NIHRDataInsights/HRCSData huggingface dataset:
Title: Neurophysiological characterisation of post-COVID fatigue
Funder: Medical Research Council

Abstract: Despite the high severity of the SARS-CoV-2 virus in a substantial minority of those infected, most people only suffer from mild initial symptoms, which typically resolve without the need for hospitalisation. However there is increasing evidence that longer term neurological sequelae can occur even after mild disease, and of these, fatigue and muscle weakness are reported with a high incidence (typically >20%). Although fatigue can be a non-specific core symptom associated with many different conditions, its detrimental impact on quality of life and productivity is well established, particularly if it develops into a chronic symptom. As a novel disease, the mechanisms behind the impact of COVID on neural function remain unclear and this is particularly so for post-COVID fatigue (pCF). Our aim is to use observational and electrophysiological methods to characterise the neural mechanisms behind pCF, particularly with respect to the sensorimotor system and to monitor their progression over time. Even after this pandemic is brought under control, by whatever measures develop over the coming months and years, the longer term sequelae will continue to reap a substantial cost both to the individuals but also to the economic recovery of the nation as a whole. By understanding the neural mechanisms of pCF we can understand the epidemiology better and make more rational decisions for interventions and treatments.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: INFECTION
Title: G2P-UK; A National Virology Consortium to address phenotypic consequences of SARSCoV-2 genomic variation
Funder: Medical Research Council

Abstract: SARS-CoV-2 has caused 1.4 million deaths and has devastated economies worldwide. As SARS-CoV-2 replicates and spreads, its RNA genome inevitably mutates. Mutations may confer altered properties of potential concern to human health, such as increased pathogenicity or transmissibility, or reduced sensitivity to prior immunity or antiviral drugs. Importantly, the imminent roll out of vaccination campaigns could provide strong selection pressure for escape from vaccine-induced immunity. We are a consortium ("G2P-UK") of UK virologists who will work openly with COG-UK and UK-CiC, to establish an experimental pipeline and shared resources (reagents, methodologies and model systems) to rapidly define the phenotypic impacts of SARS-CoV-2 mutations as they emerge. With three interconnected work packages we will obtain and distribute clinical isolates and engineered SARS-CoV-2 mutants (WP1), test the functional properties of the mutations in in vitro assays (WP2) and characterise their phenotype in culture and animal model systems (WP3). The choice of strains and mutations will be informed by a joint working group that includes COG-UK members. Current virus strains will be immediately introduced into the pipeline, to accumulate a baseline of underpinning knowledge about SARS-CoV-2 behaviour, to validate the consortium working relationships and to seed mechanistic studies suitable for further research. Then, as variants of concern are detected, they will be prioritized for co-ordinated investigation in real time. By interpreting the biological consequences of SARS-CoV-2 mutations we will inform on the associated risks and vulnerabilities related to public health policy and clinical practice, including treatment strategies, diagnostics and infection control, and vaccination.


Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.6 RESOURCES AND INFRASTRUCTURE (AETIOLOGY)
Health categories: INFECTION
Title: The UK Interstitial Lung Disease Long-COVID19 study (UKILD-Long COVID): understanding the burden of Interstitial Lung Disease in Long COVID.
Funder: Medical Research Council

Abstract: The COVID-19 pandemic has caused significant worldwide mortality and morbidity with over 3.5 million people infected and more than 350,000 people hospitalised in the UK. Current estimates suggest 10-20% of non-hospitalised patients and 40-60% of hospitalised patients have long term symptoms, including breathlessness and fatigue, so-called "Long COVID". Emerging radiological and physiological features suggest Interstitial Lung diseases (ILD) including organising pneumonia and pulmonary fibrosis occur in up to 20% of hospitalised patients, although the precise burden and natural history of Long COVID related ILD (LCILD) is not clear. Given the large number of patients with persisting breathlessness there is an urgent need to identify and prevent the development LCILD. To improve outcomes for survivors of COVID-19 we will a) determine the prevalence of ILD following COVID-19, stratified by severity of infection and treatment, b) describe the phenotypes, c) determine the natural history and d) identify pathogenic mechanisms and biomarkers of LCILD. Patients with proven COVID-19 will be recruited and the proportion of patients with symptoms, signs and investigations consistent with ILD between 3 and 6 months will be documented. In patients where ILD is suspected clinically this will be confirmed by Computerised Tomography (CT) scanning and patients will be stratified by pre-existing ILD, hospitalisation status, and therapy received. A subgroup of patients with proven LCILD will be re-consented for 12-month follow-up and deep phenotyping including 129Xenon-MRI and bronchoalveolar lavage. These data will define the burden of LCILD and inform the design of clinical trials to assess potential therapies to modulate progression of LCILD.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION
Title: TestEd: Developing and evaluating an affordable whole-system approach for early detection of viral infections in workplaces and communities
Funder: Medical Research Council

