HORMA Domain Proteins and a Trip13-like ATPase Regulate Bacterial cGAS-like Enzymes to Mediate Bacteriophage Immunity.

Bacteria are continually challenged by foreign invaders, including bacteriophages, and have evolved a variety of defenses against these invaders. Here, we describe the structural and biochemical mechanisms of a bacteriophage immunity pathway found in a broad array of bacteria, including E. coli and Pseudomonas aeruginosa. This pathway uses eukaryotic-like HORMA domain proteins that recognize specific peptides, then bind and activate a cGAS/DncV-like nucleotidyltransferase (CD-NTase) to generate a cyclic triadenylate (cAAA) second messenger; cAAA in turn activates an endonuclease effector, NucC. Signaling is attenuated by a homolog of the AAA+ ATPase Pch2/TRIP13, which binds and disassembles the active HORMA-CD-NTase complex. When expressed in non-pathogenic E. coli, this pathway confers immunity against bacteriophage λ through an abortive infection mechanism. Our findings reveal the molecular mechanisms of a bacterial defense pathway integrating a cGAS-like nucleotidyltransferase with HORMA domain proteins for threat sensing through protein detection and negative regulation by a Trip13 ATPase.

Structure and Mechanism of a Cyclic Trinucleotide-Activated Bacterial Endonuclease Mediating Bacteriophage Immunity.

Bacteria possess an array of defenses against foreign invaders, including a broadly distributed bacteriophage defense system termed CBASS (cyclic oligonucleotide-based anti-phage signaling system). In CBASS systems, a cGAS/DncV-like nucleotidyltransferase synthesizes cyclic di- or tri-nucleotide second messengers in response to infection, and these molecules activate diverse effectors to mediate bacteriophage immunity via abortive infection. Here, we show that the CBASS effector NucC is related to restriction enzymes but uniquely assembles into a homotrimer. Binding of NucC trimers to a cyclic tri-adenylate second messenger promotes assembly of a NucC homohexamer competent for non-specific double-strand DNA cleavage. In infected cells, NucC activation leads to complete destruction of the bacterial chromosome, causing cell death prior to completion of phage replication. In addition to CBASS systems, we identify NucC homologs in over 30 type III CRISPR/Cas systems, where they likely function as accessory nucleases activated by cyclic oligoadenylate second messengers synthesized by these systems' effector complexes.

One-Year Effects of High-Intensity Statin on Bioactive Lipids: Findings From the JUPITER Trial.

BACKGROUND: Statin effects extend beyond low-density lipoprotein cholesterol reduction, potentially modulating the metabolism of bioactive lipids (BALs), crucial for biological signaling and inflammation. These bioactive metabolites may serve as metabolic footprints, helping uncover underlying processes linked to pleiotropic effects of statins and yielding a better understanding of their cardioprotective properties. This study aimed to investigate the impact of high-intensity statin therapy versus placebo on plasma BALs in the JUPITER trial (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681), a randomized primary prevention trial involving individuals with low-density lipoprotein cholesterol <130 mg/dL and high-sensitivity C-reactive protein ≥2 mg/L. METHODS: Using a nontargeted mass spectrometry approach, over 11 000 lipid features were assayed from baseline and 1-year plasma samples from cardiovascular disease noncases from 2 nonoverlapping nested substudies: JUPITERdiscovery (n=589) and JUPITERvalidation (n=409). The effect of randomized allocation of rosuvastatin 20 mg versus placebo on BALs was examined by fitting a linear regression with delta values (∆=year 1-baseline) adjusted for age and baseline levels of each feature. Significant associations in discovery were analyzed in the validation cohort. Multiple comparisons were adjusted using 2-stage overall false discovery rate. RESULTS: We identified 610 lipid features associated with statin randomization with significant replication (overall false discovery rate, <0.05), including 26 with annotations. Statin therapy significantly increased levels of 276 features, including BALs with anti-inflammatory activity and arterial vasodilation properties. Concurrently, 334 features were significantly lowered by statin therapy, including arachidonic acid and proinflammatory and proplatelet aggregation BALs. By contrast, statin therapy reduced an eicosapentaenoic acid-derived hydroxyeicosapentaenoic acid metabolite, which may be related to impaired glucose metabolism. Additionally, we observed sex-related differences in 6 lipid metabolites and 6 unknown features. CONCLUSIONS: Statin allocation was significantly associated with upregulation of BALs with anti-inflammatory, antiplatelet aggregation and antioxidant properties and downregulation of BALs with proinflammatory and proplatelet aggregation activity, supporting the pleiotropic effects of statins beyond low-density lipoprotein cholesterol reduction.

