Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection

Characterization of SARS-CoV-2 Entry Genes in Skeletal Muscle and Impacts of In Vitro Versus In Vivo Infection

BACKGROUND

COVID-19 has been associated with both respiratory (diaphragm) and non-respiratory (limb) muscle atrophy. It is unclear if SARS-CoV-2 infection of skeletal muscle plays a role in these changes. This study sought to: 1) determine if cells comprising skeletal muscle tissue, particularly myofibres, express the molecular components required for SARS-CoV-2 infection; 2) assess the capacity for direct SARS-CoV-2 infection and its impact on atrophy pathway genes in myogenic cells; and 3) in an animal model of COVID-19, examine the relationship between viral infection of skeletal muscle and myofibre atrophy within the diaphragm and limb muscles.

METHODS

We used in silico bioinformatics analysis of published human single cell RNA-seq datasets, as well as direct qPCR examination of human myotubes and diaphragm biopsies, to assess expression of key genes involved in SARS-CoV-2 cellular entry. In Vitro, we determined the ability of SARS-CoV-2 to directly infect myogenic cells and employed qPCR to assess the impact on muscle atrophy pathway genes (ubiquitin-proteasome, autophagy). In vivo, the diaphragm and quadriceps of Roborovski hamsters with SARS-CoV-2 respiratory infection were examined at day 3 post-inoculation to evaluate the relationship between atrophy pathway and SARS-CoV-2 transcripts by qPCR, as well as histological measurements of myofibre morphology.

RESULTS

Angiotensin converting enzyme 2 (ACE2), the primary receptor for SARS-CoV-2, as well as cooperating proteases (furin, cathepsins B and L), are expressed by myofibres. ACE2 expression was increased 5-fold (p = 0.01) in the diaphragms of mechanically ventilated human subjects compared to controls. In Vitro, a time-dependent increase of SARS-CoV-2 transcript levels was observed in myotubes directly exposed to the virus (p = 0.002). This was associated with downregulation of the ubiquitin ligase MuRF1 (by 64%, p = 0.002) and the autophagy gene LC3B (by 31%, p = 0.009). In contrast, in vivo infection led to upregulation of MuRF1 in quadriceps (23-fold, p = 0.0007) and autophagy genes in both quadriceps (5.2-fold for Gabarapl1, p = 0.03; 7-fold for p62, p = 0.0002) and diaphragm (2.2-fold for Gabarapl1, p = 0.03; 2.3-fold for p62, p = 0.057). In infected hamsters the diaphragm lacked viral transcripts but exhibited atrophy (48% decrease in myofibre area; p = 0.02), whereas the quadriceps lacked myofibre atrophy despite elevated viral transcripts in the muscle.

CONCLUSIONS

Although myogenic cells express the genes required for SARS-CoV-2 entry and can be directly infected, there was no evident relationship between viral transcript levels and manifestations of atrophy, either in vitro or in vivo. Our results do not support direct myofibre infection by SARS-CoV-2 as a likely cause of atrophy in COVID-19.

Comprehensive computational analysis of differentially expressed miRNAs and their influence on transcriptomic signatures in prostate cancer

Prostate cancer presents a major health issue, with its progression influenced by intricate molecular factors. Notably, the interplay between miRNAs and changes in transcriptomic patterns is not fully understood. Our study seeks to bridge this knowledge gap, employing computational techniques to explore how miRNAs and transcriptomic alterations jointly regulate the development of prostate cancer. The study involved retrieving miRNA expression data from the GEO database specific to prostate cancer. Identification of DEMs was conducted using the 'limma' package in R. Integration of these DEMs with mRNA interactions was done using the MiRTarBase database. Finally, a network depicting miRNA-mRNA interactions was constructed using Cytoscape software to analyze the regulatory network of prostate cancer. The study pinpointed seven pivotal differentially expressed microRNAs (DEmiRNAs) in prostate cancer: hsa-miR-185-5p, hsa-miR-153-3p, hsa-miR-198, hsa-miR-182-5p, hsa-miR-223-3p, hsa-miR-372-3p, and hsa-miR-188-5p. These miRNAs influence key genes, including FOXO3, NFAT3, PTEN, RHOA, VEGFA, SMAD7, and CDK2, playing significant roles in both tumor suppression and oncogenesis. The analysis revealed a complex network of miRNA-mRNA interactions, comprising 1849 nodes and 3604 edges. Functional Enrichment Analysis through ClueGO highlighted 74 GO terms associated with these mRNA targets. This analysis uncovered their substantial impact on critical biological processes and molecular functions, such as cyclin-dependent protein kinase activity, mitotic DNA damage checkpoint signalling, stress-activated MAPK cascade, regulation of extrinsic apoptotic signalling pathway, and positive regulation of cell adhesion. Our analysis of miRNAs and DEGs genes revealed an intriguing mix of established and potentially novel regulators in prostate cancer development. These findings both reinforce our current understanding of prostate cancer's molecular landscape and point to unexplored pathways that could lead to novel therapeutic strategies. By mapping these regulatory relationships, our work contributes to the growing knowledge base needed for developing more targeted and effective treatments.

Therapeutic effects of platelet-derived extracellular vesicles on viral myocarditis correlate with biomolecular content

Introduction

Extracellular vesicles (EVs) can potently inhibit inflammation yet there is a lack of understanding about the impact of donor characteristics on the efficacy of EVs. The goal of this study was to determine whether the sex and age of donor platelet-derived EVs (PEV) affected their ability to inhibit viral myocarditis.

