Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12 C/ 13 C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.

Juvenile dermatomyositis (JDM) is a rare autoimmune inflammatory myopathy characterized by muscle weakness and distinctive skin rashes. Despite advancements in the clinical understanding of JDM, metabolic disturbances underlying the disease remain poorly understood. This study aimed to investigate serum metabolite differences in JDM compared to age- and sex-matched unaffected siblings (US) and unrelated healthy controls (HC), and to identify metabolite abundance differences associated with disease severity. Serum samples from JDM (n = 16) and adult dermatomyositis (DM; n = 15) patients and corresponding US and HC underwent untargeted metabolomics profiling. Multivariate, univariate, and correlation analyses were employed to identify metabolites differentiating groups and correlating with Physician Global Damage (PGD) scores. JDM patients exhibited modest but discernible alterations in serum metabolites compared to controls, many of which also correlated with PGD. Several bioactive lipids and pyroglutamic acid were upregulated in JDM and positively correlated with PGD. Changes in xanthine, methionine and N-acetylneuraminic acid also indicated increased oxidative stress and inflammation. Markers of increased energy demand and muscle damage, including acylcarnitines, creatine, 4-guanidinobutyric acid, glutamine, and phenylacetylglutamine, were differential and correlated with PGD in some cases. A metabolite abundance gradient from JDM to US to HC groups suggests that siblings help account for genetic and environmental influences on the metabolome. DM patients did not show significant serum changes compared to US. Untargeted metabolomics revealed distinct serum metabolite alterations in JDM, providing insights into disease-related metabolic perturbations. These findings enhance understanding of JDM pathophysiology and inform future large-scale, targeted studies.

Microbial metabolites play a critical role in regulating ecosystems, including the human body and its microbiota. However, understanding the physiologically relevant role of these molecules, especially through liquid chromatography tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics, poses significant challenges and often requires manual parsing of a large amount of literature, databases, and webpages. To address this gap, we established the Collaborative Microbial Metabolite Center knowledgebase (CMMC-KB), a platform that fosters collaborative efforts within the scientific community to curate knowledge about microbial metabolites. The CMMC-KB aims to collect comprehensive information about microbial molecules originating from microbial biosynthesis, drug metabolism, exposure-related molecules, food, host-derived molecules, and, whenever available, their known activities. Molecules from other sources, including host-produced, dietary, and pharmaceutical compounds, are also included. By enabling direct integration of this knowledgebase with downstream analytical tools, including molecular networking, we can deepen insights into microbiota and their metabolites, ultimately advancing our understanding of microbial ecosystems.

Per- and polyfluoroalkyl substances (PFASs) are a class of manmade chemicals linked to numerous health outcomes including cancers and increased cholesterol. Consequently, legacy PFASs such as perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) have been voluntarily phased out of production within the United States since the early 2000s. However, as replacements, emerging PFASs have been introduced over the last 25 years. The presence and accumulation of these emerging PFASs remains largely unexplored due to the complexity of their identification and quantification. Here, we analyzed a unique set of serum samples (n = 156) collected from 2003 to 2021 consisting of three groups: an autoimmune proband, a healthy sibling, and an unrelated control. Quantitative analyses revealed no discernible link between PFAS exposure and autoimmune disease diagnosis; however, nontargeted analysis revealed concerning temporal trends in emerging replacement PFASs the significance of which is unknown. We detected a concerning increase in the chlorinated PFOS-replacement 9Cl-PF3ONS and polyfluoroalkyl phosphate species including 6:2 DiPAP and perfluoroalkyl phosphinic acids (PFPis) in human serum across the US since 2003. In addition, we documented the presence of a novel PFAS chloroperfluorononylphosphonic acid (Cl-PFNPA) in human serum. These analyses provide an assessment of how PFAS exposure has changed since 2003 while exploring potential linkages to autoimmune diseases.

