A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis

BMC Systems Biology

Nagasuma Chandra¹  Praveen Anand¹  Sumanta Mukherjee¹  Priyanka Baloni¹  Soma Ghosh¹ 

[1] Department of Biochemistry, Indian Institute of Science, Bangalore, India

关键词: Protein structure;    Mycobacterium tuberculosis;    Multi-scale analysis;    Essential genes;   

Others  :  1141709
DOI  :  10.1186/1752-0509-7-132

 received in 2013-04-25, accepted in 2013-11-20,  发布年份 2013

【 摘 要 】

Background

The set of indispensable genes that are required by an organism to grow and sustain life are termed as essential genes. There is a strong interest in identification of the set of essential genes, particularly in pathogens, not only for a better understanding of the pathogen biology, but also for identifying drug targets and the minimal gene set for the organism. Essentiality is inherently a systems property and requires consideration of the system as a whole for their identification. The available experimental approaches capture some aspects but each method comes with its own limitations. Moreover, they do not explain the basis for essentiality in most cases. A powerful prediction method to recognize this gene pool including rationalization of the known essential genes in a given organism would be very useful. Here we describe a multi-level multi-scale approach to identify the essential gene pool in a deadly pathogen, Mycobacterium tuberculosis.

Results

The multi-level workflow analyses the bacterial cell by studying (a) genome-wide gene expression profiles to identify the set of genes which show consistent and significant levels of expression in multiple samples of the same condition, (b) indispensability for growth by using gene expression integrated flux balance analysis of a genome-scale metabolic model, (c) importance for maintaining the integrity and flow in a protein-protein interaction network and (d) evolutionary conservation in a set of genomes of the same ecological niche. In the gene pool identified, the functional basis for essentiality has been addressed by studying residue level conservation and the sub-structure at the ligand binding pockets, from which essential amino acid residues in that pocket have also been identified. 283 genes were identified as essential genes with high-confidence. An agreement of about 73.5% is observed with that obtained from the experimental transposon mutagenesis technique. A large proportion of the identified genes belong to the class of intermediary metabolism and respiration.

Conclusions

The multi-scale, multi-level approach described can be generally applied to other pathogens as well. The essential gene pool identified form a basis for designing experiments to probe their finer functional roles and also serve as a ready shortlist for identifying drug targets.

