1
|
Hekselman I, Vital A, Ziv-Agam M, Kerber L, Yairi I, Yeger-Lotem E. Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data. eLife 2024; 13:e84613. [PMID: 38197427 PMCID: PMC10830129 DOI: 10.7554/elife.84613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.
Collapse
Affiliation(s)
- Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Assaf Vital
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Maya Ziv-Agam
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Ido Yairi
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the NegevBe’er ShevaIsrael
| |
Collapse
|
2
|
Singhal P, Verma SS, Ritchie MD. Gene Interactions in Human Disease Studies-Evidence Is Mounting. Annu Rev Biomed Data Sci 2023; 6:377-395. [PMID: 37196359 DOI: 10.1146/annurev-biodatasci-102022-120818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.
Collapse
Affiliation(s)
- Pankhuri Singhal
- Genetics and Epigenetics Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
- Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
3
|
Simonovsky E, Sharon M, Ziv M, Mauer O, Hekselman I, Jubran J, Vinogradov E, Argov CM, Basha O, Kerber L, Yogev Y, Segrè AV, Im HK, Birk O, Rokach L, Yeger‐Lotem E. Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation. Mol Syst Biol 2023; 19:e11407. [PMID: 37232043 PMCID: PMC10407743 DOI: 10.15252/msb.202211407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.
Collapse
Affiliation(s)
- Eyal Simonovsky
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Moran Sharon
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Maya Ziv
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omry Mauer
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Idan Hekselman
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Juman Jubran
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Chanan M Argov
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Omer Basha
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Yuval Yogev
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Ayellet V Segrè
- Ocular Genomics Institute, Massachusetts Eye and EarHarvard Medical SchoolBostonMAUSA
- The Broad Institute of MIT and HarvardCambridgeMAUSA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of MedicineThe University of ChicagoChicagoILUSA
| | | | - Ohad Birk
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Lior Rokach
- Department of Software & Information Systems EngineeringBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Esti Yeger‐Lotem
- Department of Clinical Biochemistry and PharmacologyBen‐Gurion University of the NegevBeer ShevaIsrael
- The National Institute for Biotechnology in the NegevBen‐Gurion University of the NegevBeer ShevaIsrael
| |
Collapse
|
4
|
Shimada MK. Splicing Modulators Are Involved in Human Polyglutamine Diversification via Protein Complexes Shuttling between Nucleus and Cytoplasm. Int J Mol Sci 2023; 24:ijms24119622. [PMID: 37298574 DOI: 10.3390/ijms24119622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Length polymorphisms of polyglutamine (polyQs) in triplet-repeat-disease-causing genes have diversified during primate evolution despite them conferring a risk of human-specific diseases. To explain the evolutionary process of this diversification, there is a need to focus on mechanisms by which rapid evolutionary changes can occur, such as alternative splicing. Proteins that can bind polyQs are known to act as splicing factors and may provide clues about the rapid evolutionary process. PolyQs are also characterized by the formation of intrinsically disordered (ID) regions, so I hypothesized that polyQs are involved in the transportation of various molecules between the nucleus and cytoplasm to regulate mechanisms characteristic of humans such as neural development. To determine target molecules for empirical research to understand the evolutionary change, I explored protein-protein interactions (PPIs) involving the relevant proteins. This study identified pathways related to polyQ binding as hub proteins scattered across various regulatory systems, including regulation via PQBP1, VCP, or CREBBP. Nine ID hub proteins with both nuclear and cytoplasmic localization were found. Functional annotations suggested that ID proteins containing polyQs are involved in regulating transcription and ubiquitination by flexibly changing PPI formation. These findings explain the relationships among splicing complex, polyQ length variations, and modifications in neural development.
Collapse
Affiliation(s)
- Makoto K Shimada
- Center for Medical Science, Fujita Health University, Toyoake 470-1192, Japan
| |
Collapse
|
5
|
Laval F, Coppin G, Twizere JC, Vidal M. Homo cerevisiae-Leveraging Yeast for Investigating Protein-Protein Interactions and Their Role in Human Disease. Int J Mol Sci 2023; 24:9179. [PMID: 37298131 PMCID: PMC10252790 DOI: 10.3390/ijms24119179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development.
Collapse
Affiliation(s)
- Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; (F.L.); (G.C.)
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, 4000 Liège, Belgium
| | - Georges Coppin
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; (F.L.); (G.C.)
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, 4000 Liège, Belgium
| | - Jean-Claude Twizere
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; (F.L.); (G.C.)
- TERRA Teaching and Research Centre, University of Liège, 5030 Gembloux, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, 4000 Liège, Belgium
- Division of Science and Math, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA; (F.L.); (G.C.)
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
6
|
García-Vaquero ML, Gama-Carvalho M, Pinto FR, De Las Rivas J. Biological interacting units identified in human protein networks reveal tissue-functional diversification and its impact on disease. Comput Struct Biotechnol J 2022; 20:3764-3778. [PMID: 35891788 PMCID: PMC9304429 DOI: 10.1016/j.csbj.2022.07.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 07/04/2022] [Accepted: 07/04/2022] [Indexed: 12/29/2022] Open
Abstract
Biological processes are exerted by groups of physically interacting proteins. Proteins display variable biological roles depending on tissue-interactomic context. Tissue-specific protein-protein interaction networks reveal functional diversification. Most disease associated genes/proteins display tissue-specific phenotypes. Protein interaction network analysis is a valuable resource to identify disease genes.
Protein-protein interactions (PPI) play an essential role in the biological processes that occur in the cell. Therefore, the dissection of PPI networks becomes decisive to model functional coordination and predict pathological de-regulation. Cellular networks are dynamic and proteins display varying roles depending on the tissue-interactomic context. Thus, the use of centrality measures in individual proteins fall short to dissect the functional properties of the cell. For this reason, there is a need for more comprehensive, relational, and context-specific ways to analyze the multiple actions of proteins in different cells and identify specific functional assemblies within global biomolecular networks. Under this framework, we define Biological Interacting units (BioInt-U) as groups of proteins that interact physically and are enriched in a common Gene Ontology. A search strategy was applied on 33 tissue-specific (TS) PPI networks to generate BioInt libraries associated with each particular human tissue. The cross-tissue comparison showed that housekeeping assemblies incorporate different proteins and exhibit distinct network properties depending on the tissue. Furthermore, disease genes (DGs) of tissue-associated pathologies preferentially accumulate in units in the expected tissues, which in turn were more central in the TS networks. Overall, the study reveals a tissue-specific functional diversification based on the identification of specific protein units and suggests vulnerabilities specific of each tissue network, which can be applied to refine protein-disease association methods.