Methods

PEV, isolated from men and women of all ages, was compared to PEV obtained from women under 50 years of age, which we termed premenopausal PEV (pmPEV). Because of the protective effect of estrogen against myocardial inflammation, we hypothesized that pmPEV would be more effective than PEV at inhibiting myocarditis. We injected PEV, pmPEV, or vehicle control in a mouse model of viral myocarditis and examined histology, gene expression, protein profiles, and performed proteome and microRNA (miR) sequencing of EVs.

Results

We found that both PEV and pmPEV significantly inhibited myocarditis; however, PEV was more effective, which was confirmed by a greater reduction of inflammatory cells and proinflammatory and profibrotic markers determined using gene expression and immunohistochemistry. Proteome and miR sequencing of EVs revealed that PEV miRs specifically targeted antiviral, Toll-like receptor (TLR)4, and inflammasome pathways known to contribute to myocarditis while pmPEV contained general immunoregulatory miRs.

Discussion

These differences in EV content corresponded to the differing anti-inflammatory effects of the two types of EVs on viral myocarditis.

Circulating hsa-miR-320a and its regulatory network in type 1 diabetes mellitus

Introduction

Increasing evidence from human and animal model studies indicates the significant role of microRNAs (miRNAs) in pancreatic beta cell function, insulin signaling, immune responses, and pathogenesis of type 1 diabetes (T1D).

Methods

We aimed, using next-generation sequencing, to screen miRNAs from peripheral blood mononuclear cells of eight independent Kuwaiti-Arab families with T1D affected siblings, consisting of 18 T1D patients and 18 unaffected members, characterized by no parent-to-child inheritance pattern.

Results

Our analysis revealed 20 miRNAs that are differentially expressed in T1D patients compared with healthy controls. Module-based weighted gene co-expression network analysis prioritized key consensus miRNAs in T1D pathogenesis. These included hsa-miR-320a-3p, hsa-miR-139-3p, hsa-miR-200-3p, hsa-miR-99b-5p and hsa-miR-6808-3p. Functional enrichment analysis of differentially expressed miRNAs indicated that PI3K-AKT is one of the key pathways perturbed in T1D. Gene ontology analysis of hub miRNAs also implicated PI3K-AKT, along with mTOR, MAPK, and interleukin signaling pathways, in T1D. Using quantitative RT-PCR, we validated one of the key predicted miRNA-target gene-transcription factor networks in an extended cohort of children with new-onset T1D positive for islet autoantibodies. Our analysis revealed that hsa-miR-320a-3p and its key targets, including PTEN, AKT1, BCL2, FOXO1 and MYC, are dysregulated in T1D, along with their interacting partners namely BLIMP3, GSK3B, CAV1, CXCL3, TGFB, and IL10. Receiver Operating Characteristic analysis highlighted the diagnostic potential of hsa-miR-320a-3p, CAV1, GSK3B and MYC for T1D.

Discussion

Our study presents a novel link between hsa-miR-320a-3p and T1D, and highlights its key regulatory role in the network of mRNA markers and transcription factors involved in T1D pathogenesis.

Whole transcriptome landscape in HAPE under the stress of environment at high altitudes: new insights into the mechanisms of hypobaric hypoxia tolerance

Background

High altitude pulmonary edema (HAPE) is an idiopathic, noncardiogenic form of pulmonary edema that occurs at high altitudes. It is characterized by a severe clinical course and carries a significant mortality risk. Despite its clinical relevance, the molecular mechanisms underlying HAPE are not well understood.

Methods

We conducted whole-transcriptome RNA sequencing on blood samples from 6 pairs of HAPE patients and healthy controls to identify differentially expressed (DE) mRNAs, miRNAs, circRNAs, lncRNAs, along with alternative splicing (AS) events, gene fusions, and novel transcripts. To explore the regulatory dynamics, we constructed ceRNA networks and analyzed immune cell infiltration patterns, further annotating the biological functions of these transcripts. For empirical validation, we selected five circRNAs from the ceRNA network and conducted RT-qPCR on 50 paired samples. Additionally, we assessed the correlations between circRNA expression levels and clinical data to evaluate their diagnostic potential.

Results

We observed 2,023 differentially expressed mRNAs (DEmRNAs), 84 DEmiRNAs, 200 DEcircRNAs, and 3,573 DElncRNAs. A total of 139 'A3SS' events, 103 'A5SS' events, 545 'MXE' events, 14 'RI' events, and 1,482 'SE' events were identified in the AS events analysis between the two groups. Two ceRNA networks were constructed. T cells, follicular helper, and Macrophages M1 cells exhibited the strongest positive correlation (R=0.82), while naive B cells and memory B cells demonstrated the strongest negative correlation (R=-0.62). In total, the expression of three circRNAs was significantly different in a larger cohort. Hsa_circ_0058497, hsa_circ_0081006, and hsa_circ_0083220 demonstrated consistent with the RNA sequencing results. These three circRNAs strongly correlate with clinical indicators and exhibit potential as diagnostic biomarkers. Finally, we verified five genes (CXCR4, HSD17B2, ANGPTL4, TIMP3, N4BP3) that were differentially expressed in endothelial cells under normoxia and hypoxia through bioinformatics and RT-qPCR analyses.

Conclusion

This study elucidates the differential expression of coding and non-coding RNAs (ncRNAs) in HAPE, identifies new transcripts and genes, and enhances our understanding of the transcriptional characteristics of HAPE. Moreover, it highlights the potential role of circRNAs in advancing the diagnosis and treatment of HAPE.

Dysregulated Urinary Extracellular Vesicle Small RNAs in Diabetic Nephropathy: Implications for Diagnosis and Therapy

Background

Diabetic nephropathy (DN) represents a major chronic kidney disorder and a leading cause of end-stage renal disease (ESRD). Small RNAs have been showing great promise as diagnostic markers as well as drug targets. Identifying dysregulated micro RNAs (miRNAs) could help in identifying disease biomarkers and investigation of downstream interactions, shedding light on the molecular pathophysiology of DN. In this study, we analyzed small RNAs within human urinary extracellular vesicles (ECVs) from DN patients using small RNA next-generation sequencing.

Method

In this cross-sectional study, urine samples were collected from 88 participants who were divided into 3 groups: type 2 diabetes (T2D) with DN (T2D + DN, n = 20), T2D without DN (T2D - DN, n = 40), and healthy individuals (n = 28). The study focused on isolating urinary ECVs to extract and sequence small RNAs. Differentially expressed small RNAs were identified, and a functional enrichment analysis was conducted.

Results

The study revealed a distinct subset of 13 miRNAs and 10 Piwi-interacting RNAs that were significantly dysregulated in urinary ECVs of the DN group when compared to other groups. Notably, miR-151a-3p and miR-182-5p exhibited a unique expression pattern, being downregulated in the T2D - DN group, and upregulated in the T2D + DN group, thus demonstrating their effectiveness in distinguishing patients between the 2 groups. Eight driver genes were identified PTEN, SMAD2, SMAD4, VEGFA, CCND2, CDK6, LIN28B, and CHD1.

Conclusion

Our findings contribute valuable insights into the pathogenesis of DN, uncovering novel biomarkers and identifying potential therapeutic targets that may aid in managing and potentially decelerating the progression of the disease.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Background

Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.

Methods

Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.

Results

The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.

Conclusion

This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Global analysis of urinary extracellular vesicle small RNAs in autosomal dominant polycystic kidney disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent monogenic renal disease progressing to end-stage renal disease. There is a pressing need for the identification of early ADPKD biomarkers to enable timely intervention and the development of effective therapeutic approaches. Here, we profiled human urinary extracellular vesicles small RNAs by small RNA sequencing in patients with ADPKD and compared their differential expression considering healthy control individuals to identify dysregulated small RNAs and analyze downstream interaction to gain insight about molecular pathophysiology.

METHODS

This is a cross-sectional study where urine samples were collected from a total of 23 PKD1-ADPKD patients and 28 healthy individuals. Urinary extracellular vesicles were purified, and small RNA was isolated and sequenced. Differentially expressed Small RNA were identified and functional enrichment analysis of the critical miRNAs was performed to identify driver genes and affected pathways.

RESULTS

miR-320b, miR-320c, miR-146a-5p, miR-199b-3p, miR-671-5p, miR-1246, miR-8485, miR-3656, has_piR_020497, has_piR_020496 and has_piR_016271 were significantly upregulated in ADPKD patient urine extracellular vesicles and miRNA-29c was significantly downregulated. Five 'driver' target genes (FBRS, EDC3, FMNL3, CTNNBIP1 and KMT2A) were identified.

CONCLUSIONS

The findings of the present study make significant contributions to the understanding of ADPKD pathogenesis and to the identification of novel biomarkers and potential drug targets aimed at slowing disease progression in ADPKD.

Macrophage Activation Syndrome in Coinciding Pandemics of Obesity and COVID-19: Worse than Bad

Epigenetic changes have long-lasting impacts, which influence the epigenome and are maintained during cell division. Thus, human genome changes have required a very long timescale to become a major contributor to the current obesity pandemic. Whereas bidirectional effects of coronavirus disease 2019 (COVID-19) and obesity pandemics have given the opportunity to explore, how the viral microribonucleic acids (miRNAs) use the human's transcriptional machinery that regulate gene expression at a posttranscriptional level. Obesity and its related comorbidity, type 2 diabetes (T2D), and new-onset diabetes due to severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) are additional risk factors, which increase the severity of COVID-19 and its related mortality. The higher mortality rate of these patients is dependent on severe cytokine storm, which is the sum of the additional cytokine production by concomitant comorbidities and own cytokine synthesis of COVID-19. Patients with obesity facilitate the SARS-CoV-2 entry to host cell via increasing the host's cell receptor expression and modifying the host cell proteases. After entering the host cells, the SARS-CoV-2 genome directly functions as a messenger ribonucleic acid (mRNA) and encodes a set of nonstructural proteins via processing by the own proteases, main protease (Mpro), and papain-like protease (PLpro) to initiate viral genome replication and transcription. Following viral invasion, SARS-CoV-2 infection reduces insulin secretion via either inducing β-cell apoptosis or reducing intensity of angiotensin-converting enzyme 2 (ACE2) receptors and leads to new-onset diabetes. Since both T2D and severity of COVID-19 are associated with the increased serum levels of pro-inflammatory cytokines, high glucose levels in T2D aggravate SARS-CoV-2 infection. Elevated neopterin (NPT) value due to persistent interferon gamma (IFN-γ)-mediated monocyte-macrophage activation is an indicator of hyperactivated pro-inflammatory phenotype M1 macrophages. Thus, NPT could be a reliable biomarker for the simultaneously occurring COVID-19-, obesity- and T2D-induced cytokine storm. While host miRNAs attack viral RNAs, viral miRNAs target host transcripts. Eventually, the expression rate and type of miRNAs also are different in COVID-19 patients with different viral loads. It is concluded that specific miRNA signatures in macrophage activation phase may provide an opportunity to become aware of the severity of COVID-19 in patients with obesity and obesity-related T2D.

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.

Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity

BACKGROUND

The use of molecular biomarkers for COVID-19 remains unconclusive. The application of a molecular biomarker in combination with clinical ones that could help classifying aggressive patients in first steps of the disease could help clinician and sanitary system a better management of the disease. Here we characterize the role of ACE2, AR, MX1, ERG, ETV5 and TMPRSS2 for trying a better classification of COVID-19 through knowledge of the disease mechanisms.

METHODS

A total of 329 blood samples were genotyped in ACE2, MX1 and TMPRSS2. RNA analyses were also performed from 258 available samples using quantitative polymerase chain reaction for genes: ERG, ETV5, AR, MX1, ACE2, and TMPRSS2. Moreover, in silico analysis variant effect predictor, ClinVar, IPA, DAVID, GTEx, STRING and miRDB database was also performed. Clinical and demographic data were recruited from all participants following WHO classification criteria.

RESULTS

We confirm the use of ferritin (p < 0.001), D-dimer (p < 0.010), CRP (p < 0.001) and LDH (p < 0.001) as markers for distinguishing mild and severe cohorts. Expression studies showed that MX1 and AR are significantly higher expressed in mild vs severe patients (p < 0.05). ACE2 and TMPRSS2 are involved in the same molecular process of membrane fusion (p = 4.4 × 10^-3), acting as proteases (p = 0.047).

CONCLUSIONS

In addition to the key role of TMPSRSS2, we reported for the first time that higher expression levels of AR are related with a decreased risk of severe COVID-19 disease in females. Moreover, functional analysis demonstrates that ACE2, MX1 and TMPRSS2 are relevant markers in this disease.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

Background

Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients.

Methods

The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools.

Results

Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2.

Conclusion

In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Potential Biomarkers for Parkinson Disease from Functional Enrichment and Bioinformatic Analysis of Global Gene Expression Patterns of Blood and Substantia Nigra Tissues

The Parkinson disease (PD) is the second most common neurodegenerative disorder affecting the central nervous system and motor functions. The biological complexity of PD is yet to reveal potential targets for intervention or to slow the disease severity. Therefore, this study aimed to compare the fidelity of blood to substantia nigra (SN) tissue gene expression from PD patients to provide a systematic approach to predict role of the key genes of PD pathobiology. Differentially expressed genes (DEGs) from multiple microarray data sets of PD blood and SN tissue from GEO database are identified. Using the theoretical network approach and variety of bioinformatic tools, we prioritized the key genes from DEGs. A total of 540 and 1024 DEGs were identified in blood and SN tissue samples, respectively. Functional pathways closely related to PD such as ERK1 and ERK2 cascades, mitogen-activated protein kinase (MAPK) signaling, Wnt, nuclear factor-κB (NF-κB), and PI3K-Akt signaling were observed by enrichment analysis. Expression patterns of 13 DEGs were similar in both blood and SN tissues. Comprehensive network topological analysis and gene regulatory networks identified additional 10 DEGs functionally connected with molecular mechanisms of PD through the mammalian target of rapamycin (mTOR), autophagy, and AMP-activated protein kinase (AMPK) signaling pathways. Potential drug molecules were identified by chemical-protein network and drug prediction analysis. These potential candidates can be further validated in vitro/in vivo to be used as biomarkers and/or novel drug targets for the PD pathology and/or to arrest or delay the neurodegeneration over the years, respectively.

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 AbstractShared miRNA molecules among the five investigated neurodegenerative diseases and COVID-19 were identified. The two overlapping miRNAs, hsa-miR-34a and has-miR-132, present potential biomarkers of neurodegenerative sequelae after COVID-19. Furthermore, 98 common miRNAs between all five neurodegenerative diseases together and COVID-19 were identified. A KEGG and Reactome pathway enrichment analyses was performed on the list of shared miRNA target genes and finally top 20 pathways were evaluated for their potential for identification of new drug targets. A common feature of identified overlapping miRNAs and pathways is neuroinflammation. AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; COVID-19, coronavirus disease 2019; HD, Huntington's disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; MS, multiple sclerosis; PD, Parkinson's disease.

Circulating microRNAs as emerging regulators of COVID-19

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global pandemic that has high incidence rates, spreads rapidly, and has caused more than 6.5 million deaths globally to date. Currently, several drugs have been used in the clinical treatment of COVID-19, including antivirals (e.g., molnupiravir, baricitinib, and remdesivir), monoclonal antibodies (e.g., etesevimab and tocilizumab), protease inhibitors (e.g., paxlovid), and glucocorticoids (e.g., dexamethasone). Increasing evidence suggests that circulating microRNAs (miRNAs) are important regulators of viral infection and antiviral immune responses, including the biological processes involved in regulating COVID-19 infection and subsequent complications. During viral infection, both viral genes and host cytokines regulate transcriptional and posttranscriptional steps affecting viral replication. Virus-encoded miRNAs are a component of the immune evasion repertoire and function by directly targeting immune functions. Moreover, several host circulating miRNAs can contribute to viral immune escape and play an antiviral role by not only promoting nonstructural protein (nsp) 10 expression in SARS coronavirus, but among others inhibiting NOD-like receptor pyrin domain-containing (NLRP) 3 and IL-1β transcription. Consequently, understanding the expression and mechanism of action of circulating miRNAs during SARS-CoV-2 infection will provide unexpected insights into circulating miRNA-based studies. In this review, we examined the recent progress of circulating miRNAs in the regulation of severe inflammatory response, immune dysfunction, and thrombosis caused by SARS-CoV-2 infection, discussed the mechanisms of action, and highlighted the therapeutic challenges involving miRNA and future research directions in the treatment of COVID-19.

