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Alonso-Lavin AJ, Bajić D, Poyatos JF. Tolerance to NADH/NAD + imbalance anticipates aging and anti-aging interventions. iScience 2021; 24:102697. [PMID: 34195572 PMCID: PMC8239738 DOI: 10.1016/j.isci.2021.102697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/26/2021] [Accepted: 06/04/2021] [Indexed: 12/31/2022] Open
Abstract
Redox couples coordinate cellular function, but the consequences of their imbalances are unclear. This is somewhat associated with the limitations of their experimental quantification. Here we circumvent these difficulties by presenting an approach that characterizes fitness-based tolerance profiles to redox couple imbalances using an in silico representation of metabolism. Focusing on the NADH/NAD+ redox couple in yeast, we demonstrate that reductive disequilibria generate metabolic syndromes comparable to those observed in cancer cells. The tolerance of yeast mutants to redox disequilibrium can also explain 30% of the variability in their experimentally measured chronological lifespan. Moreover, by predicting the significance of some metabolites to help stand imbalances, we correctly identify nutrients underlying mechanisms of pathology, lifespan-protecting molecules, or caloric restriction mimetics. Tolerance to redox imbalances becomes, in this way, a sound framework to recognize properties of the aging phenotype while providing a consistent biological rationale to assess anti-aging interventions. We simulate how imbalances in NADH/NAD+ ratio modify cellular metabolic behavior This reveals a mechanism to understand metabolic alterations at low growth rates Tolerance to imbalance explains experimentally measured lifespan in yeast We predict lifespan-protecting metabolites in yeast, animal, and human models
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Affiliation(s)
- Alvar J. Alonso-Lavin
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
| | - Djordje Bajić
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Microbial Sciences Institute, Yale University, New Haven, CT, USA
| | - Juan F. Poyatos
- Logic of Genomic Systems Laboratory (CNB-CSIC), Darwin 3, Campus de Cantoblanco, 28049 Madrid, Spain
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, USA
- Corresponding author
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Santosh Kumar HS, Kumar V, Pattar S, Telkar S. Towards the construction of an interactome for Human WD40 protein family. Bioinformation 2016; 12:54-61. [PMID: 28104961 PMCID: PMC5237648 DOI: 10.6026/97320630012054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 02/25/2016] [Accepted: 02/25/2016] [Indexed: 12/19/2022] Open
Abstract
WD40 proteins are involved in a variety of protein-protein interactions as part of a multi-protein assembly modulating diverse and critical cellular process. It is known that several proteins of this family have been implicated in different disorders such as developmental abnormalities and cancer. However, molecular functions of many proteins in this family are yet unknown and it is of clinical interest. Therefore, it is of interest to define, construct, understand, analyze, evaluate, redefine and refine an interactome for WD40 protein family. We used data from literature mining using Cytoscape followed by linear regression analysis between Betweenness centrality and stress scores to define a model to filter the nodes in a representative WD40 interactome construction. We identified 10 ranked nodes in this analysis and subsequent microarray data selected three of them in insulin resistance that is further demonstrated in HepG2 cell culture models. We also observed the expression of GRWD1, RBBP5 and WDR5 genes during perturbation. Thus, we report hub nodes of WD40 interactome in insulin resistance. It should be noted that the pipeline using protein interaction network help find new proteins of clinical importance.
