De novo antibody discovery in human blood from full-length single B cell transcriptomics and matching haplotyped-resolved germline assemblies.

Immunoglobulin (IGH, IGK, IGL) loci in the human genome are highly polymorphic regions that encode the building blocks of the light and heavy chain IG proteins that dimerize to form antibodies. The processes of V(D)J recombination and somatic hypermutation in B cells are responsible for creating an enormous reservoir of highly specific antibodies capable of binding a vast array of possible antigens. However, the antibody repertoire is fundamentally limited by the set of variable (V), diversity (D), and joining (J) alleles present in the germline IG loci. To better understand how the germline IG haplotypes contribute to the expressed antibody repertoire, we combined genome sequencing of the germline IG loci with single-cell transcriptome sequencing of B cells from the same donor. Sequencing and assembly of the germline IG loci captured the IGH locus in a single fully-phased contig where the maternal and paternal contributions to the germline V, D, and J repertoire can be fully resolved. The B cells were collected following a measles, mumps, and rubella (MMR) vaccination, resulting in a population of cells that were activated in response to this specific immune challenge. Single-cell, full-length transcriptome sequencing of these B cells resulted in whole transcriptome characterization of each cell, as well as highly-accurate consensus sequences for the somatically rearranged and hypermutated light and heavy chain IG transcripts. A subset of antibodies synthesized based on their consensus heavy and light chain transcript sequences demonstrated binding to measles antigens and neutralization of measles live virus.

Novel epigenetic link between gestational diabetes mellitus and macrosomia.

Background & objectives: Examine maternal gestational diabetes mellitus (GDM), macrosomia and DNA methylation in candidate genes IGF1, IGF2, H19, ARHGRF11, MEST, NR3C1, ADIPOQ and RETN. Materials & methods: A total of 1145 children (572 GDM cases and 573 controls) from the Tianjin GDM study, including 177 with macrosomia, had blood DNA collection at median age 5.9 (range: 3.1-10.0). We used logistic regression to screen for associations with GDM and model macrosomia on 37 CpGs, and performed mediation analysis. Results: One CpG was associated with macrosomia at false discovery rate (FDR) <0.05 (cg14428359 in MEST); two (cg19466922 in MEST and cg26263166 in IGF2) were associated at p < 0.05 but mediated 26 and 13%, respectively. Conclusion:MEST and IGF2 were previously identified for potential involvement in fetal growth and development (Trial Registration number: NCT01554358 [ClinicalTrials.gov]).

Lay abstract Many women who get gestational diabetes during pregnancy go on to give birth to larger (macrosomic) babies. These babies then grow up to have greater risk of being overweight or obese, and all the health concerns this entails. We sought to examine whether epigenetic factors could help explain this link, by examining the blood of some children whose mothers were enrolled in a gestational diabetes study in China. We identified three sites on two different genes as being associated with both gestational diabetes and macrosomia. The way these genes work suggest a mechanism for how they contribute to macrosomia, providing a promising new avenue for future research, early detection and precision prevention (Trial Registration number: NCT01554358 [ClinicalTrials.gov]).

Prenatal gestational diabetes mellitus exposure and accelerated offspring DNA methylation age in early childhood.

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.

Correction: Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

[This corrects the article DOI: 10.1371/journal.pgen.1007813.].

Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

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.