Viral MicroRNAs Encoded by Nucleocapsid Gene of SARS-CoV-2 Are Detected during Infection, and Targeting Metabolic Pathways in Host Cells.
TL;DR: In this paper, SARS-CoV-2 v-miRNAs were identified by deep sequencing in infected Calu-3 and Vero E6 cell lines and their expression was shown to be positively associated with viral load in COVID-19 patients.
Abstract: MicroRNAs (miRNAs) are critical regulators of gene expression that may be used to identify the pathological pathways influenced by disease and cellular interactions. Viral miRNAs (v-miRNAs) encoded by both DNA and RNA viruses induce immune dysregulation, virus production, and disease pathogenesis. Given the absence of effective treatment and the prevalence of highly infective SARS-CoV-2 strains, improved understanding of viral-associated miRNAs could provide novel mechanistic insights into the pathogenesis of COVID-19. In this study, SARS-CoV-2 v-miRNAs were identified by deep sequencing in infected Calu-3 and Vero E6 cell lines. Among the ~0.1% small RNA sequences mapped to the SARS-CoV-2 genome, the top ten SARS-CoV-2 v-miRNAs (including three encoded by the N gene; v-miRNA-N) were selected. After initial screening of conserved v-miRNA-N-28612, which was identified in both SARS-CoV and SARS-CoV-2, its expression was shown to be positively associated with viral load in COVID-19 patients. Further in silico analysis and synthetic-mimic transfection of validated SARS-CoV-2 v-miRNAs revealed novel functional targets and associations with mechanisms of cellular metabolism and biosynthesis. Our findings support the development of v-miRNA-based biomarkers and therapeutic strategies based on improved understanding of the pathophysiology of COVID-19.
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TL;DR: In this paper, the authors present the recent discoveries regarding the clinical relevance and biological roles of miRNAs in coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2.
36 citations
TL;DR: In this article , the authors present the recent discoveries regarding the clinical relevance and biological roles of miRNAs in coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2.
36 citations
TL;DR: In this article, the authors discuss and evaluate evidence demonstrating the involvement of short and long ncRNAs in severe acute respiratory syndrome coronavirus disease 2019 (COVID-19) and use this information to propose hypotheses for future mechanistic and clinical studies.
Abstract: The highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as the causative agent of coronavirus disease 2019 (COVID-19) in late 2019, igniting an unprecedented pandemic. A mechanistic picture characterising the acute immunopathological disease in severe COVID-19 is developing. Non-coding RNAs (ncRNAs) constitute the transcribed but un-translated portion of the genome and, until recent decades, have been undiscovered or overlooked. A growing body of research continues to demonstrate their interconnected involvement in the immune response to SARS-CoV-2 and COVID-19 development by regulating several of its pathological hallmarks: cytokine storm syndrome, haemostatic alterations, immune cell recruitment, and vascular dysregulation. There is also keen interest in exploring the possibility of host-virus RNA-RNA and RNA-RBP interactions. Here, we discuss and evaluate evidence demonstrating the involvement of short and long ncRNAs in COVID-19 and use this information to propose hypotheses for future mechanistic and clinical studies.
22 citations
TL;DR: In this paper , the authors report the critical miRNA molecule and their regular expression in patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection and associated comorbidities.
Abstract: COVID-19 is a highly transmissible disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), affects 226 countries and continents, and has resulted in >6.2 million deaths worldwide. Despite the efforts of all scientific institutions worldwide to identify potential therapeutics, no specific drug has been approved by the FDA to treat the COVID-19 patient. SARS-CoV-2 variants of concerns make the potential of publicly known therapeutics to respond to and detect disease onset highly improbable. The quest for universal therapeutics pointed to the ability of RNA-based molecules to shield and detect the adverse effects of the COVID-19 illness. One such candidate, miRNA (microRNA), works on regulating the differential expression of the target gene post-transcriptionally. The prime focus of this review is to report the critical miRNA molecule and their regular expression in patients with COVID-19 infection and associated comorbidities. Viral and host miRNAs control the etiology of COVID-19 infection throughout the life cycle and host inflammatory response, where host miRNAs are identified as a double-edged showing as a proviral and antiviral response. The review also covered the role of viral miRNAs in mediating host cell signaling expression during disease pathology. Studying molecular interactions between the host and the SARS-CoV-2 virus during COVID-19 pathogenesis offers the chance to use miRNA-based therapeutics to reduce the severity of the illness. By utilizing an appropriate delivery vehicle, these small non-coding RNA could be envisioned as a promising biomarker in designing a practical RNAi-based treatment approach of clinical significance.
17 citations
TL;DR: This review provides a comprehensive picture of recent advancements in the understanding of the molecular basis and complexity of SARS-CoV-2 protein translation and describes the potential of translational components as targets for therapeutic interventions.
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19, which has broken out worldwide for more than two years. However, due to limited treatment, new cases of infection are still rising. Therefore, there is an urgent need to understand the basic molecular biology of SARS-CoV-2 to control this virus. SARS-CoV-2 replication and spread depend on the recruitment of host ribosomes to translate viral messenger RNA (mRNA). To ensure the translation of their own mRNAs, the SARS-CoV-2 has developed multiple strategies to globally inhibit the translation of host mRNAs and block the cellular innate immune response. This review provides a comprehensive picture of recent advancements in our understanding of the molecular basis and complexity of SARS-CoV-2 protein translation. Specifically, we summarize how this viral infection inhibits host mRNA translation to better utilize translation elements for translation of its own mRNA. Finally, we discuss the potential of translational components as targets for therapeutic interventions.
8 citations
References
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TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
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47,038 citations
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing webbased methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools
18,858 citations
TL;DR: In this article, the authors present an approach for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.
10,798 citations
TL;DR: A unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs is presented.
Abstract: Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
10,056 citations
TL;DR: BWA-MEM automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment, which is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases.
Abstract: Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date.
Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at this http URL.
Contact: hengli@broadinstitute.org
8,090 citations