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Affiliation(s)
| | - Vadlapudi Kumar
- Department of Biochemistry, Davanagere University, Shivagangothri, Davanagere - 577002, Karnataka, India
| | - Sharath Pattar
- National Bureau of Agriculturally Important Insects, Hebbal, Bengaluru, Karnataka, India
| | - Sandeep Telkar
- Department of Biotechnology and Bioinformatics, Kuvempu University,Shankaraghatta - 577451, Karnataka, India
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Mishra S, Jakkala K, Srinivasan R, Arumugam M, Ranjeri R, Gupta P, Rajeswari H, Ajitkumar P. NDK Interacts with FtsZ and Converts GDP to GTP to Trigger FtsZ Polymerisation--A Novel Role for NDK. PLoS One 2015; 10:e0143677. [PMID: 26630542 PMCID: PMC4668074 DOI: 10.1371/journal.pone.0143677] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Accepted: 11/09/2015] [Indexed: 11/19/2022] Open
Abstract
Introduction Nucleoside diphosphate kinase (NDK), conserved across bacteria to humans, synthesises NTP from NDP and ATP. The eukaryotic homologue, the NDPK, uses ATP to phosphorylate the tubulin-bound GDP to GTP for tubulin polymerisation. The bacterial cytokinetic protein FtsZ, which is the tubulin homologue, also uses GTP for polymerisation. Therefore, we examined whether NDK can interact with FtsZ to convert FtsZ-bound GDP and/or free GDP to GTP to trigger FtsZ polymerisation. Methods Recombinant and native NDK and FtsZ proteins of Mycobacterium smegmatis and Mycobacterium tuberculosis were used as the experimental samples. FtsZ polymersation was monitored using 90° light scattering and FtsZ polymer pelleting assays. The γ32P-GTP synthesised by NDK from GDP and γ32P-ATP was detected using thin layer chromatography and quantitated using phosphorimager. The FtsZ bound 32P-GTP was quantitated using phosphorimager, after UV-crosslinking, followed by SDS-PAGE. The NDK-FtsZ interaction was determined using Ni2+-NTA-pulldown assay and co-immunoprecipitation of the recombinant and native proteins in vitro and ex vivo, respectively. Results NDK triggered instantaneous polymerisation of GDP-precharged recombinant FtsZ in the presence of ATP, similar to the polymerisation of recombinant FtsZ (not GDP-precharged) upon the direct addition of GTP. Similarly, NDK triggered polymerisation of recombinant FtsZ (not GDP-precharged) in the presence of free GDP and ATP as well. Mutant NDK, partially deficient in GTP synthesis from ATP and GDP, triggered low level of polymerisation of MsFtsZ, but not of MtFtsZ. As characteristic of NDK’s NTP substrate non-specificity, it used CTP, TTP, and UTP also to convert GDP to GTP, to trigger FtsZ polymerisation. The NDK of one mycobacterial species could trigger the polymerisation of the FtsZ of another mycobacterial species. Both the recombinant and the native NDK and FtsZ showed interaction with each other in vitro and ex vivo, alluding to the possibility of direct phosphorylation of FtsZ-bound GDP by NDK. Conclusion Irrespective of the bacterial species, NDK interacts with FtsZ in vitro and ex vivo and, through the synthesis of GTP from FtsZ-bound GDP and/or free GDP, and ATP (CTP/TTP/UTP), triggers FtsZ polymerisation. The possible biological context of this novel activity of NDK is presented.
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Affiliation(s)
- Saurabh Mishra
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Kishor Jakkala
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Ramanujam Srinivasan
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Muthu Arumugam
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Raghavendra Ranjeri
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Prabuddha Gupta
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Haryadi Rajeswari
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
| | - Parthasarathi Ajitkumar
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
- * E-mail:
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Ponder EL, Freundlich JS, Sarker M, Ekins S. Computational models for neglected diseases: gaps and opportunities. Pharm Res 2013; 31:271-7. [PMID: 23990313 DOI: 10.1007/s11095-013-1170-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2013] [Accepted: 07/28/2013] [Indexed: 01/22/2023]
Abstract
Neglected diseases, such as Chagas disease, African sleeping sickness, and intestinal worms, affect millions of the world's poor. They disproportionately affect marginalized populations, lack effective treatments or vaccines, or existing products are not accessible to the populations affected. Computational approaches have been used across many of these diseases for various aspects of research or development, and yet data produced by computational approaches are not integrated and widely accessible to others. Here, we identify gaps in which computational approaches have been used for some neglected diseases and not others. We also make recommendations for the broad-spectrum integration of these techniques into a neglected disease drug discovery and development workflow.