Reconstructing the landscape of gut microbial species across 29,000 diverse individuals.

The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ∼75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across 16 regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.

Association of Physical Activity With Bioactive Lipids and Cardiovascular Events.

BACKGROUND: To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS: PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS: We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS: We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.

Effects of Diet versus Gastric Bypass on Metabolic Function in Diabetes.

BACKGROUND: Some studies have suggested that in people with type 2 diabetes, Roux-en-Y gastric bypass has therapeutic effects on metabolic function that are independent of weight loss. METHODS: We evaluated metabolic regulators of glucose homeostasis before and after matched (approximately 18%) weight loss induced by gastric bypass (surgery group) or diet alone (diet group) in 22 patients with obesity and diabetes. The primary outcome was the change in hepatic insulin sensitivity, assessed by infusion of insulin at low rates (stages 1 and 2 of a 3-stage hyperinsulinemic euglycemic pancreatic clamp). Secondary outcomes were changes in muscle insulin sensitivity, beta-cell function, and 24-hour plasma glucose and insulin profiles. RESULTS: Weight loss was associated with increases in mean suppression of glucose production from baseline, by 7.04 µmol per kilogram of fat-free mass per minute (95% confidence interval [CI], 4.74 to 9.33) in the diet group and by 7.02 µmol per kilogram of fat-free mass per minute (95% CI, 3.21 to 10.84) in the surgery group during clamp stage 1, and by 5.39 (95% CI, 2.44 to 8.34) and 5.37 (95% CI, 2.41 to 8.33) µmol per kilogram of fat-free mass per minute in the two groups, respectively, during clamp stage 2; there were no significant differences between the groups. Weight loss was associated with increased insulin-stimulated glucose disposal, from 30.5±15.9 to 61.6±13.0 µmol per kilogram of fat-free mass per minute in the diet group and from 29.4±12.6 to 54.5±10.4 µmol per kilogram of fat-free mass per minute in the surgery group; there was no significant difference between the groups. Weight loss increased beta-cell function (insulin secretion relative to insulin sensitivity) by 1.83 units (95% CI, 1.22 to 2.44) in the diet group and by 1.11 units (95% CI, 0.08 to 2.15) in the surgery group, with no significant difference between the groups, and it decreased the areas under the curve for 24-hour plasma glucose and insulin levels in both groups, with no significant difference between the groups. No major complications occurred in either group. CONCLUSIONS: In this study involving patients with obesity and type 2 diabetes, the metabolic benefits of gastric bypass surgery and diet were similar and were apparently related to weight loss itself, with no evident clinically important effects independent of weight loss. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT02207777.).

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Mapping choline metabolites in normal and transformed cells.

INTRODUCTION: Choline is an essential human nutrient that is particular important for proliferating cells, and altered choline metabolism has been associated with cancer transformation. Yet, the various metabolic fates of choline in proliferating cells have not been investigated systematically. OBJECTIVES: This study aims to map the metabolic products of choline in normal and cancerous proliferating cells. METHODS: We performed 13C-choline tracing followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of metabolic products in normal and in vitro-transformed (tumor-forming) epithelial cells, and also in tumor-derived cancer cell lines. Selected metabolites were quantified by internal standards. RESULTS: Untargeted analysis revealed 121 LCMS peaks that were 13C-labeled from choline, including various phospholipid species, but also previously unknown products such as monomethyl- and dimethyl-ethanolamines. Interestingly, we observed formation of betaine from choline specifically in tumor-derived cells. Expression of choline dehydrogenase (CHDH), which catalyzes the first step of betaine synthesis, correlated with betaine synthesis across the cell lines studied. RNAi silencing of CHDH did not affect cell proliferation, although we observed an increased fraction of G2M phase cells with some RNAi sequences, suggesting that CHDH and its product betaine may play a role in cell cycle progression. Betaine cell concentration was around 10 µM, arguing against an osmotic function, and was not used as a methyl donor. The function of betaine in these tumor-derived cells is presently unknown. CONCLUSION: This study identifies novel metabolites of choline in cancer and normal cell lines, and reveals altered choline metabolism in cancer cells.

