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Moncunill G, Scholzen A, Mpina M, Nhabomba A, Hounkpatin AB, Osaba L, Valls R, Campo JJ, Sanz H, Jairoce C, Williams NA, Pasini EM, Arteta D, Maynou J, Palacios L, Duran-Frigola M, Aponte JJ, Kocken CHM, Agnandji ST, Mas JM, Mordmüller B, Daubenberger C, Sauerwein R, Dobaño C. Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization. Sci Transl Med 2020; 12:12/543/eaay8924. [DOI: 10.1126/scitranslmed.aay8924] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 11/26/2019] [Accepted: 04/15/2020] [Indexed: 02/06/2023]
Abstract
Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity.
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Affiliation(s)
- Gemma Moncunill
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, CP 1929 Maputo, Mozambique
| | - Anja Scholzen
- Department of Medical Microbiology, Radboud University Medical Center, 6500 HB Nijmegen, Netherlands
| | - Maximillian Mpina
- Ifakara Health Institute, Bagamoyo Research and Training Centre. P.O. Box 74, Bagamoyo, Tanzania
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4002 Basel, Switzerland
- University of Basel, Petersplatz 1, 4001 Basel, Switzerland
| | - Augusto Nhabomba
- Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, CP 1929 Maputo, Mozambique
| | - Aurore Bouyoukou Hounkpatin
- Centre de Recherches Médicales de Lambaréné (CERMEL), BP 242 Lambaréné, Gabon
- Institute of Tropical Medicine and German Center for Infection Research, University of Tübingen, Wilhelmstraße 27, D-72074 Tübingen, Germany
| | - Lourdes Osaba
- Progenika Biopharma. A Grifols Company, S.A., 48160 Derio, Vizcaya, Spain
| | | | - Joseph J. Campo
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, CP 1929 Maputo, Mozambique
| | - Hèctor Sanz
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
| | - Chenjerai Jairoce
- Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, CP 1929 Maputo, Mozambique
| | - Nana Aba Williams
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
| | - Erica M. Pasini
- Department of Parasitology, Biomedical Primate Research Centre, Rijswijk, Netherlands
| | - David Arteta
- Progenika Biopharma. A Grifols Company, S.A., 48160 Derio, Vizcaya, Spain
| | - Joan Maynou
- Progenika Biopharma. A Grifols Company, S.A., 48160 Derio, Vizcaya, Spain
| | - Lourdes Palacios
- Progenika Biopharma. A Grifols Company, S.A., 48160 Derio, Vizcaya, Spain
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona). The Barcelona Institute of Science and Technology, 08028 Barcelona, Catalonia, Spain
| | - John J. Aponte
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
| | - Clemens H. M. Kocken
- Department of Parasitology, Biomedical Primate Research Centre, Rijswijk, Netherlands
| | - Selidji Todagbe Agnandji
- Centre de Recherches Médicales de Lambaréné (CERMEL), BP 242 Lambaréné, Gabon
- Institute of Tropical Medicine and German Center for Infection Research, University of Tübingen, Wilhelmstraße 27, D-72074 Tübingen, Germany
| | | | - Benjamin Mordmüller
- Institute of Tropical Medicine and German Center for Infection Research, University of Tübingen, Wilhelmstraße 27, D-72074 Tübingen, Germany
| | - Claudia Daubenberger
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4002 Basel, Switzerland
- University of Basel, Petersplatz 1, 4001 Basel, Switzerland
| | - Robert Sauerwein
- Department of Medical Microbiology, Radboud University Medical Center, 6500 HB Nijmegen, Netherlands
| | - Carlota Dobaño
- ISGlobal, Hospital Clínic–Universitat de Barcelona, E-08036 Barcelona, Catalonia, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Rua 12, Cambeve, Vila de Manhiça, CP 1929 Maputo, Mozambique
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Ramos PIP, Arge LWP, Lima NCB, Fukutani KF, de Queiroz ATL. Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets. Front Genet 2019; 10:1120. [PMID: 31798629 PMCID: PMC6863976 DOI: 10.3389/fgene.2019.01120] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 10/16/2019] [Indexed: 11/13/2022] Open
Abstract
Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.
