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For decades, variability in clinical efficacy of the widely used inflammatory bowel disease (IBD) drug 5-aminosalicylic acid (5-ASA) has been attributed, in part, to its acetylation and inactivation by gut microbes. Identification of the responsible microbes and enzyme(s), however, has proved elusive. To uncover the source of this metabolism, we developed a multi-omics workflow combining gut microbiome metagenomics, metatranscriptomics and metabolomics from the longitudinal IBDMDB cohort of 132 controls and patients with IBD. This associated 12 previously uncharacterized microbial acetyltransferases with 5-ASA inactivation, belonging to two protein superfamilies: thiolases and acyl-CoA N-acyltransferases. In vitro characterization of representatives from both families confirmed the ability of these enzymes to acetylate 5-ASA. A cross-sectional analysis within the discovery cohort and subsequent prospective validation within the independent SPARC IBD cohort (n = 208) found three of these microbial thiolases and one acyl-CoA N-acyltransferase to be epidemiologically associated with an increased risk of treatment failure among 5-ASA users. Together, these data address a longstanding challenge in IBD management, outline a method for the discovery of previously uncharacterized gut microbial activities and advance the possibility of microbiome-based personalized medicine.

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Background: Recent advances in metabolomic studies have shown promise in elucidating the biological pathways underpinning aging processes and mortality in animal models, but data in humans are lacking. We aimed to evaluate the associations between metabolite profiles, all-cause and cause-specific mortality, and longevity. Methods: Within three prospective cohorts (Nurses’ Health Study [NHS], NHSII, and Health Professional’s Follow-up Study), we measured plasma metabolites from 11,523 participants (mean age 54 years, 86% female) using high-throughput liquid chromatography-mass spectrometry. Participants were free of cardiovascular disease and cancer at blood collection. Metabolome-wide association analyses were conducted for all-cause, cardiovascular, and cancer mortality using Cox proportional hazards regression and longevity (attaining 85 years of age) using logistic regression. Both pre-defined and data-driven metabolite groups were also evaluated. We further developed a multi-metabolite profile score for all-cause mortality using an elastic-net regularized Cox model and assessed its associations with mortality and longevity. Results for all-cause mortality were replicated among 1878 participants (mean age 67 years, 58% female) from the PREDIMED trial. Results: We documented 4252 deaths (including 864 cardiovascular deaths and 1070 cancer deaths) and 3048 achieving longevity over a median follow-up of 22.6 years in the NHS/NHSII/HPFS, and 126 deaths during a follow-up of 4.7 years in the PREDIMED. Higher levels of three nucleosides (N2,N2-dimethylguanosine, pseudouridine, and N4-acetylcytidine), 4-acetamidobutanoic acid, triacylglycerols with ≤56 carbons and ≤3 double bonds, and several other lipids were associated with increased risk of all-cause mortality and corresponding decreased likelihood of longevity; whereas L-serine, L-glutamine, and TAGs with >56 carbons or >3 double bonds were associated with lower mortality risk and higher odds of achieving longevity. A multi-metabolite profile score comprising 73 metabolites was positively associated with all-cause (HR per 1-SD increment=1.25 [95% CI: 1.21, 1.30] in NHS/NHSII/HPFS and 1.47 [95% CI: 1.22, 1.78] in PREDIMED), cardiovascular (HR=1.34 [95% CI:1.24, 1.45]), and cancer mortality (HR=1.15 [95% CI:1.08, 1.24]) and inversely associated with longevity (OR=0.79 [95% CI: 0.74, 0.86]). Conclusions: We identified multiple metabolites associated with mortality and longevity. Our findings provide insights into the biological pathways that lead to death and open up new avenues to incorporate these metabolomic markers in clinical and research settings.

