Title: A molecular phenomic approach to define the high-risk NAFLD population
Funder: Guts UK

Abstract: Background Non-alcoholic fatty liver disease (NAFLD) is the commonest cause of chronic liver disease, affecting approximately 25% of the global population. The prevalence of NAFLD is increasing at approximately the same rate as obesity. NAFLD represents a “silent epidemic” with massive and disproportionate healthcare and societal impacts, particularly in Scotland which has the third highest adult prevalence rate of overweight and obesity in the world. Death rates from chronic liver disease in Scotland are 70% higher than the UK average and 60% higher than 30 years ago. However, NAFLD has a highly variable rate of progression, with a lack of effective biomarkers to predict longer-term clinical outcomes. The pan-Scotland SteatoSITE Data Commons was established to integrate human hepatic transcriptomic data, quantitative digital pathology and clinical information including outcome data, from 1000 NAFLD cases covering the entire disease spectrum from steatosis to end-stage cirrhosis. This is currently one of the largest such cohorts in the world, and detailed bioinformatic analysis of this dataset is likely to provide novel insights into NAFLD pathogenesis and disease progression. Hypothesis and objectives Our hypothesis is that comprehensive bioinformatic analysis of the SteatoSITE NAFLD Data Commons will yield diagnostic markers that predict NAFLD progression and identify novel and actionable drug-disease connections. The main objectives of the project are to define liver transcriptional changes associated with progressive NAFLD; to characterise changes in liver cellular subpopulations in progressive NAFLD; and to develop a multiplex pathology approach to improve risk stratification of patients with NAFLD. Design and methods We will undertake the first bioinformatic analysis of the entire SteatoSITE RNA-seq dataset (n=1000 NAFLD cases and n=100 normal liver controls). Transcriptional changes will be linked to liver histological features and clinical outcomes. Patients will also be stratified by baseline demographic parameters and presence of known genetic risk variants including PNPLA3, TM6SF2 and MBOAT7. Pathway and network analyses will be used to define the dysregulated pathways associated with disease progression. In addition, we will perform a complete deconvolution analysis on the full SteatoSITE dataset using single-cell RNA-seq profiles that we have recently generated for all liver cell lineages. This will allow us to determine the specific cell types that contribute to variations in gene expression seen in the bulk RNA-Seq dataset, providing a comprehensive assessment of changes in hepatic cellular composition in NAFLD. The cellular composition of each liver sample will be linked to clinical outcome, baseline demographics and histological scoring. Finally, to augment the assessment of NAFLD histopathology, we will establish a multiplex immunostaining protocol using an automated quantitative pathology imaging system. A bespoke quantitative multiplex panel for risk stratification in NAFLD will be developed using candidate prognostic biomarkers defined through our bioinformatic analysis. Clinical and scientific impact Overall, this analysis will define the key pathogenic drivers of NAFLD progression, highlighting specific cell subpopulations or molecules that could be targeted therapeutically or serve as predictive markers of patients at high-risk of adverse clinical outcomes.


Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: ORAL AND GASTROINTESTINAL