Abstract: Individuals with COVID-19 symptoms are eligible for diagnostic testing in the UK. However, due to cost and logistical constraints, pre and asymptomatic individuals are rarely tested or are screened with low-efficacy lateral flow devices, potentially allowing viral transmission. Although vaccination programmes provide grounds for optimism, there is no guarantee they will eliminate the virus if immunity is short lived, the virus mutates or there is low uptake. An efficient testing system is still needed for suppressing this and future pandemics. We have developed and validated two high-sensitivity, high-throughput approaches for detecting asymptomatic SARS-CoV-2 infection in saliva: (i) PCR-based hypercube pooling and (ii) sequence-based SwabSeq. We will directly compare the two approaches and test the hypothesis that they can be used at scale to detect asymptomatic SARS-CoV-2 infection early, reliably and affordably in up to half a million saliva samples from asymptomatic students and staff at Edinburgh University. We will survey participants to ensure our system is accessible and appropriate, and interview those who test positive to understand attitudes and behaviour regarding self-isolation. We will model the health economics of hypercube pooling and SwabSeq to compare their cost-efficiency and overall value with current testing methods. With an automated, user friendly system of participant registration, sample tracking, robotic processing and rapid result reporting, we aim to reduce the costs of regular screening to below 40p per test. This will enable testing of the whole University community twice a week, thereby minimising transmission from asymptomatic carriers. Our link to NHS Lothian Virology and its COVID service will allow positive cases to be re-tested and immediately entered into NHS public health systems. The study will provide a blueprint for large scale, regular testing to protect workplaces and communities from COVID-19 and future pandemics.


Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;4.3 INFLUENCES AND IMPACT
Health categories: INFECTION
Title: TB and COVID-19 coinfection: Investigating the clinical and biological interactions between Mycobacterium tuberculosis and SARS-CoV-2
Funder: Medical Research Council

Abstract: UKRI priority areas include understanding the interaction between SARS-CoV-2 and other pathogens. M.tb and SARS-CoV-2 have significant potential to interact and therefore impact on clinical outcomes. The two major knowledge gaps to be addressed by this project are: 1. The reciprocal biological impact of SARS-CoV-2 and M.tb on immunity to each other 2. How the pandemic has affected outcomes and the provision of TB services Research to date has focussed on retrospective studies exploring relationships between TB and COVID-19 and suggest M.tb increases susceptibility to SARS-CoV-2 and disease severity. The control of LTBI requires the maintenance of effective T-cell responses. When these are perturbed (e.g. HIV infection, anti-TNF treatment), the risk of reactivation increases significantly. SARS-CoV- 2 infections may cause lymphopenia and the impact of this on M.tb-specific T-cell response is unknown, nor is it clear if these return to normal levels. One reason for the lack of understanding of the relationship between these pathogens is the limited availability of sufficiently-sized populations of M.tb-infected patients and the clinical infrastructure to assess them. The assembled team has the appropriate expertise to address these issues and the cohorts of patients within a single joined-up health region.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: INFECTION
Title: MICA: Pharmacokinetic/Pharmacodynamic (PKPD) Model Development to Inform SARS-CoV-2 Antiviral Development
Funder: Medical Research Council

Abstract: Antivirals generally need to reduce viral load to positively influence clinical endpoints such as hospitalisation or mortality. To prioritise antivirals for Phase III trials, Phase II trials seeking significant viral load decrease versus placebo are required. Interpreting viral load is challenging since it changes with time since infection (rises then falls) and by sampling site. This proposal seeks to develop a pharmacometric nonlinear mixed effects model of SARS-CoV-2 viral dynamics. The model will be used to design and analyse efficient antiviral Phase II trials, and prioritise antivirals and antiviral combinations for Phase III. The target cell limited model, along with its common extensions (eclipse phase and innate and adaptive immune components) and simplifications (quasi-steady-state assumption between infected cell and free virus numbers), will be compared using graphical and numerical model diagnostics. A preferred model will be chosen and applied to viral load data in ongoing trials and to simulate outcomes to optimally design future trials. The model will be applied to real-world clinical data to seek subgroups of hospitalised patients who may benefit from antivirals. It will also be used to perform a model-based appraisal of whole genome sequence-derived biomarkers such as subgenomic RNA, to be assessed as a possible Phase II endpoint. This work will be carried out by a collaboration of academic, clinical and pharmaceutical industry investigators who will develop and share SARS-CoV-2 antiviral modelling best practice.