Introduction: Various forms of psychological distress, such as depression and anxiety, have been identified as risk factors for diabetes. However, there is limited evidence from population-based, epidemiologic studies investigating potential molecular mechanisms (e.g., metabolic dysregulation) linking psychological distress and diabetes development. Hypothesis: We assessed the hypothesis that a metabolite score reflecting psychological distress-related metabolic dysregulation is predictive of future diabetes risk in women. Methods: We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses’ Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 controls matched on age, race, fasting status, and time/date of blood collection. Blood samples were collected between 1989-1990 and incident diabetes was diagnosed between 1990-2012. Based on the metabolomic signature of chronic psychological distress we previously identified and validated in women (including coefficient estimates for individual metabolite associations), we calculated a weighted plasma metabolomic score of psychological distress comprised of 19 metabolites (e.g., serotonin, threonine, hippurate, biliverdin, glutamine, etc.). We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% CI for diabetes risk across quintiles of the distress-related metabolite score. Results: After adjusting for sociodemographic factors, family history of diabetes and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the distress-related metabolite score was 1.00 (reference) for Q1, 1.11 (0.77, 1.61) for Q2, 1.64 (1.14, 2.35) for Q3, 2.07 (1.42, 3.01) for Q4, and 2.35 (1.60, 3.47) for Q5. Each 1-unit increase in the score was associated with 26% higher diabetes risk (95% CI: 1.15, 1.38; p-trend Conclusions: Our results suggest that the distress-related metabolite score was significantly associated with diabetes risk in women and partly mediated the association between self-reported psychological distress and diabetes risk. These findings provide supporting evidence that metabolic dysregulation may be an important mechanism underlying the observed association between psychological distress and diabetes risk.

Background: A healthy lifestyle is associated with a lower risk of premature death. Metabolic pathways of a healthy lifestyle and their association with mortality remain to be understood. This study aimed to identify the metabolomic profile of a healthy lifestyle score and examine its prospective association with all-cause and cause-specific mortality, including death from cardiovascular disease (CVD) and cancer. Methods: The population included 12,146 participants from the Nurses’ Health Study (NHS), NHS II and Health Professionals Follow-Up Study (HPFS)(83% women, 97% white, aged 55±9y). Plasma metabolites were profiled using high-throughput liquid chromatography mass-spectrometry at baseline (NHS:1989-1990; NHSII:1996-1999; HPFS:1993-1995). The healthy lifestyle score was computed by summing the total number of healthy lifestyle factors participants adhered to from validated questionnaires at baseline: healthy diet (Alternative Healthy Eating Index, upper 40%), moderate alcohol intake (women: 5-15 g/d; men: 5-30 g/d), moderate-to-vigorous physical activity (≥30min/d), never smoking and normal BMI (18.5-24.9kg/m 2 ). Deaths were ascertained with death certificates and medical records. The metabolite profile was identified using elastic net regressions with train test validation split (70-30%). Metabolic pathways were determined using Metabolite Set Enrichment Analysis (MSEA). Multivariable-adjusted Cox proportional hazards regressions were used to estimate hazard ratios and 95% confidence intervals (HR[CI]) per unit of score of the healthy lifestyle metabolite profile with mortality risk. Results: The identified profile included 88 metabolites and correlated with the healthy lifestyle score (Pearson r=0.43-0.44; ppb =0.43; p Conclusions: In US adults, we identified a metabolite profile related to a healthy lifestyle largely reflecting lipid metabolism pathways. A higher metabolite score was associated with lower subsequent all-cause mortality risk, specifically from CVD. Findings provide novel insights into potential metabolic pathways underlying the association between a healthy lifestyle and lower premature mortality.

SummaryCellular exposure to free fatty acids (FFA) is implicated in the pathogenesis of obesity-associated diseases. However, studies to date have assumed that a few select FFAs are representative of broad structural categories, and there are no scalable approaches to comprehensively assess the biological processes induced by exposure to diverse FFAs circulating in human plasma. Furthermore, assessing how these FFA- mediated processes interact with genetic risk for disease remains elusive. Here we report the design and implementation of FALCON (Fatty Acid Library for Comprehensive ONtologies) as an unbiased, scalable and multimodal interrogation of 61 structurally diverse FFAs. We identified a subset of lipotoxic monounsaturated fatty acids (MUFAs) with a distinct lipidomic profile associated with decreased membrane fluidity. Furthermore, we developed a new approach to prioritize genes that reflect the combined effects of exposure to harmful FFAs and genetic risk for type 2 diabetes (T2D). Importantly, we found that c-MAF inducing protein (CMIP) protects cells from exposure to FFAs by modulating Akt signaling and we validated the role of CMIP in human pancreatic beta cells. In sum, FALCON empowers the study of fundamental FFA biology and offers an integrative approach to identify much needed targets for diverse diseases associated with disordered FFA metabolism.HighlightsFALCON (Fatty Acid Library for Comprehensive ONtologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effectsFALCON is applicable to many and diverse cell typesA subset of monounsaturated FAs (MUFAs) equally or more toxic than canonical lipotoxic saturated FAs (SFAs) leads to decreased membrane fluidityNew approach prioritizes genes that represent the combined effects of environmental (FFA) exposure and genetic risk for diseaseC-Maf inducing protein (CMIP) is identified as a suppressor of FFA-induced lipotoxicity via Akt-mediated signaling