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Tumor dissemination is increasingly recognized to begin early in tumor development. Although most of these early disseminated cells are cleared, some survive and persist below clinical detection, acting as reservoirs for metastatic relapse. Metastatic tumor cells often rely on interactions with local stromal cells to support their colonization. In this study, we propose that pericyte-tumor cell interactions promote dormancy induction in the early metastatic lung, enhancing disseminated tumor cell (DTC) persistence. Extravital imaging demonstrated that DTCs interact with pericytes upon extravasation into the lung. Co-culture experiments were used to assess DTC fate after pericyte contact and revealed that transient contact with pericytes reduced the proliferation of metastatic 4T1 breast cancer cells but had no effect on non-metastatic 67NR cells. In vivo , transient pericyte contact resulted in higher lung metastatic burden, driven by small, non-proliferative lesions (<6 cells), 10 days after intracardiac injection. These lesions exhibited reduced KI67 staining and EdU incorporation compared to those from monocultured cells. We further observed that primary lung pericytes transferred lyso-phospholipids (lyso-PLs) specifically to metastatic 4T1 cells through direct contact. Gene expression analysis indicated that transient pericyte contact activated pathways related to syncytium formation in metastatic cells. In normal physiology, pericytes act in a syncytium to regulate blood flow via mechanosensitive channels in response to blood pressure changes. We hypothesize that tumor cells exploit these mechanosensitive responses to trigger lyso-PL transfer from pericytes. Supporting this, calcium imaging showed higher calcium activity in pericytes co-cultured with 4T1 cells, and calcium channel inhibitors significantly reduced lyso-PL transfer. Pharmacological activation of pericyte calcium channels induced lyso-PL release, which was subsequently taken up by tumor cells. Conditioned medium from activated pericytes, containing free lyso-PLs, recapitulated the reduced proliferation observed in transient co-culture. Finally, we found our pericyte-induced dormancy signature to be associated with tumor dormancy and distant metastasis free survival latency in breast cancer patients. Together, these findings suggest that early DTCs may exploit pericyte signaling mechanisms to enter dormancy, facilitating their persistence at metastatic sites and contributing to future relapse.

Metformin (Met) and liraglutide (Lira) are preferred diabetes therapies that may improve glycemia by modulating the gut microbiome, but the mechanisms and pathways are unknown and few data exist in youth-onset type 2 diabetes (Y-T2D). In a 3-month parallel clinical trial in African American Y-T2D randomized to Met (n = 14) or Met+Lira (n = 11), we compared gut microbial composition and metabolomic profiles and determined the relationship of changes in microbial abundance with glycemia and plasma metabolites. After 3 months, Met was associated with greater relative abundance of Eubacterium and Eubacterium rectale and lower Bacteroides ovates (p < 0.05). Met+Lira was associated with greater Bacteroides fragilis and lower Streptococcus thermophilus (p < 0.05). Met group had increased (>1.5-fold) plasma cholic secondary bile acids (sulfochenodeoxycholic acid, nutriacholic acid, alpha-muricholic acid, and C24 dihydroxy bile acid; p ≤ 0.002). The change in nutriacholic acid correlated with lower fasting glucose (r = -0.7, p < 0.05). Shifts in microbiota taxa were not associated with plasma short-chain fatty acids (SCFA), hemoglobin A1c or glucose. Short-term Met and Met+Lira in Y-T2D were related to distinct shifts toward bile acid and SCFA-producing gut microbiota taxa, and secondary bile acid metabolites correlated with improved glycemia, suggesting bile acid pathways may be important modulators of glycemia in youth on metformin.Clinical trials.gov identifier: NCT02960659.

Despite extensive efforts, extracting medication exposure information from clinical records remains challenging. To complement this approach, here we show the Global Natural Product Social Molecular Networking (GNPS) Drug Library, a tandem mass spectrometry (MS/MS) based resource designed for drug screening with untargeted metabolomics. This resource integrates MS/MS references of drugs and their metabolites/analogs with standardized vocabularies on their exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data, supporting flexible summarization at multiple ontology levels to align with different research goals. We demonstrate its application by stratifying participants in a human immunodeficiency virus (HIV) cohort based on detected drug exposures. We uncover drug-associated alterations in microbiota-derived N-acyl lipids that are not captured when stratifying by self-reported medication use. Overall, GNPS Drug Library provides a scalable resource for empirical drug screening in clinical, nutritional, environmental, and other research disciplines, facilitating insights into the ecological and health consequences of drug exposures. While not intended for immediate clinical decision-making, it supports data-driven exploration of drug exposures where traditional records are limited or unreliable.