Bioinformatics insights into the genes and pathways on severe COVID-19 pathology in patients with comorbidities

Background: Coronavirus disease (COVID-19) infection is known for its severe clinical pathogenesis among individuals with pre-existing comorbidities. However, the molecular basis of this observation remains elusive. Thus, this study aimed to map key genes and pathway alterations in patients with COVID-19 and comorbidities using robust systems biology approaches. Methods: The publicly available genome-wide transcriptomic datasets from 120 COVID-19 patients, 281 patients suffering from different comorbidities (like cardiovascular diseases, atherosclerosis, diabetes, and obesity), and 252 patients with different infectious diseases of the lung (respiratory syncytial virus, influenza, and MERS) were studied using a range of systems biology approaches like differential gene expression, gene ontology (GO), pathway enrichment, functional similarity, mouse phenotypic analysis and drug target identification. Results: By cross-mapping the differentially expressed genes (DEGs) across different datasets, we mapped 274 shared genes to severe symptoms of COVID-19 patients or with comorbidities alone. GO terms and functional pathway analysis highlighted genes in dysregulated pathways of immune response, interleukin signaling, FCGR activation, regulation of cytokines, chemokines secretion, and leukocyte migration. Using network topology parameters, phenotype associations, and functional similarity analysis with ACE2 and TMPRSS2-two key receptors for this virus-we identified 17 genes with high connectivity (CXCL10, IDO1, LEPR, MME, PTAFR, PTGS2, MAOB, PDE4B, PLA2G2A, COL5A1, ICAM1, SERPINE1, ABCB1, IL1R1, ITGAL, NCAM1 and PRKD1) potentially contributing to the clinical severity of COVID-19 infection in patients with comorbidities. These genes are predicted to be tractable and/or with many existing approved inhibitors, modulators, and enzymes as drugs. Conclusion: By systemic implementation of computational methods, this study identified potential candidate genes and pathways likely to confer disease severity in COVID-19 patients with pre-existing comorbidities. Our findings pave the way to develop targeted repurposed therapies in COVID-19 patients.

Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

Identification of miRNA-mRNA-TFs regulatory network and crucial pathways involved in asthma through advanced systems biology approaches

Asthma is a life-threatening and chronic inflammatory lung disease that is posing a true global health challenge. The genetic basis of the disease is fairly well examined. However, the molecular crosstalk between microRNAs (miRNAs), target genes, and transcription factors (TFs) networks and their contribution to disease pathogenesis and progression is not well explored. Therefore, this study was aimed at dissecting the molecular network between mRNAs, miRNAs, and TFs using robust computational biology approaches. The transcriptomic data of bronchial epithelial cells of severe asthma patients and healthy controls was studied by different systems biology approaches like differentially expressed gene detection, functional enrichment, miRNA-target gene pairing, and mRNA-miRNA-TF molecular networking. We detected the differential expression of 1703 (673 up-and 1030 down-regulated) genes and 71 (41 up-and 30 down-regulated) miRNAs in the bronchial epithelial cells of asthma patients. The DEGs were found to be enriched in key pathways like IL-17 signaling (KEGG: 04657), Th1 and Th2 cell differentiation (KEGG: 04658), and the Th17 cell differentiation (KEGG: 04659) (p-values = 0.001). The results from miRNAs-target gene pairs-transcription factors (TFs) have detected the key roles of 3 miRs (miR-181a-2-3p; miR-203a-3p; miR-335-5p), 6 TFs (TFAM, FOXO1, GFI1, IRF2, SOX9, and HLF) and 32 miRNA target genes in eliciting autoimmune reactions in bronchial epithelial cells of the respiratory tract. Through systemic implementation of comprehensive system biology tools, this study has identified key miRNAs, TFs, and miRNA target gene pairs as potential tissue-based asthma biomarkers.

3’UTR of SARS-CoV-2 spike gene hijack host miR-296 or miR-520h to disturb cell proliferation and cytokine signaling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has becoming globally public health threat. Recently studies were focus on SARS-CoV-2 RNA to design vaccine and drugs. It was demonstrated that virus RNA could play as sponge to host noncoding RNAs to regulate cellular processes. Bioinformatic research predicted a series of motif on SARS-CoV-2 genome where are targets of human miRNAs. In this study, we used dual-luciferase reporter assays to validate the interaction between 3’UTR of SARS-CoV-2 S (S-3’UTR) gene and bioinformatic predicted targeting miRNAs. The growth of 293T cells and HUVECs with overexpressed S-3’UTR was determined, while miRNAs and IL6, TNF-α levels were checked in this condition. Then, miR-296 and miR-602 mimic were introduced into 293T cells and HUVECs with overexpressed S-3’UTR, respectively, to reveal the underlying regulation mechanism. In results, we screened 19 miRNAs targeting the S-3’UTR, including miR-296 and miR-602. In 293T cell, S-3’UTR could inhibit 293T cell growth through down-regulation of miR-296. By reducing miR-602, S-3’UTR could induce HUVECs cell proliferation, alter the cell cycle, reduce apoptosis, and enhanced IL6 and TNF-αlevel. In conclusion, SARS-CoV-2 RNA could play as sponge of host miRNA to disturb cell growth and cytokine signaling. It suggests an important clue for designing COVID-19 drug and vaccine.

