Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

Transcriptomic analysis of key genes and signaling pathways in sepsis-associated intestinal mucosal barrier damage

OBJECTIVES

The aim is to analyze differentially expressed genes (DEGs) in mice with sepsis-related intestinal mucosal barrier damage and to explore the diagnostic and protective mechanisms of this condition at the transcriptome level.

METHODS

Small intestinal tissues from healthy male C57BL/6J mice subjected to Cecal ligation and puncture (CLP) and sham operation were collected. High-throughput sequencing was performed using the paired-end sequencing mode of the Illumina HiSeq platform. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted on the differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was constructed using the STRING database, and hub genes were identified with Cytoscape. These hub genes were then validated using quantitative real-time polymerase chain reaction (RT-qPCR).

RESULTS

A total of 239 DEGs were identified, with 49 upregulated and 130 downregulated genes. KEGG enrichment analysis showed that these DEGs were primarily involved in cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, viral protein interactions with cytokines and their receptors, and the IL-17 signaling pathway. The top 10 hub genes were selected using the cytoHubba plugin. Experimental validation confirmed that the expression levels of TBX21, CSF3, IL-6, CXCR3, and CXCL9 matched the sequencing results.

CONCLUSION

TBX21, CSF3, IL-6,CXCR3, and CXCL9 may be potential biological markers for the diagnosis and treatment the sepsis-associated intestinal mucosal barrier.

A transcriptome-based risk model in sepsis enables prognostic prediction and drug repositioning

Septic management presented a tremendous challenge due to heterogeneous host responses. We aimed to develop a risk model for early septic stratification based on transcriptomic signature. Here, we combined genes OLAH, LY96, HPGD, and ABLIM1 into a prognostic risk score model, which demonstrated exceptional performance in septic diagnosis (AUC = 0.99-1.00) and prognosis (AUC = 0.61-0.70), outperforming that of Mars and SRS endotypes. Also, the model unveiled immunosuppressive status in high-risk patients and a poor response to hydrocortisone in low-risk individuals. Single-cell transcriptome analysis further elucidated expression patterns and effects of the four genes across immune cell types, illustrating integrated host responses reflected by this model. Upon distinct transcriptional profiles of risk subgroups, we identified fenretinide and meloxicam as therapeutic agents, which significantly improved survival in septic mice models. Our study introduced a risk model that optimized risk stratification and drug repurposing of sepsis, thereby offering a comprehensive management approach.

Identification of diagnostic candidate genes in COVID-19 patients with sepsis

PURPOSE

Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis.

PATIENTS AND METHODS

We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration.

RESULTS

The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes.

CONCLUSION

Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death

Background

Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death.

Methods

Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank.

Results

After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant.

Conclusions

The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Exploring the mechanism of Corbrin capsules in the intervention of AKI-COVID-19 based on network pharmacology combined with GEO dataset

AIM

of the study: This study used network pharmacology and the Gene Expression Omnibus (GEO) database to investigate the therapeutic effects of Corbrin capsules on acute kidney injury (AKI)-COVID-19 (coronavirus disease 2019).

MATERIALS AND METHODS

The active constituents and specific molecular targets of Corbrin capsules were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Swiss Target Prediction databases. The targets related to AKI and COVID-19 disease were obtained from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and GEO databases. A protein-protein interaction (PPI) network was constructed by utilizing Cytoscape. To enhance the analysis of pathways associated with the pathogenesis of AKI-COVID-19, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Furthermore, immune infiltration analysis was performed by using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Molecular docking was used to assess interactions between differentially expressed genes and active ingredients. Verification was performed by utilizing GEO databases and in vivo assays.

RESULTS

This study revealed an overlap of 18 significantly differentially expressed genes between the Corbrin capsules group and the AKI-COVID-19 target group. Analysis of the PPI network identified TP53, JAK2, PIK3CA, PTGS2, KEAP1, and MCL1 as the top six core protein targets with the highest degrees. The results obtained from GO and KEGG analyses demonstrated that the target genes were primarily enriched in the apoptosis and JAK-STAT signaling pathways. Moreover, the analysis of immune infiltration revealed a notable disparity in the percentage of quiescent memory CD4 + T cells. Western blot analyses provided compelling evidence suggesting that the dysregulation of 6 core protein targets could be effectively reversed by Corbrin capsules.

CONCLUSION

This study revealed the key components, targets, and pathways involved in treating AKI-related COVID-19 using Corbrin capsules. This study also provided a new understanding of the molecular mechanisms underlying this treatment.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

High-Grade Infection after Branched Endovascular Aortic Repair in Patient with Recent COVID-19 Hospitalization

In the context of the COVID-19 pandemic, the global healthcare landscape has undergone significant transformations, particularly impacting the management of complex medical conditions such as aortic aneurysms. This study focuses on a 76-year-old female patient with a history of extensive cardiovascular surgeries, including aortic valve replacement, Bentall operation, and Frozen Elephant Trunk procedure, who presented with a type II thoracoabdominal aortic aneurysm post-COVID-19 recovery. A comprehensive frailty assessment using the Modified Frailty Index and a two-phase endovascular approach for aneurysm treatment, considering the patient's frailty and complex medical history was performed. Upon successful aneurysm management, the patient's postoperative course was complicated by COVID-19 reinfection and Enterococcus faecalis superinfection, highlighting the increased risk of bacterial superinfections and the challenges posed by antimicrobial resistance in COVID-19 patients. The study underscores the necessity of vigilant postoperative surveillance and a multidisciplinary approach in managing such complex cases, highlighting the importance of personalized care strategies, integrating cardiovascular and infectious disease management, and adapting healthcare practices to the unique challenges of the pandemic. This case contributes to the evolution of knowledge on managing aortic aneurysms in the COVID-19 era, advocating for patient-centric treatment approaches and continuous research into long-term patient outcomes.

The unfolded protein response pathway as a possible link in the pathogenesis of COVID-19 and sepsis

The global impact of the COVID-19 pandemic has been substantial. Emerging evidence underscores a strong clinical connection between COVID-19 and sepsis. Numerous studies have identified the unfolded protein response (UPR) pathway as a crucial pathogenic pathway for both COVID-19 and sepsis, but it remains to be investigated whether this signaling pathway operates as a common pathogenic mechanism for both COVID-19 and sepsis. In this study, single-cell RNA-seq data and transcriptome data for COVID-19 and sepsis cases were downloaded from GEO (Gene Expression Omnibus). By analyzing the single-cell transcriptome data, we identified B cells as the critical cell subset and the UPR pathway as the critical signaling pathway. Based on the transcriptome data, a machine learning diagnostic model was then constructed using the interleaved genes of B-cell-related and UPR-pathway-related genes. We validated the diagnostic model using both internal and external datasets and found the accuracy and stability of this model to be extremely strong. Even after integrating our algorithmic model with the patient's clinical status, it continued to yield identical results, further emphasizing the reliability of this model. This study provides a novel molecular perspective on the pathogenesis of sepsis and COVID-19 at the single-cell level and suggests that these two diseases may share a common mechanism.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 on metabolic unhealthy obese patients

Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has posed a significant challenge to individuals' health. Increasing evidence shows that patients with metabolic unhealthy obesity (MUO) and COVID-19 have severer complications and higher mortality rate. However, the molecular mechanisms underlying the association between MUO and COVID-19 are poorly understood. Methods: We sought to reveal the relationship between MUO and COVID-19 using bioinformatics and systems biology analysis approaches. Here, two datasets (GSE196822 and GSE152991) were employed to extract differentially expressed genes (DEGs) to identify common hub genes, shared pathways, transcriptional regulatory networks, gene-disease relationship and candidate drugs. Results: Based on the identified 65 common DEGs, the complement-related pathways and neutrophil degranulation-related functions are found to be mainly affected. The hub genes, which included SPI1, CD163, C1QB, SIGLEC1, C1QA, ITGAM, CD14, FCGR1A, VSIG4 and C1QC, were identified. From the interaction network analysis, 65 transcription factors (TFs) were found to be the regulatory signals. Some infections, inflammation and liver diseases were found to be most coordinated with the hub genes. Importantly, Paricalcitol, 3,3',4,4',5-Pentachlorobiphenyl, PD 98059, Medroxyprogesterone acetate, Dexamethasone and Tretinoin HL60 UP have shown possibility as therapeutic agents against COVID-19 and MUO. Conclusion: This study provides new clues and references to treat both COVID-19 and MUO.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.

A hub gene signature as a therapeutic target and biomarker for sepsis and geriatric sepsis-induced ARDS concomitant with COVID-19 infection

Background

COVID-19 and sepsis represent formidable public health challenges, characterized by incompletely elucidated molecular mechanisms. Elucidating the interplay between COVID-19 and sepsis, particularly in geriatric patients suffering from sepsis-induced acute respiratory distress syndrome (ARDS), is of paramount importance for identifying potential therapeutic interventions to mitigate hospitalization and mortality risks.

Methods

We employed bioinformatics and systems biology approaches to identify hub genes, shared pathways, molecular biomarkers, and candidate therapeutics for managing sepsis and sepsis-induced ARDS in the context of COVID-19 infection, as well as co-existing or sequentially occurring infections. We corroborated these hub genes utilizing murine sepsis-ARDS models and blood samples derived from geriatric patients afflicted by sepsis-induced ARDS.

Results

Our investigation revealed 189 differentially expressed genes (DEGs) shared among COVID-19 and sepsis datasets. We constructed a protein-protein interaction network, unearthing pivotal hub genes and modules. Notably, nine hub genes displayed significant alterations and correlations with critical inflammatory mediators of pulmonary injury in murine septic lungs. Simultaneously, 12 displayed significant changes and correlations with a neutrophil-recruiting chemokine in geriatric patients with sepsis-induced ARDS. Of these, six hub genes (CD247, CD2, CD40LG, KLRB1, LCN2, RETN) showed significant alterations across COVID-19, sepsis, and geriatric sepsis-induced ARDS. Our single-cell RNA sequencing analysis of hub genes across diverse immune cell types furnished insights into disease pathogenesis. Functional analysis underscored the interconnection between sepsis/sepsis-ARDS and COVID-19, enabling us to pinpoint potential therapeutic targets, transcription factor-gene interactions, DEG-microRNA co-regulatory networks, and prospective drug and chemical compound interactions involving hub genes.

Conclusion

Our investigation offers potential therapeutic targets/biomarkers, sheds light on the immune response in geriatric patients with sepsis-induced ARDS, emphasizes the association between sepsis/sepsis-ARDS and COVID-19, and proposes prospective alternative pathways for targeted therapeutic interventions.

Non-Anticoagulant Activities of Low Molecular Weight Heparins-A Review

Low molecular weight heparins (LMWHs) are derived from heparin through chemical or enzymatic cleavage with an average molecular weight (Mw) of 2000-8000 Da. They exhibit more selective activities and advantages over heparin, causing fewer side effects, such as bleeding and heparin-induced thrombocytopenia. Due to different preparation methods, LMWHs have diverse structures and extensive biological activities. In this review, we describe the basic preparation methods in this field and compare the main principles and advantages of these specific methods in detail. Importantly, we focus on the non-anticoagulant pharmacological effects of LMWHs and their conjugates, such as preventing glycocalyx shedding, anti-inflammatory, antiviral infection, anti-fibrosis, inhibiting angiogenesis, inhibiting cell adhesion and improving endothelial function. LMWHs are effective in various diseases at the animal level, including cancer, some viral diseases, fibrotic diseases, and obstetric diseases. Finally, we briefly summarize their usage and potential applications in the clinic to promote the development and utilization of LMWHs.

