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Kutnu M, İşlerel ET, Tunçbağ N, Özcengiz G. Comparative biological network analysis for differentially expressed proteins as a function of bacilysin biosynthesis in Bacillus subtilis. Integr Biol (Camb) 2022; 14:99-110. [PMID: 35901454 DOI: 10.1093/intbio/zyac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 12/07/2021] [Accepted: 01/05/2022] [Indexed: 06/15/2023]
Abstract
The Gram-positive bacterium Bacillus subtilis produces a diverse range of secondary metabolites with different structures and activities. Among them, bacilysin is an enzymatically synthesized dipeptide that consists of L-alanine and L-anticapsin. Previous research by our group has suggested bacilysin's role as a pleiotropic molecule in its producer, B. subtilis PY79. However, the nature of protein interactions in the absence of bacilysin has not been defined. In the present work, we constructed a protein-protein interaction subnetwork by using Omics Integrator based on our recent comparative proteomics data obtained from a bacilysin-silenced strain, OGU1. Functional enrichment analyses on the resulting networks pointed to certain putatively perturbed pathways such as citrate cycle, quorum sensing and secondary metabolite biosynthesis. Various molecules, which were absent from the experimental data, were included in the final network. We believe that this study can guide further experiments in the identification and confirmation of protein-protein interactions in B. subtilis.
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Affiliation(s)
- Meltem Kutnu
- Department of Biological Sciences/Molecular Biology and Genetics, Middle East Technical University, Ankara 06800, Turkey
| | - Elif Tekin İşlerel
- Department of Biological Sciences/Molecular Biology and Genetics, Middle East Technical University, Ankara 06800, Turkey
- Department of Medical Microbiology, Faculty of Medicine, Maltepe University, Istanbul 34857, Turkey
| | - Nurcan Tunçbağ
- Department of Chemical and Biological Engineering, Koc University, Istanbul 34450, Turkey
| | - Gülay Özcengiz
- Department of Biological Sciences/Molecular Biology and Genetics, Middle East Technical University, Ankara 06800, Turkey
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2
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Sonawane AR, Aikawa E, Aikawa M. Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases. Front Cardiovasc Med 2022; 9:873582. [PMID: 35665246 PMCID: PMC9160390 DOI: 10.3389/fcvm.2022.873582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/19/2022] [Indexed: 01/18/2023] Open
Abstract
Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.
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Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masanori Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Center for Excellence in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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3
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Eyileten C, Wicik Z, Simões SN, Martins-Jr DC, Klos K, Wlodarczyk W, Assinger A, Soldacki D, Chcialowski A, Siller-Matula JM, Postula M. Thrombosis-related circulating miR-16-5p is associated with disease severity in patients hospitalised for COVID-19. RNA Biol 2022; 19:963-979. [PMID: 35938548 PMCID: PMC9361765 DOI: 10.1080/15476286.2022.2100629] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/06/2022] [Indexed: 11/21/2022] Open
Abstract
SARS-CoV-2 tropism for the ACE2 receptor, along with the multifaceted inflammatory reaction, is likely to drive the generalized hypercoagulable and thrombotic state seen in patients with COVID-19. Using the original bioinformatic workflow and network medicine approaches we reanalysed four coronavirus-related expression datasets and performed co-expression analysis focused on thrombosis and ACE2 related genes. We identified microRNAs (miRNAs) which play role in ACE2-related thrombosis in coronavirus infection and further, we validated the expressions of precisely selected miRNAs-related to thrombosis (miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p) in 79 hospitalized COVID-19 patients and 32 healthy volunteers by qRT-PCR. Consequently, we aimed to unravel whether bioinformatic prioritization could guide selection of miRNAs with a potential of diagnostic and prognostic biomarkers associated with disease severity in patients hospitalized for COVID-19. In bioinformatic analysis, we identified EGFR, HSP90AA1, APP, TP53, PTEN, UBC, FN1, ELAVL1 and CALM1 as regulatory genes which could play a pivotal role in COVID-19 related thrombosis. We also found miR-16-5p, miR-27a-3p, let-7b-5p and miR-155-5p as regulators in the coagulation and thrombosis process. In silico predictions were further confirmed in patients hospitalized for COVID-19. The expression levels of miR-16-5p and let-7b in COVID-19 patients were lower at baseline, 7-days and 21-day after admission compared to the healthy controls (p < 0.0001 for all time points for both miRNAs). The expression levels of miR-27a-3p and miR-155-5p in COVID-19 patients were higher at day 21 compared to the healthy controls (p = 0.007 and p < 0.001, respectively). A low baseline miR-16-5p expression presents predictive utility in assessment of the hospital length of stay or death in follow-up as a composite endpoint (AUC:0.810, 95% CI, 0.71-0.91, p < 0.0001) and low baseline expression of miR-16-5p and diabetes mellitus are independent predictors of increased length of stay or death according to a multivariate analysis (OR: 9.417; 95% CI, 2.647-33.506; p = 0.0005 and OR: 6.257; 95% CI, 1.049-37.316; p = 0.044, respectively). This study enabled us to better characterize changes in gene expression and signalling pathways related to hypercoagulable and thrombotic conditions in COVID-19. In this study we identified and validated miRNAs which could serve as novel, thrombosis-related predictive biomarkers of the COVID-19 complications, and can be used for early stratification of patients and prediction of severity of infection development in an individual.Abbreviations: ACE2, angiotensin-converting enzyme 2AF, atrial fibrillationAPP, Amyloid Beta Precursor ProteinaPTT, activated partial thromboplastin timeAUC, Area under the curveAβ, amyloid betaBMI, body mass indexCAD, coronary artery diseaseCALM1, Calmodulin 1 geneCaM, calmodulinCCND1, Cyclin D1CI, confidence intervalCOPD, chronic obstructive pulmonary diseaseCOVID-19, Coronavirus disease 2019CRP, C-reactive proteinCV, CardiovascularCVDs, cardiovascular diseasesDE, differentially expressedDM, diabetes mellitusEGFR, Epithelial growth factor receptorELAVL1, ELAV Like RNA Binding Protein 1FLNA, Filamin AFN1, Fibronectin 1GEO, Gene Expression OmnibushiPSC-CMs, Human induced pluripotent stem cell-derived cardiomyocytesHSP90AA1, Heat Shock Protein 90 Alpha Family Class A Member 1Hsp90α, heat shock protein 90αICU, intensive care unitIL, interleukinIQR, interquartile rangelncRNAs, long non-coding RNAsMI, myocardial infarctionMiRNA, MiR, microRNAmRNA, messenger RNAncRNA, non-coding RNANERI, network-medicine based integrative approachNF-kB, nuclear factor kappa-light-chain-enhancer of activated B cellsNPV, negative predictive valueNXF, nuclear export factorPBMCs, Peripheral blood mononuclear cellsPCT, procalcitoninPPI, Protein-protein interactionsPPV, positive predictive valuePTEN, phosphatase and tensin homologqPCR, quantitative polymerase chain reactionROC, receiver operating characteristicSARS-CoV-2, severe acute respiratory syndrome coronavirus 2SD, standard deviationTLR4, Toll-like receptor 4TM, thrombomodulinTP53, Tumour protein P53UBC, Ubiquitin CWBC, white blood cells.
