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A TRPV2 interactome-based signature for prognosis in glioblastoma patients. Oncotarget 2018; 9:18400-18409. [PMID: 29719613 PMCID: PMC5915080 DOI: 10.18632/oncotarget.24843] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2017] [Accepted: 03/01/2018] [Indexed: 11/25/2022] Open
Abstract
Proteomics aids to the discovery and expansion of protein-protein interaction networks, which are key to understand molecular mechanisms in physiology and physiopathology, but also to infer protein function in a guilt-by-association fashion. In this study we use a systematic protein-protein interaction membrane yeast two-hybrid method to expand the interactome of TRPV2, a cation channel related to nervous system development. After validation of the interactome in silico, we define a TRPV2-interactome signature combining proteomics with the available physio-pathological data in Disgenet to find interactome-disease associations, highlighting nervous system disorders and neoplasms. The TRPV2-interactome signature against available experimental data is capable of discriminating overall risk in glioblastoma multiforme prognosis, progression, recurrence, and chemotherapy resistance. Beyond the impact on glioblastoma physiopathology, this study shows that combining systematic proteomics with in silico methods and available experimental data is key to open new perspectives to define novel biomarkers for diagnosis, prognosis and therapeutics in disease.
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Pan A, Lahiri C, Rajendiran A, Shanmugham B. Computational analysis of protein interaction networks for infectious diseases. Brief Bioinform 2015; 17:517-26. [PMID: 26261187 PMCID: PMC7110031 DOI: 10.1093/bib/bbv059] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Indexed: 12/13/2022] Open
Abstract
Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.
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Remmele CW, Luther CH, Balkenhol J, Dandekar T, Müller T, Dittrich MT. Integrated inference and evaluation of host-fungi interaction networks. Front Microbiol 2015; 6:764. [PMID: 26300851 PMCID: PMC4523839 DOI: 10.3389/fmicb.2015.00764] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 07/13/2015] [Indexed: 12/18/2022] Open
Abstract
Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data.
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Affiliation(s)
| | | | | | - Thomas Dandekar
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Tobias Müller
- Department of Bioinformatics, University of Würzburg Würzburg, Germany
| | - Marcus T Dittrich
- Department of Bioinformatics, University of Würzburg Würzburg, Germany ; Department of Human Genetics, University of Würzburg Würzburg, Germany
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Abstract
Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.
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Affiliation(s)
- Didier Rognan
- Laboratory for Therapeutical Innovation
- UMR7200 CNRS-Université de Strasbourg
- MEDALIS Drug Discovery Center
- 67400 Illkirch
- France
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Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine. ACTA ACUST UNITED AC 2014. [DOI: 10.1155/2014/471836] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.
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Villoutreix BO, Kuenemann MA, Poyet JL, Bruzzoni-Giovanelli H, Labbé C, Lagorce D, Sperandio O, Miteva MA. Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology. Mol Inform 2014; 33:414-437. [PMID: 25254076 PMCID: PMC4160817 DOI: 10.1002/minf.201400040] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2014] [Accepted: 04/21/2014] [Indexed: 12/13/2022]
Abstract
[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.
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Affiliation(s)
- Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Melaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Jean-Luc Poyet
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- IUH, Hôpital Saint-LouisParis, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Heriberto Bruzzoni-Giovanelli
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CIC, Clinical investigation center, Hôpital Saint-LouisParis, France
| | - Céline Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
- CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse59000 Lille, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 InsermParis 75013, France
- Inserm, U973Paris 75013, France
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