MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors

In silico identification of novel pre-microRNA genes in Rift valley fever virus suggest new pathomechanisms for embryo-fetal dysgenesis

MicroRNAs (miRNAs) are small non-coding RNAs that regulate the post-transcriptional expression of target genes. Virus-encoded miRNAs play an important role in the replication of viruses, modulate gene expression in both the virus and host, and affect their persistence and immune evasion in hosts. This renders viral miRNAs as potential targets for therapeutic applications, especially against pathogenic viruses that infect humans and animals. Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic RNA virus that causes severe disease in both humans and livestock. High mortality among newborn lambs and abortion storms are key characteristics of an RVF outbreak. To date, limited information is available on RVFV-derived miRNAs. In this study, computational methods were used to analyse the RVFV genome for putative pre-miRNA genes, which were then analysed for the presence of mature miRNAs. We detected 19 RVFV-encoded miRNAs and identified their potential mRNAs targets in sheep (Ovis aries), the most susceptible host. The identification of significantly enriched O. aries genes in association with RVFV miRNAs will help elucidate the molecular mechanisms underlying RVFV pathogenesis and potentially uncover novel drug targets for RVFV.

Uncovering chikungunya virus-encoded miRNAs and host-specific targeted genes associated with antiviral immune responses: an integrated bioinformatics approach

Chikungunya virus (CHIKV) is a single-stranded RNA virus belonging to the genus Alphavirus and is responsible for causing Chikungunya fever, a type of arboviral fever. Despite extensive research, the pathogenic mechanism of CHIKV within host cells remains unclear. In this study, an in-silico approach was used to predict that CHIKV produces micro-RNAs that target host-specific genes associated with host cellular regulatory pathways. Putative micro-RNAs of CHIKV were predicted using the miRNAFold and Vmir RNA structure web servers, and secondary structure prediction was performed using RNAfold. Host-specific target genes were then predicted, and hub genes were identified using CytoHubba and module selection through MCODE. Functional annotations of hub genes revealed their association with various pathways, including osteoclast differentiation, neuroactive ligand-receptor interaction, and mRNA surveillance. We used the freely available dataset GSE49985 to determine the level of expression of host-specific target genes and found that two genes, F-box and leucine-rich repeat protein 16 (FBXL16) and retinoic acid receptor alpha (RARA), were down-regulated, while four genes, RNA binding protein with serine-rich domain 1 (RNPS1), RNA helicase and ATPase (UPF1), neuropeptide S receptor 1 (NPSR1), and vasoactive intestinal peptide receptor 1 (VIPR1), were up-regulated. These findings provide insight into novel miRNAs and hub genes associated with CHIKV infection and suggest potential targets for therapeutic intervention. Further experimental validation of these targets could lead to the development of effective treatments for CHIKV-mediated diseases.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Hepatitis B Virus and microRNAs: A Bioinformatics Approach

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

Species-specific microRNA discovery and target prediction in the soybean cyst nematode

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).

RNAinsecta: A tool for prediction of precursor microRNA in insects and search for their target in the model organism Drosophila melanogaster

INTRODUCTION AND BACKGROUND

Pre-MicroRNAs are the hairpin loops from which microRNAs are produced that have been found to negatively regulate gene expression in several organisms. In insects, microRNAs participate in several biological processes including metamorphosis, reproduction, immune response, etc. Numerous tools have been designed in recent years to predict novel pre-microRNA using binary machine learning classifiers where prediction models are trained with true and pseudo pre-microRNA hairpin loops. Currently, there are no existing tool that is exclusively designed for insect pre-microRNA detection.

AIM

Application of machine learning algorithms to develop an open source tool for prediction of novel precursor microRNA in insects and search for their miRNA targets in the model insect organism, Drosophila melanogaster.

METHODS

Machine learning algorithms such as Random Forest, Support Vector Machine, Logistic Regression and K-Nearest Neighbours were used to train insect true and false pre-microRNA features with 10-fold Cross Validation on SMOTE and Near-Miss datasets. miRNA targets IDs were collected from miRTarbase and their corresponding transcripts were collected from FlyBase. We used miRanda algorithm for the target searching.

RESULTS

In our experiment, SMOTE performed significantly better than Near-Miss for which it was used for modelling. We kept the best performing parameters after obtaining initial mean accuracy scores >90% of Cross Validation. The trained models on Support Vector Machine achieved accuracy of 92.19% while the Random Forest attained an accuracy of 80.28% on our validation dataset. These models are hosted online as web application called RNAinsecta. Further, searching target for the predicted pre-microRNA in Drosophila melanogaster has been provided in RNAinsecta.

MicroRNAs as powerful tool against COVID-19: Computational perspective

Severe acute respiratory syndrome coronavirus 2 is the virus that is responsible for the current pandemic, COVID-19 (SARS-CoV-2). MiRNAs, a component of RNAi technology, belong to the family of short, noncoding ssRNAs, and may be crucial in the battle against this global threat since they are involved in regulating complex biochemical pathways and may prevent viral proliferation, translation, and host expression. The complicated metabolic pathways are modulated by the activity of many proteins, mRNAs, and miRNAs working together in miRNA-mediated genetic control. The amount of omics data has increased dramatically in recent years. This massive, linked, yet complex metabolic regulatory network data offers a wealth of opportunity for iterative analysis; hence, extensive, in-depth, but time-efficient screening is necessary to acquire fresh discoveries; this is readily performed with the use of bioinformatics. We have reviewed the literature on microRNAs, bioinformatics, and COVID-19 infection to summarize (1) the function of miRNAs in combating COVID-19, and (2) the use of computational methods in combating COVID-19 in certain noteworthy studies, and (3) computational tools used by these studies against COVID-19 in several purposes. This article is categorized under: Infectious Diseases > Computational Models.

In silico analysis of genomic landscape of SARS-CoV-2 and its variant of concerns (Delta and Omicron) reveals changes in the coding potential of miRNAs and their target genes

COVID-19 related morbidities and mortalities are still continued due to the emergence of new variants of SARS-CoV-2. In the last few years, viral miRNAs have been the centre of study to understand the disease pathophysiology. In this work, we aimed to predict the change in coding potential of the viral miRNAs in SARS-CoV-2's VOCs, Delta and Omicron compared to the Reference (Wuhan origin) strain using bioinformatics tools. After ab-intio based screening by the Vmir tool and validation, we retrieved 22, 6, and 6 pre-miRNAs for Reference, Delta, and Omicron. Most of the predicted unique pre-miRNAs of Delta and Omicron were found to be encoded from the terminal and origin of the genomic sequence, respectively. Mature miRNAs identified by MatureBayes from the unique pre-miRNAs were used for target identification using miRDB. A total of 1786, 216, and 143 high-confidence target genes were captured for GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. The GO and KEGG pathways terms analysis revealed the involvement of Delta miRNAs targeted genes in the pathways such as Human cytomegalovirus infection, Breast cancer, Apoptosis, Neurotrophin signaling, and Axon guidance whereas the Sphingolipid signaling pathway was found for the Omicron. Furthermore, we focussed our analysis on target genes that were validated through GEO's (Gene Expression Omnibus) DEGs (Differentially Expressed Genes) dataset, in which FGL2, TNSF12, OGN, GDF11, and BMP11 target genes were found to be down-regulated by Reference miRNAs and YAE1 and RSU1 by Delta. Few genes were also observed to be validated among in up-regulated gene set of the GEO dataset, in which MMP14, TNFRSF21, SGMS1, and TMEM192 were related to Reference whereas ZEB2 was detected in all three strains. This study thus provides an in-silico based analysis that deciphered the unique pre-miRNAs in Delta and Omicron compared to Reference. However, the findings need future wet lab studies for validation.

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion

MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.

Deciphering the role of predicted miRNAs of polyomaviruses in carcinogenesis

Human polyomaviruses are relatively common in the general population. Polyomaviruses maintain a persistent infection after initial infection in childhood, acting as an opportunistic pathogen in immunocompromised populations and their association has been linked to carcinogenesis. A comprehensive understanding of the underlying molecular mechanisms of carcinogenesis in consequence of polyomavirus infection remains elusive. However, the critical role of viral miRNAs and their potential targets in modifying the transcriptome profile of the host remains largely unknown. Polyomavirus-derived miRNAs have the potential to play a substantial role in carcinogenesis. Employing computational approaches, putative viral miRNAs along with their target genes have been predicted and possible roles of the targeted genes in many significant biological processes have been obtained. Polyomaviruses have been observed to target intracellular signal transduction pathways through miRNA-mediated epigenetic regulation, which may contribute to cancer development. In addition, BKPyV-infected human renal cell microarray data was coupled with predicted target genes and analysis of the downregulated genes indicated that viruses target multiple signaling pathways (e.g. MAPK signaling pathway, PI3K-Akt signaling pathway, PPAR signaling pathway) in the host as well as turning off several tumor suppression genes (e.g. FGGY, EPHX2, CACNA2D3, CDH16) through miRNA-induced mechanisms, assuring cell transformation. This study provides a conceptual framework for the underlying molecular mechanisms involved in the course of carcinogenesis upon polyomavirus infection.

Identification of a Novel Small RNA Encoded in the Mouse Urokinase Receptor uPAR Gene (Plaur) and Its Molecular Target Mef2d

Urokinase receptor (uPAR) is a glycosylphosphatidylinositol (GPI)-anchored receptor of urokinase (uPA), which is involved in brain development, nerve regeneration, wound healing and tissue remodeling. We have recently shown that Plaur, which encodes uPAR, is an early response gene in murine brain. Assumingly, diverse functions of Plaur might be attributed to hypothetical, unidentified microRNAs encoded within introns of the Plaur gene. Using a bioinformatic approach we identified novel small RNAs within the Plaur gene and named them Plaur-miR1-3p and Plaur-miR1-5p. We confirmed Plaur-dependent expression of Plaur-miR1-3p and Plaur-miR1-5p in the mouse brain and mouse neuroblastoma Neuro2a cells. Utilizing an in silico MR-microT algorithm in DianaTools we selected two target genes – Mef2d and Emx2 with the highest binding scores to small RNAs selected from identified Plaur-Pre-miR1. Furthermore, sequencing of mouse brain samples for Plaur-miR1-5p target genes revealed two more genes—Nrip3 and Snrnp200. The expression of Emx2, Mef2d, and Snrnp200 in the mouse brain and Mef2d and Snrnp200 in Neuro2a cells correlated with expression of Plaur and small RNAs—Plaur-miR1-3p and Plaur-miR1-5p. Finally, we demonstrated elevated MEF2D protein expression in the mouse brain after Plaur induction and displayed activating effects of Plaur-miR1-5p on Mef2d expression in Neuro2a cells using Luciferase reporter assay. In conclusion, we have identified Plaur-miR1-3p and Plaur-miR1-5p as novel small RNAs encoded in the Plaur gene. This finding expands the current understanding of Plaur function in brain development and functioning.

A Novel miRNA Located in the HER2 Gene Shows an Inhibitory Effect on Wnt Signaling and Cell Cycle Progression

Human epidermal growth factor receptor 2 (HER2) is involved in the development of the majority of cancers. Therefore, it can be a potential target for cancer therapy. It was hypothesized that some of the broad effects of HER2 could be mediated by miRNAs that are probably embedded inside this gene. Here, we predicted and then empirically substantiated the processing and expression of a novel miRNA named HER2-miR1, located in the HER2 gene; transfection of a DNA fragment corresponding to HER2-miR1 precursor sequence (preHER2-miR1) resulted in ~4000-fold elevation of HER2-miR1 mature form in HEK293t cells. Also, the detection of HER2-miR1 in 5637, NT2, and HeLa cell lines confirmed its endogenous production. Following the HER2-miR1 overexpression, TOP/FOP flash assay and RT-qPCR results showed that Wnt signaling pathway was downregulated. Consistently, flow cytometry results revealed that overexpression of HER2-miR1 in Wnt⁺ cell lines (SW480 and HCT116) was ended in G1 arrest, unlike in Wnt⁻ cells (HEK293t). Taking everything into account, our results report the discovery of a novel miRNA that is located within the HER2 gene sequence and has a repressive impact on the Wnt signaling pathway.

Comprehensive computational analysis reveals H5N1 influenza virus-encoded miRNAs and host-specific targets associated with antiviral immune responses and protein binding

H5N1 virus (H5N1V) is highly contagious among birds and it was first detected in humans in 1997 during a poultry outbreak in Hong Kong. As the mechanism of its pathogenesis inside the host is still lacking, in this in-silico study we hypothesized that H5N1V might create miRNAs, which could target the genes associated with host cellular regulatory pathways, thus provide persistent refuge to the virus. Using bioinformatics approaches, several H5N1V produced putative miRNAs as well as the host genes targeted by these miRNAs were found. Functional enrichment analysis of targeted genes revealed their involvement in many biological pathways that facilitate their host pathogenesis. Eventually, the microarray dataset (GSE28166) was analyzed to validate the altered expression level of target genes and found the genes involved in protein binding and adaptive immune responses. This study presents novel miRNAs and their targeted genes, which upon experimental validation could facilitate in developing new therapeutics against H5N1V infection.

Non-Coding RNAs in Tuberculosis Epidemiology: Platforms and Approaches for Investigating the Genome's Dark Matter

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs' synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.

