Evaluation of a genome-scale in silico metabolic model for geobacter metallireducens by using proteomic data from a field biostimulation experiment

Applied and Environmental Microbiology

Volumn 78, Issue 24, 2012, Pages 8735-8742

  Fang, Yilin ^a   Wilkins, Michael J ^a   Yabusaki, Steven B ^a   Lipton, Mary S ^a   Long, Philip E ^b  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID TRANSPORT; BIOMASS YIELD; BIOSTIMULATION; CARBON MANAGEMENT; CONSTRAINT-BASED; DYNAMIC FIELDS; ENERGY DEVELOPMENT; FIELD EXPERIMENT; GENOME SEQUENCES; GENOME-SCALE METABOLIC MODELS; GENOME-SCALE MODEL; GEOBACTER; GROUNDWATER CLEAN-UP; IN-SILICO; IN-SILICO MODELS; METABOLIC FLUX; METABOLIC MODELS; METABOLIC PROCESS; METABOLIC REACTIONS; METAL REDUCTION; MICROBIAL METABOLISM; MODEL DISCREPANCIES; OR FLUXES; PHYSIOLOGICAL FUNCTIONS; PROTEOMIC ANALYSIS; PROTEOMICS; REACTIVE TRANSPORT MODELS; SUBSURFACE ENVIRONMENT; TERMINAL ELECTRON ACCEPTOR PROCESS;

AMINO ACIDS; EXPERIMENTS; FORECASTING; GENES; GROUNDWATER; IRON COMPOUNDS; METABOLISM; MOLECULAR BIOLOGY; PHYSIOLOGY; PROTEINS;

PHYSIOLOGICAL MODELS;

BACTERIAL PROTEIN; METAL; PROTEOME;

BIOMASS; CATALYSIS; GENOME; METABOLISM; PROTEOMICS;

ARTICLE; BIOMASS; GENETICS; GEOBACTER; GROWTH, DEVELOPMENT AND AGING; METABOLISM; OXIDATION REDUCTION REACTION; PROTEOMICS;

BACTERIAL PROTEINS; BIOMASS; GEOBACTER; METABOLIC NETWORKS AND PATHWAYS; METALS; OXIDATION-REDUCTION; PROTEOME; PROTEOMICS;

GEOBACTER METALLIREDUCENS;

EID: 84870987046     PISSN: 00992240     EISSN: 10985336     Source Type: Journal    
DOI: 10.1128/AEM.01795-12     Document Type: Article

References (70)

