Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus-Human Protein Interaction Network

Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

BACKGROUND

The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.

METHODS

We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein-protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.

RESULTS

Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.

CONCLUSIONS

Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.

PWN: enhanced random walk on a warped network for disease target prioritization

BACKGROUND

Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks.

RESULTS

We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge.

CONCLUSIONS

We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Prolonged viral shedding prediction on non-hospitalized, uncomplicated SARS-CoV-2 patients using their transcriptome data

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is identified as a highly transmissible coronavirus which threatens the world with this deadly pandemic. WHO reported that it spreads through contact, droplet, airborne, formite, fecal-oral, bloodborne, mother-to-child and animal-to-human. Hence, viral shedding has a huge impact on this pandemic. This study uses transcriptome data of coronavirus disease 2019 (COVID-19) patients to predict the prolonged viral shedding of the corresponding patient. This prediction starts with the transcriptome features which gives the lowest root mean squared value of 16.3±3.3 using top 25 feature selected using forward feature selection algorithm and linear regression algorithm. Then to see the impact of few non-molecular features in this prediction, they were added to the model one by one along with the selected transcriptome features. However, this study shows that those features do not have any impact on prolonged viral shedding prediction. Further this study predicts the day since onset in the same way. Here also top 25 transcriptome features selected using forward feature selection algorithm gives a comparably good accuracy (accuracy value of 0.74±0.1). However, the best accuracy was obtained using the best 20 features from feature importance using SVM (0.78±0.1). Moreover, adding non-molecular features shows a great impact on mutual information selected features in this prediction.

Recognition of Immune Cell Markers of COVID-19 Severity with Machine Learning Methods

COVID-19 is hypothesized to be linked to the host's excessive inflammatory immunological response to SARS-CoV-2 infection, which is regarded to be a major factor in disease severity and mortality. Numerous immune cells play a key role in immune response regulation, and gene expression analysis in these cells could be a useful method for studying disease states, assessing immunological responses, and detecting biomarkers. Here, we developed a machine learning procedure to find biomarkers that discriminate disease severity in individual immune cells (B cell, CD4+ cell, CD8+ cell, monocyte, and NK cell) using single-cell gene expression profiles of COVID-19. The gene features of each profile were first filtered and ranked using the Boruta feature selection method and mRMR, and the resulting ranked feature lists were then fed into the incremental feature selection method to determine the optimal number of features with decision tree and random forest algorithms. Meanwhile, we extracted the classification rules in each cell type from the optimal decision tree classifiers. The best gene sets discovered in this study were analyzed by GO and KEGG pathway enrichment, and some important biomarkers like TLR2, ITK, CX3CR1, IL1B, and PRDM1 were validated by recent literature. The findings reveal that the optimal gene sets for each cell type can accurately classify COVID-19 disease severity and provide insight into the molecular mechanisms involved in disease progression.

Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.

Finding Prediction of Interaction Between SARS-CoV-2 and Human Protein: A Data-Driven Approach

COVID-19 pandemic defined a worldwide health crisis into a humanitarian crisis. Amid this global emergency, human civilization is under enormous strain since no proper therapeutic method is discovered yet. A wave of research effort has been put toward the invention of therapeutics and vaccines against COVID-19. Contrarily, the spread of this fatal virus has already infected millions of people and claimed many lives all over the world. Computational biology can attempt to understand the protein–protein interactions between the viral protein and host protein. Therefore, potential viral–host protein interactions can be identified which is known as crucial information toward the discovery of drugs. In this study, an approach was presented for predicting novel interactions from maximal biclusters. Additionally, the predicted interactions are verified from biological perspectives. For this, a study was conducted on the gene ontology and KEGG-pathway in relation to the newly predicted interactions.

Identification of Novel Choroidal Neovascularization-Related Genes Using Laplacian Heat Diffusion Algorithm

Choroidal neovascularization (CNV) is a type of eye disease that can cause vision loss. In recent years, many studies have attempted to investigate the major pathological processes and molecular pathogenic mechanisms of CNV. Because many diseases are related to genes, the genes associated with CNV need to be identified. In this study, we proposed a network-based approach for identifying novel CNV-associated genes. To execute such method, we first employed a protein-protein interaction network reported in STRING. Then, we applied a network diffusion algorithm, Laplacian heat diffusion, on this network by selecting validated CNV-related genes as the seed nodes. As a result, some novel genes that had unknown but strong relationships with validated genes were identified. Furthermore, we used a screening procedure to extract the most essential genes. Eleven latent CNV-related genes were finally obtained. Extensive analyses were performed to confirm that these genes are novel CNV-related genes.