Expression analysis and function of mitochondrial genome-encoded microRNAs

MicroRNAs play a significant role in nuclear and mitochondrial anterograde and retrograde signaling. Most of the miRNAs found inside mitochondria are nuclear genome encoded, with few mitochondrial genome encoded non-coding RNAs have been reported. In this study, we have identified 13 mitochondrial genome-encoded microRNAs (mitomiRs), which were differentially expressed in breast cancer cell lines (MCF-7, MDA-MB-468, and MDA-MB-231), non-malignant breast epithelial cell line (MCF-10A), and normal and breast cancer tissue specimens. We found that mitochondrial DNA depletion and inhibition of mitochondrial transcription leads to reduced expression of mitomiRs in breast cancer cells. MitomiRs physically interact with Ago2, an RNA-induced silencing complex (RISC) protein, in the cytoplasm and inside mitochondria. MitomiRs regulate the expression of both nuclear and mitochondrial transcripts in breast cancer cells. We showed that mitomiR-5 targets PPARGC1A and regulates mtDNA copy number in breast cancer cells. MitomiRs identified in the present study may be a promising tool for expression and functional analysis in patients with a defective mitochondrial phenotype, including cancer and metabolic syndromes.

Characterization of the first microRNA in human CDH1 that affects cell cycle and apoptosis and indicates breast cancers progression

The E-cadherin protein (Cadherin 1, gene: CDH1), a master regulator of the human epithelial homeostasis, contributes to the epithelial-mesenchymal transition (EMT) which confers cell migratory features to the cells. The EMT is central to many pathophysiological changes in cancer. Therefore, a better understanding of this regulatory scenario is beneficial for therapeutic regiments. The CDH1 gene is approximately 100 kbp long and consists of 16 exons with a relatively large second intron. Since none microRNA (miRNA) has been identified in CDH1 up to now we screened the CDH1 gene for promising miRNA hairpin structures in silico. Out of the 27 hairpin structures we identified, one stable RNA fold with a promising sequence motive was selected for experimental verification. The exogenous validation of the hairpin sequence was performed by transfection of HEK293T cells and the mature miRNA sequences could be verified by quantitative polymerase chain reaction. The endogenous expression of the mature miRNA provisionally named CDH1-i2-miR-1 could be confirmed in two normal (HEK293T, HUVEK) and five cancer cell lines (MCF7, MDA-MB-231, SW480, HT-29, A549). The functional characterization by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay showed a suppression of HEK293T cell proliferation. A flow cytometry-based approach showed the ability of CDH1-i2-miR-1 to arrest transfected cells on a G2/M state while annexin staining exemplified an apoptotic effect. BAX and PTEN expression levels were affected following the overexpression with the new miRNA. The in vivo expression level was assessed in 35 breast tumor tissues and their paired nonmalignant marginal part. A fourfold downregulation in the tumor specimens compared to their marginal controls could be observed. It can be concluded that the sequence of the hub gene CDH1 harbors at least one miRNA but eventually even more relevant for the pathophysiology of breast cancer.

Computational Methods for Predicting Mature microRNAs

Tiny single-stranded noncoding RNAs with size 19-27 nucleotides serve as microRNAs (miRNAs), which have emerged as key gene regulators in the last two decades. miRNAs serve as one of the hallmarks in regulatory pathways with critical roles in human diseases. Ever since the discovery of miRNAs, researchers have focused on how mature miRNAs are produced from precursor mRNAs. Experimental methods are faced with notorious challenges in terms of experimental design, since it is time consuming and not cost-effective. Hence, different computational methods have been employed for the identification of miRNA sequences where most of them labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. This chapter provides an update and the current state of the art in this area covering various methods and 15 software suites used for prediction of mature miRNA.

MicroRNA-Mediated Regulation of the Virus Cycle and Pathogenesis in the SARS-CoV-2 Disease

In the last few years, microRNA-mediated regulation has been shown to be important in viral infections. In fact, viral microRNAs can alter cell physiology and act on the immune system; moreover, cellular microRNAs can regulate the virus cycle, influencing positively or negatively viral replication. Accordingly, microRNAs can represent diagnostic and prognostic biomarkers of infectious processes and a promising approach for designing targeted therapies. In the past 18 months, the COVID-19 infection from SARS-CoV-2 has engaged many researchers in the search for diagnostic and prognostic markers and the development of therapies. Although some research suggests that the SARS-CoV-2 genome can produce microRNAs and that host microRNAs may be involved in the cellular response to the virus, to date, not enough evidence has been provided. In this paper, using a focused bioinformatic approach exploring the SARS-CoV-2 genome, we propose that SARS-CoV-2 is able to produce microRNAs sharing a strong sequence homology with the human ones and also that human microRNAs may target viral RNA regulating the virus life cycle inside human cells. Interestingly, all viral miRNA sequences and some human miRNA target sites are conserved in more recent SARS-CoV-2 variants of concern (VOCs). Even if experimental evidence will be needed, in silico analysis represents a valuable source of information useful to understand the sophisticated molecular mechanisms of disease and to sustain biomedical applications.

Identification of putative microRNAs in the complete genome of Mycobacterium avium and their possible interaction with human transcripts

The grievous adversity regarding Mycobacterium avium is its ubiquitous nature. Isolation of the bacteria from drinking water, house dust, and soil, etc., is an alarming issue for the scientific community. The microRNAs are the molecular influencers of gene expression that act during the process of post transcription. A few reports claimed the existence of microRNAs or microRNA-like molecules in the prokaryotic species. Biogenesis of bacterial miRNAs requires their transport into the host cell. Subsequently, the host-encoded enzymes are exerted for the formation of bacterial mature miRNAs and their regulation. In our study, the screening of complete genome of Mycobacterium avium revealed six putative precursor microRNA sequences bearing typical secondary structures. The mature microRNAs were predicted in both arms of the secondary structures. A total of 12 possible mature microRNAs were identified in this study. The likely targets of the predicted mature miRNAs were searched in human 3' UTR. In the human transcriptome, 193 genes were possibly targeted by 12 mature miRNAs of Mycobacterium avium. The essential functionalities of the target genes included signal transduction, immune system, DNA binding, and response to stress.

MMV-db: vaccinomics and RNA-based therapeutics database for infectious hemorrhagic fever-causing mammarenaviruses

The recent viral outbreaks and the current pandemic situation urges us to timely address any emerging viral infections by designing therapeutic strategies. Multi-omics and therapeutic data are of great interest to develop early remedial interventions. This work provides a therapeutic data platform (Mammarenavirus (MMV)-db) for pathogenic mammarenaviruses with potential catastrophic effects on human health around the world. The database integrates vaccinomics and RNA-based therapeutics data for seven human pathogenic MMVs associated with severe viral hemorrhagic fever and lethality in humans. Protein-specific cytotoxic T lymphocytes, B lymphocytes, helper T-cell and interferon-inducing epitopes were mapped using a cluster of immune-omics-based algorithms and tools for the seven human pathogenic viral species. Furthermore, the physiochemical and antigenic properties were also explored to guide protein-specific multi-epitope subunit vaccine for each species. Moreover, highly efficacious RNAs (small Interfering RNA (siRNA), microRNA and single guide RNA (sgRNA)) after extensive genome-based analysis with therapeutic relevance were explored. All the therapeutic RNAs were further classified and listed on the basis of predicted higher efficacy. The online platform (http://www.mmvdb.dqweilab-sjtu.com/index.php) contains easily accessible data sets and vaccine designs with potential utility in further computational and experimental work. Conclusively, the current study provides a baseline data platform to secure better future therapeutic interventions against the hemorrhagic fever causing mammarenaviruses. Database URL: http://www.mmvdb.dqweilab-sjtu.com/index.php.