Collapse
Key Words
- BiU, BioInt unit
- Biological function
- CO, CORUM complex
- DEg, Differentially expressed gene
- DG, Disease gene
- Disease gene
- GO-BP, Gene Ontology biological process
- HK, Housekeeping
- Housekeeping gene
- PPI network
- PPI, Protein-protein interaction
- Protein module
- SS, Simpson's similarity
- TE, Tissue enriched
- TS, Tissue-specific
- Tissue-specific gene
- UB, Ubiquitous
Collapse
Affiliation(s)
- Marina L García-Vaquero
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal.,Cancer Research Center (CiC-IBMCC, CSIC/USAL and IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca 37007, Spain
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal
| | - Francisco R Pinto
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, Lisboa 1749-016, Portugal
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL and IBSAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL) and Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca 37007, Spain
| |
Collapse
|
7
|
Bondhus L, Varma R, Hernandez Y, Arboleda VA. Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity. Brief Bioinform 2022; 23:6582882. [PMID: 35534150 PMCID: PMC9487600 DOI: 10.1093/bib/bbac158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/06/2022] [Accepted: 04/10/2022] [Indexed: 01/28/2023] Open
Abstract
The spatial and temporal domain of a gene's expression can range from ubiquitous to highly specific. Quantifying the degree to which this expression is unique to a specific tissue or developmental timepoint can provide insight into the etiology of genetic diseases. However, quantifying specificity remains challenging as measures of specificity are sensitive to similarity between samples in the sample set. For example, in the Gene-Tissue Expression project (GTEx), brain subregions are overrepresented at 13 of 54 (24%) unique tissues sampled. In this dataset, existing specificity measures have a decreased ability to identify genes specific to the brain relative to other organs. To solve this problem, we leverage sample similarity information to weight samples such that overrepresented tissues do not have an outsized effect on specificity estimates. We test this reweighting procedure on 4 measures of specificity, Z-score, Tau, Tsi and Gini, in the GTEx data and in single cell datasets for zebrafish and mouse. For all of these measures, incorporating sample similarity information to weight samples results in greater stability of sets of genes called as specific and decreases the overall variance in the change of specificity estimates as sample sets become more unbalanced. Furthermore, the genes with the largest improvement in their specificity estimate's stability are those with functions related to the overrepresented sample types. Our results demonstrate that incorporating similarity information improves specificity estimates' stability to the choice of the sample set used to define the transcriptome, providing more robust and reproducible measures of specificity for downstream analyses.
Collapse
Affiliation(s)
- Leroy Bondhus
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095
| | - Roshni Varma
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095
| | - Yenifer Hernandez
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095
| | - Valerie A Arboleda
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095,Molecular Biology Institute, UCLA, Los Angeles, CA 90095,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, 90095,Corresponding author. Valerie A. Arboleda, 615 Charles E. Young Drive South, Los Angeles, CA 90095, USA. Tel.: +1-310-983-3568; E-mail:
| |
Collapse
|
8
|
Hekselman I, Kerber L, Ziv M, Gruber G, Yeger-Lotem E. The Organ-Disease Annotations (ODiseA) database of hereditary diseases and inflicted tissues. J Mol Biol 2022; 434:167619. [PMID: 35504357 DOI: 10.1016/j.jmb.2022.167619] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Abstract
Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.
Collapse
Affiliation(s)
- Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Lior Kerber
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maya Ziv
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Gil Gruber
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Be'er Sheva, Israel; The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| |
Collapse
|
9
|
Sharon M, Vinogradov E, Argov CM, Lazarescu O, Zoabi Y, Hekselman I, Yeger-Lotem E. The differential activity of biological processes in tissues and cell subsets can illuminate disease-related processes and cell-type identities. Bioinformatics 2022; 38:1584-1592. [PMID: 35015838 DOI: 10.1093/bioinformatics/btab883] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/09/2021] [Accepted: 01/02/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION The distinct functionalities of human tissues and cell types underlie complex phenotype-genotype relationships, yet often remain elusive. Harnessing the multitude of bulk and single-cell human transcriptomes while focusing on processes can help reveal these distinct functionalities. RESULTS The Tissue-Process Activity (TiPA) method aims to identify processes that are preferentially active or under-expressed in specific contexts, by comparing the expression levels of process genes between contexts. We tested TiPA on 1579 tissue-specific processes and bulk tissue transcriptomes, finding that it performed better than another method. Next, we used TiPA to ask whether the activity of certain processes could underlie the tissue-specific manifestation of 1233 hereditary diseases. We found that 21% of the disease-causing genes indeed participated in such processes, thereby illuminating their genotype-phenotype relationships. Lastly, we applied TiPA to single-cell transcriptomes of 108 human cell types, revealing that process activities often match cell-type identities and can thus aid annotation efforts. Hence, differential activity of processes can highlight the distinct functionality of tissues and cells in a robust and meaningful manner. AVAILABILITY AND IMPLEMENTATION TiPA code is available in GitHub (https://github.com/moranshar/TiPA). In addition, all data are available as part of the Supplementary Material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Moran Sharon
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Or Lazarescu
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Yazeed Zoabi
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.,The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| |
Collapse
|
10
|
Gonzalez-Teran B, Pittman M, Felix F, Thomas R, Richmond-Buccola D, Hüttenhain R, Choudhary K, Moroni E, Costa MW, Huang Y, Padmanabhan A, Alexanian M, Lee CY, Maven BEJ, Samse-Knapp K, Morton SU, McGregor M, Gifford CA, Seidman JG, Seidman CE, Gelb BD, Colombo G, Conklin BR, Black BL, Bruneau BG, Krogan NJ, Pollard KS, Srivastava D. Transcription factor protein interactomes reveal genetic determinants in heart disease. Cell 2022; 185:794-814.e30. [PMID: 35182466 PMCID: PMC8923057 DOI: 10.1016/j.cell.2022.01.021] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 08/20/2021] [Accepted: 01/25/2022] [Indexed: 02/08/2023]
Abstract
Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.
Collapse
Affiliation(s)
- Barbara Gonzalez-Teran
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Maureen Pittman
- Gladstone Institutes, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Franco Felix
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | | | - Desmond Richmond-Buccola
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Ruth Hüttenhain
- Gladstone Institutes, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA
| | | | | | - Mauro W Costa
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Yu Huang
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Arun Padmanabhan
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA; Division of Cardiology, Department of Medicine, University of California, San Francisco, CA, USA
| | - Michael Alexanian
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Clara Youngna Lee
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Bonnie E J Maven
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA; Developmental and Stem Cell Biology Graduate Program, University of California San Francisco, San Francisco, CA, USA
| | - Kaitlen Samse-Knapp
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Sarah U Morton
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Michael McGregor
- Gladstone Institutes, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA
| | - Casey A Gifford
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - J G Seidman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Christine E Seidman
- Department of Genetics, Harvard Medical School, Boston, MA, USA; Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Bruce D Gelb
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Bruce R Conklin
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA
| | - Brian L Black
- Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA
| | - Benoit G Bruneau
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA; Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA; Division of Cardiology, Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA
| | - Nevan J Krogan
- Gladstone Institutes, San Francisco, CA, USA; Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA; Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA.
| | - Deepak Srivastava
- Gladstone Institutes, San Francisco, CA, USA; Roddenberry Center for Stem Cell Biology and Medicine at Gladstone, San Francisco, CA, USA; Division of Cardiology, Department of Pediatrics, UCSF School of Medicine, San Francisco, CA, USA; Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA.
| |
Collapse
|
11
|
Hollander M, Do T, Will T, Helms V. Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data. FRONTIERS IN BIOINFORMATICS 2021; 1:724297. [PMID: 36303788 PMCID: PMC9581068 DOI: 10.3389/fbinf.2021.724297] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/23/2021] [Indexed: 12/25/2022] Open
Abstract
Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.
Collapse
|
12
|
Skinnider MA, Scott NE, Prudova A, Kerr CH, Stoynov N, Stacey RG, Chan QWT, Rattray D, Gsponer J, Foster LJ. An atlas of protein-protein interactions across mouse tissues. Cell 2021; 184:4073-4089.e17. [PMID: 34214469 DOI: 10.1016/j.cell.2021.06.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/05/2021] [Accepted: 06/01/2021] [Indexed: 12/20/2022]
Abstract
Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.
Collapse
Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Nichollas E Scott
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Peter Doherty Institute, Department of Microbiology and Immunology, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Anna Prudova
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David Rattray
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| |
Collapse
|
13
|
Ginsberg SD, Neubert TA, Sharma S, Digwal CS, Yan P, Timbus C, Wang T, Chiosis G. Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease. FEBS J 2021; 289:2047-2066. [PMID: 34028172 DOI: 10.1111/febs.16031] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 04/23/2021] [Accepted: 05/20/2021] [Indexed: 12/22/2022]
Abstract
The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.
Collapse
Affiliation(s)
- Stephen D Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA.,Departments of Psychiatry, Neuroscience & Physiology, The NYU Neuroscience Institute, New York University Grossman School of Medicine, NY, USA
| | - Thomas A Neubert
- Kimmel Center for Biology and Medicine at the Skirball Institute, NYU School of Medicine, New York, NY, USA
| | - Sahil Sharma
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Chander S Digwal
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Pengrong Yan
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Calin Timbus
- Department of Mathematics, Technical University of Cluj-Napoca, CJ, Romania
| | - Tai Wang
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA
| | - Gabriela Chiosis
- Program in Chemical Biology, Sloan Kettering Institute, New York, NY, USA.,Breast Cancer Medicine Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
14
|
Shemesh N, Jubran J, Dror S, Simonovsky E, Basha O, Argov C, Hekselman I, Abu-Qarn M, Vinogradov E, Mauer O, Tiago T, Carra S, Ben-Zvi A, Yeger-Lotem E. The landscape of molecular chaperones across human tissues reveals a layered architecture of core and variable chaperones. Nat Commun 2021; 12:2180. [PMID: 33846299 PMCID: PMC8042005 DOI: 10.1038/s41467-021-22369-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/23/2021] [Indexed: 12/13/2022] Open
Abstract
The sensitivity of the protein-folding environment to chaperone disruption can be highly tissue-specific. Yet, the organization of the chaperone system across physiological human tissues has received little attention. Through computational analyses of large-scale tissue transcriptomes, we unveil that the chaperone system is composed of core elements that are uniformly expressed across tissues, and variable elements that are differentially expressed to fit with tissue-specific requirements. We demonstrate via a proteomic analysis that the muscle-specific signature is functional and conserved. Core chaperones are significantly more abundant across tissues and more important for cell survival than variable chaperones. Together with variable chaperones, they form tissue-specific functional networks. Analysis of human organ development and aging brain transcriptomes reveals that these functional networks are established in development and decline with age. In this work, we expand the known functional organization of de novo versus stress-inducible eukaryotic chaperones into a layered core-variable architecture in multi-cellular organisms.
Collapse
Affiliation(s)
- Netta Shemesh
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel.,Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Juman Jubran
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Shiran Dror
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Eyal Simonovsky
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Omer Basha
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Chanan Argov
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Mehtap Abu-Qarn
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ekaterina Vinogradov
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Omry Mauer
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Tatiana Tiago
- Centre for Neuroscience and Nanotechnology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Serena Carra
- Centre for Neuroscience and Nanotechnology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Anat Ben-Zvi
- Department of Life Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, Israel.
| |
Collapse
|
15
|
Persson E, Castresana-Aguirre M, Buzzao D, Guala D, Sonnhammer ELL. FunCoup 5: Functional Association Networks in All Domains of Life, Supporting Directed Links and Tissue-Specificity. J Mol Biol 2021; 433:166835. [PMID: 33539890 DOI: 10.1016/j.jmb.2021.166835] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/18/2020] [Accepted: 01/15/2021] [Indexed: 02/07/2023]
Abstract
FunCoup (https://funcoup.sbc.su.se) is one of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence using a redundancy-weighted naïve Bayesian approach, combined with orthology transfer. FunCoup's high coverage comes from using eleven different types of evidence, and extensive transfer of information between species. Since the latest update of the database, the availability of source data has improved drastically, and user expectations on a tool for functional associations have grown. To meet these requirements, we have made a new release of FunCoup with updated source data and improved functionality. FunCoup 5 now includes 22 species from all domains of life, and the source data for evidences, gold standards, and genomes have been updated to the latest available versions. In this new release, directed regulatory links inferred from transcription factor binding can be visualized in the network viewer for the human interactome. Another new feature is the possibility to filter by genes expressed in a certain tissue in the network viewer. FunCoup 5 further includes the SARS-CoV-2 proteome, allowing users to visualize and analyze interactions between SARS-CoV-2 and human proteins in order to better understand COVID-19. This new release of FunCoup constitutes a major advance for the users, with updated sources, new species and improved functionality for analysis of the networks.
Collapse
Affiliation(s)
- Emma Persson
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Miguel Castresana-Aguirre
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Davide Buzzao
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Dimitri Guala
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.
| |
Collapse
|
16
|
Li XC, Tang ZD, Peng L, Li YY, Qian FC, Zhao JM, Ding LW, Du XJ, Li M, Zhang J, Bai XF, Zhu J, Feng CC, Wang QY, Pan J, Li CQ. Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs. Front Genet 2021; 11:590672. [PMID: 33569079 PMCID: PMC7868561 DOI: 10.3389/fgene.2020.590672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/02/2020] [Indexed: 12/25/2022] Open
Abstract
Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23–52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.
Collapse
Affiliation(s)
- Xue-Cang Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Zhi-Dong Tang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Li Peng
- Guangdong Province Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yan-Yu Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Feng-Cui Qian
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian-Mei Zhao
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Ling-Wen Ding
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
| | - Xiao-Juan Du
- The 942 Hospital of Joint Logistic Support Force of PLA, Yinchuan, China
| | - Meng Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian Zhang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Xue-Feng Bai
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jiang Zhu
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Chen-Chen Feng
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Qiu-Yu Wang
- School of Medical Informatics, Harbin Medical University, Daqing, China
| | - Jian Pan
- Department of Hematology and Oncology, Children's Hospital of Soochow University, Suzhou, China
| | - Chun-Quan Li
- School of Medical Informatics, Harbin Medical University, Daqing, China
| |
Collapse
|
17
|
Guan J, Lin Y, Wang Y, Gao J, Ji G. An analytical method for the identification of cell type-specific disease gene modules. J Transl Med 2021; 19:20. [PMID: 33407556 PMCID: PMC7788893 DOI: 10.1186/s12967-020-02690-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/22/2020] [Indexed: 12/13/2022] Open
Abstract
Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.
Collapse
Affiliation(s)
- Jinting Guan
- Department of Automation, Xiamen University, Xiamen, China. .,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
| | - Yiping Lin
- Department of Automation, Xiamen University, Xiamen, China
| | - Yang Wang
- Department of Automation, Xiamen University, Xiamen, China
| | - Junchao Gao
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Guoli Ji
- Department of Automation, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, China
| |
Collapse
|
18
|
Jubran J, Hekselman I, Novack L, Yeger-Lotem E. Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases. Comput Struct Biotechnol J 2020; 18:4024-4032. [PMID: 33363699 PMCID: PMC7744645 DOI: 10.1016/j.csbj.2020.10.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 10/16/2020] [Accepted: 10/28/2020] [Indexed: 11/30/2022] Open
Abstract
Hereditary diseases and complex traits often manifest in specific tissues, whereas their causal genes are expressed in many tissues that remain unaffected. Among the mechanisms that have been suggested for this enigmatic phenomenon is dosage-sensitive compensation by paralogs of causal genes. Accordingly, tissue-selectivity stems from dosage imbalance between causal genes and paralogs that occurs particularly in disease-susceptible tissues. Here, we used a large-scale dataset of thousands of tissue transcriptomes and applied a linear mixed model (LMM) framework to assess this and other dosage-sensitive mechanisms. LMM analysis of 382 hereditary diseases consistently showed evidence for dosage-sensitive compensation by paralogs across diseases subsets and susceptible tissues. LMM analysis of 135 candidate genes that are strongly associated with 16 tissue-selective complex traits revealed a similar tendency among half of the trait-associated genes. This suggests that dosage-sensitive compensation by paralogs affects the tissue-selectivity of complex traits, and can be used to illuminate candidate genes' modes of action. Next, we applied LMM to analyze dosage imbalance between causal genes and three classes of genetic modifiers, including regulatory micro-RNAs, pseudogenes, and genetic interactors. Our results propose modifiers as a fundamental axis in tissue-selectivity of diseases and traits, and demonstrates the power of LMM as a statistical framework for discovering treatment avenues.
Collapse
Affiliation(s)
- Juman Jubran
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| | - Lena Novack
- Soroka University Medical Center, Beer-Sheva 84101, Israel.,Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel.,The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
| |
Collapse
|
19
|
Yu L, Shi Y, Zou Q, Wang S, Zheng L, Gao L. Exploring Drug Treatment Patterns Based on the Action of Drug and Multilayer Network Model. Int J Mol Sci 2020; 21:E5014. [PMID: 32708644 PMCID: PMC7404256 DOI: 10.3390/ijms21145014] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 02/01/2023] Open
Abstract
Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein-protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease.
Collapse
Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Y.S.); (L.G.)
| | - Yayong Shi
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Y.S.); (L.G.)
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology, Chengdu 650004, China;
| | - Shuhang Wang
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Liping Zheng
- School of Computer Science and Technology, Liaocheng University, Liaocheng 252000, China;
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an 710071, China; (Y.S.); (L.G.)
| |
Collapse
|
20
|
Yadav A, Vidal M, Luck K. Precision medicine - networks to the rescue. Curr Opin Biotechnol 2020; 63:177-189. [PMID: 32199228 PMCID: PMC7308189 DOI: 10.1016/j.copbio.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.
Collapse
Affiliation(s)
- Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Current address: Institute of Molecular Biology, Mainz, Germany.
| |
Collapse
|
21
|
Basha O, Mauer O, Simonovsky E, Shpringer R, Yeger-Lotem E. ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues. Nucleic Acids Res 2020; 47:W242-W247. [PMID: 31114913 PMCID: PMC6602570 DOI: 10.1093/nar/gkz421] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/23/2019] [Accepted: 05/06/2019] [Indexed: 12/13/2022] Open
Abstract
ResponseNet v.3 is an enhanced version of ResponseNet, a web server that is designed to highlight signaling and regulatory pathways connecting user-defined proteins and genes by using the ResponseNet network optimization approach (http://netbio.bgu.ac.il/respnet). Users run ResponseNet by defining source and target sets of proteins, genes and/or microRNAs, and by specifying a molecular interaction network (interactome). The output of ResponseNet is a sparse, high-probability interactome subnetwork that connects the two sets, thereby revealing additional molecules and interactions that are involved in the studied condition. In recent years, massive efforts were invested in profiling the transcriptomes of human tissues, enabling the inference of human tissue interactomes. ResponseNet v.3 expands ResponseNet2.0 by harnessing ∼11,600 RNA-sequenced human tissue profiles made available by the Genotype-Tissue Expression consortium, to support context-specific analysis of 44 human tissues. Thus, ResponseNet v.3 allows users to illuminate the signaling and regulatory pathways potentially active in the context of a specific tissue, and to compare them with active pathways in other tissues. In the era of precision medicine, such analyses open the door for tissue- and patient-specific analyses of pathways and diseases.
Collapse
Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Omry Mauer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Eyal Simonovsky
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Rotem Shpringer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences.,National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
22
|
Lee CE, Singleton KS, Wallin M, Faundez V. Rare Genetic Diseases: Nature's Experiments on Human Development. iScience 2020; 23:101123. [PMID: 32422592 PMCID: PMC7229282 DOI: 10.1016/j.isci.2020.101123] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/25/2020] [Accepted: 04/29/2020] [Indexed: 01/25/2023] Open
Abstract
Rare genetic diseases are the result of a continuous forward genetic screen that nature is conducting on humans. Here, we present epistemological and systems biology arguments highlighting the importance of studying these rare genetic diseases. We contend that the expanding catalog of mutations in ∼4,000 genes, which cause ∼6,500 diseases and their annotated phenotypes, offer a wide landscape for discovering fundamental mechanisms required for human development and involved in common diseases. Rare afflictions disproportionately affect the nervous system in children, but paradoxically, the majority of these disease-causing genes are evolutionarily ancient and ubiquitously expressed in human tissues. We propose that the biased prevalence of childhood rare diseases affecting nervous tissue results from the topological complexity of the protein interaction networks formed by ubiquitous and ancient proteins encoded by childhood disease genes. Finally, we illustrate these principles discussing Menkes disease, an example of the discovery power afforded by rare diseases.
Collapse
Affiliation(s)
- Chelsea E Lee
- Department of Cell Biology, Emory University, Atlanta, GA 30322, USA
| | - Kaela S Singleton
- Department of Cell Biology, Emory University, Atlanta, GA 30322, USA
| | - Melissa Wallin
- Department of Cell Biology, Emory University, Atlanta, GA 30322, USA
| | - Victor Faundez
- Department of Cell Biology, Emory University, Atlanta, GA 30322, USA.
| |
Collapse
|
23
|
Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R, Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M, Deimling S, Desbuleux A, Dricot A, Gebbia M, Hardy MF, Kishore N, Knapp JJ, Kovács IA, Lemmens I, Mee MW, Mellor JC, Pollis C, Pons C, Richardson AD, Schlabach S, Teeking B, Yadav A, Babor M, Balcha D, Basha O, Bowman-Colin C, Chin SF, Choi SG, Colabella C, Coppin G, D'Amata C, De Ridder D, De Rouck S, Duran-Frigola M, Ennajdaoui H, Goebels F, Goehring L, Gopal A, Haddad G, Hatchi E, Helmy M, Jacob Y, Kassa Y, Landini S, Li R, van Lieshout N, MacWilliams A, Markey D, Paulson JN, Rangarajan S, Rasla J, Rayhan A, Rolland T, San-Miguel A, Shen Y, Sheykhkarimli D, Sheynkman GM, Simonovsky E, Taşan M, Tejeda A, Tropepe V, Twizere JC, Wang Y, Weatheritt RJ, Weile J, Xia Y, Yang X, Yeger-Lotem E, Zhong Q, Aloy P, Bader GD, De Las Rivas J, Gaudet S, Hao T, Rak J, Tavernier J, Hill DE, Vidal M, Roth FP, Calderwood MA. A reference map of the human binary protein interactome. Nature 2020; 580:402-408. [PMID: 32296183 PMCID: PMC7169983 DOI: 10.1038/s41586-020-2188-x] [Citation(s) in RCA: 621] [Impact Index Per Article: 155.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 02/14/2020] [Indexed: 12/14/2022]
Abstract
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome3, transcriptome4, and proteome5 data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.
Collapse
Affiliation(s)
- Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dae-Kyum Kim
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget E Begg
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ruth Brignall
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tiziana Cafarelli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Francisco J Campos-Laborie
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Benoit Charloteaux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dongsic Choi
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Atina G Coté
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Meaghan Daley
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steven Deimling
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alice Desbuleux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Amélie Dricot
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Madeleine F Hardy
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nishka Kishore
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Jennifer J Knapp
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - István A Kovács
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Network Science Institute, Northeastern University, Boston, MA, USA.,Wigner Research Centre for Physics, Institute for Solid State Physics and Optics, Budapest, Hungary
| | - Irma Lemmens
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miles W Mee
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Joseph C Mellor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,seqWell, Beverly, MA, USA
| | - Carl Pollis
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Aaron D Richardson
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sadie Schlabach
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget Teeking
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mariana Babor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Christian Bowman-Colin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Suet-Feung Chin
- Cancer Research UK (CRUK) Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Claudia Colabella
- Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy.,Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche "Togo Rosati" (IZSUM), Perugia, Italy
| | - Georges Coppin
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Cassandra D'Amata
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - David De Ridder
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steffi De Rouck
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Hanane Ennajdaoui
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Florian Goebels
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Liana Goehring
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anjali Gopal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Ghazal Haddad
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Elodie Hatchi
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mohamed Helmy
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yves Jacob
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Université Paris Diderot, Paris, France
| | - Yoseph Kassa
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Serena Landini
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Roujia Li
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Natascha van Lieshout
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dylan Markey
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.,Department of Biostatistics, Product Development, Genentech Inc., South San Francisco, CA, USA
| | - Sudharshan Rangarajan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - John Rasla
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ashyad Rayhan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Thomas Rolland
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adriana San-Miguel
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yun Shen
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dayag Sheykhkarimli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eyal Simonovsky
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Murat Taşan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Tejeda
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vincent Tropepe
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Claude Twizere
- Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Jochen Weile
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yu Xia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Bioengineering, McGill University, Montreal, Quebec, Canada
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Quan Zhong
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biological Sciences, Wright State University, Dayton, OH, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Suzanne Gaudet
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Janusz Rak
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Jan Tavernier
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
| | - Frederick P Roth
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. .,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada. .,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. .,Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada.