【 授权许可】

   
2013 Ghosh et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

【 参考文献 】

• [1]Arigoni F, Talabot F, Peitsch M, Edgerton MD, Meldrum E, Allet E, Fish R, Jamotte T, Curchod ML, Loferer H: A genome-based approach for the identification of essential bacterial genes. Nat Biotechnol 1998, 16(9):851-856.
• [2]Jordan IK, Rogozin IB, Wolf YI, Koonin EV: Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res 2002, 12(6):962-968.
• [3]Cole ST: Comparative mycobacterial genomics as a tool for drug target and antigen discovery. Eur Respir J Suppl 2002, 36:78s-86s.
• [4]Miesel L, Greene J, Black TA: Genetic strategies for antibacterial drug discovery. Nat Rev Genet 2003, 4(6):442-456.
• [5]Maertzdorf J, Weiner J 3rd, Kaufmann SH: Enabling biomarkers for tuberculosis control. Int J Tuberc Lung Dis 2012, 16(9):1140-1148.
• [6]Joyce AR, Palsson BO: Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach. Progress in drug research Fortschritte der Arzneimittelforschung Progres des recherches pharmaceutiques 2007, 64(265):267-309.
• [7]Chalker AF, Minehart HW, Hughes NJ, Koretke KK, Lonetto MA, Brinkman KK, Warren PV, Lupas A, Stanhope MJ, Brown JR, et al.: Systematic identification of selective essential genes in Helicobacter pylori by genome prioritization and allelic replacement mutagenesis. J Bacteriol 2001, 183(4):1259-1268.
• [8]del Rio G, Koschutzki D, Coello G: How to identify essential genes from molecular networks? BMC Syst Biol 2009, 3:102. BioMed Central Full Text
• [9]Forsyth RA, Haselbeck RJ, Ohlsen KL, Yamamoto RT, Xu H, Trawick JD, Wall D, Wang L, Brown-Driver V, Froelich JM, et al.: A genome-wide strategy for the identification of essential genes in staphylococcus aureus. Mol Microbiol 2002, 43(6):1387-1400.
• [10]Gerdes SY, Scholle MD, Campbell JW, Balazsi G, Ravasz E, Daugherty MD, Somera AL, Kyrpides NC, Anderson I, Gelfand MS, et al.: Experimental determination and system level analysis of essential genes in escherichia coli MG1655. J Bacteriol 2003, 185(19):5673-5684.
• [11]Plaimas K, Eils R, Konig R: Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst Biol 2010, 4:56. BioMed Central Full Text
• [12]Gil R, Silva FJ, Pereto J, Moya A: Determination of the core of a minimal bacterial gene set. Microbiol Mol Biol Rev 2004, 68(3):518-537.
• [13]Peters JL, Cnudde F, Gerats T: Forward genetics and map-based cloning approaches. Trends Plant Sci 2003, 8(10):484-491.
• [14]Boutros M, Ahringer J: The art and design of genetic screens: RNA interference. Nat Rev Genet 2008, 9(7):554-566.
• [15]Green RA, Kao HL, Audhya A, Arur S, Mayers JR, Fridolfsson HN, Schulman M, Schloissnig S, Niessen S, Laband K, et al.: A high-resolution C. elegans essential gene network based on phenotypic profiling of a complex tissue. Cell 2011, 145(3):470-482.
• [16]Griffin JE, Gawronski JD, Dejesus MA, Ioerger TR, Akerley BJ, Sassetti CM: High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog 2011, 7(9):e1002251.
• [17]Sassetti CM, Rubin EJ: Genetic requirements for mycobacterial survival during infection. Proc Natl Acad Sci USA 2003, 100(22):12989-12994.
• [18]Sassetti CM, Boyd DH, Rubin EJ: Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 2003, 48(1):77-84.
• [19]Zhang YJ, Ioerger TR, Huttenhower C, Long JE, Sassetti CM, Sacchettini JC, Rubin EJ: Global assessment of genomic regions required for growth in mycobacterium tuberculosis. PLoS Pathog 2012, 8(9):e1002946.
• [20]Zhang R, Lin Y: DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res 2009, 37(Database issue):D455-D458.
• [21]Chen WH, Minguez P, Lercher MJ, Bork P: OGEE: an online gene essentiality database. Nucleic Acids Res 2012, 40(Database issue):D901-D906.
• [22]Gustafson AM, Snitkin ES, Parker SC, DeLisi C, Kasif S: Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genomics 2006, 7:265. BioMed Central Full Text
• [23]Lee D-S, Burd H, Liu J, Almaas E, Wiest O, Barabási A-L, Oltvai ZN, Kapatral V: Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacteriol 2009, 191(12):4015-4024.