HantavirusesDB: Vaccinomics and RNA-based therapeutics database for the potentially emerging human respiratory pandemic agents

Hantaviruses are etiological agents of several severe respiratory illnesses in humans and their human-to-human transmission has been reported. To cope with any potential pandemic, this group of viruses needs further research and a data platform. Therefore, herein we developed a database "HantavirusesDB (HVdb)", where genomics, proteomics, immune resource, RNAi based therapeutics and information on the 3D structures of druggable targets of the Orthohantaviruses are provided on a single platform. The database allows the researchers to effectively map the therapeutic strategies by designing multi-epitopes subunit vaccine and RNA based therapeutics. Moreover, the ease of the web interface allow the users to retrieve specific information from the database. Because of the high quality and excellent functionality of the HVdb, therapeutic research of Hantaviruses can be accelerated, and data analysis might be a foundation to design better treatment strategies targeting the hantaviruses. The database is accessible at http://hvdb.dqweilab-sjtu.com/index.php.

Viral MicroRNAs Encoded by Nucleocapsid Gene of SARS-CoV-2 Are Detected during Infection, and Targeting Metabolic Pathways in Host Cells

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.

Identification and host response interaction study of SARS-CoV-2 encoded miRNA-like sequences: an in silico approach

COVID-19, a global pandemic caused by an RNA virus named SARS-CoV-2 has brought the world to a standstill in terms of infectivity, casualty, and commercial plummet. RNA viruses can encode microRNAs (miRNAs) capable of modulating host gene expression, and with that notion, we aimed to predict viral miRNA like sequences of MERS-CoV, SARS-CoV and SARS-CoV-2, analyze sequence reciprocity and investigate SARS-CoV-2 encoded potential miRNA-human genes interaction using bioinformatics tools. In this study, we retrieved 206 SARS-CoV-2 genomes, executed phylogenetic analysis, and the selected reference genome (MT434792.1) exhibited about 99% similarities among the retrieved genomes. We predicted 402, 137, and 85 putative miRNAs of MERS-CoV (NC_019843.3), SARS-CoV (NC_004718.3), and SARS-CoV-2 (MT434792.1) genome, respectively. Sequence similarity was analyzed among 624 miRNAs which revealed that the predicted miRNAs of SARS-CoV-2 share a cluster with the clad of miRNAs from MERS-CoV and SARS-CoV. Only SARS-CoV-2 derived 85 miRNAs were encountered for target prediction and 29 viral miRNAs seemed to target 119 human genes. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis suggested the involvement of respective genes in various pathways and biological processes. Finally, we focused on eight putative miRNAs influencing 14 genes that are involved in the adaptive hypoxic response, neuroinvasion and hormonal regulation, and tumorigenic progression in patients with COVID-19. SARS-CoV-2 encoded miRNAs may cause misexpression of some critical regulators and facilitate viral neuroinvasion, altered hormonal axis, and tumorigenic events in the human host. However, these propositions need validation from future studies.

CytomegaloVirusDb: Multi-omics knowledge database for cytomegaloviruses

Cytomegalovirus infection is a significant health concern and need further exploration in immunologic response mechanisms during primary and reactivated CMV infection. In this work, we evaluated the whole genomes and proteomes of different CMV species and developed an integrated open-access platform, CytomegaloVirusDb, a multi-Omics knowledge database for Cytomegaloviruses. The resource is categorized into the main sections "Genomics," "Proteomics," "Immune response," and "Therapeutics,". The database is annotated with the list of all CMV species included in the study, and available information is freely accessible at http://www.cmvdb.dqweilab-sjtu.com/index.php. Various parameters used in the analysis for each section were primarily based on the whole genome or proteome of each specie. The platform provided datasets are open to access for researchers to obtain CMV species-specific information. This will help further to explore the dynamics of CMV-specific immune response and therapeutics. This platform is a useful resource to aid in advancing research against Cytomegaloviruses.

Novel SARS-CoV-2 encoded small RNAs in the passage to humans

Motivation

The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has recently emerged as the responsible for the pandemic outbreak of the coronavirus disease (COVID-19). This virus is closely related to coronaviruses infecting bats and Malayan pangolins, species suspected to be an intermediate host in the passage to humans. Several genomic mutations affecting viral proteins have been identified, contributing to the understanding of the recent animal-to-human transmission. However, the capacity of SARS-CoV-2 to encode functional putative microRNAs (miRNAs) remains largely unexplored.

Results

We have used deep learning to discover 12 candidate stem-loop structures hidden in the viral protein-coding genome. Among the precursors, the expression of eight mature miRNAs-like sequences was confirmed in small RNA-seq data from SARS-CoV-2 infected human cells. Predicted miRNAs are likely to target a subset of human genes of which 109 are transcriptionally deregulated upon infection. Remarkably, 28 of those genes potentially targeted by SARS-CoV-2 miRNAs are down-regulated in infected human cells. Interestingly, most of them have been related to respiratory diseases and viral infection, including several afflictions previously associated with SARS-CoV-1 and SARS-CoV-2. The comparison of SARS-CoV-2 pre-miRNA sequences with those from bat and pangolin coronaviruses suggests that single nucleotide mutations could have helped its progenitors jumping inter-species boundaries, allowing the gain of novel mature miRNAs targeting human mRNAs. Our results suggest that the recent acquisition of novel miRNAs-like sequences in the SARS-CoV-2 genome may have contributed to modulate the transcriptional reprogramming of the new host upon infection.

Computationally predicted SARS-COV-2 encoded microRNAs target NFKB, JAK/STAT and TGFB signaling pathways

Recently an outbreak that emerged in Wuhan, China in December 2019, spread to the whole world in a short time and killed >1,410,000 people. It was determined that a new type of beta coronavirus called severe acute respiratory disease coronavirus type 2 (SARS-CoV-2) was causative agent of this outbreak and the disease caused by the virus was named as coronavirus disease 19 (COVID19). Despite the information obtained from the viral genome structure, many aspects of the virus-host interactions during infection is still unknown. In this study we aimed to identify SARS-CoV-2 encoded microRNAs and their cellular targets. We applied a computational method to predict miRNAs encoded by SARS-CoV-2 along with their putative targets in humans. Targets of predicted miRNAs were clustered into groups based on their biological processes, molecular function, and cellular compartments using GO and PANTHER. By using KEGG pathway enrichment analysis top pathways were identified. Finally, we have constructed an integrative pathway network analysis with target genes. We identified 40 SARS-CoV-2 miRNAs and their regulated targets. Our analysis showed that targeted genes including NFKB1, NFKBIE, JAK1-2, STAT3-4, STAT5B, STAT6, SOCS1-6, IL2, IL8, IL10, IL17, TGFBR1-2, SMAD2-4, HDAC1-6 and JARID1A-C, JARID2 play important roles in NFKB, JAK/STAT and TGFB signaling pathways as well as cells' epigenetic regulation pathways. Our results may help to understand virus-host interaction and the role of viral miRNAs during SARS-CoV-2 infection. As there is no current drug and effective treatment available for COVID19, it may also help to develop new treatment strategies.

