Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus
ABSTRACT : Commercial strains of Streptomyces clavuligerus, such as ATCC 27064, produce clavulanic acid in low quantities. Increasing the production of this metabolite is essential for decreasing the cost of clavulanic acid (CA) prescriptions, thereby promoting successful treatments against resistan...
- Autores:
-
Caicedo Montoya, Carlos Andrés
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2025
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/45690
- Acceso en línea:
- https://hdl.handle.net/10495/45690
- Palabra clave:
- Antibióticos Betalactámicos
Beta Lactam Antibiotics
RNA-seq
Ácido Clavulánico
Clavulanic Acid
Perfilación de la Expresión Génica
Gene Expression Profiling
MicroARNs
https://id.nlm.nih.gov/mesh/D000097902
https://id.nlm.nih.gov/mesh/D000081246
https://id.nlm.nih.gov/mesh/D019818
https://id.nlm.nih.gov/mesh/D020869
https://id.nlm.nih.gov/mesh/D035683
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
| id |
UDEA2_d1821cbc17faf71a6bb6eb8ea90ed407 |
|---|---|
| oai_identifier_str |
oai:bibliotecadigital.udea.edu.co:10495/45690 |
| network_acronym_str |
UDEA2 |
| network_name_str |
Repositorio UdeA |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| title |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| spellingShingle |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus Antibióticos Betalactámicos Beta Lactam Antibiotics RNA-seq Ácido Clavulánico Clavulanic Acid Perfilación de la Expresión Génica Gene Expression Profiling MicroARNs https://id.nlm.nih.gov/mesh/D000097902 https://id.nlm.nih.gov/mesh/D000081246 https://id.nlm.nih.gov/mesh/D019818 https://id.nlm.nih.gov/mesh/D020869 https://id.nlm.nih.gov/mesh/D035683 |
| title_short |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| title_full |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| title_fullStr |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| title_full_unstemmed |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| title_sort |
Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus |
| dc.creator.fl_str_mv |
Caicedo Montoya, Carlos Andrés |
| dc.contributor.advisor.none.fl_str_mv |
Ríos Estepa, Rigoberto |
| dc.contributor.author.none.fl_str_mv |
Caicedo Montoya, Carlos Andrés |
| dc.contributor.researchgroup.none.fl_str_mv |
Bioprocesos |
| dc.subject.decs.none.fl_str_mv |
Antibióticos Betalactámicos Beta Lactam Antibiotics RNA-seq Ácido Clavulánico Clavulanic Acid Perfilación de la Expresión Génica Gene Expression Profiling MicroARNs |
| topic |
Antibióticos Betalactámicos Beta Lactam Antibiotics RNA-seq Ácido Clavulánico Clavulanic Acid Perfilación de la Expresión Génica Gene Expression Profiling MicroARNs https://id.nlm.nih.gov/mesh/D000097902 https://id.nlm.nih.gov/mesh/D000081246 https://id.nlm.nih.gov/mesh/D019818 https://id.nlm.nih.gov/mesh/D020869 https://id.nlm.nih.gov/mesh/D035683 |
| dc.subject.meshuri.none.fl_str_mv |
https://id.nlm.nih.gov/mesh/D000097902 https://id.nlm.nih.gov/mesh/D000081246 https://id.nlm.nih.gov/mesh/D019818 https://id.nlm.nih.gov/mesh/D020869 https://id.nlm.nih.gov/mesh/D035683 |
| description |
ABSTRACT : Commercial strains of Streptomyces clavuligerus, such as ATCC 27064, produce clavulanic acid in low quantities. Increasing the production of this metabolite is essential for decreasing the cost of clavulanic acid (CA) prescriptions, thereby promoting successful treatments against resistant bacterial infections and making them accessible to poor communities. Understanding the regulatory pathways for clavulanic acid production is crucial for developing new genetically modified strains with enhanced metabolic capabilities for CA production. Recently, several studies have