Group 3 innate lymphoid cells (ILC3s) are RORγT+lymphocytes that are predominately enriched in mucosal tissues and produce IL-22 and IL-17A. They are the innate counterparts of Th17 cells. While Th17 lymphocytes utilize unique metabolic pathways in their differentiation program, it is unknown whether ILC3s make similar metabolic adaptations. We employed single-cell RNA sequencing and metabolomic profiling of intestinal ILC subsets to identify an enrichment of polyamine biosynthesis in ILC3s, converging on the rate-limiting enzyme ornithine decarboxylase (ODC1). In vitro and in vivo studies demonstrated that exogenous supplementation with the polyamine putrescine or its biosynthetic substrate, ornithine, enhanced ILC3 production of IL-22. Conditional deletion of ODC1 in ILC3s impaired mouse antibacterial defense againstCitrobacter rodentiuminfection, which was associated with a decrease in anti-microbial peptide production by the intestinal epithelium. Furthermore, in a model of anti-CD40 colitis, deficiency of ODC1 in ILC3s markedly reduced the production of IL-22 and severity of inflammatory colitis. We conclude that ILC3-intrinsic polyamine biosynthesis facilitates efficient defense against enteric pathogens as well as exacerbates autoimmune colitis, thus representing an attractive target to modulate ILC3 function in intestinal disease.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG.We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR).Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05).In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce Multi-set Correlation and Factor Analysis, an unsupervised integration method that enables fast inference of shared and private factors in multi-modal data. Applied to 614 ancestry-diverse participant samples across five ‘omics types, MCFA infers a shared space that captures clinically relevant molecular processes.

Seasonal influenza causes mild to severe respiratory infections and significant morbidity, especially in older adults. Transcriptomic analysis in populations across multiple flu seasons has provided insights into the molecular determinants of vaccine response. Still, the metabolic changes that underlie the immune response to influenza vaccination remain poorly characterized. We performed untargeted metabolomics to analyze plasma metabolites in a cohort of younger and older subjects before and after influenza vaccination to identify vaccine-induced molecular signatures. Metabolomic and transcriptomic data were combined to define networks of gene and metabolic signatures indicative of high and low antibody response in these individuals. We observed age-related differences in metabolic baselines and signatures of antibody response to influenza vaccination and the abundance of α-linolenic and linoleic acids, sterol esters, fatty-acylcarnitines, and triacylglycerol metabolism. We identified a metabolomic signature associated with age-dependent vaccine response, finding increased tryptophan and decreased polyunsaturated fatty acids (PUFAs) in young high responders (HRs), while fatty acid synthesis and cholesteryl esters accumulated in older HRs. Integrated metabolomic and transcriptomic analysis shows that depletion of PUFAs, which are building blocks for prostaglandins and other lipid immunomodulators, in young HR subjects at Day 28 is related to a robust immune response to influenza vaccination. Increased glycerophospholipid levels were associated with an inflammatory response in older HRs to flu vaccination. This multi-omics approach uncovered age-related molecular markers associated with influenza vaccine response and provides insight into vaccine-induced metabolic responses that may help guide development of more effective influenza vaccines.

Short and long sleep duration are associated with adverse metabolic outcomes, such as obesity and diabetes. We evaluated cross-sectional differences in metabolite levels between women with self-reported habitual short (

The application of large-scale metabolomic profiling provides new opportunities for realizing the potential of omics-based precision medicine for asthma. By leveraging data from over 14,000 individuals in four distinct cohorts, this study identifies and independently replicates 17 steroid metabolites whose levels were significantly reduced in individuals with prevalent asthma. Although steroid levels were reduced among all asthma cases regardless of medication use, the largest reductions were associated with inhaled corticosteroid (ICS) treatment, as confirmed in a 4-year low-dose ICS clinical trial. Effects of ICS treatment on steroid levels were dose dependent; however, significant reductions also occurred with low-dose ICS treatment. Using information from electronic medical records, we found that cortisol levels were substantially reduced throughout the entire 24-hour daily period in patients with asthma who were treated with ICS compared to those who were untreated and to patients without asthma. Moreover, patients with asthma who were treated with ICS showed significant increases in fatigue and anemia as compared to those without ICS treatment. Adrenal suppression in patients with asthma treated with ICS might, therefore, represent a larger public health problem than previously recognized. Regular cortisol monitoring of patients with asthma treated with ICS is needed to provide the optimal balance between minimizing adverse effects of adrenal suppression while capitalizing on the established benefits of ICS treatment.

