A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution.

RNA-binding proteins (RBPs) are critical host factors for viral infection, however, large scale experimental investigation of the binding landscape of human RBPs to viral RNAs is costly and further complicated due to sequence variation between viral strains. To fill this gap, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP-viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments on more than 100 human RBPs. We evaluated conservation of RBP binding between six other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of mutations from 11 variants of concern on protein-RNA interaction, identifying a set of gain- and loss-of-binding events, as well as predicted the regulatory impact of putative future mutations. Lastly, we linked RBPs to functional, OMICs and COVID-19 patient data from other studies, and identified MBNL1, FTO and FXR2 RBPs as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and open new avenues for therapeutic intervention.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes.

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

SLM2 Is A Novel Cardiac Splicing Factor Involved in Heart Failure due to Dilated Cardiomyopathy.

Alternative mRNA splicing is a fundamental process to increase the versatility of the genome. In humans, cardiac mRNA splicing is involved in the pathophysiology of heart failure. Mutations in the splicing factor RNA binding motif protein 20 (RBM20) cause severe forms of cardiomyopathy. To identify novel cardiomyopathy-associated splicing factors, RNA-seq and tissue-enrichment analyses were performed, which identified up-regulated expression of Sam68-Like mammalian protein 2 (SLM2) in the left ventricle of dilated cardiomyopathy (DCM) patients. In the human heart, SLM2 binds to important transcripts of sarcomere constituents, such as those encoding myosin light chain 2 (MYL2), troponin I3 (TNNI3), troponin T2 (TNNT2), tropomyosin 1/2 (TPM1/2), and titin (TTN). Mechanistically, SLM2 mediates intron retention, prevents exon exclusion, and thereby mediates alternative splicing of the mRNA regions encoding the variable proline-, glutamate-, valine-, and lysine-rich (PEVK) domain and another part of the I-band region of titin. In summary, SLM2 is a novel cardiac splicing regulator with essential functions for maintaining cardiomyocyte integrity by binding to and processing the mRNAs of essential cardiac constituents such as titin.

A concise review on the role of BDNF-AS in human disorders.

Brain-derived neurotrophic factor-antisense (BDNF-AS) is a long non-coding RNA with tens of alternatively spliced variants being transcribed from 11p14 cytogenetic band. As a naturally occurring anti-sense, it regulates expression of BDNF, a factor which as essential roles in the pathoetiology of neurodevelopmental diseases. Notably, BDNF-AS has been reported to be down-regulated in colorectal cancer, osteosarcoma, esophageal cancer, glioblastoma, prostate cancer, cervical cancer and breast cancer. This lncRNA has direct/indirect functional interactions with GSK-3ß, EZH2, miR-214, PABPC1, RAX2, DLG5, p53 and ADAR as well as RNH1/TRIM21/mTOR signaling. In prostate and breast cancers, down-regulation of BDNF-AS has been associated with poor clinical outcome. In the present review, we assessed the existing literature on the role of BDNF-AS in this process and summarized the available data in three distinct sections based of the methodology of experiments and source of expression assays. We also summarized the role of BDNF-AS in non-neoplastic conditions.

Deep neural networks for interpreting RNA-binding protein target preferences.

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.

Relationship of gray values in cone beam computed tomography and bone mineral density obtained by dual energy X-ray absorptiometry.

OBJECTIVES: The aim of this study was to assess the correlation between bone mineral density (BMD) determined with cone beam computed tomography (CBCT) gray values and BMD determined by dual energy X-ray absorptiometry (DEXA). STUDY DESIGN: Women age greater than 50 years requiring CBCT for implant treatment were included in the study. BMD was determined by calculating the mean gray value of CBCT cross-sectional images of anterior, premolar, retromolar, and tuberosity areas of the mandible and maxilla. Patients were then subjected to DEXA of the femoral neck and lumbar spine. Independent t tests, analysis of variance (ANOVA), Pearson's correlation tests, and receiver operating characteristic (ROC) evaluation were used for data analysis. RESULTS: Of 61 asymptomatic patients (mean age 64 years), 47.5% and 55.7% had abnormal BMD, based on the T-scores of the femoral neck and lumbar spine, respectively. Significant correlations were noted between the T-scores of the femoral neck and lumbar spine and the gray values of the maxillary incisor and tuberosity areas. CONCLUSIONS: A strong correlation exists between the CBCT gray values at different sites in the maxilla and the results of DEXA. A gray value less than 298 at the maxillary tuberosity can help distinguish patients with osteoporosis from normal individuals, with 66% to 67% accuracy and suggests the need for DEXA analysis.

The Distance Precision Matrix: computing networks from non-linear relationships.

MOTIVATION: Full-order partial correlation, a fundamental approach for network reconstruction, e.g. in the context of gene regulation, relies on the precision matrix (the inverse of the covariance matrix) as an indicator of which variables are directly associated. The precision matrix assumes Gaussian linear data and its entries are zero for pairs of variables that are independent given all other variables. However, there is still very little theory on network reconstruction under the assumption of non-linear interactions among variables. RESULTS: We propose Distance Precision Matrix, a network reconstruction method aimed at both linear and non-linear data. Like partial distance correlation, it builds on distance covariance, a measure of possibly non-linear association, and on the idea of full-order partial correlation, which allows to discard indirect associations. We provide evidence that the Distance Precision Matrix method can successfully compute networks from linear and non-linear data, and consistently so across different datasets, even if sample size is low. The method is fast enough to compute networks on hundreds of nodes. AVAILABILITY AND IMPLEMENTATION: An R package DPM is available at https://github.molgen.mpg.de/ghanbari/DPM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deciphering human ribonucleoprotein regulatory networks.

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP-RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP-RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

Reconstruction of gene networks using prior knowledge.

BACKGROUND: Reconstructing gene regulatory networks (GRNs) from expression data is a challenging task that has become essential to the understanding of complex regulatory mechanisms in cells. The major issues are the usually very high ratio of number of genes to sample size, and the noise in the available data. Integrating biological prior knowledge to the learning process is a natural and promising way to partially compensate for the lack of reliable expression data and to increase the accuracy of network reconstruction algorithms. RESULTS: In this manuscript, we present PriorPC, a new algorithm based on the PC algorithm. PC algorithm is one of the most popular methods for Bayesian network reconstruction. The result of PC is known to depend on the order in which conditional independence tests are processed, especially for large networks. PriorPC uses prior knowledge to exclude unlikely edges from network estimation and introduces a particular ordering for the conditional independence tests. We show on synthetic data that the structural accuracy of networks obtained with PriorPC is greatly improved compared to PC. CONCLUSION: PriorPC improves structural accuracy of inferred gene networks by using soft priors which assign to edges a probability of existence. It is robust to false prior which is not avoidable in the context of biological data. PriorPC is also fast and scales well for large networks which is important for its applicability to real data.