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Affiliation(s)
- Ceren Eyileten
- Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, Warsaw, Poland
- Genomics Core Facility, Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Zofia Wicik
- Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, Warsaw, Poland
- Center for Mathematics, Computing and Cognition, Federal University of ABC, Santo AndréBrazil
| | - Sérgio N. Simões
- Department of Informatics, Federal Institute of Espírito Santo, Serra, Brazil
| | - David C. Martins-Jr
- Center for Mathematics, Computing and Cognition, Federal University of ABC, Santo AndréBrazil
| | - Krzysztof Klos
- Department of Infectious Diseases and Allergology - Military Institute of Medicine, Warsaw, Poland
| | - Wojciech Wlodarczyk
- Department of Infectious Diseases and Allergology - Military Institute of Medicine, Warsaw, Poland
| | - Alice Assinger
- Department of Vascular Biology and Thrombosis Research, Center of Physiology and Pharmacology, Medical University of Vienna, Austria
| | - Dariusz Soldacki
- Department of Clinical Immunology, Medical University of Warsaw, Warsaw, Poland
| | - Andrzej Chcialowski
- Department of Infectious Diseases and Allergology - Military Institute of Medicine, Warsaw, Poland
| | - Jolanta M. Siller-Matula
- Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, Warsaw, Poland
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Marek Postula
- Department of Experimental and Clinical Pharmacology, Medical University of Warsaw, Center for Preclinical Research and Technology CEPT, Warsaw, Poland
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4
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Liu Z, Miller D, Li F, Liu X, Levy SF. A large accessory protein interactome is rewired across environments. eLife 2020; 9:e62365. [PMID: 32924934 PMCID: PMC7577743 DOI: 10.7554/elife.62365] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 09/04/2020] [Indexed: 12/30/2022] Open
Abstract
To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.
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Affiliation(s)
- Zhimin Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Darach Miller
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
| | - Xianan Liu
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook UniversityStony BrookUnited States
- Laufer Center for Physical and Quantitative Biology, Stony Brook UniversityStony BrookUnited States
- Joint Initiative for Metrology in BiologyStanfordUnited States
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookUnited States
- SLAC National Accelerator LaboratoryMenlo ParkUnited States
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6
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Association study based on topological constraints of protein-protein interaction networks. Sci Rep 2020; 10:10797. [PMID: 32612246 PMCID: PMC7329836 DOI: 10.1038/s41598-020-67875-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 06/15/2020] [Indexed: 12/17/2022] Open
Abstract
The non-random interaction pattern of a protein–protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Zʹ-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.
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Balasubramaniam M, Ayyadevara S, Ganne A, Kakraba S, Penthala NR, Du X, Crooks PA, Griffin ST, Shmookler Reis RJ. Aggregate Interactome Based on Protein Cross-linking Interfaces Predicts Drug Targets to Limit Aggregation in Neurodegenerative Diseases. iScience 2019; 20:248-264. [PMID: 31593839 PMCID: PMC6817627 DOI: 10.1016/j.isci.2019.09.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/20/2019] [Accepted: 09/17/2019] [Indexed: 01/02/2023] Open
Abstract
Diagnosis of neurodegenerative diseases hinges on “seed” proteins detected in disease-specific aggregates. These inclusions contain diverse constituents, adhering through aberrant interactions that our prior data indicate are nonrandom. To define preferential protein-protein contacts mediating aggregate coalescence, we created click-chemistry reagents that cross-link neighboring proteins within human, APPSw-driven, neuroblastoma-cell aggregates. These reagents incorporate a biotinyl group to efficiently recover linked tryptic-peptide pairs. Mass-spectroscopy outputs were screened for all possible peptide pairs in the aggregate proteome. These empirical linkages, ranked by abundance, implicate a protein-adherence network termed the “aggregate contactome.” Critical hubs and hub-hub interactions were assessed by RNAi-mediated rescue of chemotaxis in aging nematodes, and aggregation-driving properties were inferred by multivariate regression and neural-network approaches. Aspirin, while disrupting aggregation, greatly simplified the aggregate contactome. This approach, and the dynamic model of aggregate accrual it implies, reveals the architecture of insoluble-aggregate networks and may reveal targets susceptible to interventions to ameliorate protein-aggregation diseases. Cross-link data support a preferred hierarchy of protein accrual into aggregates Contact networks can predict proteins that contribute functionally to aggregation RNAi knockdowns of key hubs and hub connectors imply functional roles in accrual Aspirin opposes protein aggregation by reducing contactome interactions >5-fold
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Affiliation(s)
- Meenakshisundaram Balasubramaniam
- McClellan Veterans Medical Ctr., Central Arkansas Veterans Healthcare Service, Little Rock, AR 72205, USA; Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Srinivas Ayyadevara
- McClellan Veterans Medical Ctr., Central Arkansas Veterans Healthcare Service, Little Rock, AR 72205, USA; Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
| | - Akshatha Ganne
- Bioinformatics Program, University of Arkansas for Medical Sciences, University of Arkansas at Little Rock, Little Rock, AR 72205, USA
| | - Samuel Kakraba
- Bioinformatics Program, University of Arkansas for Medical Sciences, University of Arkansas at Little Rock, Little Rock, AR 72205, USA
| | - Narsimha Reddy Penthala
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Xiuxia Du
- Department of Bioinformatics & Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Peter A Crooks
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Sue T Griffin
- McClellan Veterans Medical Ctr., Central Arkansas Veterans Healthcare Service, Little Rock, AR 72205, USA; Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Robert J Shmookler Reis
- McClellan Veterans Medical Ctr., Central Arkansas Veterans Healthcare Service, Little Rock, AR 72205, USA; Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
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8
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Li M, Ni P, Chen X, Wang J, Wu FX, Pan Y. Construction of Refined Protein Interaction Network for Predicting Essential Proteins. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1386-1397. [PMID: 28186903 DOI: 10.1109/tcbb.2017.2665482] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN. In this paper, we propose a new method for constructing refined PIN by using gene expression profiles and subcellular location information. The basic idea behind refining the PIN is that two proteins should have higher possibility to physically interact with each other if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is denoted by S-PIN while the final PIN refined by our method is denoted by TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to be used in the identification of essential proteins, 10 network-based essential protein discovery methods (DC, EC, SC, BC, CC, IC, LAC, NC, BN, and DMNC) are applied on it to identify essential proteins. A comparison of TS-PIN and two other networks: S-PIN and NF-APIN (a noise-filtered active PIN constructed by using gene expression data and S-PIN) is implemented on the prediction of essential proteins by using these ten network-based methods. The comparison results show that all of the 10 network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.
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9
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Liu W, Lin L, Zhang Z, Liu S, Gao K, Lv Y, Tao H, He H. Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana. PLANTA 2019; 249:1487-1501. [PMID: 30701323 DOI: 10.1007/s00425-019-03102-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 01/28/2019] [Indexed: 05/22/2023]
Abstract
A comprehensive network of the Arabidopsis transcriptome was analyzed and may serve as a valuable resource for candidate gene function investigations. A web tool to explore module information was also provided. Arabidopsis thaliana is a widely studied model plant whose transcriptome has been substantially profiled in various tissues, development stages and other conditions. These data can be reused for research on gene function through a systematic analysis of gene co-expression relationships. We collected microarray data from National Center for Biotechnology Information Gene Expression Omnibus, identified modules of co-expressed genes and annotated module functions. These modules were associated with experiments/traits, which provided potential signature modules for phenotypes. Novel heat shock proteins were implicated according to guilt by association. A higher-order module networks analysis suggested that the Arabidopsis network can be further organized into 15 meta-modules and that a chloroplast meta-module has a distinct gene expression pattern from the other 14 meta-modules. A comparison with the rice transcriptome revealed preserved modules and KEGG pathways. All the module gene information was available from an online tool at http://bioinformatics.fafu.edu.cn/arabi/ . Our findings provide a new source for future gene discovery in Arabidopsis.
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Affiliation(s)
- Wei Liu
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China.
| | - Liping Lin
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Zhiyuan Zhang
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Siqi Liu
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Kuan Gao
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Yanbin Lv
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Huan Tao
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China
| | - Huaqin He
- School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, People's Republic of China.
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10
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Nunes F, Fernandes C, Freitas C, Marini E, Serrano M, Moran CP, Eichenberger P, Henriques AO. SpoVID functions as a non-competitive hub that connects the modules for assembly of the inner and outer spore coat layers in Bacillus subtilis. Mol Microbiol 2018; 110:576-595. [PMID: 30168214 PMCID: PMC6282716 DOI: 10.1111/mmi.14116] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2018] [Revised: 08/22/2018] [Accepted: 08/27/2018] [Indexed: 01/20/2023]
Abstract
During sporulation in Bacillus subtilis, a group of mother cell‐specific proteins guides the assembly of the coat, a multiprotein structure that protects the spore and influences many of its environmental interactions. SafA and CotE behave as party hubs, governing assembly of the inner and outer coat layers. Targeting of coat proteins to the developing spore is followed by encasement. Encasement by SafA and CotE requires E, a region of 11 amino acids in the encasement protein SpoVID, with which CotE interacts directly. Here, we identified two single alanine substitutions in E that prevent binding of SafA, but not of CotE, to SpoVID, and block encasement. The substitutions result in the accumulation of SafA, CotE and their dependent proteins at the mother cell proximal spore pole, phenocopying a spoVID null mutant and suggesting that mislocalized SafA acts as an attractor for the rest of the coat. The requirement for E in SafA binding is bypassed by a peptide with the sequence of E provided in trans. We suggest that E allows binding of SafA to a second region in SpoVID, enabling CotE to interact with E and SpoVID to function as a non‐competitive hub during spore encasement.
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Affiliation(s)
- Filipa Nunes
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Catarina Fernandes
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Carolina Freitas
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Eleonora Marini
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Mónica Serrano
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Charles P Moran
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | | | - Adriano O Henriques
- Microbial Development Laboratory, Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Oeiras, Portugal
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11
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Wang B, Xie ZR, Chen J, Wu Y. Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells. Structure 2018; 26:1414-1424.e3. [PMID: 30174150 DOI: 10.1016/j.str.2018.07.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/12/2018] [Accepted: 07/24/2018] [Indexed: 02/07/2023]
Abstract
The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.
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Affiliation(s)
- Bo Wang
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA
| | - Zhong-Ru Xie
- College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Jiawen Chen
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, New York, NY 10461, USA.
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12
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Abstract
The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.
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13
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Ordinelli A, Bernabò N, Orsini M, Mattioli M, Barboni B. Putative human sperm Interactome: a networks study. BMC SYSTEMS BIOLOGY 2018; 12:52. [PMID: 29642908 PMCID: PMC5896140 DOI: 10.1186/s12918-018-0578-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 03/27/2018] [Indexed: 11/17/2022]
Abstract
Background For over sixty years, it has been known that mammalian spermatozoa immediately after ejaculation are virtually infertile. They became able to fertilize only after they reside for long time (hours to days) within female genital tract where they complete their functional maturation, the capacitation. This process is finely regulated by the interaction with the female environment and involves, in spermatozoa, a myriad of molecules as messengers and target of signals. Since, to date, a model able to represent the molecular interaction that characterize sperm physiology does not exist, we realized the Human Sperm Interactme Network3.0 (HSIN3.0) and its main component (HSNI3.0_MC), starting from the pathway active in male germ cells. Results HSIN3.0 and HSIN3.0_MC are scale free networks, adherent to the Barabasi-Albert model, and are characterised by an ultra-small world topology. We found that they are resistant to random attacks and that are designed to respond quickly and specifically to external inputs. In addition, it has been possible to identify the most connected nodes (the hubs) and the bottlenecks nodes. This result allowed us to explore the control mechanisms active in driving sperm biochemical machinery and to verify the different levels of controls: party vs. date hubs and hubs vs. bottlenecks, thanks the availability of data from KO mice. Finally, we found that several key nodes represent molecules specifically involved in function that are thought to be not present or not active in sperm cells, such as control of cell cycle, proteins synthesis, nuclear trafficking, and immune response, thus potentially open new perspectives on the study of sperm biology. Conclusions For the first time we present a network representing putative human sperm interactome. This result gives very intriguing biological information and could contribute to the knowledge of spermatozoa, either in physiological or pathological conditions. Electronic supplementary material The online version of this article (10.1186/s12918-018-0578-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alessandra Ordinelli
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
| | - Nicola Bernabò
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.
| | | | - Mauro Mattioli
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.,Istituto Zooprofilattico Sperimentale "G. Caporale", Teramo, Italy
| | - Barbara Barboni
- Faculty of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy
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14
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Pyrogova I, Wong L. Protein complex prediction by date hub removal. Comput Biol Chem 2018; 74:407-419. [PMID: 29602640 DOI: 10.1016/j.compbiolchem.2018.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 03/13/2018] [Indexed: 02/03/2023]
Abstract
Proteins physically interact with each other and form protein complexes to perform their biological functions. The prediction of protein complexes from protein-protein interaction (PPI) network is usually difficult when the complexes are overlapping with each other in a dense region of the network. To address the problem of predicting overlapping complexes, a previously proposed network-decomposition approach is promising. It decomposes a PPI network by e.g. removing proteins with high degree (hubs) which may participate in different complexes. This motivates us to examine a list of proteins, which bind their different partners at different time or at different location (viz. date hubs), manually collected from literature, for network decomposition. Results show that the CMC complex discovery algorithm after removing date hubs recalls more overlapping complexes that were missed earlier. Further improvement in performance is achieved when we predict date hub proteins based on simple network features and remove them from PPI networks.
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Affiliation(s)
- Iana Pyrogova
- Department of Computer Science, National University of Singapore, Singapore.
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore.
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15
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Chen G, Wang Y, Wang L, Xu W. Identifying prognostic biomarkers based on aberrant DNA methylation in kidney renal clear cell carcinoma. Oncotarget 2018; 8:5268-5280. [PMID: 28029655 PMCID: PMC5354907 DOI: 10.18632/oncotarget.14134] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 11/22/2016] [Indexed: 01/09/2023] Open
Abstract
The outcome of kidney renal clear cell carcinoma (KIRC) differs even among individuals with similar clinical characteristics. DNA methylation is regarded as a regulator of gene expression in cancers, which may be a molecular marker of prognosis. In this study, we aimed to mine novel methylation markers of the prognosis of KIRC. We revealed a total of 2793 genes differentially methylated in their promoter regions (DMGs) and 2979 differentially expressed genes (DEGs) in KIRC tissues compared with normal tissues using The Cancer Genome Atlas datasets. Then, we detected 57 and 34 subpathways enriched among the DMGs and DEGs, respectively, using the R package iSubpathwayMiner. We retained 56 subpathways related to both aberrant methylation and expression based on a hypergeometric test for further analysis. An integrated gene regulatory network was constructed using the regulatory relationships between genes in the subpathways. Using the top 15% of the nodes from the network ranked by degree, survival analysis was performed. We validated four DNA methylation signatures (RAC2, PLCB2, VAV1, and PARVG) as being highly correlated with prognosis in KIRC. These findings suggest that DNA methylation might become a prognostic predictor in KIRC and could supplement histological prognostic prediction.
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Affiliation(s)
- Guang Chen
- Department of Urology, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Yihan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Lu Wang
- Department of Urology, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Wanhai Xu
- Department of Urology, The 4th Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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16
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Vandereyken K, Van Leene J, De Coninck B, Cammue BPA. Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs. FRONTIERS IN PLANT SCIENCE 2018; 9:694. [PMID: 29922309 PMCID: PMC5996676 DOI: 10.3389/fpls.2018.00694] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 05/07/2018] [Indexed: 05/20/2023]
Abstract
Plant stress responses involve numerous changes at the molecular and cellular level and are regulated by highly complex signaling pathways. Studying protein-protein interactions (PPIs) and the resulting networks is therefore becoming increasingly important in understanding these responses. Crucial in PPI networks are the so-called hubs or hub proteins, commonly defined as the most highly connected central proteins in scale-free PPI networks. However, despite their importance, a growing amount of confusion and controversy seems to exist regarding hub protein identification, characterization and classification. In order to highlight these inconsistencies and stimulate further clarification, this review critically analyses the current knowledge on hub proteins in the plant interactome field. We focus on current hub protein definitions, including the properties generally seen as hub-defining, and the challenges and approaches associated with hub protein identification. Furthermore, we give an overview of the most important large-scale plant PPI studies of the last decade that identified hub proteins, pointing out the lack of overlap between different studies. As such, it appears that although major advances are being made in the plant interactome field, defining hub proteins is still heavily dependent on the quality, origin and interpretation of the acquired PPI data. Nevertheless, many hub proteins seem to have a reported role in the plant stress response, including transcription factors, protein kinases and phosphatases, ubiquitin proteasome system related proteins, (co-)chaperones and redox signaling proteins. A significant number of identified plant stress hubs are however still functionally uncharacterized, making them interesting targets for future research. This review clearly shows the ongoing improvements in the plant interactome field but also calls attention to the need for a more comprehensive and precise identification of hub proteins, allowing a more efficient systems biology driven unraveling of complex processes, including those involved in stress responses.