revealed the pivotal role of small non-coding RNAs in regulating various metabolic processes in both prokaryotic and eukaryotic organisms. However, knowledge about small RNAs (sRNAs) in Streptomyces clavuligerus is scarce. This thesis aimed to use cutting-edge bioinformatics tools and a compendium of RNA-seq data to predict the potential coding of sRNAs that might be present in the genome of Streptomyces clavuligerus ATCC 27064. In the genome of Streptomyces clavuligerus 606 intergenic regions (IGRs) are conserved, and 272 possess a highly thermodynamically stable and conserved secondary structure, indicating the presence of non-coding RNA in these regions. The transcriptome assembly of S. clavuligerus showed that the genome is completely functional, as all the annotated genes are expressed under the conditions analyzed. From this assembly, transcripts originated from IGRs were labeled as putative sRNAs, and their differential expression during the growth curve of S. clavuligerus for clavulanic acid production was established. The interactome of these differentially expressed (DE) RNAs displayed the sRNAs as global regulators, as they can have multiple mRNA targets. Functional annotation of the target genes of DE sRNAs demonstrated that they are directly involved in secondary metabolite production, as this was one of the most enriched categories in KEGG and COG annotations. Specifically, two sRNA have the genes of the biosynthetic gene cluster of CA as targets. Thus, these molecules add an additional layer to the regulatory cascade for CA biosynthesis, and we propose them as targets for metabolic engineering to increase CA production. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-04-11T13:36:15Z |
| dc.date.issued.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/draft |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
draft |
| dc.identifier.citation.none.fl_str_mv |
C.A. Caicedo Montoya “Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus”, Tesis doctoral, Doctorado en Ingeniería Química, Universidad de Antioquia, Medellín, Antioquia, Colombia, 2024. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10495/45690 |
| identifier_str_mv |
C.A. Caicedo Montoya “Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus”, Tesis doctoral, Doctorado en Ingeniería Química, Universidad de Antioquia, Medellín, Antioquia, Colombia, 2024. |
| url |
https://hdl.handle.net/10495/45690 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
Alam, K., Islam, M. M., Islam, S., Hao, J., Abbasi, M. N., Hayat, M., … Li, A. (2023). Comparative genomics with evolutionary lineage in Streptomyces bacteria reveals high biosynthetic potentials. World Journal of Microbiology and Biotechnology, 39(2), 1–12. https://doi.org/10.1007/s11274-022-03433-y Álvarez-Álvarez, R., Rodríguez-García, A., Santamarta, I., Pérez-Redondo, R., Prieto Domínguez, A., Martínez-Burgo, Y., & Liras, P. (2014). Transcriptomic analysis of Streptomyces clavuligerus ΔccaR:: Tsr: Effects of the cephamycin C-clavulanic acid cluster regulator CcaR on global regulation. Microbial Biotechnology, 7(3), 221–231. https://doi.org/10.1111/1751-7915.12109 Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq-A Python framework to work with highthroughput sequencing data. Bioinformatics, 31(2), 166–169. https://doi.org/10.1093/bioinformatics/btu638 Aramaki, T., Blanc-Mathieu, R., Endo, H., Ohkubo, K., Kanehisa, M., Goto, S., … Valencia, A. (2020). KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics, 36(7), 2251–2252. https://doi.org/10.1093/bioinformatics/btz859 Bailey, T. L., Johnson, J., Grant, C. E., & Noble, W. S. (2015). The MEME Suite. Nucleic Acids Research, 43(W1), W39–W49. https://doi.org/10.1093/nar/gkv416 