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Stojilković N, Radović B, Vukelić D, Ćurčić M, Antonijević Miljaković E, Buha Đorđević A, Baralić K, Marić Đ, Bulat Z, Đukić-Ćosić D, Antonijević B. Involvement of toxic metals and PCBs mixture in the thyroid and male reproductive toxicity: In silico toxicogenomic data mining. Environ Res 2023; 238:117274. [PMID: 37797666 DOI: 10.1016/j.envres.2023.117274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/07/2023]
Abstract
Toxicological research is mostly limited to considering the effects of a single substance, even though the real exposure of people is reflected in their daily exposure to many different chemical substances in low-doses. This in silico toxicogenomic study aims to provide evidence for the selected environmental (organo)metals (lead, cadmium, methyl mercury) + polychlorinated biphenyls mixture involvement in the possible alteration of thyroid, and male reproductive system function, and furthermore to predict the possible toxic mechanisms of the environmental cocktail. The Comparative Toxicogenomic Database, GeneMANIA online software, and ToppGene Suite portal were used as the main tools for toxicogenomic data mining and gene ontology analysis. The results show that 35 annotated common genes between selected chemicals and endocrine system diseases can interact on the co-expression level. Our study highlighted the disruption of the cytokines, the cell's response to oxidative stress, and the influence of the transcription factors as the potential core of toxicological mechanisms of the discussed mixture's effects. The connected toxicological effects of the tested mixture were abnormal sperm cells, a disrupted level of testosterone, and thyroid hormones. The core mechanisms of these effects were inflammation, oxidative stress, disruption of androgen receptor signaling, and the alteration of the FOXO3-Keap-1/NRF2-HMOX1-NQO1 pathway signaling most likely controlled by the co-expression of overlapped genes among used chemicals. This in silico research can be used as a potential core for the determination of biomarkers that can be monitored in future further in vitro and in vivo experiments.
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Affiliation(s)
- Nikola Stojilković
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Biljana Radović
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Dragana Vukelić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Marijana Ćurčić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia.
| | - Evica Antonijević Miljaković
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Aleksandra Buha Đorđević
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Katarina Baralić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Đurđica Marić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Zorica Bulat
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Danijela Đukić-Ćosić
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
| | - Biljana Antonijević
- Department of Toxicology "Akademik Danilo Soldatović", University of Belgrade - Faculty of Pharmacy, Belgrade, Serbia
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Sbaoui Y, Nouadi B, Ezaouine A, Rida Salam M, Elmessal M, Bennis F, Chegdani F. Functional Prediction of Biological Profile During Eutrophication in Marine Environment. Bioinform Biol Insights 2022; 16:11779322211063993. [PMID: 35023908 PMCID: PMC8744080 DOI: 10.1177/11779322211063993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
In the marine environment, coastal nutrient pollution and algal blooms are increasing in many coral reefs and surface waters around the world, leading to higher concentrations of dissolved organic carbon (DOC), nitrogen (N), phosphate (P), and sulfur (S) compounds. The adaptation of the marine microbiota to this stress involves evolutionary processes through mutations that can provide selective phenotypes. The aim of this in silico analysis is to elucidate the potential candidate hub proteins, biological processes, and key metabolic pathways involved in the pathogenicity of bacterioplankton during excess of nutrients. The analysis was carried out on the model organism Escherichia coli K-12, by adopting an analysis pipeline consisting of a set of packages from the Cystoscape platform. The results obtained show that the metabolism of carbon and sugars generally are the 2 driving mechanisms for the expression of virulence factors.
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Affiliation(s)
- Yousra Sbaoui
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Badreddine Nouadi
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Abdelkarim Ezaouine
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Mohamed Rida Salam
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Mariame Elmessal
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Faiza Bennis
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
| | - Fatima Chegdani
- Immunology and Biodiversity Laboratory, Faculty of Sciences Aïn Chock, Hassan II University of Casablanca, Casablanca, Morocco
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3
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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4
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Leite GGF, Azevedo H, de Oliveira TM, Furtado DZS, Assunção NA. Cri-du-Chat Syndrome interactome network: Correlating genotypic variations to associated phenotypes. Gene Reports 2018; 11:179-87. [DOI: 10.1016/j.genrep.2018.03.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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5
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Yang Y, Song H, Chen PR. Genetically encoded photocrosslinkers for identifying and mapping protein-protein interactions in living cells. IUBMB Life 2016; 68:879-886. [DOI: 10.1002/iub.1560] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 09/03/2016] [Indexed: 12/12/2022]
Affiliation(s)
- Yi Yang
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University; Beijing China
| | - Haiping Song
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University; Beijing China
| | - Peng R. Chen
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University; Beijing China
- Peking-Tsinghua Center for Life Sciences; Beijing China
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6
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Lin S, Yin YA, Jiang X, Sahni N, Yi S. Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks. Biomed Res Int 2016; 2016:6186281. [PMID: 27403431 DOI: 10.1155/2016/6186281] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 04/23/2016] [Accepted: 05/08/2016] [Indexed: 12/26/2022]
Abstract
The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002). This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA), the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT) and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.
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Cesa LC, Mapp AK, Gestwicki JE. Direct and Propagated Effects of Small Molecules on Protein-Protein Interaction Networks. Front Bioeng Biotechnol 2015; 3:119. [PMID: 26380257 PMCID: PMC4547496 DOI: 10.3389/fbioe.2015.00119] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 08/05/2015] [Indexed: 12/15/2022] Open
Abstract
Networks of protein–protein interactions (PPIs) link all aspects of cellular biology. Dysfunction in the assembly or dynamics of PPI networks is a hallmark of human disease, and as such, there is growing interest in the discovery of small molecules that either promote or inhibit PPIs. PPIs were once considered undruggable because of their relatively large buried surface areas and difficult topologies. Despite these challenges, recent advances in chemical screening methodologies, combined with improvements in structural and computational biology have made some of these targets more tractable. In this review, we highlight developments that have opened the door to potent chemical modulators. We focus on how allostery is being used to produce surprisingly robust changes in PPIs, even for the most challenging targets. We also discuss how interfering with one PPI can propagate changes through the broader web of interactions. Through this analysis, it is becoming clear that a combination of direct and propagated effects on PPI networks is ultimately how small molecules re-shape biology.
