miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.

Detection of SARS-CoV-2 Derived Small RNAs and Changes in Circulating Small RNAs Associated with COVID-19

Cleavage of double-stranded RNA is described as an evolutionary conserved host defense mechanism against viral infection. Small RNAs are the product and triggers of post transcriptional gene silencing events. Up until now, the relevance of this mechanism for SARS-CoV-2-directed immune responses remains elusive. Herein, we used high throughput sequencing to profile the plasma of active and convalescent COVID-19 patients for the presence of small circulating RNAs. The existence of SARS-CoV-2 derived small RNAs in plasma samples of mild and severe COVID-19 cases is described. Clusters of high siRNA abundance were discovered, homologous to the nsp2 3’-end and nsp4 virus sequence. Four virus-derived small RNA sequences have the size of human miRNAs, and a target search revealed candidate genes associated with ageusia and long COVID symptoms. These virus-derived small RNAs were detectable also after recovery from the disease. The additional analysis of circulating human miRNAs revealed differentially abundant miRNAs, discriminating mild from severe cases. A total of 29 miRNAs were reduced or absent in severe cases. Several of these are associated with JAK-STAT response and cytokine storm.

High Throughput miRNA Screening Identifies miR-574-3p Hyperproductive Effect in CHO Cells

CHO is the cell line of choice for the manufacturing of many complex biotherapeutics. The constant upgrading of cell productivity is needed to meet the growing demand for these life-saving drugs. Manipulation of small non-coding RNAs—miRNAs—is a good alternative to a single gene knockdown approach due to their post-transcriptional regulation of entire cellular pathways without posing translational burden to the production cell. In this study, we performed a high-throughput screening of 2042-human miRNAs and identified several candidates able to increase cell-specific and overall production of Erythropoietin and Etanercept in CHO cells. Some of these human miRNAs have not been found in Chinese hamster cells and yet were still effective in them. We identified miR-574-3p as being able, when overexpressed in CHO cells, to improve overall productivity of Erythropoietin and Etanercept titers from 1.3 to up to 2-fold. In addition, we validated several targets of miR-574-3p and identified p300 as a main target of miR-574-3p in CHO cells. Furthermore, we demonstrated that stable CHO cell overexpressing miRNAs from endogenous CHO pri-miRNA sequences outperform the cells with human pri-miRNA sequences. Our findings highlight the importance of flanking genomic sequences, and their secondary structure features, on pri-miRNA processing offering a novel, cost-effective and fast strategy as a valuable tool for efficient miRNAs engineering in CHO cells.

Description of a CSF-Enriched miRNA Panel for the Study of Neurological Diseases

Background: The study of circulating miRNAs in CSF has gained tremendous attention during the last years, as these molecules might be promising candidates to be used as biomarkers and provide new insights into the disease pathology of neurological disorders. Objective: The main aim of this study was to describe an OpenArray panel of CSF-enriched miRNAs to offer a suitable tool to identify and characterize new molecular signatures in different neurological diseases. Methods: Two hundred and fifteen human miRNAs were selected to be included in the panel, and their expression and abundance in CSF samples were analyzed. In addition, their stability was studied in order to propose suitable endogenous controls for CSF miRNA studies. Results: miR-143-3p and miR-23a-3p were detected in all CSF samples, while another 80 miRNAs were detected in at least 70% of samples. miR-770-5p was the most abundant miRNA in CSF, presenting the lowest mean Cq value. In addition, miR-26b-5p, miR-335-5p and miR-92b-3p were the most stable miRNAs and could be suitable endogenous normalizers for CSF miRNA studies. Conclusions: These OpenArray plates might be a suitable and efficient tool to identify and characterize new molecular signatures in different neurological diseases and would improve the yield of miRNA detection in CSF.