Research activity codes: 6.1 PHARMACEUTICALS;6.9 RESOURCES AND INFRASTRUCTURE (TREATMENT EVALUATION)
Health categories: INFECTION
Title: Understanding the beneficial role and antimicrobial potential of novel human gut bacteria
Funder: Medical Research Council

Abstract: In this multidisciplinary project, I will uncover the biological roles of novel Christensenellales bacteria belonging to the human gut microbiome. The importance of Christensenellales in diverse aspects of human health is becoming increasingly recognized, but there have been a number of challenges and limitations preventing further progress in the field. Until recently, the mere existence of most of Christensenellales species colonizing the human gut was unknown, as previous studies have lacked sufficient resolution due to their reliance on the analysis of the 16S rRNA gene and the lack of reference genomes. I have now massively expanded the number of Christensenellales species and genomes currently known, which will transform our understanding of the functional capacity, host adaptation and beneficial role of Christensenellales in health. Isolating and culturing new Christensenellales species will enable me to understand their biology while opening opportunities to explore their utility as next-generation probiotics. This represents a potentially ground-breaking undertaking, as currently >98% of Christensenellales species remain uncultured and inaccessible to experimental testing. In addition, the rationale in developing these new culturing strategies may be extended to other important gut bacteria. Given my promising preliminary data, I will also test new antimicrobial peptides that may be active against multidrug-resistant pathogens. Identifying new ways to treat pathogenic bacteria causing high rates of morbidity and mortality worldwide will have a wide-reaching clinical, social and economic impact. Furthermore, the discovery of new bioactive compounds affecting the growth of gut bacteria may inform new strategies to engineer the human gut microbiome composition for improved health.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;5.2 CELLULAR AND GENE THERAPIES
Health categories: INFECTION
Title: Investigating new systems for integrity of the bacterial outer membrane
Funder: Medical Research Council

Abstract: Antimicrobial resistant bacteria pose a significant threat to human health. The outer membrane (OM) of Gram-negative bacteria acts as a physical barrier against harmful molecules, such as antibiotics. Understanding the mechanisms behind OM biogenesis is essential to explore new targets for drug design. In order to build and maintain their OM, bacteria must transport hydrophobic molecules, including lipids and proteins, across the aqueous periplasm. However, many of these mechanisms are poorly understood. My research will focus on the AsmA protein family, which are predicted to transport molecules across the periplasm to build or maintain the OM. AsmA proteins are conserved across bacteria, with homologues in humans and plants, indicating they play an important role that has been maintained throughout evolution. Furthermore, bacteria lacking AsmA proteins show severe loss of OM homeostasis. This can result in loss of virulence, decreased antibiotic resistance, and even loss of viability. However, the structures and functions of AsmA proteins remain remarkably understudied, highlighting an important area for further research. I propose to explore the function of the six AsmA proteins in E. coli using a combination of structural biology, protein biochemistry and bacterial genetics. I will solve the structure of AsmA proteins using cryo-EM. Furthermore, I will determine which components of the OM AsmA proteins directly interact with, to infer what they traffic. This will be achieved using a combination of pull-downs, mass spectrometry, and site-specific crosslinking. Finally, I will study the function of AsmA proteins at the cellular level, by constructing combinatorial gene knockouts and exploring phenotypes of these strains (e.g. cell morphology changes). Overall, this research will increase our understanding of fundamental bacterial membrane biology, while identifying potential new candidates for the treatment of antimicrobial resistant bacterial infections.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: INFECTION
Title: OpenSAFELY, ISARIC, PHOSP: tracking consequences of COVID-19 infection across UK primary and secondary care.
Funder: Medical Research Council