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Affiliation(s)
- Laura C Cesa
- Program in Chemical Biology, Life Sciences Institute, University of Michigan , Ann Arbor, MI , USA
| | - Anna K Mapp
- Program in Chemical Biology, Life Sciences Institute, University of Michigan , Ann Arbor, MI , USA ; Department of Chemistry, University of Michigan , Ann Arbor, MI , USA
| | - Jason E Gestwicki
- Program in Chemical Biology, Life Sciences Institute, University of Michigan , Ann Arbor, MI , USA ; Department of Pharmaceutical Chemistry, Institute for Neurodegenerative Diseases, University of California San Francisco , San Francisco, CA , USA
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Das J, Gayvert KM, Yu H. Predicting cancer prognosis using functional genomics data sets. Cancer Inform 2014; 13:85-8. [PMID: 25392695 PMCID: PMC4218897 DOI: 10.4137/cin.s14064] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2014] [Revised: 09/17/2014] [Accepted: 09/19/2014] [Indexed: 11/06/2022] Open
Abstract
Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.
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Affiliation(s)
- Jishnu Das
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA. ; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Kaitlyn M Gayvert
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA. ; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
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Abstract
A paragraph from the highlights of “Transcriptomics: Throwing light on dark matter” by L. Flintoft (Nature Reviews Genetics 11, 455, 2010), says: “Reports over the past few years of extensive transcription throughout eukaryotic genomes have led to considerable excitement. However, doubts have been raised about the methods that have detected this pervasive transcription and about how much of it is functional.” Since the appearance of the ENCODE project and due to follow-up work, a shift from the pervasive transcription observed from RNA-Seq data to its functional validation is gradually occurring. However, much less attention has been turned to the problem of deciphering the complexity of transcriptome data, which determines uncertainty with regard to identification, quantification and differential expression of genes and non-coding RNAs. The aim of this mini-review is to emphasize transcriptome-related problems of direct and inverse nature for which novel inference approaches are needed.
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Affiliation(s)
- Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL, USA
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10
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Das J, Fragoza R, Lee HR, Cordero NA, Guo Y, Meyer MJ, Vo TV, Wang X, Yu H. Exploring mechanisms of human disease through structurally resolved protein interactome networks. Mol Biosyst 2014; 10:9-17. [PMID: 24096645 DOI: 10.1039/c3mb70225a] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The study of the molecular basis of human disease has gained increasing attention over the past decade. With significant improvements in sequencing efficiency and throughput, a wealth of genotypic data has become available. However the translation of this information into concrete advances in diagnostic and clinical setups has proved far more challenging. Two major reasons for this are the lack of functional annotation for genomic variants and the complex nature of genotype-to-phenotype relationships. One fundamental approach to bypass these issues is to examine the effects of genetic variation at the level of proteins as they are directly involved in carrying out biological functions. Within the cell, proteins function by interacting with other proteins as a part of an underlying interactome network. This network can be determined using interactome mapping - a combination of high-throughput experimental toolkits and curation from small-scale studies. Integrating structural information from co-crystals with the network allows generation of a structurally resolved network. Within the context of this network, the structural principles of disease mutations can be examined and used to generate reliable mechanistic hypotheses regarding disease pathogenesis.
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Affiliation(s)
- Jishnu Das
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Robert Fragoza
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Hao Ran Lee
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas A Cordero
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Yu Guo
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Michael J Meyer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.,Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Tommy V Vo
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Xiujuan Wang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
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Lopes FM, Ray SS, Hashimoto RF, Cesar RM. Entropic Biological Score: a cell cycle investigation for GRNs inference. Gene 2014; 541:129-37. [DOI: 10.1016/j.gene.2014.03.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2013] [Revised: 02/17/2014] [Accepted: 03/05/2014] [Indexed: 12/21/2022]
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Jayachandran D, Ramkrishna U, Skiles J, Renbarger J, Ramkrishna D. Revitalizing personalized medicine: respecting biomolecular complexities beyond gene expression. CPT Pharmacometrics Syst Pharmacol 2014; 3:e110. [PMID: 24739991 DOI: 10.1038/psp.2014.6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 01/27/2014] [Indexed: 02/05/2023]
Abstract
Despite recent advancements in "omic" technologies, personalized medicine has not realized its fullest potential due to isolated and incomplete application of gene expression tools. In many instances, pharmacogenomics is being interchangeably used for personalized medicine, when actually it is one of the many facets of personalized medicine. Herein, we highlight key issues that are hampering the advancement of personalized medicine and highlight emerging predictive tools that can serve as a decision support mechanism for physicians to personalize treatments.
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Das J, Lee HR, Sagar A, Fragoza R, Liang J, Wei X, Wang X, Mort M, Stenson PD, Cooper DN, Yu H. Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks. Hum Mutat 2014; 35:585-93. [PMID: 24599843 DOI: 10.1002/humu.22534] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Accepted: 02/14/2014] [Indexed: 01/24/2023]
Abstract
With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.
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Affiliation(s)
- Jishnu Das
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, 14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
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Abstract
UNLABELLED INstruct is a database of high-quality, 3D, structurally resolved protein interactome networks in human and six model organisms. INstruct combines the scale of available high-quality binary protein interaction data with the specificity of atomic-resolution structural information derived from co-crystal evidence using a tested interaction interface inference method. Its web interface is designed to allow for flexible search based on standard and organism-specific protein and gene-naming conventions, visualization of protein architecture highlighting interaction interfaces and viewing and downloading custom 3D structurally resolved interactome datasets. AVAILABILITY INstruct is freely available on the web at http://instruct.yulab.org with all major browsers supported.
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Affiliation(s)
- Michael J Meyer
- Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
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15
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Abstract
It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure, and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.
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Affiliation(s)
- Longxiao Sun
- College of Information Science and Engineering, Shandong University of Science and Technology Qingdao, China
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16
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Abstract
Cell signaling is extensively wired between cellular components to sustain cell proliferation, differentiation, and adaptation. The interaction network is often manifested in how protein function is regulated through interacting with other cellular components including small molecule metabolites. While many biochemical interactions have been established as reactions between protein enzymes and their substrates and products, much less is known at the system level about how small metabolites regulate protein functions through allosteric binding. In the past decade, study of protein-small molecule interactions has been lagging behind other types of interactions. Recent technological advances have explored several high-throughput platforms to reveal many "unexpected" protein-small molecule interactions that could have profound impact on our understanding of cell signaling. These interactions will help bridge gaps in existing regulatory loops of cell signaling and serve as new targets for medical intervention. In this review, we summarize recent advances of systematic investigation of protein-metabolite/small molecule interactions, and discuss the impact of such studies and their potential impact on both biological researches and medicine.
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Affiliation(s)
- Xiyan Li
- Department of Genetics, Stanford University, Stanford, CA, USA.
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Das J, Yu H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 2012; 6:92. [PMID: 22846459 PMCID: PMC3483187 DOI: 10.1186/1752-0509-6-92] [Citation(s) in RCA: 280] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2011] [Accepted: 06/30/2012] [Indexed: 12/22/2022]
Abstract
Background A global map of protein-protein interactions in cellular systems provides key insights into the workings of an organism. A repository of well-validated high-quality protein-protein interactions can be used in both large- and small-scale studies to generate and validate a wide range of functional hypotheses. Results We develop HINT (http://hint.yulab.org) - a database of high-quality protein-protein interactomes for human, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Oryza sativa. These were collected from several databases and filtered both systematically and manually to remove low-quality/erroneous interactions. The resulting datasets are classified by type (binary physical interactions vs. co-complex associations) and data source (high-throughput systematic setups vs. literature-curated small-scale experiments). We find strong sociological sampling biases in literature-curated datasets of small-scale interactions. An interactome without such sampling biases was used to understand network properties of human disease-genes - hubs are unlikely to cause disease, but if they do, they usually cause multiple disorders. Conclusions HINT is of significant interest to researchers in all fields of biology as it addresses the ubiquitous need of having a repository of high-quality protein-protein interactions. These datasets can be utilized to generate specific hypotheses about specific proteins and/or pathways, as well as analyzing global properties of cellular networks. HINT will be regularly updated and all versions will be tracked.
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Affiliation(s)
- Jishnu Das
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.
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18
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Abstract
Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein modularity conserved during evolution that improves on the running time of previous algorithms for related problems and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes. Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins participate in multiprotein modules with good evidence in the protein interaction data of being conserved between human and Drosophila.
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Affiliation(s)
- Luqman Hodgkinson
- Division of Computer Science and the Center for Computational Biology, University of California, Berkeley, CA 94720, USA.
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Cunningham J, Estrella V, Lloyd M, Gillies R, Frieden BR, Gatenby R. Intracellular electric field and pH optimize protein localization and movement. PLoS One 2012; 7:e36894. [PMID: 22623963 PMCID: PMC3356409 DOI: 10.1371/journal.pone.0036894] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 04/11/2012] [Indexed: 01/25/2023] Open
Abstract
Mammalian cell function requires timely and accurate transmission of information from the cell membrane (CM) to the nucleus (N). These pathways have been intensively investigated and many critical components and interactions have been identified. However, the physical forces that control movement of these proteins have received scant attention. Thus, transduction pathways are typically presented schematically with little regard to spatial constraints that might affect the underlying dynamics necessary for protein-protein interactions and molecular movement from the CM to the N. We propose messenger protein localization and movements are highly regulated and governed by Coulomb interactions between: 1. A recently discovered, radially directed E-field from the NM into the CM and 2. Net protein charge determined by its isoelectric point, phosphorylation state, and the cytosolic pH. These interactions, which are widely applied in elecrophoresis, provide a previously unknown mechanism for localization of messenger proteins within the cytoplasm as well as rapid shuttling between the CM and N. Here we show these dynamics optimize the speed, accuracy and efficiency of transduction pathways even allowing measurement of the location and timing of ligand binding at the CM –previously unknown components of intracellular information flow that are, nevertheless, likely necessary for detecting spatial gradients and temporal fluctuations in ligand concentrations within the environment. The model has been applied to the RAF-MEK-ERK pathway and scaffolding protein KSR1 using computer simulations and in-vitro experiments. The computer simulations predicted distinct distributions of phosphorylated and unphosphorylated components of this transduction pathway which were experimentally confirmed in normal breast epithelial cells (HMEC).
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Affiliation(s)
- Jessica Cunningham
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida, United States of America
- Department of Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Veronica Estrella
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Mark Lloyd
- Department of Analytic Microscopy, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Robert Gillies
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida, United States of America
- Department of Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - B. Roy Frieden
- College of Optical Sciences, University of Arizona, Tucson, Arizona, United States of America
| | - Robert Gatenby
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida, United States of America
- Department of Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America
- * E-mail:
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20
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Bessarabova M, Pustovalova O, Shi W, Serebriyskaya T, Ishkin A, Polyak K, Velculescu VE, Nikolskaya T, Nikolsky Y. Functional synergies yet distinct modulators affected by genetic alterations in common human cancers. Cancer Res 2011; 71:3471-81. [PMID: 21398405 DOI: 10.1158/0008-5472.can-10-3038] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-β/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers.
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Affiliation(s)
- Marina Bessarabova
- Thomson Reuters, Healthcare & Life Science, St. Joseph, Michigan 49085, USA
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21
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Abstract
Chemical cross-linking in combination with mass spectrometry has largely been used to study protein structures and protein-protein interactions. Typically, it is used in a qualitative manner to identify cross-linked sites and provide a low-resolution topological map of the interacting regions of proteins. Here, we investigate the capability of chemical cross-linking to quantify protein-protein interactions using a model system of calmodulin and substrates melittin and mastoparan. Calmodulin is a well-characterized protein which has many substrates. Melittin and mastoparan are two such substrates which bind to calmodulin in 1:1 ratios in the presence of calcium. Both the calmodulin-melittin and calmodulin-mastoparan complexes have had chemical cross-linking strategies successfully applied in the past to investigate topological properties. We utilized an excess of immobilized calmodulin on agarose beads and formed complexes with varying quantities of mastoparan and melittin. Then, we applied disuccinimidyl suberate (DSS) chemical cross-linker, digested and detected cross-links through an LC-MS analytical method. We identified five interpeptide cross-links for calmodulin-melittin and three interpeptide cross-links for calmodulin-mastoparan. Using cross-linking sites of calmodulin-mastoparan, we demonstrated that mastoparan also binds in two orientations to calmodulin. We quantitatively demonstrated that both melittin and mastoparan preferentially bind to calmodulin in a parallel fashion, which is opposite to the preferred binding mode of the majority of known calmodulin binding peptides. We also demonstrated that the relative abundances of cross-linked peptide products quantitatively reflected the abundances of the calmodulin peptide complexes formed.
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Affiliation(s)
- Juan D Chavez
- Department of Genome Sciences, University of Washington, PO Box 358050, Seattle, Washington 98195, United States
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22
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Abstract
Phenotypic variations of an organism may arise from alterations of cellular networks, ranging from the complete loss of a gene product to the specific perturbation of a single molecular interaction. In interactome networks that are modeled as nodes (macromolecules) connected by edges (interactions), these alterations can be thought of as node removal and edge-specific or "edgetic" perturbations, respectively. Here we present two complementary strategies, forward and reverse edgetics, to investigate the phenotypic outcomes of edgetic perturbations of binary protein-protein interaction networks. Both approaches are based on the yeast two-hybrid system (Y2H). The first allows the determination of the interaction profile of proteins encoded by alleles with known phenotypes to identify edgetic alleles. The second is used to directly isolate edgetic alleles for subsequent in vivo characterization.
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Affiliation(s)
- Benoit Charloteaux
- Department of Cancer Biology, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA
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23
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Shi W, Bessarabova M, Dosymbekov D, Dezso Z, Nikolskaya T, Dudoladova M, Serebryiskaya T, Bugrim A, Guryanov A, Brennan RJ, Shah R, Dopazo J, Chen M, Deng Y, Shi T, Jurman G, Furlanello C, Thomas RS, Corton JC, Tong W, Shi L, Nikolsky Y. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. Pharmacogenomics J 2010; 10:310-23. [PMID: 20676069 PMCID: PMC2920075 DOI: 10.1038/tpj.2010.35] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
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Affiliation(s)
- W Shi
- GeneGo Inc., St Joseph, MI, USA
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Gatenby RA, Frieden BR. Coulomb interactions between cytoplasmic electric fields and phosphorylated messenger proteins optimize information flow in cells. PLoS One 2010; 5:e12084. [PMID: 20711447 PMCID: PMC2920310 DOI: 10.1371/journal.pone.0012084] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2010] [Accepted: 07/14/2010] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Normal cell function requires timely and accurate transmission of information from receptors on the cell membrane (CM) to the nucleus. Movement of messenger proteins in the cytoplasm is thought to be dependent on random walk. However, Brownian motion will disperse messenger proteins throughout the cytosol resulting in slow and highly variable transit times. We propose that a critical component of information transfer is an intracellular electric field generated by distribution of charge on the nuclear membrane (NM). While the latter has been demonstrated experimentally for decades, the role of the consequent electric field has been assumed to be minimal due to a Debye length of about 1 nanometer that results from screening by intracellular Cl- and K+. We propose inclusion of these inorganic ions in the Debye-Huckel equation is incorrect because nuclear pores allow transit through the membrane at a rate far faster than the time to thermodynamic equilibrium. In our model, only the charged, mobile messenger proteins contribute to the Debye length. FINDINGS Using this revised model and published data, we estimate the NM possesses a Debye-Huckel length of a few microns and find this is consistent with recent measurement using intracellular nano-voltmeters. We demonstrate the field will accelerate isolated messenger proteins toward the nucleus through Coulomb interactions with negative charges added by phosphorylation. We calculate transit times as short as 0.01 sec. When large numbers of phosphorylated messenger proteins are generated by increasing concentrations of extracellular ligands, we demonstrate they generate a self-screening environment that regionally attenuates the cytoplasmic field, slowing movement but permitting greater cross talk among pathways. Preliminary experimental results with phosphorylated RAF are consistent with model predictions. CONCLUSION This work demonstrates that previously unrecognized Coulomb interactions between phosphorylated messenger proteins and intracellular electric fields will optimize information transfer from the CM to the NM in cells.
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Affiliation(s)
- Robert A Gatenby
- Department of Radiology, Moffitt Cancer Center, Tampa, Florida, United States of America.
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25
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Benschop JJ, Brabers N, van Leenen D, Bakker LV, van Deutekom HWM, van Berkum NL, Apweiler E, Lijnzaad P, Holstege FCP, Kemmeren P. A consensus of core protein complex compositions for Saccharomyces cerevisiae. Mol Cell 2010; 38:916-28. [PMID: 20620961 DOI: 10.1016/j.molcel.2010.06.002] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2010] [Revised: 04/02/2010] [Accepted: 05/16/2010] [Indexed: 11/28/2022]
Abstract
Analyses of biological processes would benefit from accurate definitions of protein complexes. High-throughput mass spectrometry data offer the possibility of systematically defining protein complexes; however, the predicted compositions vary substantially depending on the algorithm applied. We determine consensus compositions for 409 core protein complexes from Saccharomyces cerevisiae by merging previous predictions with a new approach. Various analyses indicate that the consensus is comprehensive and of high quality. For 85 out of 259 complexes not recorded in GO, literature search revealed strong support in the form of coprecipitation. New complexes were verified by an independent interaction assay and by gene expression profiling of strains with deleted subunits, often revealing which cellular processes are affected. The consensus complexes are available in various formats, including a merge with GO, resulting in 518 protein complex compositions. The utility is further demonstrated by comparison with binary interaction data to reveal interactions between core complexes.
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Affiliation(s)
- Joris J Benschop
- Department of Physiological Chemistry, University Medical Centre Utrecht, 3584 CG Utrecht, The Netherlands
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26
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Abstract
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context of biological networks. We show that there is both utility and unexplored potential in adopting manifold learning for network inference purposes. In particular, the following advantages are highlighted: (a) fusion with diagnostic statistical tools designed to assign significance to protein interactions based on pre-selected topological features; (b) dissection into components of the interactome in order to elucidate global and local connectivity organization; (c) relevance of embedding the interactome in reduced dimensions for biological validation purposes. We have compared the performances of three well-known techniques--kernel-PCA, RADICAL ICA, and ISOMAP--relatively to their power of mapping the interactome onto new coordinate dimensions where important associations among proteins can be detected, and then back projected such that the corresponding sub-interactomes are reconstructed. This recovery has been done selectively, by using significant information according to a robust statistical procedure, and then standard biological annotation has been provided to validate the results. We expect that a byproduct of using subspace analysis by the proposed techniques is a possible calibration of interactome modularity studies. Supplementary Material is available online at www.libertonlinec.com.