Circular RNA–MicroRNA–MRNA interaction predictions in SARS-CoV-2 infection

Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host–pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.

miRCOVID-19: Potential Targets of Human miRNAs in SARS-CoV-2 for RNA-Based Drug Discovery

Sense-antisense interactions of long and short RNAs in human cells are integral to post-transcriptional gene regulation, in particular that of mRNAs by microRNAs. Many viruses, including severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 (the causative agent of coronavirus disease 2019, COVID-19), have RNA genomes, and interactions between host and viral RNAs, while known to be functional in other viral diseases, have not yet been investigated in COVID-19. To remedy this gap in knowledge, we present miRCOVID-19, a computational meta-analysis framework identifying the predicted binding sites of human microRNAs along the SARS-CoV-2 RNA genome. To highlight the potential relevance of SARS-CoV-2-genome-complementary miRNAs to COVID-19 pathogenesis, we assessed their expression in COVID-19-relevant tissues using public transcriptome data. miRCOVID-19 identified 14 high-confidence mature miRNAs that are highly likely to interact with the SARS-CoV-2 genome and are expressed in diverse respiratory epithelial and immune cell types that are relevant to COVID-19 pathogenesis. As a proof of principle, we have shown that human miR-122, a previously known co-factor of another RNA virus, the hepatitis C virus (HCV) whose genome it binds as a prerequisite for pathogenesis, was predicted to also bind the SARS-CoV-2 RNA genome with high affinity, suggesting the perspective of repurposing anti-HCV RNA-based drugs, such as Miravirsen, to treat COVID-19. Our study is the first to identify all high-confidence binding sites of human miRNAs in the SARS-CoV-2 genome using multiple tools. Our work directly facilitates experimental validation of the reported targets, which would accelerate RNA-based drug discovery for COVID-19 and has the potential to provide new avenues for treating symptomatic COVID-19, and block SARS-CoV-2 replication, in humans.

Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs, there are examples of miRNA like sequences from RNA viruses with regulatory functions. In the case of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with viral replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA-based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Overall, 950 hairpin structured sequences were extracted from the virus genome and based on the prediction results, 29 of them could be precursor miRNAs. Targeting analysis showed that 30 viral mature miRNA-like sequences could target 1,367 different human genes. PANTHER gene function analysis results indicated that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription, metabolism, defense system and several signaling pathways such as Wnt and EGFR signalings. Protein class-based grouping of targeted human genes showed that host transcription might be one of the main targets of the virus since 96 genes involved in transcriptional processes were potential targets of predicted viral miRNAs. For instance, basal transcription machinery elements including several components of human mediator complex (MED1, MED9, MED12L, MED19), basal transcription factors such as TAF4, TAF5, TAF7L and site-specific transcription factors such as STAT1 were found to be targeted. In addition, many known human miRNAs appeared to be able to target viral genes involved in viral life cycle such as S, M, N, E proteins and ORF1ab, ORF3a, ORF8, ORF7a and ORF10. Considering the fact that miRNA-based therapies have been paid attention, based on the findings of this study, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.

Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression that have been found in more than 200 diverse organisms. Although it is still not fully established if RNA viruses could generate miRNAs that would target their own genes or alter the host gene expression, there are examples of miRNAs functioning as an antiviral defense mechanism. In the case of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there are several mechanisms that would make miRNAs impact the virus, like interfering with replication, translation and even modulating the host expression. In this study, we performed a machine learning based miRNA prediction analysis for the SARS-CoV-2 genome to identify miRNA-like hairpins and searched for potential miRNA – based interactions between the viral miRNAs and human genes and human miRNAs and viral genes. Our PANTHER gene function analysis results indicate that viral derived miRNA candidates could target various human genes involved in crucial cellular processes including transcription. For instance, a transcriptional regulator, STAT1 and transcription machinery might be targeted by virus-derived miRNAs. In addition, many known human miRNAs appear to be able to target viral genes. Considering the fact that miRNA-based therapies have been successful before, comprehending mode of actions of miRNAs and their possible roles during SARS-CoV-2 infections could create new opportunities for the development and improvement of new therapeutics.