Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations.

Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

Original Publication: [London] : BioMed Central, 2000-

Gene Expression Profiling* ; Gene Expression Regulation, Bacterial* ; Gene Regulatory Networks* ; Proteomics*; Algorithms ; Bacteria/genetics ; Bayes Theorem ; Computational Biology/methods ; Computer Simulation ; Signal Transduction ; Systems Biology/methods

Background: Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications.
Methods: We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation.
Results: The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context.
Conclusions: The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

NPJ Syst Biol Appl. 2017 Jun 2;3:14. (PMID: 28649441)
Appl Environ Microbiol. 2018 Jan 17;84(3):. (PMID: 29150517)
Wellcome Open Res. 2019 Aug 30;4:14. (PMID: 37744419)
Bioinform Biol Insights. 2015 Apr 29;9:61-74. (PMID: 25983554)
Appl Microbiol Biotechnol. 2013 Sep;97(17):7543-52. (PMID: 23877580)
Genome Biol. 2006;7(5):R36. (PMID: 16686963)
Nature. 2009 Jan 15;457(7227):309-12. (PMID: 19148099)
Bioinformatics. 2012 Nov 1;28(21):2811-8. (PMID: 22923292)
PLoS Comput Biol. 2013;9(12):e1003290. (PMID: 24367245)
Mol Biosyst. 2015 Aug;11(8):2247-54. (PMID: 26057862)
Bioinformatics. 2013 Aug 15;29(16):2068-70. (PMID: 23818512)
Elife. 2015 Aug 18;4:. (PMID: 26284497)
Appl Microbiol Biotechnol. 2013 Sep;97(17):7529-41. (PMID: 23720034)
Bioinformatics. 2014 Apr 1;30(7):923-30. (PMID: 24227677)
BMC Genomics. 2014 Dec 15;15:1107. (PMID: 25511286)
BMC Bioinformatics. 2006 May 08;7:249. (PMID: 16681847)
PLoS One. 2016 Sep 26;11(9):e0163011. (PMID: 27669408)
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5448-51. (PMID: 19964678)
J Comput Biol. 2000;7(3-4):601-20. (PMID: 11108481)
Bioinformatics. 2009 Jan 15;25(2):286-7. (PMID: 18826957)
J Bacteriol. 2012 Mar;194(6):1515-22. (PMID: 22247510)
Bioinformatics. 2014 Aug 1;30(15):2114-20. (PMID: 24695404)
Genome Biol. 2014;15(12):550. (PMID: 25516281)
Microb Inform Exp. 2013 Apr 10;3(1):2. (PMID: 23575213)
J Bacteriol. 2009 Sep;191(18):5877-8. (PMID: 19617360)
Microorganisms. 2015 Oct 21;3(4):707-24. (PMID: 27682113)
Biophys J. 2007 Jun 1;92(11):3755-63. (PMID: 17350995)
Gut. 2015 Nov;64(11):1732-43. (PMID: 25614621)
Sci Signal. 2009 Jun 30;2(77):ra31. (PMID: 19567914)
Science. 2005 Apr 22;308(5721):523-9. (PMID: 15845847)
Microb Biotechnol. 2017 Sep;10(5):1191-1193. (PMID: 28771998)
Biophys J. 2007 May 15;92(10):3407-24. (PMID: 17277187)
PLoS Comput Biol. 2013;9(1):e1002803. (PMID: 23341757)
Sci Signal. 2013 Aug 13;6(288):ra68. (PMID: 23943608)
Am J Pathol. 2007 Jul;171(1):252-62. (PMID: 17591970)
J Proteome Res. 2011 Apr 1;10(4):1794-805. (PMID: 21254760)
Circ Res. 2016 Jun 10;118(12):1906-17. (PMID: 27140435)
Sci Rep. 2016 Apr 26;6:24967. (PMID: 27113331)
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Dec;76(6 Pt 1):061909. (PMID: 18233871)
BMC Cancer. 2018 May 18;18(1):569. (PMID: 29776351)
ISME J. 2013 Jan;7(1):110-21. (PMID: 22763648)
Appl Microbiol Biotechnol. 2014 Oct;98(19):8133-44. (PMID: 25104030)
Am J Physiol. 1998 Jan;274(1):C82-7. (PMID: 9458715)
Nat Protoc. 2007;2(8):1896-906. (PMID: 17703201)
Ecotoxicol Environ Saf. 2000 Mar;45(3):198-207. (PMID: 10702338)
PeerJ. 2017 Oct 17;5:e3859. (PMID: 29062598)
Nat Methods. 2012 Mar 04;9(4):357-9. (PMID: 22388286)
Trends Ecol Evol. 2010 Jul;25(7):410-8. (PMID: 20488578)
Mol Biosyst. 2013 Jul;9(7):1576-83. (PMID: 23525368)
Nat Commun. 2019 Jan 8;10(1):71. (PMID: 30622249)
Nat Rev Microbiol. 2010 Sep;8(9):623-33. (PMID: 20676145)
Curr Opin Microbiol. 2015 Feb;23:133-40. (PMID: 25483350)
J Neurosci Res. 2009 Feb 15;87(3):683-90. (PMID: 18816790)
BMC Bioinformatics. 2006 Feb 07;7:56. (PMID: 16464248)
Mol Syst Biol. 2009;5:239. (PMID: 19156131)
FEBS Lett. 2008 Nov 12;582(27):3776-82. (PMID: 18930050)
J Bacteriol. 2004 Jun;186(11):3663-9. (PMID: 15150257)
BMC Bioinformatics. 2018 Apr 24;19(1):155. (PMID: 29699481)
Curr Genomics. 2013 Apr;14(2):91-110. (PMID: 24082820)
Biosystems. 2009 Apr;96(1):86-103. (PMID: 19150482)
Mol Cell Proteomics. 2014 Sep;13(9):2513-26. (PMID: 24942700)
Front Microbiol. 2011 Feb 10;2:17. (PMID: 21687411)
Biophys J. 2014 Jan 21;106(2):467-78. (PMID: 24461022)
ISME J. 2015 Aug;9(8):1693-9. (PMID: 25535936)
Nat Methods. 2016 Sep;13(9):731-40. (PMID: 27348712)
PLoS Comput Biol. 2008 Feb 29;4(2):e1000005. (PMID: 18463702)
Mol Biosyst. 2015 Aug;11(8):2238-46. (PMID: 26118552)
Annu Rev Biophys. 2010;39:43-59. (PMID: 20192769)
Front Microbiol. 2018 Dec 12;9:3059. (PMID: 30631311)

031A600A/B Bundesministerium für Bildung und Forschung; 2014-6545 Vetenskapsrådet; NTER/SYSAPP/14/05 Fonds National de la Recherche Luxembourg; ERASysAPP SystemsX (CH); 205321 173020 Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; 1161007 Fondecyt

Keywords: Acidophiles; Approximate Bayesian computation; Biological signalling simulations; Biomining; Gene regulatory networks; Machine learning; Multispecies bacterial community interactions

Date Created: 20200123 Date Completed: 20200406 Latest Revision: 20240329

20240329

PMC6975020

10.1186/s12859-019-3337-9

31964336