Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes

IFIH1 loss of function predisposes to inflammatory and SARS-CoV-2-related infectious diseases

The IFIH1 gene, encoding melanoma differentiation-associated protein 5 (MDA5), is an indispensable innate immune regulator involved in the early detection of viral infections. Previous studies described MDA5 dysregulation in weakened immunological responses, and increased susceptibility to microbial infections and autoimmune disorders. Monoallelic gain-of-function of the IFIH1 gene has been associated with multisystem disorders, namely Aicardi-Goutieres and Singleton-Merten syndromes, while biallelic loss causes immunodeficiency. In this study, nine patients suffering from recurrent infections, inflammatory diseases, severe COVID-19 or multisystem inflammatory syndrome in children (MIS-C) were identified with putative loss-of-function IFIH1 variants by whole-exome sequencing. All patients revealed signs of lymphopaenia and an increase in inflammatory markers, including CRP, amyloid A, ferritin and IL-6. One patient with a pathogenic homozygous variant c.2807+1G>A was the most severe case showing immunodeficiency and glomerulonephritis. The c.1641+1G>C variant was identified in the heterozygous state in patients suffering from periodic fever, COVID-19 or MIS-C, while the c.2016delA variant was identified in two patients with inflammatory bowel disease or MIS-C. There was a significant association between IFIH1 monoallelic loss of function and susceptibility to infections in males. Expression analysis showed that PBMCs of one patient with a c.2016delA variant had a significant decrease in ISG15, IFNA and IFNG transcript levels, compared to normal PBMCs, upon stimulation with Poly(I:C), suggesting that MDA5 receptor truncation disrupts the immune response. Our findings accentuate the implication of rare monogenic IFIH1 loss-of-function variants in altering the immune response, and severely predisposing patients to inflammatory and infectious diseases, including SARS-CoV-2-related disorders.

In silico analysis of the impact of toxic metals on COVID-19 complications: molecular insights

COVID-19 can cause a range of complications, including cardiovascular, renal, and/or respiratory insufficiencies, yet little is known of its potential effects in persons exposed to toxic metals. The aim of this study was to answer this question with in silico toxicogenomic methods that can provide molecular insights into COVID-19 complications owed to exposure to arsenic, cadmium, lead, mercury, nickel, and chromium. For this purpose we relied on the Comparative Toxicogenomic Database (CTD), GeneMANIA, and ToppGene Suite portal and identified a set of five common genes (IL1B, CXCL8, IL6, IL10, TNF) for the six metals and COVID-19, all of which code for pro-inflammatory and anti-inflammatory cytokines. The list was expanded with additional 20 related genes. Physical interactions are the most common between the genes affected by the six metals (77.64 %), while the dominant interaction between the genes affected by each metal separately is co-expression (As 56.35 %, Cd 64.07 %, Pb 71.5 %, Hg 81.91 %, Ni 64.28 %, Cr 88.51 %). Biological processes, molecular functions, and pathways in which these 25 genes participate are closely related to cytokines and cytokine storm implicated in the development of COVID-19 complications. In other words, our findings confirm that exposure to toxic metals, alone or in combinations, might escalate COVID-19 severity.

Integrated analysis of RNA-seq datasets reveals novel targets and regulators of COVID-19 severity

During the COVID-19 pandemic, RNA-seq datasets were produced to investigate the virus-host relationship. However, much of these data remains underexplored. To improve the search for molecular targets and biomarkers, we performed an integrated analysis of multiple RNA-seq datasets, expanding the cohort and including patients from different countries, encompassing severe and mild COVID-19 patients. Our analysis revealed that severe COVID-19 patients exhibit overexpression of genes coding for proteins of extracellular exosomes, endomembrane system, and neutrophil granules (e.g., S100A9, LY96, and RAB1B), which may play an essential role in the cellular response to infection. Concurrently, these patients exhibit down-regulation of genes encoding components of the T cell receptor complex and nucleolus, including TP53, IL2RB, and NCL Finally, SPI1 may emerge as a central transcriptional factor associated with the up-regulated genes, whereas TP53, MYC, and MAX were associated with the down-regulated genes during COVID-19. This study identified targets and transcriptional factors, lighting on the molecular pathophysiology of syndrome coronavirus 2 infection.

Upper Airway Epithelial Tissue Transcriptome Analysis Reveals Immune Signatures Associated with COVID-19 Severity in Ghanaians

The immunological signatures driving the severity of coronavirus disease 19 (COVID-19) in Ghanaians remain poorly understood. We performed bulk transcriptome sequencing of nasopharyngeal samples from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected Ghanaians with mild and severe COVID-19, as well as healthy controls to characterize immune signatures at the primary SARS-CoV-2 infection site and identify drivers of disease severity. Generally, a heightened antiviral response was observed in SARS-CoV-2-infected Ghanaians compared with uninfected controls. COVID-19 severity was associated with immune suppression, overexpression of proinflammatory cytokines, including CRNN, IL1A, S100A7, and IL23A, and activation of pathways involved in keratinocyte proliferation. SAMD9L was among the differentially regulated interferon-stimulated genes in our mild and severe disease cohorts, suggesting that it may play a critical role in SARS-CoV-2 pathogenesis. By comparing our data with a publicly available dataset from a non-African (Indians) (GSE166530), an elevated expression of antiviral response-related genes was noted in COVID-19-infected Ghanaians. Overall, the study describes immune signatures driving COVID-19 severity in Ghanaians and identifies immune drivers that could serve as potential prognostic markers for future outbreaks or pandemics. It further provides important preliminary evidence suggesting differences in antiviral response at the upper respiratory interface in sub-Saharan Africans (Ghanaians) and non-Africans, which could be contributing to the differences in disease outcomes. Further studies using larger datasets from different populations will expand on these findings.

Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence

With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.

Genetic determinants of severe COVID-19 in young Asian and Middle Eastern patients: a case series

Studies of genetic factors associated with severe COVID-19 in young adults have been limited in non-Caucasian populations. Here, we clinically characterize a case series of patients with COVID-19, who were otherwise healthy, young adults (N = 55; mean age 34.1 ± SD 5.0 years) from 16 Asian, Middle Eastern, and North African countries. Using whole exome sequencing, we identify rare, likely deleterious variants affecting 16 immune-related genes in 17 out of 55 patients (31%), including 7 patients (41% of all carriers or 12.7% of all patients) who harbored multiple such variants mainly in interferon and toll-like receptor genes. Protein network analysis as well as transcriptomic analysis of nasopharyngeal swabs from an independent COVID-19 cohort (N = 50; 42% Asians and 22% Arabs) revealed that most of the altered genes, as identified by whole exome sequencing, and the associated molecular pathways were significantly altered in COVID-19 patients. Genetic variants tended to be associated with mortality, intensive care admission, and ventilation support. Our clinical cases series, genomic and transcriptomic findings suggest a possible role for interferon pathway genes in severe COVID-19 and highlight the importance of extending genetic studies to diverse populations to better understand the human genetics of disease.

