IMS MRL logo
University + MRL logo
CORD logo

BIONASH

Identifying NAFLD/NASH-specific molecular lipid networks of diagnostic, prognostic and therapeutic biomarker value.

 

Context and strategic relevance of this application:

The project proposed is integrated within the IMS MRC MDU unit programme focused on Lipotoxity as a pathogenic mechanism of the Metabolic Syndrome under the direction of Prof Vidal-Puig. This programme of research collaborates closely with Dr Jules Griffin at the MRC HNR in aspects related to lipidomics technologies, with Dr Julio Saez Rodriguez-at EMBL-EBI in aspects relates to Systems Biology modelling and with Dr Mike Allison at Cambridge University NHS Foundation Trust, who leads the Cambridge NAFLD Service. We have formalised this collaborative network creating the Cambridge BIONASH Group.

 

This fellowship is strategically very important to: a) increase the translational potential of Prof Vidal-Puig’s lab. One of the most internationally competitive disease oriented research programmes within the MRC MDU; b) to strengthen the links amongst internationally established research programmes (MRC MDU, EMBL-EBI, HNR MRC) with the clinical research programme in NAFLD at Cambridge University NHS Foundation Trust. c) enable the translational elements of other competitive lipotoxicity-related programmes in the campus (adipose, macrophage, skeletal muscle). In summary this fellowship has the potential to enable a qualitative change of quality in the map of metabolic research across the Campus and in the wider Cambridge academic environment.

 

 

Specific Project.

Background:

What is NAFLD/NASH? This represents a spectrum of pathology from isolated hepatic triglyceride accumulation (steatosis); through hepatic lipid accumulation with inflammation (non-alcoholic steatohepatitis, NASH); and ultimately progressing to fibrosis/cirrhosis and potentially hepatocellular carcinoma. (1). NAFLD is strongly associated with the Metabolic Syndrome (MetS)(3), representing the clustering in the same individual of obesity, type 2 diabetes mellitus (T2DM), hypertension and dyslipidaemia

 

NAFLD/NASH is an important problem: Non Alcoholic Fatty Liver Disease (NAFLD) affects 30% of the population in developed countries and is recognized as one of the most common liver disorders and a major public health problem. Over 100 million people are affected in the US alone, and its prevalence is rapidly growing in parallel with Metabolic Syndrome (MetS), particularly in association with obesity and diabetes. The economic burden on primary care due to NAFLD is estimated at $76B in the US and Europe.

 

What are the key challenges in the area?

• Problem 1: Lack of mechanistic understanding of the development of NASH.

• Problem 2: Lack of robust non-invasive diagnostic tests (diagnostic biomarkers) to identify patients with NASH

• Problem 3: Lack of tools (biomarkers) to determine those who have/will get progressive hepatic fibrosis, with sequelae of cirrhosis, liver cancer risk and liver-related death.

• Problem 4: Lack of specific proven treatments specifically for NASH

 

Our hypothesis is that one of the main pathogenic factors driving the evolution from NAFLD to NASH is the accumulation of specific lipid species in hepatocytes resulting in liver injury (Lipotoxicity . Thus identification of EBPOD-BIONASH Vidal-Puig, Rodriguez-Saez, Griffin, Allison hepatic and circulating lipid-related biomarkers could identify individuals firstly, who have NASH as opposed to benign steatosis, and, secondly, who are developing, or are destined to develop, progressive NASH.

 

General objectives: 1) To identify specific mechanisms of disease (pathogenically relevant biomarkers), which are amenable to pharmacological intervention and 2) To identify accurate diagnostic, staging and prognostic biomarkers that facilitate patient stratification for therapeutic intervention.

 

Research strategy: Our approach is to utilise biological samples from well characterised (histology) patients with different stages of NAFLD/NASH as well as appropriate healthy controls. We will use a Systems Biology hypothesis to identify relevant mechanisms that could provide the rational for identification of hepatic and circulating biomarkers that could identify individuals with increased susceptibility to progression from NAFLD to NASH. This approach will make extensive use of OMICs approaches and data integration (see below).

 

Methodology:

Human samples: The Cambridge BIONASH Group has LREC-approved studies and have over the last few years accumulated the most complete collection in UK of clinical data, frozen liver tissue, serum and plasma samples and genomic DNA from over 150 patients with biopsy-proven NAFLD (Allison, CUFT). These patients include the whole spectrum of severity of NAFLD and cover a range of BMIs and degrees of insulin resistance. These biological samples represent a unique resource and have attracted collaborative interest from several 2020 programmes. Currently, we are preparing second stage proposal for in-depth analysis of these samples.

 

Profiling technology: Extensive use of OMICs approaches: a) lipidomic studies and bioinformatics analysis (Griffin, HNR, Vidal-Puig MDU MRC) (7,12-16,21,22)(Oresic, Hanninen et al. 2008), b) transcriptome (Vidal-Puig, MRC MDU) (5,8,9)and c) proteome (Sanger, Vidal-Puig, Saez- Rodriguez, EMBL-EBI) (17,19,20)approaches and d) data integration using bioinformatics tools (Saez- Rodriguez, EMBL-EBI, and Vidal-Puig, MRC MDU)(2,4,6,10,11,18) to combine heterogeneous datasets into models of cell functionality. Particularly relevant to this fellowship, the candidate will be working in close collaboration with EMBL-EBI and MRC NHR, MDU in aspects related to the computational analysis and development of new systems biology tools appropriate for integration of different tiers of biological information and subsequent mapping as lipid networks to understand the complex regulatory pathways and circuits that are perturbed in NAFLD. The candidate will have access to a wide network of collaborators with expertise in systems biology of lipids (Oresic, Dopazo). Specifically, we will use detailed models of human metabolism available (Reactome, KEGG, etc.) and extend them as required to map omic data and produce hypothetical biomarkers based on relevant changes in gene activities and lipid composition. A family of methodologies derived from Flux Balance Analysis (FBA) allow the study of the relationship between lipid (or, in general, metabolite) levels and gene/protein activities, providing a mathematical framework to integrate these data. These methods can be used to detect and prioritize mechanism-based biomarkers for validation in subsequent stages of this proposal and also identify key regulatory points that may be attractive targets for drug or diet intervention.

 

Output: These biomarkers are expected to be useful for patient stratification and early identification of patients with different disease outcomes. They will also provide us with mechanistic insight into the disease which can subsequently be verified in cell culture, animal models and larger human cohorts through our international partnerships FP7 Flip, Horizon2020 (Chris Day) http://www.flip-fp7.eu/