A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform.

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph TM platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Promises and challenges of populational proteomics in health and disease.

Advances in proteomic assay technologies has significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvement in multiplexed protein assays have facilitated quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest ranging, and potentially the most disease relevant, blood circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with the affinity-based array platforms in terms of speed, cost and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remains considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations and data integration as population scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs and financing must be solved.

Plasma proteome association with coronary heart disease and carotid intima media thickness: results from the KORA F4 study.

BACKGROUND AND AIMS: Atherosclerosis is the main cause of stroke and coronary heart disease (CHD), both leading mortality causes worldwide. Proteomics, as a high-throughput method, could provide helpful insights into the pathological mechanisms underlying atherosclerosis. In this study, we characterized the associations of plasma protein levels with CHD and with carotid intima-media thickness (CIMT), as a surrogate measure of atherosclerosis. METHODS: The discovery phase included 1000 participants from the KORA F4 study, whose plasma protein levels were quantified using the aptamer-based SOMAscan proteomics platform. We evaluated the associations of plasma protein levels with CHD using logistic regression, and with CIMT using linear regression. For both outcomes we applied two models: an age-sex adjusted model, and a model additionally adjusted for body mass index, smoking status, physical activity, diabetes status, hypertension status, low density lipoprotein, high density lipoprotein, and triglyceride levels (fully-adjusted model). The replication phase included a matched case-control sample from the independent KORA F3 study, using ELISA-based measurements of galectin-4. Pathway analysis was performed with nominally associated proteins (p-value < 0.05) from the fully-adjusted model. RESULTS: In the KORA F4 sample, after Bonferroni correction, we found CHD to be associated with five proteins using the age-sex adjusted model: galectin-4 (LGALS4), renin (REN), cathepsin H (CTSH), and coagulation factors X and Xa (F10). The fully-adjusted model yielded only the positive association of galectin-4 (OR = 1.58, 95% CI = 1.30-1.93), which was successfully replicated in the KORA F3 sample (OR = 1.40, 95% CI = 1.09-1.88). For CIMT, we found four proteins to be associated using the age-sex adjusted model namely: cytoplasmic protein NCK1 (NCK1), insulin-like growth factor-binding protein 2 (IGFBP2), growth hormone receptor (GHR), and GDNF family receptor alpha-1 (GFRA1). After assessing the fully-adjusted model, only NCK1 remained significant (ß = 0.017, p-value = 1.39e-06). Upstream regulators of galectin-4 and NCK1 identified from pathway analysis were predicted to be involved in inflammation pathways. CONCLUSIONS: Our proteome-wide association study identified galectin-4 to be associated with CHD and NCK1 to be associated with CIMT. Inflammatory pathways underlying the identified associations highlight the importance of inflammation in the development and progression of CHD.

The E3 ubiquitin-protein ligase UHRF1 promotes adipogenesis and limits fibrosis by suppressing GPNMB-mediated TGF-ß signaling.

The E3 ubiquitin-ligase UHRF1 is an epigenetic regulator coordinating DNA methylation and histone modifications. However, little is known about how it regulates adipogenesis or metabolism. In this study, we discovered that UHRF1 is a key regulatory factor for adipogenesis, and we identified the altered molecular pathways that UHRF1 targets. Using CRISPR/Cas9-based knockout strategies, we discovered the whole transcriptomic changes upon UHRF1 deletion. Bioinformatics analyses revealed that key adipogenesis regulators such PPAR-γ and C/EBP-α were suppressed, whereas TGF-ß signaling and fibrosis markers were upregulated in UHRF1-depleted differentiating adipocytes. Furthermore, UHRF1-depleted cells showed upregulated expression and secretion of TGF-ß1, as well as the glycoprotein GPNMB. Treating differentiating preadipocytes with recombinant GPNMB led to an increase in TGF-ß protein and secretion levels, which was accompanied by an increase in secretion of fibrosis markers such as MMP13 and a reduction in adipogenic conversion potential. Conversely, UHRF1 overexpression studies in human cells demonstrated downregulated levels of GPNMB and TGF-ß, and enhanced adipogenic potential. In conclusion, our data show that UHRF1 positively regulates 3T3-L1 adipogenesis and limits fibrosis by suppressing GPNMB and TGF-ß signaling cascade, highlighting the potential relevance of UHRF1 and its targets to the clinical management of obesity and linked metabolic disorders.

Loss of the TRPM4 channel in humans causes immune dysregulation with defective monocyte migration.