Role of CD27 and SAMHD1 and their genetic susceptibility to COVID-19

SARS-CoV-2, which initiated the worldwide COVID-19 epidemic in 2019, has rapidly emerged and spread, resulting in significant public health challenges worldwide. The COVID-19 severity signs and their association with specific genes have been investigated to better comprehend this phenomenon. In this study, several genes were investigated to see whether they correspond with COVID-19 sickness severity. This research aims to determine and evaluate certain gene expression levels associated with the immune system, as these genes were reported to play important roles in immune control during the COVID-19 outbreak. We analyzed two immunity-linked genes: CD27 and SAMHD1 in COVID-19 patients' samples using RT-PCR, compared them to the samples from recovered, immunized, and healthy individuals. These data were examined to determine the potential relationships between clinical patterns, illness severity, and progression, and SARS-CoV-2 infection immunology. We observed that CD27 gene expression was higher in COVID-19 vaccinated and control groups, but lower in active and recovered COVID-19 patients. On the other hand, SAMHD1 gene expression was elevated in infected and recovered COVID-19 groups. According to our study, the proteins CD27 and SAMHD1 are essential for controlling the immunological response to COVID-19. Changes in their expression levels could increase the susceptibility of patients to severe complications associated with the disease. Therefore, the gene expression level of these proteins could serve as viable prognostic markers for COVID-19.

Differential expression of biomarkers in saliva related to SARS-CoV-2 infection in patients with mild, moderate and severe COVID-19

BACKGROUND

Severe COVID-19 is a disease characterized by profound dysregulation of the innate immune system. There is a need to identify highly reliable prognostic biomarkers that can be rapidly assessed in body fluids for early identification of patients at higher risk for hospitalization and/or death. This study aimed to assess whether differential gene expression of immune response molecules and cellular enzymes, detected in saliva samples of COVID-19 patients, occurs according to disease severity staging.

METHODS

In this cross-sectional study, subjects with a COVID-19 diagnosis were classified as having mild, moderate, or severe disease based on clinical features. Transcripts of genes encoding 6 biomarkers, IL-1β, IL-6, IL-10, C-reactive protein, IDO1 and ACE2, were measured by RT‒qPCR in saliva samples of patients and COVID-19-free individuals.

RESULTS

The gene expression levels of all 6 biomarkers in saliva were significantly increased in severe disease patients compared to mild/moderate disease patients and healthy controls. A significant strong inverse relationship between oxemia and the level of expression of the 6 biomarkers (Spearman's correlation coefficient between -0.692 and -0.757; p < 0.001) was found.

CONCLUSIONS

Biomarker gene expression determined in saliva samples still needs to be validated as a potentially valuable predictor of severe clinical outcomes early at the onset of COVID-19 symptoms.

SARS-CoV-2 infection severity and mortality is modulated by repeat-mediated regulation of alternative splicing

Like single-stranded RNA viruses, SARS-CoV-2 hijacks the host transcriptional machinery for its own replication. Numerous traditional differential gene expression-based investigations have examined the diverse clinical symptoms caused by SARS-CoV-2 infection. The virus, on the other hand, also affects the host splicing machinery, causing host transcriptional dysregulation, which can lead to diverse clinical outcomes. Hence, in this study, we performed host transcriptome sequencing of 125 hospital-admitted COVID-19 patients to understand the transcriptomic differences between the severity sub-phenotypes of mild, moderate, severe, and mortality. We performed transcript-level differential expression analysis, investigated differential isoform usage, looked at the splicing patterns within the differentially expressed transcripts (DET), and elucidated the possible genome regulatory features. Our DTE analysis showed evidence of diminished transcript length and diversity as well as altered promoter site usage in the differentially expressed protein-coding transcripts in the COVID-19 mortality patients. We also investigated the potential mechanisms driving the alternate splicing and discovered a compelling differential enrichment of repeats in the promoter region and a specific enrichment of SINE (Alu) near the splicing sites of differentially expressed transcripts. These findings suggested a repeat-mediated plausible regulation of alternative splicing as a potential modulator of COVID-19 disease severity. In this work, we emphasize the role of scarcely elucidated functional role of alternative splicing in influencing COVID-19 disease severity sub-phenotypes, clinical outcomes, and its putative mechanism. IMPORTANCE The wide range of clinical symptoms reported during the COVID-19 pandemic inherently highlights the numerous factors that influence the progression and prognosis of SARS-CoV-2 infection. While several studies have investigated the host response and discovered immunological dysregulation during severe infection, most of them have the common theme of focusing only up to the gene level. Viruses, especially RNA viruses, are renowned for hijacking the host splicing machinery for their own proliferation, which inadvertently puts pressure on the host transcriptome, exposing another side of the host response to the pathogen challenge. Therefore, in this study, we examine host response at the transcript-level to discover a transcriptional difference that culminates in differential gene-level expression. Importantly, this study highlights diminished transcript diversity and possible regulation of transcription by differentially abundant repeat elements near the promoter region and splicing sites in COVID-19 mortality patients, which together with differentially expressed isoforms hold the potential to elaborate disease severity and outcome.

The Janus face of proliferating plasmablasts in dengue and COVID-19 infections

Introduction

B cells play an integral role in the immune response to both dengue fever and COVID-19. Prior scRNAseq analyses of peripheral plasmablasts in COVID-19 have revealed a heterogeneous population with distinct cell subsets associated with proliferation; prior studies in patients with dengue fever have likewise shown the presence of proliferative pre-plasmablasts in the circulation. These findings may have implications for disease severity. In this study, we sought to gain a mechanistic understanding of the intracellular processes in naive and memory B cells that are associated with and may lead to an expanded proliferative plasmablast population in the circulation.

Methods

We analyzed age-controlled (pediatric and adult), peripheral blood mononuclear cell scRNAseq datasets from patients infected with either dengue (primary or secondary) or COVID-19 (non-severe or severe) from previously published studies. Our preliminary analysis showed that pediatric patients with dengue and adults with COVID-19 had an expanded proliferative plasmablast (p-PB) population. By contrast, neither the adults with dengue nor the children with COVID-19 in our dataset had p-PBs. We used this distinctive preliminary signature to guide our analyses design and expanded our analyses to naive and memory B cells.

Results

In age/disease conditions with and without p-PBs, we found differences in cell sensing and activation, including via the B cell receptor and downstream signal transduction. Likewise, inflammation was mediated differently: relative to groups without p-PBs, those with p-PBs had increased expression of interferon response and S100 genes (particularly severe COVID-19). Furthermore, several transcription factors at the nexus of activation, inflammation, and cell fate decisions were expressed differently in groups with and without p-PBs.

Discussion

We used dengue and COVID-19 infections in adult and pediatric patients (focusing on naive B, memory B, and plasmablast cells) as a model to better understand the mechanisms that may give rise to p-PB populations in the circulation. Our results indicate that a more pro-inflammatory state in naive and memory B cells correlated with - and could influence the generation of- proliferating plasmablasts. Further exploration of these mechanisms will have implications for immune memory, vaccine development, and post-viral autoimmune syndromes.

Coagulopathy and thromboembolic events a pathogenic mechanism of COVID-19 associated with mortality: An updated review

During 2019, the SARS-CoV-2 emerged from China, and during months, COVID-19 spread in many countries around the world. The expanding data about pathogenesis of this virus could elucidate the exact mechanism by which COVID-19 caused death in humans. One of the pathogenic mechanisms of this disease is coagulation. Coagulation disorders that affect both venous and arterial systems occur in patients with COVID-19. The possible mechanism involved in the coagulation could be excessive inflammation induced by SARS-CoV-2. However, it is not yet clear well how SARS-CoV-2 promotes coagulopathy. However, some factors, such as pulmonary endothelial cell damage and some anticoagulant system disorders, are assumed to have an important role. In this study, we assessed conducted studies about COVID-19-induced coagulopathy to obtain clearer vision of the wide range of manifestations and possible pathogenesis mechanisms.

Dysregulated early transcriptional signatures linked to mast cell and interferon responses are implicated in COVID-19 severity

Background

Dysregulated immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are thought to underlie the progression of coronavirus disease 2019 (COVID-19) to severe disease. We sought to determine whether early host immune-related gene expression could predict clinical progression to severe disease.

