Skip to main content
SpringerLink
Log in

Dynamic interactive events in gene regulation using E. coli dehydrogenase as a model

  • Original Paper
  • Published:
Functional & Integrative Genomics Aims and scope Submit manuscript

Abstract

Different approaches in gene expression analysis always provide a snapshot view of cellular events. During the bacterial growth, the decisions are dynamically made with participation of various genes and their interactions with modulating factors. We have selected Escherichia coli dehydrogenases as a model to capture these interactions. We have treated the cells with hydrogen peroxide with very low level and asked the questions how cellular physiology has modulated itself to survive post-shock conditions. We hypothesized that while global regulators and associated gene network dictate the overall cellular intelligence, specific redox-sensitive classes of enzymes like dehydrogenase-mediated modulation could provide the option to cell for survival under peroxide after-effect. To understand the dynamic gene interaction, we used multidimensional scaling of genes and overlaid with minimum spanning tree to understand the clustering patterns under different conditions. Study shows that under peroxide after-effect, it is the interplay of ArcA (global regulator), with ldhA (involved in intermediary metabolism) and ndh (managing co-factor NADH), that emerges as modulating association. Knockout mutants of global regulators confirmed the promoter activity trend through gene expression change for dehydrogenases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Anjem A, Imlay JA (2012) Targets of hydrogen peroxide stress mononuclear iron enzymes are primary. J Biol Chem 287:15544–15556

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Antiqueira L, Jangab SC, Costac LF (2012) Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli. Mol BioSyst 8:3028–3035

    Article  CAS  PubMed  Google Scholar 

  • Basak S, Jiang R (2012) Enhancing E. coli tolerance towards oxidative stress via engineering its global regulator cAMP receptor protein (CRP). PLoS ONE 7(12):e51179

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Carey LB, van Dijk D, Sloot PMA, Kaandorp JA, Segal E (2013) Promoter sequence determines the relationship between expression level and noise. PLoS Biol 11(4):e1001528

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Chatfield C (2004) The analysis of time series, an introduction, sixth edition. Chapman & Hall/CRC, New York, p 333

    Google Scholar 

  • Chiang SM, Schellhorn HE (2012) Regulators of oxidative stress response genes in Escherichia coli and their functional conservation in bacteria. Arch Biochem Biophys 525:161–9

    Article  CAS  PubMed  Google Scholar 

  • Durfee T, Hansen AM, Zhi H, Blattner FR, Jin DJ (2008) Transcription profiling of the stringent response in Escherichia coli. J Bacteriol 190:1084

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Feng Y, Cronan JE (2012) Crosstalk of Escherichia coli FadR with global regulators in expression of fatty acid transport genes. PLoS ONE 7(9):e46275

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gama-Castro S, Salgado H, Peralta-Gil M, Santos-Zavaleta A, Muñiz-Rascado L et al (2011) RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res 39:D98–105

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Georgiou G (2002) How to flip the (Redox) switch. Cell 111:607–610

    Article  CAS  PubMed  Google Scholar 

  • Gerosa L, Kochanowski K, Heinemann M, Sauer U (2013) Dissecting specific and global transcriptional regulation of bacterial gene expression. Mol Syst Biol 9:658

    Article  PubMed Central  PubMed  Google Scholar 

  • Guan Q, Haroon S, Bravo DG, Will JL, Gasch AP (2012) Cellular memory of acquired stress resistance in Saccharomyces cerevisiae. Genetics 192:495–505

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gutierrez-Rios RM, Freyre-Gonzalez JA, Resendis O, Collado-Vides J, Saier M et al (2007) Identification of regulatory network topological units coordinating the genome wise transcriptional response to glucose in Escherichia coli. BMC Microbiol 7:53

    Article  PubMed Central  PubMed  Google Scholar 

  • Han JJ (2008) Understanding biological functions through molecular networks. Cell Res 18:224–237

    Article  CAS  PubMed  Google Scholar 

  • Holm A, Blank LM, Oldiges M, Schmid A, Solem C et al (2010) Metabolic and transcriptional response to cofactor perturbations in Escherichia coli. J Biol Chem 285:17498–17506

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Imlay J (2013) The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium. Nat Rev Microbiol. doi:10.1038/nrmicro3032

    PubMed Central  PubMed  Google Scholar 

  • Jiang GR, Nikolova S, Clark DP (2001) Regulation of the ldhA gene, encoding the fermentative lactate dehydrogenase of Escherichia coli. Microbiology 147:2437–2446

