Search

Objective. To identify the key genetic and epigenetic mechanisms involved in the wound healing process after injury of the oral mucosa. Materials and Methods. Gene expression profiling datasets pertaining to rapid wound healing of oral mucosa were identified using the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed to identify differentially expressed genes (DEGs) during oral mucosal wound healing. Next, functional enrichment analysis was performed to identify the biological processes (BPs) and signaling pathways relevant to these DEGs. A protein-protein interaction (PPI) network was constructed to identify hub DEGs. Interaction networks were constructed for both miRNA-target DEGs and DEGs-transcription factors. A DEGs-chemical compound interaction network and a miRNA-small molecular interaction network were also constructed. Results. DEGs were found significantly enriched in several signaling pathways including arachidonic acid metabolism, cell cycle, p53, and ECM-receptor interaction. Hub genes, GABARAPL1, GABARAPL2, HDAC5, MAP1LC3A, AURKA, and PLK1, were identified via PPI network analysis. Two miRNAs, miR-34a-5p and miR-335-5p, were identified as pivotal players in the miRNA-target DEGs network. Four transcription factors FOS, PLAU, BCL6, and RORA were found to play key roles in the TFs-DEGs interaction network. Several chemical compounds including Valproic acid, Doxorubicin, Nickel, and tretinoin and small molecular drugs including atorvastatin, 17β-estradiol, curcumin, and vitamin D3 were noted to influence oral mucosa regeneration by regulating the expression of healing-associated DEGs/miRNAs. Conclusion. Genetic and epigenetic mechanisms and specific drugs were identified as significant molecular mechanisms and entities relevant to oral mucosal healing. These may be valuable potential targets for experimental research.

We read with interest the recent article by Chan et al (Gut, 2020; 0:1–10) analysing Fusobacterium diversity in the southern Chinese population.1 Fusobacterium nucleatum has been proven to be involved in the occurrence and progression of colorectal cancer (CRC); therefore, F. nucleatum has attracted more academic interest than other Fusobacterium species.2–5 Nevertheless, Chan et al ’s study made a critical observation: individuals in China (Hong Kong, Shenzhen and Zhejiang) with and without cancer harbour non-nucleatum types of Fusobacterium species as the dominating taxa. Similarly, Geva-Zatorsky et al reported that F. varium can exert a greater phenotypical impact in the host than F. nucleatum .6 Herein, we analysed the fusobacterial diversity in a Chinese population comprising 7009 individuals across 14 districts to provide a comprehensive understanding of Fusobacterium lineage distributions and their correlation with host parameters.7 8 Because the present dataset is based on 16S rRNA gene V4 sequencing, we first proved that these sequences can be …

