In Silico Identification and Clinical Validation of a Novel Long Non-Coding RNA/mRNA/miRNA Molecular Network for Potential Biomarkers for Discriminating SARS CoV-2 Infection Severity

Illuminating the pathogenic role of SARS-CoV-2: Insights into competing endogenous RNAs (ceRNAs) regulatory networks

The appearance of SARS-CoV-2 in 2019 triggered a significant economic and health crisis worldwide, with heterogeneous molecular mechanisms that contribute to its development are not yet fully understood. Although substantial progress has been made in elucidating the mechanisms behind SARS-CoV-2 infection and therapy, it continues to rank among the top three global causes of mortality due to infectious illnesses. Non-coding RNAs (ncRNAs), being integral components across nearly all biological processes, demonstrate effective importance in viral pathogenesis. Regarding viral infections, ncRNAs have demonstrated their ability to modulate host reactions, viral replication, and host-pathogen interactions. However, the complex interactions of different types of ncRNAs in the progression of COVID-19 remains understudied. In recent years, a novel mechanism of post-transcriptional gene regulation known as "competing endogenous RNA (ceRNA)" has been proposed. Long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and viral ncRNAs function as ceRNAs, influencing the expression of associated genes by sequestering shared microRNAs. Recent research on SARS-CoV-2 has revealed that disruptions in specific ceRNA regulatory networks (ceRNETs) contribute to the abnormal expression of key infection-related genes and the establishment of distinctive infection characteristics. These findings present new opportunities to delve deeper into the underlying mechanisms of SARS-CoV-2 pathogenesis, offering potential biomarkers and therapeutic targets. This progress paves the way for a more comprehensive understanding of ceRNETs, shedding light on the intricate mechanisms involved. Further exploration of these mechanisms holds promise for enhancing our ability to prevent viral infections and develop effective antiviral treatments.

Non-coding RNAs expression in SARS-CoV-2 infection: pathogenesis, clinical significance, and therapeutic targets

The coronavirus disease 2019 (COVID-19) pandemic has been looming globally for three years, yet the diagnostic and treatment methods for COVID-19 are still undergoing extensive exploration, which holds paramount importance in mitigating future epidemics. Host non-coding RNAs (ncRNAs) display aberrations in the context of COVID-19. Specifically, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) exhibit a close association with viral infection and disease progression. In this comprehensive review, an overview was presented of the expression profiles of host ncRNAs following SARS-CoV-2 invasion and of the potential functions in COVID-19 development, encompassing viral invasion, replication, immune response, and multiorgan deficits which include respiratory system, cardiac system, central nervous system, peripheral nervous system as well as long COVID. Furthermore, we provide an overview of several promising host ncRNA biomarkers for diverse clinical scenarios related to COVID-19, such as stratification biomarkers, prognostic biomarkers, and predictive biomarkers for treatment response. In addition, we also discuss the therapeutic potential of ncRNAs for COVID-19, presenting ncRNA-based strategies to facilitate the development of novel treatments. Through an in-depth analysis of the interplay between ncRNA and COVID-19 combined with our bioinformatic analysis, we hope to offer valuable insights into the stratification, prognosis, and treatment of COVID-19.

Computational Prediction of RNA-RNA Interactions between Small RNA Tracks from Betacoronavirus Nonstructural Protein 3 and Neurotrophin Genes during Infection of an Epithelial Lung Cancer Cell Line: Potential Role of Novel Small Regulatory RNA

Whether RNA-RNA interactions of cytoplasmic RNA viruses, such as Betacoronavirus, might end in the biogenesis of putative virus-derived small RNAs as miRNA-like molecules has been controversial. Even more, whether RNA-RNA interactions of wild animal viruses may act as virus-derived small RNAs is unknown. Here, we address these issues in four ways. First, we use conserved RNA structures undergoing negative selection in the genomes of SARS-CoV, MERS-CoV, and SARS-CoV-2 circulating in different bat species, intermediate animals, and human hosts. Second, a systematic literature review was conducted to identify Betacoronavirus-targeting hsa-miRNAs involved in lung cell infection. Third, we employed sophisticated long-range RNA-RNA interactions to refine the seed sequence homology of hsa-miRNAs with conserved RNA structures. Fourth, we used high-throughput RNA sequencing of a Betacoronavirus-infected epithelial lung cancer cell line (Calu-3) to validate the results. We proposed nine potential virus-derived small RNAs: two vsRNAs in SARS-CoV (Bats: SB-vsRNA-ORF1a-3p; SB-vsRNA-S-5p), one vsRNA in MERS-CoV (Bats: MB-vsRNA-ORF1b-3p), and six vsRNAs in SARS-CoV-2 (Bats: S2B-vsRNA-ORF1a-5p; intermediate animals: S2I-vsRNA-ORF1a-5p; and humans: S2H-vsRNA-ORF1a-5p, S2H-vsRNA-ORF1a-3p, S2H-vsRNA-ORF1b-3p, S2H-vsRNA-ORF3a-3p), mainly encoded by nonstructural protein 3. Notably, Betacoronavirus-derived small RNAs targeted 74 differentially expressed genes in infected human cells, of which 55 upregulate the molecular mechanisms underlying acute respiratory distress syndrome (ARDS), and the 19 downregulated genes might be implicated in neurotrophin signaling impairment. These results reveal a novel small RNA-based regulatory mechanism involved in neuropathogenesis that must be further studied to validate its therapeutic use.

