1887

Abstract

is a micro-organism with great potential for industry due to its stress-endurance traits and easy manipulation of the metabolism. However, optimization is still required to improve production yields. In the last years, manipulation of bacterial small non-coding RNAs (ncRNAs) has been recognized as an effective tool to improve the production of industrial compounds. So far, very few ncRNAs are annotated in beyond the generally conserved. In the present study, was cultivated in a two-compartment scale-down bioreactor that simulates large-scale industrial bioreactors. We performed RNA-Seq of samples collected at distinct locations and time-points to predict novel and potentially important ncRNAs for the adaptation of to bioreactor stress conditions. Instead of using a purely genomic approach, we have rather identified regions of putative ncRNAs with high expression levels using two different programs (Artemis and sRNA detect). Only the regions identified with both approaches were considered for further analysis and, in total, 725 novel ncRNAs were predicted. We also found that their expression was not constant throughout the bioreactor, showing different patterns of expression with time and position. This is the first work focusing on the ncRNAs whose expression is triggered in a bioreactor environment. This information is of great importance for industry, since it provides possible targets to engineer more effective strains for large-scale production.

Loading

Article metrics loading...

/content/journal/micro/10.1099/mic.0.000875
2019-12-20
2020-01-27
Loading full text...

Full text loading...

References

  1. Palleroni NJ. Genus Pseudomonas In Krieg NR, Holt JG. (editors) Bergey’s Manual of Systematic Bacteriology. 1 Baltimore, MD: USA Williams & Wilkins; 1984; pp 141– 199
    [Google Scholar]
  2. Nikel PI, Martínez-García E, de Lorenzo V. Biotechnological domestication of pseudomonads using synthetic biology. Nat Rev Microbiol 2014;12: 368– 379 [CrossRef]
    [Google Scholar]
  3. Ramos J-L, Sol Cuenca M, Molina-Santiago C, Segura A, Duque E et al. Mechanisms of solvent resistance mediated by interplay of cellular factors in Pseudomonas putida. FEMS Microbiol Rev 2015;39: 555– 566 [CrossRef]
    [Google Scholar]
  4. Bagdasarian M, Lurz R, Rückert B, Franklin FC, Bagdasarian MM et al. Specific-purpose plasmid cloning vectors. II. broad host range, high copy number, RSF1010-derived vectors, and a host-vector system for gene cloning in Pseudomonas. Gene 1981;16: 237– 247 [CrossRef]
    [Google Scholar]
  5. Regenhardt D, Heuer H, Heim S, Fernandez DU, Strömpl C et al. Pedigree and taxonomic credentials of Pseudomonas putida strain KT2440. Environ Microbiol 2002;4: 912– 915 [CrossRef]
    [Google Scholar]
  6. Register F. Appendix E, certified host-vector systems. 1982;47 17197
  7. Nelson KE, Weinel C, Paulsen IT, Dodson RJ, Hilbert H et al. Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440. Environ Microbiol 2002;4: 799– 808 [CrossRef]
    [Google Scholar]
  8. Loeschcke A, Thies S. Pseudomonas putida-a versatile host for the production of natural products. Appl Microbiol Biotechnol 2015;99: 6197– 6214 [CrossRef]
    [Google Scholar]
  9. Waters LS, Storz G. Regulatory RNAs in bacteria. Cell 2009;136: 615– 628 [CrossRef]
    [Google Scholar]
  10. Ferrara S, Brugnoli M, De Bonis A, Righetti F, Delvillani F et al. Comparative profiling of Pseudomonas aeruginosa strains reveals differential expression of novel unique and conserved small RNAs. PLoS One 2012;7: e36553 [CrossRef]
    [Google Scholar]
  11. Filiatrault MJ, Stodghill PV, Bronstein PA, Moll S, Lindeberg M et al. Transcriptome analysis of Pseudomonas syringae identifies new genes, noncoding RNAs, and antisense activity. J Bacteriol 2010;192: 2359– 2372 [CrossRef]
    [Google Scholar]
  12. Gómez-Lozano M, Marvig RL, Molin S, Long KS. Genome-wide identification of novel small RNAs in Pseudomonas aeruginosa. Environ Microbiol 2012;14: 2006– 2016 [CrossRef]
    [Google Scholar]
  13. Gómez-Lozano M, Marvig RL, Molina-Santiago C, Tribelli PM, Ramos J-L et al. Diversity of small RNAs expressed in Pseudomonas species. Environ Microbiol Rep 2015;7: 227– 236 [CrossRef]
    [Google Scholar]
  14. Wurtzel O, Sesto N, Mellin JR, Karunker I, Edelheit S et al. Comparative transcriptomics of pathogenic and non-pathogenic listeria species. Mol Syst Biol 2012;8: 583 [CrossRef]
    [Google Scholar]
  15. D'Arrigo I, Bojanovič K, Yang X, Holm Rau M, Long KS. Genome-wide mapping of transcription start sites yields novel insights into the primary transcriptome of Pseudomonas putida. Environ Microbiol 2016;18: 3466– 3481 [CrossRef]
    [Google Scholar]
  16. Frank S, Klockgether J, Hagendorf P, Geffers R, Schöck U et al. Pseudomonas putida KT2440 genome update by cDNA sequencing and microarray transcriptomics. Environ Microbiol 2011;13: 1309– 1326 [CrossRef]