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Affiliation(s)
- Katy Vandereyken
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Jelle Van Leene
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Barbara De Coninck
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Division of Crop Biotechnics, KU Leuven, Heverlee, Belgium
| | - Bruno P. A. Cammue
- Centre of Microbial and Plant Genetics, KU Leuven, Heverlee, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- *Correspondence: Bruno P. A. Cammue
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17
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Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions. Int J Mol Sci 2017; 18:ijms18122761. [PMID: 29257115 PMCID: PMC5751360 DOI: 10.3390/ijms18122761] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/15/2022] Open
Abstract
Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization of the disorder-enriched hub protein-protein interactions (PPIs), including both hubs and their interactors, and by analyzing their enrichment among disease-associated proteins. We focused on the human interactome, given its high degree of completeness and relevance to the analysis of the disease-linked proteins. We quantified and investigated numerous functional and structural characteristics of the disorder-enriched hub PPIs, including protein binding, structural stability, evolutionary conservation, several categories of functional sites, and presence of over twenty types of posttranslational modifications (PTMs). We showed that the disorder-enriched hub PPIs have a significantly enlarged number of disordered protein binding regions and long intrinsically disordered regions. They also include high numbers of targeting, catalytic, and many types of PTM sites. We empirically demonstrated that these hub PPIs are significantly enriched among 11 out of 18 considered classes of human diseases that are associated with at least 100 human proteins. Finally, we also illustrated how over a dozen specific human hubs utilize intrinsic disorder for their promiscuous PPIs.
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18
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Wang X, Jin Y. Predicted networks of protein-protein interactions in Stegodyphus mimosarum by cross-species comparisons. BMC Genomics 2017; 18:716. [PMID: 28893204 PMCID: PMC5594591 DOI: 10.1186/s12864-017-4085-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 08/23/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Stegodyphus mimosarum is a candidate model organism belonging to the class Arachnida in the phylum Arthropoda. Studies on the biology of S. mimosarum over the past several decades have consisted of behavioral research and comparison of gene sequences based on the assembled genome sequence. Given the lack of systematic protein analyses and the rich source of information in the genome, we predicted the relationships of proteins in S. mimosarum by bioinformatics comparison with genome-wide proteins from select model organisms using gene mapping. RESULTS The protein-protein interactions (PPIs) of 11 organisms were integrated from four databases (BioGrid, InAct, MINT, and DIP). Here, we present comprehensive prediction and analysis of 3810 proteins in S. mimosarum with regard to interactions between proteins using PPI data of organisms. Interestingly, a portion of the protein interactions conserved among Saccharomyces cerevisiae, Homo sapiens, Arabidopsis thaliana, and Drosophila melanogaster were found to be associated with RNA splicing. In addition, overlap of predicted PPIs in reference organisms, Gene Ontology, and topology models in S. mimosarum are also reported. CONCLUSIONS Addition of Stegodyphus, a spider representative of interactomic research, provides the possibility of obtaining deeper insights into the evolution of PPI networks among different animal species. This work largely supports the utility of the "stratus clouds" model for predicted PPIs, providing a roadmap for integrative systems biology in S. mimosarum.
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Affiliation(s)
- Xiu Wang
- Institute of Ecology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China.,Institute of Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China
| | - Yongfeng Jin
- Institute of Biochemistry, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, ZJ310058, People's Republic of China.
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Paci P, Colombo T, Fiscon G, Gurtner A, Pavesi G, Farina L. SWIM: a computational tool to unveiling crucial nodes in complex biological networks. Sci Rep 2017; 7:44797. [PMID: 28317894 PMCID: PMC5357943 DOI: 10.1038/srep44797] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 02/14/2017] [Indexed: 12/14/2022] Open
Abstract
SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called “fight-club hubs”, characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called “switch genes”, appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.
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Affiliation(s)
- Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.,SysBio Centre for Systems Biology, Rome, 00185, Italy
| | - Teresa Colombo
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Aymone Gurtner
- Department of Research, Advanced Diagnostics, and Technological Innovation, Translational Research Area, Regina Elena National Cancer Institute, Rome, Italy
| | - Giulio Pavesi
- Department of Biosciences, University of Milan, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, "Sapienza" University, Rome, Italy
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20
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Zheng H, Wang C, Wang H. Analysis of Organization of the Interactome Using Dominating Sets: A Case Study on Cell Cycle Interaction Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:282-289. [PMID: 28368806 DOI: 10.1109/tcbb.2015.2459712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this study, a minimum dominating set based approach was developed and implemented as a Cytoscape plugin to identify critical and redundant proteins in a protein interaction network. We focused on the investigation of the properties associated with critical proteins in the context of the analysis of interaction networks specific to cell cycle in both yeast and human. A total of 132 yeast genes and 129 human proteins have been identified as critical nodes while 950 in yeast and 980 in human have been categorized as redundant nodes. A clear distinction between critical and redundant proteins was observed when examining their topological parameters including betweenness centrality, suggesting a central role of critical proteins in the control of a network. The significant differences in terms of gene coexpression and functional similarity were observed between the two sets of proteins in yeast. Critical proteins were found to be enriched with essential genes in both networks and have a more deleterious effect on the network integrity than their redundant counterparts. Furthermore, we obtained statistically significant enrichments of proteins that govern human diseases including cancer-related and virus-targeted genes in the corresponding set of critical proteins.
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21
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Ehsani Ardakani MJ, Safaei A, Arefi Oskouie A, Haghparast H, Haghazali M, Mohaghegh Shalmani H, Peyvandi H, Naderi N, Zali MR. Evaluation of liver cirrhosis and hepatocellular carcinoma using Protein-Protein Interaction Networks. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2016; 9:S14-S22. [PMID: 28224023 PMCID: PMC5310795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
AIM In the current study, we analysised only the articles that investigate serum proteome profile of cirrhosis patients or HCC patients versus healthy controls. BACKGROUND Increased understanding of cancer biology has enabled identification of molecular events that lead to the discovery of numerous potential biomarkers in diseases. Protein-protein interaction networks is one of aspect that could elevate the understanding level of molecular events and protein connections that lead to the identification of genes and proteins associated with diseases. METHODS Gene expression data, including 63 gene or protein names for hepatocellular carcinoma and 29 gene or protein names for cirrhosis, were extracted from a number of previous investigations. The networks of related differentially expressed genes were explored using Cytoscape and the PPI analysis methods such as MCODE and ClueGO. Centrality and cluster screening identified hub genes, including APOE, TTR, CLU, and APOA1 in cirrhosis. RESULTS CLU and APOE belong to the regulation of positive regulation of neurofibrillary tangle assembly. HP and APOE involved in cellular oxidant detoxification. C4B and C4BP belong to the complement activation, classical pathway and acute inflammation response pathway. Also, it was reported TTR, TFRC, VWF, CLU, A2M, APOA1, CKAP5, ZNF648, CASP8, and HSP27 as hubs in HCC. In HCC, these include A2M that are corresponding to platelet degranulation, humoral immune response, and negative regulation of immune effector process. CLU belong to the reverse cholesterol transport, platelet degranulation and human immune response. APOA1 corresponds to the reverse cholesterol transport, platelet degranulation and humoral immune response, as well as negative regulation of immune effector process pathway. CONCLUSION In conclusion, this study suggests that there is a common molecular relationship between cirrhosis and hepatocellular cancer that may help with identification of target molecules for early treatment that is essential in cancer therapy.
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Affiliation(s)
- Mohammad Javad Ehsani Ardakani
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Akram Safaei
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Afsaneh Arefi Oskouie
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Hesam Haghparast
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterologyand Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehrdad Haghazali
- Behbood Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Mohaghegh Shalmani
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterologyand Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Peyvandi
- Hearing Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nosratollah Naderi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterologyand Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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22
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Charon J, Theil S, Nicaise V, Michon T. Protein intrinsic disorder within the Potyvirus genus: from proteome-wide analysis to functional annotation. MOLECULAR BIOSYSTEMS 2016; 12:634-52. [PMID: 26699268 DOI: 10.1039/c5mb00677e] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Within proteins, intrinsically disordered regions (IDRs) are devoid of stable secondary and tertiary structures under physiological conditions and rather exist as dynamic ensembles of inter-converting conformers. Although ubiquitous in all domains of life, the intrinsic disorder content is highly variable in viral genomes. Over the years, functional annotations of disordered regions at the scale of the whole proteome have been conducted for several animal viruses. But to date, similar studies applied to plant viruses are still missing. Based on disorder prediction tools combined with annotation programs and evolutionary studies, we analyzed the intrinsic disorder content in Potyvirus, using a 10-species dataset representative of this genus diversity. In this paper, we revealed that: (i) the Potyvirus proteome displays high disorder content, (ii) disorder is conserved during Potyvirus evolution, suggesting a functional advantage of IDRs, (iii) IDRs evolve faster than ordered regions, and (iv) IDRs may be associated with major biological functions required for the Potyvirus cycle. Notably, the proteins P1, Coat protein (CP) and Viral genome-linked protein (VPg) display a high content of conserved disorder, enriched in specific motifs mimicking eukaryotic functional modules and suggesting strategies of host machinery hijacking. In these three proteins, IDRs are particularly conserved despite their high amino acid polymorphism, indicating a link to adaptive processes. Through this comprehensive study, we further investigate the biological relevance of intrinsic disorder in Potyvirus biology and we propose a functional annotation of potyviral proteome IDRs.
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Affiliation(s)
- Justine Charon
- UMR Biologie du Fruit et Pathologie, INRA, Villenave d'Ornon cedex, France. and UMR Biologie du Fruit et Pathologie, Université de Bordeaux, Villenave d'Ornon cedex, France
| | - Sébastien Theil
- UMR Biologie du Fruit et Pathologie, INRA, Villenave d'Ornon cedex, France. and UMR Biologie du Fruit et Pathologie, Université de Bordeaux, Villenave d'Ornon cedex, France
| | - Valérie Nicaise
- UMR Biologie du Fruit et Pathologie, INRA, Villenave d'Ornon cedex, France. and UMR Biologie du Fruit et Pathologie, Université de Bordeaux, Villenave d'Ornon cedex, France
| | - Thierry Michon
- UMR Biologie du Fruit et Pathologie, INRA, Villenave d'Ornon cedex, France. and UMR Biologie du Fruit et Pathologie, Université de Bordeaux, Villenave d'Ornon cedex, France
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Platig J, Castaldi PJ, DeMeo D, Quackenbush J. Bipartite Community Structure of eQTLs. PLoS Comput Biol 2016; 12:e1005033. [PMID: 27618581 PMCID: PMC5019382 DOI: 10.1371/journal.pcbi.1005033] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 06/23/2016] [Indexed: 11/18/2022] Open
Abstract
Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network “hub” SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community (“core SNPs”) and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits. Large-scale studies have identified thousands of genetic variants associated with different phenotypes without explaining their function. Expression quantitative trait locus analysis associates the compendium of genetic variants with expression levels of individual genes, providing the opportunity to link those variants to functions. But the complexity of those associations has caused most analyses to focus solely on genetic variants immediately adjacent to the genes they may influence. We describe a method that embraces the complexity, representing all variant-gene associations as a bipartite graph. The graph contains highly modular, functional communities in which disease-associated variants emerge as those likely to perturb the structure of the network and the function of the genes in these communities.
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Affiliation(s)
- John Platig
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America
- * E-mail:
| | - Peter J. Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Division of General Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dawn DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - John Quackenbush
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard Chan School of Public Health, Boston, Massachusetts, United States of America
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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24
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Young JG, Hébert-Dufresne L, Allard A, Dubé LJ. Growing networks of overlapping communities with internal structure. Phys Rev E 2016; 94:022317. [PMID: 27627327 DOI: 10.1103/physreve.94.022317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Indexed: 06/06/2023]
Abstract
We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships, and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.
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Affiliation(s)
- Jean-Gabriel Young
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
| | - Laurent Hébert-Dufresne
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
| | - Antoine Allard
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain
- Universitat de Barcelona Institute of Complex Systems (UBICS), Universitat de Barcelona, Barcelona, Spain
| | - Louis J Dubé
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
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Muetze T, Goenawan IH, Wiencko HL, Bernal-Llinares M, Bryan K, Lynn DJ. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks. F1000Res 2016; 5:1745. [PMID: 27853512 PMCID: PMC5105880 DOI: 10.12688/f1000research.9118.2] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2016] [Indexed: 01/21/2023] Open
Abstract
Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such
contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. Availability: CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store (
http://apps.cytoscape.org/apps/chat).
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Affiliation(s)
- Tanja Muetze
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Ivan H Goenawan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Heather L Wiencko
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Meath, Ireland
| | - Manuel Bernal-Llinares
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Kenneth Bryan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - David J Lynn
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia; School of Medicine, Flinders University, Bedford Park, Australia
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Muetze T, Goenawan IH, Wiencko HL, Bernal-Llinares M, Bryan K, Lynn DJ. Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks. F1000Res 2016; 5:1745. [PMID: 27853512 PMCID: PMC5105880 DOI: 10.12688/f1000research.9118.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/26/2016] [Indexed: 07/30/2023] Open
Abstract
UNLABELLED Highly connected nodes (hubs) in biological networks are topologically important to the structure of the network and have also been shown to be preferentially associated with a range of phenotypes of interest. The relative importance of a hub node, however, can change depending on the biological context. Here, we report a Cytoscape app, the Contextual Hub Analysis Tool (CHAT), which enables users to easily construct and visualize a network of interactions from a gene or protein list of interest, integrate contextual information, such as gene expression or mass spectrometry data, and identify hub nodes that are more highly connected to contextual nodes (e.g. genes or proteins that are differentially expressed) than expected by chance. In a case study, we use CHAT to construct a network of genes that are differentially expressed in Dengue fever, a viral infection. CHAT was used to identify and compare contextual and degree-based hubs in this network. The top 20 degree-based hubs were enriched in pathways related to the cell cycle and cancer, which is likely due to the fact that proteins involved in these processes tend to be highly connected in general. In comparison, the top 20 contextual hubs were enriched in pathways commonly observed in a viral infection including pathways related to the immune response to viral infection. This analysis shows that such contextual hubs are considerably more biologically relevant than degree-based hubs and that analyses which rely on the identification of hubs solely based on their connectivity may be biased towards nodes that are highly connected in general rather than in the specific context of interest. AVAILABILITY CHAT is available for Cytoscape 3.0+ and can be installed via the Cytoscape App Store ( http://apps.cytoscape.org/apps/chat).
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Affiliation(s)
- Tanja Muetze
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Ivan H. Goenawan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Heather L. Wiencko
- Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre, Teagasc, Meath, Ireland
| | - Manuel Bernal-Llinares
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - Kenneth Bryan
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
| | - David J. Lynn
- EMBL Australia Biomedical Informatics Group, Infection & Immunity Theme, South Australian Medical and Health Research Institute, Adelaide, Australia
- School of Medicine, Flinders University, Bedford Park, Australia
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Design principles for clinical network-based proteomics. Drug Discov Today 2016; 21:1130-8. [DOI: 10.1016/j.drudis.2016.05.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2015] [Revised: 04/18/2016] [Accepted: 05/20/2016] [Indexed: 01/10/2023]
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Hu Z, Jiang K, Frank MB, Chen Y, Jarvis JN. Complexity and Specificity of the Neutrophil Transcriptomes in Juvenile Idiopathic Arthritis. Sci Rep 2016; 6:27453. [PMID: 27271962 PMCID: PMC4895221 DOI: 10.1038/srep27453] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Accepted: 05/19/2016] [Indexed: 12/17/2022] Open
Abstract
NIH projects such as ENCODE and Roadmap Epigenomics have revealed surprising complexity in the transcriptomes of mammalian cells. In this study, we explored transcriptional complexity in human neutrophils, cells generally regarded as nonspecific in their functions and responses. We studied distinct human disease phenotypes and found that, at the gene, gene isoform, and miRNA level, neutrophils exhibit considerable specificity in their transcriptomes. Thus, even cells whose responses are considered non-specific show tailoring of their transcriptional repertoire toward specific physiologic or pathologic contexts. We also found that miRNAs had a global impact on neutrophil transcriptome and are associated with innate immunity in juvenile idiopathic arthritis (JIA). These findings have important implications for our understanding of the link between genes, non-coding transcripts and disease phenotypes.
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Affiliation(s)
- Zihua Hu
- Center for Computational Research, New York State Center of Excellence in Bioinformatics &Life Sciences, State University of New York at Buffalo, Buffalo, NY 14260, USA.,Department of Ophthalmology, Department of Biostatistics, Department of Medicine, State University of New York at Buffalo, Buffalo, NY 14260, USA.,SUNY Eye Institute, Buffalo, NY 14260, USA
| | - Kaiyu Jiang
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA
| | - Mark Barton Frank
- Arthritis &Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Yanmin Chen
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA
| | - James N Jarvis
- Department of Pediatrics, Division of Allergy/Immunology/Rheumatology, University at Buffalo, Buffalo, NY 14203, USA.,Graduate Program in Genetics, Genomics, &Bioinformatics, University at Buffalo, Buffalo, NY 14203, USA
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Charitou T, Bryan K, Lynn DJ. Using biological networks to integrate, visualize and analyze genomics data. Genet Sel Evol 2016; 48:27. [PMID: 27036106 PMCID: PMC4818439 DOI: 10.1186/s12711-016-0205-1] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 03/16/2016] [Indexed: 12/22/2022] Open
Abstract
Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.
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Affiliation(s)
- Theodosia Charitou
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia.,Systems Biology Ireland, University College Dublin, Belfield 4, Ireland.,Teagasc, The Agriculture and Food Development Authority, Co Meath, Ireland
| | - Kenneth Bryan
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia
| | - David J Lynn
- EMBL Australia Group, Infection and Immunity, South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, 5000, Australia. .,School of Medicine, Flinders University, Bedford Park, SA, 5042, Australia.
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30
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Verkhivker GM. Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation. MOLECULAR BIOSYSTEMS 2016; 12:3146-65. [DOI: 10.1039/c6mb00298f] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The computational analysis of residue interaction networks dissects the allosteric effects of inhibitor-induced BRAF kinase dimerization and paradoxical activation.
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Affiliation(s)
- G. M. Verkhivker
- Graduate Program in Computational and Data Sciences
- Department of Computational Sciences
- Schmid College of Science and Technology
- Chapman University
- Orange
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31
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Kiran M, Nagarajaram HA. Interaction and localization diversities of global and local hubs in human protein–protein interaction networks. MOLECULAR BIOSYSTEMS 2016; 12:2875-82. [DOI: 10.1039/c6mb00104a] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Hubs, the highly connected nodes in protein–protein interaction networks (PPINs), are associated with several characteristic properties and are known to perform vital roles in cells.
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Affiliation(s)
- M. Kiran
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
| | - H. A. Nagarajaram
- Laboratory of Computational Biology
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500 001
- India
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Castrillo JI, Oliver SG. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks. Methods Mol Biol 2016; 1303:3-48. [PMID: 26235058 DOI: 10.1007/978-1-4939-2627-5_1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.
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Affiliation(s)
- Juan I Castrillo
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK,
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33
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Simões SN, Martins DC, Pereira CAB, Hashimoto RF, Brentani H. NERI: network-medicine based integrative approach for disease gene prioritization by relative importance. BMC Bioinformatics 2015; 16 Suppl 19:S9. [PMID: 26696568 PMCID: PMC4686785 DOI: 10.1186/1471-2105-16-s19-s9] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Complex diseases are characterized as being polygenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing, gene expression measurements (transcriptome), and protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which more studied proteins tend to have more connections, degrading the results quality. Additionally, methods using only PPI networks can provide only static and non-specific results, since the topologies of these networks are not specific of a given disease. Results The goal of this work is to develop a methodology that integrates PPI networks with disease specific data sources, such as GWAS and gene expression, to find genes more specific of a given complex disease. After the integration of PPI networks and gene expression data, the resulting network is used to connect genes related to the disease through the shortest paths that have the greatest concordance between their gene expressions. Both case and control expression data are used separately and, at the end, the most altered genes between the two conditions are selected. To evaluate the method, schizophrenia was adopted as case study. Conclusion Results show that the proposed method successfully retrieves differentially coexpressed genes in two conditions, while avoiding the bias from literature. Moreover we were able to achieve a greater concordance in the selection of important genes from different microarray studies of the same disease and to produce a more specific gene set related to the studied disease.
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Comprehensive analysis of lncRNA-mRNA co-expression patterns identifies immune-associated lncRNA biomarkers in ovarian cancer malignant progression. Sci Rep 2015; 5:17683. [PMID: 26631459 PMCID: PMC4668366 DOI: 10.1038/srep17683] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 11/03/2015] [Indexed: 02/08/2023] Open
Abstract
Ovarian cancer (OV) is the most common and lethal gynecological tumor with a poor prognosis for women; however, the regulatory roles of the long non-coding RNAs (lncRNAs) in ovarian malignant progression are insufficiently understood. Here, we investigated the expression patterns of lncRNAs and mRNAs in the high-throughput molecular profiles of 399 OV patients and constructed a functional lncRNA-mRNA co-expression network across OV malignant progression. We found that two protective lncRNAs, RP11-284N8.3.1 and AC104699.1.1, were not only differentially expressed throughout the progression of malignant OV but were also independently predictive of the survival of patients with different OV stages. A functional analysis of the two lncRNAs predicted their roles in immune system activation and other anti-tumor processes in the OV microenvironment. Integrating these two lncRNAs into an OV risk model was able to significantly stratify patients into different risk groups. Overall, our analysis effectively provides insights into the lncRNA association with malignant OV progression. The two-lncRNA signature is a candidate biomarker for the prognosis of patients with OV and may enable a more accurate prediction of survival.
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Pritykin Y, Ghersi D, Singh M. Genome-Wide Detection and Analysis of Multifunctional Genes. PLoS Comput Biol 2015; 11:e1004467. [PMID: 26436655 PMCID: PMC4593560 DOI: 10.1371/journal.pcbi.1004467] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 07/19/2015] [Indexed: 12/25/2022] Open
Abstract
Many genes can play a role in multiple biological processes or molecular functions. Identifying multifunctional genes at the genome-wide level and studying their properties can shed light upon the complexity of molecular events that underpin cellular functioning, thereby leading to a better understanding of the functional landscape of the cell. However, to date, genome-wide analysis of multifunctional genes (and the proteins they encode) has been limited. Here we introduce a computational approach that uses known functional annotations to extract genes playing a role in at least two distinct biological processes. We leverage functional genomics data sets for three organisms—H. sapiens, D. melanogaster, and S. cerevisiae—and show that, as compared to other annotated genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be more central in protein interaction networks, tend to be more evolutionarily conserved, and are more likely to be essential. We also find that multifunctional genes are significantly more likely to be involved in human disorders. These same features also hold when multifunctionality is defined with respect to molecular functions instead of biological processes. Our analysis uncovers key features about multifunctional genes, and is a step towards a better genome-wide understanding of gene multifunctionality. Almost every aspect of cellular function depends on protein activity. In spite of being fine-tuned to carry out highly specific functions, proteins can also multitask. Experimental studies have identified genes and proteins endowed with more than one molecular function, or participating in very different biological processes. These studies suggest that the degree of functional plasticity exhibited by proteins might go well beyond a simple “one protein—one function” relationship. However, systematic studies of the properties of multifunctional genes (and their encoded proteins) have been limited. Here we present a computational framework to identify putative multifunctional genes, and compare their properties with those of other genes. We find that multifunctional genes are significantly different from other genes with respect to their physicochemical properties, expression profiles, and interaction properties. We also observe that multifunctional genes tend to be more conserved, and that a greater fraction of them are associated with human disorders. Taken together, these results represent a step towards a more complete understanding of the role multifunctional genes play in the functional organization of the cell.