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Affiliation(s)
- Pablo Ivan Pereira Ramos
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Luis Willian Pacheco Arge
- Laboratório de Genética Molecular e Biotecnologia Vegetal, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Kiyoshi F. Fukutani
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Fundação José Silveira, Salvador, Brazil
| | - Artur Trancoso L. de Queiroz
- Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
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McCusker JP, Dumontier M, Yan R, He S, Dordick JS, McGuinness DL. Finding melanoma drugs through a probabilistic knowledge graph. PeerJ Comput Sci 2017; 3:e106. [PMID: 37133296 PMCID: PMC10151034 DOI: 10.7717/peerj-cs.106] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 12/27/2016] [Indexed: 05/04/2023]
Abstract
Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.
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Affiliation(s)
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
| | - Rui Yan
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Sylvia He
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Jonathan S. Dordick
- Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
- Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Deborah L. McGuinness
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
- Center for Biotechnology & Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
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Herrando-Grabulosa M, Mulet R, Pujol A, Mas JM, Navarro X, Aloy P, Coma M, Casas C. Novel Neuroprotective Multicomponent Therapy for Amyotrophic Lateral Sclerosis Designed by Networked Systems. PLoS One 2016; 11:e0147626. [PMID: 26807587 PMCID: PMC4726541 DOI: 10.1371/journal.pone.0147626] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 01/05/2016] [Indexed: 12/20/2022] Open
Abstract
Amyotrophic Lateral Sclerosis is a fatal, progressive neurodegenerative disease characterized by loss of motor neuron function for which there is no effective treatment. One of the main difficulties in developing new therapies lies on the multiple events that contribute to motor neuron death in amyotrophic lateral sclerosis. Several pathological mechanisms have been identified as underlying events of the disease process, including excitotoxicity, mitochondrial dysfunction, oxidative stress, altered axonal transport, proteasome dysfunction, synaptic deficits, glial cell contribution, and disrupted clearance of misfolded proteins. Our approach in this study was based on a holistic vision of these mechanisms and the use of computational tools to identify polypharmacology for targeting multiple etiopathogenic pathways. By using a repositioning analysis based on systems biology approach (TPMS technology), we identified and validated the neuroprotective potential of two new drug combinations: Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine. In addition, we estimated their molecular mechanisms of action in silico and validated some of these results in a well-established in vitro model of amyotrophic lateral sclerosis based on cultured spinal cord slices. The results verified that Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine promote neuroprotection of motor neurons and reduce microgliosis.
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Affiliation(s)
- Mireia Herrando-Grabulosa
- Group of Neuroplasticity and Regeneration, Institut de Neurociències and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Bellaterra, Barcelona, Spain
| | - Roger Mulet
- Anaxomics Biotech SL, Barcelona, Catalonia, Spain
| | - Albert Pujol
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Catalonia, Spain
| | | | - Xavier Navarro
- Group of Neuroplasticity and Regeneration, Institut de Neurociències and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Bellaterra, Barcelona, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Mireia Coma
- Anaxomics Biotech SL, Barcelona, Catalonia, Spain
- * E-mail: (CC); (MC)
| | - Caty Casas
- Group of Neuroplasticity and Regeneration, Institut de Neurociències and Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona and Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Bellaterra, Barcelona, Spain
- * E-mail: (CC); (MC)
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Linghu B, Franzosa EA, Xia Y. Construction of functional linkage gene networks by data integration. Methods Mol Biol 2013. [PMID: 23192549 DOI: 10.1007/978-1-62703-107-3_14] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.