• [24]Suthers PF, Dasika MS, Kumar VS, Denisov G, Glass JI, Maranas CD: A genome-scale metabolic reconstruction of mycoplasma genitalium, iPS189. PLoS Comput Biol 2009, 5(2):e1000285.
• [25]Schilling CH, Covert MW, Famili I, Church GM, Edwards JS, Palsson BO: Genome-scale metabolic model of helicobacter pylori 26695. J Bacteriol 2002, 184(16):4582-4593.
• [26]Oberhardt MA, Puchałka J, Fryer KE, Dos Santos VAM, Papin JA: Genome-scale metabolic network analysis of the opportunistic pathogen pseudomonas aeruginosa PAO1. J Bacteriol 2008, 190(8):2790-2803.
• [27]Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW: A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150(2):389-401.
• [28]Chandra N: Computational approaches for drug target identification in pathogenic diseases. Expert Opin Drug Discov 2011, 6(10):975-979.
• [29]Conant GC, Wagner A: Duplicate genes and robustness to transient gene knock-downs in caenorhabditis elegans. Proc Biol Sci 2004, 271(1534):89-96.
• [30]Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, Gordon SV, Eiglmeier K, Gas S, Barry CE 3rd, et al.: Deciphering the biology of mycobacterium tuberculosis from the complete genome sequence. Nature 1998, 393(6685):537-544.
• [31]Global Tuberculosis Control. World Health Organisation; 2011. http://www.who.int/tb/publications/global_report/2011/en/ webcite
• [32]Goldman RC, Plumley KV, Laughon BE: The evolution of extensively drug resistant tuberculosis (XDR-TB): history, status and issues for global control. Infect Disord Drug Targets 2007, 7(2):73-91.
• [33]Anand P, Sankaran S, Mukherjee S, Yeturu K, Laskowski R, Bhardwaj A, Bhagavat R, Brahmachari SK, Chandra N: Structural annotation of mycobacterium tuberculosis proteome. PLoS One 2011, 6(10):e27044.
• [34]Vashisht R, Mondal AK, Jain A, Shah A, Vishnoi P, Priyadarshini P, Bhattacharyya K, Rohira H, Bhat AG, Passi A, et al.: Crowd sourcing a new paradigm for interactome driven drug target identification in mycobacterium tuberculosis. PLoS One 2012, 7(7):e39808.
• [35]Bebek G, Koyuturk M, Price ND, Chance MR: Network biology methods integrating biological data for translational science. Brief Bioinform 2012, 13(4):446-459.
• [36]Vilaprinyo E, Alves R, Sorribas A: Minimization of biosynthetic costs in adaptive gene expression responses of yeast to environmental changes. PLoS Comput Biol 2010, 6(2):e1000674.
• [37]Boshoff HI, Myers TG, Copp BR, McNeil MR, Wilson MA, Barry CE 3rd: The transcriptional responses of mycobacterium tuberculosis to inhibitors of metabolism: novel insights into drug mechanisms of action. J Biol Chem 2004, 279(38):40174-40184.
• [38]Jeong H, Oltvai ZN, Barabsi A: Prediction of protein essentiality based on genomic data. ComPlexUs 2003, 1:19-28.
• [39]Lew JM, Kapopoulou A, Jones LM, Cole ST: TubercuList--10 years after. Tuberculosis (Edinb) 2011, 91(1):1-7.
• [40]Banu S, Honore N, Saint-Joanis B, Philpott D, Prevost MC, Cole ST: Are the PE-PGRS proteins of mycobacterium tuberculosis variable surface antigens? Mol Microbiol 2002, 44(1):9-19.
• [41]Mukhopadhyay S, Balaji KN: The PE and PPE proteins of mycobacterium tuberculosis. Tuberculosis (Edinb) 2011, 91(5):441-447.
• [42]Sampson SL: Mycobacterial PE/PPE proteins at the host-pathogen interface. Clin Dev Immunol 2011, 2011:497203.
• [43]Orth JD, Thiele I, Palsson BO: What is flux balance analysis? Nature Biotechnol 2010, 28(3):245-248.
• [44]Raman K, Chandra N: Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 2009, 10(4):435-449.
• [45]Jamshidi N, Palsson BO: Investigating the metabolic capabilities of mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 2007, 1:26. BioMed Central Full Text
• [46]Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE: Interpreting expression data with metabolic flux models: predicting mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 2009, 5(8):e1000489.
• [47]Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, McFadden J: GSMN-TB: a web-based genome-scale network model of mycobacterium tuberculosis metabolism. Genome Biol 2007, 8(5):R89. BioMed Central Full Text
