Molecular probes to diagnose pathoadapatations in bacterial infections

慢性细菌感染对医院和社会环境中的NHS提出了重大挑战，并导致抗菌素耐药性的威胁日益增加。细菌在宿主体内进化和多样化，特别是在免疫功能低下的个体中，导致持续的、难以控制的感染。这种“病理适应”过程导致表型变化，如蛋白质表达改变、对抗菌剂的抗性或毒力性状的获得或丧失。因此，可靠地鉴定病理适应对于成功设计和治疗细菌感染至关重要。目前，病理适应性可以使用分子组学技术来鉴定，这些技术集中于基因表达、蛋白质组学或代谢组学。然而，这些方法在技术上要求很高，昂贵且耗时，突出了对更快诊断的需求。我们提出了一种新的分子探针为基础的战略，快速识别流行病学上重要的病理类型的基础上与毒力，宿主定植和抗菌药物耐药性的细菌表面特性。在初步实验中，我们已经证明，荧光糖聚合物阵列可以区分一系列的模式生物的基因型-机会性呼吸道病原体铜绿假单胞菌。我们的方法可以可靠地区分不同的工程转座子插入突变体在其显示的毒力因子，和铜绿假单胞菌分离株之间起源于囊性纤维化（CF）患者，证明了我们的系统的潜力，以确定典型的持续性临床感染的病理适应。我们将在此基础上创建全面的多维糖聚合物传感器阵列，以区分与肺部感染相关的临床铜绿假单胞菌致病型。传感器阵列将被纳入光纤技术中，以在铜绿假单胞菌肺部感染的典型混合群体的表面上生长时识别致病型。我们提出的工作有一个明确的转化途径，临床使用，由公众和患者参与和参与（PPIE）的原则指导。除了提供新的诊断工具，用于经济有效和快速监测细菌感染，为治疗策略提供信息，包括抗生素的选择，我们的阵列技术还可用于诊断其他类型的微生物病原体，包括呼吸道病毒。

Chronic bacterial infections present a significant challenge to the NHS within hospital and social settings and contribute to the growing threat of antimicrobial resistance. Bacteria evolve and diversify within hosts, particularly in immunocompromised individuals, leading to persistent, difficult to manage infections. This process of 'pathoadaptation' leads to phenotypic changes such as altered protein expression, resistance to antimicrobials, or gain or loss of virulence traits. Reliable identification of pathoadaptations is hence crucial for the successful design and treatment of bacterial infections. Currently, pathoadaptations can be identified using molecular omics techniques focussing on gene expression, proteomics or metabolomics. However, these approaches are technically demanding, expensive and time-intensive, highlighting the need for faster diagnostics. We propose a novel molecular probe-based strategy to rapidly identify epidemiologically important pathotypes based on bacterial surface properties linked to virulence, host colonisation and antimicrobial resistance. In preliminary experiments, we have demonstrated that arrays of fluorescent glycopolymers can differentiate a range of genotypes of a model organism - the opportunistic respiratory pathogen Pseudomonas aeruginosa. Our method can reliably distinguish engineered transposon-insertion mutants differing in their display of virulence factors, and between P. aeruginosa isolates originating from cystic fibrosis (CF) patients, demonstrating the potential of our system to identify pathoadaptations typical for persistent clinical infections. We will build on this foundation to create comprehensive, multidimensional glycopolymer sensor arrays to differentiate clinical P. aeruginosa pathotypes associated with lung infections. The sensor arrays will be incorporated within fibre-optic technology to characterise pathotypes when grown on surfaces in mixed populations typical for P. aeruginosa lung infections. Our proposed work has a clear translational pathway to clinical use, guided by principles of Public and Patient Involvement and Engagement (PPIE). Besides providing new diagnostic tools for cost-effective and rapid monitoring of bacterial infections, informing treatment strategies including choice of antibiotics, our array technology could be used to diagnose other types of microbial pathogens including respiratory viruses.