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Affiliation(s)
- Elisabetta Marras
- CRS4 Bioinformatics Laboratory, Polaris Science and Technology Park, Sardinia, Italy
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27
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Abstract
Physical interactions mediated by proteins are critical for most cellular functions and altogether form a complex macromolecular "interactome" network. Systematic mapping of protein-protein, protein-DNA, protein-RNA, and protein-metabolite interactions at the scale of the whole proteome can advance understanding of interactome networks with applications ranging from single protein functional characterization to discoveries on local and global systems properties. Since the early efforts at mapping protein-protein interactome networks a decade ago, the field has progressed rapidly giving rise to a growing number of interactome maps produced using high-throughput implementations of either binary protein-protein interaction assays or co-complex protein association methods. Although high-throughput methods are often thought to necessarily produce lower quality information than low-throughput experiments, we have recently demonstrated that proteome-scale interactome datasets can be produced with equal or superior quality than that observed in literature-curated datasets derived from large numbers of small-scale experiments. In addition to performing all experimental steps thoroughly and including all necessary controls and quality standards, careful verification of all interacting pairs and validation tests using independent, orthogonal assays are crucial to ensure the release of interactome maps of the highest possible quality. This chapter describes a high-quality, high-throughput binary protein-protein interactome mapping pipeline that includes these features.
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Affiliation(s)
- Matija Dreze
- Center for Cancer Systems Biology (CCSB), Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
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28
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Abstract
The idea that multi-scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental "great ideas" as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems-level understanding of "What is Life" may materialize as one of the major ideas of biology.
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29
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Pinzón A, Barreto E, Bernal A, Achenie L, González Barrios AF, Isea R, Restrepo S. Computational models in plant-pathogen interactions: the case of Phytophthora infestans. Theor Biol Med Model 2009; 6:24. [PMID: 19909526 PMCID: PMC2787490 DOI: 10.1186/1742-4682-6-24] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Accepted: 11/12/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Phytophthora infestans is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between P. infestans and one of its hosts, Solanum tuberosum. MODELING AND CONCLUSION Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.
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Affiliation(s)
- Andrés Pinzón
- Mycology and Phytopathology Laboratory, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
- Bioinformatics center, Colombian EMBnet node, Biotechnology Institute, National University of Colombia, Bogotá, Colombia
| | - Emiliano Barreto
- Bioinformatics center, Colombian EMBnet node, Biotechnology Institute, National University of Colombia, Bogotá, Colombia
| | - Adriana Bernal
- Mycology and Phytopathology Laboratory, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Luke Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg Virginia, USA
| | - Andres F González Barrios
- Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Los Andes University, Bogotá, Colombia
| | - Raúl Isea
- Fundación IDEA, Centro de Biociencias, Hoyo de la puerta, Baruta 1080, Venezuela
| | - Silvia Restrepo
- Mycology and Phytopathology Laboratory, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
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30
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Dreze M, Charloteaux B, Milstein S, Vidalain PO, Yildirim MA, Zhong Q, Svrzikapa N, Romero V, Laloux G, Brasseur R, Vandenhaute J, Boxem M, Cusick ME, Hill DE, Vidal M. 'Edgetic' perturbation of a C. elegans BCL2 ortholog. Nat Methods 2009; 6:843-9. [PMID: 19855391 DOI: 10.1038/nmeth.1394] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2009] [Accepted: 09/28/2009] [Indexed: 02/03/2023]
Abstract
Genes and gene products do not function in isolation but within highly interconnected “interactome” networks, modeled as graphs of nodes and edges representing macromolecules and interactions between them, respectively. We propose to investigate genotype-phenotype associations by methodical use of alleles that lack single interactions, while retaining all others, in contrast to genetic approaches designed to eliminate gene products completely. We describe an integrated strategy based on the reverse yeast two-hybrid system to isolate and characterize such edge-specific, or “edgetic” alleles. We establish a proof-of-concept with CED-9, a C. elegans BCL2 ortholog involved in apoptosis. Using ced-9 edgetic alleles, we uncover a new potential functional link between apoptosis and a centrosomal protein, demonstrating both the interest and efficiency of our strategy. This approach is amenable to higher throughput and is particularly applicable to interactome network analysis in organisms for which transgenesis is straightforward.
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31
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Mazzucchelli S, De Palma A, Riva M, D'Urzo A, Pozzi C, Pastori V, Comelli F, Fusi P, Vanoni M, Tortora P, Mauri P, Regonesi ME. Proteomic and biochemical analyses unveil tight interaction of ataxin-3 with tubulin. Int J Biochem Cell Biol 2009; 41:2485-92. [PMID: 19666135 DOI: 10.1016/j.biocel.2009.08.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2009] [Revised: 07/29/2009] [Accepted: 08/01/2009] [Indexed: 10/20/2022]
Abstract
Ataxin-3 consists of an N-terminal globular Josephin domain and an unstructured C-terminal region containing a stretch of consecutive glutamines that triggers an inherited neurodegenerative disorder, spinocerebellar ataxia type 3, when its length exceeds a critical threshold. The pathology results from protein misfolding and intracellular accumulation of fibrillar amyloid-like aggregates. Plenty of work has been carried out to elucidate the protein's physiological role(s), which has shown that ataxin-3 is multifunctional; it acts as a transcriptional repressor, and also has polyubiquitin-binding/ubiquitin-hydrolase activity. In addition, a recent report shows that it participates in sorting misfolded protein to aggresomes, close to the microtubule-organizing center. Since a thorough understanding of the protein's physiological role(s) requires the identification of all the molecular partners interacting with ataxin-3, we pursued this goal by taking advantage of two-dimensional chromatography coupled to tandem mass spectrometry. We found that different ataxin-3 constructs, including the sole Josephin domain, bound alpha- and beta-tubulin from soluble rat brain extracts. Coimmunoprecipitation experiments confirmed this interaction. Also, normal ataxin-3 overexpressed in COS7 cultured cells partially colocalized with microtubules, whereas an expanded variant only occasionally did so, probably due to aggregation. Furthermore, by surface plasmon resonance we determined a dissociation constant of 50-70nM between ataxin-3 and tubulin dimer, which strongly supports the hypothesis of a direct interaction of this protein with microtubules in vivo. These findings suggest an involvement of ataxin-3 in directing aggregated protein to aggresomes, and shed light on the mode of interaction among the different molecular partners participating in the process.
