metabolites. We verified protein expression by immunoblotting and demonstrated that the cell lines generate P450-specific metabolites from probe substrates. To increase analytical throughput, we used a pooling strategy where 36 chemicals were grouped into 12 unique mixtures, each mixture containing 6 randomly selected compounds, and each compound being present in two separate mixtures. Each mixture of compounds was incubated with 8 different P450 cell lines with cell extracts analyzed at 0 and 2 h. Extracts were analyzed using liquid chromatography-high resolution mass spectrometry. Cell lines selectively metabolized test substrates, with pazopanib metabolized by CYP3A4 and CYP2C8 cells, bupropion by CYP2B6, and {beta}-naphthoflavone by CYP1A2 for example, showing substrate-enzyme specificity. Predicted metabolites from the remaining 33 compounds as well as many unidentified m/z features were detected. We also show that a specific metabolite generated by CYP2B6 cells, but not detected in the S9 system, was identified in human samples. Our data show that incubating these cell lines with chemical mixtures accelerated characterization of xenobiotic chemical space, while simultaneously allowing for the contributions of specific P450 enzymes to be identified. Significance statementHigh resolution mass spectrometry enables the identification of exposures to drugs and other xenobiotics in human samples. This paper demonstrates a workflow for high throughput production of xenobiotic metabolites using a panel of engineered cytochrome P450-expressing hepatoma cells. Active substrate-enzyme pairs can be identified using this workflow and generated metabolites can be used as surrogate standards to validate xenobiotic detection in humans.

pharmacology and toxicology↗

bioRxiv2021

Cross-species metabolomic analysis of DDT and Alzheimer's disease-associated tau toxicity

BackgroundThe formation of hyperphosphorylated tau (p-tau) protein tangles in neurons is a pathological marker of Alzheimers disease (AD). Exposure to the pesticide dichlorodiphenyltrichloroethane (DDT) has been associated with increased risk of AD. ObjectivesTo determine if there was a connection between DDT exposure and tau toxicity we investigated whether exposure to DDT can exacerbate tau protein toxicity in C. elegans. In addition, we examined the association between p-tau protein and metabolism in a human population study and in a transgenic C. elegans strain neuronally expressing a mutant tau protein fragment that is prone to aggregation. MethodsIn the human population study, we used a metabolome-wide association framework to determine the association between p-tau measured in the cerebrospinal fluid (CSF) and metabolomic features measured in both plasma (n = 142) and CSF (n = 78) using high-resolution metabolomics (HRM). Using the same HRM method, we determined changes in metabolomic features in the transgenic C. elegans strain compared to its control strain. Metabolites associated with p-tau in both species were analyzed for overlap. We also examined the effect of DDT and aggregating tau protein on growth, swim behavior, mitochondrial function, metabolism, learning, and lifespan in C. elegans. ResultsPlasma and CSF-derived features associated with p-tau level were related to drug, amino acid, fatty acid and mitochondrial metabolism pathways. Five metabolites overlapped between plasma and C. elegans, and 4 between CSF and C. elegans. DDT exacerbated the inhibitory effect of aggregating tau protein on growth and basal respiration. In the presence of aggregating tau protein, DDT induced more curling and was associated with reduced levels of amino acids but increased levels of uric acid and adenosylselenohomocysteine. Developmental exposure to DDT blunted the lifespan reduction caused by aggregating tau protein. ConclusionThe model organism C. elegans can complement human studies by providing a means to study mechanisms of environmental toxicants. Specifically, our C. elegans data show that DDT exposure and tau protein aggregation both inhibit mitochondrial function and DDT exposure can exacerbate the mitochondrial inhibitory effects of tau protein aggregation providing a plausible explanation for the observed human associations.

pharmacology and toxicology↗

bioRxiv2021

mTOR-activating mutations in RRAGD cause kidney tubulopathy and cardiomyopathy (KICA) syndrome

BackgroundOver the last decaces, advances in genetic techniques have resulted in the identification of rare hereditary disorders of renal magnesium and salt handling. Nevertheless, {+/-}20% of all tubulopathy patients remain without genetic diagnosis. Here, we explore a large multicentric patient cohort with a novel inherited salt-losing tubulopathy, hypomagnesemia and dilated cardiomyopathy (DCM). MethodsWhole exome and genome sequencings were performed with various subsequent functional analyses of identified RRAGD variants in vitro. ResultsIn 8 children from unrelated families with a tubulopathy characterized by hypomagnesemia, hypokalemia, salt-wasting, and nephrocalcinosis, we identified heterozygous missense variants in RRAGD that mostly occurred de novo. Six of these patients additionally suffered from DCM requiring heart transplantation in 3 of them. An additional dominant variant in RRAGD was simultaneously identified in eight members of a large family with a similar renal phenotype. RRAGD encodes GTPase RagD mediating amino acid signaling to the mechanistic target of rapamycin complex 1 (mTORC1). RagD expression along the mammalian nephron include the thick ascending limb and the distal convoluted tubule. The identified RRAGD variants were shown to induce a constitutive activation of mTOR signaling in vitro, ConclusionsOur findings establish a novel disease phenotype combining kidney tubulopathy and cardiomyopathy (KICA) caused by an activation of mTOR signaling suggesting a critical role of Rag GTPase D for renal electrolyte handling and cardiac function. SIGNIFICANCE STATEMENTHere, we report on heterozygous variants in RRAGD in patients with profound hypomagnesemia, renal salt wasting, nephrocalcinosis, and dilated cardiomyopathy. The identified RagD variants induce a constitutive activation of mTOR signaling in vitro. These findings not only establish a novel monogenic disorder of the kidney tubule, but demonstrate the essential role of mTOR signaling for distal tubular electrolyte handling and cardiac function.

genetics↗

bioRxiv2020

Metabolic effects of the schizophrenia-associated 3q29 deletion are sex-specific and uncoupled from behavioral phenotypes

The 1.6 Mb 3q29 deletion is associated with developmental and psychiatric phenotypes. Reduced birthweight and a high prevalence of feeding disorders in patients suggest underlying metabolic dysregulation. We investigated 3q29 deletion-induced metabolic changes using the B6.Del16+/Bdh1-Tfrc mouse model. We found that B6.Del16+/Bdh1-Tfrc animals preferentially use dietary lipids as an energy source. Untargeted metabolomics showed a strong sex-dependent effect of the 3q29 deletion on fat metabolism. A high-fat diet (HFD) partially rescued the 3q29 deletion-associated weight deficit in females, but not males. Untargeted metabolomics after HFD revealed persistent fat metabolism alterations in females. The HFD did not affect B6.Del16+/Bdh1-Tfrc behavioral phenotypes, suggesting that 3q29 deletion-associated metabolic and behavioral outcomes are uncoupled. Our data indicate a HFD intervention in 3q29 deletion syndrome may improve weight phenotypes without exacerbating behavioral manifestations. Our study also highlights the importance of assessing sex in metabolic studies and suggests mechanisms underlying 3q29 deletion-associated metabolic phenotypes are sex-specific.

genetics↗

bioRxiv2020

Untargeted Metabolomics Shows Alterations in Homocysteine, Lipids, and Fatty Acids Predicting Memory Decline in Healthy Middle-Aged Individuals

INTRODUCTIONSome aspects of memory start declining in the fifth decade which may be related to systemic metabolic changes. These changes have not been fully identified. This is the first Metabolome-Wide Association Study of the human plasma for the longitudinal change in memory in healthy adults. METHODSUltra-high resolution mass spectrometry with liquid chromatography was performed on 207 University employees plasma. RESULTSFrom 10,201 measured metabolic features, 558 differed between those experiencing change vs no change in memory (False Discovery Rate, FDR< 0.2). Differentially abundant metabolites were observed in lipid and fatty acid metabolism pathways: glycerophospholipid (p=0.0003), fatty acid (p=0.0003) and linoleate (p=0.0003) pathways. Within these pathways, higher homocysteine (OR for memory decline=1.09, FDR=0.19) and lower arachidonic acid (OR=0.97, FDR=0.19), sterol (OR=0.92, FDR=0.02), acetylcholine (OR=0.78, FDR=0.19), carnitine (OR=0.75, FDR=0.19) and linoleic acid (OR=0.74, FDR=0.19) were associated memory decline. DISCUSSIONAltered systemic lipid and fatty acid are linked with early memory decline in middle-aged individuals.

neuroscience↗

bioRxiv2018

Levodopa and dopamine dynamics in Parkinson’s disease metabolomics

Parkinsons disease (PD) is a progressive neurological disorder caused by a combination of genetic and environmental factors. Metabolomics is a powerful tool that can be used to screen for potential biomarkers, exogenous toxicants, and metabolic network changes associated with disease states. Here, we used high-resolution metabolomics to compare over 10,000 plasma metabolic features from older adults with and without PD in an untargeted approach. We performed a network analysis that demonstrates that the presence of the PD drug levodopa influences variation observed between PD and control patients. Metabolome wide association studies and discrimination analysis identified significant differentiation in the metabolomics profile of older adults with and without PD. Notably, 15 metabolic features (ten of which we putatively identified) differed between PD and control adults with p < 0.05 and a corrected false discovery rate less than 20%. Furthermore, 13 metabolic networks were identified to be functionally different between PD and non-PD patients. Lastly, the dopaminergic toxic intermediate DOPAL differed between PD and non-PD populations, which supports the dopaminergic sequestration model of PD. These individual metabolites and metabolic networks have been implicated in past PD pathogenesis models, including the beta-carboline harmalol and the glycosphingolipid metabolism pathway including the ganglioside GM2. We recommend that future studies take into account the confounding effects of levodopa in metabolomic analyses of disease versus control patients, and encourage validation of several promising metabolic markers of PD.

neuroscience↗

bioRxiv2017

xMWAS: an R package for data-driven integration and differential network analysis

SummaryIntegrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Further-more, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, an R package for data integration, network visualization, clustering, differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.\n\nAvailabilityhttps://sourceforge.net/projects/xmwas/\n\nContactkuppal2@emory.edu

bioinformatics↗