RATIONALE:Reproducible analytical instrumentation system performance is critical for mass spectrometry, particularly metabolomics, aptly named system suitability testing. We identified a need based on literature reports that stated only 2% of papers performed system suitability testing. METHODS:We report MassQLab, built upon open-source, vendor-agnostic software called the mass spectrometry query language (MassQL). MassQL, implemented in MassQLab, provides freedom for researchers to choose their analyte/s, mass spectrometry system (including liquid chromatography-mass spectrometry), and metrics of performance. RESULTS:In this report, we describe the use of MassQLab, demonstrate the construction of the required MassQL query, common metrics of performance (i.e., extracted ion chromatograms), uncommon metrics (i.e., MS/MS product ion spectra), and discuss insights gained about performance-including issues requiring correction prior to sample analysis. CONCLUSIONS:MassQLab is a flexible solution for system suitability testing for mass spectrometry-based analytical measurements. Deficits in analytical performance, while unavoidable and rare, were noted prior to data collection and corrected. The open-source and adaptable nature of MassQLab will empower researchers and lead to improved implementation of system suitability testing.

The analysis of small carboxyl-containing metabolites (CCMs), such as tricarboxylic acid (TCA) cycle intermediates, provides highly useful information about the metabolic state of cells. However, their detection using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) methods can face sensitivity and specificity challenges given their low ionization efficiency and the presence of isomers. Ion mobility spectrometry (IMS), such as trapped ion mobility spectrometry (TIMS), provides additional specificity, but further signal loss can occur during the mobility separation process. We, therefore, developed a solution to boost CCM ionization and chromatographic separation as well as leverage specificity of IMS. Inspired by carbodiimide-mediated coupling of carboxylic acids with 4-bromo-N-methylbenzylamine (4-BNMA) for quantitative analysis, we newly report the benefits of this reagent for TIMS-based measurement. We observed a pronounced (orders of magnitude) increase in signal and enhanced isomer separations, particularly by LC. We found that utilization of a brominated reagent, such as 4-BNMA, offered unique benefits for untargeted CCM measurement. Derivatized CCMs displayed shifted mobility out of the metabolite and lipid region of the TIMS-MS space as well as characteristic isotope patterns, which were leveraged for data mining with Mass Spectrometry Query Language (MassQL) and indication of the number of carboxyl groups. The utility of our LC-ESI-TIMS-MS/MS method with 4-BMA derivatization was demonstrated via the characterization of alterations in CCM expression in bone marrow-derived macrophages upon activation with lipopolysaccharide. While metabolic reprogramming in activated macrophages has been characterized previously, especially with respect to TCA cycle intermediates, we report a novel finding that isomeric itaconic, mesaconic, and citraconic acid increase after 24 h, indicating possible roles in the inflammatory response.

Annotation is the process of assigning features in mass spectrometry metabolomics data sets to putative chemical structures or "analytes." The purpose of this study was to identify challenges in the annotation of untargeted mass spectrometry metabolomics datasets and suggest strategies to overcome them. Toward this goal, we analyzed an extract of the plant ashwagandha (Withania somnifera) using liquid chromatography-mass spectrometry on two different platforms (an Orbitrap and Q-ToF) with various acquisition modes. The resulting 12 datasets were shared with ten teams that had established expertise in metabolomics data interpretation. Each team annotated at least one positive ion dataset using their own approaches. Eight teams selected the positive ion mode data-dependent acquisition (DDA) data collected on the Orbitrap platform, so the results reported for that dataset were chosen for an in-depth comparison. We compiled and cross-checked the annotations of this dataset from each laboratory to arrive at a "consensus annotation," which included 142 putative analytes, of which 13 were confirmed by comparison with standards. Each team only reported a subset (24 to 57%) of the analytes in the consensus list. Correct assignment of ion species (clusters and fragments) in MS spectra was a major bottleneck. In many cases, in-source redundant features were mistakenly considered to be independent analytes, causing annotation errors and resulting in overestimation of sample complexity. Our results suggest that better tools/approaches are needed to effectively assign feature identity, group related mass features, and query published spectral and taxonomic data when assigning putative analyte structures.

