Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs.

DNA-methyltransferase inhibitors (DNMTis), such as azacitidine and decitabine, are used clinically to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Decitabine activates the transcription of endogenous retroviruses (ERVs), which can induce immune response by acting as cellular double-stranded RNAs (dsRNAs). Yet, the posttranscriptional regulation of ERV dsRNAs remains uninvestigated. Here, we find that the viral mimicry and subsequent cell death in response to decitabine require the dsRNA-binding protein Staufen1 (Stau1). We show that Stau1 directly binds to ERV RNAs and stabilizes them in a genome-wide manner. Furthermore, Stau1-mediated stabilization requires a long noncoding RNA TINCR, which enhances the interaction between Stau1 and ERV RNAs. Analysis of a clinical patient cohort reveals that MDS and AML patients with lower Stau1 and TINCR expressions exhibit inferior treatment outcomes to DNMTi therapy. Overall, our study reveals the posttranscriptional regulatory mechanism of ERVs and identifies the Stau1-TINCR complex as a potential target for predicting the efficacy of DNMTis and other drugs that rely on dsRNAs.

GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning.

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6 pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.1.

High-Intensity Aerobic Exercise Suppresses Cancer Growth by Regulating Skeletal Muscle-Derived Oncogenes and Tumor Suppressors.

High-intensity aerobic exercise (90% of the maximal heart rate) can effectively suppress cancer cell proliferation in vivo. However, the molecular effects of exercise and its relevance to cancer prevention remain uninvestigated. In this study, mice with colorectal cancer were subjected to high-intensity aerobic exercise, and mRNA-seq analysis was performed on the heart, lungs, and skeletal muscle tissues to analyze the genome-wide molecular effects of exercise. The skeletal muscle-derived genes with exercise-dependent differential expression were further evaluated for their effects on colorectal cancer cell viability. Compared to the results obtained for the control groups (healthy and cancer with no exercise), the regular and high-intensity aerobic physical activity in the mice produced positive results in comprehensive parameters (i.e., food intake, weight gain, and survival rate). A heatmap of differentially expressed genes revealed markedly different gene expression patterns among the groups. RNA-seq analysis of 23,282 genes expressed in the skeletal muscle yielded several anticancer effector genes (e.g., Trim63, Fos, Col1a1, and Six2). Knockdown and overexpression of selected anticancer genes repressed CT26 murine colorectal carcinoma cell proliferation by 20% (p < 0.05). Our findings, based on the aerobic exercise cancer mouse model, suggest that high-intensity aerobic exercise results in a comprehensive change in the expression patterns of genes, particularly those that can affect cancer cell viability. Such an approach may identify key exercise-regulated genes that can help the body combat cancer.