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Affiliation(s)
- Serena Mazzucchelli
- Dipartimento di Biotecnologie e Bioscienze, Università di Milano-Bicocca, Piazza della Scienza 2, I-20126 Milano, Italy
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Ravi D, Wiles AM, Bhavani S, Ruan J, Leder P, Bishop AJR. A network of conserved damage survival pathways revealed by a genomic RNAi screen. PLoS Genet 2009; 5:e1000527. [PMID: 19543366 PMCID: PMC2688755 DOI: 10.1371/journal.pgen.1000527] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2009] [Accepted: 05/19/2009] [Indexed: 11/18/2022] Open
Abstract
Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS-induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into "pathway nodes" qualitatively improved the interactome organization, revealing a highly organized "MMS survival network." We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis.
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Affiliation(s)
- Dashnamoorthy Ravi
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Amy M. Wiles
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Selvaraj Bhavani
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Jianhua Ruan
- Department of Computer Science, University of Texas at San Antonio, San Antonio, Texas, United States of America
| | - Philip Leder
- Harvard Medical School, Department of Genetics, Harvard University, Boston, Massachusetts, United States of America
| | - Alexander J. R. Bishop
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
- Harvard Medical School, Department of Genetics, Harvard University, Boston, Massachusetts, United States of America
- * E-mail:
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33
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Oberdorf R, Kortemme T. Complex topology rather than complex membership is a determinant of protein dosage sensitivity. Mol Syst Biol 2009; 5:253. [PMID: 19293832 PMCID: PMC2671925 DOI: 10.1038/msb.2009.9] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2008] [Accepted: 02/06/2009] [Indexed: 11/23/2022] Open
Abstract
The ‘balance hypothesis' predicts that non-stoichiometric variations in concentrations of proteins participating in complexes should be deleterious. As a corollary, heterozygous deletions and overexpression of protein complex members should have measurable fitness effects. However, genome-wide studies of heterozygous deletions in Saccharomyces cerevisiae and overexpression have been unable to unambiguously relate complex membership to dosage sensitivity. We test the hypothesis that it is not complex membership alone but rather the topology of interactions within a complex that is a predictor of dosage sensitivity. We develop a model that uses the law of mass action to consider how complex formation might be affected by varying protein concentrations given a protein's topological positioning within the complex. Although we find little evidence for combinatorial inhibition of complex formation playing a major role in overexpression phenotypes, consistent with previous results, we show significant correlations between predicted sensitivity of complex formation to protein concentrations and both heterozygous deletion fitness and protein abundance noise levels. Our model suggests a mechanism for dosage sensitivity and provides testable predictions for the effect of alterations in protein abundance noise.
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Affiliation(s)
- Richard Oberdorf
- Graduate Group in Biophysics, University of California, San Francisco, CA 94158-2330, USA
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Abstract
Background The architectural structure of cellular networks provides a framework for innovations as well as constraints for protein evolution. This issue has previously been studied extensively by analyzing protein interaction networks. However, it is unclear how signaling networks influence and constrain protein evolution and conversely, how protein evolution modifies and shapes the functional consequences of signaling networks. In this study, we constructed a human signaling network containing more than 1,600 nodes and 5,000 links through manual curation of signaling pathways, and analyzed the dN/dS values of human-mouse orthologues on the network. Results We revealed that the protein dN/dS value decreases along the signal information flow from the extracellular space to nucleus. In the network, neighbor proteins tend to have similar dN/dS ratios, indicating neighbor proteins have similar evolutionary rates: co-fast or co-slow. However, different types of relationships (activating, inhibitory and neutral) between proteins have different effects on protein evolutionary rates, i.e., physically interacting protein pairs have the closest evolutionary rates. Furthermore, for directed shortest paths, the more distant two proteins are, the less chance they share similar evolutionary rates. However, such behavior was not observed for neutral shortest paths. Fast evolving signaling proteins have two modes of evolution: immunological proteins evolve more independently, while apoptotic proteins tend to form network components with other signaling proteins and share more similar evolutionary rates, possibly enhancing rapid information exchange between apoptotic and other signaling pathways. Conclusion Major network constraints on protein evolution in protein interaction networks previously described have been found for signaling networks. We further uncovered how network characteristics affect the evolutionary and co-evolutionary behavior of proteins and how protein evolution can modify the existing functionalities of signaling networks. These new insights provide some general principles for understanding protein evolution in the context of signaling networks.
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Cusick ME, Yu H, Smolyar A, Venkatesan K, Carvunis AR, Simonis N, Rual JF, Borick H, Braun P, Dreze M, Vandenhaute J, Galli M, Yazaki J, Hill DE, Ecker JR, Roth FP, Vidal M. Literature-curated protein interaction datasets. Nat Methods 2009; 6:39-46. [PMID: 19116613 PMCID: PMC2683745 DOI: 10.1038/nmeth.1284] [Citation(s) in RCA: 234] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
High quality datasets are needed to understand how global and local properties of protein-protein interaction, or “interactome”, networks relate to biological mechanisms, and to guide research on individual proteins. Evaluations of existing curation of protein interaction experiments reported in the literature find that curation can be error prone and possibly of lower quality than commonly assumed.
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Affiliation(s)
- Michael E Cusick
- Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA.
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Abstract
Prioritization, or ranking, of gene lists is becoming increasingly important for analyzing data generated from high-throughput assays like expression profiling and RNAi-based screening. This is especially the case when specific genes in a list need to be further validated using low-throughput experiments. In addition to gene set overlap enrichment methods, a complementary approach is to examine molecular interaction networks. These can provide putative functional insights based on gene connectivity, especially when many genes contain little or no annotation. For bench and computational biologists alike, using networks requires an informed selection of interaction data for network construction and strategies for managing network complexity. Moreover, graph theory and social network analysis methods can be used to isolate critical subnetworks and quantify network properties. Here, I discuss the basic components of networks, implications of their structure for functional interpretation, and common metrics used for prioritization. Although this is still an ongoing area of research, networks are providing new ways for gauging pathway impact in large-scale data sets.
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Nibbe RK, Markowitz S, Myeroff L, Ewing R, Chance MR. Discovery and scoring of protein interaction subnetworks discriminative of late stage human colon cancer. Mol Cell Proteomics 2008; 8:827-45. [PMID: 19098285 DOI: 10.1074/mcp.m800428-mcp200] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
We used a systems biology approach to identify and score protein interaction subnetworks whose activity patterns are discriminative of late stage human colorectal cancer (CRC) versus control in colonic tissue. We conducted two gel-based proteomics experiments to identify significantly changing proteins between normal and late stage tumor tissues obtained from an adequately sized cohort of human patients. A total of 67 proteins identified by these experiments was used to seed a search for protein-protein interaction subnetworks. A scoring scheme based on mutual information, calculated using gene expression data as a proxy for subnetwork activity, was developed to score the targets in the subnetworks. Based on this scoring, the subnetwork was pruned to identify the specific protein combinations that were significantly discriminative of late stage cancer versus control. These combinations could not be discovered using only proteomics data or by merely clustering the gene expression data. We then analyzed the resultant pruned subnetwork for biological relevance to human CRC. A number of the proteins in these smaller subnetworks have been associated with the progression (CSNK2A2, PLK1, and IGFBP3) or metastatic potential (PDGFRB) of CRC. Others have been recently identified as potential markers of CRC (IFITM1), and the role of others is largely unknown in this disease (CCT3, CCT5, CCT7, and GNA12). The functional interactions represented by these signatures provide new experimental hypotheses that merit follow-on validation for biological significance in this disease. Overall the method outlines a quantitative approach for integrating proteomics data, gene expression data, and the wealth of accumulated legacy experimental data to discover significant protein subnetworks specific to disease.
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Affiliation(s)
- Rod K Nibbe
- Department of Pharmacology, Case Western Reserve University, Cleveland, Ohio 44106, USA.
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Bonifaci N, Berenguer A, Díez J, Reina O, Medina I, Dopazo J, Moreno V, Pujana MA. Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes. BMC Med Genomics 2008; 1:62. [PMID: 19094230 PMCID: PMC2628924 DOI: 10.1186/1755-8794-1-62] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2008] [Accepted: 12/18/2008] [Indexed: 12/24/2022] Open
Abstract
Background Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined. Methods We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence. Results First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the "omic" properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk. Conclusion This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins.
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Affiliation(s)
- Núria Bonifaci
- Bioinformatics and Biostatistics Unit, and Translational Research Laboratory, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet, Barcelona, Spain.
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Venkatesan K, Rual JF, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh KI, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet AS, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, Rau K, Boone C, Braun P, Cusick ME, Roth FP, Hill DE, Tavernier J, Wanker EE, Barabási AL, Vidal M. An empirical framework for binary interactome mapping. Nat Methods 2008; 6:83-90. [PMID: 19060904 PMCID: PMC2872561 DOI: 10.1038/nmeth.1280] [Citation(s) in RCA: 609] [Impact Index Per Article: 38.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2008] [Accepted: 11/10/2008] [Indexed: 01/05/2023]
Abstract
Several attempts have been made at systematically mapping protein-protein interaction, or “interactome” networks. However, it remains difficult to assess the quality and coverage of existing datasets. We describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human are superior in precision to literature-curated interactions supported by only a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ~130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the human genome project, estimates of protein interaction data quality and interactome size are critical to establish the magnitude of the task of comprehensive human interactome mapping and to illuminate a path towards this goal.
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Affiliation(s)
- Kavitha Venkatesan
- Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, 1 Jimmy Fund Way, Boston, MA 02115, USA
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Yu J, Pacifico S, Liu G, Finley RL. DroID: the Drosophila Interactions Database, a comprehensive resource for annotated gene and protein interactions. BMC Genomics 2008; 9:461. [PMID: 18840285 PMCID: PMC2572628 DOI: 10.1186/1471-2164-9-461] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2008] [Accepted: 10/07/2008] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Charting the interactions among genes and among their protein products is essential for understanding biological systems. A flood of interaction data is emerging from high throughput technologies, computational approaches, and literature mining methods. Quick and efficient access to this data has become a critical issue for biologists. Several excellent multi-organism databases for gene and protein interactions are available, yet most of these have understandable difficulty maintaining comprehensive information for any one organism. No single database, for example, includes all available interactions, integrated gene expression data, and comprehensive and searchable gene information for the important model organism, Drosophila melanogaster. DESCRIPTION DroID, the Drosophila Interactions Database, is a comprehensive interactions database designed specifically for Drosophila. DroID houses published physical protein interactions, genetic interactions, and computationally predicted interactions, including interologs based on data for other model organisms and humans. All interactions are annotated with original experimental data and source information. DroID can be searched and filtered based on interaction information or a comprehensive set of gene attributes from Flybase. DroID also contains gene expression and expression correlation data that can be searched and used to filter datasets, for example, to focus a study on sub-networks of co-expressed genes. To address the inherent noise in interaction data, DroID employs an updatable confidence scoring system that assigns a score to each physical interaction based on the likelihood that it represents a biologically significant link. CONCLUSION DroID is the most comprehensive interactions database available for Drosophila. To facilitate downstream analyses, interactions are annotated with original experimental information, gene expression data, and confidence scores. All data in DroID are freely available and can be searched, explored, and downloaded through three different interfaces, including a text based web site, a Java applet with dynamic graphing capabilities (IM Browser), and a Cytoscape plug-in. DroID is available at http://www.droidb.org.
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Affiliation(s)
- Jingkai Yu
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, 540 East Canfield Ave., Detroit, MI 48201, USA.