Many existing mass spectral libraries focus on human or microbially derived molecules. Few plant-specific MS2 databases exist, making annotation of botanical samples difficult. To fill this gap in mass spectrometry data availability, the Library Enabling Annotation of Botanical Natural Products (LEAFBot) was constructed. Using a flow injection mass spectrometry method that allowed for rapid throughput data collection, the MS2 spectra of >300 pure botanical secondary metabolites were experimentally measured and complied into a single library housed in the Global Natural Products Social Molecular Networking (GNPS) spectral database. Of these compounds, over 20% were not present in the existing GNPS database, and 11% were not present in any of three main mass spectral databases (GNPS, Metlin, and MassBank). Additionally, LEAFBot contains a wider range of adducts compared to other plant-based mass spectral libraries, enabling more effective annotation of unknown features. The LEAFBot database represents a new resource to the mass spectrometry and metabolomics community seeking to characterize plant-based samples. The possibility of searching against a taxonomically specific library decreases the likelihood of false positives in database searches, and the ease of adding new spectra, following procedures outlined herein, will enable community-lead expansion of the database.

O049 / #570 Topic: AS04 - Biomarkers ABSTRACT CONCURRENT SESSION 08: RECENT ADVANCES IN LUPUS BIOMARKERS 23-05-2025 1:40 PM - 2:40 PM Recently, interest has increased in the role of metabolites and metabolic pathways in autoimmunity and SLE. Evidence suggests that immune cells are influenced by metabolic programs. Several studies identified metabolites with different levels in SLE cases compared to controls. It is unknown whether metabolite levels are associated with SLE disease activity or are correlated with other biomarkers of SLE disease activity such as DNA methylation. Metabolites, and their correlations with other markers, might serve as indicators of immune cell function and improve our ability to successfully treat patients. Using a cohort of SLE patients recruited during a flare and followed up over time, we aimed to identify whether changes in metabolites were associated with flare remission and whether these changes were correlated with changes in DNA methylation. Forty multiethnic SLE patients were recruited during a rheumatologist-confirmed flare and returned to the clinic approximately 3 months later. At both visits, we obtained whole blood and generated untargeted metabolomics data from plasma (LC-QTOF) and DNA methylation profiles (Illumina EPIC array). Clinical data, including SLEDAI SELENA and medications, were collected at each visit. Remission was defined as SLEDAI=0 at the follow-up visit. We identified metabolites whose changes over time were associated with remission status, after adjusting for follow-up time and medications, using linear regression models. Previously in this study sample and using a similar statistical approach, we identified 291 DNA methylation sites whose changes over time were associated with remission. Significant metabolite changes (FDR q<0.05) were tested for their association with DNA methylation changes at these 291 sites using correlation coefficients. Sixteen SLE patients were in remission at the follow-up visit. Remitters and nonremitters did not differ significantly by race, ethnicity, age, disease activity or symptoms at the baseline flare, or medications. We identified 9 metabolite changes associated with remission status (Figure 1). These included oleic acid (P= 5.49×10^−8) and 2 isomers of adenine (P= 3.77×10^−5, each). For nonremitters, these metabolite levels changed very little between visits. For remitters, 4 metabolites increased while 5 decreased between visits. We identified 57 significantly correlated metabolite-DNA methylation pairs. This included a strong correlation between oleic acid and a DNA methylation site within the body of EBF1, an interferon response gene and key transcription factor of B cell specification (correlation = -0.79, p=1.1×10^−7). We also identified a strong correlation between adenine and a DNA methylation site within the body of IL12B, another interferon response gene which encodes a cytokine that acts on T and natural killer cells (correlation = 0.70, p=1.30×10^−6). Figure 1. Nine metabolites had levels that changed between flare and follow-up visits differently by remission status (FDR q<0.05). Colors represented patient’s remission status. Bold line represented mean change in metabolite by remission status. Current treatments do not adequately prevent SLE flares or disease-related organ damage. Understanding the biological markers and pathways associated with remission after a flare might improve our ability to successfully treat patients. Our results showed that changes in several metabolites, including oleic acid and adenine, were associated with remission status and were correlated with changes in DNA methylation at SLE-relevant loci. These might be promising targets for future therapeutics and help us understand the underlying biology of SLE. Acknowledgments: This work was funded in part by U01DP005120 CDC.