Computational analysis revealed miRNAs produced by Chikungunya virus target genes associated with antiviral immune responses and cell cycle regulation

Chikungunya virus (CHIKV) that causes chikungunya fever, is an alphavirus that belongs to the Togaviridae family containing a single-stranded RNA genome. Mosquitoes of the Aedes species act as the vectors for this virus and can be found in the blood, which can be passed from an infected person to a mosquito through mosquito bites. CHIKV has drawn much attention recently because of its potential of causing an epidemic. As the detailed mechanism of its pathogenesis inside the host system is still lacking, in this in silico research we have hypothesized that CHIKV might create miRNAs, which would target the genes associated with host cellular regulatory pathways, thereby providing the virus with prolonged refuge. Using bioinformatics approaches we found several putative miRNAs produced by CHIKV. Then we predicted the genes of the host targeted by these miRNAs. Functional enrichment analysis of these targeted genes shows the involvement of several biological pathways regulating antiviral immune stimulation, cellular proliferation, and cell cycle, thereby provide themselves with prolonged refuge and facilitate their pathogenesis, which in turn may lead to disease conditions. Finally, we analyzed a publicly available microarray dataset (GSE49985) to determine the altered expression levels of the targeted genes and found genes associated with pathways such as cell differentiation, phagocytosis, T-cell activation, response to cytokine, autophagy, Toll-like receptor signaling, RIG-I like receptor signaling and apoptosis. Our finding presents novel miRNAs and their targeted genes, which upon experimental validation could facilitate in developing new therapeutics to combat CHIKV infection and minimize CHIKV mediated diseases.

SARS-CoV-2 encoded microRNAs are involved in the process of virus infection and host immune response

The outbreak of COVID-19 caused by SARS-CoV-2 is spreading worldwide, with the pathogenesis mostly unclear. Both virus and host-derived microRNA (miRNA) play essential roles in the pathology of virus infection. This study aims to uncover the mechanism for SARS-CoV-2 pathogenicity from the perspective of miRNA. We scanned the SARS-CoV-2 genome for putative miRNA genes and miRNA targets and conducted in vivo experiments to validate the virus-encoded miRNAs and their regulatory role on the putative targets. One of such virus-encoded miRNAs, MR147-3p, was overexpressed that resulted in significantly decreased transcript levels of all of the predicted targets in human,i.e., EXOC7, RAD9A, and TFE3 in the virus-infected cells. The analysis showed that the immune response and cytoskeleton organization are two of the most notable biological processes regulated by the infection-modulated miRNAs. Additionally, the genomic mutation of SARS-CoV-2 contributed to the changed miRNA repository and targets, suggesting a possible role of miRNAs in the attenuated phenotype of SARS-CoV-2 during its evolution. This study provided a comprehensive view of the miRNA-involved regulatory system during SARS-CoV-2 infection, indicating possible antiviral therapeutics against SARS-CoV-2 through intervening miRNA regulation.

Structural and Functional Characteristics of miRNAs in Five Strategic Millet Species and Their Utility in Drought Tolerance

Millets are the strategic food crops in arid and drought-prone ecologies. Millets, by virtue of nature, are very well-adapted to drought conditions and able to produce sustainable yield. Millets have important nutrients that can help prevent micro-nutrient malnutrition. As a result of the adverse effect of climate change and widespread malnutrition, millets have attained a strategic position to sustain food and nutritional security. Although millets can adapt well to the drought ecologies where other cereals fail completely, the yield level is very low under stress. There is a tremendous opportunity to increase the genetic potential of millet crops in dry lands when the genetics of the drought-tolerance mechanism is fully explained. MicroRNAs (miRNAs) are the class of small RNAs that control trait expression. They are part of the gene regulation but little studied in millets. In the present study, novel miRNAs and gene targets were identified from the genomic resources of pearl millet, sorghum, foxtail millet, finger millet, and proso millet through in silico approaches. A total of 1,002 miRNAs from 280 families regulating 23,158 targets were identified using different filtration criteria in five millet species. The unique as well as conserved structural features and functional characteristics of miRNA across millets were explained. About 84 miRNAs were conserved across millets in different species combinations, which explained the evolutionary relationship of the millets. Further, 215 miRNAs controlling 155 unique major drought-responsive genes, transcription factors, and protein families revealed the genetics of drought tolerance that are accumulated in the millet genomes. The miRNAs regulating the drought stress through specific targets or multiple targets showed through a network analysis. The identified genes regulated by miRNA genes could be useful in developing functional markers and used for yield improvement under drought in millets as well as in other crops.

Popular Computational Tools Used for miRNA Prediction and Their Future Development Prospects

MicroRNAs (miRNAs) are 19-24 nucleotide (nt)-long noncoding, single-stranded RNA molecules that play significant roles in regulating the gene expression, growth, and development of plants and animals. From the year that miRNAs were first discovered until the beginning of the twenty-first century, researchers used experimental methods such as cloning and sequencing to identify new miRNAs and their roles in the posttranscriptional regulation of protein synthesis. Later, in the early 2000s, informatics approaches to the discovery of new miRNAs began to be implemented. With increasing knowledge about miRNA, more efficient algorithms have been developed for computational miRNA prediction. The miRNA research community, hoping for greater coverage and faster results, has shifted from cumbersome and expensive traditional experimental approaches to computational approaches. These computational methods started with homology-based comparisons of known miRNAs with orthologs in the genomes of other species; this method could identify a known miRNA in new species. Second-generation sequencing and next-generation sequencing of mRNA at different developmental stages and in specific tissues, in combination with a better search and alignment algorithm, have accelerated the process of predicting novel miRNAs in a particular species. Using the accumulated annotated miRNA sequence information, researchers have been able to design ab initio algorithms for miRNA prediction independent of genome sequence knowledge. Here, the methods recently used for miRNA computational prediction are summarized and classified into the following four categories: homology-based, target-based, scoring-based, and machine-learning-based approaches. Finally, the future developmental directions of miRNA prediction methods are discussed.

Identification of candidate microRNAs from Ostreid herpesvirus-1 (OsHV-1) and their potential role in the infection of Pacific oysters (Crassostrea gigas)

Oyster production is an economic activity of great interest worldwide. Recently, oysters have been suffering significant mortalities from OsHV-1infection, which has resulted in substantial economic loses in several countries around the world. Understanding viral pathogenicity mechanisms is of central importance for the establishment of disease control measures. Thus, the present work aimed to identify and characterize miRNAs from OsHV-1 as well as to predict their target transcripts in the virus and the host. OsHV-1 genome was used for the in silico discovery of pre-miRNAs. Subsequently, viral and host target transcripts of the OsHV-1 miRNAs were predicted according to the base pairing interaction between mature miRNAs and mRNA 3' untranslated regions (UTRs). Six unique pre-miRNAs were found in different regions of the viral genome, ranging in length from 85 to 172 nucleotides. A complex network of self-regulation of viral gene expression mediated by the miRNAs was identified. These sequences also seem to have a broad ability to regulate the expression of host immune-related genes, especially those associated with pathogen recognition. Our results suggest that OsHV-1 encodes miRNAs with important functions in the infection process, inducing self-regulation of viral transcripts, as well as affecting the regulation of Pacific oyster transcripts related to immunity. Understanding the molecular basis of host-pathogen interactions can help mitigate the recurrent events of oyster mass mortalities by OsHV-1 observed worldwide.

