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PhD Studentship: What Is It Like To Have Advanced Dementia? Using Remote Measurement of EEG and Physiological Markers of Sleep, Arousal and Awareness in People With Advanced Alzheimer's Disease

University of Exeter - Neuroscience

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students, International Students
Funding amount: £19,237
Hours: Full Time, Part Time
Placed On: 17th September 2024
Closes: 4th November 2024
Reference: 5234

About the GW4 BioMed2 Doctoral Training Partnership

The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities, with opportunities to participate in interdisciplinary and 'team science'. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 58 students over 3 cohorts in its second phase.

Project Information

Research Theme: Infection, Immunity, Antimicrobial Resistance & Repair

Summary: The spread of antimicrobial resistance (AMR) is a slow-moving pandemic, identified by the WHO as a top-10 threat facing humanity. Staphylococcus aureus is an opportunistic human pathogen with high levels of antimicrobial resistance and it is a WHO priority to develop novel therapeutic solutions against this species. Phages (viruses that infect bacteria) are increasingly recognized as potential therapeutic modalities. In this project, the student will carry out large scale infection assays with diverse S. aureus isolates and phages to identify which phages are the most effective inhibitors of S. aureus growth, and tease apart why this is the case.

Project Description: The spread of antimicrobial resistance (AMR) is a slow-moving pandemic and has been identified by the WHO as one of the top 10 threats facing humanity. Mobile genetic elements (MGEs), such as phages and plasmids, play a key role in AMR dissemination but also offer a promising basis for non-antibiotic therapies. The activity of MGEs is fundamentally shaped by bacterial defenses, which can suppress AMR spread and limit the efficacy of phage-based therapies. Well-known bacterial defenses include Restriction-Modification (RM) and CRISPR-Cas, but it is now recognized that bacteria carry more than 100 defense systems. These defenses act at different stages of the MGE lifecycle: some cleave MGE genomes immediately following infection, others interfere with MGE transcription or replication, or induce cell death or dormancy responses. Large-scale systematic studies are necessary to develop a broader understanding of how these defenses integrate and shape bacteriaphage interactions, which is essential to outflank AMR. We predict that bacterial defenses can interact synergistically or antagonistically, and that such combinations occur more and less frequently than expected by chance, respectively. To identify interactions supporting this hypothesis, we will identify known defense genes in publicly available whole genome sequences and analyze their co-occurrence patterns using phylogenetically controlled models. Next, we will use a diverse collection of over 500 sequenced S. aureus isolates that differ in geographical, clinical origin, ecology and host. Using bioinformatics pipelines, we will build phylogenies, and identify known defense genes in these isolates. On these 500 isolates, we will perform large-scale infection assays using a panel of 15 phages that can infect S. aureus. This panel captures diverse phage families with distinct life styles, genome sizes, and replication mechanisms, allowing us to test whether certain defenses are specific to particular phage types and whether combinations of defenses provide complementary or overlapping resistance ranges. Our team has recently developed high-throughput analyses of phage resistance and infectivity based on OD600 plate reader assays, which will be adopted in this project. We will analyze the resulting infectivity/resistance data using phylogenetically controlled statistical models to examine how defense types and combinations correlate with phage resistance phenotypes, and to what extent this depends on the phage type.

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