Longitudinal single-cell clinical studies enable tracking within-individual cellular dynamics, but methods for modeling temporal phenotypic changes and estimating power remain limited. We present scLASER, a framework detecting time-dependent cellular neighborhood dynamics and simulating longitudinal single-cell datasets for power estimation. Across benchmark experiments, scLASER shows consistently higher sensitivity than traditional cluster--based approaches, with particularly pronounced gains in rare cell types and non-linear temporal patterns. Applications to inflammatory bowel disease (95,813 cells, 38 patients) reveal treatment-responsive NOTCH3+ stromal trajectories with high cell type discrimination (AUC > 0.92), while analysis of COVID-19 data (188,181 cells, 84 patients) identifies three distinct axes of T cell activity (cytotoxic effector, NK immunoreceptor signaling, and interferon-stimulated gene programs) over disease progression. scLASER enables robust longitudinal single-cell analysis and optimization of study design.

Background Type 1 diabetes is believed to be associated with early genetic and environmental stressors. Epigenetic age acceleration (EAA) is also associated with environmental stressors and the pathogenesis of many chronic diseases. This study explored longitudinal changes in EAA among individuals at high risk for type 1 diabetes.Methods DNA methylation was measured longitudinally in subjects from the Diabetes Autoimmunity Study in the Young cohort, 2547 children born 1993–2006 at high risk for type 1 diabetes. Data were collected before and after islet autoimmunity (IA) seroconversion, a preclinical type 1 diabetes stage. EAA was estimated from DNA methylation using an epigenetic clock appropriate for pediatric blood samples. A linear mixed model was used to test for differences in EAA between 85 type 1 diabetes cases and 85 controls, before and after IA seroconversion.Results Change in EAA significantly differed between cases and controls (p=0.02). EAA significantly decreased in cases, from pre-IA to post-IA seroconversion by 0.367 units (95% CI −0.64 to 0.09, p=0.01), but not in controls (0.045, 95% CI 0.23 to 0.32, p=0.75).Conclusion These results suggest that EAA occurs in children who develop type 1 diabetes prior to IA seroconversion, highlighting the potential role of early environmental stressors in disease pathogenesis.

CONTEXT:Increased levels of 25-hydroxyvitamin D (25OHD) have been protective against islet autoimmunity (IA), a preclinical type 1 diabetes (T1D) disease state. However, the role of 25OHD and downstream metabolites in progression from IA to T1D is not well understood. OBJECTIVE:We hypothesized that downstream vitamin D metabolites and metabolite ratios would be associated with progression from IA to T1D. METHODS:Among participants at high genetic risk for T1D who developed IA (n = 143) in the Diabetes Autoimmunity Study in the Young (DAISY), a T1D birth cohort study, we quantified vitamin D3, 25OHD2, 3-epi-25OHD3, 25OHD3, 24,25(OH)2D3, and 1α,25(OH)2D3 metabolites from plasma samples using LC-MS/MS. We calculated the vitamin D metabolite ratio (VMR) (ie, 24,25(OH)2D3/25OHD3) and the epimer ratio (3-epi-25OHD3/25OHD3). We also tested the correlation between metabolite levels and gene expression in a subset of participants (n = 53). RESULTS:A total of 57/143 progressed to T1D. Higher VMR (hazard ratio (HR) per 1 SD increase: 0.65; 95% CI: 0.49-0.88) and levels of 24,25(OH)2D3 (HR per 1 SD increase: 0.72; 95% CI: 0.55-0.94) at IA seroconversion were associated with a lower risk of progression to T1D, adjusting for seroconversion age, season, HLA-DR3/4 genotype, and ancestry. Functional enrichment analysis suggests higher VMR resulted in a gene expression pattern in whole blood characteristic of decreased activation of inflammatory pathways related to neutrophil infiltration. CONCLUSION:A higher VMR, a more functional measure of vitamin D status, was protective against T1D progression, perhaps by decreasing activation of inflammatory pathways.