Methods

We analysed the expression of 579 immunological genes in peripheral blood mononuclear cells taken early after symptom onset using the NanoString nCounter and compared SARS-CoV-2 negative controls with SARS-CoV-2 positive subjects with mild (SARS+ Mild) and Moderate/Severe disease to evaluate disease outcomes. Biobanked plasma samples were also assessed for type I (IFN-α2a and IFN-β), type II (IFN-γ) and type III (IFN-λ1) interferons (IFNs) as well as 10 additional cytokines using multiplex immunoassays.

Results

We identified 19 significantly deregulated genes in 62 SARS-CoV-2 positive subject samples within 5 days of symptom onset and 58 SARS-CoV-2 negative controls and found that type I interferon (IFN) signalling (MX1, IRF7, IFITM1, IFI35, STAT2, IRF4, PML, BST2, STAT1) and genes encoding proinflammatory cytokines (TNF, TNFSF4, PTGS2 and IL1B) were upregulated in both SARS+ groups. Moreover, we found that FCER1, involved in mast cell activation, was upregulated in the SARS+ Mild group but significantly downregulated in the SARS+ Moderate/Severe group. In both SARS+ groups we discovered elevated interferon type I IFN-α2a, type II IFN and type III IFN λ1 plasma levels together with higher IL-10 and IL-6. These results indicate that those with moderate or severe disease are characterised by deficiencies in a mast cell response together with IFN hyper-responsiveness, suggesting that early host antiviral immune responses could be a cause and not a consequence of severe COVID-19.

Conclusions

This study suggests that early host immune responses linking defects in mast cell activation with host interferon responses correlates with more severe outcomes in COVID-19. Further characterisation of this pathway could help inform better treatment for vulnerable individuals.

Vitamin B12 attenuates leukocyte inflammatory signature in COVID-19 via methyl-dependent changes in epigenetic markings

COVID-19 induces chromatin remodeling in host immune cells, and it had previously been shown that vitamin B12 downregulates some inflammatory genes via methyl-dependent epigenetic mechanisms. In this work, whole blood cultures from moderate or severe COVID-19 patients were used to assess the potential of B12 as adjuvant drug. The vitamin normalized the expression of a panel of inflammatory genes still dysregulated in the leukocytes despite glucocorticoid therapy during hospitalization. B12 also increased the flux of the sulfur amino acid pathway, that regulates the bioavailability of methyl. Accordingly, B12-induced downregulation of CCL3 strongly and negatively correlated with the hypermethylation of CpGs in its regulatory regions. Transcriptome analysis revealed that B12 attenuates the effects of COVID-19 on most inflammation-related pathways affected by the disease. As far as we are aware, this is the first study to demonstrate that pharmacological modulation of epigenetic markings in leukocytes favorably regulates central components of COVID-19 physiopathology.

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.

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules ( lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning algorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach

SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42979-023-01703-6.

Selection of Ideal Reference Genes for Gene Expression Analysis in COVID-19 and Mucormycosis

Selection of reference genes during real-time quantitative PCR (qRT-PCR) is critical to determine accurate and reliable mRNA expression. Nonetheless, not a single study has investigated the expression stability of candidate reference genes to determine their suitability as internal controls in SARS-CoV-2 infection or COVID-19-associated mucormycosis (CAM). Using qRT-PCR, we determined expression stability of the nine most commonly used housekeeping genes, namely, TATA-box binding protein (TBP), cyclophilin (CypA), β-2-microglobulin (B2M), 18S rRNA (18S), peroxisome proliferator-activated receptor gamma (PPARG) coactivator 1 alpha (PGC-1α), glucuronidase beta (GUSB), hypoxanthine phosphoribosyltransferase 1 (HPRT-1), β-ACTIN, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in patients with COVID-19 of various severities (asymptomatic, mild, moderate, and severe) and those with CAM. We used statistical algorithms (delta-CT [threshold cycle], NormFinder, BestKeeper, GeNorm, and RefFinder) to select the most appropriate reference gene and observed that clinical severity profoundly influences expression stability of reference genes. CypA demonstrated the most consistent expression irrespective of disease severity and emerged as the most suitable reference gene in COVID-19 and CAM. Incidentally, GAPDH, the most commonly used reference gene, showed the maximum variations in expression and emerged as the least suitable. Next, we determined expression of nuclear factor erythroid 2-related factor 2 (NRF2), interleukin-6 (IL-6), and IL-15 using CypA and GAPDH as internal controls and show that CypA-normalized expression matches well with the RNA sequencing-based expression of these genes. Further, IL-6 expression correlated well with the plasma levels of IL-6 and C-reactive protein, a marker of inflammation. In conclusion, GAPDH emerged as the least suitable and CypA as the most suitable reference gene in COVID-19 and CAM. The results highlight the expression variability of housekeeping genes due to disease severity and provide a strong rationale for identification of appropriate reference genes in other chronic conditions as well. IMPORTANCE Gene expression studies are critical to develop new diagnostics, therapeutics, and prognostic modalities. However, accurate determination of expression requires data normalization with a reference gene, whose expression does not vary across different disease stages. Misidentification of a reference gene can produce inaccurate results. Unfortunately, despite the global impact of COVID-19 and an urgent unmet need for better treatment, not a single study has investigated the expression stability of housekeeping genes across the disease spectrum to determine their suitability as internal controls. Our study identifies CypA and then TBP as the two most suitable reference genes for COVID-19 and CAM. Further, GAPDH, the most commonly used reference gene in COVID-19 studies, turned out to be the least suitable. This work fills an important gap in the field and promises to facilitate determination of an accurate expression of genes to catalyze development of novel molecular diagnostics and therapeutics for improved patient care.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

SARS-CoV-2 induces "cytokine storm" hyperinflammatory responses in RA patients through pyroptosis

Background

The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs.

Methods

We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design.

Results

COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways (Network map of SARS-CoV-2 signaling pathway, activation of NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways (IL, nuclear factor-kappa B (NF-κB), TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1.

Conclusions

Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression.

Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19

COVID-19 pandemic poses a severe threat to public health. However, so far, there are no effective drugs for COVID-19. Transcriptomic changes and key genes related to Th2 cells in COVID-19 have not been reported. These genes play an important role in host interactions with SARS-COV-2 and may be used as promising target. We analyzed five COVID-19-associated GEO datasets (GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627) using the xCell algorithm and weighted gene co-expression network analysis (WGCNA). Results showed that 5 closely correlated modular genes to COVID-19 and Th2 cell enrichment levels, including purple, blue, pink, tan and turquoise, were intersected with differentially expressed genes (DEGs) and 648 shared genes were obtained. GO and KEGG pathway enrichment analyses revealed that they were enriched in cell proliferation, differentiation, and immune responses after virus infection. The most significantly enriched pathway involved the regulation of viral life cycle. Three key genes, namely CCNB1, BUB1, and UBE2C, may clarify the pathogenesis of COVID-19 associated with Th2 cells. 11 drug candidates were identified that could down-regulate three key genes using the cMAP database and demonstrated strong drugs binding energies aganist the three keygenes using molecular docking methods. BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.