1. Aklujkar M, et al. 2010. The genome of Geobacter bemidjiensis, exemplar for the subsurface clade of Geobacter species that predominate in Fe(III)-reducing subsurface environments. BMC Genomics 11:490. doi:10.1186/ 1471-2164-11-490.
2. Anderson RT, et al. 2003. Stimulating the in situ activity of Geobacter species to remove uranium from the groundwater of a uraniumcontaminated aquifer. Appl. Environ. Microbiol. 69:5884-5891.
3. Aniszewski A. 2011. Particular application of a mathematical transport model incorporating sub-surface reactive pollutants. Acta Geophys. 59: 110-123.
4. Baek KH, et al. 2007. Monitoring of microbial diversity and activity during bioremediation of crude OH-contaminated soil with different treatments. J. Microbiol. Biotechnol. 17:67-73.
5. Baker MD, Wolanin PM, Stock JB. 2006. Signal transduction in bacterial chemotaxis. Bioessays 28:9-22.
6. Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. 2004. Bacterial persistence as a phenotypic switch. Science 305:1622-1625.
7. Brown LR. 2010. Microbial enhanced oil recovery (MEOR). Curr. Opin. Microbiol. 13:316-320.
8. Callister SJ, et al. 2006. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5:277-286.
9. Callister SJ, et al. 2010. Analysis of biostimulated microbial communities from two field experiments reveals temporal and spatial differences in proteome profiles. Environ. Sci. Technol. 44:8897-8903.
10. Chambon JC, Broholm MM, Binning PJ, Bjerg PL. 2010. Modeling multi-component transport and enhanced anaerobic dechlorination processes in a single fracture-clay matrix system. J. Contam. Hydrol. 112:77-90.
11. Chandler DP, et al. 2010. Monitoring microbial community structure and dynamics during in situ U(VI) bioremediation with a field-portable microarray analysis system. Environ. Sci. Technol. 44:5516-5522.
12. Chang YJ, et al. 2005. Microbial incorporation of C-13-labeled acetate at the field scale: detection of microbes responsible for reduction of U(VI). Environ. Sci. Technol. 39:9039-9048.
13. Chung BKS, et al. 2010. Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement. Microb. Cell Fact. 9:50.
14. Colijin C, et al. 2009. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5:e1000489. doi:10.1371/journal.pcbi.1000489.
15. Durot M, Bourguignon PY, Schachter V. 2009. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol. Rev. 33:164-190.
16. Edwards JS, Covert M, Palsson B. 2002. Metabolic modeling of microbes: the flux-balance approach. Environ. Microbiol. 4:133-140.
17. Fang YL, et al. 2011. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model. J. Contam. Hydrol. 122:96-103.
18. Fang YL, Yabusaki SB, Morrison SJ, Amonette JP, Long PE. 2009. Multicomponent reactive transport modeling of uranium bioremediation field experiments. Geochim. Cosmochim. Acta 73:6029-6051.
19. Finneran KT, Anderson RT, Nevin KP, Lovley DR. 2002. Potential for bioremediation of uranium-contaminated aquifers with microbial U(VI) reduction. Soil Sediment Contam. 11:339-357.
20. Greskowiak J, Prommer H, Massmann G, Nutzmann G. 2006. Modeling seasonal redox dynamics and the corresponding fate of the pharmaceutical residue phenazone during artificial recharge of groundwater. Environ. Sci. Technol. 40:6615-6621.
21. Holmes DE, Finneran KT, O'Neill RA, Lovley DR. 2002. Enrichment of members of the family Geobacteraceae associated with stimulation of dissimilatory metal reduction in uranium-contaminated aquifer sediments. Appl. Environ. Microbiol. 68:2300-2306.
22. Holmes DE, et al. 2008. Transcriptome of Geobacter uraniireducens growing in uranium-contaminated subsurface sediments. ISME J. 3:216-230.
23. Holmes DE, Nevin KP, Lovley DR. 2004. In situ expression of nifD in Geobacteraceae in subsurface sediments. Appl. Environ. Microbiol. 70: 7251-7259.
24. Hunter KS, Wang YF, Van Cappellen P. 1998. Kinetic modeling of microbially driven redox chemistry of subsurface environments: coupling transport, microbial metabolism and geochemistry. J. Hydrol. 209:53-80.
25. Jenneman GE, Knapp RM, McInerney MJ, Menzie DE, Revus DE. 1984. Experimental studies of in situ microbial enhanced oil-recovery. SPE J. 24:33-37.
26. Kaszycki P, Petryszak P, Pawlike M, Koloczek H. 2011. Ex situ bioremediation of soil polluted with oily waste: the use of specialized microbial consortia for process bioaugmentation. Ecol. Chem. Eng. S 18:83-92.
27. Kauffman KJ, Prakash P, Edwards JS. 2003. Advances in flux balance analysis. Curr. Opin. Biotechnol. 14:491-496.
28. Kerkof LJ, Williams KH, Long PE, McGuinness LR. 2011. Phase preference by active, acetate-utilizing bacteria at the Rifle, CO Integrated Field Research Challenge site. Environ. Sci. Technol. 45:1250-1256.
29. King EL, Tuncay K, Ortoleva P, Meile C. 2009. In silico Geobacter sulfurreducens metabolism and its representation in reactive transport models. Appl. Environ. Microbiol. 75:83-92.
30. Kjeldsen KR, Nielsen J. 2009. In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network. Biotechnol. Bioeng. 102:583-597.
31. Knabe S. 1984. Overview of microbial enhanced oil recovery. Oil Gas J. 82:59-60.
32. Korotkevych O, et al. 2011. Functional adaptation of microbial communities from jet fuel-contaminated soil under bioremediation treatment: simulation of pollutant rebound. FEMS Microbiol. Ecol. 78:137-149.
33. Lee J, Yun H, Feist AM, Palsson BO, Lee SY. 2008. Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Appl. Microbiol. Biotechnol. 80:849-862.
34. Lewis NE, et al. 2010. Omic data from evolved E-coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6:390.
35. Li JA, et al. 2011. Interactions of microbial-enhanced oil recovery processes. Transport Porous Media 87:77-104.
36. Li L, Steefel CI, Kowalsky MB, Englert A, Hubbard SS. 2010. Effects of physical and geochemical heterogeneities on mineral transformation and biomass accumulation during biostimulation experiments at Rifle, Colorado. J. Contam. Hydrol. 112:45-63.