INTRODUCTION:Seroconversion (SV) marks islet autoimmunity (IA) onset and preclinical type 1 diabetes (T1D), yet the contributions beyond T and B lymphocytes remain unclear. We evaluated DNA methylation (DNAm)-derived immune cell ratios between T1D cases and controls around SV. RESEARCH DESIGN AND METHODS:High-resolution immune cell-type deconvolution of peripheral blood DNAm from nested case-control samples in the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and the Environmental Determinants of Diabetes in the Young (TEDDY; n=166) estimated immune cell proportions at pre-SV (the latest visit before SV) and at SV (the first visit with persistent detected autoantibodies) to construct immune cell ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Linear models compared T1D cases to matched T1D controls (IA negative) at pre-SV, SV, and the change across time points. RESULTS:From pre-SV to SV, controls showed expected developmental increases in B-memory/naive, B-CD4T-CD8T memory/naive, and NLR, while cases failed to follow these patterns, with attenuated trajectories of 35%, 38%, and 21%, respectively. Pre-SV, cases had 15% higher NLR and 9% lower CD4T/CD8T. At SV, the combined B-CD4T-CD8T memory/naive ratio was 26% reduced in cases. CONCLUSIONS:These patterns may reflect increased neutrophil activation or pancreatic infiltration, altered CD4 and CD8 T cell balance, and delayed or disrupted immune maturation with the persistence or expansion of naive B and T cells or impaired transition to memory B and T subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during T1D pathogenesis and support methylation-derived immune cell ratios as potential biomarkers for risk stratification and mechanistic insight.

CONTEXT:This is the first study to examine the association between variants of the glucagon-like-peptide-1 receptor gene (GLP-1R) and metabolic characteristics among youth. OBJECTIVE:We explored separate associations of 3 GLP-1R polymorphisms (rs10305420, rs6923761, and rs1042044) with body mass index (BMI) trajectories and markers of glucose-insulin homeostasis. METHODS:Mixed models examined associations between GLP-1R polymorphisms and trajectories of BMI. Linear models examined associations of GLP-1R polymorphisms with glucose and insulin concentrations across oral glucose tolerance test (OGTT), insulin sensitivity (HOMA2-IR), insulin secretion (insulinogenic index and HOMA2-%B), and β-cell function (oral disposition index). RESULTS:Rs10305420 and rs6923761, but not rs1042044, were associated with growth and metabolic characteristics in early life. Rs6923761 genotype GG was associated with faster BMI growth velocity, when compared to carriers of the minor allele (difference in velocity [95% CI]: 0.16/year [0.07-0.24] at age 10), which led to significantly higher average BMI by age 16 (average difference [95% CI]: 1.29 [0.22-2.37]). Rs10305420 CC and rs6923761 GG genotypes had higher HOMA2-IR (β [95% CI]: 1.19% [1.06-1.32] and 1.13% [1.01-1.26], respectively) compared to minor allele carriers. Rs10305420 CC had higher HOMA2-%B (β [95% CI]: 1.09% [1.01-1.17]), and higher stimulated insulin secretion at 30 minutes (β [95% CI]: 27.62 μIU/mL [3.00-25.24]) and 120 minutes (β [95% CI]: 18.94 μIU/mL [1.04-36.84]), when compared to carriers of the minor allele. CONCLUSION:GLP-1R polymorphisms are associated with faster BMI growth across development, and lower estimated insulin sensitivity and higher compensatory insulin secretion during adolescence. GLP-1R polymorphisms should be considered in future pediatric studies of genetic susceptibility for obesity and diabetes.