    CAS  PubMed  Google Scholar 

  • Joyce AR, Reed JL, White A, Edwards R, Osterman A et al (2006) Experimental and computational assessment of conditionally essential genes in Escherichia coli. J Bacteriol 188(23):8259–71

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Korshunov S, Imlay J (2010) Two sources of endogenous hydrogen peroxide in Escherichia coli. Mol Microbiol 75(6):1389–401

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kumar R, Shimizu K (2011) Transcriptional regulation of main metabolic pathways of cyoA, cydB, fnr, and fur gene knockout Escherichia coli in C-limited and N-limited aerobic continuous cultures. Microb Cell Fact 10:3

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Li Y, Li M, Zhang X, Yang P, Liang Q, Qi Q (2013) A novel whole-phase succinate fermentation strategy with high volumetric productivity in engineered Escherichia coli. Bioresource Technol 149:333–340

    Article  CAS  Google Scholar 

  • Maklashina E, Cecchini G, Dikanov SA (2013) Defining a direction: electron transfer and catalysis in Escherichia coli complex II enzymes. Biochim Biophys Acta 1827(5):668–78

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Martinez-Antonio A, Collado-Vides J (2003) Identifying global regulators in transcriptional regulatory networks in bacteria. Curr Opin Microbiol 6:482–489

    Article  CAS  PubMed  Google Scholar 

  • Matsumura I (2012) Bacterial cells as model factories. Am J Oper Res 3:81–86

    Article  Google Scholar 

  • Melo AMP, Bandeiras TM, Teixeira M (2004) New insights into type II NAD(P)H:quinine oxidoreductases. Microbiol Mol Biol R 68:603–616

    Article  CAS  Google Scholar 

  • Myers KS, Yan H, Ong IM, Chung D, Liang K et al (2013) Genome-scale analysis of Escherichia coli FNR reveals complex features of transcription factor binding. PLoS Genet 9(6):e1003565

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Nam H, Conrad TM, Lewis NE (2011) The role of cellular objectives and selective pressures in metabolic pathway evolution. Curr opin biotech 22:595–600

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Neidhardt FC, Ingraham JL, Low KB, Magasanik B, Schaechter M, Umbarger H (1987) Escherichia coli and Salmonella typhimurium: cellular and molecular biology, 2nd edn. ASM Press, Washington, DC

    Google Scholar 

  • Pease AJ, Wolf RE Jr (1994) Determination of the growth rate-regulated steps in expression of the Escherichia coli K-12 gnd gene. J Bacteriol 176:115–22

    PubMed Central  CAS  PubMed  Google Scholar 

  • Perkins TJ, Swain PS (2009) Strategies for cellular decision-making. Mol Syst Biol 5:1–15

    Article  Google Scholar 

  • Peskov K, Mogilevskaya E, Demin O (2012) Kinetic modelling of central carbon metabolism in Escherichia coli. FEBS J 279:3374–3385

    Article  CAS  PubMed  Google Scholar 

  • Pomposiello PJ, Demple B (2001) Redox-operated genetic switches: the SoxR and OxyR transcription factors. Trends Biotechnol 19(3):109–114

    Article  CAS  PubMed  Google Scholar 

  • Puranik S, Shaligram S, Paliwal V, Raje DV, Kapley A, Purohit HJ (2012) Demonstration of sequential adaptation strategy for developing salt tolerance in bacteria for wastewater treatment: a study using Escherichia coli as model. Bioresour Technol 121:282–289

    Article  CAS  PubMed  Google Scholar 

  • Ragan MA, McInerney JO, Lake JA (2009) The network of life: genome beginnings and evolution. Phil Trans R Soc B 364:2169–2175

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Rodriguez C, Kwon O, Georgellis D (2004) Effect of D-lactate on the physiological activity of the ArcB sensor kinase in Escherichia coli. J Bacteriol 186(7):2085–90

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Rui B, Shen T, Zhou H, Liu J, Chen J et al (2010) A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress. BMC Syst Biol 4:122

    Article  PubMed Central  PubMed  Google Scholar 

  • Sasson V, Shachrai I, Bren A, Dekel E, Alon U (2012) Mode of regulation and the insulation of bacterial gene expression. Mol Cell 46:399–407

    Article  CAS  PubMed  Google Scholar 

  • Schneider BL, Hernandez VJ, Reitzer L (2013) Putrescine catabolism is a metabolic response to several stresses in Escherichia coli. Mol Microbiol 88:537–50

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Schuetz R, Zamboni N, Zampieri M, Heinemann M, Sauer U (2012) Multidimensional optimality of microbial metabolism. Science 336:601–4

