An integrative analysis identifying transcriptional features and key genes involved in COVID-19

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Glycolipid transfer protein knockout disrupts vesicle trafficking to the plasma membrane

The glycolipid transfer protein (GLTP) has been linked to many cellular processes aside from its best-known in vitro function as a lipid transport protein. It has been proposed to act as a sensor and regulator of glycosphingolipid homeostasis in cells. Furthermore, through its previously determined interaction with the endoplasmic reticulum (ER) membrane protein VAP-A (vesicle-associated membrane protein-associated protein A), GLTP may also be involved in facilitating vesicular transport in cells. In this study, we characterized the phenotype of CRISPR/Cas9-mediated GLTP knockout HeLa cells. We showed that motility, three-dimensional growth, and cellular metabolism were all altered by GLTP knockout. Expression of a GLTP mutant incapable of binding VAP disrupted cell spheroid formation, indicating that the GLTP-VAP interaction is linked to cellular adhesion, cohesion, and three-dimensional growth. Most notably, we found evidence that GLTP, through its interaction with VAP-A, affects vesicular trafficking, marking the first cellular process discovered to be directly impacted by a change in GLTP expression.

An experimental in silico study on COVID-19: Response of neutrophil-related genes to antibiotics

Background and Aims

All components of the immune system are involved in alleviating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Further research is required to provide detailed insights into COVID-19-related immune compartments and pathways. In addition, a significant percentage of hospitalized COVID-19 patients suspect bacterial infections and antimicrobial resistance occurs following antibiotics treatment. The aim of this study was to evaluate the possible effects of antibiotics on the response of neutrophil-related genes in SARS-CoV-2 patients by an experimental in silico study.

Methods

The two data sets GSE1739 and GSE21802 including 10 SARS positive patients and 35 influenza A (H1N1) patients were analyzed, respectively. Differentially expressed genes (DEGs) between these two data sets were determined by GEO2R analysis and the Venn diagram online tool. After determining the hub genes involved in immune responses, the expression of these genes in 30 COVID-19 patients and 30 healthy individuals was analyzed by real-time polymerase chain reaction (PCR). All patients received antibiotics, including levofloxacin, colistin, meropenem, and ceftazidime.

Results

GEO2R analysis detected 240 and 120 DEGs in GSE21802 and GSE1739, respectively. Twenty DEGs were considered as enriched hub genes involved in immune processes such as neutrophil degranulation, neutrophil activation, and antimicrobial humoral response. The central nodes were attributed to the genes of neutrophil elastase (ELANE), arginase 1 (ARG-1), lipocalin 2 (LCN2), and defensin 4 (DEFA4). Compared to the healthy subjects, the expression of LCN2 and DEFA4 were significantly reduced in COVID-19 patients. However, no significant differences were observed in the ELANE and AGR-1 levels between COVID-19 subjects and the control group.

Conclusions

Activation and degranulation of neutrophils were observed mainly in SARS, and H1N1 infection processes and antibiotics administration could affect neutrophil activity during viral infection. It can be suggested that antibiotics can decrease inflammation by restoring the expression of neutrophil-related genes in COVID-19 patients.

Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples

Background

While our battle with the COVID-19 pandemic continues, a multitude of Omics data have been generated from patient samples in various studies. Translation of these data into clinical interventions against COVID-19 remains to be accomplished. Exploring host response to COVID-19 in the upper respiratory tract can unveil prognostic markers and therapeutic targets.

Methods

We conducted a meta-analysis of published transcriptome and proteome profiles of respiratory samples of COVID-19 patients to shortlist high confidence upregulated host factors. Subsequently, mRNA overexpression of selected genes was validated in nasal swabs from a cohort of COVID-19 positive/negative, symptomatic/asymptomatic individuals. Guided by this analysis, we sought to check for potential drug targets. An FDA-approved drug, Auranofin, was tested against SARS-CoV-2 replication in cell culture and Syrian hamster challenge model.

Findings

The meta-analysis and validation in the COVID-19 cohort revealed S100 family genes (S100A6, S100A8, S100A9, and S100P) as prognostic markers of severe COVID-19. Furthermore, Thioredoxin (TXN) was found to be consistently upregulated. Auranofin, which targets Thioredoxin reductase, was found to mitigate SARS-CoV-2 replication in vitro. Furthermore, oral administration of Auranofin in Syrian hamsters in therapeutic as well as prophylactic regimen reduced viral replication, IL-6 production, and inflammation in the lungs.

Interpretation

Elevated mRNA level of S100s in the nasal swabs indicate severe COVID-19 disease, and FDA-approved drug Auranofin mitigated SARS-CoV-2 replication in preclinical hamster model.

Funding

This study was supported by the DBT-IISc partnership program (DBT (IED/4/2020-MED/DBT)), the Infosys Young Investigator award (YI/2019/1106), DBT-BIRAC grant (BT/CS0007/CS/02/20) and the DBT-Wellcome Trust India Alliance Intermediate Fellowship (IA/I/18/1/503613) to ST lab.

Immunofibrotic drivers of impaired lung function in postacute sequelae of SARS-CoV-2 infection

BACKGROUNDIndividuals recovering from COVID-19 frequently experience persistent respiratory ailments, which are key elements of postacute sequelae of SARS-CoV-2 infection (PASC); however, little is known about the underlying biological factors that may direct lung recovery and the extent to which these are affected by COVID-19 severity.METHODSWe performed a prospective cohort study of individuals with persistent symptoms after acute COVID-19, collecting clinical data, pulmonary function tests, and plasma samples used for multiplex profiling of inflammatory, metabolic, angiogenic, and fibrotic factors.RESULTSSixty-one participants were enrolled across 2 academic medical centers at a median of 9 weeks (interquartile range, 6-10 weeks) after COVID-19 illness: n = 13 participants (21%) had mild COVID-19 and were not hospitalized, n = 30 participants (49%) were hospitalized but were considered noncritical, and n = 18 participants (30%) were hospitalized and in the intensive care unit (ICU). Fifty-three participants (85%) had lingering symptoms, most commonly dyspnea (69%) and cough (58%). Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and diffusing capacity for carbon monoxide (DLCO) declined as COVID-19 severity increased (P < 0.05) but these values did not correlate with respiratory symptoms. Partial least-squares discriminant analysis of plasma biomarker profiles clustered participants by past COVID-19 severity. Lipocalin-2 (LCN2), MMP-7, and HGF identified by our analysis were significantly higher in the ICU group (P < 0.05), inversely correlated with FVC and DLCO (P < 0.05), and were confirmed in a separate validation cohort (n = 53).CONCLUSIONSubjective respiratory symptoms are common after acute COVID-19 illness but do not correlate with COVID-19 severity or pulmonary function. Host response profiles reflecting neutrophil activation (LCN2), fibrosis signaling (MMP-7), and alveolar repair (HGF) track with lung impairment and may be novel therapeutic or prognostic targets.FundingNational Heart, Lung, and Blood Institute (K08HL130557 and R01HL142818), American Heart Association (Transformational Project Award), the DeLuca Foundation Award, a donation from Jack Levin to the Benign Hematology Program at Yale University, and Duke University.

Expression of SARS-CoV-2 entry genes ACE2 and TMPRSS2 at single cell resolution in the peripartum decidua

Angiotensin-converting enzyme 2 (ACE2) is the key receptor for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). However, the susceptibility of the decidua to infection during the peripartum period has not been explored, even though this may affect vertical transmission. The objective of this study was to investigate the expression of ACE2 and related genes in the decidua during delivery. Here, single-cell RNA sequencing was used to characterize the transcriptomes of decidual cells before and after the onset of labor. During the peripartum period, ACE2 expression was highly heterogeneous. ACE2 was expressed principally in decidual stromal cells, uterine smooth muscle cells, and extravillous trophoblasts. Comparison of the transcriptomes of ACE2-positive and ACE2-negative cells indicated that ACE2-positive cells exhibited integrin clusters on the cell surface interactions. ACE2-positive cells were compared before and after labor onset. After delivery, the number of ACE2-positive cells was slightly higher than before delivery. Before labor onset, ACE2-positive decidual stromal cells were in the regulation of membrane protein ectodomain proteolysis cluster. After labor onset, the upregulated genes changed to include cell junction assembly genes. The susceptibility of decidual cells to SARS-CoV-2 infection is thus heterogeneous during the peripartum period.

STAT1 N-terminal domain discriminatively controls type I and type II IFN signaling

The seven signal transducers of transcription (STATs) are cytokine-inducible modular transcription factors. They transmit the stimulation of cells with type I interferons (IFN-α/IFN-β) and type II interferon (IFN-ɣ) into altered gene expression patterns. The N-terminal domain (NTD) of STAT1 is a surface for STAT1/STAT1 homodimer and STAT1/STAT2 heterodimer formation and allows the cooperative DNA binding of STAT1. We investigated whether the STAT1 NTD-mediated dimerization affected the IFN-induced tyrosine phosphorylation of STAT1, its nuclear translocation, STAT1-dependent gene expression, and IFN-dependent antiviral defense. We reconstituted human STAT1-negative and STAT2-negative fibrosarcoma cells with STAT1, NTD-mutated STAT1 (STAT1AA), STAT1 with a mutated DNA-binding domain (DBD), or STAT2. We treated these cells with IFN-α and IFN-ɣ to assess differences between IFN-α-induced STAT1 homo- and heterodimers and IFN-ɣ-induced STAT1 homodimers. Our data demonstrate that IFNs induce the phosphorylation of STAT1 and STAT1AA at Y701 and their nuclear accumulation. We further reveal that STAT1AA can be phosphorylated in response to IFN-α in the absence of STAT2 and that IFN-ɣ-induced STAT1AA can activate gene expression directly. However, STAT1AA largely fails to bind STAT2 and to activate IFN-α-induced expression of endogenous antiviral STAT1/STAT2 target proteins. Congruent herewith, both an intact STAT1 NTD and STAT2 are indispensable to establish an antiviral state with IFN-α. These data provide new insights into the biological importance of the STAT1 NTD.

Immuno-fibrotic drivers of impaired lung function in post-acute sequelae of SARS-CoV-2 infection (PASC)

Introduction:

Subjects recovering from COVID-19 frequently experience persistent respiratory ailments; however, little is known about the underlying biological factors that may direct lung recovery and the extent to which these are affected by COVID-19 severity.

Methods:

We performed a prospective cohort study of subjects with persistent symptoms after acute COVID-19, collecting clinical data, pulmonary function tests, and plasma samples used for multiplex profiling of inflammatory, metabolic, angiogenic, and fibrotic factors.

Results:

Sixty-one subjects were enrolled across two academic medical centers at a median of 9 weeks (interquartile range 6–10) after COVID-19 illness: n=13 subjects (21%) mild/non-hospitalized, n=30 (49%) hospitalized/non-critical, and n=18 subjects (30%) hospitalized/intensive care (“ICU”). Fifty-three subjects (85%) had lingering symptoms, most commonly dyspnea (69%) and cough (58%). Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and diffusing capacity for carbon monoxide (DLCO) declined as COVID-19 severity increased (P<0.05), but did not correlate with respiratory symptoms. Partial least-squares discriminant analysis of plasma biomarker profiles clustered subjects by past COVID-19 severity. Lipocalin 2 (LCN2), matrix metalloproteinase-7 (MMP-7), and hepatocyte growth factor (HGF) identified by the model were significantly higher in the ICU group (P<0.05) and inversely correlated with FVC and DLCO (P<0.05), and were confirmed in a separate validation cohort (n=53).

Conclusions:

Subjective respiratory symptoms are common after acute COVID-19 illness but do not correlate with COVID-19 severity or pulmonary function. Host response profiles reflecting neutrophil activation (LCN2), fibrosis signaling (MMP-7), and alveolar repair (HGF) track with lung impairment and may be novel therapeutic or prognostic targets.

Funding:

The study was funded in part by the NHLBI (K08HL130557 to BDK and R01HL142818 to HJC), the DeLuca Foundation Award (AP), a donation from Jack Levin to the Benign Hematology Program at Yale, and Divisional/Departmental funds from Duke University.