Background Poor sleep is associated with human immunodeficiency virus (HIV), particularly among women with HIV (WWH), although mechanisms are unclear. We explored cross-sectional associations between sleep disruption and tryptophan-kynurenine (T/K) pathway activation, measured by the kynurenine-to-tryptophan ratio (K:T). Methods HIV-uninfected women (HIV–) and WWH aged 35–70 years and on stable antiretroviral therapy were included. Sleep metrics were measured using wrist actigraphy. Plasma T/K pathway metabolites were measured using liquid chromatography–tandem mass spectrometry. Multivariate linear regression models examined relationships between K:T and actigraphy-based sleep metrics by HIV status. Results WWH (n = 153) and HIV– women (n = 151) were demographically similar. Among WWH, median CD4 was 751 cells/µL; 92% had undetectable HIV RNA. Compared to HIV– women, WWH had higher K:T (P < .001) and kynurenine (P = .01) levels but similar tryptophan levels (P = .25). Higher K:T was associated with more wake bouts (P = .001), more time awake after sleep onset (P = .01), and lower sleep efficiency (P = .03) in WWH only. Conclusions HIV infection was associated with T/K pathway activation; this activation was associated with poorer sleep efficiency and more fragmented sleep. While longitudinal studies are needed to elucidate the directionality of these associations, these findings may help identify treatments to reduce sleep disruption in WWH by targeting residual inflammation and T/K pathway activation.

Background: Bile acids are synthesized from cholesterol in the liver and play pivotal roles in nutrient absorption and metabolic regulation. Altered bile acid metabolism is related to dyslipidemia, and microRNA-34a (miR-34a) may play a pivotal role in the treatment of lipid homeostasis by regulating the bile acid biosynthesis. Hypothesis: We investigated whether weight-loss diet-induced changes in primary and secondary bile acids were associated with the improvements in lipid profiles. We also tested the role of circulating miR-34a in the altered bile acid metabolism in adults with overweight and obesity. Methods: A total of 520 participants of a weight-loss dietary intervention (the POUNDS Lost trial) with data on bile acids and miR-34a were included in the present analysis. Circulating levels of miR-34a and bile acid subtypes (primary and secondary unconjugated bile acids and their taurine-/glycine-conjugates) were measured at baseline and 6 months after the intervention. Outcome measurements were improvements in lipids (triglycerides and cholesterol) in response to the interventions. Results: At baseline, higher levels of primary bile acids (chenodeoxycholate (CDCA), glycocholate (GCA), taurocholate (TCA), and glycochenodeoxycholate (GCDCA) were associated with elevated levels of triglycerides ( P FDR P FDR Conclusions: Changes in the specific primary and secondary bile acid subtypes were related to the improvements in lipid profiles in response to weight-loss diet interventions. Circulating miR-34a might in part contribute to the alterations in the primary bile acid subtypes in obese adults.

Introduction: C-reactive protein (CRP) and GlycA are established biomarkers of inflammation. Regular exercise tends to decrease CRP and GlycA levels. However, the molecules underlying CRP and GlycA and their responses to exercise training are less known. Hypothesis: We hypothesized that distinct protein signatures exist for both baseline levels and exercise responsiveness of CRP and GlycA, however with some overlap of proteins across signatures. Methods: Measures were performed before and after 20 weeks of exercise training in 652 Black and White adults from the HERITAGE Family Study. 4,979 circulating proteins were measured using SomaScan. High-sensitivity CRP was measured using automated assays. GlycA was quantified by NMR spectroscopy (LabCorp). Linear mixed models were used to test: 1) Association of baseline proteins with baseline hsCRP and GlycA and 2) Association of change in protein with changes in hsCRP and GlycA. Models were adjusted for age, sex, race, BMI, with family membership as a random variable, with change models also adjusting for baseline trait value. Significance was determined as FDR Results: Baseline levels (r=0.51, p-40 < p < 1.2x10 -08 ), with serum amyloid A-2 and A-1 (SAA2, SAA) among the top 6 hits for 3 of 4 models (Table 1). Conclusions: More unique proteins were associated with GlycA levels at both timepoints, with most hsCRP proteins overlapping with GlycA proteins. Overall, a substantial number of proteins are associated with CRP and GlycA levels regardless of when they are measured, which has potential implications for using proteins as signatures of these inflammatory biomarkers.