Therapeutic prospects of ceRNAs in COVID-19

Since the end of 2019, COVID-19 caused by SARS-CoV-2 has spread worldwide, and the understanding of the new coronavirus is in a preliminary stage. Currently, immunotherapy, cell therapy, antiviral therapy, and Chinese herbal medicine have been applied in the clinical treatment of the new coronavirus; however, more efficient and safe drugs to control the progress of the new coronavirus are needed. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) may provide new therapeutic targets for novel coronavirus treatments. The first aim of this paper is to review research progress on COVID-19 in the respiratory, immune, digestive, circulatory, urinary, reproductive, and nervous systems. The second aim is to review the body systems and potential therapeutic targets of lncRNAs, miRNAs, and circRNAs in patients with COVID-19. The current research on competing endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA) in SARS-CoV-2 is summarized. Finally, we predict the possible therapeutic targets of four lncRNAs, MALAT1, NEAT1, TUG1, and GAS5, in COVID-19. Importantly, the role of PTEN gene in the ceRNA network predicted by lncRNA MALAT1 and lncRNA TUG1 may help in the discovery and clinical treatment of effective drugs for COVID-19.

miRNA expression in COVID-19

Coronavirus disease 2019 (COVID-19) is regarded as a challenge in health system. Several studies have assessed the immune-related aspect of this disorder to identify the host-related factors that affect the course of COVID-19. microRNAs (miRNAs) as potent regulators of immune responses have gained much attention in this regard. Recent studies have shown aberrant expression of miRNAs in COVID-19 in association with disease course. Differentially expressed miRNAs have been enriched in pathways related with inflammation and antiviral immune response. miRNAs have also been regarded as potential therapeutic targets in COVID-19, particularly for management of pathological consequences of COVID-19. In the current review, we summarize the data about dysregulation of miRNAs in COVID-19.

Immunomodulatory LncRNA on antisense strand of ICAM-1 augments SARS-CoV-2 infection-associated airway mucoinflammatory phenotype

Noncoding RNAs are important regulators of mucoinflammatory response, but little is known about the contribution of airway long noncoding RNAs (lncRNAs) in COVID-19. RNA-seq analysis showed a more than 4-fold increased expression of IL-6, ICAM-1, CXCL-8, and SCGB1A1 inflammatory factors; MUC5AC and MUC5B mucins; and SPDEF, FOXA3, and FOXJ1 transcription factors in COVID-19 patient nasal samples compared with uninfected controls. A lncRNA on antisense strand to ICAM-1 or LASI was induced 2-fold in COVID-19 patients, and its expression was directly correlated with viral loads. A SARS-CoV-2-infected 3D-airway model largely recapitulated these clinical findings. RNA microscopy and molecular modeling indicated a possible interaction between viral RNA and LASI lncRNA. Notably, blocking LASI lncRNA reduced the SARS-CoV-2 replication and suppressed MUC5AC mucin levels and associated inflammation, and select LASI-dependent miRNAs (e.g., let-7b-5p and miR-200a-5p) were implicated. Thus, LASI lncRNA represents an essential facilitator of SARS-CoV-2 infection and associated airway mucoinflammatory response.

Gene Network Analysis of the Transcriptome Impact of SARS-CoV-2 Interacting MicroRNAs in COVID-19 Disease

According to the World Health Organization (WHO), as of June 2022, over 536 million confirmed COVID-19 disease cases and over 6.3 million deaths had been globally reported. COVID-19 is a multiorgan disease involving multiple intricated pathological mechanisms translated into clinical, biochemical, and molecular changes, including microRNAs. MicroRNAs are essential post-transcriptional regulators of gene expression, being involved in the modulation of most biological processes. In this study, we characterized the biological impact of SARS-CoV-2 interacting microRNAs differentially expressed in COVID-19 disease by analyzing their impact on five distinct tissue transcriptomes. To this end, we identified the microRNAs’ predicted targets within the list of differentially expressed genes (DEGs) in tissues affected by high loads of SARS-CoV-2 virus. Next, we submitted the tissue-specific lists of the predicted microRNA-targeted DEGs to gene network functional enrichment analysis. Our data show that the upregulated microRNAs control processes such as mitochondrial respiration and cytokine and cell surface receptor signaling pathways in the heart, lymph node, and kidneys. In contrast, downregulated microRNAs are primarily involved in processes related to the mitotic cell cycle in the heart, lung, and kidneys. Our study provides the first exploratory, systematic look into the biological impact of the microRNAs associated with COVID-19, providing a new perspective for understanding its multiorgan physiopathology.

Clinical and molecular evaluation of patients with ovarian cancer in the context of drug resistance to chemotherapy

The present study aimed to evaluate changes in the expression patterns at the gene and protein levels associated with drug resistance. The study group included 48 women who had a histopathologically confirmed diagnosis of stage I-IV ovarian cancer, they were divided into two subgroups (groups A and B). In group A, there were 36 patients in whom surgical treatment was supplemented with first-line chemotherapy according to current standards. Within this patient group, 5 had stage I (14%), 5 had stage II (14%), 25 had stage III (69%), and 1 had stage IV ovarian cancer (3%). Drug resistance was found after the third cycle of chemotherapy in 17 patients (71%) and after the sixth cycle in 7 patients (29%). Group B included 12 women with type I ovarian cancer, including 11 with stage I and 1 patient with stage IV ovarian cancer. The oncological treatment required only surgery. The control group (C) included 50 women in whom the uterus and adnexa were surgically removed for non-oncological reasons. Significantly higher levels of carcinoma antigen 125 CA-125 and human epididymis protein 4 HE4 were observed in group A and in menopausal women. Moreover, drug resistance was associated with significantly higher levels of CA-125 (p < 0.05). The genes UBA2, GLO1, STATH, and TUFT1 were differentiated in test samples from control samples. Moreover, drug resistance was associated with significantly higher expression of GLO1. The results of these assessments indicated the strong link between UBA2 and hsa-miR-133a-3p and hsa-miR-133b; GLO1 and hsa-miR-561-5p; STATH and hsa-miR-137-3p and hsa-miR-580-3p; and TUFT1 and hsa-miR-1233-3p and hsa-miR-2052. Correlation analysis showed a significant correlation between CA-125 and HE4 levels. Moreover, a significant correlation between TUFT1 mRNA and UBA2, GLO1, STATH (negative correlation), and TUFT1 in relation to CA-125 and HE4 (p < 0.05) was noted in all patients. In view of the lack of screening tests for ovarian cancer, the occurrence of the described correlation may be inscribed as an attempt to establish an assay that meets the criteria of a screening test and thus increase the early diagnosis of ovarian cancer.

