Noninvasive Fetal Genotyping by Droplet Digital PCR to Identify Maternally Inherited Monogenic Diabetes Variants

Background: Babies of women with heterozygous pathogenic glucokinase (GCK) variants causing mild fasting hyperglycemia are at risk of macrosomia if they do not inherit the variant. Conversely, babies who inherit a pathogenic hepatocyte nuclear factor 4α (HNF4A) diabetes variant are at increased risk of high birth weight. Noninvasive fetal genotyping for maternal pathogenic variants would inform pregnancy management. Methods: Droplet digital PCR was used to quantify reference and variant alleles in cell-free DNA extracted from blood from 38 pregnant women heterozygous for a GCK or HNF4A variant and to determine fetal fraction by measurement of informative maternal and paternal variants. Droplet numbers positive for the reference/alternate allele together with the fetal fraction were used in a Bayesian analysis to derive probability for the fetal genotype. The babies' genotypes were ascertained postnatally by Sanger sequencing. Results: Droplet digital PCR assays for GCK or HNF4A variants were validated for testing in all 38 pregnancies. Fetal fraction of ≥2% was demonstrated in at least 1 cell-free DNA sample from 33 pregnancies. A threshold of ≥0.95 for calling homozygous reference genotypes and ≤0.05 for heterozygous fetal genotypes allowed correct genotype calls for all 33 pregnancies with no false-positive results. In 30 of 33 pregnancies, a result was obtained from a single blood sample. Conclusions: This assay can be used to identify pregnancies at risk of macrosomia due to maternal monogenic diabetes variants.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.A.T. Hattersley and M.H. Shepherd are supported by the NIHR Exeter Clinical Research Facility, which is a partnership between the University of Exeter Medical School College of Medicine and Health, and Royal Devon and Exeter NHS Foundation Trust

Simultaneously monitoring immune response and microbial infections during pregnancy through plasma cfRNA sequencing

Plasma cell-free RNA (cfRNA) encompasses a broad spectrum of RNA species that can be derived from both human cells and microbes. Because cfRNA is fragmented and of low concentration, it has been challenging to profile its transcriptome using standard RNA-seq methods.We assessed several recently developed RNA-seq methods on cfRNA samples. We then analyzed the dynamic changes of both the human transcriptome and the microbiome of plasma during pregnancy from 60 women.cfRNA reflects a well-orchestrated immune modulation during pregnancy: an up-regulation of antiinflammatory genes and an increased abundance of antimicrobial genes. We observed that the plasma microbiome remained relatively stable during pregnancy. The bacteria Ureaplasma shows an increased prevalence and increased abundance at postpartum, which is likely to be associated with postpartum infection. We demonstrated that cfRNA-seq can be used to monitor viral infections. We detected a number of human pathogens in our patients, including an undiagnosed patient with a high load of human parvovirus B19 virus (B19V), which is known to be a potential cause of complications in pregnancy.Plasma cfRNA-seq demonstrates the potential to simultaneously monitor immune response and microbial infections during pregnancy

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases