Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

Abstract

BackgroundDiabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.ResultsIn this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.ConclusionsThe results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Endocytosis and membrane receptor internalization implication of F-BAR protein Carom

“…We carried out an analysis of their protein-protein interactions by using String database (19, 20) (Figure 4). Solid lines indicate known interaction deposited in NCBI Gene database which was established based on Affinity Capture-MS, Affinity Capture-RNA, Affinity Capture-Western, Reconstituted Complex and Two-hybrid experimental data.…”

“…Subcellular localization of Carom, its endocytic partner and membrane receptors were determined using Compartments database established by manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods (20) (Table 4). Numbers are indicated confidential levels.…”

Shared pathological pathways of Alzheimer's disease with specific comorbidities: current perspectives and interventions

“…It seems likely that only a combined analysis will define a patient's specific signature and assist with robust biomarker validation. Importantly, multi‐omics integrative approaches can be used both for studies that focus on causal mechanisms (Gao et al., ; Popp et al., ) and drug targets (Stempler et al., ) and for establishing disease diagnostic biomarkers (Ghidoni et al., ; Tang et al., ). For instance, the pioneering work of Nho et al .…”

Multilayer view of pathogenic SNVs in human interactome throughin-silicoedgetic profiling

“…As a first case-study, we carried out the analysis of an interaction network centered around proteins associated with type 2 diabetes mellitus (T2DM). The important role of protein-protein interactions in T2DM was recently proposed [28][29][30]. Most of these works focused on integrating different sources of data to discover novel candidate genes for T2DM.…”