Introduction: Elevated plasma triglycerides (TG) are associated with risk of cardiovascular disease and are modifiable through lifestyle interventions such as regular exercise. However, TG responses to regular exercise are characterized by significant inter-individual differences. Hypothesis: We hypothesized that baseline levels of circulating proteins and metabolites are associated with TG response to exercise and can predict exercise-induced changes in plasma TG traits. Methods: We measured circulating proteins (n=4979 proteins) and metabolites (n=300) in 650 Black and White adults of the HERITAGE Family Study who completed 20 weeks of exercise training and had complete data on TG traits. We investigated two TG-related traits that significantly improved with training: fasting TG and large TG-rich lipoprotein particle concentration (LTRLP). The association between baseline analyte values and exercise-induced changes in TG traits were examined using linear mixed models adjusted for age, sex, race, BMI, baseline trait value, and the random effect of family membership. Significance was determined as FDR Results: Regular exercise significantly reduced plasma TG (-1.03±1.3 mmol/L, p=0.01) and LTRLP (-0.24±2.2 nmol/L, p= 0.007), with their training-induced changes moderately correlated (r=0.4, pTable 1 ). Similarly, elastic net regression yielded distinct models of 99 analytes for TG and 315 analytes for LTRLP responses to exercise with accuracy of RMSE=1.28 mmol/L for TG and 2.16 nmol/L for the LTRLP model. Conclusions: We identified panels of proteins and metabolites associated with exercise-induced changes in TG traits and demonstrate, within our data, circulating analytes hold promise for predicting the exercise responsiveness of plasma TG-related traits.

PurposeTo better understand the etiologic pathways in glaucoma, we aimed to identify pre-diagnostic plasma metabolites associated with glaucoma risk.MethodsIn a case-control study from the Nurses’ Health Study (NHS), NHSII and Health Professionals Follow-Up Study (HPFS), 599 incident primary open-angle glaucoma (POAG) cases (mean time between blood draw and diagnosis was 10.3 years) were 1:1 matched to 599 controls. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 367 metabolites from 17 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 NMR metabolites (Nightingale, Finland; version 2020) were measured in serum samples from 2,238 prevalent glaucoma cases and 44,723 controls. Metabolites were probit-score transformed for normality; multiple logistic regression was used to identify metabolites associated with POAG in NHS/NHSII/HPFS and glaucoma in UK Biobank. In NHS/NHSII/HPFS, we also used Metabolite Set Enrichment Analysis to identify metabolite classes associated with POAG. All analyses adjusted for established glaucoma risk factors. False discovery rate (FDR) and number of effective tests (NEF) were used to adjust for multiple comparisons.ResultsNine metabolite classes were associated (FDRConclusionsHigher levels of glycerides (diglycerides and triglycerides) and phospholipids were adversely associated with glaucoma in both the NHS/NHSII/HPFS and the UK Biobank, suggesting that they play an important role in glaucoma pathogenesis.PRÉCISHigher glyceride and phospholipid levels in pre-diagnostic plasma was associated with glaucoma risk in three cohorts and were associated with prevalent glaucoma in the UK Biobank. Altered lipid metabolism may be etiologically important in glaucoma.