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Affiliation(s)
- Elizabeth L Ponder
- Center for Emerging and Neglected Diseases, Berkeley, 444A Li Ka Shing Center, Berkeley, California, 94720-3370, USA,
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 512] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Affiliation(s)
- Nagasuma Chandra
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India ,
| | - Jyothi Padiadpu
- Indian Institute of Science, Department of Biochemistry,
Bangalore – 560012, India
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7
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Kim HU, Sohn SB, Lee SY. Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J 2011; 7:330-42. [DOI: 10.1002/biot.201100159] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2011] [Revised: 09/09/2011] [Accepted: 10/08/2011] [Indexed: 11/08/2022]
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Systems biology of tuberculosis. Tuberculosis (Edinb) 2011; 91:487-96. [DOI: 10.1016/j.tube.2011.02.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 02/09/2011] [Accepted: 02/14/2011] [Indexed: 01/28/2023]
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Chandra N. Computational approaches for drug target identification in pathogenic diseases. Expert Opin Drug Discov 2011; 6:975-9. [DOI: 10.1517/17460441.2011.611128] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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10
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Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 2011; 6:1290-307. [PMID: 21886097 DOI: 10.1038/nprot.2011.308] [Citation(s) in RCA: 980] [Impact Index Per Article: 75.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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Affiliation(s)
- Jan Schellenberger
- Bioinformatics Program, University of California San Diego, La Jolla, California, USA
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Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development. Tuberculosis (Edinb) 2011; 91:479-86. [PMID: 21782516 DOI: 10.1016/j.tube.2011.06.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 05/11/2011] [Accepted: 06/12/2011] [Indexed: 11/23/2022]
Abstract
It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery.
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Baths V, Roy U, Singh T. Disruption of cell wall fatty acid biosynthesis in Mycobacterium tuberculosis using a graph theoretic approach. Theor Biol Med Model 2011; 8:5. [PMID: 21453530 PMCID: PMC3087688 DOI: 10.1186/1742-4682-8-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Accepted: 03/31/2011] [Indexed: 12/01/2022] Open
Abstract
Fatty acid biosynthesis of Mycobacterium tuberculosis was analyzed using graph theory and influential (impacting) proteins were identified. The graphs (digraphs) representing this biological network provide information concerning the connectivity of each protein or metabolite in a given pathway, providing an insight into the importance of various components in the pathway, and this can be quantitatively analyzed. Using a graph theoretic algorithm, the most influential set of proteins (sets of {1, 2, 3}, etc.), which when eliminated could cause a significant impact on the biosynthetic pathway, were identified. This set of proteins could serve as drug targets. In the present study, the metabolic network of Mycobacterium tuberculosis was constructed and the fatty acid biosynthesis pathway was analyzed for potential drug targeting. The metabolic network was constructed using the KEGG LIGAND database and subjected to graph theoretical analysis. The nearness index of a protein was used to determine the influence of the said protein on other components in the network, allowing the proteins in a pathway to be ordered according to their nearness indices. A method for identifying the most strategic nodes to target for disrupting the metabolic networks is proposed, aiding the development of new drugs to combat this deadly disease.
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Affiliation(s)
- Veeky Baths
- Department of Biological Sciences, Birla Institute of Technology & Science (BITS) Pilani K K BIRLA Goa Campus, Goa 403 726, India.
