Single-Cell Transcriptome Profiling of Pancreatic Islets From Early Diabetic Mice Identifies Anxa10 for Ca2+ Allostasis Toward ß-Cell Failure.

Type 2 diabetes is a progressive disorder denoted by hyperglycemia and impaired insulin secretion. Although a decrease in ß-cell function and mass is a well-known trigger for diabetes, the comprehensive mechanism is still unidentified. Here, we performed single-cell RNA sequencing of pancreatic islets from prediabetic and diabetic db/db mice, an animal model of type 2 diabetes. We discovered a diabetes-specific transcriptome landscape of endocrine and nonendocrine cell types with subpopulations of ß- and α-cells. We recognized a new prediabetic gene, Anxa10, that was induced by and regulated Ca2+ influx from metabolic stresses. Anxa10-overexpressed ß-cells displayed suppression of glucose-stimulated intracellular Ca2+ elevation and potassium-induced insulin secretion. Pseudotime analysis of ß-cells predicted that this Ca2+-surge responder cluster would proceed to mitochondria dysfunction and endoplasmic reticulum stress. Other trajectories comprised dedifferentiation and transdifferentiation, emphasizing acinar-like cells in diabetic islets. Altogether, our data provide a new insight into Ca2+ allostasis and ß-cell failure processes. ARTICLE HIGHLIGHTS: The transcriptome of single-islet cells from healthy, prediabetic, and diabetic mice was studied. Distinct ß-cell heterogeneity and islet cell-cell network in prediabetes and diabetes were found. A new prediabetic ß-cell marker, Anxa10, regulates intracellular Ca2+ and insulin secretion. Diabetes triggers ß-cell to acinar cell transdifferentiation.

Th1 cytokine endotype discriminates and predicts severe complications in COVID-19

Treatment of severe and critical cases of coronavirus disease 2019 (COVID-19) is still a top priority in public health. Previously, we reported distinct Th1 cytokines related to the pathophysiology of severe COVID-19 condition. In the present study, we investigated the association of Th1 and Th2 cytokine/chemokine endotypes with cell-mediated immunity via multiplex immunophenotyping, single-cell RNA-Seq analysis of peripheral blood mononuclear cells, and analysis of the clinical features of COVID-19 patients. Based on serum cytokine and systemic inflammatory markers, COVID-19 cases were classified into four clusters of increasing (I-IV) severity. Two prominent clusters were of interest and could be used as prognostic reference for a targeted treatment of severe COVID-19 cases. Cluster III reflected severe/critical pathology and was characterized by decreased in CCL17 levels and increase in IL-6, C-reactive protein CXCL9, IL-18, and IL-10 levels. The second cluster (Cluster II) showed mild to moderate pathology and was characterized by predominated CXCL9 and IL-18 levels, levels of IL-6 and CRP were relatively low. Cluster II patients received anti-inflammatory treatment in early-stage, which may have led prevent disease prognosis which is accompanied to IL-6 and CRP induction. In Cluster III, a decrease in the proportion of effector T cells with signs of T cell exhaustion was observed. This study highlights the mechanisms of endotype clustering based on specific inflammatory markers in related the clinical outcome of COVID-19.

DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.

Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1st- and 2nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%-9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.

Gene expression profile prospectively predicts peritoneal relapse after curative surgery of gastric cancer.

BACKGROUND: Peritoneal relapse is the most common pattern of tumor progression in advanced gastric cancer. Clinicopathological findings are sometimes inadequate for predicting peritoneal relapse. The aim of this study was to identify patients at high risk of peritoneal relapse in a prospective study based on molecular prediction. METHODS: RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly, we constructed a molecular prediction system and validated its robustness and prognostic validity by 500 times multiple validation by repeated random sampling in a retrospective set of 56 (38 relapse-free and 18 peritoneal-relapse) patients. Secondly, we applied this prediction to 85 patients of the prospective set to assess predictive accuracy and prognostic validity. RESULTS: In the retrospective phase, repeated random validation yielded approximately 68% predictive accuracy and a 22-gene expression profile associated with peritoneal relapse was identified. The prediction system identified patients with poor prognosis. In the prospective phase, the molecular prediction yielded 76.9% overall accuracy. Kaplan-Meier analysis of peritoneal-relapse-free survival showed a significant difference between the "good signature group" and "poor signature group" (log-rank p = 0.0017). Multivariate analysis by Cox regression hazards model identified the molecular prediction as the only independent prognostic factor for peritoneal relapse. CONCLUSIONS: Gene expression profile inherent to primary gastric cancer tissues can be useful in prospective prediction of peritoneal relapse after curative surgery, potentially allowing individualized postoperative management to improve the prognosis of patients with advanced gastric cancer.

Prediction of efficacy of anti-TNF biologic agent, infliximab, for rheumatoid arthritis patients using a comprehensive transcriptome analysis of white blood cells.

Introduction of biologics, such as infliximab, to the therapy of rheumatoid arthritis (RA) patients has revolutionized the treatment of this disease. However, biomarkers for predicting the efficacy of the drug at an early phase of treatment for selecting real responders have not been found. We here present predictive markers based on a thorough transcriptome analysis of white blood cells from RA patients. RNA from whole blood cells of consecutive 42 patients before the first infusion was analyzed with microarrays for training studies. Samples from the subsequent 26 consecutive patients were used for a prospective study. We categorized the results into no inflammation and residual inflammation groups using the serum C-reactive protein (CRP) level at 14weeks after the first infusion. The accuracy of prediction in our study was 65.4%.