How does maternal tissue immunity determines fetal growth and offspring health?
Supervisor: Professor Francesco Colucci, Professor in Immunology (firstname.lastname@example.org)
Principle Supervisor Department: Obstetrics and Gynaecology
Fetal growth restriction (FGR), caused by maternal, placental or fetal malfunctions, affects ~10M neonates yearly and correlates with adult onset of chronic conditions. A knowledge gap on the cellular and molecular networks that regulate placental and fetal development limits discovery of mechanisms underlying FGR. We study placental related FGR due to inadequate uterine natural killer (uNK) cell function or gestational infection. uNK cells mediate vascular changes necessary for placental development. We have discovered that the NKG2A receptor educates uNK cell function and its absence causes FGR with insufficient vascular remodelling, altered placental gene expression and abnormal brain development. Preliminary data show that an ATP-sensitive potassium (KATP) channel is upregulated in NKG2A-educated uNK cells, suggesting new links between cell metabolism and function. Gestational cytomegalovirus (CMV) activates uNK cells but perturbs the maternal-fetal interface (MFI), causing FGR. To advance fundamental understanding of MFI networks in health and disease, we want to determine:
– the genes involved in the NKG2A pathway in human cells
– how the KATP channel affects uNK cell function
– how inadequate uNK cell function and gestational CMV perturb the MFI
– the link between these perturbations and offspring health.
The human NKG2A transcriptome will be defined by bulk RNA-sequencing in purified uNK cells. The role of the KATP channel will be analysed in human cell assays and NK-specific KATP KO mice. MFI disruptions caused by inadequate uNK cell function or CMV infection will be determined using single-cell multi-omics. Links between MFI perturbations and offspring health will be established by quantifying phenotypes in offspring immune system, heart and brain by flow cytometry, morphometric, physiological and AI-driven approaches.
This project will deliver transformative outcomes to advance understanding of FGR mechanisms and its effects on offspring.
A prediction model for Ventilator Associated Pneumonia in the critically ill child.
Supervisor: Dr Nazima Pathan, Associate Professor (email@example.com)
Principle Supervisor Department: Paediatrics
Ventilator-associated pneumonia (VAP) is defined as pneumonia occurring after 48h mechanical ventilation, and accounts for 20% of hospital acquired infections in paediatric intensive care unit (PICU) patients. With a reported prevalence of 3-20/1000 PICU bed days, development of VAP is associated with significant risk of harm. Associated mortality ranges from ~20-50%, and morbidity includes prolonged recovery time and relapse, elevating healthcare costs (estimated at around $10,000 per patient).
Diagnosing and managing VAP remains one of the greatest challenges to intensive care and microbiology clinicians. It is poorly defined and complex in pathophysiology, resulting in antimicrobial decision making on subjective assessment of clinical signs and symptoms due to low diagnostic yield from routine culture-based assays. The current best practice for diagnosis uses consensus criteria such as the European CDC framework, which is cumbersome and not widely implemented in a clinical timeframe.
Objective to reliably identify pre-symptomatic respiratory infection in critically ill patients at risk of VAP in order to take this forward as a clinical diagnostic tool.
1. Identify the optimal sampling strategy for a predictive test of VAP.
2. Investigate how and when oropharyngeal and lower respiratory tract dysbiosis leads to the clinical manifestation of VAP.
3. Identify the inflammatory markers activated by lower respiratory tract dysbiosis which indicate recognition of a ‘hostile’ threat to the host.
4. Integrate temporal data from microbial and host profiling to develop a model separating VAP from non-infectious respiratory failure.
5. Create a predictive algorithm using microbial and host indicators of VAP to take forward for clinical testing.
The study proposed is a prospective, multi-centre cohort study of 200 children >1 month admitted to PICU for a non-infectious primary diagnosis and expected to require >48h mechanical ventilation. Study participants will be classified through post-hoc evaluation of anonymised clinical, radiological and microbiological data using the European CDC (ECDC) criteria for the diagnosis of VAP11.
Work Package 1: Microbial changes in the LRTi predictive of VAP
We will collect serial samples of endotracheal fluid (collected during routine suction) and oro-pharyngeal swabs for metagenomic sequencing, applying dimension reduction techniques and standard alpha and beta metrics to track diversity shifts over time, with a focus on identifying early signatures of the transition to a dominant microbial taxon and/or a decline in previously dominant microbiota. In parallel, we will evaluate and interpret machine-learning models trained to predict VAP using microbial taxa abundance, microbial diversity, and/or the presence of microbial communities as predictors.
Work Package 2: Inflammatory changes associated with development of VAP
We will profile the inflammatory response to identify the inflection point where the host recognises pathogen infiltration as infection. We will characterise markers of innate and adaptive immunity in bronchial fluid and plasma using the Bio-Rad 20-plex human immunotherapy cytokine panel12. At transcriptional level, we will explore the expression profile of inflammasome and inflammation related genes in lung and blood immune cells to identify patterns associated with VAP.
Work Package 3: Development of an integrated framework for prediction of VAP.
We believe the host-microbiota interplay holds precursor signals leading to VAP. Our analysis will integrate data from both, to analyse factors and patterns predictive of VAP. We will use a logistic regression approach comparing a validation cohort of patients from definite VAP and non-VAP patients to derive a probability score of infection. We will train machine learning models to predict microbial abundance using expression profiles of the host inflammatory response, patient demographics, clinical signs and outcomes
Identification and characterisation of novel antiviral restriction factors
Supervisor: Professor Michael Weekes, Professor of Viral Immunology (firstname.lastname@example.org)
Principle Supervisor Department: Cambridge Institute for Medical Research
Antiviral restriction factors (ARF) are a critical element of cellular innate immunity, representing the first barrier to viral infection that can determine outcome. We aim to identify and characterise novel ARF and their viral antagonists, since therapeutic interruption of viral antagonism can enable restoration of endogenous antiviral activity.
We employ a number of human pathogens, in particular Human Cytomegalovirus (HCMV), Monkeypox virus (MPXV) and its vaccine, Modified Vaccinia Ankara (MVA). Our systematic proteomic analyses determine which cellular factors each pathogen targets for destruction, since we have shown these to be enriched in novel ARFs. For example, we recently developed a multiplexed proteomic technique that identified proteins degraded in the proteasome or lysosome very early during HCMV infection (Nightingale et al, Cell Host & Microbe 2018). A shortlist of 35 proteins were degraded with high confidence, and we have since shown that several are novel ARF, with characterisation of these factors forming ongoing projects. Application to MVA infection indicated further candidates, and identified novel mechanisms of vaccine action (Albarnaz et al, in review, https://www.researchsquare.com/article/rs-1850393/v1). Furthermore, interactome screens can identify the viral factor(s) responsible for targeting each ARF, and indicate mechanism (Nobre et al eLife 2019).
This project will now identify and characterise critical pan-viral ARF, which can restrict diverse viruses. For the most potent, we will determine both the mechanism of restriction and the mechanism of virally mediated protein degradation. In order to prioritise the most important factors, there will also be the opportunity to use novel multiplexed proteomic screens.