Background: We investigated the association between prenatal GDM exposure and offspring DNA methylation (DNAm) age at 3-10 years of age in the Tianjin GDM Observational Study.
Methods: This study ...included 578 GDM and 578 non-GDM mother-child pairs. Children underwent an exam with anthropometric measurements and blood draw for DNAm analysis (Illumina 850 K array) at a median age of 5.9 years (range 3.1-10.2). DNAm age was calculated using two epigenetic clock algorithms (Horvath and Hannum). The residual resulting from regressing DNAm age on chronological age was used as a metric for age acceleration.
Results: Chronological age was positively correlated with Horvath DNAm age (r = 0.53, p < 0.0001) and Hannum DNAm age (r = 0.38, p < 0.0001). Offspring age acceleration was higher in the GDM group than non-GDM group after adjustment for potential confounders (Horvath: 4.96 months higher, adjusted for sex, pre-pregnancy BMI, cell-type proportions, and technical bias, p = 0.0002; Hannum: 11.2 months higher, adjusted for cell-type proportions and technical bias, p < 0.0001). Increased offspring DNAm age acceleration was associated with increased offspring weight-for-age Z-score, BMI-for-age-Z-score, waist circumference, body fat percentage, subscapular skinfold, suprailiac skinfold, upper-arm circumference, and blood pressure; findings were stronger in the GDM group.
Conclusions: We found that offspring of women with GDM exhibit accelerated epigenetic age compared to control participants, independent of other maternal factors. Epigenetic age in offspring was associated with cardiometabolic risk factors, suggesting that GDM and GDM-associated factors may have long-term effects on offspring epigenetic age and contribute to health outcomes.
Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances ...including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Splicing factor mutations are recurrent genetic alterations in blood disorders, highlighting the importance of alternative splicing regulation in hematopoiesis. Specifically, mutations in splicing ...factor 3B subunit 1 (SF3B1) are implicated in the pathogenesis of myelodysplastic syndromes (MDS) and linked to a high-risk of leukemic transformation in clonal hematopoiesis (CH). SF3B1 mutations are associated with aberrant RNA splicing, leading to increased cryptic 3' splice site (ss) usage and MDS with ring sideroblasts phenotype.
The study of mutant SF3B1-driven splicing aberrations in humans has been hampered by the inability to distinguish mutant and wildtype single cells in patient samples and the inadequate coverage of short-read sequencing over splice junctions. To overcome these limitations, we developed GoT-Splice by integrating Genotyping of Transcriptomes (GoT; Nam et al. 2019) with Nanopore long-read single-cell transcriptome profiling and CITE-seq (Fig. A). This allowed for the simultaneous single-cell profiling of protein and gene expression, somatic mutation status, and alternative splicing. Our method selectively enriched full-length sequencing reads with the accurate structure, enabling the capture of higher number of junctions per cell and greater coverage uniformity vs. short-read sequencing (10x Genomics; Fig. B, C).
We applied GoT-Splice to CD34+ bone marrow progenitor cells from MDS (n = 15,436 cells across 3 patients; VAF: 0.38-0.4) to study how SF3B1 mutations corrupt human hematopoiesis (Fig. D). High-resolution mapping of SF3B1 mut vs. SF3B1 wt hematopoietic progenitors revealed an increasing fitness advantage of SF3B1 mut cells towards the megakaryocytic-erythroid lineage, resulting in an expansion of SF3B1 muterythroid progenitor (EP) cells (Fig. E, F). Accordingly, SF3B1 mutEP cells displayed higher protein expression of erythroid lineage markers, CD71 and CD36, vs. SF3B1 wt cells (Fig. G). In these SF3B1 mutEP cells, we identified up-regulation of genes involved in regulation of cell cycle and checkpoint controls (e.g., CCNE1, TP53), and mRNA translation (eIFs gene family; Fig. H).