Obesity and metabolic dysfunction drive sex-associated differential disease profiles in hACE2-mice challenged with SARS-CoV-2

Severe outcomes from SARS-CoV-2 infection are highly associated with preexisting comorbid conditions like hypertension, diabetes, and obesity. We utilized the diet-induced obesity (DIO) model of metabolic dysfunction in K18-hACE2 transgenic mice to model obesity as a COVID-19 comorbidity. Female DIO, but not male DIO mice challenged with SARS-CoV-2 were observed to have shortened time to morbidity compared to controls. Increased susceptibility to SARS-CoV-2 in female DIO was associated with increased viral RNA burden and interferon production compared to males. Transcriptomic analysis of the lungs from all mouse cohorts revealed sex- and DIO-associated differential gene expression profiles. Male DIO mice after challenge had decreased expression of antibody-related genes compared to controls, suggesting antibody producing cell localization in the lung. Collectively, this study establishes a preclinical comorbidity model of COVID-19 in mice where we observed sex- and diet-specific responses that begin explaining the effects of obesity and metabolic disease on COVID-19 pathology.

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Transcriptomic analysis of infected lungs revealed unique sex-dependent differences

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Obese female mice have high viral RNA burden and interferon production in the lung

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Male mice have altered antibody and T cell response gene profiles after viral challenge

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Metabolic dysfunction comorbidity can be studied in the hACE2 mouse model of COVID-19

IFI44 is an immune evasion biomarker for SARS-CoV-2 and Staphylococcus aureus infection in patients with RA

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic of severe coronavirus disease 2019 (COVID-19). Staphylococcus aureus is one of the most common pathogenic bacteria in humans, rheumatoid arthritis (RA) is among the most prevalent autoimmune conditions. RA is a significant risk factor for SARS-CoV-2 and S. aureus infections, although the mechanism of RA and SARS-CoV-2 infection in conjunction with S. aureus infection has not been elucidated. The purpose of this study is to investigate the biomarkers and disease targets between RA and SARS-CoV-2 and S. aureus infections using bioinformatics analysis, to search for the molecular mechanisms of SARS-CoV-2 and S. aureus immune escape and potential drug targets in the RA population, and to provide new directions for further analysis and targeted development of clinical treatments.

Methods

The RA dataset (GSE93272) and the S. aureus bacteremia (SAB) dataset (GSE33341) were used to obtain differentially expressed gene sets, respectively, and the common differentially expressed genes (DEGs) were determined through the intersection. Functional enrichment analysis utilizing GO, KEGG, and ClueGO methods. The PPI network was created utilizing the STRING database, and the top 10 hub genes were identified and further examined for functional enrichment using Metascape and GeneMANIA. The top 10 hub genes were intersected with the SARS-CoV-2 gene pool to identify five hub genes shared by RA, COVID-19, and SAB, and functional enrichment analysis was conducted using Metascape and GeneMANIA. Using the NetworkAnalyst platform, TF-hub gene and miRNA-hub gene networks were built for these five hub genes. The hub gene was verified utilizing GSE17755, GSE55235, and GSE13670, and its effectiveness was assessed utilizing ROC curves. CIBERSORT was applied to examine immune cell infiltration and the link between the hub gene and immune cells.

Results

A total of 199 DEGs were extracted from the GSE93272 and GSE33341 datasets. KEGG analysis of enrichment pathways were NLR signaling pathway, cell membrane DNA sensing pathway, oxidative phosphorylation, and viral infection. Positive/negative regulation of the immune system, regulation of the interferon-I (IFN-I; IFN-α/β) pathway, and associated pathways of the immunological response to viruses were enriched in GO and ClueGO analyses. PPI network and Cytoscape platform identified the top 10 hub genes: RSAD2, IFIT3, GBP1, RTP4, IFI44, OAS1, IFI44L, ISG15, HERC5, and IFIT5. The pathways are mainly enriched in response to viral and bacterial infection, IFN signaling, and 1,25-dihydroxy vitamin D3. IFI44, OAS1, IFI44L, ISG15, and HERC5 are the five hub genes shared by RA, COVID-19, and SAB. The pathways are primarily enriched for response to viral and bacterial infections. The TF-hub gene network and miRNA-hub gene network identified YY1 as a key TF and hsa-mir-1-3p and hsa-mir-146a-5p as two important miRNAs related to IFI44. IFI44 was identified as a hub gene by validating GSE17755, GSE55235, and GSE13670. Immune cell infiltration analysis showed a strong positive correlation between activated dendritic cells and IFI44 expression.

Conclusions

IFI144 was discovered as a shared biomarker and disease target for RA, COVID-19, and SAB by this study. IFI44 negatively regulates the IFN signaling pathway to promote viral replication and bacterial proliferation and is an important molecular target for SARS-CoV-2 and S. aureus immune escape in RA. Dendritic cells play an important role in this process. 1,25-Dihydroxy vitamin D3 may be an important therapeutic agent in treating RA with SARS-CoV-2 and S. aureus infections.

Noninvasive nasopharyngeal proteomics of COVID-19 patient identify abnormalities related to complement and coagulation cascade and mucosal immune system

Serum or plasma have been the primary focus of proteomics studies for COVID-19 to identity biomarkers and potential drug targets. The nasal mucosal environment which consists of lipids, mucosal immune cells, and nasal proteome, has been largely neglected but later revealed to have critical role combating SARS-CoV-2 infection. We present a bottom-up proteomics investigation of the host response to SARS-CoV-2 infection in the nasopharyngeal environment, featuring a noninvasive approach using proteins in nasopharyngeal swabs collected from groups of 76 SARS-CoV-2 positive and 76 negative patients. Results showed that 31 significantly down-regulated and 6 up-regulated proteins were identified (p < 0.05, log₂ FC > 1.3) in SARS-CoV-2 positive patient samples as compared to the negatives; these proteins carry potential value as markers for the early detection of COVID-19, disease monitoring, as well as be drug targets. The down-regulation of coagulation factor 5 indicates a thrombotic abnormality in COVID-19 patients and the decreased IgG4 suggests an abnormal immune response at the point of entry in human nasopharyngeal environment, which is in consistent with KEGG and GO pathway analysis. Our study also demonstrated that mass spectrometry proteomics analysis of nasopharyngeal swabs can be used as a powerful early approach to evaluate host response to SARS-CoV-2 viral infection.

RNA sequence analysis of nasopharyngeal swabs from asymptomatic and mildly symptomatic patients with COVID-19

OBJECTIVES

The characterization of asymptomatic and mildly symptomatic patients with COVID-19 by observing changes in gene expression profile and possible bacterial coinfection is relevant to be investigated. We aimed to identify transcriptomic and coinfection profiles in both groups of patients.

METHODS

A ribonucleic acid (RNA) sequence analysis on nasopharyngeal swabs were performed using a shotgun sequencing pipeline. Differential gene analysis, viral genome assembly, and metagenomics analysis were further performed using the retrieved data.

RESULTS

Both groups of patients underwent a cilia modification and mRNA splicing. Modulations in macroautophagy, epigenetics, and cell cycle processes were observed specifically in the asymptomatic group. Modulation in the RNA transport was found specifically in the mildly symptomatic group. The mildly symptomatic group showed modulation in the RNA transport and upregulation of autophagy regulator genes and genes in the complement system. No link between viral variants and disease severity was found. Microbiome analysis revealed the elevation of Streptococcus pneumoniae and Veillonella parvula proportion in symptomatic patients.

CONCLUSION

A reduction in the autophagy influx and modification in the epigenetic profile might be involved in halting the disease progression. A global dysregulation of RNA processing and translation might cause more severe outcomes in symptomatic individuals. Coinfection by opportunistic microflora should be taken into account when assessing the possible outcome of SARS-CoV-2 infection.