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Affiliation(s)
- Yuri Pritykin
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
- Lewis–Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Dario Ghersi
- Lewis–Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
- * E-mail: (DG); (MS)
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
- Lewis–Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail: (DG); (MS)
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Chen Y, Wang S, Zhang L, Xie T, Song S, Huang J, Zhang Y, Ouyang L, Liu B. Identification of ULK1 as a novel biomarker involved in miR-4487 and miR-595 regulation in neuroblastoma SH-SY5Y cell autophagy. Sci Rep 2015; 5:11035. [PMID: 26183158 PMCID: PMC4505320 DOI: 10.1038/srep11035] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 05/11/2015] [Indexed: 02/05/2023] Open
Abstract
Autophagy, referring to an evolutionarily conserved, multi-step lysosomal degradation process, has been well-known to be initiated by Unc-51 like kinase 1 (ULK1) with some links to Parkinson's disease (PD). MicroRNAs (miRNAs), small and non-coding endogenous RNAs 22 ~ 24 nucleotides (nt) in length, have been demonstrated to play an essential role for modulating autophagy. Recently, the relationships between miRNAs and autophagy have been widely reported in PD; however, how microRNAs regulate autophagy still remains in its infancy. Thus, in this study, we computationally constructed the ULK1-regulated autophagic kinase subnetwork in PD and further identified ULK1 able to negatively regulate p70(S6K) in starvation-induced autophagy of neuroblastoma SH-SY5Y cells. Combination of in silico prediction and microarray analyses, we identified that miR-4487 and miR-595 could target ULK1 and experimentally verified they could negatively or positively regulate ULK1-mediated autophagy. In conclusion, these results may uncover the novel ULK1-p70(S6K) autophagic pathway, as well as miR-4487 and miR-595 as new ULK1 target miRNAs. Thus, these findings would provide a clue to explore ULK1 and its target miRNAs as potential biomarkers in the future PD therapy.
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Affiliation(s)
- Yi Chen
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shuya Wang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Northwestern University, Feinberg School of Medicine, 303 East Chicago Avenue, Chicago, Illinois 60611, USA
| | - Lan Zhang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Tao Xie
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Sicheng Song
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jian Huang
- School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Yonghui Zhang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
- Collaborative Innovation Center for Biotherapy, Department of Pharmacology & Pharmaceutical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Liang Ouyang
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bo Liu
- Department of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
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Siwiak M, Zielenkiewicz P. Co-regulation of translation in protein complexes. Biol Direct 2015; 10:18. [PMID: 25909184 PMCID: PMC4409705 DOI: 10.1186/s13062-015-0048-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Accepted: 03/13/2015] [Indexed: 11/23/2022] Open
Abstract
Background Co-regulation of gene expression has been known for many years, and studied widely both globally and for individual genes. Nevertheless, most analyses concerned transcriptional control, which in case of physically interacting proteins and protein complex subunits may be of secondary importance. This research is the first quantitative analysis that provides global-scale evidence for translation co-regulation among associated proteins. Results By analyzing the results of our previous quantitative model of translation, we have demonstrated that protein production rates plus several other translational parameters, such as mRNA and protein abundance, or number of produced proteins from a gene, are well concerted between stable complex subunits and party hubs. This may be energetically favorable during synthesis of complex building blocks and ensure their accurate production in time. In contrast, for connections with regulatory particles and date hubs translational co-regulation is less visible, indicating that in these cases maintenance of accurate levels of interacting particles is not necessarily beneficial. Conclusions Similar results obtained for distantly related model organisms, Saccharomyces cerevisiae and Homo sapiens, suggest that the phenomenon of translational co-regulation applies to the variety of living organisms and concerns many complex constituents. This phenomenon was also observed among the set of functionally linked proteins from Escherichia coli operons. This leads to the conclusion that translational regulation of a protein should always be studied with respect to the expression of its primary interacting partners. Reviewers This article was reviewed by Sandor Pongor and Claus Wilke. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0048-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marlena Siwiak
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, Warsaw, 02-106, Poland.
| | - Piotr Zielenkiewicz
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, Warsaw, 02-106, Poland. .,Laboratory of Plant Molecular Biology, Faculty of Biology, Warsaw University, Pawinskiego 5a, Warsaw, 02-106, Poland.
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Srihari S, Yong CH, Patil A, Wong L. Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes. FEBS Lett 2015; 589:2590-602. [PMID: 25913176 DOI: 10.1016/j.febslet.2015.04.026] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 04/14/2015] [Accepted: 04/14/2015] [Indexed: 12/30/2022]
Abstract
Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.
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Affiliation(s)
- Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland 4067, Australia.
| | - Chern Han Yong
- Department of Computer Science, National University of Singapore, Singapore 117417, Singapore
| | - Ashwini Patil
- Human Genome Centre, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore 117417, Singapore
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Berenstein AJ, Piñero J, Furlong LI, Chernomoretz A. Mining the modular structure of protein interaction networks. PLoS One 2015; 10:e0122477. [PMID: 25856434 PMCID: PMC4391834 DOI: 10.1371/journal.pone.0122477] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 02/11/2015] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. METHODOLOGY We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed to what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera's cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. RESULTS As a case study we considered a set of aging related proteins and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter/intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge.
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Affiliation(s)
- Ariel José Berenstein
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and Instituto de Física de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Pabellón 1, Ciudad Universitaria, Buenos Aires, Argentina
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer del Dr. Aiguader, 88, 08003—Barcelona, Spain
| | - Laura Inés Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Universitat Pompeu Fabra (UPF), Carrer del Dr. Aiguader, 88, 08003—Barcelona, Spain
| | - Ariel Chernomoretz
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and Instituto de Física de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Pabellón 1, Ciudad Universitaria, Buenos Aires, Argentina
- Laboratorio de Biología de Sistemas Integrativa, Fundación Instituto Leloir, Buenos Aires, Argentina
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Bhattacharyya M, Chakrabarti S. Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malar J 2015; 14:70. [PMID: 25879642 PMCID: PMC4333160 DOI: 10.1186/s12936-015-0562-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 01/14/2015] [Indexed: 12/28/2022] Open
Abstract
Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12936-015-0562-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Madhumita Bhattacharyya
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Biology, Kolkata, 700032, West Bengal, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council of Scientific and Industrial Research-Indian Institute of Chemical Biology, Kolkata, 700032, West Bengal, India.
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Kumar G, Gotesman M, El-Matbouli M. Interaction of Tetracapsuloides bryosalmonae, the causative agent of proliferative kidney disease, with host proteins in the kidney of Salmo trutta. Parasitol Res 2015; 114:1721-7. [PMID: 25663070 PMCID: PMC4412511 DOI: 10.1007/s00436-015-4357-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Accepted: 01/22/2015] [Indexed: 12/23/2022]
Abstract
Tetracapsuloides bryosalmonae (Myxozoa) is the causative agent of proliferative kidney disease in various species of salmonids which are found in Europe and North America. Less information about the interactions of T. bryosalmonae proteins with salmonid proteins during parasite development is known. In this study, anti-T. bryosalmonae monoclonal antibody-linked to N-hydroxysuccinimide-activated spin columns were used to purify parasite and host proteins from the kidneys of infected and non-infected brown trout (Salmo trutta) Linnaeus, 1758. The samples were next analyzed by electrospray ionization coupled to mass spectrometry to identify proteins that may be involved in the infection and proliferation of T. bryosalmonae within the brown trout host. A total of 6 parasite proteins and 40 different host proteins were identified in this analysis. The identified host proteins function in various processes, which include host defense, enzymatic, and structural components. In conjunction with modern molecular based tools, such siRNA, gene replacement, or gene disruption, this data can ultimately be used to develop novel control methods for T. bryosalmonae, based on the proteins or pathways identified in this study.
