A human phenome-interactome network of protein complexes implicated in genetic disorders

Lage, Kasper; Karlberg, E. Olof; Størling, Zenia M.; Olason, Pall I.; Pedersen, Anders Gorm; Rigina, Olga; Hinsby, Anders M.; Tümer, Zeynep; Pociot, Flemming; Tommerup, Niels; Moreau, Yves; Brunak, Søren

doi:10.1038/nbt1295

Cited by 840 publications

(826 citation statements)

References 54 publications

Supporting

Mentioning

808

Contrasting

Unclassified

Order By: Relevance

“…Network analysis Protein-protein interaction (PPI) networks were created for each list of differentially expressed proteins and first-order neighbours using InWeb [21]. The networks were visualised in Cytoscape [22].…”

Section: Methodsmentioning

confidence: 99%

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

et al. 2017

Self Cite

View full text Add to dashboard Cite

Aims/hypothesis Type 1 diabetes is an endocrine disease where a long preclinical phase, characterised by immune cell infiltration in the islets of Langerhans, precedes elevated blood glucose levels and disease onset. Although several studies have investigated the role of the immune system in this process of insulitis, the importance of the beta cells themselves in the initiation of type 1 diabetes is less well understood. The aim of this study was to investigate intrinsic differences present in the islets from diabetes-prone NOD mice before the onset of insulitis. Methods The islet transcriptome and proteome of 2-3-week-old mice was investigated by microarray and 2-dimensional difference gel electrophoresis (2D-DIGE), respectively. Subsequent analyses using sophisticated pathway analysis and ranking of differentially expressed genes and proteins based on their relevance in type 1 diabetes were performed. Results In the preinsulitic period, alterations in general pathways related to metabolism and cell communication were already present. Additionally, our analyses pointed to an important role for post-translational modifications (PTMs), especially citrullination by PAD2 and protein misfolding due to low expression levels of protein disulphide isomerases (PDIA3, 4 and 6), as causative mechanisms that induce beta cell stress and potential auto-antigen generation. Conclusions/interpretation We conclude that the pancreatic islets, irrespective of immune differences, may contribute to the initiation of the autoimmune process. Data availability All microarray data are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-5264.

show abstract

Section: Methodsmentioning

confidence: 99%

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Effective utilization of these large-scale data has been validated useful in analyzing individual disease proteins or related complexes. For example, Lage et al prioritized disease proteins based on a systematic analysis of human protein complexes comprising gene products implicated in many different categories of human disease [26]. Vanunu et al provided a global network-based method for prioritizing disease proteins and inferring protein complex associations with a disease of interest [27].…”

Section: Introductionmentioning

confidence: 99%

Prioritizing protein complexes implicated in human diseases by network optimization

et al. 2014

View full text Add to dashboard Cite

BackgroundThe detection of associations between protein complexes and human inherited diseases is of great importance in understanding mechanisms of diseases. Dysfunctions of a protein complex are usually defined by its member disturbance and consequently result in certain diseases. Although individual disease proteins have been widely predicted, computational methods are still absent for systematically investigating disease-related protein complexes.ResultsWe propose a method, MAXCOM, for the prioritization of candidate protein complexes. MAXCOM performs a maximum information flow algorithm to optimize relationships between a query disease and candidate protein complexes through a heterogeneous network that is constructed by combining protein-protein interactions and disease phenotypic similarities. Cross-validation experiments on 539 protein complexes show that MAXCOM can rank 382 (70.87%) protein complexes at the top against protein complexes constructed at random. Permutation experiments further confirm that MAXCOM is robust to the network structure and parameters involved. We further analyze protein complexes ranked among top ten for breast cancer and demonstrate that the SWI/SNF complex is potentially associated with breast cancer.ConclusionsMAXCOM is an effective method for the discovery of disease-related protein complexes based on network optimization. The high performance and robustness of this approach can facilitate not only pathologic studies of diseases, but also the design of drugs targeting on multiple proteins.

show abstract

“…In which, the problem is considered as a classification one, where a classifier is learned from training data; then the learned classifier is used to predict whether or not a test/candidate gene is a disease gene. Briefly, at the early, machine learningbased studies usually approached disease gene prediction as a binary classification problem [9], where the learning samples are comprised of positive training samples and negative training samples [9] such as decision trees (DT) [10,11] k-nearest neighbor (kNN) [12], naive Bayesian classifier [13,14], binary support vector machine classifier [15][16][17], artificial neural network (ANN) techniques [18] and random forest (RF) [9]. In these binary classifier-based methods, positive training samples are constructed from known disease genes, whereas negative training samples are the remaining which are not known to be associated with diseases.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based disease similarity network for disease gene prediction

Dang²

2016

Vietnam J Comput Sci

View full text Add to dashboard Cite

Finding underlying molecular mechanisms of diseases is one of the important issues in biomedical research. In which, prediction of novel disease-associated genes is mostly focused. Many methods have been proposed based on biological networks and shown effectively for the problem. These network-based methods are usually relied on a "disease module" principle that functionally similar genes are associated with similar phenotypes or diseases. Among them, methods solely based on gene/protein networks only exploit that principle by structural modules in the gene/protein networks. Meanwhile, others based on integration of these networks with a disease similarity network better exploit the principle and consequently result in higher prediction performance. In these studies, the disease similarity network is extracted from a disease similarity matrix which was calculated using text mining techniques on OMIM records. Considering that diseases have been recently well annotated by human phenotype ontology (i.e., a controlled vocabulary database) and semantic similarity measures can be used to calculate similarities among them. Therefore, it would be more accurate to construct disease similarity network based on semantic similarity measures on phenotype ontol- Experiment results show that the ontology-based disease similarity network much improves the prediction performance compared to the one based on OMIM records, irrespective of gene/protein networks. In addition, we show ability of our method in predicting novel Alzheimer's disease-associated genes, in which 19 out of top 100 ranked candidate genes are supported with evidences from literature.

show abstract

A human phenome-interactome network of protein complexes implicated in genetic disorders

Cited by 840 publications

References 54 publications

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis

Prioritizing protein complexes implicated in human diseases by network optimization

Ontology-based disease similarity network for disease gene prediction

Contact Info

Product

Resources

About