Bauer, J. S., Fillinger, S., Förstner, K., Herbig, A., Jones, A. C., Flinspach, K., … Apel, A. K. (2017). dRNA-seq transcriptional profiling of the FK506 biosynthetic gene cluster in Streptomyces tsukubaensis NRRL18488 and general analysis of the transcriptome. RNA Biology, 14(11), 1617–1626. https://doi.org/10.1080/15476286.2017.1341020 Bilyk, O., & Luzhetskyy, A. (2016). Metabolic engineering of natural product biosynthesis in actinobacteria. Current Opinion in Biotechnology, 42, 98–107. https://doi.org/10.1016/j.copbio.2016.03.008 Blin, K., Shaw, S., Augustijn, H. E., Reitz, Z. L., Biermann, F., Alanjary, M., … Weber, T. (2023). AntiSMASH 7.0: New and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Research, 51(W1), W46–W50. https://doi.org/10.1093/nar/gkad344 Boisset, S.; Geissmann, T.; Huntzinger, E.; Fechter, P.; Bendridi, N.; Possedko, M.; Chevalier, C.; Helfer, A.C.; Benito, Y.; Jacquier, A.; et al (2007). Staphylococcus aureus RNAIII coordinately represses the synthesis of virulence factors and the transcription regulator rot by an antisense mechanism. Genes Dev. 21, 1353–1366. https://doi.org/10.1101/gad.423507 Brantl, S., & Müller, P (2021). Cis- and trans-encoded small regulatory RNAs in Bacillus subtilis. Microorganisms. 9, 1865. https://doi.org/10.3390/microorganisms9091865 Bushell, M. E., Kirk, S., Zhao, H. J., & Avignone-Rossa, C. A. (2006). Manipulation of the physiology of clavulanic acid biosynthesis with the aid of metabolic flux analysis. Enzyme and Microbial Technology, 39(1), 149–157. https://doi.org/10.1016/j.enzmictec.2006.01.017 Bustamante, M. C. C., Costa, C. L. L., Esperança, M. N., Mazziero, V. T., Cerri, M. O., & Badino, A. C. (2024). Effect of impeller type on cellular morphology and production of clavulanic acid by Streptomyces clavuligerus. Brazilian Journal of Microbiology, 55(2), 1167–1177. https://doi.org/10.1007/s42770-024-01306-0 Caicedo-Montoya, C., Manzo-Ruiz, M., & Rios-Estepa, R. (2021). Pan-genome of the genus Streptomyces and prioritization of biosynthetic gene clusters with potential to produce antibiotic compounds. Frontiers in Microbiology, 12(September). https://doi.org/10.3389/fmicb.2021.677558 Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560 Chevez-Guardado, R., & Peña-Castillo, L. (2021). Promotech: a general tool for bacterial promoter recognition. Genome Biology, 22(1), 1–16. https://doi.org/10.1186/s13059- 021-02514-9 Desnoyers, G., Bouchard, M. P., & Massé, E. (2013). New insights into small RNA-dependent translational regulation in prokaryotes. Trends in Genetics, 29(2), 92–98. https://doi.org/10.1016/j.tig.2012.10.004 Dutta, T., & Srivastava, S. (2018). Small RNA-mediated regulation in bacteria: A growing palette of diverse mechanisms. Gene, 656, 60–72. https://doi.org/10.1016/j.gene.2018.02.068 Galperin, M. Y., Wolf, Y. I., Makarova, K. S., Alvarez, R. V., Landsman, D., & Koonin, E. V. (2021). COG database update: Focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Research, 49(D1), D274–D281. https://doi.org/10.1093/nar/gkaa1018 Georg, J., & Hess, W. R. (2018). Widespread antisense transcription in prokaryotes. Microbiology Spectrum, 6(4). https://doi.org/10.1128/microbiolspec.rwr-0029-2018 Gottesman, S., & Storz, G. (2011). Bacterial small RNA regulators: Versatile roles and rapidly evolving variations. Cold Spring Harbor Perspectives in Biology, 3(12), 1–16. https://doi.org/10.1101/cshperspect.a003798 Groher, F., & Suess, B. (2014). Synthetic riboswitches - A tool comes of age. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms. https://doi.org/10.1016/j.bbagrm.2014.05.005 /doi.org/10.1093/nar/gkx428 Gruber, A. R., Findeiß, S., Washietl, S., Hofacker, I. L., & Stadler, P. F. (2010). RNAz 2.0: improved noncoding RNA detection. In Pacific Symposium on Biocomputing 2010 (pp. 69–79). https://doi.org/10.1142/9789814295291_0009 Helmann, J.D. (2019), Where to begin? Sigma factors and the selectivity of transcription initiation in bacteria. Mol Microbiol, 112: 335-347. https://doi.org/10.1111/mmi.14309 Heueis, N., Vockenhuber, M.-P., & Suess, B. (2014). Small non-coding RNAs in Streptomycetes. RNA Biology, 11(5), 464–469. https://doi.org/10.4161/rna.28262 Hindra, Moody, M. J., Jones, S. E., & Elliot, M. A. (2014). Complex intra-operonic dynamics mediated by a small RNA in Streptomyces coelicolor. PloS One, 9(1), e85856. https://doi.org/10.1371/journal.pone.0085856 Hwang, S., Lee, N., Choe, D., Lee, Y., Kim, W., Jeong, Y., … Cho, B.-K. (2021). Elucidating the regulatory elements for transcription termination and posttranscriptional processing in the Streptomyces clavuligerus genome. MSystems, 6(3). https://doi.org/10.1128/msystems.01013-20 Jackson, L.A., Day, M., Allen, J., Scott, E. & Dyer, D.W (2017). Iron-regulated small RNA expression as Neisseria gonorrhoeae FA 1090 transitions into stationary phase growth. BMC Genom. 18, 317. https://doi.org/10.1186/s12864-017-3684-8. Jeong, Y., Kim, J.-N., Kim, M. W., Bucca, G., Cho, S., Yoon, Y. J., … Cho, B.-K. (2016). The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2). Nature Communications, 7, 11605. https://doi.org/10.1038/ncomms11605 Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., … Petrov, A. I. (2021). Rfam 14: Expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research, 49(D1), D192–D200. https://doi.org/10.1093/nar/gkaa1047 Kang, Y. J., Yang, D. C., Kong, L., Hou, M., Meng, Y. Q., Wei, L., & Gao, G. (2017). CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Research, 45(W1), W12–W16. https://doi.org/10.1093/nar/gkx428 Katz, K., Shutov, O., Lapoint, R., Kimelman, M., Rodney Brister, J., & O’Sullivan, C. (2022). The Sequence Read Archive: A decade more of explosive growth. Nucleic Acids Research, 50(D1), D387–D390. https://doi.org/10.1093/nar/gkab1053 Kingsford, C. L., Ayanbule, K., & Salzberg, S. L. (2007). Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake. Genome Biology, 8(2). https://doi.org/10.1186/gb-2007-8-2-r22 Kopylova, E., Noé, L., Touzet, H. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24): 3211-3217. Kurt, A., Álvarez-Álvarez, R., Liras, P., & Özcengiz, G. (2013). Role of the cmcH-ccaR intergenic region and ccaR overexpression in cephamycin C biosynthesis in Streptomyces clavuligerus. Applied Microbiology and Biotechnology, 97(13), 5869–5880. https://doi.org/10.1007/s00253-013-4721-4 Langmead, B., & Salzberg, S. L. (2013). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359. https://doi.org/10.1038/nmeth.1923.Fast Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., Mcgettigan, P. A., McWilliam, H., … Higgins, D. G. (2007). Clustal W and Clustal X version 2.0. Bioinformatics, 23(21), 2947– 2948. https://doi.org/10.1093/bioinformatics/btm404 Lee, N., Hwang, S., Lee, Y., Cho, S., Palsson, B., & Cho, B. K. (2019). Synthetic biology tools for novel secondary metabolite discovery in Streptomyces. Journal of Microbiology and Biotechnology, 29(5), 667–686. https://doi.org/10.4014/jmb.1904.04015 Lee, Y., Lee, N., Hwang, S., Kim, W., Cho, S., Palsson, B. O., & Cho, B. K. (2022). Genome-scale analysis of genetic regulatory elements in Streptomyces avermitilis MA-4680 using transcript boundary information. BMC Genomics, 23(1). https://doi.org/10.1186/s12864-022-08314-0 Lee, Y., Lee, N., Jeong, Y., Hwang, S., Kim, W., Cho, S., … Cho, B. K. (2019). The transcription unit architecture of Streptomyces lividans TK24. Frontiers in Microbiology, 10. https://doi.org/10.3389/fmicb.2019.02074 