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Generation and robustness of Boolean networks to model Clostridium difficile infection

  • Dante TravisanyEmail author
  • Eric Goles
  • Mauricio Latorre
  • María-Paz Cortés
  • Alejandro Maass
Article
  • 36 Downloads

Abstract

One of the more common healthcare associated infection is Chronic diarrhea. This disease is caused by the bacterium Clostridium difficile which alters the normal composition of the human gut flora. The most successful therapy against this infection is the fecal microbial transplant (FMT). They displace C. difficile and contribute to gut microbiome resilience, stability and prevent further episodes of diarrhea. The microorganisms in the FMT their interactions and inner dynamics reshape the gut microbiome to a healthy state. Even though microbial interactions play a key role in the development of the disease, currently, little is known about their dynamics and properties. In this context, a Boolean network model for C. difficile infection (CDI) describing one set of possible interactions was recently presented. To further explore the space of possible microbial interactions, we propose the construction of a neutral space conformed by a set of models that differ in their interactions, but share the final community states of the gut microbiome under antibiotic perturbation and CDI. To begin with the analysis, we use the previously described Boolean network model and we demonstrate that this model is in fact a threshold Boolean network (TBN). Once the TBN model is set, we generate and use an evolutionary algorithm to explore to identify alternative TBNs. We organize the resulting TBNs into clusters that share similar dynamic behaviors. For each cluster, the associated neutral graph is constructed and the most relevant interactions are identified. Finally, we discuss how these interactions can either affect or prevent CDI.

Keywords

Threshold network Neutral space Evolutionary computation Microbiome Clostridium difficile infection 

Notes

Acknowledgements

This work was supported by Basal grant of the Center for Mathematical Modeling AFB170001 (UMI2807 UCHILE-CNRS), Center for Genome Regulation FONDAP 15090007 (D.T., M.C., M.L., A.M.), CONICYT PFCHA/Beca Doctorado Nacional 2015/FOLIO 21150895 (D.T.), FONDECYT 11150679 (M.L.), ECOS C16E01 (E.G.) and Internal Grant of the Universidad Adolfo Ibañez (E.G.). We also thank to the National Laboratory for High Performance Computing NLHPC (ECM-02).

Supplementary material

11047_2019_9730_MOESM1_ESM.pdf (3.6 mb)
Supplementary material 1 (pdf 3662 KB)