Abstract: The prevalence and severity of health consequences for patients who have had COVID-19 are not currently known. There is also little data on which patients are most at risk of ongoing health and care needs. We must understand post-covid prognosis and risks to inform choices around prevention and treatment, design services, predict need, inform patients about their risks and prognosis, mitigate individuals' risks, and improve clinical outcomes. We will link data and combine expertise from three key projects to provide an unprecedented, comprehensive, longitudinal, patient-level view on COVID-19 patients in England: OpenSAFELY, running across patients' full primary and secondary care electronic health records (40% of patients in England, rising to 95% during the course of the project). ISARIC, with detailed data on >80,000 COVID-19 patients' in-hospital presentation and management. PHOSP, collecting bespoke symptom and laboratory data over 12 months on 10,000 hospitalised COVID-19 survivors. Using OpenSAFELY EHR data linked to ISARIC and PHOSP cohort data we will: - Assess risk of specific diagnoses, presentations, treatments, investigation findings, and symptoms that are elevated following COVID-19, in hospitalised and non-hospitalised patients. - Evaluate the impact of age, ethnicity, prior medical history, COVID-19 disease severity and in-hospital treatment on variation in recovery and complications. - Evaluate the extent to which PHOSP findings generalise to non-admitted COVID-19 patients. - Describe the impact of "long COVID" on health service utilisation to help predict service design and need. - Estimate excess morbidity associated with COVID-19. - Develop tools to inform shielding policy based on long-term outcomes. - Share open code resources.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: INFECTION
Title: Smart and scalable epidemic prediction and control
Funder: Medical Research Council

Abstract: Mathematical models form the first defence against emergent infectious diseases, inferring key transmission drivers, forecasting epidemic burden and informing non-pharmaceutical interventions (NPIs). However, the reliability of models as public health tools depends materially and subtly on their scale or structure. Fine scale models simulating every spatiotemporal or sociodemographic transmission heterogeneity may yield unreliable forecasts due to scarce data or untestable assumptions. Coarse models averaging these heterogeneities (e.g. nationwide) may be overconfident or only able to support blunt NPIs (e.g. lockdowns). Policy informed by improperly selected models can be risky, costly and ineffective. Solving this model selection problem is fundamentally challenging. The most reliable scale for modelling varies with space, time or reactively to NPIs and is influenced by mostly unknown performance limits imposed by noisy data, behavioural feedback and practical policy constraints. I propose to engineer smart models to resolve these challenges. Renewal epidemic models are tractable but accurate, while hierarchies realistically describe dynamics at multiple scales. Minimum description length and decentralised control theory offer proven means of penalising model structure by descriptive power and algorithms for exposing and exploiting limits due to feedback, delays and noise. Fusing these methods, which are new to epidemiology, with input from policy scientists I will develop novel hierarchical models that smartly restructure to circumvent performance limits and pinpoint the optimal scales for combatting pandemics at any time. Applying these on key SARS and COVID-19 datasets, I will derive robust early-indicators of resurgence and persistence, probe and overcome information bottlenecks limiting control and prediction and devise practical NPI strategies that cooperate across scales to more efficiently suppress future outbreaks. Smart models can inform reliable policy.


Research activity codes: 3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: INFECTION
Title: From alternative hosts to alternative interventions: identifying drivers of epidemic dynamics for Japanese encephalitis virus in Bangladesh
Funder: Medical Research Council

Abstract: Japanese encephalitis virus (JEV) is usually transmitted between pigs and mosquitoes. I will determine how JEV epidemics occur in northern Bangladesh where density of cattle is high and density of pigs is low. Cattle are often the preferred host for JEV vectors but cannot transmit the virus. I will test the hypothesis that alternative host species are both necessary and sufficient for JEV epidemics in this region, determine whether host density and community composition are predictive of JEV risk and use results to inform control strategies. The objectives to achieve these aims are: i) development of mechanistic models of JEV transmission; ii) field work to determine the relative contribution of alternative hosts to transmission and test model-based predictions of JEV risk; and iii) use validated models to quantify the potential impact of alternative interventions on transmission. I will use stochastic models, incorporating non-homogeneous mixing between hosts and vectors, to determine the conditions under which onward transmission is possible, accounting for high density of dead-end hosts. Models will be parametrised using data collected on livestock production, vector abundance and feeding patterns, and fitted to estimates of livestock seroprevalence and JEV prevalence in vectors. Models will be validated by testing predictions of the differences in JEV risk between areas of contrasting livestock density and community composition. Ecological interventions, determined through stakeholder engagement, will then be assessed using models. I will use findings to produce policy-ready documents to help inform targeted human vaccination, the potential effects of combining alternative control strategies and assess risk of JEV spread to new regions. This project will contribute specifically to knowledge required to implement JE control in Bangladesh but also more generally on the potential for mitigating JEV risk elsewhere through sustainable approaches to control.


Research activity codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: INFECTION