Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

Long non-coding RNAs in biomarking COVID-19: a machine learning-based approach

BACKGROUND

The coronavirus pandemic that started in 2019 has caused the highest mortality and morbidity rates worldwide. Data on the role of long non-coding RNAs (lncRNAs) in coronavirus disease 2019 (COVID-19) is scarce. We aimed to elucidate the relationship of three important lncRNAs in the inflammatory states, H19, taurine upregulated gene 1 (TUG1), and colorectal neoplasia differentially expressed (CRNDE) with key factors in inflammation and fibrosis induction including signal transducer and activator of transcription3 (STAT3), alpha smooth muscle actin (α-SMA), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) in COVID-19 patients with moderate to severe symptoms.

METHODS

Peripheral blood mononuclear cells from 28 COVID-19 patients and 17 healthy controls were collected. The real-time quantitative polymerase chain reaction (RT-qPCR) was performed to evaluate the expression of RNAs and lncRNAs. Western blotting analysis was also performed to determine the expression levels of STAT3 and α-SMA proteins. Machine learning and receiver operating characteristic (ROC) curve analysis were carried out to evaluate the distinguishing ability of lncRNAs.

RESULTS

The expression levels of H19, TUG1, and CRNDE were significantly overexpressed in COVID-19 patients compared to healthy controls. Moreover, STAT3 and α-SMA expression levels were remarkedly increased at both transcript and protein levels in patients with COVID-19 compared to healthy subjects and were correlated with Three lncRNAs. Likewise, IL-6 and TNF-α were considerably upregulated in COVID-19 patients. Machine learning and ROC curve analysis showed that CRNDE-H19 panel has the proper ability to distinguish COVID-19 patients from healthy individuals (area under the curve (AUC) = 0.86).

CONCLUSION

The overexpression of three lncRNAs in COVID-19 patients observed in this study may align with significant manifestations of COVID-19. Furthermore, their co-expression with STAT3 and α-SMA, two critical factors implicated in inflammation and fibrosis induction, underscores their potential involvement in exacerbating cardiovascular, pulmonary and common symptoms and complications associated with COVID-19. The combination of CRNDE and H19 lncRNAs seems to be an impressive host-based biomarker panel for screening and diagnosis of COVID-19 patients from healthy controls. Research into lncRNAs can provide a robust platform to find new viral infection-related mediators and propose novel therapeutic strategies for viral infections and immune disorders.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.

Identification of Key Genes Related to Immune Cells in Patients with COVID-19 Via Integrated Bioinformatics-Based Analysis

COVID-19 has spread all over the world which poses a serious threat to social economic development and public health. Despite enormous progress has been made in the prevention and treatment of COVID-19, the specific mechanism and biomarker related to disease severity or prognosis have not been clarified yet. Our study intended to further explore the diagnostic markers of COVID-19 and their relationship with serum immunology by bioinformatics analysis. The datasets about COVID-19 were downloaded from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were selected via the limma package. Then, weighted gene co-expression network analysis (WGCNA) was conducted to identify the critical module associated with the clinic status. The intersection DEGs were processed for further enrichment analysis. The final diagnostic genes for COVID-19 were selected and verified through special bioinformatics algorithms. There were significant DEGs between the normal and COVID-19 patients. These genes were mainly enriched in cell cycle, complement and coagulation cascade, extracellular matrix (ECM) receptor interaction, and the P53 signaling pathway. As much as 357 common intersected DEGs were selected in the end. These DEGs were enriched in organelle fission, mitotic cell cycle phase transition, DNA helicase activity, cell cycle, cellular senescence, and P53 signaling pathway. Our study also identified CDC25A, PDCD6, and YWAHE were potential diagnostic markers of COVID-19 with the AUC (area under curve), 0.958 (95% CI 0.920-0.988), 0.941(95% CI 0.892-0.980), and 0.929 (95% CI 0.880-0.971). Moreover, CDC25A, PDCD6, and YWAHE were correlated with plasma cells, macrophages M0, T cells CD4 memory resting, T cells CD8, dendritic cells, and NK cells. Our study discovered that CDC25A, PDCD6, and YWAHE can be used as diagnostic markers for COVID-19. Moreover, these biomarkers were also closely associated with immune cell infiltration, which plays a pivotal role in the diagnosis and progression of COVID-19.

Transcriptomic identification of genes expressed in invasive S. aureus diabetic foot ulcer infection

Introduction

Infection in diabetic foot ulcers (DFUs) is one of the major complications associated with patients with diabetes. Staphylococcus aureus is the most common offending pathogen in patients with infected DFU. Previous studies have suggested the application of species-specific antibodies against S. aureus for diagnosis and monitoring treatment response. Early and accurate identification of the main pathogen is critical for management of DFU infection. Understanding the host immune response against species-specific infection may facilitate diagnosis and may suggest potential intervention options to promote healing infected DFUs. We sought to investigate evolving host transcriptome associated with surgical treatment of S. aureus- infected DFU.

Methods

This study compared the transcriptome profile of 21 patients with S. aureus- infected DFU who underwent initial foot salvage therapy with irrigation and debridement followed by intravenous antibiotic therapy. Blood samples were collected at the recruitment (0 weeks) and 8 weeks after therapy to isolate peripheral blood mononuclear cells (PBMCs). We analyzed the PBMC expression of transcriptomes at two different time points (0 versus 8 weeks). Subjects were further divided into two groups at 8 weeks: healed (n = 17, 80.95%) versus non-healed (n = 4, 19.05%) based on the wound healing status. DESeq2 differential gene analysis was performed.

Results and discussion

An increased expression of IGHG1, IGHG2, IGHG3, IGLV3-21, and IGLV6-57 was noted during active infection at 0 weeks compared with that at 8 weeks. Lysine- and arginine-rich histones (HIST1H2AJ, HIST1H2AL, HIST1H2BM, HIST1H3B, and HIST1H3G) were upregulated at the initial phase of active infection at 0 weeks. CD177 and RRM2 were also upregulated at the initial phase of active infection (0 weeks) compared with that at 8 weeks of follow-up. Genes of heat shock protein members (HSPA1A, HSPE1, and HSP90B1) were high in not healed patients compared with that in healed patients 8 weeks after therapy. The outcome of our study suggests that the identification of genes evolution based on a transcriptomic profiling could be a useful tool for diagnosing infection and assessing severity and host immune response to therapies.

Humans with inherited MyD88 and IRAK-4 deficiencies are predisposed to hypoxemic COVID-19 pneumonia

X-linked recessive deficiency of TLR7, a MyD88- and IRAK-4-dependent endosomal ssRNA sensor, impairs SARS-CoV-2 recognition and type I IFN production in plasmacytoid dendritic cells (pDCs), thereby underlying hypoxemic COVID-19 pneumonia with high penetrance. We report 22 unvaccinated patients with autosomal recessive MyD88 or IRAK-4 deficiency infected with SARS-CoV-2 (mean age: 10.9 yr; 2 mo to 24 yr), originating from 17 kindreds from eight countries on three continents. 16 patients were hospitalized: six with moderate, four with severe, and six with critical pneumonia, one of whom died. The risk of hypoxemic pneumonia increased with age. The risk of invasive mechanical ventilation was also much greater than in age-matched controls from the general population (OR: 74.7, 95% CI: 26.8-207.8, P < 0.001). The patients' susceptibility to SARS-CoV-2 can be attributed to impaired TLR7-dependent type I IFN production by pDCs, which do not sense SARS-CoV-2 correctly. Patients with inherited MyD88 or IRAK-4 deficiency were long thought to be selectively vulnerable to pyogenic bacteria, but also have a high risk of hypoxemic COVID-19 pneumonia.

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.

New insights into extracellular and intracellular redox status in COVID-19 patients

BACKGROUND

The imbalance of redox homeostasis induces hyper-inflammation in viral infections. In this study, we explored the redox system signature in response to SARS-COV-2 infection and examined the status of these extracellular and intracellular signatures in COVID-19 patients.

METHOD

The multi-level network was constructed using multi-level data of oxidative stress-related biological processes, protein-protein interactions, transcription factors, and co-expression coefficients obtained from GSE164805, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and healthy controls. Top genes were designated based on the degree and closeness centralities. The expression of high-ranked genes was evaluated in PBMCs and nasopharyngeal (NP) samples of 30 COVID-19 patients and 30 healthy controls. The intracellular levels of GSH and ROS/O₂^• - and extracellular oxidative stress markers were assayed in PBMCs and plasma samples by flow cytometry and ELISA. ELISA results were applied to construct a classification model using logistic regression to differentiate COVID-19 patients from healthy controls.

RESULTS

CAT, NFE2L2, SOD1, SOD2 and CYBB were 5 top genes in the network analysis. The expression of these genes and intracellular levels of ROS/O₂^• - were increased in PBMCs of COVID-19 patients while the GSH level decreased. The expression of high-ranked genes was lower in NP samples of COVID-19 patients compared to control group. The activity of extracellular enzymes CAT and SOD, and the total oxidant status (TOS) level were increased in plasma samples of COVID-19 patients. Also, the 2-marker panel of CAT and TOS and 3-marker panel showed the best performance.

CONCLUSION

SARS-COV-2 disrupts the redox equilibrium in immune cells and the upper respiratory tract, leading to exacerbated inflammation and increased replication and entrance of SARS-COV-2 into host cells. Furthermore, utilizing markers of oxidative stress as a complementary validation to discriminate COVID-19 from healthy controls, seems promising.

Review of Medical Studies on COVID-19 During the Pandemic Period

Due to the COVID-19 pandemic, both the university hospital and the city hospital have faced a significant patient load in our city. During this period, academic articles were written that contributed significantly to the literature on both hospitals struggling with patient density. In our study, we aimed to compile medical articles about COVID-19 in our city using the Web of Science and PubMed database.