Transcriptional states in pancreatic cancer can stratify patients by response to chemotherapy and clinical outcomes. These include the classical and basal-like states as well as a newly identified neural-like progenitor (NRP) state, which we have previously found to be enriched in primary patient tumors treated with neoadjuvant chemotherapy and radiotherapy. While several transcription factor drivers of classical and basal-like identity have been described, key regulators of the NRP state are unknown. Through in silico approaches, we identified candidate transcription factors of the NRP state, including GLIS3, a Krüppel-like zinc finger protein that mediates neuroendocrine fate during pancreatic development and differentiation of human embryonic stem cells into posterior neural progenitor cells. Our understanding of biologic and clinically-relevant attributes of transcriptional cell states remains limited by state-specific biases in various preclinical models. Existing human cell lines maintained as two-dimensional cultures tend to preferentially represent the basal-like state, whereas human three-dimensional organoid models grown in standard culture conditions best reflect the classical state. These phenotypes are therefore impacted by culture conditions as well as underlying genetic features. Furthermore, most murine pancreatic cancer models do not fully reflect the classical vs. basal-like state heterogeneity observed in humans. To enable systematic study of the classical, basal-like and NRP phenotypes, we developed isogenic KP (KrasG12D/+;Trp53FL/FL) murine organoids with a germline dCas9-VPR system to enable facile overexpression of state-specific transcription factors through CRISPR activation approaches. Quantitative PCR, RNA-sequencing, and proteomics confirmed Gata6, deltaN Trp63, and Glis3 as drivers of classical, basal-like, and NRP identity, respectively. DeltaN Trp63 organoids were further differentiated by loss of luminal morphology. Pairwise comparisons of global transcriptional alterations suggest the greatest similarities between the Gata6- and Glis3-overexpressed models, which is consistent with enhanced associations between classical and NRP states in patient tumors. Finally, although basal-like and NRP states are associated with poorer response to multi-agent chemotherapy, state-specific therapeutic sensitivities to other treatments remain incompletely defined. We therefore performed drug sensitivity assays with a panel of targeted therapies and unveiled state-specific sensitivities. These data were corroborated by drug sensitivity profiling of human patient-derived organoids and cell lines. Taken together, these results suggest a framework for defining cell state-specific vulnerabilities that may aid in stratifying and treating pancreatic cancer patients with new therapies. Citation Format: Jimmy A. Guo, Jennifer Su, Ananya Jambhale, Julien Dilly, Connor J. Hennessey, Carina Shiau, Patrick Yu, Steven Wang, Junning Wang, Laleh Abbassi, James Neiswender, Tate Bertea, Annan Yang, Qijia Yu, Peter Westcott, Jason Schenkel, Daniel Y. Kim, Hannah I. Hoffman, Grissel Cervantes Jaramillo, Giselle A. Uribe, Westley W. Wu, Arnav Mehta, David Ting, Julian A. Pacheco, Amy Deik, Clary Clish, Francisca Vazquez, Brian Wolpin, Aviv Regev, William A. Freed-Pastor, Joseph D. Mancias, Tyler Jacks, William L. Hwang, Andrew J. Aguirre. Systematic dissection of transcriptional states in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A052.

Cardiovascular disease (CVD) is a major cause of mortality among people with diabetic kidney disease (DKD). The pathophysiology is inadequately explained by traditional CVD risk factors. The uremic solutes trimethylamine-N-oxide (TMAO) and asymmetric and symmetric dimethylarginine (ADMA, SDMA) have been linked to CVD in kidney failure with replacement therapy (KFRT), but data are limited in populations with diabetes and less severe kidney disease.Observational cohort.Random subcohort of 555 REGARDS (Reasons for Geographic and Racial Differences in Stroke) study participants with diabetes and estimated glomerular filtration rate (eGFR) 60 mL/min/1.73 mADMA, SDMA, and TMAO assayed by liquid chromatography-mass spectrometry in plasma and urine.Cardiovascular mortality (primary outcome); all-cause mortality and incident KFRT (secondary outcomes).Plasma concentrations and ratios of urine to plasma concentrations of ADMA, SDMA, and TMAO were tested for association with outcomes. Adjusted Cox regression models were fitted and hazard ratios of outcomes calculated per standard deviation and per doubling, and as interquartile comparisons.The mean baseline eGFR was 44 mL/min/1.73 mSingle cohort, restricted to patients with diabetes and eGFR 60 mL/min/1.73 mHigher plasma concentrations and lower ratios of urine to plasma concentrations of uremic solutes were independently associated with cardiovascular and all-cause mortality in DKD. Associations of ratios of urine to plasma concentrations with mortality suggest a connection between renal uremic solute clearance and CVD pathogenesis.

Introduction Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of upper airway obstruction during sleep resulting in oxygen desaturation and sleep fragmentation, and associated with increased risk of adverse health outcomes. Metabolites are being increasingly used for biomarker discovery and evaluation of disease processes and progression. We aimed to develop a metabolomic risk score (MRS) for OSA and identify individual metabolites associated with OSA to provide insights into the pathogenesis of OSA. Methods We studied 219 metabolites and their associations the apnea hypopnea index (AHI) and with OSA, defined as AHI , in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (n=3507) using two methods: (1) association analysis of individual metabolites, and (2) least absolute shrinkage and selection operator (LASSO) regression to identify a subset of metabolites that are jointly associated with OSA, and develop an MRS. We then validated the results in Multi-Ethnic Study of Atherosclerosis (MESA) (n=475), an independent dataset. Results HCHS/SOL participants were 41.72 years old on average, 50.7% female, and 10.2% had OSA. MESA individuals were 68.45 years old on average, 56.2% females, and 46.7% had OSA. When assessing the associations between OSA/AHI and individual metabolites, we identified seven metabolites significantly and positively associated with OSA in HCHS/SOL (FDR p Conclusion We developed an MRS that replicated in an independent multi-ethnic dataset, demonstrating the robustness of metabolomic-based OSA risk score to population heterogeneity. Replicated metabolite associations may provide insights into OSA-related molecular and metabolic mechanisms. Support (If Any) Support for metabolomics data was graciously provided by the JLH Foundation (Houston, Texas). The Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke, and the Office of Dietary Supplements. The authors thank the staff and participants of HCHS/SOL for their important contributions. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420. MESA Family is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support is provided by grants and contracts R01HL071051, R01HL071205, R01HL071250, R01HL071251, R01HL071258, R01HL071259, and by the National Center for Research Resources, Grant UL1RR033176. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung and Blood Institute (NHLBI). ). Metabolomics for “NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)” (phs001416) was performed at Broad Institute and Beth Israel Metabolomics Platform (HHSN268201600038I). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study was supported by NHLBI grant R35HL135818.