Seroconversion (SV) marks the initiation of islet autoimmunity (IA) and pre-clinical phase of type 1 diabetes, yet the contributions of immune cells beyond cytotoxic T cells remain unclear. We applied high-resolution immune cell-type deconvolution using peripheral blood DNA methylation data from nested case-control samples of the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and The Environmental Determinants of Diabetes in the Young (TEDDY; n=166) to estimate immune cell proportions across pre-SV and SV timepoints and construct functional ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Using linear models, we evaluated differences between type 1 diabetes cases and controls at pre-SV, SV, and the change across timepoints. Pre-SV, cases had higher NLR and lower CD4T/CD8T cell ratios. At SV, the combined B-CD4T-CD8T memory/naïve ratio was reduced in cases. From pre-SV to SV, cases showed attenuations in NLR, B-memory/naïve, and B-CD4T-CD8T memory/naïve ratios. These patterns may reflect delayed or disrupted immune maturation with the persistence or expansion of naïve cells or impaired transition to memory subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during type 1 diabetes pathogenesis and support immune cell ratios as potential biomarkers for risk stratification and mechanistic insight.

OBJECTIVE Exposure to maternal gestational diabetes mellitus (GDM) is associated with childhood BMI. Among youth, we explored whether three different glucagon-like peptide 1 receptor gene (GLP-1R) polymorphisms modified the associations between 1) GDM and BMI trajectories and 2) GDM and markers of glucose-insulin homeostasis. RESEARCH DESIGN AND METHODS For 464 participants from the Exploring Perinatal Outcomes Among Children (EPOCH) study, microarray genotyping was performed during childhood (∼10 years). BMI trajectories across childhood and adolescence were characterized using repeated measurements from research visits and medical record abstraction. Markers of glucose-insulin homeostasis were derived from one oral glucose tolerance test in adolescence (∼16 years). Linear models assessed effect modification by GLP-1R polymorphisms. RESULTS Among youth with at least one minor allele of rs10305420 (CT or TT) or rs1042044 (CA or AA), but not among major allele homozygotes, exposure to GDM was associated with higher average BMI. For rs6923761, participants who were exposed to GDM and were major allele homozygotes (i.e., genotype GG) had significantly higher average BMI than all other participants in the cohort. No polymorphisms modified the association between GDM and markers of glucose-insulin homeostasis during adolescence. CONCLUSIONS GLP-1R polymorphisms modify the association between GDM and BMI growth among youth. Further studies are needed to replicate these findings, and to better understand the mechanisms by which GLP-1R polymorphisms lead to heterogeneity in offspring BMI growth.

OBJECTIVE:Multiple studies have reported an inverse association between self-reported smoking during pregnancy and offspring type 1 diabetes (T1D) risk. We investigated the association between DNA methylation (DNAm) smoke exposure scores, parental self-reported smoking, and islet autoimmunity (IA) and T1D risk in children at high risk of T1D. RESEARCH DESIGN AND METHODS:We used longitudinal data from the Diabetes Autoimmunity Study in the Young cohort, including 205 IA case and 206 control participants (87 and 88 were T1D case and control participants, respectively), matched by age, race/ethnicity, and sample availability. DNAm profiles were obtained from cord or peripheral blood using the Infinium Human Methylation 450K or EPIC BeadChip. Three published DNAm smoking scores were calculated at every time point. To estimate in utero smoke exposure, participant-specific intercepts were derived from mixed-effects models of longitudinal DNAm scores. These intercepts strongly correlated with cord blood scores (r = 0.85-0.95; n = 179), indicating their utility as proxies for in utero smoke exposure. Associations with IA/T1D were evaluated using logistic regression, adjusting for HLA-DR3/4, first-degree relative status, and sex. RESULTS:Multivariable models showed both maternally reported smoking during pregnancy and higher DNAm smoking scores to be associated with lower risk of IA and T1D. Maternal smoking showed a strong inverse association with IA (odds ratio [OR] 0.24; 95% CI 0.10-0.54). Rauschert and McCartney DNAm scores showed consistent inverse associations with both outcomes (OR 0.65-0.83 for SD increase). CONCLUSIONS:Our study supports existing literature indicating in utero smoke exposure is associated with reduced IA and T1D risk. Further research is essential to uncover the underlying mechanisms.