References

  1. Antharam VC, Li EC, Ishmael A, Sharma A, Mai V, Rand KH, Wang GP (2013) Intestinal dysbiosis and depletion of butyrogenic bacteria in clostridium difficile infection and nosocomial diarrhea. J Clin Microbiol 51(9):2884–2892CrossRefGoogle Scholar
  2. Aracena J, Goles E, Moreira A, Salinas L (2009) On the robustness of update schedules in boolean networks. Biosystems 97(1):1–8CrossRefGoogle Scholar
  3. Arias CA, Murray BE (2012) The rise of the enterococcus: beyond vancomycin resistance. Nat Rev Microbiol 10(4):266–278CrossRefGoogle Scholar
  4. Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, No D, Liu H, Kinnebrew M, Viale A et al (2015) Precision microbiome reconstitution restores bile acid mediated resistance to clostridium difficile. Nature 517(7533):205–208CrossRefGoogle Scholar
  5. Buffie CG, Jarchum I, Equinda M, Lipuma L, Gobourne A, Viale A, Ubeda C, Xavier J, Pamer EG (2012) Profound alterations of intestinal microbiota following a single dose of clindamycin results in sustained susceptibility to clostridium difficile-induced colitis. Infect Immun 80(1):62–73CrossRefGoogle Scholar
  6. Chang JY, Antonopoulos DA, Kalra A, Tonelli A, Khalife WT, Schmidt TM, Young VB (2008) Decreased diversity of the fecal microbiome in recurrent clostridium difficile-associated diarrhea. J Infect Dis 197(3):435–438CrossRefGoogle Scholar
  7. Ciliberti S, Martin OC, Wagner A (2007a) Innovation and robustness in complex regulatory gene networks. Proc Natl Acad Sci 104(34):13591–13596CrossRefGoogle Scholar
  8. Ciliberti S, Martin OC, Wagner A (2007b) Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLoS Comput Biol 3(2):e15MathSciNetCrossRefGoogle Scholar
  9. Ferrada E, Wagner A (2008) Protein robustness promotes evolutionary innovations on large evolutionary time-scales. Proc R Soc Lond B Biol Sci 275(1643):1595–1602CrossRefGoogle Scholar
  10. Goles E, Montalva M, Ruz GA (2013) Deconstruction and dynamical robustness of regulatory networks: application to the yeast cell cycle networks. Bull Math Biol 75(6):939–966MathSciNetCrossRefzbMATHGoogle Scholar
  11. Gordon JI (2012) Honor thy gut symbionts redux. Science 336(6086):1251–1253CrossRefGoogle Scholar
  12. Jörg T, Martin OC, Wagner A (2008) Neutral network sizes of biological rna molecules can be computed and are not atypically small. BMC Bioinform 9(1):464CrossRefGoogle Scholar
  13. Kau AL, Ahern PP, Griffin NW, Goodman AL, Gordon JI (2011) Human nutrition, the gut microbiome, and immune system: envisioning the future. Nature 474(7351):327CrossRefGoogle Scholar
  14. Kurten K (1988) Correspondence between neural threshold networks and kauffman boolean cellular automata. J Phys A Math Gen 21(11):L615MathSciNetCrossRefzbMATHGoogle Scholar
  15. Martin OC, Wagner A et al (2007) New structural variation in evolutionary searches of rna neutral networks. Biosystems 90(2):475–485CrossRefGoogle Scholar
  16. McInnes L, Healy J, Astels S (2017) hdbscan: hierarchical density based clustering. J Open Source Softw 2:205.  https://doi.org/10.21105/joss.00205 CrossRefGoogle Scholar
  17. Müssel C, Hopfensitz M, Kestler HA (2010) Boolnetan r package for generation, reconstruction and analysis of boolean networks. Bioinformatics 26(10):1378–1380CrossRefGoogle Scholar
  18. Ozaki E, Kato H, Kita H, Karasawa T, Maegawa T, Koino Y, Matsumoto K, Takada T, Nomoto K, Tanaka R et al (2004) Clostridium difficile colonization in healthy adults: transient colonization and correlation with enterococcal colonization. J Med Microbiol 53(2):167–172CrossRefGoogle Scholar
  19. Pérez-Cobas AE, Artacho A, Ott SJ, Moya A, Gosalbes MJ, Latorre A (2014) Structural and functional changes in the gut microbiota associated to clostridium difficile infection. Front Microbiol 5:335Google Scholar
  20. Pérez-Cobas AE, Moya A, Gosalbes MJ, Latorre A (2015) Colonization resistance of the gut microbiota against clostridium difficile. Antibiotics 4(3):337–357CrossRefGoogle Scholar
  21. Reeves AE, Koenigsknecht MJ, Bergin IL, Young VB (2012) Suppression of clostridium difficile in the gastrointestinal tracts of germfree mice inoculated with a murine isolate from the family lachnospiraceae. Infect Immun 80(11):3786–3794CrossRefGoogle Scholar
  22. Robert F (2012) Discrete iterations: a metric study, vol 6. Springer, BerlinGoogle Scholar
  23. Rodriguez C, Taminiau B, Korsak N, Avesani V, Van Broeck J, Brach P, Delmée M, Daube G (2016) Longitudinal survey of clostridium difficile presence and gut microbiota composition in a belgian nursing home. BMC Microbiol 16(1):229CrossRefGoogle Scholar
  24. Rosenberg E, Sharon G, Atad I, Zilber-Rosenberg I (2010) The evolution of animals and plants via symbiosis with microorganisms. Environ Microbiol Rep 2(4):500–506CrossRefGoogle Scholar
  25. Ruz GA, Goles E (2012) Reconstruction and update robustness of the mammalian cell cycle network. In: IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), 2012, pp 397–403. IEEEGoogle Scholar
  26. Ruz GA, Goles E (2013) Learning gene regulatory networks using the bees algorithm. Neural Comput Appl 22(1):63–70CrossRefGoogle Scholar
  27. Ruz GA, Goles E, Montalva M, Fogel GB (2014) Dynamical and topological robustness of the mammalian cell cycle network: a reverse engineering approach. Biosystems 115:23–32CrossRefGoogle Scholar
  28. Schubert E, Koos A, Emrich T, Züfle A, Schmid KA, Zimek A (2015) A framework for clustering uncertain data. PVLDB 8(12):1976–1979. URL http://www.vldb.org/pvldb/vol8/p1976-schubert.pdf. Accessed 19 June 2017
  29. Stein RR, Bucci V, Toussaint NC, Buffie CG, Rätsch G, Pamer EG, Sander C, Xavier JB (2013) Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput Biol 9(12):e1003388CrossRefGoogle Scholar
  30. Steinway SN, Biggs MB, Loughran TP Jr, Papin JA, Albert R (2015) Inference of network dynamics and metabolic interactions in the gut microbiome. PLoS Comput Biol 11(6):e1004338CrossRefGoogle Scholar
  31. Wagner A (2013) Robustness and evolvability in living systems. Princeton University Press, PrincetonCrossRefGoogle Scholar
  32. Walia R, Garg S, Song Y, Girotra M, Cuffari C, Fricke WF, Dutta SK (2014) Efficacy of fecal microbiota transplantation in 2 children with recurrent clostridium difficile infection and its impact on their growth and gut microbiome. J Pediatr Gastroenterol Nutr 59(5):565–570CrossRefGoogle Scholar
  33. Wuensche A (1999) Classifying cellular automata automatically: finding gliders, filtering, and relating space-time patterns, attractor basins, and the z parameter. Complexity 4(3):47–66.  https://doi.org/10.1002/(SICI)1099-0526(199901/02)4:3<47::AID-CPLX9>3.0.CO;2-V MathSciNetCrossRefGoogle Scholar
  34. Wuensche A, Lesser M (1992) Global dynamics of cellular automata: an atlas of basin of attraction fields of one-dimensional cellular automata. Andrew Wuensche, ReadingzbMATHGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Facultad de Ingeniería y CienciasUniversidad Adolfo IbáñezSantiagoChile
  2. 2.Centro de Modelamiento Matemático, UMI-CNRS-2807Universidad de ChileSantiagoChile
  3. 3.Centro para la Regulación del GenomaUniversidad de ChileSantiagoChile
  4. 4.Laboratorio de Bioinformática y Expresión Génica, INTAUniversidad de ChileSantiagoChile
  5. 5.Instituto de Ciencias de la IngenieríaUniversidad de O’HigginsRancaguaChile

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