The Chemokines CXC, CC and C in the Pathogenesis of COVID-19 Disease and as Surrogates of Vaccine-Induced Innate and Adaptive Protective Responses

COVID-19 is one of the progressive viral pandemics that originated from East Asia. COVID-19 or SARS-CoV-2 has been shown to be associated with a chain of physio-pathological mechanisms that are basically immunological in nature. In addition, chemokines have been proposed as a subgroup of chemotactic cytokines with different activities ranging from leukocyte recruitment to injury sites, irritation, and inflammation to angiostasis and angiogenesis. Therefore, researchers have categorized the chemotactic elements into four classes, including CX3C, CXC, CC, and C, based on the location of the cysteine motifs in their structures. Considering the severe cases of COVID-19, the hyperproduction of particular chemokines occurring in lung tissue as well as pro-inflammatory cytokines significantly worsen the disease prognosis. According to the studies conducted in the field documenting the changing expression of CXC and CC chemokines in COVID-19 cases, the CC and CXC chemokines contribute to this pandemic, and their impact could reflect the development of reasonable strategies for COVID-19 management. The CC and the CXC families of chemokines are important in host immunity to viral infections and along with other biomarkers can serve as the surrogates of vaccine-induced innate and adaptive protective responses, facilitating the improvement of vaccine efficacy. Furthermore, the immunogenicity elicited by the chemokine response to adenovirus vector vaccines may constitute the basis of vaccine-induced immune thrombotic thrombocytopaenia.

Considerations for Novel COVID-19 Mucosal Vaccine Development

Mucosal surfaces are the first contact sites of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most SARS-CoV-2 vaccines induce specific IgG responses but provide limited mucosal immunity. Cytokine B-cell activation factor (BAFF) and A proliferation-inducing ligand (APRIL) in the tumor necrosis factor (TNF) superfamily play key immunological functions during B cell development and antibody production. Furthermore, homeostatic chemokines, such as C-X-C motif chemokine ligand 13 (CXCL13), chemokine (C–C motif) ligand 19 (CCL19), and CCL21, can induce B- and T-cell responses to infection and promote the formation of inducible bronchus-associated lymphoid tissues (iBALT), where specific local immune responses and memory cells are generated. We reviewed the role of BAFF, APRIL, CXCL13, CCL19, and CCL21 in the activation of local B-cell responses and antibody production, and the formation of iBALT in the lung following viral respiratory infections. We speculate that mucosal vaccines may offer more efficient protection against SARS-CoV-2 infection than systematic vaccines and hypothesize that a novel SARS-CoV-2 mRNA mucosal vaccine using BAFF/APRIL or CXCL13 as immunostimulants combined with the spike protein-encoding mRNA may enhance the efficiency of the local immune response and prevent the early stages of SARS-CoV-2 replication and the rapid viral clearance from the airways.

A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity

Coronavirus Disease-19 (COVID-19) symptoms range from mild to severe illness; the cause for this differential response to infection remains unknown. Unravelling the immune mechanisms acting at different levels of the colonization process might be key to understand these differences. We carried out a multi-tissue (nasal, buccal and blood; n = 156) gene expression analysis of immune-related genes from patients affected by different COVID-19 severities, and healthy controls through the nCounter technology. Mild and asymptomatic cases showed a powerful innate antiviral response in nasal epithelium, characterized by activation of interferon (IFN) pathway and downstream cascades, successfully controlling the infection at local level. In contrast, weak macrophage/monocyte driven innate antiviral response and lack of IFN signalling activity were present in severe cases. Consequently, oral mucosa from severe patients showed signals of viral activity, cell arresting and viral dissemination to the lower respiratory tract, which ultimately could explain the exacerbated innate immune response and impaired adaptative immune responses observed at systemic level. Results from saliva transcriptome suggest that the buccal cavity might play a key role in SARS-CoV-2 infection and dissemination in patients with worse prognosis. Co-expression network analysis adds further support to these findings, by detecting modules specifically correlated with severity involved in the abovementioned biological routes; this analysis also provides new candidate genes that might be tested as biomarkers in future studies. We also found tissue specific severity-related signatures mainly represented by genes involved in the innate immune system and cytokine/chemokine signalling. Local immune response could be key to determine the course of the systemic response and thus COVID-19 severity. Our findings provide a framework to investigate severity host gene biomarkers and pathways that might be relevant to diagnosis, prognosis, and therapy.

Weighted Gene Co-Expression Network Analysis to Identify Potential Biological Processes and Key Genes in COVID-19-Related Stroke

The purpose of this research was to explore the underlying biological processes causing coronavirus disease 2019- (COVID-19-) related stroke. The Gene Expression Omnibus (GEO) database was utilized to obtain four COVID-19 datasets and two stroke datasets. Thereafter, we identified key modules via weighted gene co-expression network analysis, following which COVID-19- and stroke-related crucial modules were crossed to identify the common genes of COVID-19-related stroke. The common genes were intersected with the stroke-related hub genes screened via Cytoscape software to discover the critical genes associated with COVID-19-related stroke. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for common genes associated with COVID-19-related stroke, and the Reactome database was used to annotate and visualize the pathways involved in the key genes. Two COVID-19-related crucial modules and one stroke-related crucial module were identified. Subsequently, the top five genes were screened as hub genes after visualizing the genes of stroke-related critical module using Cytoscape. By intersecting the COVID-19- and stroke-related crucial modules, 28 common genes for COVID-19-related stroke were identified. ITGA2B and ITGB3 have been further identified as crucial genes of COVID-19-related stroke. Functional enrichment analysis indicated that both ITGA2B and ITGB3 were involved in integrin signaling and the response to elevated platelet cytosolic Ca²⁺, thus regulating platelet activation, extracellular matrix- (ECM-) receptor interaction, the PI3K-Akt signaling pathway, and hematopoietic cell lineage. Therefore, platelet activation, ECM-receptor interaction, PI3K-Akt signaling pathway, and hematopoietic cell lineage may represent the potential biological processes associated with COVID-19-related stroke, and ITGA2B and ITGB3 may be potential intervention targets for COVID-19-related stroke.

Genetic and Clinical Characteristics of Patients in the Middle East With Multisystem Inflammatory Syndrome in Children

IMPORTANCE

Clinical, genetic, and laboratory characteristics of Middle Eastern patients with multisystem inflammatory syndrome in children (MIS-C) have not yet been documented.

OBJECTIVE

To assess the genetic and clinical characteristics of patients with MIS-C of primarily Arab and Asian origin.

DESIGN, SETTING, AND PARTICIPANTS

A prospective, multicenter cohort study was conducted from September 1, 2020, to August 31, 2021, in the United Arab Emirates and Jordan. Forty-five patients with MIS-C and a matched control group of 25 healthy children with a confirmed SARS-CoV-2 infection status were recruited. Whole exome sequencing in all 70 participants was performed to identify rare, likely deleterious variants in patients with MIS-C and to correlate genetic findings with the clinical course of illness.

EXPOSURES

SARS-CoV-2.

MAIN OUTCOMES AND MEASURES

Fever, organ system complications, laboratory biomarkers, whole exome sequencing findings, treatments, and clinical outcomes were measured. The Mann-Whitney U test was used to assess the association between genetic variants and MIS-C attributes. The Fisher exact test was used to compute the genetic burden in MIS-C relative to controls.