BackgroundType 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).MethodsThis epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7).ResultsThe median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions.ConclusionDifferentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.

Background:An animal's ability to discriminate between differing wavelengths of light (i.e., color vision) is mediated, in part, by a subset of photoreceptor cells that express opsins with distinct absorption spectra. In Drosophila R7 photoreceptors, expression of the rhodopsin molecules, Rh3 or Rh4, is determined by a stochastic process mediated by the transcription factor spineless. The goal of this study was to identify additional factors that regulate R7 cell fate and opsin choice using a Genome Wide Association Study (GWAS) paired with transcriptome analysis via RNA-Seq. Results:We examined Rh3 and Rh4 expression in a subset of fully-sequenced inbred strains from the Drosophila Genetic Reference Panel and performed a GWAS to identify 42 naturally-occurring polymorphisms-in proximity to 28 candidate genes-that significantly influence R7 opsin expression. Network analysis revealed multiple potential interactions between the associated candidate genes, spineless and its partners. GWAS candidates were further validated in a secondary RNAi screen which identified 12 lines that significantly reduce the proportion of Rh3 expressing R7 photoreceptors. Finally, using RNA-Seq, we demonstrated that all but four of the GWAS candidates are expressed in the pupal retina at a critical developmental time point and that five are among the 917 differentially expressed genes in sevenless mutants, which lack R7 cells. Conclusions:Collectively, these results suggest that the relatively simple, binary cell fate decision underlying R7 opsin expression is modulated by a larger, more complex network of regulatory factors. Of particular interest are a subset of candidate genes with previously characterized neuronal functions including neurogenesis, neurodegeneration, photoreceptor development, axon growth and guidance, synaptogenesis, and synaptic function.

Background Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible. Methods We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of two recent algorithms, based on networks (scGWAS) or single-cell disease scores (scDRS), according to accuracy/sensitivity and interpretability ([M. J. Zhang et al. 2022][1]; [Jia et al. 2022][2]). While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,1,73 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC) ([F. Zhang et al. 2023][3]; [Ishigaki et al. 2022][4]; [Smillie et al. 2019][5]; de [Lange et al. 2017][6]). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools’ resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls. Results We first found that single-cell based tool scDRS called superior numbers of supported cell states, like MERTK+ myeloid cells in RA, which were overlooked by network-based scGWAS. While scGWAS was advantageous for gene exploration, scDRS captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5’ scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn’s disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-65 [2]: #ref-20 [3]: #ref-64 [4]: #ref-16 [5]: #ref-49 [6]: #ref-25

BackgroundUnderstanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible.MethodsWe present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools’ resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.ResultsWe first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5’ scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn’s disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.

Tracking trajectories of body size in children provides insight into chronic disease risk. One measure of pediatric body size is body mass index (BMI), a function of height and weight. Errors in measuring height or weight may lead to incorrect assessment of BMI. Yet childhood measures of height and weight extracted from electronic medical records often include values which seem biologically implausible in the context of a growth trajectory. Removing biologically implausible values reduces noise in the data, and thus increases the ease of modeling associations between exposures and childhood BMI trajectories, or between childhood BMI trajectories and subsequent health conditions. We developed open-source algorithms (available on github) for detecting and removing biologically implausible values in pediatric trajectories of height and weight. A Monte Carlo simulation experiment compared the sensitivity, specificity and speed of our algorithms to three published algorithms. The comparator algorithms were selected because they used trajectory information, had open-source code, and had published verification studies. Simulation inputs were derived from longitudinal epidemiological cohorts. Our algorithms had higher specificity, with similar sensitivity and speed, when compared to the three published algorithms. The results suggest that our algorithms should be adopted for cleaning longitudinal pediatric growth data.