    Article  CAS  PubMed  Google Scholar 

  • Schurig-Briccio LA, Rintoul MR, Volentini SI, Far’ias RN, Baldoma L (2008) A critical phosphate concentration in the stationary phase maintains ndh gene expression and aerobic respiratory chain activity in Escherichia coli. FEMS Microbiol Lett 284:76–83

    Article  CAS  PubMed  Google Scholar 

  • Shalel-Levanon S, San KY, Bennet GN (2005) Effect of ArcA and FNR on the expression of genes related to the oxygen regulation and glycolysis pathway in Escherichia coli under microaerobic growth conditions. Biotechnol Bioeng 92:147–159

    Article  CAS  PubMed  Google Scholar 

  • Shannon P, Markiel A, Ozier O, Baliga NS et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–504

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Shimizu K (2013) Metabolic regulation of a bacterial cell system with emphasis on Escherichia coli metabolism. ISRN Biochem. doi:10.1155/2013/645983

    Google Scholar 

  • Siddiquee KA, Arauzo-Bravo MJ, Shimizu K (2004) Effect of a pyruvate kinase (pykF-gene) knockout mutation on the control of gene expression and metabolic fluxes in Escherichia coli. FEMS Microbial Lett 235:25–33

    Article  CAS  Google Scholar 

  • Trevin˜o S III, Sun Y, Cooper TF, Bassler KE (2012) Robust detection of hierarchical communities from Escherichia coli gene expression data. PLoS Comput Biol 8, e1002391

  • Zaslaver A, Bren A, Ronen M, Itzkovitz S, Kikoin I et al (2006) A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat Methods 3:623–628

    Article  CAS  PubMed  Google Scholar 

  • Zhao Z, Rothery RA, Weiner JH (2006) Effects of site-directed mutations in Escherichia coli succinate dehydrogenase on the enzyme activity and production of superoxide radicals. Biochem Cell Biol 84:1013–1021

    Article  CAS  PubMed  Google Scholar 

  • Zwietering MH, Jongenburger I, Rombouts FM, Riet KV (1990) Modeling of bacterial growth curve. Appl and Environ Microb 56:1875–1881

    CAS  Google Scholar 

Download references

Acknowledgments

One of the authors Sampada Puranik is supported by CSIR SRF fellowship. We thank Dr. Dhananjay V. Raje, MDS Bio-analytics, Nagpur, for their support in the analysis of data. The work is supported by grant from CSIR Project ESC0108.

Author information

Authors and Affiliations

  1. Environmental Genomics Division, CSIR-National Environmental Engineering Research Institute, 440020, Nehru Marg, India

    Sampada Puranik & Hemant J. Purohit

Authors
  1. Sampada Puranik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hemant J. Purohit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hemant J. Purohit.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Fig. 1

Base network for major dehydrogenases. Interaction database from Regulon dB was used to derive the network which consists of global and local regulators and their associated target genes. Considering the proximity and relatedness of all the nodes to dehydrogenases, three categories of genes were defined. First category consisted of regulators (global and local) relevant in terms of after-effects on dehydrogenases. In the second category, genes that are part of the core network along with dehydrogenases were included and the category was referred as ‘Related genes’. Third category consisted of genes with nodes excluded from the core network, yet present in base network and showing relevance to dehydrogenases. This category was referred as ‘Peripheral genes’. (GIF 195 kb)

High Resolution Image (TIFF 158 kb)

Supplementary Fig. 2

(a-c) Image plot showing cross-correlation of dehydrogenases genes with selected genes. Relationships for the three scenarios a) Seed culture without H2O2, b) Seed culture with 0.5 mM H2O2 and c) Seed culture with 1.0 mM H2O2 are shown in the figure. Lag values correspond to maximum cross-correlation between the compared activities. Strength of relationship is indicated by the color code. Actual correlation values are shown in Supplementary Table 2. (GIF 145 kb)

(GIF 132 kb)

(GIF 134 kb)

High Resolution Image (TIFF 76 kb)

High Resolution Image (TIFF 65 kb)

High Resolution Image (TIFF 70 kb)

Supplementary Table 1

Primers designed using DNA Star software for real time PCR. (DOCX 13 kb)

Supplementary Table 2

Cross-correlation values for different dehydrogenases compared to selected genes along with lag values corresponding to maximum correlation (XLSX 47 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Puranik, S., Purohit, H.J. Dynamic interactive events in gene regulation using E. coli dehydrogenase as a model. Funct Integr Genomics 15, 175–188 (2015). https://doi.org/10.1007/s10142-014-0418-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10142-014-0418-8

Keywords

Search

Navigation