RESULTS

A total of 45 patients with MIS-C (23 [51.1%] male; 30 [66.7%] of Middle Eastern origin; mean [SD] age, 6.7 [3.6] years) and 25 controls (17 [68.0%] male; 24 [96.0%] of Middle Eastern origin; mean [SD] age 7.4 [4.0] years) participated in the study. Key inflammatory markers were significantly dysregulated in all patients with MIS-C. Mucocutaneous and gastrointestinal manifestations were each reported in 36 patients (80.0%; 95% CI, 66.1%-89.1%), cardiac findings were reported in 22 (48.9%; 95% CI, 35.0%-63.0%), and neurologic findings were reported in 14 (31.1%; 95% CI, 19.5%-45.6%). Rare, likely deleterious heterozygous variants in immune-related genes, including TLR3, TLR6, IL22RA2, IFNB1, and IFNA6, were identified in 19 patients (42.2%; 95% CI, 29.0%-56.7%), of whom 7 had multiple variants. There was higher enrichment of genetic variants in patients relative to controls (29 vs 3, P < .001). Patients with those variants tended to have earlier disease onset (7 patients [36.8%; 95% CI, 19.1%-58.9%] with genetic findings vs 2 [7.7%; 95% CI, 2.1%-24.1%] without genetic findings were younger than 3 years at onset) and resistance to treatment (8 patients [42.1%; 95% CI, 23.1%-63.7%] with genetic findings vs 3 patients [11.5%; 95% CI, 4.0%-29.0%] without genetic findings received 2 doses of intravenous immunoglobulin).

CONCLUSIONS AND RELEVANCE

The results of this cohort study suggest that rare, likely deleterious genetic variants may contribute to MIS-C disease. This finding paves the way for additional studies with larger, diverse populations to fully characterize the genetic contribution to this new disease entity.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background:

SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.

Methods:

The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.

Results:

The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.

Conclusion:

This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.

Multi-omics evaluation of SARS-CoV-2 infected mouse lungs reveals dynamics of host responses

The outbreak of Coronavirus disease 2019 (COVID-19) throughout the world has caused millions of death, while the dynamics of host responses and the underlying regulation mechanisms during SARS-CoV-2 infection are not well depicted. Lung tissues from a mouse model sensitized to SARS-CoV-2 infection were serially collected at different time points for evaluation of transcriptome, proteome, and phosphoproteome. We showed the ebb and flow of several host responses in the lung across the viral infection. The signaling pathways and kinases regulating networks were alternated at different phases of infection. This multiplex evaluation also revealed that many kinases of the CDK and MAPK family were interactive and served as functional hubs in mediating the signal transduction during SARS-CoV-2 infection. Our study not only revealed the dynamics of lung pathophysiology and their underlying molecular mechanisms during SARS-CoV-2 infection, but also highlighted some molecules and signaling pathways that might guide future investigations on COVID-19 therapies.

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Multi-omics analysis profiles temporal host responses in SARS-CoV-2 infected lungs

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Signaling pathways and kinase regulating networks are dynamically altered

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The CDK and MAPK family are interactive and involved in regulating host responses

Molecular and Clinical Prognostic Biomarkers of COVID-19 Severity and Persistence

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), poses several challenges to clinicians, due to its unpredictable clinical course. The identification of laboratory biomarkers, specific cellular, and molecular mediators of immune response could contribute to the prognosis and management of COVID-19 patients. Of utmost importance is also the detection of differentially expressed genes, which can serve as transcriptomic signatures, providing information valuable to stratify patients into groups, based on the severity of the disease. The role of biomarkers such as IL-6, procalcitonin, neutrophil–lymphocyte ratio, white blood cell counts, etc. has already been highlighted in recently published studies; however, there is a notable amount of new evidence that has not been summarized yet, especially regarding transcriptomic signatures. Hence, in this review, we assess the latest cellular and molecular data and determine the significance of abnormalities in potential biomarkers for COVID-19 severity and persistence. Furthermore, we applied Gene Ontology (GO) enrichment analysis using the genes reported as differentially expressed in the literature in order to investigate which biological pathways are significantly enriched. The analysis revealed a number of processes, such as inflammatory response, and monocyte and neutrophil chemotaxis, which occur as part of the complex immune response to SARS-CoV-2.

SARS-CoV-2 genomes from Saudi Arabia implicate nucleocapsid mutations in host response and increased viral load

Monitoring SARS-CoV-2 spread and evolution through genome sequencing is essential in handling the COVID-19 pandemic. Here, we sequenced 892 SARS-CoV-2 genomes collected from patients in Saudi Arabia from March to August 2020. We show that two consecutive mutations (R203K/G204R) in the nucleocapsid (N) protein are associated with higher viral loads in COVID-19 patients. Our comparative biochemical analysis reveals that the mutant N protein displays enhanced viral RNA binding and differential interaction with key host proteins. We found increased interaction of GSK3A kinase simultaneously with hyper-phosphorylation of the adjacent serine site (S206) in the mutant N protein. Furthermore, the host cell transcriptome analysis suggests that the mutant N protein produces dysregulated interferon response genes. Here, we provide crucial information in linking the R203K/G204R mutations in the N protein to modulations of host-virus interactions and underline the potential of the nucleocapsid protein as a drug target during infection.

Time-Dependent Molecular Motifs of Pulmonary Fibrogenesis in COVID-19

(1) Background: In COVID-19 survivors there is an increased prevalence of pulmonary fibrosis of which the underlying molecular mechanisms are poorly understood; (2) Methods: In this multicentric study, n = 12 patients who succumbed to COVID-19 due to progressive respiratory failure were assigned to an early and late group (death within ≤7 and >7 days of hospitalization, respectively) and compared to n = 11 healthy controls; mRNA and protein expression as well as biological pathway analysis were performed to gain insights into the evolution of pulmonary fibrogenesis in COVID-19; (3) Results: Median duration of hospitalization until death was 3 (IQR25-75, 3–3.75) and 14 (12.5–14) days in the early and late group, respectively. Fifty-eight out of 770 analyzed genes showed a significantly altered expression signature in COVID-19 compared to controls in a time-dependent manner. The entire study group showed an increased expression of BST2 and IL1R1, independent of hospitalization time. In the early group there was increased activity of inflammation-related genes and pathways, while fibrosis-related genes (particularly PDGFRB) and pathways dominated in the late group; (4) Conclusions: After the first week of hospitalization, there is a shift from pro-inflammatory to fibrogenic activity in severe COVID-19. IL1R1 and PDGFRB may serve as potential therapeutic targets in future studies.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes

Background

The recent advancements in high-throughput sequencing have resulted in the availability of annotated genomes, as well as of multi-omics data for many living organisms. This has increased the need for graphic tools that allow the concurrent visualization of genomes and feature-associated multi-omics data on single publication-ready plots.

Results

We present chromoMap, an R package, developed for the construction of interactive visualizations of chromosomes/chromosomal regions, mapping of any chromosomal feature with known coordinates (i.e., protein coding genes, transposable elements, non-coding RNAs, microsatellites, etc.), and chromosomal regional characteristics (i.e. genomic feature density, gene expression, DNA methylation, chromatin modifications, etc.) of organisms with a genome assembly. ChromoMap can also integrate multi-omics data (genomics, transcriptomics and epigenomics) in relation to their occurrence across chromosomes. ChromoMap takes tab-delimited files (BED like) or alternatively R objects to specify the genomic co-ordinates of the chromosomes and elements to annotate. Rendered chromosomes are composed of continuous windows of a given range, which, on hover, display detailed information about the elements annotated within that range. By adjusting parameters of a single function, users can generate a variety of plots that can either be saved as static image or as HTML documents.

Conclusions

ChromoMap’s flexibility allows for concurrent visualization of genomic data in each strand of a given chromosome, or of more than one homologous chromosome; allowing the comparison of multi-omic data between genotypes (e.g. species, varieties, etc.) or between homologous chromosomes of phased diploid/polyploid genomes. chromoMap is an extensive tool that can be potentially used in various bioinformatics analysis pipelines for genomic visualization of multi-omics data.

Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies

The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.