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Bhat AG, Vashisht R, Chandra N. Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid. SYSTEMS AND SYNTHETIC BIOLOGY 2011; 4:299-309. [PMID: 22132057 DOI: 10.1007/s11693-011-9075-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 01/07/2011] [Accepted: 02/03/2011] [Indexed: 10/18/2022]
Abstract
UNLABELLED Complex biological systems exhibit a property of robustness at all levels of organization. Through different mechanisms, the system tries to sustain stress such as due to starvation or drug exposure. To explore whether reconfiguration of the metabolic networks is used as a means to achieve robustness, we have studied possible metabolic adjustments in Mtb upon exposure to isoniazid (INH), a front-line clinical drug. The redundancy in the genome of M. tuberculosis (Mtb) makes it an attractive system to explore if alternate routes of metabolism exist in the bacterium. While the mechanism of action of INH is well studied, its effect on the overall metabolism is not well characterized. Using flux balance analysis, inhibiting the fluxes flowing through the reactions catalyzed by Rv1484, the target of INH, significantly changes the overall flux profiles. At the pathway level, activation or inactivation of certain pathways distant from the target pathway, are seen. Metabolites such as NADPH are shown to reduce drastically, while fatty acids tend to accumulate. The overall biomass also decreases with increasing inhibition levels. Inhibition studies, pathway level clustering and comparison of the flux profiles with the gene expression data indicate the activation of folate metabolism, ubiquinone metabolism, and metabolism of certain amino acids. This analysis provides insights useful for target identification and designing strategies for combination therapy. Insights gained about the role of individual components of a system and their interactions will also provide a basis for reconstruction of whole systems through synthetic biology approaches. ELECTRONIC SUPPLEMENTARY MATERIAL The online version of this article (doi:10.1007/s11693-011-9075-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ashwini G Bhat
- Bioinformatics Centre, Indian Institute of Science, Bangalore, India
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Lee DY, Chung BKS, Yusufi FN, Selvarasu S. In silico genome-scale modeling and analysis for identifying anti-tubercular drug targets. Drug Dev Res 2010. [DOI: 10.1002/ddr.20408] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Ekins S, Freundlich JS, Choi I, Sarker M, Talcott C. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. Trends Microbiol 2010; 19:65-74. [PMID: 21129975 DOI: 10.1016/j.tim.2010.10.005] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2010] [Revised: 10/15/2010] [Accepted: 10/29/2010] [Indexed: 01/31/2023]
Abstract
We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, USA.
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Beste DJV, McFadden J. System-level strategies for studying the metabolism of Mycobacterium tuberculosis. MOLECULAR BIOSYSTEMS 2010; 6:2363-72. [PMID: 20938502 PMCID: PMC3172586 DOI: 10.1039/c003757p] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2010] [Accepted: 09/08/2010] [Indexed: 12/01/2022]
Abstract
Despite decades of research many aspects of the biology of Mycobacterium tuberculosis remain unclear and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking M. tuberculosis's metabolism and pathogenesis have renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes whereas recent advances in computational systems biology and high throughput experimental technologies now allow metabolism to be studied on a genome scale. Here we discuss the progress being made in applying system level approaches to studying the metabolism of this important pathogen. The information from these studies will fundamentally change our approach to tuberculosis research and lead to new targets for therapeutic drugs and vaccines.
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Affiliation(s)
- Dany J. V. Beste
- Faculty of Health and Medical Sciences , University of Surrey , Guildford GU2 7XH , UK . ; ; Fax: +44 (0)1483-300374 ; Tel: +44 (0)1483-696494
| | - Johnjoe McFadden
- Faculty of Health and Medical Sciences , University of Surrey , Guildford GU2 7XH , UK . ; ; Fax: +44 (0)1483-300374 ; Tel: +44 (0)1483-696494
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Abstract
Despite decades of research, many aspects of the biology of Mycobacterium tuberculosis remain unclear, and this is reflected in the antiquated tools available to treat and prevent tuberculosis and consequently this disease remains a serious public health problem. Important discoveries linking the metabolism of M. tuberculosis and pathogenesis has renewed interest in this area of research. Previous experimental studies were limited to the analysis of individual genes or enzymes, whereas recent advances in computational systems biology and high-throughput experimental technologies now allows metabolism to be studied on a genome scale. In the present article, we discuss the progress being made in applying system-level approaches to study the metabolism of this important pathogen.
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Raman K. Construction and analysis of protein-protein interaction networks. AUTOMATED EXPERIMENTATION 2010; 2:2. [PMID: 20334628 PMCID: PMC2834675 DOI: 10.1186/1759-4499-2-2] [Citation(s) in RCA: 101] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2009] [Accepted: 02/15/2010] [Indexed: 12/28/2022]
Abstract
Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.
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Affiliation(s)
- Karthik Raman
- Department of Biochemistry, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland.
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Raman K, Bhat AG, Chandra N. A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis. MOLECULAR BIOSYSTEMS 2009; 6:516-30. [PMID: 20174680 DOI: 10.1039/b912129c] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.
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Affiliation(s)
- Karthik Raman
- Bioinformatics Centre, Indian Institute of Science, Bangalore - 560012, India.
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