Lessons learned from patient outcomes when lowering hemoglobin transfusion thresholds during COVID-19 blood shortages

OBJECTIVES

This study examines whether patient outcomes were affected when the hemoglobin (Hb) transfusion threshold was lowered by 1 g/dL during COVID-19-related blood shortages.

METHODS

Outcomes of lowered Hb thresholds (from <7 to <6 g/dL) were defined by 14-month intervals in 2 patient groups (prepandemic [January 2019-February 2020] and pandemic [April 2020-May 2021]). We evaluated patient admissions, pretransfusion (if transfused) or nadir admission (if not transfused) Hb levels between 5.0 and 8.0 g/dL, and total red blood cell (RBC) transfusions during admission (if transfused). Baseline variables and outcomes were selected from electronic health records. Primary COVID-19-related admissions were excluded. Regression analysis was conducted to determine outcomes.

RESULTS

Those in the prepandemic group (1976) and pandemic group (1547) were transfused. Fewer RBCs (2186, vs 3337) were used in the prepandemic group than in the pandemic group, respectively. Those in the prepandemic group had significantly higher rates of hypertension and diabetes as well as more smokers. Significant differences were observed when comparing the number of procedures and incidence of sepsis between the patient groups. Similar patterns were observed for the not transfused and transfused subgroups.

CONCLUSIONS

Patient outcomes were not affected after implementing lower Hb pretransfusion thresholds. Although confounding factors were mitigated, some may have been associated with procedures or sepsis. Proactive patient blood management strategies during COVID-19-related blood shortages may include adopting lower Hb thresholds.

Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning

Background

To explicate the pathogenic mechanisms of cuproptosis, a newly observed copper induced cell death pattern, in Coronavirus disease 2019 (COVID-19).

Methods

Cuproptosis-related subtypes were distinguished in COVID-19 patients and associations between subtypes and immune microenvironment were probed. Three machine algorithms, including LASSO, random forest, and support vector machine, were employed to identify differentially expressed genes between subtypes, which were subsequently used for constructing cuproptosis-related risk score model in the GSE157103 cohort to predict the occurrence of COVID-19. The predictive values of the cuproptosis-related risk score were verified in the GSE163151 cohort, GSE152418 cohort and GSE171110 cohort. A nomogram was created to facilitate the clinical use of this risk score, and its validity was validated through a calibration plot. Finally, the model genes were validated using lung proteomics data from COVID-19 cases and single-cell data.

Results

Patients with COVID-19 had higher significantly cuproptosis level in blood leukocytes compared to patients without COVID-19. Two cuproptosis clusters were identified by unsupervised clustering approach and cuproptosis cluster A characterized by T cell receptor signaling pathway had a better prognosis than cuproptosis cluster B. We constructed a cuproptosis-related risk score, based on PDHA1, PDHB, MTF1 and CDKN2A, and a nomogram was created, which both showed excellent predictive values for COVID-19. And the results of proteomics showed that the expression levels of PDHA1 and PDHB were significantly increased in COVID-19 patient samples.

Conclusion

Our study constructed and validated an cuproptosis-associated risk model and the risk score can be used as a powerful biomarker for predicting the existence of SARS-CoV-2 infection.

The influence of COVID-19 on colorectal cancer was investigated using bioinformatics and systems biology techniques

Introduction

Coronavirus disease 2019 (COVID-19) is a global pandemic and highly contagious, posing a serious threat to human health. Colorectal cancer (CRC) is a risk factor for COVID-19 infection. Therefore, it is vital to investigate the intrinsic link between these two diseases.

Methods

In this work, bioinformatics and systems biology techniques were used to detect the mutual pathways, molecular biomarkers, and potential drugs between COVID-19 and CRC.

Results

A total of 161 common differentially expressed genes (DEGs) were identified based on the RNA sequencing datasets of the two diseases. Functional analysis was performed using ontology keywords, and pathway analysis was also performed. The common DEGs were further utilized to create a protein-protein interaction (PPI) network and to identify hub genes and key modules. The datasets revealed transcription factors-gene interactions, co-regulatory networks with DEGs-miRNAs of common DEGs, and predicted possible drugs as well. The ten predicted drugs include troglitazone, estradiol, progesterone, calcitriol, genistein, dexamethasone, lucanthone, resveratrol, retinoic acid, phorbol 12-myristate 13-acetate, some of which have been investigated as potential CRC and COVID-19 therapies.

Discussion

By clarifying the relationship between COVID-19 and CRC, we hope to provide novel clues and promising therapeutic drugs to treat these two illnesses.

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms

Background

COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms.

Methods

We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the "Limma" package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses.

Results

We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH.

Conclusion

Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.

Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.

Single-Cell Transcriptomics Reveals Evidence of Endothelial Dysfunction in the Brains of COVID-19 Patients with Implications for Glioblastoma Progression

BACKGROUND

Endothelial dysfunction is implicated in various inflammatory diseases such as ischemic stroke, heart attack, organ failure, and COVID-19. Recent studies have shown that endothelial dysfunction in the brain is attributed to excessive inflammatory responses caused by the SARS-CoV-2 infection, leading to increased permeability of the blood-brain barrier and consequently neurological damage. Here, we aim to examine the single-cell transcriptomic landscape of endothelial dysfunction in COVID-19 and its implications for glioblastoma (GBM) progression.

METHODS

Single-cell transcriptome data GSE131928 and GSE159812 were obtained from the gene expression omnibus (GEO) to analyze the expression profiles of key players in innate immunity and inflammation between brain endothelial dysfunction caused by COVID-19 and GBM progression.

RESULTS

Single-cell transcriptomic analysis of the brain of COVID-19 patients revealed that endothelial cells had undergone significant transcriptomic changes, with several genes involved in immune responses and inflammation upregulated. Moreover, transcription factors were observed to modulate this inflammation, including interferon-regulated genes.

CONCLUSIONS

The results indicate a significant overlap between COVID-19 and GBM in the context of endothelial dysfunction, suggesting that there may be an endothelial dysfunction link connecting severe SARS-CoV-2 infection in the brain to GBM progression.

Identification of Common Dysregulated Genes in COVID-19 and Hypersensitivity Pneumonitis: A Systems Biology and Machine Learning Approach

A comprehensive knowledge on systems biology of severe acute respiratory syndrome coronavirus 2 is crucial for differential diagnosis of COVID-19. Interestingly, the radiological and pathological features of COVID-19 mimic that of hypersensitivity pneumonitis (HP), another pulmonary fibrotic phenotype. This motivated us to explore the overlapping pathophysiology of COVID-19 and HP, if any, and using a systems biology approach. Two datasets were obtained from the Gene Expression Omnibus database (GSE147507 and GSE150910) and common differentially expressed genes (DEGs) for both diseases identified. Fourteen common DEGs, significantly altered in both diseases, were found to be implicated in complement activation and growth factor activity. A total of five microRNAs (hsa-miR-1-3p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-16-5p, and hsa-miR-34b-5p) and five transcription factors (KLF6, ZBTB7A, ELF1, NFIL3, and ZBT33) exhibited highest interaction with these common genes. Next, C3, CFB, MMP-9, and IL1A were identified as common hub genes for both COVID-19 and HP. Finally, these top-ranked genes (hub genes) were evaluated using random forest classifier to discriminate between the disease and control group (coronavirus disease 2019 [COVID-19] vs. controls, and HP vs. controls). This supervised machine learning approach demonstrated 100% and 87.6% accuracy in differentiating COVID-19 from controls, and HP from controls, respectively. These findings provide new molecular leads that inform COVID-19 and HP diagnostics and therapeutics research and innovation.