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Affiliation(s)
- Gokhlesh Kumar
- Clinical Division of Fish Medicine, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, Veterinärplatz 1, 1210, Vienna, Austria
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Palumbo MC, Zenoni S, Fasoli M, Massonnet M, Farina L, Castiglione F, Pezzotti M, Paci P. Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development. THE PLANT CELL 2014; 26:4617-35. [PMID: 25490918 PMCID: PMC4311215 DOI: 10.1105/tpc.114.133710] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We developed an approach that integrates different network-based methods to analyze the correlation network arising from large-scale gene expression data. By studying grapevine (Vitis vinifera) and tomato (Solanum lycopersicum) gene expression atlases and a grapevine berry transcriptomic data set during the transition from immature to mature growth, we identified a category named "fight-club hubs" characterized by a marked negative correlation with the expression profiles of neighboring genes in the network. A special subset named "switch genes" was identified, with the additional property of many significant negative correlations outside their own group in the network. Switch genes are involved in multiple processes and include transcription factors that may be considered master regulators of the previously reported transcriptome remodeling that marks the developmental shift from immature to mature growth. All switch genes, expressed at low levels in vegetative/green tissues, showed a significant increase in mature/woody organs, suggesting a potential regulatory role during the developmental transition. Finally, our analysis of tomato gene expression data sets showed that wild-type switch genes are downregulated in ripening-deficient mutants. The identification of known master regulators of tomato fruit maturation suggests our method is suitable for the detection of key regulators of organ development in different fleshy fruit crops.
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Affiliation(s)
- Maria Concetta Palumbo
- Institute for Computing Applications "Mauro Picone," National Research Council, 00185 Rome, Italy
| | - Sara Zenoni
- Dipartimento di Biotecnologie, Università degli Studi di Verona, 37134 Verona, Italy
| | - Marianna Fasoli
- Dipartimento di Biotecnologie, Università degli Studi di Verona, 37134 Verona, Italy
| | - Mélanie Massonnet
- Dipartimento di Biotecnologie, Università degli Studi di Verona, 37134 Verona, Italy
| | - Lorenzo Farina
- Department of Computer, Control, and Management Engineering, "Sapienza" University of Rome, 00185 Rome, Italy
| | - Filippo Castiglione
- Institute for Computing Applications "Mauro Picone," National Research Council, 00185 Rome, Italy
| | - Mario Pezzotti
- Dipartimento di Biotecnologie, Università degli Studi di Verona, 37134 Verona, Italy
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti," National Research Council, 00185 Rome, Italy SysBio Centre for Systems Biology, 00185 Rome, Italy
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Ehlert K, Loewe L. Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations. J Chem Phys 2014; 141:204109. [PMID: 25429935 DOI: 10.1063/1.4901114] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
To respect the nature of discrete parts in a system, stochastic simulation algorithms (SSAs) must update for each action (i) all part counts and (ii) each action's probability of occurring next and its timing. This makes it expensive to simulate biological networks with well-connected "hubs" such as ATP that affect many actions. Temperature and volume also affect many actions and may be changed significantly in small steps by the network itself during fever and cell growth, respectively. Such trends matter for evolutionary questions, as cell volume determines doubling times and fever may affect survival, both key traits for biological evolution. Yet simulations often ignore such trends and assume constant environments to avoid many costly probability updates. Such computational convenience precludes analyses of important aspects of evolution. Here we present "Lazy Updating," an add-on for SSAs designed to reduce the cost of simulating hubs. When a hub changes, Lazy Updating postpones all probability updates for reactions depending on this hub, until a threshold is crossed. Speedup is substantial if most computing time is spent on such updates. We implemented Lazy Updating for the Sorting Direct Method and it is easily integrated into other SSAs such as Gillespie's Direct Method or the Next Reaction Method. Testing on several toy models and a cellular metabolism model showed >10× faster simulations for its use-cases-with a small loss of accuracy. Thus we see Lazy Updating as a valuable tool for some special but important simulation problems that are difficult to address efficiently otherwise.
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Affiliation(s)
- Kurt Ehlert
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Laurence Loewe
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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Wang H, Zheng H. Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:1264-1270. [PMID: 26357062 DOI: 10.1109/tcbb.2014.2318715] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from gene ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity derived from both GO molecular function and cellular component hierarchies. Relating to three-dimensional structures, we found that both single- and multi-interface proteins could become date hubs coordinating multiple functions performed at different times while party hubs are mainly multi-interface proteins. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the dynamic organization in human interactome is far more complex than the dichotomy of hubs observed in the yeast cell cycle.
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Abstract
The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
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Affiliation(s)
- Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology
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Schramm SJ, Jayaswal V, Goel A, Li SS, Yang YH, Mann GJ, Wilkins MR. Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations. Proteomics 2014; 13:3393-405. [PMID: 24166987 DOI: 10.1002/pmic.201200570] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Revised: 09/11/2013] [Accepted: 10/07/2013] [Indexed: 01/01/2023]
Abstract
High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.
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Affiliation(s)
- Sarah-Jane Schramm
- Sydney Medical School, Westmead Millennium Institute for Medical Research, The University of Sydney, Sydney, NSW, Australia; Melanoma Institute Australia, Sydney, NSW, Australia
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Lage K. Protein-protein interactions and genetic diseases: The interactome. Biochim Biophys Acta Mol Basis Dis 2014; 1842:1971-1980. [PMID: 24892209 DOI: 10.1016/j.bbadis.2014.05.028] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 05/07/2014] [Accepted: 05/24/2014] [Indexed: 12/27/2022]
Abstract
Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.
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Affiliation(s)
- Kasper Lage
- Department of Surgery and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Stevens A, Meyer S, Hanson D, Clayton P, Donn RP. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis. Arthritis Res Ther 2014; 16:R109. [PMID: 24886659 PMCID: PMC4062926 DOI: 10.1186/ar4559] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 04/29/2014] [Indexed: 02/06/2023] Open
Abstract
Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the functionality of the biological pathways within the MEN and to statistically rank the proteins. JIA gene expression data were integrated with the MEN and clusters of functionally important proteins derived using MCODE. Results A JIA interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN, representing the most functionally related components of the network, comprised of seven clusters, with distinct functional characteristics. Four gene expression datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial fluid monocytes, were mapped onto the MEN and a list of genes enriched for functional significance identified. This analysis revealed the genes of greatest potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were derived for oligoarticular and RF-ve polyarticular JIA respectively. Conclusions This first report of the application of network biology to JIA, integrating genetic association findings and gene expression data, has prioritised protein clusters for functional validation and identified new pathways for targeted pharmacological intervention.
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Choi D, Choi J, Kang B, Lee S, Cho YH, Hwang I, Hwang D. iNID: an analytical framework for identifying network models for interplays among developmental signaling in Arabidopsis. MOLECULAR PLANT 2014; 7:792-813. [PMID: 24380880 DOI: 10.1093/mp/sst173] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs.
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Affiliation(s)
- Daeseok Choi
- School of Interdisciplinary Bioscience and Bioengineering, POSTECH, 790-784, Pohang, Republic of Korea
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Pastrello C, Pasini E, Kotlyar M, Otasek D, Wong S, Sangrar W, Rahmati S, Jurisica I. Integration, visualization and analysis of human interactome. Biochem Biophys Res Commun 2014; 445:757-73. [DOI: 10.1016/j.bbrc.2014.01.151] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 01/24/2014] [Indexed: 02/06/2023]
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