565 Background: Metabolomic profiling of plasma from mUC pts has not been comprehensively examined. Plasma metabolomics may capture the effects of interactions between the malignancy, host and therapy. We hypothesized that Identifying metabolites in plasma from patients with mUC receiving an ICI or PBC may shed valuable insights regarding tumor biology and mechanisms of resistance. Methods: We obtained 0.2 ml plasma before and after starting therapy from pts with mUC receiving an ICI or PBC at the Dana-Farber Cancer Institute. Plasma metabolomic profiling was conducted at the Broad Institute using 3 complementary liquid chromatography tandem mass spectrometry (LC-MS)-based metabolomics platforms. We measured 648 metabolites at baseline prior to starting ICI/PBC and at a second time point in each subject following initiation of ICI/PBC. Metabolite levels were assumed to be normally distributed with log transformation to transform distributions to be approximately symmetric. We performed Wilcoxon-rank sum test to compare the levels of metabolites before and after initiation of the ICI or PBC (significance at p < 0.05). Results: Plasma was available at baseline and during therapy in 53 mUC pts (ICI: n = 43; PBC: n = 10). The median age was 68 (range: 39-86) years and 42 (82.3%) were male. The median time from baseline to the second time point was 4.7 months (range: 0.7-90.2). The ICIs administered were atezolizumab (n = 20), pembrolizumab (n = 16), nivolumab (n = 5), and durvalumab + tremelimumab (n = 1). We identified 20 metabolites that were significantly increased in post-PBC plasma samples (vs. pre-PBC) and 19 metabolites increased in post-ICI (vs. pre-ICI) samples (p < 0.05). All altered metabolites except one (Uracil) were exclusive for each treatment group. The most significant metabolites that increased following initiation of the ICI and PBC are shown in the Table. Evaluation of the association of plasma metabolomics with clinical outcomes and toxicities is ongoing. Conclusions: This is the first report, to our knowledge, of comprehensive metabolomic plasma profiling of pre- and post-ICI and PBC pts with mUC. The metabolomic changes after ICI appear distinct from those seen after PBC. Furthermore, our study sheds insights on potential mechanisms of resistance and new therapeutic targets in pts with mUC.[Table: see text]

Human cancers with activating RAS mutations are typically highly aggressive and treatment-refractory, yet RAS mutation itself is insufficient for tumorigenesis, due in part to profound metabolic stress induced by RAS activation. Here we show that loss of REDD1, a stress-induced metabolic regulator, is sufficient to reprogram lipid metabolism and drive progression of RAS mutant cancers. Redd1 deletion in genetically engineered mouse models (GEMMs) of KRAS-dependent pancreatic and lung adenocarcinomas converts preneoplastic lesions into invasive and metastatic carcinomas. Metabolic profiling reveals that REDD1-deficient/RAS mutant cells exhibit enhanced uptake of lysophospholipids and lipid storage, coupled to augmented fatty acid oxidation that sustains both ATP levels and ROS-detoxifying NADPH. Mechanistically, REDD1 loss triggers HIF-dependent activation of a lipid storage pathway involving PPARγ and the prometastatic factor CD36. Correspondingly, decreased REDD1 expression and a signature of REDD1 loss predict poor outcomes selectively in RAS mutant but not RAS wild-type human lung and pancreas carcinomas. Collectively, our findings reveal the REDD1-mediated stress response as a novel tumor suppressor whose loss defines a RAS mutant tumor subset characterized by reprogramming of lipid metabolism, invasive and metastatic progression, and poor prognosis. This work thus provides new mechanistic and clinically relevant insights into the phenotypic heterogeneity and metabolic rewiring that underlies these common cancers.

