Identifying the Interaction Between Tuberculosis and SARS-CoV-2 Infections via Bioinformatics Analysis and Machine Learning.

The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein-protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB.

Huang ZM ，Kang JQ ，Chen PZ ，Deng LF ，Li JX ，He YX ，Liang J ，Huang N ，Luo TY ，Lan QW ，Chen HK ，Guo XG ... -  《-》

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

Huang T ，He J ，Zhou X ，Pan H ，He F ，Du A ，Yu B ，Jiang N ，Li X ，Yuan K ，Wang Z ... -  《Frontiers in Cellular and Infection Microbiology》

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

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. 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. 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. 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.

Yan C ，Niu Y ，Wang X 《Frontiers in Immunology》

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Mahmud SMH ，Al-Mustanjid M ，Akter F ，Rahman MS ，Ahmed K ，Rahman MH ，Chen W ，Moni MA ... -  《-》

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear. This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein-protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC. A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein-protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC. This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.

Fang C ，Sun H ，Wen J ，Wu X ，Wu Q ，Zhai D ... -  《-》