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| |
Collapse
|
24
|
Basha O, Argov CM, Artzy R, Zoabi Y, Hekselman I, Alfandari L, Chalifa-Caspi V, Yeger-Lotem E. Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes. Bioinformatics 2020; 36:2821-2828. [DOI: 10.1093/bioinformatics/btaa034] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 01/07/2020] [Accepted: 01/16/2020] [Indexed: 01/19/2023] Open
Abstract
Abstract
Motivation
Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.
Results
Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.
Summary
Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
Availability and implementation
Datasets are available as part of the Supplementary data.
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Raviv Artzy
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Yazeed Zoabi
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Liad Alfandari
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Vered Chalifa-Caspi
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
25
|
Hekselman I, Yeger-Lotem E. Mechanisms of tissue and cell-type specificity in heritable traits and diseases. Nat Rev Genet 2020; 21:137-150. [DOI: 10.1038/s41576-019-0200-9] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2019] [Indexed: 02/07/2023]
|
26
|
Jiang L, Xue C, Dai S, Chen S, Chen P, Sham PC, Wang H, Li M. DESE: estimating driver tissues by selective expression of genes associated with complex diseases or traits. Genome Biol 2019; 20:233. [PMID: 31694669 PMCID: PMC6836538 DOI: 10.1186/s13059-019-1801-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 08/25/2019] [Indexed: 02/08/2023] Open
Abstract
The driver tissues or cell types in which susceptibility genes initiate diseases remain elusive. We develop a unified framework to detect the causal tissues of complex diseases or traits according to selective expression of disease-associated genes in genome-wide association studies (GWASs). This framework consists of three components which run iteratively to produce a converged prioritization list of driver tissues. Additionally, this framework also outputs a list of prioritized genes as a byproduct. We apply the framework to six representative complex diseases or traits with GWAS summary statistics, which leads to the estimation of the lung as an associated tissue of rheumatoid arthritis.
Collapse
Affiliation(s)
- Lin Jiang
- Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China.,Department of Pituitary Tumour Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Chao Xue
- Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, 510080, China
| | - Sheng Dai
- Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Shangzhen Chen
- Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Peikai Chen
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Pak Chung Sham
- Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Haijun Wang
- Department of Pituitary Tumour Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Miaoxin Li
- Zhongshan School of Medicine, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, China. .,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, 510080, China. .,Department of Psychiatry, The Centre for Genomic Sciences, State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
27
|
Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
Collapse
MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
Collapse
Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| |
Collapse
|
28
|
Amado F, Calheiros-Lobo MJ, Ferreira R, Vitorino R. Sample Treatment for Saliva Proteomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1073:23-56. [DOI: 10.1007/978-3-030-12298-0_2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
29
|
Boczek E, Gaglia G, Olshina M, Sarraf S. The first Autumn School on Proteostasis: from molecular mechanisms to organismal consequences. Cell Stress Chaperones 2019; 24:481-492. [PMID: 31073902 PMCID: PMC6527634 DOI: 10.1007/s12192-019-00998-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/15/2019] [Indexed: 12/12/2022] Open
Abstract
The first Autumn School on Proteostasis was held at the Mediterranean Institute for Life Sciences (MedILS) in Split, Croatia, from November 12th-16th, 2018, bringing together 44 graduate students and postdoctoral fellows and 22 principal investigators from around the world. This meeting was geared towards providing students with an in-depth understanding of the field of proteostasis, with the aim of broadening their perspectives of the field. Session topics covered multiple aspects of cellular and organismal proteostasis, including fundamental principles, responses to heat shock, aging and disease, and protein folding, misfolding, and degradation. The structure of the meeting and the restricted number of participants afforded the students and postdocs the opportunity to interact with principal investigators to discuss not only their latest research, but also their career prospects and progression in a close, supportive environment.
Collapse
Affiliation(s)
- Edgar Boczek
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Giorgio Gaglia
- Brigham Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Maya Olshina
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shireen Sarraf
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD USA
| |
Collapse
|
30
|
Barshir R, Hekselman I, Shemesh N, Sharon M, Novack L, Yeger-Lotem E. Role of duplicate genes in determining the tissue-selectivity of hereditary diseases. PLoS Genet 2018; 14:e1007327. [PMID: 29723191 PMCID: PMC5953478 DOI: 10.1371/journal.pgen.1007327] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 05/15/2018] [Accepted: 03/21/2018] [Indexed: 11/18/2022] Open
Abstract
A longstanding puzzle in human genetics is what limits the clinical manifestation of hundreds of hereditary diseases to certain tissues, while their causal genes are expressed throughout the human body. A general conception is that tissue-selective disease phenotypes emerge when masking factors operate in unaffected tissues, but are specifically absent or insufficient in disease-manifesting tissues. Although this conception has critical impact on the understanding of disease manifestation, it was never challenged in a systematic manner across a variety of hereditary diseases and affected tissues. Here, we address this gap in our understanding via rigorous analysis of the susceptibility of over 30 tissues to 112 tissue-selective hereditary diseases. We focused on the roles of paralogs of causal genes, which are presumably capable of compensating for their aberration. We show for the first time at large-scale via quantitative analysis of omics datasets that, preferentially in the disease-manifesting tissues, paralogs are under-expressed relative to causal genes in more than half of the diseases. This was observed for several susceptible tissues and for causal genes with varying number of paralogs, suggesting that imbalanced expression of paralogs increases tissue susceptibility. While for many diseases this imbalance stemmed from up-regulation of the causal gene in the disease-manifesting tissue relative to other tissues, it was often combined with down-regulation of its paralog. Notably in roughly 20% of the cases, this imbalance stemmed only from significant down-regulation of the paralog. Thus, dosage relationships between paralogs appear as important, yet currently under-appreciated, modifiers of disease manifestation.
Collapse
Affiliation(s)
- Ruth Barshir
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Netta Shemesh
- Department of Life Sciences, Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Moran Sharon
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lena Novack
- Department of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| |
Collapse
|
31
|
Basha O, Shpringer R, Argov CM, Yeger-Lotem E. The DifferentialNet database of differential protein-protein interactions in human tissues. Nucleic Acids Res 2018; 46:D522-D526. [PMID: 29069447 PMCID: PMC5753382 DOI: 10.1093/nar/gkx981] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 09/19/2017] [Accepted: 10/10/2017] [Indexed: 11/22/2022] Open
Abstract
DifferentialNet is a novel database that provides users with differential interactome analysis of human tissues (http://netbio.bgu.ac.il/diffnet/). Users query DifferentialNet by protein, and retrieve its differential protein-protein interactions (PPIs) per tissue via an interactive graphical interface. To compute differential PPIs, we integrated available data of experimentally detected PPIs with RNA-sequencing profiles of tens of human tissues gathered by the Genotype-Tissue Expression consortium (GTEx) and by the Human Protein Atlas (HPA). We associated each PPI with a score that reflects whether its corresponding genes were expressed similarly across tissues, or were up- or down-regulated in the selected tissue. By this, users can identify tissue-specific interactions, filter out PPIs that are relatively stable across tissues, and highlight PPIs that show relative changes across tissues. The differential PPIs can be used to identify tissue-specific processes and to decipher tissue-specific phenotypes. Moreover, they unravel processes that are tissue-wide yet tailored to the specific demands of each tissue.
