scholarly journals DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

2015 ◽  
Vol 9 ◽  
pp. BBI.S35237 ◽  
Author(s):  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Disease Diagnosis ◽  
Scoring Method ◽  
Protein Protein Interaction ◽  
Disease Associations ◽  
Statistical Relationships ◽  
Complex Relationships

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein-protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease-disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

scholarly journals Multi-Data Aspects of Protein Similarity with a Learning Technique to Identify Drug-Disease Associations

2021 ◽  
Vol 11 (7) ◽  
pp. 2914
Author(s):  
Satanat Kitsiranuwat ◽  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
High Performance ◽  
Drug Repositioning ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Biological Information ◽  
P Value ◽  
Protein Protein Interaction ◽  
Proteomic Data ◽  
Disease Associations ◽  
Functional Pathway

Drug repositioning has been proposed to develop drugs for diseases. However, the similarity in a single aspect may not be sufficient to reveal hidden information. Therefore, we established protein–protein similarity vectors (PPSVs) based on potential similarities in various types of biological information associated with proteins, including their network topology, proteomic data, functional analysis, and druggable property. Based on the proposed PPSVs, a separate drug–disease matrix was constructed for individual to prevent characteristics from being obscured between diseases. The classification technique was employed for prediction. The results showed that more than half of the tested disease models exhibited high performance, with overall F1 scores of more than 80%. Furthermore, comparing all diseases using traditional methods in one run, we obtained an (area under the curve) AUC of 98.9%. All candidate drugs were then tested in clinical trials (p-value < 2.2 × 10−16) and were known drugs based on their functions (p-value < 0.05). An analysis revealed that, in the functional aspect, the confidence value of an interaction in the protein–protein interaction network and the functional pathway score were the best descriptors for prediction. Based on the learning processes of PPSVs with an isolated disease, the classifier exhibited high performance in predicting and identifying new potential drugs for that disease.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Synchronization of human retinal pigment epithelial-1 cells in mitosis

2020 ◽  
Vol 133 (18) ◽  
pp. jcs247940
Author(s):  
Stacey J. Scott ◽  
Kethan S. Suvarna ◽  
Pier Paolo D'Avino
Keyword(s):  
Cell Lines ◽  
Large Scale ◽  
Retinal Pigment Epithelial ◽  
Retinal Pigment ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Pigment Epithelial ◽  
Adenocarcinoma Cells ◽  
Sequential Treatments

ABSTRACTHuman retinal pigment epithelial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here, we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4 and Cdk6 inhibitor PD 0332991 (palbociclib) and the microtubule-depolymerizing drug nocodazole. With this method, the vast majority (80–90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Moreover, the cells fully recovered and re-entered the cell cycle after the palbociclib–nocodazole block. Finally, we show that this protocol could be successfully employed for the characterization of the protein–protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

scholarly journals Synchronization of human retinal pigment ephitilial-1 (RPE-1) cells in mitosis

2020 ◽  
Author(s):  
Stacey J. Scott ◽  
Kethan Suvarna ◽  
Pier Paolo D’Avino
Keyword(s):  
Cell Lines ◽  
Large Scale ◽  
Retinal Pigment ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Adenocarcinoma Cells ◽  
Sequential Treatments ◽  
Dna Synthesis Inhibitors ◽  
Protein Protein Interaction Network

ABSTRACTHuman retinal pigment ephitilial-1 (RPE-1) cells are increasingly being used as a model to study mitosis because they represent a non-transformed alternative to cancer cell lines, such as HeLa cervical adenocarcinoma cells. However, the lack of an efficient method to synchronize RPE-1 cells in mitosis precludes their application for large-scale biochemical and proteomics assays. Here we report a protocol to synchronize RPE-1 cells based on sequential treatments with the Cdk4/6 inhibitor PD 0332991 (palbociclib) and the microtubule depolymerizing drug nocodazole. With this method, the vast majority (80-90%) of RPE-1 cells arrested at prometaphase and exited mitosis synchronously after release from nocodazole. Furthermore, we show that this protocol could be successfully employed for the characterization of the protein-protein interaction network of the kinetochore protein Ndc80 by immunoprecipitation coupled with mass spectrometry. This synchronization method significantly expands the versatility and applicability of RPE-1 cells to the study of cell division and might be applied to other cell lines that do not respond to treatments with DNA synthesis inhibitors.

scholarly journals Germline variation networks in the PI3K/AKT pathway corresponding to familial high-incidence lung cancer pedigrees

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Huan Lin ◽  
Gong Zhang ◽  
Xu-chao Zhang ◽  
Xin-lei Lian ◽  
Wen-zhao Zhong ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Large Scale ◽  
Interaction Network ◽  
Akt Pathway ◽  
Nucleotide Polymorphisms ◽  
Healthy Individuals ◽  
Protein Protein Interaction ◽  
Germline Variation ◽  
Major Predictor ◽  
Validation Set

Abstract Background There were scarcely germline variants of familial lung cancer (LC) identified. We conducted an study with whole-exome sequencing of pedigrees with familial lung cancer to analyze the potential genetic susceptibility. Methods Probands with the highest hereditary background were identified by our large-scale epidemiological study and five ones were enrolled as a learning set. The germline SNPs (single-nucleotide polymorphisms) of other five similar probands, four healthy individuals in the formerly pedigrees and three patients with sporadic LC were used as a validation set, controlled by three healthy individuals without family history of any cancer. The network of mutated genes was generated using STRING-DB and visualized using Cytoscape. Results Specific and shared somatic mutations and germline SNPs were not the shared cause of familial lung cancer. However, individual germline SNPs showed distinct protein-protein interaction network patterns in probands versus healthy individuals and patients with sporadic lung cancer. SNP-containing genes were enriched in the PI3K/AKT pathway. These results were validated in the validation set. Furthermore, patients with familial lung cancer were distinguished by many germline variations in the PI3K/AKT pathway by a simple SVM classification method. It is worth emphasizing that one person with many germline variations in the PI3K/AKT pathway developed lung cancer during follow-up. Conclusions The phenomenon that the enrichments of germline SNPs in the PI3K/AKT pathway might be a major predictor of familial susceptibility to lung cancer.

scholarly journals Protein-protein-interaction Network Organization of the Hypusine Modification System

2012 ◽  
Vol 11 (11) ◽  
pp. 1289-1305 ◽  
Author(s):  
Henning Sievert ◽  
Simone Venz ◽  
Oscar Platas-Barradas ◽  
Vishnu M. Dhople ◽  
Martin Schaletzky ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Affinity Purification ◽  
Interaction Network ◽  
Initiation Factor ◽  
Post Translational Modification ◽  
Comprehensive Overview ◽  
Data Set ◽  
Modification System ◽  
Protein Protein Interaction

Hypusine modification of eukaryotic initiation factor 5A (eIF-5A) represents a unique and highly specific post-translational modification with regulatory functions in cancer, diabetes, and infectious diseases. However, the specific cellular pathways that are influenced by the hypusine modification remain largely unknown. To globally characterize eIF-5A and hypusine-dependent pathways, we used an approach that combines large-scale bioreactor cell culture with tandem affinity purification and mass spectrometry: “bioreactor-TAP-MS/MS.” By applying this approach systematically to all four components of the hypusine modification system (eIF-5A1, eIF-5A2, DHS, and DOHH), we identified 248 interacting proteins as components of the cellular hypusine network, with diverse functions including regulation of translation, mRNA processing, DNA replication, and cell cycle regulation. Network analysis of this data set enabled us to provide a comprehensive overview of the protein-protein interaction landscape of the hypusine modification system. In addition, we validated the interaction of eIF-5A with some of the newly identified associated proteins in more detail. Our analysis has revealed numerous novel interactions, and thus provides a valuable resource for understanding how this crucial homeostatic signaling pathway affects different cellular functions.

scholarly journals Ten genes are considered as potential biomarkers for the diagnosis of dermatomyositis

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260511
Author(s):  
Lu Xiao ◽  
Wei Xiao ◽  
Shudian Lin
Keyword(s):  
Characteristic Curve ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Type I ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Healthy Control ◽  
Potential Biomarkers

Objective This study aimed to identify the biomarkers and mechanisms for dermatomyositis (DM) progression at the transcriptome level through a combination of microarray and bioinformatic analyses. Method Microarray datasets for skeletal muscle of DM and healthy control (HC) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using GEO2R. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein–protein interaction network was constructed to identify hub genes. The top 10 hub genes were validated by other GEO datasets. The diagnostic accuracy of the top 10 hub genes for DM was evaluated using the area under the curve of the receiver operating characteristic curve. Result A total of 63 DEGs were identified between 10 DM samples and 9 HC samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that DEGs are mostly enriched in response to virus, defense response to virus, and type I interferon signaling pathway. 10 hub genes and 3 gene cluster modules were identified by Cytoscape. The identified hub genes were verified by GSE1551 and GSE11971 datasets and proven to be potential biomarkers for the diagnosis of DM. Conclusion Our work identified 10 valuable genes as potential biomarkers for the diagnosis of DM and explored the potential underlying molecular mechanism of the disease.

scholarly journals A SURVEY ON DIFFERENT METHODS USED FOR ANALYSIS OF PROTEIN-PROTEIN INTERACTION NETWORK

2020 ◽  
pp. 446-452
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
3D Structure ◽  
Interaction Network ◽  
Protein Detection ◽  
Human Interaction ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Advantages And Disadvantages

Bio information system is one of the prominent fields for analyzing of biological process. The main objective of Bioinformatics is to identify the disease and analysis the cause for disease. Protein- Protein Interactions (PPI) is used to analyze the structure of protein sequence and visualization in 3D structure. Many methodologies have been used to analysis the cancer causing protein detection using PPI network. Protein-Protein Interaction networks are used for the drug discovery for a particular disease in humans using protein interactions in Human Interaction Networks. There are many advantages and disadvantages while analyzing the different methods, so different analysis and results of large scale data is gathered. It is used for feature directions for the purpose of data modeling and analyzing to be implemented by using different machine learning and deep learning techniques and 3d visualization.Here different analysis methods have been surveyed for the future directions

Sign in / Sign up

Export Citation Format

Share Document