• [48]Fang X, Wallqvist A, Reifman J: Development and analysis of an in vivo-compatible metabolic network of mycobacterium tuberculosis. BMC Syst Biol 2010, 4:160. BioMed Central Full Text
• [49]Barabasi AL, Oltvai ZN: Network biology: understanding the cell’s functional organization. Nat Rev Genet 2004, 5(2):101-113.
• [50]Cabusora L, Sutton E, Fulmer A, Forst CV: Differential network expression during drug and stress response. Bioinformatics 2005, 21(12):2898-2905.
• [51]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28(1):27-30.
• [52]Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol 1990, 215(3):403-410.
• [53]Golby P, Nunez J, Cockle PJ, Ewer K, Logan K, Hogarth P, Vordermeier HM, Hinds J, Hewinson RG, Gordon SV: Characterization of two in vivo-expressed methyltransferases of the mycobacterium tuberculosis complex: antigenicity and genetic regulation. Microbiology 2008, 154(Pt 4):1059-1067.
• [54]Hsu T, Hingley-Wilson SM, Chen B, Chen M, Dai AZ, Morin PM, Marks CB, Padiyar J, Goulding C, Gingery M, et al.: The primary mechanism of attenuation of bacillus Calmette-Guerin is a loss of secreted lytic function required for invasion of lung interstitial tissue. Proc Natl Acad Sci USA 2003, 100(21):12420-12425.
• [55]Parish T, Smith DA, Roberts G, Betts J, Stoker NG: The senX3-regX3 two-component regulatory system of mycobacterium tuberculosis is required for virulence. Microbiology 2003, 149(Pt 6):1423-1435.
• [56]Rengarajan J, Bloom BR, Rubin EJ: Genome-wide requirements for mycobacterium tuberculosis adaptation and survival in macrophages. Proc Natl Acad Sci USA 2005, 102(23):8327-8332.
• [57]Rickman L, Scott C, Hunt DM, Hutchinson T, Menendez MC, Whalan R, Hinds J, Colston MJ, Green J, Buxton RS: A member of the cAMP receptor protein family of transcription regulators in mycobacterium tuberculosis is required for virulence in mice and controls transcription of the rpfA gene coding for a resuscitation promoting factor. Mol Microbiol 2005, 56(5):1274-1286.
• [58]Savvi S, Warner DF, Kana BD, McKinney JD, Mizrahi V, Dawes SS: Functional characterization of a vitamin B12-dependent methylmalonyl pathway in mycobacterium tuberculosis: implications for propionate metabolism during growth on fatty acids. J Bacteriol 2008, 190(11):3886-3895.
• [59]Venugopal A, Bryk R, Shi S, Rhee K, Rath P, Schnappinger D, Ehrt S, Nathan C: Virulence of mycobacterium tuberculosis depends on lipoamide dehydrogenase, a member of three multienzyme complexes. Cell Host Microbe 2011, 9(1):21-31.
• [60]Zahrt TC, Deretic V: Mycobacterium tuberculosis signal transduction system required for persistent infections. Proc Natl Acad Sci USA 2001, 98(22):12706-12711.
• [61]Ducati RG, Breda A, Basso LA, Santos DS: Purine salvage pathway in mycobacterium tuberculosis. Curr Med Chem 2011, 18(9):1258-1275.
• [62]Lyon RH, Hall WH, Costas-Martinez C: Utilization of amino acids during growth of mycobacterium tuberculosis in rotary cultures. Infect Immun 1970, 1(6):513-520.
• [63]Parker WB, Long MC: Purine metabolism in mycobacterium tuberculosis as a target for drug development. Curr Pharm Des 2007, 13(6):599-608.
• [64]Timm J, Post FA, Bekker LG, Walther GB, Wainwright HC, Manganelli R, Chan WT, Tsenova L, Gold B, Smith I: Differential expression of iron-, carbon-, and oxygen-responsive mycobacterial genes in the lungs of chronically infected mice and tuberculosis patients. Proc Natl Acad Sci 2003, 100(24):14321.
• [65]Metris A, Reuter M, Gaskin D, Baranyi J, van Vliet A: In vivo and in silico determination of essential genes of campylobacter jejuni. BMC Genomics 2011, 12(1):535. BioMed Central Full Text
• [66]Xu P, Ge X, Chen L, Wang X, Dou Y, Xu JZ, Patel JR, Stone V, Evans K, Kitten T: Genome-wide essential gene identification in streptococcus sanguinis. Sci Rep 2011, 1:125.
• [67]Dotsch A, Klawonn F, Jarek M, Scharfe M, Blocker H, Haussler S: Evolutionary conservation of essential and highly expressed genes in pseudomonas aeruginosa. BMC Genomics 2011, 11:234.