Exploration of molecular targets and mechanisms of Chinese medicinal formula Acacia Catechu -Scutellariae Radix in the treatment of COVID-19 by a systems pharmacology strategy

Coronavirus disease 2019 (COVID‐19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). In China, the Acacia catechu (AC)‐Scutellariae Radix (SR) formula has been widely used for pulmonary infection in clinical practice for several centuries. However, the potential role and mechanisms of this formula against COVID‐19 remains unclear. The present study was designed to dissect the active ingredients, molecular targets, and the therapeutic mechanisms of AC‐SR formula in the treatment of COVID‐19 based on a systems pharmacology strategy integrated by ADME screening, target prediction, network analysis, GO and KEGG enrichment analysis, molecular docking, and molecular dynamic (MD) simulations. Finally, Quercetin, Fisetin(1‐), kaempferol, Wogonin, Beta‐sitosterol, Baicalein, Skullcapflavone II, Stigmasterol were primarily screened to be the potentially effective active ingredients against COVID‐19. The hub‐proteins were TP53, JUN, ESR1, MAPK1, Akt1, HSP90AA1, TNF, IL‐6, SRC, and RELA. The potential mechanisms of AC‐SR formula in the treatment of COVID‐19 were the TNF signaling pathway, PI3K‐Akt signaling pathway and IL‐17 signaling pathway, etc. Furthermore, virtual docking revealed that baicalein, (+)‐catechin and fisetin(1‐) exhibited high affinity to SARS‐CoV‐2 3CLpro, which has validated by the FRET‐based enzymatic inhibitory assays with the IC₅₀ of 11.3, 23.8, and 44.1 μM, respectively. And also, a concentration‐dependent inhibition of baicalein, quercetin and (+)‐catechin against SARS‐CoV‐2 ACE2 was observed with the IC₅₀ of 138.2, 141.3, and 348.4 μM, respectively. These findings suggested AC‐SR formula exerted therapeutic effects involving “multi‐compounds and multi‐targets.” It might be working through directly inhibiting the virus, improving immune function, and reducing the inflammatory in response to anti‐COVID‐19. Ultimately, this study would provide new perspective for discovering potential drugs and mechanisms against COVID‐19.

In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Nucleocapsid Through Molecular Docking-Based Drug Repurposing

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, and its effects on people worldwide continue to grow. Protein-targeted therapeutics are currently unavailable for this virus. As with other coronaviruses, the nucleocapsid (N) protein is the most conserved RNA-binding structural protein of SARS-CoV-2. The N protein is an appealing target because of its functional role in viral transcription and replication. Therefore, molecular docking method for structure-based drug design was used to investigate the binding energy and binding modes of various anti-N inhibitors in depth. The inhibitors selected were originally developed to target stress granules and other molecules involved in RNA biology, and were either FDA-approved or in the process of clinical trials for COVID-19. We aimed at targeting the N-terminal RNA binding domain (NTD) for molecular docking-based screening, on the basis of the first resolved crystal structure of SARS-CoV-2 N protein (PDB ID: 6M3M) and C-terminal domain (CTD) dimerization of the nucleocapsid phosphoprotein of SARS-COV-2 (PDB ID: 6WJI). Silmitasertib, nintedanib, ternatin, luteolin, and fedratinib were found to interact with RNA binding sites and to form a predicted protein interface with high binding energy. Similarly, silmitasertib, sirolimus-rapamycin, dovitinib, nintedanib, and fedratinib were found to interact with the SARS-CoV-2 N protein at its CTD dimerization sites, according to previous studies. In addition, we investigated an information gap regarding the relationships among the energetic landscape and stability and drug binding of the SARS-CoV-2 N NTD and CTD. Our in silico results clearly indicated that several tested drugs as potent putative inhibitors for COVID-19 therapeutics, thus indicating that they should be further validated as treatments to slow the spread of SARS-CoV-2.

The Key Genes Underlying Pathophysiology Correlation Between the Acute Myocardial Infarction and COVID-19

Introduction

Accumulating evidences disclose that COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a marked effect on acute myocardial infarction (AMI). Nevertheless, the underlying pathophysiology correlation between the AMI and COVID-19 remains vague.

Materials and Methods

Bioinformatics analyses of the altered transcriptional profiling of peripheral blood mononuclear cells (PBMCs) in patients with AMI and COVID-19 were implemented, including identification of differentially expressed genes and common genes between AMI and COVID-19, protein–protein interactions, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, TF-genes and miRNA coregulatory networks, to explore their biological functions and potential roles in the pathogenesis of COVID-19-related AMI.

Conclusion

Our bioinformatic analyses of gene expression profiling of PBMCs in patients with AMI and COVID-19 provide us with a unique view regarding underlying pathophysiology correlation between the two vital diseases.

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

BACKGROUND

Tuberculosis (TB) is a major infectious disease, where incomplete information about host genetics and immune responses is hindering the development of transformative therapies. This study characterized the immune cell landscape and blood transcriptomic profile of patients with pulmonary TB (PTB) to identify the potential therapeutic biomarkers.