RATIONALE:Spironolactone is a steroidal drug prescribed for a variety of medical conditions and is extensively metabolized quickly after administration. Measurement of spironolactone and its metabolites remains challenging using mass spectrometry (MS) due to in-source fragmentation and relatively poor ionization using electrospray ionization. Therefore, improved methods of measurements are needed, particularly in the case of small sample volumes. METHODS:Girard's reagent P (GP) derivatization of spironolactone was employed to improve response and provide an MS-based solution to the measurement of spironolactone and its metabolites. We performed ultra-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS) and ion mobility spectrometry (IMS)-high-resolution mass spectrometry (HRMS) to fully characterize the GP derivatization products. Analytes were studied in positive ionization mode, and MS/MS was performed using nonresonance and resonance excitation collision-induced dissociation. RESULTS:We observed the successful GP derivatization of spironolactone and its metabolites using authentic chemical standards. A signal enhancement of 1-2 orders of magnitude was observed for GP-derivatized versions of spironolactone and its metabolites. Further, GP derivatization eliminated in-source fragmentation. Finally, we performed GP derivatization and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) in a small volume of murine serum (20 μL) from spironolactone-treated and control animals and observed multiple spironolactone metabolites only in the spironolactone-treated group. CONCLUSIONS:GP derivatization was proven to have advantageous mass spectral performance (e.g., limiting in-source fragmentation, enhancing signals, and eliminating isobaric analytes) for spironolactone and its metabolites. This work and the detailed characterization using ultra-high-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and IMS serve as the foundation for future developments in reaction optimization and/or quantitative assay development.

In the brain, the hippocampus is enriched with mineralocorticoid receptors (MR; Nr3c2), a ligand-dependent transcription factor stimulated by the stress hormone corticosterone in rodents. Recently, we discovered that MR is required for the acquisition and maintenance of many features of mouse area CA2 neurons. Notably, we observed that immunofluorescence for the vesicular glutamate transporter 2 (vGluT2), likely representing afferents from the supramammillary nucleus (SuM), was disrupted in the embryonic, but not postnatal, MR knockout mouse CA2. To test whether pharmacological perturbation of MR activity in utero similarly disrupts CA2 connectivity, we implanted slow-release pellets containing the MR antagonist spironolactone in mouse dams during mid-gestation. After confirming that at least one likely active metabolite crossed from the dams’ serum into the embryonic brains, we found that spironolactone treatment caused a significant reduction of CA2 axon fluorescence intensity in the CA1 stratum oriens, where CA2 axons preferentially project, and that vGluT2 staining was significantly decreased in both CA2 and dentate gyrus in spironolactone-treated animals. We also found that spironolactone-treated animals exhibited increased reactivity to novel objects, an effect similar to what is seen with embryonic or postnatal CA2-targeted MR knockout. However, we found no difference in preference for social novelty between the treatment groups. We infer these results to suggest that persistent or more severe disruptions in MR function may be required to interfere with this type of social behavior. These findings do indicate, though, that developmental disruption in MR signaling can have persistent effects on hippocampal circuitry and behavior.

CONTEXT Pubertal girls with higher total body fat (TBF) demonstrate higher androgen levels. The cause of this association is unknown but is hypothesized to relate to insulin resistance. OBJECTIVE To investigate the association between higher TBF and higher androgens in pubertal girls using untargeted metabolomics. METHODS Serum androgens were determined using a quantitative mass spectrometry-based assay. Metabolomic samples were analyzed using liquid chromatography high-resolution mass spectrometry. Associations between TBF or BMI Z-score (exposure) and metabolomic features (outcome) and between metabolomic features (exposure) and serum hormones (outcome) were examined using gaussian generalized estimating equation models with the outcome lagged by one study visit. Benjamini-Hochberg false discovery rate (FDR) adjusted p-values were calculated to account for multiple testing. RaMP-DB (Relational database of Metabolomic Pathways) was used to conduct enriched pathway analyses among features nominally associated with body composition or hormones. RESULTS Sixty-six pubertal, pre-menarchal girls (aged 10.9 ± 1.39 SD years; 60% White, 24% Black, 16% Other; 63% normal weight, 37% overweight/obese) contributed an average of 2.29 blood samples. BMI and TBF were negatively associated with most features including raffinose (a plant trisaccharide) and several bile acids. For BMI, RaMP-DB identified many enriched pathways related to bile acids. Androstenedione also showed strong negative associations with raffinose and bile acids. CONCLUSIONS Metabolomic analyses of samples from pubertal girls did not identify an insulin resistance signature to explain the association between higher TBF and androgens. Instead, we identified potential novel signaling pathways that may involve raffinose or bile acid action at the adrenal gland.

