Objective To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. Materials and methods We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. Results We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Discussion Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls. Conclusions Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.

Autophagy is essential for maintaining glucose homeostasis, but the mechanism by which energy deprivation activates autophagy is not fully understood. We show that Mec1/ATR, a member of the DNA damage response pathway, is essential for glucose starvation-induced autophagy. Mec1, Atg13, Atg1, and the energy-sensing kinase Snf1 are recruited to mitochondria shortly after glucose starvation. Mec1 is recruited through the adaptor protein Ggc1. Snf1 phosphorylates Mec1 on the mitochondrial surface, leading to recruitment of Atg1 to mitochondria. Furthermore, the Snf1-mediated Mec1 phosphorylation and mitochondrial recruitment of Atg1 are essential for maintaining mitochondrial respiration during glucose starvation, and active mitochondrial respiration is required for energy deprivation-activated autophagy. Thus, formation of a Snf1-Mec1-Atg1 module on mitochondria governs energy deprivation-induced autophagy by regulating mitochondrial respiration.

Keywords: B cell r Hypoxia-inducible factor-1α r Rheumatoid arthritis r Synovial fibroblast r T cell Additional supporting information may be found in the online version of this article at the publisher's web-site

Natural antibodies, particularly natural IgM, are proved to play indispensable roles in the immune defenses against common infections. More recently, the protective roles of these natural IgM were also recognized in autoimmune diseases. They are mainly produced by B-1 and innate-like B cells (ILBs). Human CD19+CD27+IgD+ B cells, also termed as un-switched memory B cells, were proposed to be a kind of ILBs. However, functional features and characteristics of these cells in rheumatoid arthritis (RA) remained poorly understood. In this study, we found that human CD27+IgD+ B cells could produce natural antibody-like IgM. Under RA circumstance, the frequencies of these cells were significantly decreased. Moreover, the IgM-producing capacities of these cells were also dampened. Interestingly, the BCR repertoire of these cells was altered in RA, demonstrating decreased diversity with preferential usage alteration from VH3-23D to VH1-8. Single cell sequencing further revealed the proinflammatory biased features of these cells in RA. These CD27+IgD+ B cells were negatively correlated with RA patient disease activities and clinical manifestations. After effective therapy with disease remission in RA, these cells could be recovered. Taken together, these results have revealed that CD27+IgD+ B cells were impaired in RA with dysfunctional features, which might contribute to the disease perpetuation.