Type II interferon signaling in the brain during a viral infection with age-dependent pathogenesis

Comprehensive analyses identify potential biomarkers for encephalitis in HIV infection

Human immunodeficiency virus encephalitis (HIVE) is a severe neurological complication after HIV infection. Evidence shows that genetic factors play an important role in HIVE. The aim of the present study was to identify new potential therapeutic targets for HIVE. Differentially expressed gene (DEG), functional annotation and pathway, and protein-protein interaction analyses were performed to identify the hub genes associated with HIVE. Gene co-expression analysis was carried out to confirm the association between the hub genes and HIVE. Finally, the role of the hub genes in HIVE therapy was evaluated by conducting drug-gene interaction analysis. A total of 20 overlapping DEGs closely related to HIVE were identified. Functional annotation and pathway enrichment analysis indicated that the markedly enriched DEG terms included ion transport, type II interferon signaling, and synaptic signaling. Moreover, protein-protein interaction analysis revealed that 10 key HIVE-related genes were hub genes, including SCN8A, CDK5R2, GRM5, SCN2B, IFI44L, STAT1, SLC17A7, ISG15, FGF12, and FGF13. Furthermore, six hub genes were co-expressed with HIVE-associated host genes in human brain tissue. Finally, three hub genes (STAT1, ISG15, and SCN2B) interacted with several inflammation-associated drugs. These findings suggested that SCN8A, CDK5R2, GRM5, SCN2B, IFI44L, STAT1, SLC17A7, ISG15, FGF12, and FGF13 may be new targets for diagnosis and therapy of HIVE.

Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases

The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs.