A quantitative approach to study indirect effects among disease proteins in the human protein interaction network

<p>Abstract</p> <p>Background</p> <p>Systems biology makes it possible to study larger and more intricate systems than before, so it is now possible to look at the molecular basis of several diseases in parallel. Analyzing the interaction network of proteins in the cell can be the key to understand how complex processes lead to diseases. Novel tools in network analysis provide the possibility to quantify the key interacting proteins in large networks as well as proteins that connect them. Here we suggest a new method to study the relationships between topology and functionality of the protein-protein interaction network, by identifying key mediator proteins possibly maintaining indirect relationships among proteins causing various diseases.</p> <p>Results</p> <p>Based on the i2d and OMIM databases, we have constructed (i) a network of proteins causing five selected diseases (DP, disease proteins) plus their interacting partners (IP, non-disease proteins), the DPIP network and (ii) a protein network showing only these IPs and their interactions, the IP network. The five investigated diseases were (1) various cancers, (2) heart diseases, (3) obesity, (4) diabetes and (5) autism. We have quantified the number and strength of IP-mediated indirect effects between the five groups of disease proteins and hypothetically identified the most important mediator proteins linking heart disease to obesity or diabetes in the IP network. The results present the relationship between mediator role and centrality, as well as between mediator role and functional properties of these proteins.</p> <p>Conclusions</p> <p>We show that a protein which plays an important indirect mediator role between two diseases is not necessarily a hub in the PPI network. This may suggest that, even if hub proteins and disease proteins are trivially of great interest, mediators may also deserve more attention, especially if disease-disease associations are to be understood. Identifying the hubs may not be sufficient to understand particular pathways. We have found that the mediators between heart diseases and obesity, as well as heart diseases and diabetes are of relatively high functional importance in the cell. The mediator proteins suggested here should be experimentally tested as products of hypothetical disease-related proteins.</p

TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/

Low serum osteoprotegerin levels in normoalbuminuric type 1 diabetes mellitus

“The original publication is available at www.springerlink.com”. Copyright Springer [Full text of this article is not available in the UHRA]The aim of this study is to establish whether abnormal mineral metabolism is present in patients with type 1 DM with normal renal function and in the absence of microalbuminuria. Serum levels of 1,25-dihydroxyvitamin D, osteoprotegerin (OPG) and receptor activator for nuclear factor kappa β ligand (RANKL) and other determinants of bone metabolism were measured in 35 patients with type 1 DM and in 25 age-, sex- and ethnicity-matched healthy controls. Serum OPG (1.98 vs. 2.98 pmol/l: P = 0.001), 1,25-dihydroxyvitamin D (41.1 vs. 48.2 pmol/l: P = 0.035) and magnesium (0.84 vs. 0.89 mmol/l P = 0.029) levels were significantly lower in patients with type 1 DM compared to normal controls. RANKL levels were similar in both groups. The groups did not differ with respect to calcium, phosphate, PTH, 25-hydroxyvitamin D, tubular reabsorption of phosphate and cross-linked N-telopeptides of type 1-collagen levels. Abnormalities of mineral metabolism including low serum OPG and 1,25-dihydroxyvitamin D levels occur in patients with type 1 DM with normal renal function and in the absence of microalbuminuria. These abnormalities may promote altered bone metabolism and vascular pathology.Peer reviewe