Next, while SF3B1 mut cells showed the expected increase of cryptic 3' splicing vs. SF3B1 wt cells (Fig. I), they exhibited distinct cryptic 3' ss usage as a function of hematopoietic progenitor cell identity, displaying stage-specific aberrant splicing during erythroid maturation (Fig. J). In less differentiated EP cells, we observed mis-splicing of genes involved in iron homeostasis, such as the hypoxia-inducible factor HIF1A, and key regulators of erythroid cell growth, such as SEPT2. At later stages, we observed mis-splicing of BAX, a pro-apoptotic member of the Bcl-2 gene family and transcriptional target of p53, and erythroid-specific genes (e.g., PPOX). We further predicted 54% of the aberrantly spliced mRNAs to introduce premature stop codons, promoting RNA degradation through nonsense-mediated decay (NMD). In line with this notion, we observed a significant decrease in expression of NMD-inducing genes in SF3B1 mut vs . SF3B1 wt EP cells (Fig. K).
Lastly, splicing factor mutations observed in CH subjects provide an opportunity to interrogate the downstream impact of SF3B1 mutations prior to development of disease. Like MDS, by applying GoT-splice to CD34+ progenitor cells from SF3B1 mut CH subjects (n = 9,007 cells across 2 subjects; VAF: 0.15-0.22; Fig. L), we revealed increased mutant cell frequency in EP cells (Fig. M) with concomitant increased expression of genes involved in mRNA translation (Fig. N), consistent with SF3B1 mutation causing mis-splicing injury to translational machinery and ineffective erythropoiesis. Notably, CH patients already exhibited cell-type specific cryptic 3' ss usage in SF3B1 mut cells (Fig. O).
In summary, we developed a novel multi-omics single-cell toolkit to examine the impact of splicing factor mutations on cellular fitness directly in human samples. With this approach, we showed that, while SF3B1 mutations arise in uncommitted HSCs, their effect on fitness increases with differentiation into committed EPs, in line with the mutant SF3B1-driven dyserythropoiesis phenotype. We revealed that SF3B1 mutations exert cell-type specific mis-splicing that leads to abnormal erythropoiesis. Finally, we demonstrated that the impact of SF3B1 mutations on EP cells begins before disease onset, as observed in CH subjects.
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Dai: Oxford Nanopore Technologies: Current Employment. Beaulaurier: Oxford Nanopore Technologies: Current Employment. Drong: Oxford Nanopore Technologies: Current Employment. Hickey: Oxford Nanopore Technologies: Current Employment. Juul: Oxford Nanopore Technologies: Current Employment. Wiseman: Astex: Research Funding; Novartis: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy; StemLine: Consultancy. Harrington: Oxford Nanopore Technologies: Current Employment. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy. Abdel-Wahab: H3B Biomedicine: Consultancy, Research Funding; Foundation Medicine Inc: Consultancy; Merck: Consultancy; Prelude Therapeutics: Consultancy; LOXO Oncology: Consultancy, Research Funding; Lilly: Consultancy; AIChemy: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Envisagenics Inc.: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees.
Abstract
Context
As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice.
Objective
Utilizing polygenic risk prediction, we aim to ...identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.
Design, Patients, and Methods
Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS.
Results
The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: “morbid obesity”, “type 2 diabetes”, “hypercholesterolemia”, “disorders of lipid metabolism”, “hypertension”, and “sleep apnea” reaching phenome-wide significance.