ObjectiveThis study aimed to identify the programmed death ligand-1 (PDL1, also termed as CD274) and its positively correlated immune checkpoint genes (ICGs) and to determine the immune subtypes of CD274-centered ICG combinations in oral and squamous cell carcinoma (OSCC).Materials and MethodsFirstly, the 95 ICGs obtained via literature reviews were identified in the Cancer Genome Atlas (TCGA) database in relation to OSCC, and such 88 ICG expression profiles were extracted. ICGs positively correlated with CD274 were utilized for subsequent analysis. The relationship between ICGs positively correlated with CD274 and immunotherapy biomarkers (tumor mutation burden (TMB), and adaptive immune resistance pathway genes) was investigated, and the relationships of these genes with OSCC clinical features were explored. The prognostic values of CD274 and its positively correlated ICGs and also their associated gene pairs were revealed using the survival analysis.ResultsEight ICGs, including CTLA4, ICOS, TNFRSF4, CD27, B- and T-lymphocyte attenuator (BTLA), ADORA2A, CD40LG, and CD28, were found to be positively correlated with CD274. Among the eight ICGs, seven ICGs (CTLA4, ICOS, TNFRSF4, CD27, BTLA, CD40LG, and CD28) were significantly negatively correlated with TMB. The majority of the adaptive immune resistance pathway genes were positively correlated with ICGs positively correlated with CD274. The survival analysis utilizing the TCGA-OSCC data showed that, although CD274 was not significantly associated with overall survival (OS), the majority of ICGs positively correlated with CD274 (BTLA, CD27, CTLA4, CD40LG, CD28, ICOS, and TNFRSF4) were significantly correlated with OS, whereby their low-expression predicted a favorable prognosis. The survival analysis based on the gene pair subtypes showed that the combination subtypes of CD274_low/BTLA_low, CD274_low/CD27_low, CD274_low/CTLA4_low, CD8A_high/BTLA_low, CD8A_high/CD27_low, and CD8A_high/CTLA4_low predicted favorable OS.ConclusionThe results in this study provide a theoretical basis for prognostic immune subtyping of OSCC and highlight the importance of developing future immunotherapeutic strategies for treating oral cancer.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression1–3. Diagnostic applications have been proposed for inflammatory bowel disease diagnosis and prognosis4, colorectal cancer prescreening5 and therapeutic choices in melanoma6. Noninvasive sampling could facilitate large-scale public health applications, including early diagnosis and risk assessment in metabolic7 and cardiovascular diseases8. To understand the generalizability of microbiota-based diagnostic models of metabolic disease, we characterized the gut microbiota of 7,009 individuals from 14 districts within 1 province in China. Among phenotypes, host location showed the strongest associations with microbiota variations. Microbiota-based metabolic disease models developed in one location failed when used elsewhere, suggesting that such models cannot be extrapolated. Interpolated models performed much better, especially in diseases with obvious microbiota-related characteristics. Interpolation efficiency decreased as geographic scale increased, indicating a need to build localized baseline and disease models to predict metabolic risks. The definition of a 'healthy' microbiome is impacted by geographic regional variations.

Dysbiosis, departure of the gut microbiome from a healthy state, has been suggested to be a powerful biomarker of disease incidence and progression

Salmonella Typhimurium is an intracellular pathogen that causes gastroenteritis in humans. Aided by a battery of effector proteins, S. Typhimurium resides intracellularly in a specialized vesicle, called the Salmonella-containing vacuole (SCV) that utilizes the host endocytic vesicular transport pathway (VTP). Here, we probed the possible role of SUMOylation, a post-translation modification pathway, in SCV biology. Proteome analysis by complex mass-spectrometry (MS/MS) revealed a dramatically altered SUMO-proteome (SUMOylome) in S. Typhimurium-infected cells. RAB7, a component of VTP, was key among several crucial proteins identified in our study. Detailed MS/MS assays, in vitro SUMOylation assays and structural docking analysis revealed SUMOylation of RAB7 (RAB7A) specifically at lysine 175. A SUMOylation-deficient RAB7 mutant (RAB7K175R) displayed longer half-life, was beneficial to SCV dynamics and functionally deficient. Collectively, the data revealed that RAB7 SUMOylation blockade by S. Typhimurium ensures availability of long-lived but functionally compromised RAB7, which was beneficial to the pathogen. Overall, this SUMOylation-dependent switch of RAB7 controlled by S. Typhimurium is an unexpected mode of VTP pathway regulation, and unveils a mechanism of broad interest well beyond Salmonella-host crosstalk. This article has an associated First Person interview with the first author of the paper.

Supplementary Data: contains Supplementary Methods and Materials, Supplementary Results, Supplementary Figures and Legends, References for Supplementary Data. (DOCX 3627 kb)

In the version of this article originally published, in the sentence “Applying the same approach to obesity (Fig. 2b), MetS (Fig. 2c) and fatty liver (Fig. 2d) yielded similar results,” two figure panels were cited incorrectly. The data for obesity are in Fig. 2c, and the data for MetS are in Fig. 2b. The sentence has been updated with the correct citations in the print, PDF and HTML versions of the article.