Cellular metabolism is a major regulator of immune response, but it is difficult to study the metabolic status of an individual immune cell using current technologies. Here, we present Compass, an algorithm to characterize the metabolic landscape of single cells in silico based on single-cell RNA-Seq and flux balance analysis. We used Compass to study the landscape of metabolic heterogeneity in Th17 cells and search for novel metabolic regulators of their inflammatory function. In central carbon metabolism, Compass predicted a metabolic switch between glycolysis and fatty acid oxidation that mirrors the Th17 vs. Treg phenotype, which we validated through transcriptomic, metabolic and functional assays. The TCA cycle was predicted to break at two points, both of which have been independently identified by other groups in M1 macrophage polarization. Surprisingly, and contrary to common immunometabolic understanding, Compass predicted that glycolysis too was divided into functional modules, and that one of them supported an anti-inflammatory phenotype. We validate the paradoxical prediction and demonstrate that inhibition of this module promotes a pro-inflammatory transcriptional program in Th17 cells, resulting in neuroinflammation in an adoptive transfer model of autoimmune disease. In conclusion, Compass is a widely applicable algorithm to characterize metabolic states at single cell resolution. It allows associating cellular metabolic states with effector functions and detection of metabolic targets that regulate effector phenotypes. We expect it to become a widely used tool with the increasing availability of single-cell RNA-Seq data, spurred by efforts such as the human cell atlas.

Increased expression of the Tribbles pseudokinase 1 gene (TRIB1) is associated with lower plasma levels of LDL cholesterol and triglycerides, higher levels of HDL cholesterol and decreased risk of coronary artery disease and myocardial infarction. We identified a class of tricyclic glycal core-based compounds that upregulate TRIB1 expression in human HepG2 cells and phenocopy the effects of genetic TRIB1 overexpression as they inhibit expression of triglyceride synthesis genes and ApoB secretion in cells. In addition to predicted effects related to downregulation of VLDL assembly and secretion these compounds also have unexpected effects as they upregulate expression of LDLR and stimulate LDL uptake. This activity profile is unique and favorably differs from profiles produced by statins or other lipoprotein targeting therapies. BRD8518, the initial lead compound from the tricyclic glycal class, exhibited stereochemically dependent activity and the potency far exceeding previously described benzofuran BRD0418. Gene expression profiling of cells treated with BRD8518 demonstrated the anticipated changes in lipid metabolic genes and revealed a broad stimulation of early response genes. Consistently, we found that BRD8518 activity is MEK1/2 dependent and the treatment of HepG2 cells with BRD8518 stimulates ERK1/2 phosphorylation. In agreement with down-regulation of genes controlling triglyceride synthesis and assembly of lipoprotein particles, the mass spectrometry analysis of cell extracts showed reduced rate of incorporation of stable isotope labeled glycerol into triglycerides in BRD8518 treated cells. Furthermore, we describe medicinal chemistry efforts that led to identification of BRD8518 analogs with enhanced potency and pharmacokinetic properties suitable for in vivo studies.

Introduction: Altered acylcarnitine concentrations may reflect impaired mitochondrial metabolism and are implicated in cardiovascular disease (CVD). Disturbances in carnitine metabolism have been observed in HIV infection, but it is unknown whether this is related to CVD risk in HIV infected people. Methods: Twenty-six acylcarnitine species were profiled with ultrahigh-performance liquid chromatography/tandem mass spectrometry in 705 men and women with or at risk of HIV infection in the Multicenter AIDS Cohort Study and the Women’s Interagency HIV Study. Using a weighted score approach to define aggregate levels of plasma acylcarnitines, we assessed the associations of short-chain (C2-C7), medium-chain (C8-C14) and long-chain acylcarnitine (C16-C26) with incident carotid plaque, over 7-year follow-up, defined as a carotid artery region with focal intima-media thickness>1.5mm among those with no baseline carotid plaque. Results: The mean age was 45 years and 70% were non-white. The majority (70%) had HIV, and 68% of 394 participants on antiretroviral therapy (ART) had undetectable HIV viral load. Over 7 years, 108 participants developed carotid plaque. Comparing HIV-infected with HIV-uninfected participants, some individual acylcarnitines were higher (C3, C16, C20, C26) while others were lower (C8, C10, C20:4) (all P Conclusion: Plasma short-chain acylcarnitines were associated with increased risk of carotid plaque formation, independent of traditional CVD risk factors, especially in HIV-infected individuals and those with poor control of HIV viral load.