37. Li L, Steefel CI, Williams KH, Wilkins MJ, Hubbard SS. 2009. Mineral transformation and biomass accumulation associated with uranium bioremediation at Rifle, Colorado. Environ. Sci. Technol. 43:5429-5435.
38. Loew LM, Schaff JC. 2001. The Virtual Cell: a software environment for computational cell biology. Trends Biotechnol. 19:401-406.
39. Mahadevan R, et al. 2006. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl. Environ. Microbiol. 72:1558-1568.
40. Mahadevan R, et al. 2008. Characterizing regulation of metabolism in Geobacter sulfurreducens through genome-wide expression data and sequence analysis. OMICS 12:33-59.
41. Mayer KU, Frind EO, Blowes DW. 2002. Multicomponent reactive transport modeling in variably saturated porous media using a generalized formulation for kinetically controlled reactions. Water Resour. Res. 38:1174.
42. Molnaa BA, Grubbs RB. 1989. Bioremediation of petroleum contaminated soils using a microbial consortia as inoculums, p 219-232. In Calabrese EJ, Kostecki PT (ed), Petroleum contaminated soils, vol 2. Lewis Publishers, Chelsea, MI.
43. Mouser PJ, et al. 2009. Influence of heterogeneous ammonium availability on bacterial community structure and the expression of nitrogen fixation and ammonium transporter genes during in situ bioremediation of uraniumcontaminated groundwater. Environ. Sci. Technol. 43:4386-4392.
44. Nielsen SM, Shapiro AA, Michelsen ML, Stenby EH. 2010. 1D simulations for microbial enhanced oil recovery with metabolite partitioning. Transport Porous Media 85:785-802.
45. Polpitiya AD, et al. 2008. DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24:1556-1558.
46. Price ND, Papin JA, Schilling CH, Palsson BO. 2003. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol. 21:162-169.
47. Reed JL, Vo TD, Schilling CH, Palsson BO. 2003. An expanded genomescale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4:R54.
48. Resat H, Bailey V, McCue LA, Konopka A. 2011. Modeling microbial dynamics in heterogeneous environments: growth on soil carbon sources. Microb. Ecol. 63:883-897. doi:10.1007/s00248-011-9965-x.
49. Ros M, Rodriguez I, Garcia C, Hernandez T. 2010. Microbial communities involved in the bioremediation of an aged recalcitrant hydrocarbon polluted soil by using organic amendments. Bioresour. Technol. 101: 6916-6923.
50. Salvage KM, Yeh GT. 1998. Modeling chemically and microbiologically reactive contaminant transport in porous media, p 263-270. In Burganos VN, et al (ed), Computational methods in water resources XII, vol 1. Computational methods in contamination and remediation of water resources. WIT Press, Wessex Institute of Technology, Southampton, United Kingdom.
51. Schafer D, Schafer W, Kinzelbach W. 1998. Simulation of reactive processes related to biodegradation in aquifers: 1. Structure of the threedimensional reactive transport model. J. Contam. Hydrol. 31:167-186.
52. Scheibe TD, et al. 2009. Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation. Microb. Biotechnol. 2:274-286.
53. Sinha S, et al. 2009. Microbial transformation of xenobiotics for environmental bioremediation. Afr. J. Biotechnol. 8:6016-6027.
54. Sun J, et al. 2009. Genome-scale constraint-based modeling of Geobacter metallireducens. BMC Syst. Biol. 3:15. doi:10.1186/1752-0509-3-15.
55. Sun Y, Petersen JN, Bear J, Clement TP, Hooker BS. 1999. Modeling microbial transport and biodegradation in a dual-porosity system. Transport Porous Media 35:49-65.
56. Thiele I, Jamshidi N, Fleming RMT, Palsson BO. 2009. Genome-scale reconstruction of Escherichia coli's transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput. Biol. 5:e1000312. doi:10.1371/journal.pcbi.1000312.
57. Thiele I, Vo TD, Price ND, Palsson BO. 2005. Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol. 187:5818-5830.
58. Thullner M, Van Cappellen P, Regnier P. 2005. Modeling the impact of microbial activity on redox dynamics in porous media. Geochim. Cosmochim. Acta 69:5005-5019.
59. Tang YJ, et al. 2007. Flux analysis of central metabolic pathways in Geobacter metallireducens during reduction of soluble Fe(III)-nitrilotriacetic acid. Appl. Environ. Microbiol. 73:3859-3864.
60. Varma A, Palsson BO. 1994. Metabolic flux balancing: basic concepts, scientific and practical use. Nat. Biotechnol. 12:994-998.
61. Vrionis HA, et al. 2005. Microbiological and geochemical heterogeneity in an in situ uranium bioremediation field site. Appl. Environ. Microbiol. 71:6308-6318.
62. Widiastuti H, et al. 2011. Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis. Biotechnol. Bioeng. 108:655-665.
63. Wilkins MJ, et al. 2009. Proteogenomic monitoring of Geobacter physiology during stimulated uranium bioremediation. Appl. Environ. Microbiol. 75:6591-6599.
64. Williams KH, et al. 2011. Acetate availability and its influence on sustainable bioremediation of uranium-contaminated groundwater. Geomicrobiol. J. 28:519-539.
65. Woo SH, Park JM. 2004. Microbial degradation and enhanced bioremediation of polycyclic aromatic hydrocarbons. J. Ind. Eng. Chem. 10:16-23.
66. Yabusaki SB, et al. 2007. Uranium removal from groundwater via in situ biostimulation: field-scale modeling of transport and biological processes. J. Contam. Hydrol. 93:216-235.
67. Yabusaki SB, et al. 2011. Variably saturated flow and multicomponent biogeochemical reactive transport modeling of a uranium bioremediation field experiment. J. Contam. Hydrol. 126:271-290.
68. Yeh GT, et al. 2010. Numerical modeling of coupled fluid flow and thermal and reactive biogeochemical transport in porous and fractured media. Comput. Geosci. 14:147-170.
69. Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T. 2010. Integrating quantitative proteomics and metabolomics with a genomescale metabolic network model. Bioinformatics 26:i255-i260.
70. Zhuang K, et al. 2011. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5:305-316.