Collapse
Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Rotem Shpringer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Chanan M Argov
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
32
|
Kumar S, Blangero J, Curran JE. Induced Pluripotent Stem Cells in Disease Modeling and Gene Identification. Methods Mol Biol 2018; 1706:17-38. [PMID: 29423791 DOI: 10.1007/978-1-4939-7471-9_2] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Experimental modeling of human inherited disorders provides insight into the cellular and molecular mechanisms involved, and the underlying genetic component influencing, the disease phenotype. The breakthrough development of induced pluripotent stem cell (iPSC) technology represents a quantum leap in experimental modeling of human diseases, providing investigators with a self-renewing and, thus, unlimited source of pluripotent cells for targeted differentiation. In principle, the entire range of cell types found in the human body can be interrogated using an iPSC approach. Therefore, iPSC technology, and the increasingly refined abilities to differentiate iPSCs into disease-relevant target cells, has far-reaching implications for understanding disease pathophysiology, identifying disease-causing genes, and developing more precise therapeutics, including advances in regenerative medicine. In this chapter, we discuss the technological perspectives and recent developments in the application of patient-derived iPSC lines for human disease modeling and disease gene identification.
Collapse
Affiliation(s)
- Satish Kumar
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, School of Medicine, 1214 W Schunior St, Edinburg, TX, 78541, USA.
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, School of Medicine, 1214 W Schunior St, Edinburg, TX, 78541, USA
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, School of Medicine, 1214 W Schunior St, Edinburg, TX, 78541, USA
| |
Collapse
|
33
|
Zhao Y, Song WM, Zhang F, Zhou MM, Zhang W, Walsh MJ, Zhang B. Distinct distributions of genomic features of the 5’ and 3’ partners of coding somatic cancer gene fusions: arising mechanisms and functional implications. Oncotarget 2017; 8:66769-66783. [PMID: 28977995 PMCID: PMC5620135 DOI: 10.18632/oncotarget.10734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 06/06/2016] [Indexed: 11/25/2022] Open
Abstract
The genomic features and arising mechanisms of coding cancer somatic gene fusions (CSGFs) largely remain elusive. In this study, we show the gene origin stratification pattern of CSGF partners that fusion partners in human cancers are significantly enriched for genes with the gene age ofEuteleostomes and with the gene family age of Bilateria. GC skew (a measurement of G, C nucleotide content bias, (G-C)/(G+C)) is a useful measurement to indicate the DNA leading strand, lagging strand, replication origin, and replication terminal and DNA-RNA R-loop formation. We find that GC skew bias at the 5 prime (5′) but not the 3 prime (3’) partners of CSGFs, coincident with the polarity feature of gene expression breadth that the 5’ partners are more ubiquitous while the 3’ fusion partners are more tissue specific in general. We reveal distinct length and composition distributions of 5’ and 3’ of CSGFs, including sequence features corresponded to the 5’ untranslated regions (UTRs), 3’ UTRs, and the N-terminal sequences of the encoded proteins. Oncogenic somatic gene fusions are most enriched for the 5’ and 3’ genes’ somatic amplification alongside a substantial proportion of other types of combinations. At the function level, 5’ partners of CSGFs appear more likely to be tumour suppressor genes while many 3’ partners appear to be proto-oncogene. Such distinct polarities of CSGFs at the evolutionary, structural, genomic and functional levels indicate the heterogeneous arsing mechanisms of CSGFs including R-loops and suggest potential novel targeted therapeutics specific to CSGF functional categories.
Collapse
Affiliation(s)
- Yongzhong Zhao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
- Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Won-Min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
- Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Fan Zhang
- Department of Medicine, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Ming-Ming Zhou
- Department of Structural and Chemical Biology, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Weijia Zhang
- Department of Medicine, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| | - Martin J. Walsh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
- Department of Structural and Chemical Biology, Icahn School of Medicine at Mount Sinai, NY 10029, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY 10029, USA
- Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, NY 10029, USA
| |
Collapse
|
34
|
Feiglin A, Allen BK, Kohane IS, Kong SW. Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders. Cell Syst 2017; 5:140-148.e2. [PMID: 28822752 PMCID: PMC5928498 DOI: 10.1016/j.cels.2017.06.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 03/21/2017] [Accepted: 06/29/2017] [Indexed: 01/23/2023]
Abstract
Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.
Collapse
Affiliation(s)
- Ariel Feiglin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Bryce K Allen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
35
|
Caldera M, Buphamalai P, Müller F, Menche J. Interactome-based approaches to human disease. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.04.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|
36
|
Will T, Helms V. Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare. BMC SYSTEMS BIOLOGY 2017; 11:44. [PMID: 28376810 PMCID: PMC5379774 DOI: 10.1186/s12918-017-0400-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 01/26/2017] [Indexed: 12/24/2022]
Abstract
BACKGROUND Differential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell's transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks. RESULTS Besides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control. CONCLUSIONS Applying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/ .
Collapse
Affiliation(s)
- Thorsten Will
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123 Germany
- Graduate School of Computer Science, Saarland University, Campus E1.3, Saarbrücken, 66123 Germany
| | - Volkhard Helms
- Center for Bioinformatics, Saarland University, Campus E2.1, Saarbrücken, 66123 Germany
| |
Collapse
|
37
|
A Differentiation Transcription Factor Establishes Muscle-Specific Proteostasis in Caenorhabditis elegans. PLoS Genet 2016; 12:e1006531. [PMID: 28036392 PMCID: PMC5201269 DOI: 10.1371/journal.pgen.1006531] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 12/08/2016] [Indexed: 02/07/2023] Open
Abstract
Safeguarding the proteome is central to the health of the cell. In multi-cellular organisms, the composition of the proteome, and by extension, protein-folding requirements, varies between cells. In agreement, chaperone network composition differs between tissues. Here, we ask how chaperone expression is regulated in a cell type-specific manner and whether cellular differentiation affects chaperone expression. Our bioinformatics analyses show that the myogenic transcription factor HLH-1 (MyoD) can bind to the promoters of chaperone genes expressed or required for the folding of muscle proteins. To test this experimentally, we employed HLH-1 myogenic potential to genetically modulate cellular differentiation of Caenorhabditis elegans embryonic cells by ectopically expressing HLH-1 in all cells of the embryo and monitoring chaperone expression. We found that HLH-1-dependent myogenic conversion specifically induced the expression of putative HLH-1-regulated chaperones in differentiating muscle cells. Moreover, disrupting the putative HLH-1-binding sites on ubiquitously expressed daf-21(Hsp90) and muscle-enriched hsp-12.2(sHsp) promoters abolished their myogenic-dependent expression. Disrupting HLH-1 function in muscle cells reduced the expression of putative HLH-1-regulated chaperones and compromised muscle proteostasis during and after embryogenesis. In turn, we found that modulating the expression of muscle chaperones disrupted the folding and assembly of muscle proteins and thus, myogenesis. Moreover, muscle-specific over-expression of the DNAJB6 homolog DNJ-24, a limb-girdle muscular dystrophy-associated chaperone, disrupted the muscle chaperone network and exposed synthetic motility defects. We propose that cellular differentiation could establish a proteostasis network dedicated to the folding and maintenance of the muscle proteome. Such cell-specific proteostasis networks can explain the selective vulnerability that many diseases of protein misfolding exhibit even when the misfolded protein is ubiquitously expressed. Molecular chaperones protect proteins from misfolding and aggregation. In multi-cellular organisms, the composition and expression levels of chaperones vary between tissues. However, little is known of how such differential expression is regulated. We hypothesized that the cellular differentiation that regulates the cell-type specific expression program may be involved in establishing a cell-type specific chaperone network. To test this possibility, we addressed the myogenic commitment transcription factor HLH-1 (CeMyoD) that converts embryonic cells to muscle cells in Caenorhabditis elegans. We demonstrated that HLH-1 regulates the expression of muscle chaperones during muscle differentiation. Moreover, we showed that HLH-1-dependent expression of chaperones is required for embryonic development and muscle function. We propose that cellular differentiation results in cell-specific differences in the chaperone network that may be detrimental in terms of the susceptibility of neurons and muscle cells to protein misfolding diseases.
