scholarly journals Walking on a Tissue-Specific Disease-Protein-Complex Heterogeneous Network for the Discovery of Disease-Related Protein Complexes

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Thibault Jacquemin ◽  
Rui Jiang
Keyword(s):  
Protein Complex ◽  
Heterogeneous Network ◽  
High Performance ◽  
Protein Complexes ◽  
Specific Protein ◽  
Disease Genes ◽  
Computational Systems Biology ◽  
Related Protein ◽  
Tissue Specific ◽  
Disease Protein

Besides the pinpointing of individual disease-related genes, associating protein complexes to human inherited diseases is also of great importance, because a biological function usually arises from the cooperative behaviour of multiple proteins in a protein complex. Moreover, knowledge about disease-related protein complexes could also enhance the inference of disease genes and pathogenic genetic variants. Here, we have designed a computational systems biology approach to systematically analyse potential relationships between diseases and protein complexes. First, we construct a heterogeneous network which is composed of a disease-disease similarity layer, a tissue-specific protein-protein interaction layer, and a protein complex membership layer. Then, we propose a random walk model on this disease-protein-complex network for identifying protein complexes that are related to a query disease. With a series of leave-one-out cross-validation experiments, we show that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We further predict a landscape of associations between human diseases and protein complexes. This landscape can be used to facilitate the inference of disease genes, thereby benefiting studies on pathology of diseases.

scholarly journals A convex optimization approach for identification of human tissue-specific interactomes

2016 ◽  
Author(s):  
Shahin Mohammadi ◽  
Ananth Grama
Keyword(s):  
Human Tissue ◽  
Drug Targets ◽  
Protein Complexes ◽  
Disease Genes ◽  
Optimization Approach ◽  
Cell Type Specificity ◽  
Tissue Specific ◽  
Parkinson’S Diseases ◽  
Coordinated Expression ◽  
Validation Tests

AbstractMotivation:Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell-type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/ selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.Results:We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (ESEA, GO Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state of the art techniques. Finally, using case studies of Alzheimer’s and Parkinson’s diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets.Availability:http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html

RoDiCE: robust differential protein co-expression analysis for cancer complexome

2021 ◽  
Author(s):  
Yusuke Matsui ◽  
Yuichi Abe ◽  
Kohei Uno ◽  
Satoru Miyano
Keyword(s):  
Protein Complex ◽  
Drug Targets ◽  
Large Scale ◽  
Protein Complexes ◽  
Specific Protein ◽  
Identification Accuracy ◽  
Supplementary Information ◽  
Full Spectrum ◽  
Differential Protein ◽  
Proteomic Data

Abstract Motivation The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression. Results We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures. Availability and implementation https://github.com/ymatts/RoDiCE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Syntaxin 1A Gene Is Negatively Regulated in a Cell/Tissue Specific Manner by YY1 Transcription Factor, Which Binds to the −183 to −137 Promoter Region Together with Gene Silencing Factors Including Histone Deacetylase

Biomolecules ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 146
Author(s):  
Takahiro Nakayama ◽  
Toshiyuki Fukutomi ◽  
Yasuo Terao ◽  
Kimio Akagawa
Keyword(s):  
Transcription Factor ◽  
Gene Silencing ◽  
Protein Complex ◽  
Promoter Region ◽  
Neuronal Cell ◽  
Syntaxin 1A ◽  
Tissue Specific ◽  
Cell Tissue ◽  
Specific Manner ◽  
A Cell

The HPC-1/syntaxin 1A (Stx1a) gene, which is involved in synaptic transmission and neurodevelopmental disorders, is a TATA-less gene with several transcription start sites. It is activated by the binding of Sp1 and acetylated histone H3 to the −204 to +2 core promoter region (CPR) in neuronal cell/tissue. Furthermore, it is depressed by the association of class 1 histone deacetylases (HDACs) to Stx1a–CPR in non-neuronal cell/tissue. To further clarify the factors characterizing Stx1a gene silencing in non-neuronal cell/tissue not expressing Stx1a, we attempted to identify the promoter region forming DNA–protein complex only in non-neuronal cells. Electrophoresis mobility shift assays (EMSA) demonstrated that the −183 to −137 OL2 promoter region forms DNA–protein complex only in non-neuronal fetal rat skin keratinocyte (FRSK) cells which do not express Stx1a. Furthermore, the Yin-Yang 1 (YY1) transcription factor binds to the −183 to −137 promoter region of Stx1a in FRSK cells, as shown by competitive EMSA and supershift assay. Chromatin immunoprecipitation assay revealed that YY1 in vivo associates to Stx1a–CPR in cell/tissue not expressing Stx1a and that trichostatin A treatment in FRSK cells decreases the high-level association of YY1 to Stx1a-CPR in default. Reporter assay indicated that YY1 negatively regulates Stx1a transcription. Finally, mass spectrometry analysis showed that gene silencing factors, including HDAC1, associate onto the −183 to −137 promoter region together with YY1. The current study is the first to report that Stx1a transcription is negatively regulated in a cell/tissue-specific manner by YY1 transcription factor, which binds to the −183 to −137 promoter region together with gene silencing factors, including HDAC.

scholarly journals Genome-wide inferring gene–phenotype relationship by walking on the heterogeneous network

2010 ◽  
Vol 26 (9) ◽  
pp. 1219-1224 ◽  
Author(s):  
Yongjin Li ◽  
Jagdish C. Patra
Keyword(s):  
Heterogeneous Network ◽  
Gene Network ◽  
Genetic Diseases ◽  
Supplementary Information ◽  
Disease Genes ◽  
Phenotypic Data ◽  
Disease Associations ◽  
Improved Performance ◽  
Leave One Out ◽  
Phenotype Network

Abstract Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease genes is to elucidate the gene–phenotype relationships. Mutations in functionally related genes may result in similar phenotypes. It is reasonable to predict disease-causing genes by integrating phenotypic data and genomic data. Some genetic diseases are genetically or phenotypically similar. They may share the common pathogenetic mechanisms. Identifying the relationship between diseases will facilitate better understanding of the pathogenetic mechanism of diseases. Results: In this article, we constructed a heterogeneous network by connecting the gene network and phenotype network using the phenotype–gene relationship information from the OMIM database. We extended the random walk with restart algorithm to the heterogeneous network. The algorithm prioritizes the genes and phenotypes simultaneously. We use leave-one-out cross-validation to evaluate the ability of finding the gene–phenotype relationship. Results showed improved performance than previous works. We also used the algorithm to disclose hidden disease associations that cannot be found by gene network or phenotype network alone. We identified 18 hidden disease associations, most of which were supported by literature evidence. Availability: The MATLAB code of the program is available at http://www3.ntu.edu.sg/home/aspatra/research/Yongjin_BI2010.zip Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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