Deep Neural Networks for Classification of LC-MS Spectral Peaks.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

Thiopeptide antibiotics stimulate biofilm formation in Bacillus subtilis.

Bacteria have evolved the ability to produce a wide range of structurally complex natural products historically called "secondary" metabolites. Although some of these compounds have been identified as bacterial communication cues, more frequently natural products are scrutinized for antibiotic activities that are relevant to human health. However, there has been little regard for how these compounds might otherwise impact the physiology of neighboring microbes present in complex communities. Bacillus cereus secretes molecules that activate expression of biofilm genes in Bacillus subtilis. Here, we use imaging mass spectrometry to identify the thiocillins, a group of thiazolyl peptide antibiotics, as biofilm matrix-inducing compounds produced by B. cereus. We found that thiocillin increased the population of matrix-producing B. subtilis cells and that this activity could be abolished by multiple structural alterations. Importantly, a mutation that eliminated thiocillin's antibiotic activity did not affect its ability to induce biofilm gene expression in B. subtilis. We go on to show that biofilm induction appears to be a general phenomenon of multiple structurally diverse thiazolyl peptides and use this activity to confirm the presence of thiazolyl peptide gene clusters in other bacterial species. Our results indicate that the roles of secondary metabolites initially identified as antibiotics may have more complex effects--acting not only as killing agents, but also as specific modulators of microbial cellular phenotypes.

A Web Service Framework for Interactive Analysis of Metabolomics Data.

Analyzing mass spectrometry-based metabolomics data presents a major challenge to metabolism researchers, as it requires downloading and processing large data volumes through complex "pipelines", even in cases where only a single metabolite or peak is of interest. This presents a significant hurdle for data sharing, reanalysis, or meta-analysis of existing data sets, whether locally stored or available from public repositories. Here we introduce mzAccess, a software system that provides interactive, online access to primary mass spectrometry data in real-time via a Web service protocol, circumventing the need for bulk data processing. mzAccess allows querying instrument data for spectra, chromatograms, or two-dimensional MZ-RT areas in either profile or centroid modes through a simple, uniform interface that is independent of vendor or instrument type. Using a cache mechanism, mzAccess achieves response times in the millisecond range for typical liquid chromatography-mass spectrometry (LC-MS) peaks, enabling real-time browsing of large data sets with hundreds or even thousands of samples. By simplifying access to metabolite data, we hope that this system will help enable data sharing and reanalysis in the metabolomics field.

Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ∼3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.

Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism.

Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells.

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography-high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine.

MS/MS networking guided analysis of molecule and gene cluster families.

The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779(T). The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.

Comparative genomics and metabolic profiling of the genus Lysobacter.

BACKGROUND: Lysobacter species are Gram-negative bacteria widely distributed in soil, plant and freshwater habitats. Lysobacter owes its name to the lytic effects on other microorganisms. To better understand their ecology and interactions with other (micro)organisms, five Lysobacter strains representing the four species L. enzymogenes, L. capsici, L. gummosus and L. antibioticus were subjected to genomics and metabolomics analyses. RESULTS: Comparative genomics revealed a diverse genome content among the Lysobacter species with a core genome of 2,891 and a pangenome of 10,028 coding sequences. Genes encoding type I, II, III, IV, V secretion systems and type IV pili were highly conserved in all five genomes, whereas type VI secretion systems were only found in L. enzymogenes and L. gummosus. Genes encoding components of the flagellar apparatus were absent in the two sequenced L. antibioticus strains. The genomes contained a large number of genes encoding extracellular enzymes including chitinases, glucanases and peptidases. Various nonribosomal peptide synthase (NRPS) and polyketide synthase (PKS) gene clusters encoding putative bioactive metabolites were identified but only few of these clusters were shared between the different species. Metabolic profiling by imaging mass spectrometry complemented, in part, the in silico genome analyses and allowed visualisation of the spatial distribution patterns of several secondary metabolites produced by or induced in Lysobacter species during interactions with the soil-borne fungus Rhizoctonia solani. CONCLUSIONS: Our work shows that mining the genomes of Lysobacter species in combination with metabolic profiling provides novel insights into the genomic and metabolic potential of this widely distributed but understudied and versatile bacterial genus.