Collapse
|
38
|
Basha O, Barshir R, Sharon M, Lerman E, Kirson BF, Hekselman I, Yeger-Lotem E. The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues. Nucleic Acids Res 2016; 45:D427-D431. [PMID: 27899616 PMCID: PMC5210565 DOI: 10.1093/nar/gkw1088] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 01/08/2023] Open
Abstract
Knowledge of the molecular interactions of human proteins within tissues is important for identifying their tissue-specific roles and for shedding light on tissue phenotypes. However, many protein–protein interactions (PPIs) have no tissue-contexts. The TissueNet database bridges this gap by associating experimentally-identified PPIs with human tissues that were shown to express both pair-mates. Users can select a protein and a tissue, and obtain a network view of the query protein and its tissue-associated PPIs. TissueNet v.2 is an updated version of the TissueNet database previously featured in NAR. It includes over 40 human tissues profiled via RNA-sequencing or protein-based assays. Users can select their preferred expression data source and interactively set the expression threshold for determining tissue-association. The output of TissueNet v.2 emphasizes qualitative and quantitative features of query proteins and their PPIs. The tissue-specificity view highlights tissue-specific and globally-expressed proteins, and the quantitative view highlights proteins that were differentially expressed in the selected tissue relative to all other tissues. Together, these views allow users to quickly assess the unique versus global functionality of query proteins. Thus, TissueNet v.2 offers an extensive, quantitative and user-friendly interface to study the roles of human proteins across tissues. TissueNet v.2 is available at http://netbio.bgu.ac.il/tissuenet.
Collapse
Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Ruth Barshir
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Moran Sharon
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Eugene Lerman
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Binyamin F Kirson
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel .,National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| |
Collapse
|
39
|
Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res 2016; 45:D408-D414. [PMID: 27794551 PMCID: PMC5210659 DOI: 10.1093/nar/gkw985] [Citation(s) in RCA: 280] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 09/28/2016] [Accepted: 10/14/2016] [Indexed: 01/01/2023] Open
Abstract
The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.
Collapse
Affiliation(s)
- Gregorio Alanis-Lobato
- Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
| | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg Universität, Mainz, Germany
- Institute of Molecular Biology, Mainz, Germany
| | - Martin H Schaefer
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| |
Collapse
|
40
|
Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes. Sci Rep 2016; 6:35228. [PMID: 27752041 PMCID: PMC5067504 DOI: 10.1038/srep35228] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Accepted: 09/26/2016] [Indexed: 01/13/2023] Open
Abstract
Protein interactions play significant roles in complex diseases. We analyzed peripheral blood mononuclear cells (PBMC) transcriptome using a multi-method strategy. We constructed a tissue-specific interactome (T2Di) and identified 420 molecular signatures associated with T2D-related comorbidity and symptoms, mainly implicated in inflammation, adipogenesis, protein phosphorylation and hormonal secretion. Apart from explaining the residual associations within the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study, the T2Di signatures were enriched in pathogenic cell type-specific regulatory elements related to fetal development, immunity and expression quantitative trait loci (eQTL). The T2Di revealed a novel locus near a well-established GWAS loci AChE, in which SRRT interacts with JAZF1, a T2D-GWAS gene implicated in pancreatic function. The T2Di also included known anti-diabetic drug targets (e.g. PPARD, MAOB) and identified possible druggable targets (e.g. NCOR2, PDGFR). These T2Di signatures were validated by an independent computational method, and by expression data of pancreatic islet, muscle and liver with some of the signatures (CEBPB, SREBF1, MLST8, SRF, SRRT and SLC12A9) confirmed in PBMC from an independent cohort of 66 T2D and 66 control subjects. By combining prior knowledge and transcriptome analysis, we have constructed an interactome to explain the multi-layered regulatory pathways in T2D.
Collapse
|
41
|
Abstract
Genes carrying mutations associated with genetic diseases are present in all human cells; yet, clinical manifestations of genetic diseases are usually highly tissue-specific. Although some disease genes are expressed only in selected tissues, the expression patterns of disease genes alone cannot explain the observed tissue specificity of human diseases. Here we hypothesize that for a disease to manifest itself in a particular tissue, a whole functional subnetwork of genes (disease module) needs to be expressed in that tissue. Driven by this hypothesis, we conducted a systematic study of the expression patterns of disease genes within the human interactome. We find that genes expressed in a specific tissue tend to be localized in the same neighborhood of the interactome. By contrast, genes expressed in different tissues are segregated in distinct network neighborhoods. Most important, we show that it is the integrity and the completeness of the expression of the disease module that determines disease manifestation in selected tissues. This approach allows us to construct a disease-tissue network that confirms known and predicts unexpected disease-tissue associations.
Collapse
|
42
|
Zhang XF, Ou-Yang L, Dai DQ, Wu MY, Zhu Y, Yan H. Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks. BMC Bioinformatics 2016; 17:358. [PMID: 27612563 PMCID: PMC5016887 DOI: 10.1186/s12859-016-1233-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Accepted: 08/31/2016] [Indexed: 12/31/2022] Open
Abstract
Background Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading. Results In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions. Conclusions Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China
| | - Le Ou-Yang
- College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
| | - Meng-Yun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China
| | - Yuan Zhu
- School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China
| | - Hong Yan
- Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
| |
Collapse
|
43
|
Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. MOLECULAR BIOSYSTEMS 2016; 12:2953-64. [PMID: 27510223 DOI: 10.1039/c6mb00476h] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.
Collapse
Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden.
| | | |
Collapse
|
44
|
Identification of Tissue-Specific Protein-Coding and Noncoding Transcripts across 14 Human Tissues Using RNA-seq. Sci Rep 2016; 6:28400. [PMID: 27329541 PMCID: PMC4916594 DOI: 10.1038/srep28400] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 06/01/2016] [Indexed: 12/15/2022] Open
Abstract
Many diseases and adverse drug reactions exhibit tissue specificity. To better understand the tissue-specific expression characteristics of transcripts in different human tissues, we deeply sequenced RNA samples from 14 different human tissues. After filtering many lowly expressed transcripts, 24,729 protein-coding transcripts and 1,653 noncoding transcripts were identified. By analyzing highly expressed tissue-specific protein-coding transcripts (TSCTs) and noncoding transcripts (TSNTs), we found that testis expressed the highest numbers of TSCTs and TSNTs. Brain, monocytes, ovary, and heart expressed more TSCTs than the rest tissues, whereas brain, placenta, heart, and monocytes expressed more TSNTs than other tissues. Co-expression network constructed based on the TSCTs and TSNTs showed that each hub TSNT was co-expressed with several TSCTs, allowing functional annotation of TSNTs. Important biological processes and KEGG pathways highly related to the specific functions or diseases of each tissue were enriched with the corresponding TSCTs. These TSCTs and TSNTs may participate in the tissue-specific physiological or pathological processes. Our study provided a unique data set and systematic analysis of expression characteristics and functions of both TSCTs and TSNTs based on 14 distinct human tissues, and could facilitate future investigation of the mechanisms behind tissue-specific diseases and adverse drug reactions.
