Phospholipase D Family Member 4, a Transmembrane Glycoprotein with No Phospholipase D Activity, Expression in Spleen and Early Postnatal Microglia

BACKGROUND: Phospholipase D (PLD) catalyzes conversion of phosphatidylcholine into choline and phosphatidic acid, leading to a variety of intracellular signal transduction events. Two classical PLDs, PLD1 and PLD2, contain phosphatidylinositide-binding PX and PH domains and two conserved His-x-Lys-(x)(4)-Asp (HKD) motifs, which are critical for PLD activity. PLD4 officially belongs to the PLD family, because it possesses two HKD motifs. However, it lacks PX and PH domains and has a putative transmembrane domain instead. Nevertheless, little is known regarding expression, structure, and function of PLD4. METHODOLOGY/PRINCIPAL FINDINGS: PLD4 was analyzed in terms of expression, structure, and function. Expression was analyzed in developing mouse brains and non-neuronal tissues using microarray, in situ hybridization, immunohistochemistry, and immunocytochemistry. Structure was evaluated using bioinformatics analysis of protein domains, biochemical analyses of transmembrane property, and enzymatic deglycosylation. PLD activity was examined by choline release and transphosphatidylation assays. Results demonstrated low to modest, but characteristic, PLD4 mRNA expression in a subset of cells preferentially localized around white matter regions, including the corpus callosum and cerebellar white matter, during the first postnatal week. These PLD4 mRNA-expressing cells were identified as Iba1-positive microglia. In non-neuronal tissues, PLD4 mRNA expression was widespread, but predominantly distributed in the spleen. Intense PLD4 expression was detected around the marginal zone of the splenic red pulp, and splenic PLD4 protein recovered from subcellular membrane fractions was highly N-glycosylated. PLD4 was heterologously expressed in cell lines and localized in the endoplasmic reticulum and Golgi apparatus. Moreover, heterologously expressed PLD4 proteins did not exhibit PLD enzymatic activity. CONCLUSIONS/SIGNIFICANCE: Results showed that PLD4 is a non-PLD, HKD motif-carrying, transmembrane glycoprotein localized in the endoplasmic reticulum and Golgi apparatus. The spatiotemporally restricted expression patterns suggested that PLD4 might play a role in common function(s) among microglia during early postnatal brain development and splenic marginal zone cells

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Sample Complexity of Learning Multi-value Opinions in Social Networks

We consider how many users we need to query in order to estimate the extent to which multi-value opinions (information) have propagated in a social network. For example, if the launch date of a new product has changed many times, the company might want to know to which people the most current information has reached. In the propagation model we consider, the social network is represented as a directed graph, and an agent (node) updates its state if it receives a stronger opinion (updated information) and then forwards the opinion in accordance with the direction of its edges. Previous work evaluated opinion propagation in a social network by using the probably approximately correct (PAC) learning framework and considered only binary opinions. In general, PAC learnability, i.e., the finiteness of the number of samples needed, is not guaranteed when generalizing from a binary-value model to a multi-value model. We show that the PAC learnability of multi-value opinions propagating in a social network. We first prove that the number of samples needed in a multi-opinion model is sufficient for (k−1)log(k−1) times the number of samples needed in a binary-opinion model, when k (≥3) is the number of opinions. We next prove that the upper and lower bounds on the number of samples needed to learn a multi-opinion model can be determined from the Natarajan dimension, which is a generalization of the Vapnik-Chervonenkis dimension

Sample Complexity ofLearning Multi-value Opinions inSocial Networks

We consider how many users we need to query in order to estimate the extent to which multi-value opinions (information) have propagated in a social network. For example, if the launch date of a new product has changed many times, the company might want to know to which people the most current information has reached. In the propagation model we consider, the social network is represented as a directed graph, and an agent (node) updates its state if it receives a stronger opinion (updated information) and then forwards the opinion in accordance with the direction of its edges. Previous work evaluated opinion propagation in a social network by using the probably approximately correct (PAC) learning framework and considered only binary opinions. In general, PAC learnability, i.e., the finiteness of the number of samples needed, is not guaranteed when generalizing from a binary-value model to a multi-value model. We show that the PAC learnability of multi-value opinions propagating in a social network. We first prove that the number of samples needed in a multi-opinion model is sufficient for (k−1)log(k−1) times the number of samples needed in a binary-opinion model, when k (≥3) is the number of opinions. We next prove that the upper and lower bounds on the number of samples needed to learn a multi-opinion model can be determined from the Natarajan dimension, which is a generalization of the Vapnik-Chervonenkis dimension

Indium-Catalyzed Annulation of o-Acylanilines with Alkoxyheteroarenes: Synthesis of Heteroaryl[b]quinolines and Subsequent Transformation to Cryptolepine Derivatives

We disclose herein the first synthetic method that is capable of offering heteroaryl[b]quinolines (HA[b]Qs) with structural diversity, which include tricyclic and tetracyclic structures with (benzo)thienyl, (benzo)furanyl, and indolyl rings. The target HA[b]Q is addressed by the annulation of o-acylanilines and MeO–heteroarenes with the aid of an indium Lewis acid that effectively works to make two different types of the N–C and C–C bonds in one batch. A series of indolo[3,2-b]quinolines prepared here can be subsequently transformed to structurally unprecedented cryptolepine derivatives. Mechanistic studies showed that the N–C bond formation is followed by the C–C bond formation. The indium-catalyzed annulation reaction thus starts with the nucleophilic attack of the NH2 group of o-acylanilines to the MeO-connected carbon atom of the heteroaryl ring in an SNAr fashion, and thereby the N–C bond is formed. The resulting intermediate then cyclizes to make the C–C bond through the nucleophilic attack of the heteroaryl-ring-based carbon atom to the carbonyl carbon atom, providing the HA[b]Q after aromatizing dehydration

Flexible Reward Plans to Elicit Truthful Predictions in Crowdsourcing

We develop a flexible reward plan to elicit truthful predictive probability distribution over a set of uncertain events from workers.  In our reward plan, the principal can assign rewards for incorrect predictions according to her similarity between events.  In the spherical proper scoring rule, a worker's expected utility is represented as the inner product of her truthful predictive probability and her declared probability. We generalize the inner product by introducing a reward matrix that defines a reward for each prediction-outcome pair. We show that if the reward matrix is symmetric and positive definite, the spherical proper scoring rule guarantees the maximization of a worker's expected utility when she truthfully declares her prediction