Mass spectral molecular networking of living microbial colonies.

Integrating the governing chemistry with the genomics and phenotypes of microbial colonies has been a "holy grail" in microbiology. This work describes a highly sensitive, broadly applicable, and cost-effective approach that allows metabolic profiling of live microbial colonies directly from a Petri dish without any sample preparation. Nanospray desorption electrospray ionization mass spectrometry (MS), combined with alignment of MS data and molecular networking, enabled monitoring of metabolite production from live microbial colonies from diverse bacterial genera, including Bacillus subtilis, Streptomyces coelicolor, Mycobacterium smegmatis, and Pseudomonas aeruginosa. This work demonstrates that, by using these tools to visualize small molecular changes within bacterial interactions, insights can be gained into bacterial developmental processes as a result of the improved organization of MS/MS data. To validate this experimental platform, metabolic profiling was performed on Pseudomonas sp. SH-C52, which protects sugar beet plants from infections by specific soil-borne fungi [R. Mendes et al. (2011) Science 332:1097-1100]. The antifungal effect of strain SH-C52 was attributed to thanamycin, a predicted lipopeptide encoded by a nonribosomal peptide synthetase gene cluster. Our technology, in combination with our recently developed peptidogenomics strategy, enabled the detection and partial characterization of thanamycin and showed that it is a monochlorinated lipopeptide that belongs to the syringomycin family of antifungal agents. In conclusion, the platform presented here provides a significant advancement in our ability to understand the spatiotemporal dynamics of metabolite production in live microbial colonies and communities.

A genome-wide CRISPR screen reveals that antagonism of glutamine metabolism sensitizes head and neck squamous cell carcinoma to ferroptotic cell death.

Glutamine is a conditionally essential amino acid for the growth and survival of rapidly proliferating cancer cells. Many cancers are addicted to glutamine, and as a result, targeting glutamine metabolism has been explored clinically as a therapeutic approach. Glutamine-catalyzing enzymes are highly expressed in primary and metastatic head and neck squamous cell carcinoma (HNSCC). However, the nature of the glutamine-associated pathways in this aggressive cancer type has not been elucidated. Here, we explored the therapeutic potential of a broad glutamine antagonist, DRP-104 (sirpiglenastat), in HNSCC tumors and aimed at shedding light on glutamine-dependent pathways in this disease. We observed a potent antitumoral effect of sirpiglenastat in HPV- and HPV+ HNSCC xenografts. We conducted a whole-genome CRISPR screen and metabolomics analyses to identify mechanisms of sensitivity and resistance to glutamine metabolism blockade. These approaches revealed that glutamine metabolism blockade results in the rapid buildup of polyunsaturated fatty acids (PUFAs) via autophagy nutrient-sensing pathways. Finally, our analysis demonstrated that GPX4 mediates the protection of HNSCC cells from accumulating toxic lipid peroxides; hence, glutamine blockade sensitizes HNSCC cells to ferroptosis cell death upon GPX4 inhibition. These findings demonstrate the therapeutic potential of sirpiglenastat in HNSCC and establish a novel link between glutamine metabolism and ferroptosis, which may be uniquely translated into targeted glutamine-ferroptosis combination therapies.

Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a rare and fatal vascular disease with heterogeneous clinical manifestations. To date, molecular determinants underlying the development of PAH and related outcomes remain poorly understood. Herein, we identify pulmonary primary oxysterol and bile acid synthesis (PPOBAS) as a previously unrecognized pathway central to PAH pathophysiology. Mass spectrometry analysis of 2,756 individuals across five independent studies revealed 51 distinct circulating metabolites that predicted PAH-related mortality and were enriched within the PPOBAS pathway. Across independent single-center PAH studies, PPOBAS pathway metabolites were also associated with multiple cardiopulmonary measures of PAH-specific pathophysiology. Furthermore, PPOBAS metabolites were found to be increased in human and rodent PAH lung tissue and specifically produced by pulmonary endothelial cells, consistent with pulmonary origin. Finally, a poly-metabolite risk score comprising 13 PPOBAS molecules was found to not only predict PAH-related mortality but also outperform current clinical risk scores. This work identifies PPOBAS as specifically altered within PAH and establishes needed prognostic biomarkers for guiding therapy in PAH.