Conclusions
Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome–phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
Abstract only Background: Gestational diabetes mellitus (GDM) is a known risk factor for macrosomia, with recent estimates suggesting that between 15-45% of newborns of mothers with GDM are ...macrosomic (vs. 12% in non-GDM mothers). This study’s objectives were to explore associations between both maternal GDM and children’s macrosomia and DNA methylation of eight genes selected based on a literature review of candidates potentially involved in GDM and obesogenic pathways ( IGF1 , IGF2 , H19 , ARHGRF11 , MEST , NR3C1 , Adiponectin , and RETN ). Methods: Data were taken from the Tianjin GDM Observational study, in the 4th-largest city in China; subjects were ages 24-49 and diagnosed with GDM between 2005-2009. Baseline surveys were completed from 2009-2011 for 580 enrolled women-child pairs; an additional 580 women-child pairs without GDM and frequency matched on child sex and birth date were enrolled. We examined 572 GDM cases vs. 573 non-GDM controls; of these 177 children were born with macrosomia (114 to women with GDM, p<0.001). Anthropometric measurements of all enrolled women were completed as part of usual prenatal care; blood draws for DNA methylation analysis (using the Illumina 850K array) were collected from children (median age 5.9 years, range 3.1-10.0). We used logistic regression for all analyses and adjusted for maternal height, age, smoking status, pre-pregnancy overweight/obesity, weight gain during pregnancy, parity, and hypertensive disorders of pregnancy as well as child sex and gestational age at delivery. FDR adjustment was used to correct all candidate gene CpG analyses for multiple testing, with FDR-adjusted P<0.05 considered statistically significant. Results: After analysis of 345 CpGs in eight target genes, one CpG was associated with macrosomia (cg14428359) and one with GDM (cg19466922) at FDR < 0.05; both CpGs were located in the gene MEST (3’ and 5’ untranslated regions, respectively). One additional CpG site in the promoter region of MEST (cg05862114) was associated with both GDM and macrosomia before FDR adjustment. All three CpGs were hypomethylated in both children of GDM mothers and macrosomia cases. Conclusions: MEST is a paternally imprinted gene that is highly expressed in fetal and placental tissue, and believed to play an important role in fetal development. It has also been found to have elevated expression in adipose tissue; epigenetic regulation of MEST may play an important role in the link between GDM and macrosomia.
Polycystic ovary syndrome (PCOS) is a complex multifactorial disorder with a substantial genetic component. However, the clinical manifestations of PCOS are heterogeneous with notable differences ...between lean and obese women, implying a different pathophysiology manifesting in differential body mass index (BMI). We performed a meta-analysis of genome-wide association study (GWAS) data from six well-characterised cohorts, using a case-control study design stratified by BMI, aiming to identify genetic variants associated with lean and overweight/obese PCOS subtypes.
The study comprised 254,588 women (5,937 cases and 248,651 controls) from individual studies performed in Australia, Estonia, Finland, the Netherlands and United States of America, and separated according to three BMI stratifications (lean, overweight and obese). Genome-wide association analyses were performed for each stratification within each cohort, with the data for each BMI group meta-analysed using METAL software. Almost half of the total study population (47%, n = 119,584) were of lean BMI (≤ 25 kg/m
). Two genome-wide significant loci were identified for lean PCOS, led by rs12000707 within DENND1A (P = 1.55 × 10
) and rs2228260 within XBP1 (P = 3.68 × 10
). One additional locus, LINC02905, was highlighted as significantly associated with lean PCOS through gene-based analyses (P = 1.76 × 10
). There were no significant loci observed for the overweight or obese sub-strata when analysed separately, however, when these strata were combined, an association signal led by rs569675099 within DENND1A reached genome-wide significance (P = 3.22 × 10
) and a gene-based association was identified with ERBB4 (P = 1.59 × 10
). Nineteen of 28 signals identified in previous GWAS, were replicated with consistent allelic effect in the lean stratum. There were less replicated signals in the overweight and obese groups, and only 4 SNPs were replicated in each of the three BMI strata.
Genetic variation at the XBP1, LINC02905 and ERBB4 loci were associated with PCOS within unique BMI strata, while DENND1A demonstrated associations across multiple strata, providing evidence of both distinct and shared genetic features between lean and overweight/obese PCOS-affected women. This study demonstrated that PCOS-affected women with contrasting body weight are not only phenotypically distinct but also show variation in genetic architecture; lean PCOS women typically display elevated gonadotrophin ratios, lower insulin resistance, higher androgen levels, including adrenal androgens, and more favourable lipid profiles. Overall, these findings add to the growing body of evidence supporting a genetic basis for PCOS as well as differences in genetic patterns relevant to PCOS BMI-subtype.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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