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The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential despite being trained with less data and with no prior concept of what features define mRNAs. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.

Background The molecular mechanisms behind cerebral aneurysm formation and rupture remain poorly understood. In the past decade, microRNAs (miRNAs) have been shown to be key regulators in a host of biological processes. They are non-coding RNA molecules, approximately 21 nucleotides long, which post-transcriptionally inhibit mRNAs by attenuating protein translation and promoting mRNA degradation. We attempted to profile the miRNA and mRNA interactions and expression levels in cerebral aneurysm tissue from human subjects. Methods We performed a prospective case-control study in human subjects in order to characterize the differential expression of mRNA and miRNA in unruptured cerebral aneurysms compared to control tissue [healthy superficial temporal arteries (STA)]. Ion Torrent was used for deep RNA sequencing. Affymetrix miRNA microarrays were used to analyze miRNA expression, whereas Nanostring nCounter technology was used for validation of identified targets. Results Overall, 7 unruptured cerebral aneurysm and 10 STA specimens were collected. We identified several differentially expressed genes in aneurysm tissue with MMP-13 (Fold change 7.21) and various collagen genes (COL1A1, COL5A1, COL5A2) being among the most upregulated. In addition, multiple miRNAs were significantly differentially expressed, with miR-21 (Fold change 16.97) being the most upregulated, and miR-143-5p (Fold change −11.14) being the most downregulated. From these, miR-21, miR-143, and miR-145 had several significantly anti-correlated target genes in our cohort, associated with smooth muscle cell function, extracellular matrix remodeling, inflammation signaling, and lipid accumulation. All these processes are crucial in the pathophysiology of cerebral aneurysms. Conclusion Our analysis identified differentially expressed genes and miRNAs in unruptured human cerebral aneurysms, suggesting the possibility of a role for miRNAs in aneurysm formation. Further investigation for their importance as therapeutic targets is needed.

The epidermal permeability barrier (EPB) prevents organisms from dehydration and infection. The transcriptional regulation of EPB development is poorly understood. We demonstrate here that transcription factor COUP-TF-interacting protein 1 (CTIP1/BCL11A; hereafter CTIP1) is highly expressed in the developing murine epidermis. Germline deletion of Ctip1 (Ctip1 −/−) results in EPB defects accompanied by compromised epidermal differentiation, drastic reduction in profilaggrin processing, reduced lamellar bodies in granular layers and significantly altered lipid composition. Transcriptional profiling of Ctip1 −/− embryonic skin identified altered expression of genes encoding lipid-metabolism enzymes, skin barrier-associated transcription factors and junctional proteins. CTIP1 was observed to interact with genomic elements within the regulatory region of the gene encoding the differentiation-associated gene, Fos-related antigen2 (Fosl2) and lipid-metabolism-related gene, Fatty acid elongase 4 (Elvol4), and the expression of both was altered in Ctip1 −/− mice. CTIP1 appears to play a role in EPB establishment of via direct or indirect regulation of a subset of genes encoding proteins involved in epidermal differentiation and lipid metabolism. These results identify potential, CTIP1-regulated avenues for treatment of skin disorders involving EBP defects.

Abstract:The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machinedriven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.