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Affiliation(s)
- Bolan Linghu
- Translational Sciences Department, Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
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Gonçalves JP, Francisco AP, Moreau Y, Madeira SC. Interactogeneous: disease gene prioritization using heterogeneous networks and full topology scores. PLoS One 2012. [PMID: 23185389 PMCID: PMC3501465 DOI: 10.1371/journal.pone.0049634] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Disease gene prioritization aims to suggest potential implications of genes in disease susceptibility. Often accomplished in a guilt-by-association scheme, promising candidates are sorted according to their relatedness to known disease genes. Network-based methods have been successfully exploiting this concept by capturing the interaction of genes or proteins into a score. Nonetheless, most current approaches yield at least some of the following limitations: (1) networks comprise only curated physical interactions leading to poor genome coverage and density, and bias toward a particular source; (2) scores focus on adjacencies (direct links) or the most direct paths (shortest paths) within a constrained neighborhood around the disease genes, ignoring potentially informative indirect paths; (3) global clustering is widely applied to partition the network in an unsupervised manner, attributing little importance to prior knowledge; (4) confidence weights and their contribution to edge differentiation and ranking reliability are often disregarded. We hypothesize that network-based prioritization related to local clustering on graphs and considering full topology of weighted gene association networks integrating heterogeneous sources should overcome the above challenges. We term such a strategy Interactogeneous. We conducted cross-validation tests to assess the impact of network sources, alternative path inclusion and confidence weights on the prioritization of putative genes for 29 diseases. Heat diffusion ranking proved the best prioritization method overall, increasing the gap to neighborhood and shortest paths scores mostly on single source networks. Heterogeneous associations consistently delivered superior performance over single source data across the majority of methods. Results on the contribution of confidence weights were inconclusive. Finally, the best Interactogeneous strategy, heat diffusion ranking and associations from the STRING database, was used to prioritize genes for Parkinson’s disease. This method effectively recovered known genes and uncovered interesting candidates which could be linked to pathogenic mechanisms of the disease.
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Affiliation(s)
- Joana P. Gonçalves
- Knowledge Discovery and Bioinformatics Group, INESC-ID, Lisbon, Portugal
- Computer Science and Engineering Department, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
- * E-mail: (JPG); (SCM)
| | - Alexandre P. Francisco
- Knowledge Discovery and Bioinformatics Group, INESC-ID, Lisbon, Portugal
- Computer Science and Engineering Department, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
| | - Yves Moreau
- Electrical Engineering Department, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Sara C. Madeira
- Knowledge Discovery and Bioinformatics Group, INESC-ID, Lisbon, Portugal
- Computer Science and Engineering Department, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
- * E-mail: (JPG); (SCM)
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Abstract
In order to ensure their function(s) in the cell, proteins are organized in machineries, underlaid by a complex network of interactions. Identifying protein interactions is thus crucial to our understanding of cell functioning. Technical advances in molecular biology and genomic technology now allow for the systematic study of the interactions occurring in a given organism. This review first presents the techniques readily available to microbiologists for studying protein-protein interactions in bacteria, as well as their usability for high-throughput studies. Two types of techniques need to be considered: (1) the isolation of protein complexes from the organism of interest by affinity purification, and subsequent identification of the complex partners by mass spectrometry and (2) two-hybrid techniques, in general based on the production of two recombinant proteins whose interaction has to be tested in a reporter cell. Next, we summarize the bacterial interactomes already published. Finally, the strengths and pitfalls of the techniques are discussed, together with the potential prospect of interactome studies in bacteria.