BACKGROUND: TRPM4 is a broadly expressed, calcium-activated, monovalent cation channel that regulates immune cell function in mice and cell lines. Clinically, however, partial loss- or gain-of-function mutations in TRPM4 lead to arrhythmia and heart disease, with no documentation of immunologic disorders. OBJECTIVE: To characterize functional cellular mechanisms underlying the immune dysregulation phenotype in a proband with a mutated TRPM4 gene. METHODS: We employed a combination of biochemical, cell biological, imaging, omics analyses, flow cytometry, and gene editing approaches. RESULTS: We report the first human cases to our knowledge with complete loss of the TRPM4 channel, leading to immune dysregulation with frequent bacterial and fungal infections. Single-cell and bulk RNA sequencing point to altered expression of genes affecting cell migration, specifically in monocytes. Inhibition of TRPM4 in T cells and the THP-1 monocyte cell line reduces migration. More importantly, primary T cells and monocytes from TRPM4 patients migrate poorly. Finally, CRISPR knockout of TRPM4 in THP-1 cells greatly reduces their migration potential. CONCLUSION: Our results demonstrate that TRPM4 plays a critical role in regulating immune cell migration, leading to increased susceptibility to infections.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus.

BACKGROUND: The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS: Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS: We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION: Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study.

INTRODUCTION: Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites. RESEARCH DESIGN AND METHODS: This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin. RESULTS: Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models. CONCLUSIONS: Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Nanoparticle enrichment mass-spectrometry proteomics identifies protein-altering variants for precise pQTL mapping.

Proteogenomics studies generate hypotheses on protein function and provide genetic evidence for drug target prioritization. Most previous work has been conducted using affinity-based proteomics approaches. These technologies face challenges, such as uncertainty regarding target identity, non-specific binding, and handling of variants that affect epitope affinity binding. Mass spectrometry-based proteomics can overcome some of these challenges. Here we report a pQTL study using the Proteograph™ Product Suite workflow (Seer, Inc.) where we quantify over 18,000 unique peptides from nearly 3000 proteins in more than 320 blood samples from a multi-ethnic cohort in a bottom-up, peptide-centric, mass spectrometry-based proteomics approach. We identify 184 protein-altering variants in 137 genes that are significantly associated with their corresponding variant peptides, confirming target specificity of co-associated affinity binders, identifying putatively causal cis-encoded proteins and providing experimental evidence for their presence in blood, including proteins that may be inaccessible to affinity-based proteomics.

Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions.

Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development because they provide genetic evidence to support protein function, suggest relationships between cis- and trans-associated proteins, and link proteins to disease endpoints. Using Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank, we identified 4,248 associations with 2,821 ratios between protein levels (rQTLs). rQTLs were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. Conducting a GWAS on ratios increased the number of discovered genetic signals by 24.7%. The approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large-scale GWAS.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease.

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

Retinal nerve fibre layer thinning and corneal nerve loss in patients with Bardet-Biedl syndrome.

BACKGROUND: Bardet-Biedl syndrome (BBS) is an autosomal recessive, genetically heterogeneous, pleiotropic disorder caused by variants in genes involved in the function of the primary cilium. We have harnessed genomics to identify BBS and ophthalmic technologies to describe novel features of BBS. CASE PRESENTATION: A patient with an unclear diagnosis of syndromic type 2 diabetes mellitus, another affected sibling and unaffected siblings and parents were sequenced using DNA extracted from saliva samples. Corneal confocal microscopy (CCM) and retinal spectral domain optical coherence tomography (SD-OCT) were used to identify novel ophthalmic features in these patients. The two affected individuals had a homozygous variant in C8orf37 (p.Trp185*). SD-OCT and CCM demonstrated a marked and patchy reduction in the retinal nerve fiber layer thickness and loss of corneal nerve fibers, respectively. CONCLUSION: This report highlights the use of ophthalmic imaging to identify novel retinal and corneal abnormalities that extend the phenotype of BBS in a patient with syndromic type 2 diabetes.

Using trials of caloric restriction and bariatric surgery to explore the effects of body mass index on the circulating proteome.

Thousands of proteins circulate in the bloodstream; identifying those which associate with weight and intervention-induced weight loss may help explain mechanisms of diseases associated with adiposity. We aimed to identify consistent protein signatures of weight loss across independent studies capturing changes in body mass index (BMI). We analysed proteomic data from studies implementing caloric restriction (Diabetes Remission Clinical trial) and bariatric surgery (By-Band-Sleeve), using SomaLogic and Olink Explore1536 technologies, respectively. Linear mixed models were used to estimate the effect of the interventions on circulating proteins. Twenty-three proteins were altered in a consistent direction after both bariatric surgery and caloric restriction, suggesting that these proteins are modulated by weight change, independent of intervention type. We also integrated Mendelian randomisation (MR) estimates of the effect of BMI on proteins measured by SomaLogic from a UK blood donor cohort as a third line of causal evidence. These MR estimates provided further corroborative evidence for a role of BMI in regulating the levels of six proteins including alcohol dehydrogenase-4, nogo receptor and interleukin-1 receptor antagonist protein. These results indicate the importance of triangulation in interrogating causal relationships; further study into the role of proteins modulated by weight in disease is now warranted.

Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome.