Collapse
|
45
|
Liu W, Sun Z, Xie H. The analyses of human inherited disease and tissue-specific proteins in the interaction network. J Biomed Inform 2016; 61:10-8. [PMID: 27012904 DOI: 10.1016/j.jbi.2016.03.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 03/04/2016] [Accepted: 03/17/2016] [Indexed: 01/17/2023]
Abstract
OBJECTIVES With the announcement of human proteome and interaction data, it becomes possible to comprehensively investigate the tissue-expression and network properties of inherited disease proteins. In this study, our goal was to develop methods to map the disease and expression data and analyze the disorder-tissue associations. METHODS In this paper, we manually classified the human disease proteins into 22 disorder classes and systematically analyzed the properties of disease proteins in different disorder classes. Then, we investigated the similarity of different disorder classes by computing the overlap of different disorder proteins and networks. We proposed two novel measures, Enrichment Ratio and P-value for comparative analysis of disease proteins across tissues and revealed the associations between disorder classes and tissues/cells. RESULTS Compared with non-disease proteins, disease proteins tend to express in more tissues, have higher expression levels and interact with more other proteins in the network. The overlap percentages of networks are much higher than those of proteins, implying that different disorder classes usually influence each other by means of their interacting neighbors. The metabolic, muscular and hematologic proteins are related with most tissues/cells, and cancer proteins are closely associated with the disorders in immune cells. CONCLUSION This paper provided novel methods to investigate proteome-wide disease proteins and their interacting networks in order to understand different disease's associations.
Collapse
Affiliation(s)
- Wei Liu
- College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha 410073, China.
| | - Zhiqiang Sun
- College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha 410073, China
| | - Hongwei Xie
- College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Changsha 410073, China
| |
Collapse
|
46
|
Schaefer MH, Serrano L. Cell type-specific properties and environment shape tissue specificity of cancer genes. Sci Rep 2016; 6:20707. [PMID: 26856619 PMCID: PMC4746590 DOI: 10.1038/srep20707] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 01/11/2016] [Indexed: 12/21/2022] Open
Abstract
One of the biggest mysteries in cancer research remains why mutations in certain genes cause cancer only at specific sites in the human body. The poor correlation between the expression level of a cancer gene and the tissues in which it causes malignant transformations raises the question of which factors determine the tissue-specific effects of a mutation. Here, we explore why some cancer genes are associated only with few different cancer types (i.e., are specific), while others are found mutated in a large number of different types of cancer (i.e., are general). We do so by contrasting cellular functions of specific-cancer genes with those of general ones to identify properties that determine where in the body a gene mutation is causing malignant transformations. We identified different groups of cancer genes that did not behave as expected (i.e., DNA repair genes being tissue specific, immune response genes showing a bimodal specificity function or strong association of generally expressed genes to particular cancers). Analysis of these three groups demonstrates the importance of environmental impact for understanding why certain cancer genes are only involved in the development of some cancer types but are rarely found mutated in other types of cancer.
Collapse
Affiliation(s)
- Martin H Schaefer
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, Barcelona, Spain
| | - Luis Serrano
- EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, Barcelona, Spain
| |
Collapse
|
47
|
Will T, Helms V. PPIXpress: construction of condition-specific protein interaction networks based on transcript expression. Bioinformatics 2015; 32:571-8. [PMID: 26508756 DOI: 10.1093/bioinformatics/btv620] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 10/20/2015] [Indexed: 12/13/2022] Open
Abstract
UNLABELLED Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress. AVAILABILITY AND IMPLEMENTATION PPIXpress is available at https://sourceforge.net/projects/ppixpress/.
Collapse
Affiliation(s)
- Thorsten Will
- Center for Bioinformatics and Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
| | | |
Collapse
|
48
|
Cornish AJ, Filippis I, David A, Sternberg MJE. Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types. Genome Med 2015; 7:95. [PMID: 26330083 PMCID: PMC4557825 DOI: 10.1186/s13073-015-0212-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 07/31/2015] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Each cell type found within the human body performs a diverse and unique set of functions, the disruption of which can lead to disease. However, there currently exists no systematic mapping between cell types and the diseases they can cause. METHODS In this study, we integrate protein-protein interaction data with high-quality cell-type-specific gene expression data from the FANTOM5 project to build the largest collection of cell-type-specific interactomes created to date. We develop a novel method, called gene set compactness (GSC), that contrasts the relative positions of disease-associated genes across 73 cell-type-specific interactomes to map genes associated with 196 diseases to the cell types they affect. We conduct text-mining of the PubMed database to produce an independent resource of disease-associated cell types, which we use to validate our method. RESULTS The GSC method successfully identifies known disease-cell-type associations, as well as highlighting associations that warrant further study. This includes mast cells and multiple sclerosis, a cell population currently being targeted in a multiple sclerosis phase 2 clinical trial. Furthermore, we build a cell-type-based diseasome using the cell types identified as manifesting each disease, offering insight into diseases linked through etiology. CONCLUSIONS The data set produced in this study represents the first large-scale mapping of diseases to the cell types in which they are manifested and will therefore be useful in the study of disease systems. Overall, we demonstrate that our approach links disease-associated genes to the phenotypes they produce, a key goal within systems medicine.
Collapse
Affiliation(s)
- Alex J Cornish
- Department of Life Sciences, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
| | - Ioannis Filippis
- Department of Life Sciences, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
| | - Alessia David
- Department of Life Sciences, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
| | - Michael J E Sternberg
- Department of Life Sciences, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
| |
Collapse
|
49
|
Xu J, Li Y, Lu J, Pan T, Ding N, Wang Z, Shao T, Zhang J, Wang L, Li X. The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types. Nucleic Acids Res 2015; 43:8169-82. [PMID: 26304537 PMCID: PMC4787795 DOI: 10.1093/nar/gkv853] [Citation(s) in RCA: 140] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Accepted: 08/11/2015] [Indexed: 12/14/2022] Open
Abstract
Cross-talk between competitive endogenous RNAs (ceRNAs) through shared miRNAs represents a novel layer of gene regulation that plays important roles in the physiology and development of cancers. However, a global view of their system-level properties across various types of cancers is still unknown. Here, we constructed the mRNA related ceRNA–ceRNA interaction landscape across 20 cancer types by systematically analyzing molecular profiles of 5203 tumors and miRNA regulations. Our study highlights the conserved features shared by pan-cancer and higher similarity within similar origin cell type. Moreover, a core ceRNA network was identified. Function analysis identified a common theme of cancer hallmarks, however they exhibit phenotype-specific connectivity patterns. Besides, we found a marked rewiring in the ceRNA program between various cancers, and further revealed conserved and rewired network ceRNA hubs in each cancer, which were tensely competitive interactions to constitute conserved and cancer-specific modules. By providing mechanistic linkage between known cancer miRNAs, their mediated ceRNA–ceRNA interactions, and the associations with known cancer hallmarks, the inferred cancer ceRNA–ceRNA interaction landscape will serve as a powerful public resource for further biological discoveries of tumorigenesis.
Collapse
Affiliation(s)
- Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongsheng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jianping Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Pan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Zishan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tingting Shao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Jinwen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin 150081, Heilongjiang Province, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| |
Collapse
|
50
|
Yeger-Lotem E, Sharan R. Human protein interaction networks across tissues and diseases. Front Genet 2015; 6:257. [PMID: 26347769 PMCID: PMC4541328 DOI: 10.3389/fgene.2015.00257] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 07/17/2015] [Indexed: 11/13/2022] Open
Abstract
Protein interaction networks are an important framework for studying protein function, cellular processes, and genotype-to-phenotype relationships. While our view of the human interaction network is constantly expanding, less is known about networks that form in biologically important contexts such as within distinct tissues or in disease conditions. Here we review efforts to characterize these networks and to harness them to gain insights into the molecular mechanisms underlying human disease.
Collapse
Affiliation(s)
- Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev Beer-Sheva, Israel
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University Tel Aviv, Israel
| |
Collapse
|