PARP16 is a tail-anchored endoplasmic reticulum protein required for the PERK- and IRE1α-mediated unfolded protein response

Poly(ADP-ribose) polymerases (PARPs; also known as ADP-ribosyl transferase D proteins) modify acceptor proteins with ADP-ribose modifications of varying length (reviewed in refs 1, 2, 3). PARPs regulate key stress response pathways, including DNA damage repair and the cytoplasmic stress response. Here, we show that PARPs also regulate the unfolded protein response (UPR) of the endoplasmic reticulum (ER). Human PARP16 (also known as ARTD15) is a tail-anchored ER transmembrane protein required for activation of the functionally related ER stress sensors PERK and IRE1α during the UPR. The third identified ER stress sensor, ATF6, is not regulated by PARP16. As is the case for other PARPs that function during stress, the enzymatic activity of PARP16 is upregulated during ER stress when it ADP-ribosylates itself, PERK and IRE1α. ADP-ribosylation by PARP16 is sufficient for activating PERK and IRE1α in the absence of ER stress, and is required for PERK and IRE1α activation during the UPR. Modification of PERK and IRE1α by PARP16 increases their kinase activities and the endonuclease activity of IRE1α. Interestingly, the carboxy-terminal luminal tail of PARP16 is required for PARP16 function during ER stress, suggesting that it transduces stress signals to the cytoplasmic PARP catalytic domain.National Cancer Institute (U.S.) (Cancer Center Support Core Grant P30-CA14051)National Institutes of Health (U.S.) (Grant 5R01 GM087465-02)Kathy and Curt Marble Cancer Research FundJeptha H. and Emily V. Wade FundVirginia and D.K. Ludwig Fund for Cancer Researc

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido

TCF7L2 Polymorphism, Weight Loss and Proinsulin∶Insulin Ratio in the Diabetes Prevention Program

Aims: TCF7L2 variants have been associated with type 2 diabetes, body mass index (BMI), and deficits in proinsulin processing and insulin secretion. Here we sought to test whether these effects were apparent in high-risk individuals and modify treatment responses. Methods: We examined the potential role of the TCF7L2 rs7903146 variant in predicting resistance to weight loss or a lack of improvement of proinsulin processing during 2.5-years of follow-up participants (N = 2,994) from the Diabetes Prevention Program (DPP), a randomized controlled trial designed to prevent or delay diabetes in high-risk adults. Results: We observed no difference in the degree of weight loss by rs7903146 genotypes. However, the T allele (conferring higher risk of diabetes) at rs7903146 was associated with higher fasting proinsulin at baseline (P, 0.001), higher baseline proinsulin: insulin ratio (p<0.0001) and increased proinsulin: insulin ratio over a median of 2.5 years of follow-up (P = 0.003). Effects were comparable across treatment arms. Conclusions: The combination of a lack of impact of the TCF7L2 genotypes on the ability to lose weight, but the presence of a consistent effect on the proinsulin: insulin ratio over the course of DPP, suggests that high-risk genotype carriers at this locus can successfully lose weight to counter diabetes risk despite persistent deficits in insulin production

Association of Type 1 Diabetes vs Type 2 Diabetes Diagnosed During Childhood and Adolescence With Complications During Teenage Years and Young Adulthood

The burden and determinants of complications and comorbidities in contemporary youth-onset diabetes are unknown

Reed_et_al_2019.zip

This .zip package contains data files for the article: Reed et al. 2019. Prairie plant phenology driven more by temperature than moisture in climate manipulations across a latitudinal gradient in the Pacific Northwest, USA. Ecology and Evolution. Please see the README.txt file for a description of each data file