Xerostomia (subjective complaint of dry mouth) is commonly associated with salivary gland hypofunction. Molecular mechanisms associated with xerostomia pathobiology are poorly understood, thus hampering drug development. Our objectives were to (i) use text-mining tools to investigate xerostomia and dry mouth concepts, (ii) identify associated molecular interactions involving genes as candidate drug targets, and (iii) determine how drugs currently used in clinical trials may impact these genes and associated pathways. PubMed and PubMed Central were used to identify search terms associated with xerostomia and/or dry mouth. Search terms were queried in pubmed2ensembl. Protein–protein interaction (PPI) networks were determined using the gene/protein network visualization program search tool for recurring instances of neighboring genes (STRING). A similar program, Cytoscape, was used to determine PPIs of overlapping gene sets. The drug–gene interaction database (DGIdb) and the clinicaltrials.gov database were used to identify potential drug targets from the xerostomia/dry mouth PPI gene set. We identified 64 search terms in common between xerostomia and dry mouth. STRING confirmed PPIs between identified genes (CL = 0.90). Cytoscape analysis determined 58 shared genes, with cytokine–cytokine receptor interaction representing the most significant pathway (p = 1.29 × 10−23) found in the Kyoto encyclopedia of genes and genomes (KEGG). Fifty-four genes in common had drug interactions, per DGIdb analysis. Eighteen drugs, targeting the xerostomia/dry mouth PPI network, have been evaluated for xerostomia, head and neck cancer oral complications, and Sjögren’s Syndrome. The PPI network genes IL6R, EGFR, NFKB1, MPO, and TNFSF13B constitute a possible biomarker signature of xerostomia. Validation of the candidate biomarkers is necessary to better stratify patients at the genetic and molecular levels to facilitate drug development or to monitor response to treatment.

Background The COVID-19 pandemic has led to over 820,000 deaths for almost 24 million confirmed cases worldwide, as of August 27th, 2020, per WHO report. Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, obesity, and cancer. There are currently no effective treatments. Our objective was to complete a meta-analysis to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational approach. Results SNP datasets were downloaded from publicly available GWAS catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using MAGMA program. SNP annotation program was used to analyze MAGMA-identified genes. COVID-19 comorbidities from six disease categories were found to have significant associated pathways, which were validated by Q-Q plots (p<0.05). The top 250 human mRNA gene expressions for SNP-affected pathways, extracted from publicly accessible gene expression profiles, were evaluated for significant pathways. Protein-protein interactions of identified differentially expressed genes, visualized with STRING program, were significant (p<0.05). Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. Conclusion Pathways potentially affected by or affecting SARS-CoV-2 infection were identified in underlying medical conditions likely to confer susceptibility and/or severity to COVID-19. Our findings have implications in COVID-19 treatment development. Keywords: SARS-CoV-2, COVID-19, comorbidity, susceptibility, severity