ABSTRACT Vaccinia virus (VACV) infection induces prominent changes in host cell metabolism. Little is known about the global metabolic reprogramming that takes place in whole tissue during viral infection. Here, we performed a longitudinal metabolomics study in VACV-infected mouse skin to investigate metabolic changes in the tissue during infection. We assessed metabolites in homogenized skin of the ear pinnae over time in the presence or absence of antigen-specific T cells using untargeted mass spectrometry. VACV infection induced several significant metabolic changes in the tissue, including in the levels of nucleic acid metabolites (reflecting the impact of viral replication on the skin metabolome). Furthermore, monocyte- and antiviral T cell-produced metabolites, such as itaconic acid and glutamine, were significantly increased following infection, highlighting the immune response’s contribution to the global skin metabolome. Additional RNA-Seq of infected skin tissue recapitulated metabolic changes identified by metabolomics analysis. Overall, our study reveals the metabolic balance of viral replication and the antiviral immune response in the skin, elucidating metabolic pathways that could contribute to cutaneous poxvirus control in vivo . IMPORTANCE Human poxvirus infections have caused significant public health burdens both historically and recently during the unprecedented global Mpox virus outbreak. Although vaccinia virus (VACV) infection of mice is a commonly used model to explore the anti-poxvirus immune response, little is known about the metabolic changes that occur in vivo during infection. We hypothesized that the metabolome of VACV-infected skin would reflect the increased energetic requirements of both virus-infected cells and immune cells recruited to sites of infection. Therefore, we profiled whole VACV-infected skin using untargeted mass spectrometry to define the metabolome during infection, complementing these experiments with flow cytometry and transcriptomics. We identified specific metabolites, including nucleotides, itaconic acid, and glutamine, that were differentially expressed during VACV infection. Together, this study offers insight into both virus-specific and immune-mediated metabolic pathways that could contribute to the clearance of cutaneous poxvirus infection.

Arginase 1 (ARG1), the enzyme catalyzing the conversion of arginine to ornithine and urea, is a hallmark of IL-10 producing immunosuppressive M2 macrophages, however ARG1 activity in T cells is disputed. Here we demonstrate that Arg1, but not Arg2, expression induction is a key feature of lung CD4 T cells during mouse in vivo influenza infection. Ablation of CD4 T cell-intrinsic Arg1 unexpectedly accelerated both the virus-specific Th1 effector response and its IL-10-associated contraction. Biologically, this led to efficient viral clearance, yet significantly reduced lung pathology. Surprisingly, loss of Arg1 in CD4 T cells did not result in disturbed intracellular ornithine or polyamine levels. Instead, by employing unbiased transcriptomic and metabolomic approaches, we found that Arg1 deficiency triggered altered glutamine metabolism, and rebalancing the glutamine flux normalized the Arg1 deficient T cell response. Further, the role of Arg1 in CD4 T cells was distinct from that of its’ isoenzyme Arg2, as ablation of CD4 T cell intrinsic Arg2 resulted in normal Th1 responses, and instead altered Th2 and Th17 responses. Finally, CD4 T cells from rare patients with a deficiency in ARG1, or from healthy donors with CRISPR-Cas9-mediated ARG1 deletion, recapitulated the mouse data, demonstrating that ARG1 also plays a CD4 T cell intrinsic role in human Th1 responses. Collectively, CD4 T cell-intrinsic ARG1, functions as an unexpected pace-keeper of human and mouse T helper 1 (Th1) responses with implications for Th1-associated tissue pathologies. Supported by grants from NIH (5K22HL125593 to M.K.) the Intramural Research Program of the NIH (NIDDK ZIA/DK075149 to B.A. and NHLBI ZIA/HL006223 to C.K.)