METHODS

The blood transcriptome profile of patients with PTB and controls were used for fractionating immune cell populations with the CIBERSORT algorithm and then to identify differentially expressed genes (DEGs) with R/Bioconductor packages. Later, systems biology investigations (such as semantic similarity, gene correlation, and graph theory parameters) were implemented to prioritize druggable genes contributing to the immune cell alterations in patients with TB. Finally, real time-PCR (RT-PCR) was used to confirm gene expression levels.

RESULTS

Patients with PTB had higher levels of four immune subpopulations like CD8⁺ T cells (P = 1.9 × 10^-8), natural killer (NK) cells resting (P = 6.3 × 10^-5), monocytes (P = 6.4 × 10^-6), and neutrophils (P = 1.6 × 10^-7). The functional enrichment of 624 DEGs identified in the blood transcriptome of patients with PTB revealed major dysregulation of T cell-related ontologies and pathways (q ≤ 0.05). Of the 96 DEGs shared between transcriptome and immune cell types, 39 overlapped with TB meta-profiling genetic signatures, and their semantic similarity analysis with the remaining 57 genes, yielded 45 new candidate TB markers. This study identified 9 CD8⁺ T cell-associated genes (ITK, CD2, CD6, CD247, ZAP70, CD3D, SH2D1A, CD3E, and IL7R) as potential therapeutic targets of PTB by combining computational druggability and co-expression (r² ≥ |0.7|) approaches.

CONCLUSION

The changes in immune cell proportion and the downregulation of T cell-related genes may provide new insights in developing therapeutic compounds against chronic TB.

microRNAs and Inflammatory Immune Response in SARS-CoV-2 Infection: A Narrative Review

The current SARS-CoV-2 pandemic has emerged as an international challenge with strong medical and socioeconomic impact. The spectrum of clinical manifestations of SARS-CoV-2 is wide, covering asymptomatic or mild cases up to severe and life-threatening complications. Critical courses of SARS-CoV-2 infection are thought to be driven by the so-called “cytokine storm”, derived from an excessive immune response that induces the release of proinflammatory cytokines and chemokines. In recent years, non-coding RNAs (ncRNAs) emerged as potential diagnostic and therapeutic biomarkers in both inflammatory and infectious diseases. Therefore, the identification of SARS-CoV-2 miRNAs and host miRNAs is an important research topic, investigating the host–virus crosstalk in COVID-19 infection, trying to answer the pressing question of whether miRNA-based therapeutics can be employed to tackle SARS-CoV-2 complications. In this review, we aimed to directly address ncRNA role in SARS-CoV-2-immune system crosstalk upon COVID-19 infection, particularly focusing on inflammatory pathways and cytokine storm syndromes.

Bima A, Almukadi H, Ibrahim Kutbi H, Jayasheela Shetty P, Ahmad Shaik N, Banaganapalli B. Identifying Significant Genes and Functionally Enriched Pathways in Familial Hypercholesterolemia Using Integrated Gene Co-Expression Network Analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.

Viral and Host Genetic and Epigenetic Biomarkers Related to SARS-CoV-2 Cell Entry, Infection Rate, and Disease Severity

The rapid spread of COVID-19 outbreak lead to a global pandemic declared in March 2020. The common features of corona virus family helped to resolve structural characteristics and entry mechanism of SARS-CoV-2. However, rapid mutagenesis leads to the emergence of new strains that may have different reproduction rates or infectivity and may impact the course and severity of the disease. Host related factors may also play a role in the susceptibility for infection as well as the severity and outcomes of the COVID-19. We have performed a literature and database search to summarize potential viral and host-related genomic and epigenomic biomarkers, such as genetic variability, miRNA, and DNA methylation in the molecular pathway of SARS-CoV-2 entry into the host cell, that may be related to COVID-19 susceptibility and severity. Bioinformatics tools may help to predict the effect of mutations in the spike protein on the binding to the ACE2 receptor and the infectivity of the strain. SARS-CoV-2 may also target several transcription factors and tumour suppressor genes, thus influencing the expression of different host genes and affecting cell signalling. In addition, the virus may interfere with RNA expression in host cells by exploiting endogenous miRNA and its viral RNA. Our analysis showed that numerous human miRNA may form duplexes with different coding and non-coding regions of viral RNA. Polymorphisms in human genes responsible for viral entry and replication, as well as in molecular damage response and inflammatory pathways may also contribute to disease prognosis and outcome. Gene ontology analysis shows that proteins encoded by such polymorphic genes are highly interconnected in regulation of defense response. Thus, virus and host related genetic and epigenetic biomarkers may help to predict the course of the disease and the response to treatment.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

Potential COVID-19 therapeutic approaches targeting angiotensin-converting enzyme 2; An updated review

COVID-19 has spread swiftly throughout the world posing a global health emergency. The significant numbers of deaths attributed to this pandemic have researchers battling to understand this new, dangerous virus. Researchers are looking to find possible treatment regimens and develop effective therapies. This study aims to provide an overview of published scientific information on potential treatments, emphasizing angiotensin-converting enzyme II (ACE2) inhibitors as one of the most important drug targets. SARS-CoV-2 receptor-binding domain (RBD); as a viral attachment or entry inhibitor against SARS-CoV-2, human recombinant soluble ACE2; as a genetically modified soluble form of ACE2 to compete with membrane-bound ACE2, and microRNAs (miRNAs); as a negative regulator of the expression of ACE2/TMPRSS2 to inhibit SARS-CoV2 entry into cells, are the potential therapeutic approaches discussed thoroughly in this article. This review provides the groundwork for the ongoing development of therapeutic agents and effective treatments against SARS-COV-2.

Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.