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Bizzarri M, Cucina A, Conti F, D’Anselmi F. Beyond the oncogene paradigm: understanding complexity in cancerogenesis. Acta Biotheor 2008; 56:173-96. [PMID: 18288572 DOI: 10.1007/s10441-008-9047-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2007] [Accepted: 02/06/2008] [Indexed: 12/13/2022]
Abstract
In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a 'system biology disease'. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.
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Pisabarro AG, Perez G, Lavin JL, Ramirez L. Genetic networks for the functional study of genomes. Briefings in Functional Genomics and Proteomics 2008; 7:249-63. [DOI: 10.1093/bfgp/eln026] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Pieroni E, de la Fuente van Bentem S, Mancosu G, Capobianco E, Hirt H, de la Fuente A. Protein networking: insights into global functional organization of proteomes. Proteomics 2008; 8:799-816. [PMID: 18297653 DOI: 10.1002/pmic.200700767] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.
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Affiliation(s)
- Enrico Pieroni
- CRS4 Bioinformatica, c/o Parco Tecnologico POLARIS, Pula, Italy
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Gohlke JM, Armant O, Parham FM, Smith MV, Zimmer C, Castro DS, Nguyen L, Parker JS, Gradwohl G, Portier CJ, Guillemot F. Characterization of the proneural gene regulatory network during mouse telencephalon development. BMC Biol 2008; 6:15. [PMID: 18377642 PMCID: PMC2330019 DOI: 10.1186/1741-7007-6-15] [Citation(s) in RCA: 78] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2007] [Accepted: 03/31/2008] [Indexed: 12/22/2022] Open
Abstract
Background The proneural proteins Mash1 and Ngn2 are key cell autonomous regulators of neurogenesis in the mammalian central nervous system, yet little is known about the molecular pathways regulated by these transcription factors. Results Here we identify the downstream effectors of proneural genes in the telencephalon using a genomic approach to analyze the transcriptome of mice that are either lacking or overexpressing proneural genes. Novel targets of Ngn2 and/or Mash1 were identified, such as members of the Notch and Wnt pathways, and proteins involved in adhesion and signal transduction. Next, we searched the non-coding sequence surrounding the predicted proneural downstream effector genes for evolutionarily conserved transcription factor binding sites associated with newly defined consensus binding sites for Ngn2 and Mash1. This allowed us to identify potential novel co-factors and co-regulators for proneural proteins, including Creb, Tcf/Lef, Pou-domain containing transcription factors, Sox9, and Mef2a. Finally, a gene regulatory network was delineated using a novel Bayesian-based algorithm that can incorporate information from diverse datasets. Conclusion Together, these data shed light on the molecular pathways regulated by proneural genes and demonstrate that the integration of experimentation with bioinformatics can guide both hypothesis testing and hypothesis generation.
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Affiliation(s)
- Julia M Gohlke
- Environmental Systems Biology Group, Laboratory of Molecular Toxicology, National Institute of Environmental Health Sciences, RTP, NC 27709, USA.
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Abstract
Cellular functions are almost always the result of the coordinated action of several proteins, interacting in protein complexes, pathways or networks. Progress made in devising suitable tools for analysis of protein-protein interactions, have recently made it possible to chart interaction networks on a large-scale. The aim of this review is to provide a short overview of the most promising contributions of interaction networks to human biology, structural biology and human genetics.
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Affiliation(s)
- Samuel Bader
- EMBL, Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg, Germany
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Lalonde S, Ehrhardt DW, Loqué D, Chen J, Rhee SY, Frommer WB. Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations. Plant J 2008; 53:610-635. [PMID: 18269572 DOI: 10.1111/j.1365-313x.2007.03332.x] [Citation(s) in RCA: 109] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597-609). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.
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Affiliation(s)
- Sylvie Lalonde
- Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USA.
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Abstract
The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.
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Affiliation(s)
- Muhammed A Yildirim
- Center for Cancer Systems Biology (CCSB), Harvard Medical School, 44 Binney St., Boston, Massachusetts 02115, USA
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49
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Abstract
In these two companion papers, we provide an overview and a brief history of the multiple roots, current developments and recent advances of integrative systems biology and identify multiscale integration as its grand challenge. Then we introduce the fundamental principles and the successive steps that have been followed in the construction of the scale relativity theory, and discuss how scale laws of increasing complexity can be used to model and understand the behaviour of complex biological systems. In scale relativity theory, the geometry of space is considered to be continuous but non-differentiable, therefore fractal (i.e., explicitly scale-dependent). One writes the equations of motion in such a space as geodesics equations, under the constraint of the principle of relativity of all scales in nature. To this purpose, covariant derivatives are constructed that implement the various effects of the non-differentiable and fractal geometry. In this first review paper, the scale laws that describe the new dependence on resolutions of physical quantities are obtained as solutions of differential equations acting in the scale space. This leads to several possible levels of description for these laws, from the simplest scale invariant laws to generalized laws with variable fractal dimensions. Initial applications of these laws to the study of species evolution, embryogenesis and cell confinement are discussed.
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Affiliation(s)
- Charles Auffray
- Functional Genomics and Systems Biology for Health, UMR 7091-LGN, CNRS/Pierre & Marie Curie University-Paris VI, 7 rue Guy Moquet-BP 8, 94801 Villejuif Cedex, France.
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Abstract
Since the functional state of a protein-protein interaction network depends on gene expression, a fundamental question is what relationships exist between protein interaction network and gene regulation. In particular, microRNAs have recently emerged as a major class of post-transcriptional regulators that influences a large proportion of genes in higher eukaryotes. Here we show that protein connectivity in the human protein-protein interaction network is positively correlated with the number of microRNA target-site types in the 3' untranslated regions of the gene encoding the protein and that interacting proteins tend to share more microRNA target-site types than random pairs. Moreover, our results demonstrate that microRNA targeting propensity for genes in different biological processes can be largely explained by their protein connectivity. Finally, we show that for hub proteins, microRNA regulation complexity is negatively correlated with clustering coefficient, suggesting that microRNA regulation is more important to inter-modular hubs than to intramodular ones. Taken together, our study provides the first evidence for global correlation between microRNA repression and protein-protein interactions.
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Affiliation(s)
- Han Liang
- Department of Ecology and Evolution, University of Chicago, IL 60637, USA
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