    [Google Scholar]
  17. Löffler M, Simen JD, Jäger G, Schäferhoff K, Freund A et al. Engineering E. coli for large-scale production - Strategies considering ATP expenses and transcriptional responses. Metab Eng 2016;38: 73– 85 [CrossRef]
    [Google Scholar]
  18. Vallon T, Simon O, Rendgen-Heugle B, Frana S, Mückschel B et al. Applying systems biology tools to study n-butanol degradation in Pseudomonas putida KT2440. Eng Life Sci 2015;15: 760– 771 [CrossRef]
    [Google Scholar]
  19. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011. 2011;17 3
  20. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9: 357– 359 [CrossRef]
    [Google Scholar]
  21. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25: 2078– 2079 [CrossRef]
    [Google Scholar]
  22. Peña-Castillo L, Grüell M, Mulligan ME, Lang AS. Detection of bacterial small transcripts from RNA-Seq data: a comparative assessment. Pac Symp Biocomput 2016;21: 456– 467
    [Google Scholar]
  23. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26: 841– 842 [CrossRef]
    [Google Scholar]
  24. Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA. Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 2012;28: 464– 469 [CrossRef]
    [Google Scholar]
  25. Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 2013;41: e108 [CrossRef]
    [Google Scholar]
  26. Babicki S, Arndt D, Marcu A, Liang Y, Grant JR et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res 2016;44: W147– W153 [CrossRef]
    [Google Scholar]
  27. Kalvari I, Argasinska J, Quinones-Olvera N, Nawrocki EP, Rivas E et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res 2018;46: D335– D342 [CrossRef]
    [Google Scholar]
  28. Bojanovič K, D'Arrigo I, Long KS. Global transcriptional responses to osmotic, oxidative, and imipenem stress conditions in Pseudomonas putida. Appl Environ Microbiol 2017;83: e03236– 16 [CrossRef]
    [Google Scholar]
  29. Edgar R, Domrachev M, Lash AE. Gene expression Omnibus: NCBI gene expression and hybridization array data Repository. Nucleic Acids Res 2002;30: 207– 210 [CrossRef]
    [Google Scholar]
  30. Zhang Y, Huang H, Zhang D, Qiu J, Yang J et al. A review on recent computational methods for predicting noncoding RNAs. Biomed Res Int 2017;2017: 9139504 14 [CrossRef]
    [Google Scholar]
  31. Chao Y, Papenfort K, Reinhardt R, Sharma CM, Vogel J. An atlas of Hfq-bound transcripts reveals 3' UTRs as a genomic reservoir of regulatory small RNAs. Embo J 2012;31: 4005– 4019 [CrossRef]
    [Google Scholar]
  32. Valentin-Hansen P, Eriksen M, Udesen C. The bacterial Sm-like protein Hfq: a key player in RNA transactions. Mol Microbiol 2004;51: 1525– 1533 [CrossRef]
    [Google Scholar]
  33. Olejniczak M, Storz G. ProQ/FinO-domain proteins: another ubiquitous family of RNA matchmakers?. Mol Microbiol 2017;104: 905– 915 [CrossRef]
    [Google Scholar]
  34. Liu JM, Camilli A. A broadening world of bacterial small RNAs. Curr Opin Microbiol 2010;13: 18– 23 [CrossRef]
    [Google Scholar]
  35. Opdyke JA, Kang J-G, Storz G, GadY SG. A small-RNA regulator of acid response genes in Escherichia coli. J Bacteriol 2004;186: 6698– 6705 [CrossRef]
    [Google Scholar]
  36. Jäger D, Pernitzsch SR, Richter AS, Backofen R, Sharma CM et al. An archaeal sRNA targeting cis- and trans-encoded mRNAs via two distinct domains. Nucleic Acids Res 2012;40: 10964– 10979 [CrossRef]
    [Google Scholar]
  37. Melamed S, Peer A, Faigenbaum-Romm R, Gatt YE, Reiss N et al. Global mapping of small RNA-Target interactions in bacteria. Mol Cell 2016;63: 884– 897 [CrossRef]
    [Google Scholar]
  38. Sayed N, Jousselin A, Felden B. A cis-antisense RNA acts in trans in Staphylococcus aureus to control translation of a human cytolytic peptide. Nat Struct Mol Biol 2011;19: 105– 112 [CrossRef]
    [Google Scholar]
  39. Wade JT, Grainger DC. Pervasive transcription: illuminating the dark matter of bacterial transcriptomes. Nat Rev Microbiol 2014;12: 647– 653 [CrossRef]
    [Google Scholar]
  40. Lybecker M, Zimmermann B, Bilusic I, Tukhtubaeva N, Schroeder R. The double-stranded transcriptome of Escherichia coli. Proc Natl Acad Sci USA 2014;111: 3134– 3139 [CrossRef]
    [Google Scholar]
  41. Lasa I, Toledo-Arana A, Dobin A, Villanueva M, de los Mozos IR et al. Genome-Wide antisense transcription drives mRNA processing in bacteria. Proc Natl Acad Sci USA 2011;108: 20172– 20177 [CrossRef]
    [Google Scholar]
  42. Kwenda S, Gorshkov V, Ramesh AM, Naidoo S, Rubagotti E et al. Discovery and profiling of small RNAs responsive to stress conditions in the plant pathogen Pectobacterium atrosepticum. BMC Genomics 2016;17: 47 [CrossRef]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/micro/10.1099/mic.0.000875
Loading
/content/journal/micro/10.1099/mic.0.000875
Loading

Data & Media loading...

Supplements

Supplementary material 1

EXCEL
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error