• [68]Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, et al.: Clustal W and Clustal X version 2.0. Bioinformatics 2007, 23(21):2947-2948.
• [69]Padiadpu J, Mukherjee S, Chandra N: Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs. J Biomol Struct Dyn 2013, 31(1):44-58.
• [70]Kalidas Y, Chandra N: PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. J Struct Biology 2008, 161(1):31-42.
• [71]Futcher B, Latter GI, Monardo P, McLaughlin CS, Garrels JI: A sampling of the yeast proteome. Mol Cell Biol 1999, 19(11):7357-7368.
• [72]Orntoft TF, Thykjaer T, Waldman FM, Wolf H, Celis JE: Genome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas. Mol Cell Proteomics 2002, 1(1):37-45.
• [73]Raman K, Yeturu K, Chandra N: targetTB: a target identification pipeline for mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis. BMC Syst Biol 2008, 2:109. BioMed Central Full Text
• [74]Ji Y, Zhang B, Van Horn SF, Warren P, Woodnutt G, Burnham MK, Rosenberg M: Identification of critical staphylococcal genes using conditional phenotypes generated by antisense RNA. Science 2001, 293(5538):2266-2269.
• [75]Salama NR, Shepherd B, Falkow S: Global transposon mutagenesis and essential gene analysis of helicobacter pylori. J Bacteriol 2004, 186(23):7926-7935.
• [76]Glass JI, Assad-Garcia N, Alperovich N, Yooseph S, Lewis MR, Maruf M, Hutchison CA 3rd, Smith HO, Venter JC: Essential genes of a minimal bacterium. Proc Natl Acad Sci USA 2006, 103(2):425-430.
• [77]Liberati NT, Urbach JM, Miyata S, Lee DG, Drenkard E, Wu G, Villanueva J, Wei T, Ausubel FM: An ordered, nonredundant library of pseudomonas aeruginosa strain PA14 transposon insertion mutants. Proc Natl Acad Sci USA 2006, 103(8):2833-2838.
• [78]Barve A, Rodrigues JF, Wagner A: Superessential reactions in metabolic networks. Proc Natl Acad Sci USA 2012, 109(18):E1121-E1130.
• [79]Li M, Zhang H, Wang JX, Pan Y: A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Syst Biol 2012, 6:15. BioMed Central Full Text
• [80]Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, et al.: NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res 2011, 39(suppl 1):D1005-D1010.
• [81]Edgar R, Domrachev M, Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30(1):207-210.
• [82]Schellenberger J, Park JO, Conrad TM, Palsson BO: BiGG: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics 2010, 11:213. BioMed Central Full Text
• [83]Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, et al.: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0. Nature protocols 2011, 6(9):1290-1307.
• [84]Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al.: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011, 39(Database issue):D561-D568.
• [85]Haw R, Hermjakob H, D’Eustachio P, Stein L: Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics 2011, 11(18):3598-3613.
• [86]Wang Y, Cui T, Zhang C, Yang M, Huang Y, Li W, Zhang L, Gao C, He Y, Li Y, et al.: Global protein-protein interaction network in the human pathogen mycobacterium tuberculosis H37Rv. J Proteome Res 2010, 9(12):6665-6677.
• [87]Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27(3):431-432.
• [88]Robert WF: Algorithm 97: shortest path. Commun ACM 1962, 5(6):345.
• [89]Kramer LD, Presser SB, Houk EJ, Hardy JL: Effect of the anesthetizing agent triethylamine on western equine encephalomyelitis and St. Louis encephalitis viral titers in mosquitoes (Diptera: Culicidae). J Med Entomol 1990, 27(6):1008-1010.
• [90]Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Feng Z, Gilliland GL, Iype L, Jain S, et al.: The protein data bank. Acta Crystallogr D 2002, 58(Pt 6 No 1):899-907.
• [91]Huang B, Schroeder M: LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 2006, 6:19. BioMed Central Full Text
• [92]Hernandez M, Ghersi D, Sanchez R: SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res 2009, 37(Web Server issue):W413-W416.
• [93]Oliveros JC: VENNY. An interactive tool for comparing lists with Venn Diagrams. 2007. http://bioinfogp.cnb.csic.es/tools/venny/ webcite