Interplay between SARS-CoV-2-derived miRNAs, immune system, vitamin D pathway and respiratory system

The new coronavirus pandemic started in China in 2019. The intensity of the disease can range from mild to severe, leading to death in many cases. Despite extensive research in this area, the exact molecular nature of virus is not fully recognized; however, according to pieces of evidence, one of the mechanisms of virus pathogenesis is through the function of viral miRNAs. So, we hypothesized that SARS-CoV-2 pathogenesis may be due to targeting important genes in the host with its miRNAs, which involved in the respiratory system, immune pathways and vitamin D pathways, thus possibly contributing to disease progression and virus survival. Potential miRNA precursors and mature miRNA were predicted and confirmed based on the virus genome. The next step was to predict and identify their target genes and perform functional enrichment analysis to recognize the biological processes connected with these genes in the three pathways mentioned above through several comprehensive databases. Finally, cis-acting regulatory elements in 5' regulatory regions were analysed, and the analysis of available RNAseq data determined the expression level of genes. We revealed that thirty-nine mature miRNAs could theoretically derive from the SARS-CoV-2 genome. Functional enrichment analysis elucidated three highlighted pathways involved in SARS-CoV-2 pathogenesis: vitamin D, immune system and respiratory system. Our finding highlighted genes' involvement in three crucial molecular pathways and may help develop new therapeutic targets related to SARS-CoV-2.

Methylation of Host Genes Associated with Coronavirus Infection from Birth to 26 Years

DNA methylation (DNAm) patterns over time at 1146 CpGs on coronavirus-related genes were assessed to understand whether the varying differences in susceptibility, symptoms, and the outcomes of the SARS-CoV-2 infection in children and young adults could be explained through epigenetic alterations in a host cell’s transcriptional apparatus to coronaviruses. DNAm data from the Isle of Wight birth cohort (IOWBC) at birth, 10, 18, and 26 years of age were included. Linear mixed models with repeated measurements stratified by sex were used to examine temporal patterns, and cluster analysis was performed to identify CpGs following similar patterns. CpGs on autosomes and sex chromosomes were analyzed separately. The association of identified CpGs and expression of their genes were evaluated. Pathway enrichment analyses of the genes was conducted at FDR = 0.05. DNAm at 635 of the 1146 CpGs on autosomes showed statistically significant time effects (FDR = 0.05). The 635 CpGs were classified into five clusters with each representing a unique temporal pattern of DNAm. Of the 29 CpGs on sex chromosomes, DNAm at seven CpGs in males and eight CpGs in females showed time effects (FDR = 0.05). Sex-specific and non-specific associations of DNAm with gene expression were found at 24 and 93 CpGs, respectively. Genes which mapped the 643 CpGs represent 460 biological processes. We suggest that the observed variability in DNAm with advancing age may partially explain differing susceptibility, disease severity, and mortality of coronavirus infections among different age groups.

Consequences of coronavirus infections for primitive and mature hematopoietic cells: new insights and why it matters

PURPOSE OF REVIEW

In recent history there have been three outbreaks of betacoronavirus infections in humans, with the most recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; causing Coronavirus disease 2019 [COVID-19]) outbreak leading to over two million deaths, with a rapidly rising death toll. Much remains unknown about host cells and tissues affected by coronavirus infections, including the hematopoietic system. Here, we discuss the recent findings examining effects that coronavirus infection or exposure has on hematopoietic cells and the clinical implications for these effects.

RECENT FINDINGS

Recent studies have centered on SARS-CoV-2, demonstrating that hematopoietic stem and progenitor cells and mature immune cells may be susceptible to infection and are impacted functionally by exposure to SARS-CoV-2 Spike protein. These findings have important implications regarding hematologic complications arising from COVID-19 and other coronavirus-induced disease, which we discuss here.

SUMMARY

Infection with coronaviruses sometimes leads to hematologic complications in patients, and these hematologic complications are associated with poorer prognosis. These hematologic complications may be caused by coronavirus direct infection or impact on primitive hematopoietic cells or mature immune cells, by indirect effects on these cells, or by a combination thereof. It is important to understand how hematologic complications arise in order to seek new treatments to improve patient outcomes.

Computationally approached inhibition potential of Tinospora cordifolia towards COVID-19 targets

The recent emergence of novel coronavirus (SARS-CoV-2) has been a major threat to human society, as the challenge of finding suitable drug or vaccine is not met till date. With increasing morbidity and mortality, the need for novel drug candidates is under great demand. The investigations are progressing towards COVID-19 therapeutics. Among the various strategies employed, the use of repurposed drugs is competing along with novel drug inventions. Based on the therapeutic significance, the chemical constituents from the extract of Tinospora cordifolia belonging to various classes like alkaloids, lignans, steroids and terpenoids are investigated as potential drug candidates for COVID-19. The inhibition potential of the proposed compounds against viral spike protein and human receptor ACE2 were evaluated by computational molecular modeling (Auto dock), along with their ADME/T properties. Prior to docking, the initial geometry of the compounds were optimized by Density functional theory (DFT) method employing B3LYP hybrid functional and 6-311 +  + G (d,p) basis set. The results of molecular docking and ADME/T studies have revealed 6 constituents as potential drug candidates that can inhibit the binding of SARS-CoV-2 spike protein with the human receptor ACE2 protein. The narrowed down list of constituents from Tinospora cordifolia paved way for further tuning their ability to inhibit COVID-19 by modifying the chemical structures and by employing computational geometry optimization and docking methods.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-021-00666-7.