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Deciphering host-oral microbiome interactions and mechanisms for prevention of dysbiosis and antimicrobial resistance

Deciphering host-oral microbiome interactions and mechanisms for prevention of dysbiosis and antimicrobial resistance
解读宿主-口腔微生物组相互作用以及预防生态失调和抗菌素耐药性的机制
批准号:
2613554
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金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --

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英文摘要
Host-microbiome interactions play a key role in determining health and disease status. By deciphering the intricate signals within host and microbiome profiles we will advance knowledge for maintaining health and preventing dysbiosis. The significance of the microbiome on host health has led to the emergence of new therapeutic approaches focused on the prescribed manipulation of the host microbiome, either by decreasing harmful taxa or reinstating missing beneficial taxa and the functional roles they perform.A rich and diverse microbiome is necessary for host organisms, as it contributes to the smooth development and functioning of important physiological processes. We will focus on the oral microbiota and its role maintaining the oral mucosal barrier. Reducing the portals for systemic translocation of organisms and disruption to systemic health from ensuing infections. This is particularly problematic in individuals whose immune system is compromised, highlighting the importance of understanding the underlying biological mechanisms at work.The oral cavity comprises microbes that attach to surfaces in communities called biofilms. These are highly regulated with complex interdependency of organisms that adapt to changes in the wider environment. These biofilms are essential to health with multiple functions e.g. contributing to the maintenance of mucosa barrier that prevents invasion of disease-promoting species. Co-cultures allow for the study of cell-cell interactions that more closely mimic complex tissue structures (e.g. human cells-biofilm). Many studies are advancing understanding of the role of the microbiome as a therapeutic agent and its significance in human health.Aims:WP1:To advance current understanding of the taxonomic and functional profiles of the oral microbiome in health and in dysbiosis, and screen for AMR genes harboured by the oral microbiome.Understanding the role of the microbiome in human health relies on detailed characterisation. The microbiome composition is complex, comprised of highly diverse communities with many micro-organisms not yet culturable. We will use our previous dataset (both metagenomic and meta-transcriptomic), supplemented with publicly available data (including associated host cell data) to summarise the taxonomical, functional and resistome profiles observed in oral microbiomes in health and in caries, gingivitis and periodontitis.WP2:To construct and characterise co-cultured biofilms with oral epithelia cells.1. Using our established methods, we will collect oral biofilm samples from healthy volunteers and incubate them in co-culture with an oral epithelial cell line. To establish a dysbiosis state, typical of either caries, gingivitis or periodontitis we will incubate samples in the presence of disease-inducing media.2. We will measure the effect of defined therapeutic strategies to revert dysbiosis/ restore healthy microbial profiles. This offers potential to explore GSK products e.g. active components of oral health products or other pre/probiotics, or antibiotics.3. The effect of the microbial biofilms and therapeutic agents on human epithelial cell line gene expression will be monitored to understand the immune response changes observed from shifting microbiomes. We will work closely with our Glasgow colleagues, to help interpret human cell expression data from transcriptomic work.WP3:To design in silico pipelines for oral health and prevention of dysbiosis modelling.Data generated from WP2 from surveying cell-microbe interactions will produce highly relevant information, which supplemented with those gathered in WP1 will help the design of robust in-silico oral health and disease modelling and predictions using a machine learning approach. We aim for the developed tool to provide a rapid screening methodology that can be used in the future to comprehensively screen for large panels of compounds, drugs, and metabolites to identify the promising ones for
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