Proteome-based pathway modelling of psychiatric disorders

The etiopathogenesis of many psychiatric illnesses remains unclear and a variety of these diseases can coexist, partly mimicking each other while contributing to and distorting symptomatic expressions. To understand the processes involved, it is necessary to unravel signalling pathways, complex interaction networks and metabolic alterations involving a plethora of anatomical components. When addressing such largely obscure mechanisms, primary data mainly based on genomics and differential gene expression patterns turns out to be of limited usefulness. Numerous direct as well as very indirect processes modulate and dissociate gene expression from protein functions and physiological effects. Proteomics approaches that utilise metabolic labelling and high-throughput mass spectrometry to provide proteome dynamics data need to be utilised. However, the data thus gathered encompasses a complex assembly of numerous types of intermixed cells, representing biological processes that occur in both time and space across several scalar levels. The complexities represented are such that to analytically approach these diseases, a systems standpoint becomes necessary. This implies multiple experimental interrogations in an iterative interplay between experimentation and modelling. While this may be reasonably considered in the context of in vitro systems, it can hardly be contemplated when addressing CNS tissues from heterogeneous human origins, thereby imposing serious constraints upon the investigation of human cognitive disorders. In this article, the authors expose a paradigm that addresses and alleviates at least some of these major difficulties. Based on the reasoned utilisation of trait animal models and human material, this approach has already started to deliver novel and directly exploitable knowledge.