Introduction: A family history of type 1 diabetes (T1D) increases T1D risk, but the increase is lower for maternal compared to paternal T1D. We aimed to identify epigenetic markers of this parent-of-origin effect by testing whether the effect of DNA methylation on T1D risk differs by T1D family history in The Environmental Determinants of Diabetes in the Young (TEDDY) Study. Methods: For 106 T1D cases and 99 matched controls in TEDDY, methylation was measured in 1,424 peripheral blood samples collected prospectively from 3-75 months of age using the MethylationEPIC Beadchip. Following data processing, we performed an epigenome-wide association study across 534,790 CpGs using linear regression adjusted for age, sex, and HLA-DR3/4. We used an interaction term to test whether the difference in mean longitudinal methylation (%) between T1D cases and controls differed by T1D family history (affected: mother, N=19; father or sibling, N=50; none, N=136). Results: We identified 141 CpGs where the effect of methylation on T1D risk differed by T1D family history (FDR-adjusted Pinteraction<0.01). Among children exposed to maternal T1D in utero, methylation levels differed between cases and controls; however, in those with no T1D family history or with an affected father or sibling there was no difference in methylation. In those with an affected mother, the largest effect sizes included hypomethylation in T1D cases near glucose metabolism genes ASTN2 (-11.3%) and ACOT7 (-7.8%), and hypermethylation near PTEN (11.3%), a key insulin signaling gene. Over 24% (35/141) of CpGs localized in previously identified loci exhibiting allele-specific methylation, implicating genetic-epigenetic interplay. Conclusion: We identified epigenetic changes preceding T1D that differ by T1D family history. At these loci, methylation differences occurred only among children exposed to T1D in utero and localized near glucose metabolism genes, suggesting epigenetic mechanisms may be involved in the long-described maternal effect in T1D risk. Disclosure R.K. Johnson: None. S.D. Slack: None. L.A. Vanderlinden: None. K. Hohsfield: None. P.M. Carry: None. S. Onengut-Gumuscu: None. S.S. Rich: None. M. Rewers: Advisory Panel; Sanofi. Other Relationship; Sanofi. Consultant; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Consultant; Provention Bio, Inc. Research Support; Hemsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases. K. Kechris: None. J.M. Norris: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust (2103-05094). The TEDDY Study is a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Juvenile Diabetes Research Foundation (JDRF), and Centers for Disease Control and Prevention (CDC).

BackgroundOxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins.MethodsWe conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10−8).ResultsThe SNP rs143070873 was associated with (p < 5×10−8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis.ConclusionGenetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.

Abstract The androgen receptor (AR) is important in the development of both experimental and human bladder cancer. However, the role of AR in bladder cancer growth and progression is less clear, with literature indicating that more advanced stage and grade disease are associated with reduced AR expression. To determine the mechanisms underlying these relationships, we profiled AR-expressing human bladder cancer cells by AR chromatin immunoprecipitation sequencing and complementary transcriptomic approaches in response to in vitro stimulation by the synthetic androgen R1881. In vivo functional genomics consisting of pooled shRNA or pooled open reading frame libraries was employed to evaluate 97 genes that recapitulate the direction of expression associated with androgen stimulation. Interestingly, we identified CD44, the receptor for hyaluronic acid, a potent biomarker and driver of progressive disease in multiple tumor types, as significantly associated with androgen stimulation. CRISPR-based mutagenesis of androgen response elements associated with CD44 identified a novel silencer element leading to the direct transcriptional repression of CD44 expression. In human patients with bladder cancer, tumor AR and CD44 mRNA and protein expression were inversely correlated, suggesting a clinically relevant AR–CD44 axis. Collectively, our work describes a novel mechanism partly explaining the inverse relationship between AR and bladder cancer tumor progression and suggests that AR and CD44 expression may be useful for prognostication and therapeutic selection in primary bladder cancer. Significance: This study describes novel AREs that suppress CD44 and an expected inverse correlation of AR-CD44 expression observed in human bladder tumors.