Modeling SARS-CoV-2 Infection in Mice Using Lentiviral hACE2 Vectors Infers Two Modes of Immune Responses to SARS-CoV-2 Infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a severe global pandemic. Mice models are essential to investigate infection pathology, antiviral drugs, and vaccine development. However, wild-type mice lack the human angiotensin-converting enzyme 2 (hACE2) that mediates SARS-CoV-2 entry into human cells and consequently are not susceptible to SARS-CoV-2 infection. hACE2 transgenic mice could provide an efficient COVID-19 model, but are not always readily available, and practically restricted to specific strains. Therefore, there is a dearth of additional mouse models for SARS-CoV-2 infection. We applied lentiviral vectors to generate hACE2 expression in interferon receptor knock-out (IFNAR1^−/−) mice. Lenti-hACE2 transduction supported SARS-CoV-2 replication in vivo, simulating mild acute lung disease. Gene expression analysis revealed two modes of immune responses to SARS-CoV-2 infection: one in response to the exposure of mouse lungs to SARS-CoV-2 particles in the absence of productive viral replication, and the second in response to productive SARS-CoV-2 infection. Our results infer that immune response to immunogenic elements on incoming virus or in productively infected cells stimulate diverse immune effectors, even in absence of type I IFN signaling. Our findings should contribute to a better understanding of the immune response triggered by SARS-CoV-2 and to further elucidate COVID-19.

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.

Chemokine-Based Therapeutics for the Treatment of Inflammatory and Fibrotic Convergent Pathways in COVID-19

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 betacoronavirus and has taken over 761,426 American lives as of the date of publication and will likely result in long-term, if not permanent, tissue damage for countless patients. COVID-19 presents with diverse and multisystemic pathologic processes, including a hyperinflammatory response, acute respiratory distress syndrome (ARDS), vascular injury, microangiopathy, tissue fibrosis, angiogenesis, and widespread thrombosis across multiple organs, including the lungs, heart, kidney, liver, and brain. C-X-C chemokines contribute to these pathologies by attracting inflammatory mediators, the disruption of endothelial cell integrity and function, and the initiation and propagation of the cytokine storm. Among these, CXCL10 is recognized as a critical contributor to the hyperinflammatory state and poor prognosis in COVID-19. CXCL10 is also known to regulate growth factor-induced fibrosis, and recent evidence suggests the CXCL10-CXCR3 signaling system may be vital in targeting convergent pro-inflammatory and pro-fibrotic pathways. This review will explore the mechanistic role of CXCL10 and related chemokines in fibrotic complications associated with COVID-19 and the potential of CXCL10-targeted therapeutics for early intervention and long-term treatment of COVID-19-induced fibrosis.

Detection of Antibodies Against the SARS-CoV-2 Spike Protein and Analysis of the Peripheral Blood Mononuclear Cell Transcriptomic Profile, 15 Years After Recovery From SARS

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows a high degree of homology with SARS-CoV. They share genes, protein sequences, clinical manifestations, and cellular entry patterns. Thus, SARS research may serve helpful in gaining a better understanding of the current coronavirus disease 2019 (COVID-19) pandemic. Serum antibodies from convalescent patients with SARS collected in 2018 were used to target the recombinant SARS-CoV-2 spike protein via a chemiluminescence microsphere immunoassay. Antibodies of convalescent patients with SARS exhibited serous immune cross-reactivity with the SARS-CoV-2 spike protein. The serous antibodies, excluding S22 of convalescent patients with SARS, did not competitively inhibit the binding of SARS-CoV-2 spike protein to ACE2. T cellular immunity research was conducted in vitro using peripheral blood mononuclear cells (PBMCs) stimulated by pooled peptide epitopes 15 years post-infection. Interferon gamma was detected and the PBMC transcriptomic profile was obtained. The heatmap of the transcriptomic profile showed that mRNAs and circRNAs of the SARS group clustered together after being stimulated by the peptide epitope pool. Differentially expressed mRNAs were most significantly enriched in immunity and signal transduction (P < 0.01). SARS elicits cytokine and chemokine responses, partially consistent with previously published data about COVID-19. Overall, our results indicate that antibodies from convalescent patients with SARS persisted for 15 years and displayed immune cross-reactivity with the SARS-CoV-2 spike protein. The immune status of patients with SARS 15 years post-infection may provide a better understanding of the future immune status of patients with COVID-19.

Single-Cell and Bulk RNASeq Profiling of COVID-19 Patients Reveal Immune and Inflammatory Mechanisms of Infection-Induced Organ Damage

The SARS-CoV-2 virus’s ability to induce hypercytokinemia and cause multiple organ failure makes it imperative to find effective treatments. To understand the mechanism of viral infection and its effects on organ tissues, we analyzed multiple single-cell and bulk RNAseq data from COVID-19 patients’ organ samples. Various levels of severity of infection were accounted for, with comparative analyses between mild, moderate, and severely infected patients. Our analysis uncovered an upregulation of the innate immune response via several inflammatory genes, IL-2, IL-6, IL-8, IL-17A, and NF-κB. Consequently, we found that the upregulation of these downstream effects can lead to organ injury. The downregulated pathways such as eukaryotic initiation factor 2 (eIF2) and eIF4-mediated host translation, were found to lead to an increased viral translation. We also found that the loss of inhibitory peptides can suppress an overactive innate immune response via NF-κB and interleukin-mediated pathways. Investigation of viral-host protein mapping showed that the interaction of viral proteins with host proteins correlated with the down- and upregulation of host pathways such as decreased eIF2-mediated host translation and increased hypertrophy and fibrosis. Inflammation was increased via the stimulation of pro-inflammatory cytokines and suppression of host translation pathways that led to reduced inflammatory inhibitors. Cardiac hypertrophy and organ fibrosis were the results of increased inflammation in organs of severe and critical patients. Finally, we identified potential therapeutic targets for the treatment of COVID-19 and its deleterious effects on organs. Further experimental investigation would conclusively determine the effects of COVID-19 infection on organs other than the lungs and the effectiveness of the proposed therapeutic targets.

Cytokine storm in the pathophysiology of COVID-19: Possible functional disturbances of miRNAs

SARS-CoV-2, as the causative agent of COVID-19, is an enveloped positives-sense single-stranded RNA virus that belongs to the Beta-CoVs sub-family. A sophisticated hyper-inflammatory reaction named cytokine storm is occurred in patients with severe/critical COVID-19, following an imbalance in immune-inflammatory processes and inhibition of antiviral responses by SARS-CoV-2, which leads to pulmonary failure, ARDS, and death. The miRNAs are small non-coding RNAs with an average length of 22 nucleotides which play various roles as one of the main modulators of genes expression and maintenance of immune system homeostasis. Recent evidence has shown that Homo sapiens (hsa)-miRNAs have the potential to work in three pivotal areas including targeting the virus genome, regulating the inflammatory signaling pathways, and reinforcing the production/signaling of IFNs-I. However, it seems that several SARS-CoV-2-induced interfering agents such as viral (v)-miRNAs, cytokine content, competing endogenous RNAs (ceRNAs), etc. preclude efficient function of hsa-miRNAs in severe/critical COVID-19. This subsequently leads to increased virus replication, intense inflammatory processes, and secondary complications development. In this review article, we provide an overview of hsa-miRNAs roles in viral genome targeting, inflammatory pathways modulation, and IFNs responses amplification in severe/critical COVID-19 accompanied by probable interventional factors and their function. Identification and monitoring of these interventional elements can help us in designing the miRNAs-based therapy for the reduction of complications/mortality rate in patients with severe/critical forms of the disease.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSIONS

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

Novel ACE2 protein interactions relevant to COVID-19 predicted by evolutionary rate correlations

Angiotensin-converting enzyme 2 (ACE2) is the cell receptor that the coronavirus SARS-CoV-2 binds to and uses to enter and infect human cells. COVID-19, the pandemic disease caused by the coronavirus, involves diverse pathologies beyond those of a respiratory disease, including micro-thrombosis (micro-clotting), cytokine storms, and inflammatory responses affecting many organ systems. Longer-term chronic illness can persist for many months, often well after the pathogen is no longer detected. A better understanding of the proteins that ACE2 interacts with can reveal information relevant to these disease manifestations and possible avenues for treatment. We have undertaken an approach to predict candidate ACE2 interacting proteins which uses evolutionary inference to identify a set of mammalian proteins that “coevolve” with ACE2. The approach, called evolutionary rate correlation (ERC), detects proteins that show highly correlated evolutionary rates during mammalian evolution. Such proteins are candidates for biological interactions with the ACE2 receptor. The approach has uncovered a number of key ACE2 protein interactions of potential relevance to COVID-19 pathologies. Some proteins have previously been reported to be associated with severe COVID-19, but are not currently known to interact with ACE2, while additional predicted novel ACE2 interactors are of potential relevance to the disease. Using reciprocal rankings of protein ERCs, we have identified strongly interconnected ACE2 associated protein networks relevant to COVID-19 pathologies. ACE2 has clear connections to coagulation pathway proteins, such as Coagulation Factor V and fibrinogen components FGA, FGB, and FGG, the latter possibly mediated through ACE2 connections to Clusterin (which clears misfolded extracellular proteins) and GPR141 (whose functions are relatively unknown). ACE2 also connects to proteins involved in cytokine signaling and immune response (e.g. XCR1, IFNAR2 and TLR8), and to Androgen Receptor (AR). The ERC prescreening approach has elucidated possible functions for relatively uncharacterized proteins and possible new functions for well-characterized ones. Suggestions are made for the validation of ERC-predicted ACE2 protein interactions. We propose that ACE2 has novel protein interactions that are disrupted during SARS-CoV-2 infection, contributing to the spectrum of COVID-19 pathologies.

Gene Expression Risk Scores for COVID-19 Illness Severity

BACKGROUND

The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

METHODS

We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.

RESULTS

Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

CONCLUSION

These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

Relative expression of proinflammatory molecules in COVID-19 patients who manifested disease severities

Aggressive immune response, due to overexpressed proinflammatory molecules, has been characterized in coronavirus disease 2019 (COVID‐19) patients. Some of those mediators have a dual and opposite role on immune systems at play behind differential disease severities. We investigated the expression of some cytokines and chemokines in COVID‐19 patients in Bangladesh. We diagnosed the patients by detecting severe acute respiratory syndrome coronavirus 2 RNA in nasal swab samples by the real‐time RT‐PCR method. Thirty adult patients were preselected based on their disease severities and grouped into mild, moderate, and severe cases. Nine healthy volunteers participated in this study as a control. Relative expression of nine cytokines/chemokine in total leukocytes was semi‐quantified in SYBRgreen‐based real‐time quantitative reverse‐transcriptase polymerase chain reaction. We performed statistical tests on transformed log data using SPSS 24.0. At the onset of symptoms (Day 1), angiotensin‐converting enzyme 2 (ACE2) (p < 0.05) and interleukin (IL)‐6 (p > 0.05) were upregulated in all COVID‐19 groups, although the expression levels did not significantly correlate with disease severities. However, expressions of IL‐6, monocyte chemotactic protein‐1, macrophage inflammatory protein‐1α, tumor necrosis factor‐α (TNF‐α), RANTES (regulated upon activation, normal T cell expressed and secreted), and ACE2, on Day 14, were positively correlated with disease severities. Relative viral load at Day 1 showed no significant correlation with cytokine expression but had a significant positive correlation with RANTES and ACE2 expression on Day 14 (p < 0.05). Male patients had a higher level of IL‐6 than female patients on Day 1 (p < 0.05). All COVID‐19 patients showed upregulated cytokines and chemokines on Day 14 compared to Day 1 except TNF‐α. Female patients had a higher expression of ACE2 and IL‐12 on Day 14. Upregulated cytokines/chemokines at the convalescent stage, especially IL‐6, may help in targeting anticytokine therapy in post‐COVID‐19 patients' management.

Mucosal immunity to severe acute respiratory syndrome coronavirus 2 infection

PURPOSE OF REVIEW

Despite its crucial role in protection against viral infections, mucosal immunity has been largely understudied in the context of coronavirus disease 2019 (COVID-19). This review outlines the current evidence about the role of mucosal immune responses in the clearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, as well as potential mucosal mechanisms of protection against (re-)infection.

RECENT FINDINGS

The angiotensin-converting enzyme 2 cellular entry receptor for SARS-CoV-2 is most highly expressed in the upper respiratory tract and most SARS-CoV-2 shedding occurs from the upper respiratory tract. Viral shedding peaks early during infection around the onset of symptoms, before dropping rapidly in most individuals within 7 days of symptom onset, suggesting mucosal inhibition of viral infection. Serum and mucosal immunoglobulin G and immunoglobulin M responses were found to be strongly correlated in infected patients, whereas correlations were much weaker for immunoglobulin A (IgA). Mucosal IgA responses have been detected in infected cases in the absence of serum antibody responses, with mucosal antibody levels correlating strongly with virus neutralization. Bulk and single-cell RNA sequencing analysis of nasopharyngeal swabs and bronchoalveolar lavage samples of COVID-19 patients revealed the induction of mucosal chemokine and cytokine genes, complement pathways, Janus Kinase/Signal Transducer and Activator of Transcription signaling and cytotoxic T cells.

SUMMARY

Although most clinical studies focus on antibodies and cellular immunity in peripheral blood, mucosal immune responses in the respiratory tract play a key role in the early restriction of viral replication and the clearance of SARS-CoV-2. Identification of mucosal biomarkers associated with viral clearance will allow monitoring of infection-induced immunity. Further studies are needed to understand how the systemic immunological endpoints measured in vaccination studies translate to mucosal protection against SARS-CoV-2 infection.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

A computational analysis of transcriptional profiles from CD8(+) T lymphocytes reveals potential mechanisms of HIV/AIDS control and progression

Cytotoxic and noncytotoxic CD8+ T lymphocyte responses are essential for the control of HIV infection. Understanding the mechanisms underlying HIV control in elite controllers (ECs), which maintain undetectable viral load in the absence of antiretroviral therapy, may facilitate the development of new effective therapeutic strategies. We developed an original pipeline for an analysis of the transcriptional profiles of CD8+ cells from ECs, treated and untreated progressors. Hierarchical cluster analysis of CD8+ cells' transcription profiles allowed us to identify five distinct groups (EC groups 1-5) of ECs. The transcriptional profiles of EC group 1 were opposite to those of groups 2-4 and similar to those of the treated progressors, which can be associated with residual activation and dysfunction of CD8+ T-lymphocytes. The profiles of groups 2-4 were associated with different numbers of differentially expressed genes compared to healthy controls, but the corresponding genes shared the same cellular processes. These three groups were associated with increased metabolism, survival, proliferation, and the absence of an "exhausted" phenotype, compared to both untreated progressors and healthy controls. The CD8+ lymphocytes from these groups of ECs may contribute to the control under HIV replication and slower disease progression. The EC group 5 was indistinguishable from normal. Application of master regulator analysis allowed us to identify 22 receptors, including interferon-gamma, interleukin-2, and androgen receptors, which may be responsible for the observed expression changes and the functional states of CD8+ cells from ECs. These receptors can be considered potential targets of therapeutic intervention, which may decelerate disease progression.