BACKGROUND: B vitamins such as folate (B9), B6, and B12 are key in one carbon metabolism, which generates methyl donors for DNA methylation. Several studies have linked differential methylation to self-reported intakes of folate and B12, but these estimates can be imprecise, while metabolomic biomarkers can offer an objective assessment of dietary intakes. We explored blood metabolomic biomarkers of folate and vitamins B6 and B12, to carry out epigenome-wide analyses across up to three European cohorts. Associations between self-reported habitual daily B vitamin intakes and 756 metabolites (Metabolon Inc.) were assessed in serum samples from 1064 UK participants from the TwinsUK cohort. The identified B vitamin metabolomic biomarkers were then used in epigenome-wide association tests with fasting blood DNA methylation levels at 430,768 sites from the Infinium HumanMethylation450 BeadChip in blood samples from 2182 European participants from the TwinsUK and KORA cohorts. Candidate signals were explored for metabolite associations with gene expression levels in a subset of the TwinsUK sample (n = 297). Metabolomic biomarker epigenetic associations were also compared with epigenetic associations of self-reported habitual B vitamin intakes in samples from 2294 European participants. RESULTS: Eighteen metabolites were associated with B vitamin intakes after correction for multiple testing (Bonferroni-adj. p < 0.05), of which 7 metabolites were available in both cohorts and tested for epigenome-wide association. Three metabolites - pipecolate (metabolomic biomarker of B6 and folate intakes), pyridoxate (marker of B6 and folate) and docosahexaenoate (DHA, marker of B6) - were associated with 10, 3 and 1 differentially methylated positions (DMPs), respectively. The strongest association was observed between DHA and DMP cg03440556 in the SCD gene (effect = 0.093 ± 0.016, p = 4.07E-09). Pyridoxate, a catabolic product of vitamin B6, was inversely associated with CpG methylation near the SLC1A5 gene promoter region (cg02711608 and cg22304262) and with SLC7A11 (cg06690548), but not with corresponding changes in gene expression levels. The self-reported intake of folate and vitamin B6 had consistent but non-significant associations with the epigenetic signals. CONCLUSION: Metabolomic biomarkers are a valuable approach to investigate the effects of dietary B vitamin intake on the human epigenome.

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Genetic determinants of Vitamin D deficiency in the Middle Eastern Qatari population: a genome-wide association study.

Introduction: Epidemiological studies have consistently revealed that Vitamin D deficiency is most prevalent in Middle Eastern countries. However, research on the impact of genetic loci and polygenic models related to Vitamin D has primarily focused on European populations. Methods: We conducted the first genome-wide association study to identify genetic determinants of Vitamin D levels in Middle Easterners using a whole genome sequencing approach in 6,047 subjects from the Qatar Biobank (QBB) project. We performed a GWAS meta-analysis, combining the QBB cohort with recent European GWAS data from the UK Biobank (involving 345,923 individuals). Additionally, we evaluated the performance of European-derived polygenic risk scores using UK Biobank data in the QBB cohort. Results: Our study identified an association between a variant in a known locus for the group-specific component gene (GC), specifically rs2298850 (p-value = 1.71 × 10-08, Beta = -0.1285), and Vitamin D levels. Furthermore, our GWAS meta-analysis identified two novel variants at a known locus on chromosome 11, rs67609747 and rs1945603, that reached the GWAS significance threshold. Notably, we observed a moderately high heritability of Vitamin D, estimated at 18%, compared to Europeans. Despite the lower predictive performance of Vitamin D levels in Qataris compared to Europeans, the European-derived polygenic risk scores exhibited significant links to Vitamin D deficiency risk within the QBB cohort. Conclusion: This novel study reveals the genetic architecture contributing to Vitamin D deficiency in the Qatari population, emphasizing the genetic heterogeneity across different populations.

Progesterone induces meiosis through two obligate co-receptors with PLA2 activity.

The steroid hormone progesterone (P4) regulates multiple aspects of reproductive and metabolic physiology. Classical P4 signaling operates through nuclear receptors that regulate transcription. In addition, P4 signals through membrane P4 receptors (mPRs) in a rapid nongenomic modality. Despite the established physiological importance of P4 nongenomic signaling, its detailed signal transduction remains elusive. Here, using Xenopus oocyte maturation as a well-established physiological readout of nongenomic P4 signaling, we identify the lipid hydrolase ABHD2 (α/ß hydrolase domain-containing protein 2) as an essential mPRß co-receptor to trigger meiosis. We show using functional assays coupled to unbiased and targeted cell-based lipidomics that ABHD2 possesses a phospholipase A2 (PLA2) activity that requires both P4 and mPRß. This PLA2 activity bifurcates P4 signaling by inducing mPRß clathrin-dependent endocytosis and producing lipid messengers that are G-protein coupled receptors agonists. Therefore, P4 drives meiosis by inducing the ABHD2 PLA2 activity that requires both mPRß and ABHD2 as obligate co-receptors.