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Kariminejad R, Lind-Thomsen A, Tümer Z, Erdogan F, Ropers HH, Tommerup N, Ullmann R, Møller RS. High frequency of rare copy number variants affecting functionally related genes in patients with structural brain malformations. Hum Mutat 2011; 32:1427-35. [DOI: 10.1002/humu.21585] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Accepted: 07/26/2011] [Indexed: 01/20/2023]
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Fahey ME, Bennett MJ, Mahon C, Jäger S, Pache L, Kumar D, Shapiro A, Rao K, Chanda SK, Craik CS, Frankel AD, Krogan NJ. GPS-Prot: a web-based visualization platform for integrating host-pathogen interaction data. BMC Bioinformatics 2011; 12:298. [PMID: 21777475 PMCID: PMC3213248 DOI: 10.1186/1471-2105-12-298] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Accepted: 07/22/2011] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The increasing availability of HIV-host interaction datasets, including both physical and genetic interactions, has created a need for software tools to integrate and visualize the data. Because these host-pathogen interactions are extensive and interactions between human proteins are found within many different databases, it is difficult to generate integrated HIV-human interaction networks. RESULTS We have developed a web-based platform, termed GPS-Prot http://www.gpsprot.org, that allows for facile integration of different HIV interaction data types as well as inclusion of interactions between human proteins derived from publicly-available databases, including MINT, BioGRID and HPRD. The software has the ability to group proteins into functional modules or protein complexes, generating more intuitive network representations and also allows for the uploading of user-generated data. CONCLUSIONS GPS-Prot is a software tool that allows users to easily create comprehensive and integrated HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.
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Affiliation(s)
- Marie E Fahey
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, 1700 4th Street, San Francisco, CA 94158, USA
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HelmCoP: an online resource for helminth functional genomics and drug and vaccine targets prioritization. PLoS One 2011; 6:e21832. [PMID: 21760913 PMCID: PMC3132748 DOI: 10.1371/journal.pone.0021832] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2011] [Accepted: 06/08/2011] [Indexed: 12/31/2022] Open
Abstract
A vast majority of the burden from neglected tropical diseases result from helminth infections (nematodes and platyhelminthes). Parasitic helminthes infect over 2 billion, exerting a high collective burden that rivals high-mortality conditions such as AIDS or malaria, and cause devastation to crops and livestock. The challenges to improve control of parasitic helminth infections are multi-fold and no single category of approaches will meet them all. New information such as helminth genomics, functional genomics and proteomics coupled with innovative bioinformatic approaches provide fundamental molecular information about these parasites, accelerating both basic research as well as development of effective diagnostics, vaccines and new drugs. To facilitate such studies we have developed an online resource, HelmCoP (Helminth Control and Prevention), built by integrating functional, structural and comparative genomic data from plant, animal and human helminthes, to enable researchers to develop strategies for drug, vaccine and pesticide prioritization, while also providing a useful comparative genomics platform. HelmCoP encompasses genomic data from several hosts, including model organisms, along with a comprehensive suite of structural and functional annotations, to assist in comparative analyses and to study host-parasite interactions. The HelmCoP interface, with a sophisticated query engine as a backbone, allows users to search for multi-factorial combinations of properties and serves readily accessible information that will assist in the identification of various genes of interest. HelmCoP is publicly available at: http://www.nematode.net/helmcop.html.
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Geschwind DH, Konopka G. Neuroscience in the era of functional genomics and systems biology. Nature 2009; 461:908-15. [PMID: 19829370 DOI: 10.1038/nature08537] [Citation(s) in RCA: 145] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Advances in genetics and genomics have fuelled a revolution in discovery-based, or hypothesis-generating, research that provides a powerful complement to the more directly hypothesis-driven molecular, cellular and systems neuroscience. Genetic and functional genomic studies have already yielded important insights into neuronal diversity and function, as well as disease. One of the most exciting and challenging frontiers in neuroscience involves harnessing the power of large-scale genetic, genomic and phenotypic data sets, and the development of tools for data integration and mining. Methods for network analysis and systems biology offer the promise of integrating these multiple levels of data, connecting molecular pathways to nervous system function.
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Affiliation(s)
- Daniel H Geschwind
- Program in Neurogenetics and Neurobehavioural Genetics, Department of Neurology and Semel Institute, David Geffen School of Medicine, Los Angeles, California 90095, USA.