An in-silico approach to study the possible interactions of miRNA between human and SARS-CoV2

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The rising epidemics of SARS-CoV2 has globally made a serious concern.

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Thus, understanding the virus-host relationship remains a serious concern.

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Here, we adopted the in-silico approach to picturized the similarities between the miRNAs of SARS-CoV2 genomes and human.

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Further, the assessments and prediction of miRNAs helped us to analyze and understand the mechanism of pathogenesis.

Background

The progressive SARS-CoV2 outbreaks worldwide have evoked global investigation. Despite the numerousin-silico approaches, the virus-host relationship remains a serious concern. MicroRNAs are the small non-coding RNAs that help in regulating gene profiling. The current study utilized miRNA prediction tools along with the PANTHER classification system to demonstrate association and sequence similarities shared between miRNAs of SARS-CoV2 and human host.

Method

An in-silico approach was carried out using Vmir analyzer to predict miRNAs from SARS-CoV2 viral genomes. Predicted miRNAs from SARS-CoV2 viral genomes were used for effective hybridization sequence identification along the nucleotide similarities with human miRNAs from miRbase database. Further, it was proceeded to analyze the gene ontology using miRDB with PANTHER classification.

Result

Based on the prediction and analysis, we have identified 22 potential miRNAs from five genomes of SARS-CoV2 linked with 12 human miRNAs. Analysis of human miRNAs hsa-mir-1267, hsa-mir-1-3p, hsa-mir-5683 were found shared between all the five viral SARS-CoV2 miRNAs. Further, PANTHER classification analyzed the gene-ontology being carried by these associations showed that 44 genes were involved in biological functions that includes genes specific for signaling pathway, immune complex generation, enzyme binding with effective role in the virus-host relationship.

Conclusion

Our analysis concludes that the genes identified in this study can be effective in analyzing the virus-host interaction. It also provides a new direction to understand viral pathogenesis with a probable new way to link, that can be used to understand and relate the miRNAs of the virus to the host conditions.

CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2

In December 2019, the Chinese city of Wuhan was the center of origin of a pneumonia-like disease outbreak with an unknown causative pathogen. The CDC, China, managed to track the source of infection to a novel coronavirus (2019-nCoV; SARS-CoV-2) that shares approximately 79.6% of its genome with SARS-CoV. The World Health Organization (WHO) initially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) and later characterized it as a global pandemic on March 11, 2020. Due to the novel nature of this virus, there is an urgent need for vaccines and therapeutics to control the spread of SARS-CoV-2 and its associated disease, COVID-19. Global efforts are underway to circumvent its further spread and treat COVID-19 patients through experimental vaccine formulations and therapeutic interventions, respectively. In the absence of any effective therapeutics, we have devised h bioinformatics-based approaches to accelerate global efforts in the fight against SARS-CoV-2 and to assist researchers in the initial phase of vaccine and therapeutics development. In this study, we have performed comprehensive meta-analyses and developed an integrative resource, “CoronaVR” (http://bioinfo.imtech.res.in/manojk/coronavr/). Predominantly, we identified potential epitope-based vaccine candidates, siRNA-based therapeutic regimens, and diagnostic primers. The resource is categorized into the main sections “Genomes,” “Epitopes,” “Therapeutics,” and Primers.” The genome section harbors different components, viz, genomes, a genome browser, phylogenetic analysis, codon usage, glycosylation sites, and structural analysis. Under the umbrella of epitopes, sub-divisions, namely cross-protective epitopes, B-cell (linear/discontinuous), T-cell (CD4⁺/CD8⁺), CTL, and MHC binders, are presented. The therapeutics section has different sub-sections like siRNA, miRNAs, and sgRNAs. Further, experimentally confirmed and designed diagnostic primers are earmarked in the primers section. Our study provided a set of shortlisted B-cell and T-cell (CD4⁺ and CD8⁺) epitopes that can be experimentally tested for their incorporation in vaccine formulations. The list of selected primers can be used in testing kits to identify SARS-CoV-2, while the recommended siRNAs, sgRNAs, and miRNAs can be used in therapeutic regimens. We foresee that this resource will help in advancing the research against coronaviruses.

Predicting novel microRNA: a comprehensive comparison of machine learning approaches

MOTIVATION

The importance of microRNAs (miRNAs) is widely recognized in the community nowadays because these short segments of RNA can play several roles in almost all biological processes. The computational prediction of novel miRNAs involves training a classifier for identifying sequences having the highest chance of being precursors of miRNAs (pre-miRNAs). The big issue with this task is that well-known pre-miRNAs are usually few in comparison with the hundreds of thousands of candidate sequences in a genome, which results in high class imbalance. This imbalance has a strong influence on most standard classifiers, and if not properly addressed in the model and the experiments, not only performance reported can be completely unrealistic but also the classifier will not be able to work properly for pre-miRNA prediction. Besides, another important issue is that for most of the machine learning (ML) approaches already used (supervised methods), it is necessary to have both positive and negative examples. The selection of positive examples is straightforward (well-known pre-miRNAs). However, it is difficult to build a representative set of negative examples because they should be sequences with hairpin structure that do not contain a pre-miRNA.

RESULTS

This review provides a comprehensive study and comparative assessment of methods from these two ML approaches for dealing with the prediction of novel pre-miRNAs: supervised and unsupervised training. We present and analyze the ML proposals that have appeared during the past 10 years in literature. They have been compared in several prediction tasks involving two model genomes and increasing imbalance levels. This work provides a review of existing ML approaches for pre-miRNA prediction and fair comparisons of the classifiers with same features and data sets, instead of just a revision of published software tools. The results and the discussion can help the community to select the most adequate bioinformatics approach according to the prediction task at hand. The comparative results obtained suggest that from low to mid-imbalance levels between classes, supervised methods can be the best. However, at very high imbalance levels, closer to real case scenarios, models including unsupervised and deep learning can provide better performance.

Epigenetic Regulator miRNA Pattern Differences Among SARS-CoV, SARS-CoV-2, and SARS-CoV-2 World-Wide Isolates Delineated the Mystery Behind the Epic Pathogenicity and Distinct Clinical Characteristics of Pandemic COVID-19

A detailed understanding of the molecular mechanism of SARS-CoV-2 pathogenesis is still elusive, and there is a need to address its deadly nature and to design effective therapeutics. Here, we present a study that elucidates the interplay between the SARS-CoV and SARS-CoV-2 viruses' and host's miRNAs, an epigenetic regulator, as a mode of pathogenesis; and we explored how the SARS-CoV and SARS-CoV-2 infections differ in terms of their miRNA-mediated interactions with the host and the implications this has in terms of disease complexity. We have utilized computational approaches to predict potential host and viral miRNAs and their possible roles in different important functional pathways. We have identified several putative host antiviral miRNAs that can target the SARS viruses and also predicted SARS viruses-encoded miRNAs targeting host genes. In silico predicted targets were also integrated with SARS-infected human cell microarray and RNA-seq gene expression data. A comparison between the host miRNA binding profiles on 67 different SARS-CoV-2 genomes from 24 different countries with respective country's normalized death count surprisingly uncovered some miRNA clusters, which are associated with increased death rates. We have found that induced cellular miRNAs can be both a boon and a bane to the host immunity, as they have possible roles in neutralizing the viral threat; conversely, they can also function as proviral factors. On the other hand, from over representation analysis, our study revealed that although both SARS-CoV and SARS-CoV-2 viral miRNAs could target broad immune-signaling pathways; only some of the SARS-CoV-2 miRNAs are found to uniquely target some immune-signaling pathways, such as autophagy, IFN-I signaling, etc., which might suggest their immune-escape mechanisms for prolonged latency inside some hosts without any symptoms of COVID-19. Furthermore, SARS-CoV-2 can modulate several important cellular pathways that might lead to the increased anomalies in patients with comorbidities like cardiovascular diseases, diabetes, breathing complications, etc. This might suggest that miRNAs can be a key epigenetic modulator behind the overcomplications amongst the COVID-19 patients. Our results support that miRNAs of host and SARS-CoV-2 can indeed play a role in the pathogenesis which can be further concluded with more experiments. These results will also be useful in designing RNA therapeutics to alleviate the complications from COVID-19.

Roles of Noncoding RNAs in Islet Biology

The discovery that most mammalian genome sequences are transcribed to ribonucleic acids (RNA) has revolutionized our understanding of the mechanisms governing key cellular processes and of the causes of human diseases, including diabetes mellitus. Pancreatic islet cells were found to contain thousands of noncoding RNAs (ncRNAs), including micro-RNAs (miRNAs), PIWI-associated RNAs, small nucleolar RNAs, tRNA-derived fragments, long non-coding RNAs, and circular RNAs. While the involvement of miRNAs in islet function and in the etiology of diabetes is now well documented, there is emerging evidence indicating that other classes of ncRNAs are also participating in different aspects of islet physiology. The aim of this article will be to provide a comprehensive and updated view of the studies carried out in human samples and rodent models over the past 15 years on the role of ncRNAs in the control of α- and β-cell development and function and to highlight the recent discoveries in the field. We not only describe the role of ncRNAs in the control of insulin and glucagon secretion but also address the contribution of these regulatory molecules in the proliferation and survival of islet cells under physiological and pathological conditions. It is now well established that most cells release part of their ncRNAs inside small extracellular vesicles, allowing the delivery of genetic material to neighboring or distantly located target cells. The role of these secreted RNAs in cell-to-cell communication between β-cells and other metabolic tissues as well as their potential use as diabetes biomarkers will be discussed. © 2020 American Physiological Society. Compr Physiol 10:893-932, 2020.

Genome-wide computational prediction of miRNAs in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed target genes involved in pulmonary vasculature and antiviral innate immunity

The current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China threatened humankind worldwide. The coronaviruses contains the largest RNA genome among all other known RNA viruses, therefore the disease etiology can be understood by analyzing the genome sequence of SARS-CoV-2. In this study, we used an ab-intio based computational tool VMir to scan the complete genome of SARS-CoV-2 to predict pre-miRNAs. The potential pre-miRNAs were identified by ViralMir and mature miRNAs were recognized by Mature Bayes. Additionally, predicted mature miRNAs were analysed against human genome by miRDB server to retrieve target genes. Besides that we also retrieved GO (Gene Ontology) terms for pathways, functions and cellular components. We predicted 26 mature miRNAs from genome of SARS-CoV-2 that targets human genes involved in pathways like EGF receptor signaling, apoptosis signaling, VEGF signaling, FGF receptor signaling. Gene enrichment tool analysis and substantial literature evidences suggests role of genes like BMPR2 and p53 in pulmonary vasculature and antiviral innate immunity respectively. Our findings may help research community to understand virus pathogenesis.

NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus

Nipah virus (NiV) is an emerging and priority pathogen from the Paramyxoviridae family with a high fatality rate. It causes various diseases such as respiratory ailments and encephalitis and poses a great threat to humans and livestock. Despite various efforts, there is no approved antiviral treatment available. Therefore, to expedite and assist the research, we have developed an integrative resource NipahVR (http://bioinfo.imtech.res.in/manojk/nipahvr/) for the multi-targeted putative therapeutics and epitopes for NiV. It is structured into different sections, i.e. genomes, codon usage, phylogenomics, molecular diagnostic primers, therapeutics (siRNAs, sgRNAs, miRNAs) and vaccine epitopes (B-cell, CTL, MHC-I and -II binders). Most decisively, potentially efficient therapeutic regimens targeting different NiV proteins and genes were anticipated and projected. We hope this computational resource would be helpful in developing combating strategies against this deadly pathogen. Database URL: http://bioinfo.imtech.res.in/manojk/nipahvr/.

PCV2 Regulates Cellular Inflammatory Responses through Dysregulating Cellular miRNA-mRNA Networks

Porcine circovirus type 2 (PCV2) is closely linked to postweaning multisystemic wasting syndrome (PMWS) and other PCV-associated diseases (PCVADs), which influence the global pig industry. MicroRNAs (miRNAs) are evolutionarily conserved classes of endogenous small non-coding RNA that regulate almost every cellular process. According to our previous transcription study, PCV2 infection causes up-regulation of genes related to inflammation. To reveal the function of miRNAs in PCV2 infection and PCV2-encoded miRNAs, next generation sequencing and data analysis was performed to explore miRNA expression in PCV2-infected cells and non-infected cells. Data analysis found some small RNAs matched the PCV2 genome but PCV2 does not express miRNAs in an in vitro infection (PK-15 cells). More than 297 known and 427 novel miRNAs were identified, of which 44 miRNAs were differently expressed (DE). The pathways of inflammation mediated by chemokine and cytokine signaling pathway (P00031), were more perturbed in PCV2-infected cells than in mock controls. DE miRNAs and DE mRNA interaction network clearly revealed that PCV2 regulates the cellular inflammatory response through dysregulating the cellular miRNA-mRNA network. MiRNA overexpression and down-expression results demonstrated that miRNA dysregulation could affect PCV2 infection-induced cellular inflammatory responses. Our study revealed that host miRNA can be dysregulated by PCV2 infection and play an important role in PCV2-modulated inflammation.

Trends in the development of miRNA bioinformatics tools

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

An Approach to Identify Individual Functional Single Nucleotide Polymorphisms and Isoform MicroRNAs

MicroRNAs (miRNAs) and single nucleotide polymorphisms (SNPs) play important roles in disease risk and development, especially cancer. Importantly, when SNPs are located in pre-miRNAs, they affect their splicing mechanism and change the function of miRNAs. To improve disease risk assessment, we propose an approach and developed a software tool, IsomiR_Find, to identify disease/phenotype-related SNPs and isomiRs in individuals. Our approach is based on the individual's samples, with SNP information extracted from the 1000 Genomes Project. SNPs were mapped to pre-miRNAs based on whole-genome coordinates and then SNP-pre-miRNA sequences were constructed. Moreover, we developed matpred2, a software tool to identify the four splicing sites of mature miRNAs. Using matpred2, we identified isomiRs and then verified them by searching within individual miRNA sequencing data. Our approach yielded biomarkers for biological experiments, mined functions of miRNAs and SNPs, improved disease risk assessment, and provided a way to achieve individualized precision medicine.

Genome-wide hairpins datasets of animals and plants for novel miRNA prediction

This article makes available several genome-wide datasets, which can be used for training microRNA (miRNA) classifiers. The hairpin sequences available are from the genomes of: Homo sapiens, Arabidopsis thaliana, Anopheles gambiae, Caenorhabditis elegans and Drosophila melanogaster. Each dataset provides the genome data divided into sequences and a set of computed features for predictions. Each sequence has one label: i) “positive”: meaning that it is a well-known pre-miRNA, according to miRBase v21; or ii) “unlabeled”: indicating that the sequence has not (yet) a known function and could be a possible candidate to novel pre-miRNA. Due to the fact that selecting an informative feature set is very important for a good pre-miRNA classifier, a representative feature set with large discriminative power has been calculated and it is provided, as well, for each genome. This feature set contains typical information about sequence, topology and structure. Dataset was publically shared in https://sourceforge.net/projects/sourcesinc/files/mirdata/.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

In silico analysis revealed Zika virus miRNAs associated with viral pathogenesis through alteration of host genes involved in immune response and neurological functions

BACKGROUND

The concurrent Zika Virus (ZIKV) outbreaks in the United States and Northeast Brazil have evoked global surveillance. Zika infection has been correlated with severe clinical symptoms, such as microcephaly, Guillain-Barré syndrome, and other congenital brain abnormalities. Recent data suggest that ZIKV predominantly targets neural progenitor cells leading to neurological impairment. Despite the clinical evidence, detailed experimental mechanism of ZIKV neurotropic pathogenesis has not been fully understood yet. Here we hypothesized that ZIKV produces miRNAs, which target essential host genes involved in various cellular pathways facilitating their survival through immune evasion and progression of disease during brain development.

METHODS

From genome sequence information using several bioinformatic tools, we predicted pri-miRNAs, pre-miRNAs, and finally the mature miRNAs produced by ZIKV. We also identified their target genes and performed functional enrichment analysis to identify the biological processes associated with these genes. Finally, we analyzed a publicly available RNA-seq data set to determine the altered expression level of the targeted genes.

RESULTS

From ZIKV genome sequence, we identified and validated 47 putative novel miRNAs. Functional enrichment of the targeted genes demonstrates the involvement of various biological pathways regulating cellular signaling, neurological functions, cancer, and fetal development. The expression analysis of these genes showed that ZIKV-produced miRNAs downregulate the key genes involved in these pathways, which in turn may lead to impaired brain development.

CONCLUSIONS

Our finding proposes novel ZIKV miRNAs and their targets, which upon experimental validation could help developing new therapeutics to combat ZIKV infection and minimize ZIKV-mediated pathologies.

Host and MTB genome encoded miRNA markers for diagnosis of tuberculosis

MicroRNAs (miRNAs) are a class of noncoding RNA molecules which are involved in various cellular and physiological processes. Previously, studies have identified several miRNAs that are potential diagnostic biomarkers for various infectious diseases including tuberculosis. We have performed small RNA sequencing using the Ion Torrent PGM platform in extra pulmonary tuberculosis (EPTB) subject's serum samples to identify circulating miRNAs during mycobacterium tuberculosis (MTB) infection. Our analysis identified 20 circulating miRNAs upregulated and 5 miRNAs downregulated during MTB infection in patient's serum. In addition, we have identified 6 MTB genome encoded miRNAs upregulated in EPTB patient's serum samples. Taqman based qRT-PCR analysis of host-genome encoded (hsa-miR-146a-5p and hsa-miR-125b-5p) and MTB genome encoded (MTB-miR5) miRNAs showed increased expression in a cohort of 52 healthy, pulmonary tuberculosis (PTB) and extra pulmonary tuberculosis (EPTB) patients serum samples. Our study identified for the first time a panel of host and MTB genome specific differentially expressed circulating miRNAs in serum samples of an Indian patient cohort with tuberculosis infection with a potential as a non-invasive diagnostic biomarker for tuberculosis infection.

Computational prediction of microRNAs in marine bacteria of the genus Thalassospira

MicroRNAs (miRNAs) are key players in regulation of gene expression at post-transcription level in eukaryotic cells. MiRNAs have been intensively studied in plants, animals and viruses. The investigations of bacterial miRNAs have gained less attention, except for the recent studies on miRNAs derived from Streptococcus mutans ATCC 25175 and Escherichia coli DH10B. In this study, high-throughput sequencing approach was employed to investigate the miRNA population in bacteria of the genus Thalassospira using both the miRDeep2 and CID-miRNA methods. A total of 984 putative miRNAs were identified in 9 species of the genus Thalassospira using both miRDeep and CID-miRNA analyses. Fifty seven conserved putative miRNAs were found in different species of the genus Thalassospira, and up to 6 miRNAs were found to be present at different locations in the T. alkalitolerans JCM 18968^T, T. lucentensis QMT2^T and T. xianhensis P-4^T. None of the putative miRNAs was found to share sequence to the reported miRNAs in E. coli DH10B and S. mutans ATCC 25175. The findings provide a comprehensive list of computationally identified miRNAs in 9 bacterial species of the genus Thalassospira and addressed the existing knowledge gap on the presence of miRNAs in the Thalassospira genomes.

Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features

The significant role of microRNAs (miRNAs) in various biological processes and diseases has been widely studied and reported in recent years. Several computational methods associated with mature miRNA identification suffer various limitations involving canonical biological features extraction, class imbalance, and classifier performance. The proposed classifier, miRFinder, is an accurate alternative for the identification of mature miRNAs. The structured-sequence features were proposed to precisely extract miRNA biological features, and three algorithms were selected to obtain the canonical features based on the classifier performance. Moreover, the center of mass near distance training based on K-means was provided to improve the class imbalance problem. In particular, the AdaBoost-SVM algorithm was used to construct the classifier. The classifier training process focuses on incorrectly classified samples, and the integrated results use the common decision strategies of the weak classifier with different weights. In addition, the all mature miRNA sites were predicted by different classifiers based on the features of different sites. Compared with other methods, the performance of the classifiers has a high degree of efficacy for the identification of mature miRNAs. MiRFinder is freely available at https://github.com/wangying0128/miRFinder .