Dysfunctional ERG signaling drives pulmonary vascular aging and persistent fibrosis.

Vascular dysfunction is a hallmark of chronic diseases in elderly. The contribution of the vasculature to lung repair and fibrosis is not fully understood. Here, we performed an epigenetic and transcriptional analysis of lung endothelial cells (ECs) from young and aged mice during the resolution or progression of bleomycin-induced lung fibrosis. We identified the transcription factor ETS-related gene (ERG) as putative orchestrator of lung capillary homeostasis and repair, and whose function is dysregulated in aging. ERG dysregulation is associated with reduced chromatin accessibility and maladaptive transcriptional responses to injury. Loss of endothelial ERG enhances paracrine fibroblast activation in vitro, and impairs lung fibrosis resolution in young mice in vivo. scRNA-seq of ERG deficient mouse lungs reveales transcriptional and fibrogenic abnormalities resembling those associated with aging and human lung fibrosis, including reduced number of general capillary (gCap) ECs. Our findings demonstrate that lung endothelial chromatin remodeling deteriorates with aging leading to abnormal transcription, vascular dysrepair, and persistent fibrosis following injury.

Stage-Specific Non-Coding RNA Expression Patterns during In Vitro Human B Cell Differentiation into Antibody Secreting Plasma Cells.

The differentiation of B cells into antibody secreting plasma cells (PCs) is governed by a strict regulatory network that results in expression of specific transcriptomes along the activation continuum. In vitro models yielding significant numbers of PCs phenotypically identical to the in vivo state enable investigation of pathways, metabolomes, and non-coding (ncRNAs) not previously identified. The objective of our study was to characterize ncRNA expression during human B cell activation and differentiation. To achieve this, we used an in vitro system and performed RNA-seq on resting and activated B cells and PCs. Characterization of coding gene transcripts, including immunoglobulin (Ig), validated our system and also demonstrated that memory B cells preferentially differentiated into PCs. Importantly, we identified more than 980 ncRNA transcripts that are differentially expressed across the stages of activation and differentiation, some of which are known to target transcription, proliferation, cytoskeletal, autophagy and proteasome pathways. Interestingly, ncRNAs located within Ig loci may be targeting both Ig and non-Ig-related transcripts. ncRNAs associated with B cell malignancies were also identified. Taken together, this system provides a platform to study the role of specific ncRNAs in B cell differentiation and altered expression of those ncRNAs involved in B cell malignancies.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. RESULTS: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). CONCLUSIONS: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Tissue-specific BMAL1 cistromes reveal that rhythmic transcription is associated with rhythmic enhancer-enhancer interactions.

The mammalian circadian clock relies on the transcription factor CLOCK:BMAL1 to coordinate the rhythmic expression of thousands of genes. Consistent with the various biological functions under clock control, rhythmic gene expression is tissue-specific despite an identical clockwork mechanism in every cell. Here we show that BMAL1 DNA binding is largely tissue-specific, likely because of differences in chromatin accessibility between tissues and cobinding of tissue-specific transcription factors. Our results also indicate that BMAL1 ability to drive tissue-specific rhythmic transcription is associated with not only the activity of BMAL1-bound enhancers but also the activity of neighboring enhancers. Characterization of physical interactions between BMAL1 enhancers and other cis-regulatory regions by RNA polymerase II chromatin interaction analysis by paired-end tag (ChIA-PET) reveals that rhythmic BMAL1 target gene expression correlates with rhythmic chromatin interactions. These data thus support that much of BMAL1 target gene transcription depends on BMAL1 capacity to rhythmically regulate a network of enhancers.