Loss-of-function PTPRD mutations lead to increased STAT3 activation and sensitivity to STAT3 inhibition in head and neck cancer

Background Protein tyrosine phosphatase receptor type D (PTPRD) is a putative tumor suppressor in several cancers including head and neck squamous cell carcinoma (HNSCC). STAT3 is a frequently hyperactivated oncogene in HNSCC. As STAT3 is a direct substrate of PTPRD, we sought to determine the genetic or epigenetic alterations of PTPRD that contribute to overactive STAT3 in HNSCC. Methods We analyzed data from The Cancer Genome Atlas (TCGA) and our previous whole-exome sequencing study and summarized the mutation, methylation, and copy number status of PTPRD in HNSCC and other cancers. In vitro studies involved standard transfection and MTT protocols, as well as methylation-specific PCR. Results Our findings indicate that PTPRD mutation, rather thanmethylation or copy number alteration, is the primary mechanism by which PTPRD function is lost in HNSCC.We demonstrate that overexpression of wild-type PTPRD in HNSCC cells significantly inhibits growth and STAT3 activation while PTPRD mutants do not, suggesting thatmutation may lead to loss of function and subsequent hyper-phosphorylation of PTPRD substrates, especially STAT3. Importantly, we determined that HNSCC cells harboring an endogenous PTPRD mutation are more sensitive to STAT3 blockade than PTPRD wild-type cells.We additionally found that PTPRD mRNA expression does not correlate with pSTAT3 expression, suggesting that alterations that manifest through altered mRNA expression, including hypermethylation and gene copy number alterations, do not significantly contribute to STAT3 overactivation in HNSCC. Copyright

Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Correction to: Nature Communications https://doi.org/10.1038/s41467-020-19366-9, published online 5 January 2021. The original version of this Article contained an error in Fig. 2, in which panels a and b were inadvertently swapped. This has now been corrected in the PDF and HTML versions of the Article

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe

Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Correction to: Nature Communications https://doi.org/10.1038/s41467-020-19366-9, published online 5 January 2021. The original version of this Article contained an error in Fig. 2, in which panels a and b were inadvertently swapped. This has now been corrected in the PDF and HTML versions of the Article

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.</p