Bioinformatics and systems-biology approach to identify common pathogenic mechanisms for COVID-19 and systemic lupus erythematosus

BACKGROUND

The Coronavirus disease 2019 (COVID-19) pandemic has brought a heavy burden to the world, interestingly, it shares many clinical symptoms with systemic lupus erythematosus (SLE). It is unclear whether there is a similar pathological process between COVID-9 and SLE. In addition, we don't know how to treat SLE patients with COVID-19. In this study, we analyse the potential similar pathogenesis between SLE and COVID-19 and explore their possible drug regimens using bioinformatics and systems biology approaches.

METHODS

The common differentially expressed genes (DEGs) were extracted from the COVID-19 datasets and the SLE datasets for functional enrichment, pathway analysis and candidate drug analysis.

RESULT

Based on the two transcriptome datasets between COVID-19 and SLE, 325 common DEGs were selected. Hub genes were identified by protein-protein interaction (PPI) analysis. few found a variety of similar functional changes between COVID-19 and SLE, which may be related to the pathogenesis of COVID-19. Besides, we explored the related regulatory networks. Then, through drug target matching, we found many candidate drugs for patients with COVID-19 only or COVID-19 combined with SLE.

CONCLUSION

COVID-19 and SLE patients share many common hub genes, related pathways and regulatory networks. Based on these common targets, we found many potential drugs that could be used in treating patient with COVID-19 or COVID-19 combined with SLE.

Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV

BACKGROUND

The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.

METHODS

COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.

RESULTS

In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.

CONCLUSION

In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

Identification of Key Genes Potentially Related to Intervertebral Disk Degeneration by Microarray Analysis

Aims: This study was designed to investigate differentially expressed genes (DEGs) in the annulus fibrosus (AF), nucleus pulposus (NP), and whole blood (WB) of intervertebral disk degeneration (IDD) patients. Materials and Methods: We retrieved microarray data set GSE70362, which contains the gene expression profiles of 24 AF and 24 NP samples from the Gene Expression Omnibus and identified DEGs in degenerative AF (AF-DEGs) and NP (NP-DEGs) samples compared with nondegenerative samples. We also examined gene expression profiles in WB from patients with IDD and healthy volunteers to identify DEGs in WB (WB-DEGs). We performed functional analyses on the DEGs common to AF-DEGs, NP-DEGs, and WB-DEGs. Expression of the common DEGs was partially validated by quantitative real-time-polymerase chain reaction (QRT-PCR). Results: In total, 846 AF-DEGs, 902 NP-DEGs, and 862 WB-DEGs were identified, and 22 DEGs were common among the three groups. Functional analyses showed that the common DEGs were enriched in 33 biological processes, 16 cellular components, 4 molecular functions, and 9 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways; 13 of the common DEGs were included in the protein-protein interaction (PPI) network and superoxide dismutase 2 (SOD2) was identified as a hub gene in the PPI network. The QRT-PCR results for the expression of the genes protein disulfide isomerase family A member 4, FKBP prolyl isomerase 11, ectonucleotide pyrophosphatase/phosphodiesterase 4, SOD2, and actin binding LIM protein 1, were consistent with the gene chip hybridization results. Conclusions: This study identified key genes for future investigations of the underlying molecular mechanisms of IDD. These genes may provide future targets for the clinical treatment and diagnosis of IDD.