Function and evolution of local repeats in the Firre locus

More than half the human and mouse genomes are comprised of repetitive sequences, such as transposable elements (TEs), which have been implicated in many biological processes. In contrast, much less is known about other repeats, such as local repeats that occur in multiple instances within a given locus in the genome but not elsewhere. Here, we systematically characterize local repeats in the genomic locus of the Firre long noncoding RNA (lncRNA). We find a conserved function for the RRD repeat as a ribonucleic nuclear retention signal that is sufficient to retain an otherwise cytoplasmic mRNA in the nucleus. We also identified a repeat, termed R0, that can function as a DNA enhancer element within the intronic sequences of Firre. Collectively, our data suggest that local repeats can have diverse functionalities and molecular modalities in the Firre locus and perhaps more globally in other lncRNAs

High‐throughput identification of RNA nuclear enrichment sequences

Abstract In the post‐genomic era, thousands of putative noncoding regulatory regions have been identified, such as enhancers, promoters, long noncoding RNAs (lncRNAs), and a cadre of small peptides. These ever‐growing catalogs require high‐throughput assays to test their functionality at scale. Massively parallel reporter assays have greatly enhanced the understanding of noncoding DNA elements en masse. Here, we present a massively parallel RNA assay (MPRNA) that can assay 10,000 or more RNA segments for RNA‐based functionality. We applied MPRNA to identify RNA‐based nuclear localization domains harbored in lncRNAs. We examined a pool of 11,969 oligos densely tiling 38 human lncRNAs that were fused to a cytosolic transcript. After cell fractionation and barcode sequencing, we identified 109 unique RNA regions that significantly enriched this cytosolic transcript in the nucleus including a cytosine‐rich motif. These nuclear enrichment sequences are highly conserved and over‐represented in global nuclear fractionation sequencing. Importantly, many of these regions were independently validated by single‐molecule RNA fluorescence in situ hybridization. Overall, we demonstrate the utility of MPRNA for future investigation of RNA‐based functionalities

A gene expression atlas of embryonic neurogenesis in Drosophila reveals complex spatiotemporal regulation of lncRNAs

Cell type specification during early nervous system development in Drosophila melanogaster requires precise regulation of gene expression in time and space. Resolving the programs driving neurogenesis has been a major challenge owing to the complexity and rapidity with which distinct cell populations arise. To resolve the cell type-specific gene expression dynamics in early nervous system development, we have sequenced the transcriptomes of purified neurogenic cell types across consecutive time points covering crucial events in neurogenesis. The resulting gene expression atlas comprises a detailed resource of global transcriptome dynamics that permits systematic analysis of how cells in the nervous system acquire distinct fates. We resolve known gene expression dynamics and uncover novel expression signatures for hundreds of genes among diverse neurogenic cell types, most of which remain unstudied. We also identified a set of conserved long noncoding RNAs (lncRNAs) that are regulated in a tissue-specific manner and exhibit spatiotemporal expression during neurogenesis with exquisite specificity. lncRNA expression is highly dynamic and demarcates specific subpopulations within neurogenic cell types. Our spatiotemporal transcriptome atlas provides a comprehensive resource for investigating the function of coding genes and noncoding RNAs during crucial stages of early neurogenesis

A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies