Single nucleotide polymorphisms conferring susceptibility to leukemia and oral mucositis: a multi-center pilot study of patients prior to conditioning therapy for hematopoietic cell transplant.

PURPOSE: Leukemias have been associated with oral manifestations, reflecting susceptibility to cancer therapy-induced oral mucositis. We sought to identify SNPs associated with both leukemia and oral mucositis (OM). METHODS: Whole exome sequencing was performed on leukemia and non-cancer blood disorder (ncBD) patients' saliva samples (N = 50) prior to conditioning therapy. WHO OM grading scores were determined: moderate to severe (OM2-4) vs. none to mild (OM0-1). Reads were processed using Trim Galorev0.6.7, Bowtie2v2.4.1, Samtoolsv1.10, Genome Analysis Toolkit (GATK)v4.2.6.1, and DeepVariantv1.4.0. We utilized the following pipelines: P1 analysis with PLINK2v3.7, SNP2GENEv1.4.1 and MAGMAv1.07b, and P2 [leukemia (N = 42) vs. ncBDs (N = 8)] and P3 [leukemia + OM2-4 (N = 18) vs. leukemia + OM0-1 (N = 24)] with Z-tests of genotypes and protein-protein interaction determination. GeneCardsSuitev5.14 was used to identify phenotypes (P1 and P2, leukemia; P3, oral mucositis) and average disease-causing likelihood and DGIdb for drug interactions. P1 and P2 genes were analyzed with CytoScape plugin BiNGOv3.0.3 to retrieve overrepresented Gene Ontology (GO) terms and Ensembl's VEP for SNP outcomes. RESULTS: In P1, 457 candidate SNPs (28 genes) were identified and 21,604 SNPs (1016 genes) by MAGMAv1.07b. Eighteen genes were associated with "leukemia" per VarElectv5.14 analysis and predicted to be deleterious. In P2 and P3, 353 and 174 SNPs were significant, respectively. STRINGv12.0 returned 77 and 32 genes (C.L. = 0.7) for P2 and P3, respectively. VarElectv5.14 determined 60 genes from P2 associated with "leukemia" and 11 with "oral mucositis" from P3. Overrepresented GO terms included "cellular process," "signaling," "hemopoiesis," and "regulation of immune response." CONCLUSIONS: We identified candidate SNPs possibly conferring susceptibility to develop leukemia and oral mucositis.

Identification of single nucleotide polymorphisms (SNPs) associated with chronic graft-versus-host disease in patients undergoing allogeneic hematopoietic cell transplantation.

INTRODUCTION: Chronic graft-versus-host disease (cGVHD) is a debilitating side effect of allogeneic hematopoietic cell transplantation (HCT), affecting the quality of life of patients. We used whole exome sequencing to identify candidate SNPs and complete a multi-marker gene-level analysis using a cohort of cGVHD( +) (N = 16) and cGVHD( -) (N = 66) HCT patients. METHODS: Saliva samples were collected from HCT patients (N = 82) pre-conditioning in a multi-center study from March 2011 to May 2018. Exome sequencing was performed and FASTQ files were processed for sequence alignments. Significant SNPs were identified by logistic regression using PLINK2v3.7 and Fisher's exact test. One cGVHD( -) patient sample was excluded from further analysis since no SNP was present in at least 10% of the sample population. The FUMA platform's SNP2GENE was utilized to annotate SNPs and generate a MAGMA output. Chromatin state visualization of lead SNPs was completed using Epilogos tool. FUMA's GENE2FUNC was used to obtain gene function and tissue expression from lead genomic loci. RESULTS: Logistic regression classified 986 SNPs associated with cGVHD( +). SNP2GENE returned three genomic risk loci, four lead SNPs, 48 candidate SNPs, seven candidate GWAS tagged SNPs, and four mapped genes. Fisher's exact test identified significant homozygous genotypes of four lead SNPs (p < 0.05). GENE2FUNC analysis of multi-marker SNP sets identified one positional gene set including lead SNPs for KANK1 and KDM4C and two curated gene sets including lead SNPs for PTPRD, KDM4C, and/or KANK1. CONCLUSIONS: Our data suggest that SNPs in three genes located on chromosome 9 confer genetic susceptibility to cGVHD in HCT patients. These genes modulate STAT3 expression and phosphorylation in cancer pathogenesis. The findings may have implications in the modulation of pathways currently targeted by JAK inhibitors in cGVHD clinical trials.

Human oral mucosa and oral microbiome interactions following supragingival plaque reconstitution in healthy volunteers: a diet-controlled balanced design proof-of-concept model to investigate oral pathologies.

Changes in the oral microbiome may contribute to oral pathologies, especially in patients undergoing cancer therapy. Interactions between oral microbiome and oral mucosa may exacerbate inflammation. We determined whether probiotic-controlled plaque formation could impact proximal oral mucosa gene expression profiles in healthy volunteers. A 3-weeks balanced sample collection design from healthy volunteers (HVs) was implemented. At Week-1 plaques samples and labial mucosa brush biopsies were obtained from HVs in the morning (N = 4) and/or in the afternoon (N = 4), and groups were flipped at Week-3. A fruit yogurt and tea diet were given 2-4hrs before sample collection. mRNA gene expression analysis was completed using RNA-Seq and DESeq2. Bacterial taxa relative abundance was determined by 16S HOMINGS. Bacterial diversity changes and metabolic pathway enrichment were determined using PRIMERv7 and LEfSe programs. Alpha- and beta-diversities did not differ morning (AM) vs. afternoon (PM). The most affected KEGG pathway was Toll-like receptor signaling in oral mucosa. Eighteen human genes and nine bacterial genes were differentially expressed in plaque samples. Increased activity for 'caries-free' health-associated calcifying Corynebacterium matruchotii and reduced activity for Aggregatibacter aphrophilus, an opportunistic pathogen, were observed. Microbial diversity was not altered after 8 hours plaque formation in healthy individuals as opposed to gene expression.

Cutaneous Microbiome Profiles Following Chlorhexidine Treatment in a 72-Hour Daily Follow-Up Paired Design: a Pilot Study.

Venous catheter-related bloodstream infections represent a significant problem in the United States. Our objective was to determine daily changes in skin microbiome profiles up to 72h postchlorhexidine treatment. Left and right forearm skin swab samples were obtained from 10 healthy volunteers over 72h at 24h intervals. Dorsal surface of left arm was treated with chlorohexidine gluconate (CHG) at initial time point (T = 0), while the right arm remained untreated (control). Swab samples were obtained shortly before (T = 0) and after CHG treatment (T = 24-48-72h). Bacterial DNA extraction, 16S rRNA gene V1-V3 sequencing and taxonomic annotation were performed using ZymoBIOMICS pipeline. PERMANOVA, linear discriminant and bacterial interaction network analyses were performed. A total of 13 total phyla, 273 genera, and 950 total species were detected across all time points, CHG-treated or CHG-untreated. Most abundant species included Cutibacterium acnes, Staphylococcus epidermidis, and Rothia Mucilaginosa. Low biomass-related inconsistent taxa detection was observed. PERMANOVA suggested a marginal difference between CHG-treated and CHG-untreated microbiome profiles (Genera: P(perm) = 0.0531; Species: P(perm) = 0.0450). Bacterial interaction network guided PERMANOVA analyses detected a microbiome change over time, suggesting a consistent CHG treatment-specific change. LEfSe identified Finegoldia magna, Bacillus pumilus, Bacillus thermoamylovorans as the only distinctive species. These species were more abundant and/or present post-CHG treatment in the CHG-treated group. These findings suggest that the skin microbiome was not significantly different 24, 48, or 72h after CHG treatment. Previous culture-based studies have found similar results after 24h. Future studies will be needed to determine the mechanisms of bacterial regrowth after CHG treatment. IMPORTANCE Annually, over 80,000 central line infections occur in the United States. Understanding the pathogenesis of these infections is crucial. Chlorhexidine is the most commonly used skin preparation before line placement. We hypothesized that the use of chlorhexidine and dressings will alter the normal arm skin microbiome over a period of 72h. We used 16S-rRNA gene next generation sequencing (NGS) to determine the forearm skin microbiome of volunteers. The left arm was swabbed with chlorhexidine and the right arm served as control. The skin microbiome returned to normal after 24h. Our NGS results confirm findings of two previous culture-based studies. Relative abundance of Bacillus spp. in the chlorhexidine-treated samples was increased, consistent with one previous study. Based on the results of this pilot study, we will need to measure viable bacteria during a 24h time course following chlorhexidine treatment to understand the source of skin microbiome replenishment.

A Computational Text Mining-Guided Meta-Analysis Approach to Identify Potential Xerostomia Drug Targets.

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.

Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach.

The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q-Q plots (p < 0.05). Protein-protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.

Haemophilus pittmaniae and Leptotrichia spp. Constitute a Multi-Marker Signature in a Cohort of Human Papillomavirus-Positive Head and Neck Cancer Patients.

OBJECTIVES: Human papillomavirus (HPV) is a known etiological factor of oropharyngeal head and neck cancer (HNC). HPV positivity and periodontal disease have been associated with higher HNC risk, suggesting a role for oral bacterial species. Our objective was to determine oral microbiome profiles in HNC patients (HPV-positive and HPV-negative) and in healthy controls (HC). METHODS: Saliva samples and swabs of buccal mucosa, supragingival plaque, and tongue were collected from HNC patients (N = 23 patients, n = 92 samples) before cancer therapy. Next-generation sequencing (16S-rRNA gene V3-V4 region) was used to determine bacterial taxa relative abundance (RA). ß-Diversities of HNC HPV+ (N = 16 patients, n = 64 samples) and HNC HPV- (N = 7 patients, n = 28 samples) groups were compared using PERMANOVA (pMonte Carlo < 0.05). LEfSe discriminant analysis was performed to identify differentiating taxa (Log LDA > 2.0). RA differences were analyzed by Mann-Whitney U-test (α = 0.05). CombiROC program was used to determine multi-marker bacterial signatures. The Microbial Interaction Network Database (MIND) and LitSuggest online tools were used for complementary analyses. RESULTS: HNC vs. HC and HNC HPV+ vs. HNC HPV- ß-diversities differed significantly (pMonte Carlo < 0.05). Streptococcus was the most abundant genus for HNC and HC groups, while Rothia mucilaginosa and Haemophilus parainfluenzae were the most abundant species in HNC and HC patients, respectively, regardless of antibiotics treatment. LEfSe analysis identified 43 and 44 distinctive species for HNC HPV+ and HNC HPV- groups, respectively. In HNC HPV+ group, 26 periodontal disease-associated species identified by LefSe had a higher average RA compared to HNC HPV- group. The significant species included Alloprevotella tannerae, Fusobacterium periodonticum, Haemophilus pittmaniae, Lachnoanaerobaulum orale, and Leptotrichia spp. (Mann-Whitney U-test, p < 0.05). Of 43 LEfSe-identified species in HPV+ group, 31 had a higher RA compared to HPV- group (Mann-Whitney U-test, p < 0.05). MIND analysis confirmed interactions between Haemophilus and Leptotrichia spp., representing a multi-marker signature per CombiROC analysis [area under the curve (AUC) > 0.9]. LitSuggest correctly classified 15 articles relevant to oral microbiome and HPV status. CONCLUSION: Oral microbiome profiles of HNC HPV+ and HNC HPV- patients differed significantly regarding periodontal-associated species. Our results suggest that oral bacterial species (e.g., Leptotrichia spp.), possessing unique niches and invasive properties, coexist with HPV within HPV-induced oral lesions in HNC patients. Further investigation into host-microbe interactions in HPV-positive HNC patients may shed light into cancer development.

Oral Microbiome Signatures in Hematological Cancers Reveal Predominance of Actinomyces and Rothia Species.

The endogenous microbiome of healthy individuals in oral cavities is diverse, representing over 700 bacterial species. Imbalance in oral and gut microbiome composition and associated gene expression has been linked to different forms of hematological (blood) cancers. Our objective is to compare oral microbiome profiles of patients with blood cancers (BC group: N = 39 patients, n = 124 oral samples) to those of healthy control subjects (HC group: N = 27 subjects, n = 100 oral samples). Saliva samples and swabs of buccal mucosa, supragingival plaque, and tongue were collected from blood cancer patients and healthy controls. Next-generation sequencing (16S-rRNA gene V3-V4 region) was used to determine the relative abundance of bacterial taxa present at the genus and species levels. Differences in oral microbiome beta-diversity were determined using multivariate permutational analysis of variance (PERMANOVA). Linear discriminant analysis (LDA) effect size (LEfSe) analysis was performed to identify differentiating bacterial taxa in pairwise comparisons. The PATRICv3.6.7 online tool was used to determine the predominance of potential pathogenicity in the BC group. The oral microbiome beta-diversities of the BC and HC groups differed and corresponded to a reduced alpha-diversity in the BC group. LEfSe analysis showed significant LDA scores for Actinomyces and Rothia spp., differentiating the BC group from the HC group. In silico analysis using PATRICv3.6.7 demonstrated that the groups of bacteria possessing traits of "antibiotic resistance", "oral pathogen", and "virulence" was enriched in the BC group. Although 56% of the BC patients received antibiotics within two weeks of the oral bacterial sampling, Actinomyces genus remained the top differentiating feature in the BC group regardless of the administration of antibiotics, while Rothia dentocariosa was detected as the top differentiating feature in the BC patients who did not receive antibiotics, but not in those who received antibiotics. Further investigation is needed to better understand the interactions of certain oral species with the host immune system to better characterize clinically relevant associations with hematological cancers.

Lasting Gammaproteobacteria profile changes characterized hematological cancer patients who developed oral mucositis following conditioning therapy.

Background: Oral mucositis (OM) is a common side effect of conditioning therapy implemented before hematopoietic stem cell transplantation (HSCT). The role of oral microbiome in OM is not fully elucidated. Objective: To determine oral microbiome profile changes post-conditioning in HSCT patients who developed moderate OM, or mild to no OM. Design: Patient groups were: Muc0-1 with OM-score = 0-1 (43 paired samples) and Muc2 with WHO OM-score = 2 (36 paired samples). Bacterial DNA was isolated from oral samples (saliva, swabs of buccal mucosa, tongue, and supragingival plaque) at pre-conditioning (T 0 ), post-conditioning mucositis onset (T Muc ), and one-year post-conditioning (T Year ). 16S-rRNA gene next-generation sequencing was used to determine the relative abundance (RA) of >700 oral species. Alpha-diversity, beta-diversity and linear discriminant analyses (LDA) were performed Muc2 versus Muc0-1. Results: Muc2 oral microbiome alpha- and beta-diversity differed between T 0 and T Muc . Muc2 alpha-diversity and Muc0-1 beta-diversity did not differ between T 0 and T Year . T 0 to T Muc LDA scores were significant in Muc2 for Gammaproteobacteria. For Muc2 patients, the average RA decreased for Haemophilus parainfluenza, a species known as mucosal surfaces protector, but increased for Escherichia-Shigella genera. Conclusions: Post-conditioning OM might contribute to long-term oral microbiome changes affecting Gammaproteobacteria, in HSCT patients.