Shared miRNA landscapes of COVID-19 and neurodegeneration confirm neuroinflammation as an important overlapping feature

Introduction

Development and worsening of most common neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, have been associated with COVID-19 However, the mechanisms associated with neurological symptoms in COVID-19 patients and neurodegenerative sequelae are not clear. The interplay between gene expression and metabolite production in CNS is driven by miRNAs. These small non-coding molecules are dysregulated in most common neurodegenerative diseases and COVID-19.

Methods

We have performed a thorough literature screening and database mining to search for shared miRNA landscapes of SARS-CoV-2 infection and neurodegeneration. Differentially expressed miRNAs in COVID-19 patients were searched using PubMed, while differentially expressed miRNAs in patients with five most common neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis) were searched using the Human microRNA Disease Database. Target genes of the overlapping miRNAs, identified with the miRTarBase, were used for the pathway enrichment analysis performed with Kyoto Encyclopedia of Genes and Genomes and Reactome.

Results

In total, 98 common miRNAs were found. Additionally, two of them (hsa-miR-34a and hsa-miR-132) were highlighted as promising biomarkers of neurodegeneration, as they are dysregulated in all five most common neurodegenerative diseases and COVID-19. Additionally, hsa-miR-155 was upregulated in four COVID-19 studies and found to be dysregulated in neurodegeneration processes as well. Screening for miRNA targets identified 746 unique genes with strong evidence for interaction. Target enrichment analysis highlighted most significant KEGG and Reactome pathways being involved in signaling, cancer, transcription and infection. However, the more specific identified pathways confirmed neuroinflammation as being the most important shared feature.

Discussion

Our pathway based approach has identified overlapping miRNAs in COVID-19 and neurodegenerative diseases that may have a valuable potential for neurodegeneration prediction in COVID-19 patients. Additionally, identified miRNAs can be further explored as potential drug targets or agents to modify signaling in shared pathways.

Graphical Abstract

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets

Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studies involving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.

Long Non-Coding RNAs as Cellular Metabolism and Haematopoiesis Regulators

Long non-coding RNAs (lncRNAs) are a category of non-coding RNAs (ncRNAs) that are more than 200 bases long and play major regulatory roles in a wide range of biologic processes, including hematopoeisis and metabolism. Metabolism in cells is an immensely complex process that involves the interconnection and unification of numerous signaling pathways. A growing body of affirmation marks that lncRNAs do participate in metabolism, both directly and indirectly, via metabolic regulation of enzymes and signaling pathways, respectively. The complexities are disclosed by the latest studies demonstrating how lncRNAs could indeed alter tissue-specific metabolism. We have entered a new realm for discovery that is both intimidating and intriguing. Understanding the different functions of lncRNAs in various cellular pathways aids in the advancement of predictive and therapeutic capabilities for a wide variety of myelodysplastic and metabolic disorders. This review has tried to give an overview of the different ncRNAs and their effects on hematopoiesis and metabolism. We have focused on the pathway of action of several lncRNAs and have also delved into their prognostic value. Their use as biomarkers and possible therapeutic targets has also been discussed. SIGNIFICANCE STATEMENT: This review has tried to give an overview of the different ncRNAs and their effects on hematopoiesis and metabolism. The pathway of action of several lncRNAs and their prognostic value was discussed. Their use as biomarkers and possible therapeutic targets has also been elaborated.

Cardiovascular signatures of COVID-19 predict mortality and identify barrier stabilizing therapies

Background

Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe coronavirus disease 2019 (COVID-19), indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted.

Methods

Prospectively evaluating the prevalence of circulating inflammatory, cardiac, and EC activation markers as well as developing a microRNA atlas in 241 unvaccinated patients with suspected SARS-CoV-2 infection allowed for prognostic value assessment using a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred.

Findings

Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction, which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N.

Interpretation

Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis.

Funding Information

This work was directly supported by grant funding from the Ted Rogers Center for Heart Research, Toronto, Ontario, Canada and the Peter Munk Cardiac Center, Toronto, Ontario, Canada.