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12
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Linghu B, Snitkin ES, Hu Z, Xia Y, Delisi C. Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol 2009; 10:R91. [PMID: 19728866 PMCID: PMC2768980 DOI: 10.1186/gb-2009-10-9-r91] [Citation(s) in RCA: 180] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2009] [Revised: 07/09/2009] [Accepted: 09/03/2009] [Indexed: 11/16/2022] Open
Abstract
An evidence-weighted functional-linkage network of human genes reveals associations among diseases that share no known disease genes and have dissimilar phenotypes
We integrate 16 genomic features to construct an evidence-weighted functional-linkage network comprising 21,657 human genes. The functional-linkage network is used to prioritize candidate genes for 110 diseases, and to reliably disclose hidden associations between disease pairs having dissimilar phenotypes, such as hypercholesterolemia and Alzheimer's disease. Many of these disease-disease associations are supported by epidemiology, but with no previous genetic basis. Such associations can drive novel hypotheses on molecular mechanisms of diseases and therapies.
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Affiliation(s)
- Bolan Linghu
- Bioinformatics Program, Boston University, 24 Cummington Street, Boston, MA 02215, USA.
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Bureeva S, Zvereva S, Romanov V, Serebryiskaya T. Manual annotation of protein interactions. Methods Mol Biol 2009; 563:75-95. [PMID: 19597781 DOI: 10.1007/978-1-60761-175-2_5] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein interactions are the basic building blocks for assembly of pathways and networks. Almost any biologically meaningful functionality (for instance, linear signaling pathways, chains of metabolic reactions, transcription factor dimmers, protein complexes of transcriptosome, gene-disease associations) can be represented as a combination of binary relationships between "network objects" (genes, proteins, RNA species, bioactive compounds). Naturally, the assembled pathways and networks are only as good as their "weakest" link (i.e., a wrongly assigned interaction), and the errors multiply in multi-step pathways. Therefore, the utility of "systems biology" is fundamentally dependent on quality and relevance of protein interactions. The second important parameter is the sheer number of interactions assembled in the database. One needs a "critical mass" of species-specific interactions in order to build cohesive networks for a gene list, not a constellation of non-connected proteins and protein pairs. The third issue is semantic consistency between interactions of different types. Transient physical signal transduction interactions, reactions of endogenous metabolism, transcription factor-promoter binding, and kinetic drug-target interactions are all very different in nature. Yet, they have to fit well into one database format and be consistent in order to be useful in reconstruction of cellular processes.High-quality protein interactions are available in peer-reviewed "small experiment" literature and, to a much smaller extent, patents. However, it is very challenging to find the interactions, annotate with searchable (and computable) parameters, catalogue in the database format in computer readable form, and assemble into a database. There are hundreds of thousands of mammalian interactions scattered in tens of thousands of papers in a few thousands of scientific journals. There are no widely used standards for reporting the interactions in scientific texts and, therefore, text-mining tools have only limited applicability. In order to generate a meaningful database of protein interactions, one needs a well-developed technology of manual curation, equipped with computational solutions, managerial procedures, quality control, and users' feedback. Here we describe our ever-evolving annotation approach, the important annotation issues and our solutions, and the mammalian protein interactions database MetaBase which we have been working on for over 8 years.
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Chautard E, Thierry-Mieg N, Ricard-Blum S. Interaction networks: from protein functions to drug discovery. A review. ACTA ACUST UNITED AC 2008; 57:324-33. [PMID: 19070972 DOI: 10.1016/j.patbio.2008.10.004] [Citation(s) in RCA: 84] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2008] [Accepted: 10/17/2008] [Indexed: 02/07/2023]
Abstract
Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.
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Affiliation(s)
- E Chautard
- UMR 5086 CNRS, institut de biologie et chimie des protéines, université Lyon 1, IFR, 128 biosciences Lyon-Gerland, 7, passage du Vercors, 69367 Lyon cedex 07, France
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