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Developing interactome analysis approaches to profile multi-organ metabolic adaptation across healthspan

(This project has been filled - no further applications will be accepted)

Project supervisor: Dr Andrew Philp

Non-academic partner: Cambridge Cell Networks Ltd (CCNet)

Project Title: Developing interactome analysis approaches to profile multi-organ metabolic adaptation across healthspan

Project outline:

Healthy ageing is a multi-factorial process, involving interaction and cross-talk between numerous components of individual cells - including genes, proteins, transcription factors and metabolites (1, 2). Given this complexity of regulation, systems biology based approaches are required to study this ‘interactome’ between different biochemical/gene networks and physiological/pathological processes (1).

We have recently begun to apply such approaches to study the interaction between metabolic and transcriptomic datasets in rodent models of obesity. Combining RNA-Seq and metabolomic profiling has allowed us to identify ~50 exercise responsive genes affected by high-fat feeding, thought to be involved in muscle remodeling and force transfer. Preliminary interactome analysis suggests that co-ordinated repression of genes involved in amino acid metabolism may also underlie this insulin resistant phenotype. Our aim is to now develop this systems modeling approach to the context of human ageing, by studying human blood, muscle and adipose tissue metabolomic, proteomic and transcriptomic changes across healthspan. Our working hypothesis is that impairments in skeletal muscle gene/protein/metabolite networks precede changes in adipose and serum and underlie impairments in muscle function.

Aims of the project:

We propose to partner with CCNet given their established track record of expertise in analysis, modelling and interpretation of large, interacting datasets of biological relevance. Biological samples to be used in this study will be generated through ongoing studies in the Philp laboratory examining skeletal muscle function across healthspan. Drs Philp and Dunn have a track record of collaboration relating to global metabolomic analysis in skeletal muscle samples.


  1. Stevens et al., (2014) J. Mol. Endocrinol. 52; R79-R93. (2) Lewis et al., (2010) Sci. Trans. Med. 33; 1-13.

Interview dates: 27th, 28th & 29th January 2016