Leonard, S., Meyer, S., Lacour, S., Nasser, W., Hommais, F., & Reverchon, S. (2019). APERO: A genome-wide approach for identifying bacterial small RNAs from RNA-Seq data. Nucleic Acids Research, 47(15). https://doi.org/10.1093/nar/gkz485 Levine, E., & Hwa, T. (2008). Small RNAs establish gene expression thresholds. Current Opinion in Microbiology, 11(6), 574–579. https://doi.org/10.1016/j.mib.2008.09.016 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352 Li, R., & Townsend, C. A. (2006). Rational strain improvement for enhanced clavulanic acid production by genetic engineering of the glycolytic pathway in Streptomyces clavuligerus. Metabolic Engineering, 8(3), 240–252. https://doi.org/10.1016/j.ymben.2006.01.003 Liras, P., & Martín, J. F. (2021). Streptomyces clavuligerus: The omics era. journal of industrial Microbiology and Biotechnology, 48(9–10). https://doi.org/10.1093/jimb/kuab072 Liu, W. B., Shi, Y., Yao, L. L., Zhou, Y., & Ye, B. C. (2013). Prediction and characterization of small non-coding RNAs related to secondary metabolites in Saccharopolyspora erythraea. PLoS ONE, 8(11), 1–11. https://doi.org/10.1371/journal.pone.0080676 Llorens-Rico, V., Cano, J., Kamminga, T., Gil, R., Latorre, A., Chen, W.-H., … Lluch-Senar, M. (2016). Bacterial antisense RNAs are mainly the product of transcriptional noise. Science Advances, 2(3), e1501363–e1501363. https://doi.org/10.1126/sciadv.1501363 Lorenz, R., Bernhart, S. H., Siederdissen, C. H. zu, Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011). ViennaRNA Package 2.0. Algorithms for molecular biology, 6(26). https://doi.org/10.1186/1748-7188-6-26 Mann, M., Wright, P. R., & Backofen, R. (2017). IntaRNA 2.0: Enhanced and customizable prediction of RNA-RNA interactions. Nucleic Acids Research, 45(W1), W435–W439. https://doi.org/10.1093/nar/gkx279 Martín-Sánchez, L., Singh, K. S., Avalos, M., Van Wezel, G. P., Dickschat, J. S., & Garbeva, P. (2019). Phylogenomic analyses and distribution of terpene synthases among Streptomyces. Beilstein Journal of Organic Chemistry, 15, 1181–1193. https://doi.org/10.3762/bjoc.15.115 Melamed, S., Peer, A., Faigenbaum-Romm, R., Gatt, Y. E., Reiss, N., Bar, A., … Margalit, H. (2016). Global Mapping of small RNA-target interactions in bacteria. Molecular Cell, 63, 884–897. https://doi.org/10.1016/j.molcel.2016.07.026 Menard, G., Silard, C., Suriray, M., Rouillon, A., & Augagneur, Y. (2022) Thirty years of sRNAmediated regulation in Staphylococcus Aureus: From initial discoveries to in vivo biological implications. Int. J. Mol. Sci. 23, 7346. https://doi.org/10.3390/ijms23137346. Mentz, A., Neshat, A., Pfeifer-Sancar, K., Pühler, A., Rückert, C., & Kalinowski, J. (2013). Comprehensive discovery and characterization of small RNAs in Corynebacterium glutamicum ATCC 13032. BMC Genomics, 14, 714. https://doi.org/10.1186/1471-2164- 14-714 Mingyar, E., Mühling, L., Kulik, A., Winkler, A., Wibberg, D., Kalinowski, J., … Stegmann, E. (2021). A regulator based “semi-targeted” approach to activate silent biosynthetic gene clusters. International Journal of Molecular Sciences, 22(14). https://doi.org/10.3390/ijms22147567 Moody, M. J., Young, R. A., Jones, S. E., & Elliot, M. A. (2013). Comparative analysis of noncoding RNAs in the antibiotic-producing Streptomyces bacteria. BMC Genomics, 14(1), 558. https://doi.org/10.1186/1471-2164-14-558 Muzellec, B., Teleńczuk, M., Cabeli, V., & Andreux, M. (2023). PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics, 39(9). https://doi.org/10.1093/bioinformatics/btad547 Nawrocki, E. P., & Eddy, S. R. (2013). Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics, 29(22), 2933–2935. https://doi.org/10.1093/bioinformatics/btt509 Nussenzweig P. M., and Marraffini L. A. (2020). Molecular mechanisms of CRISPR-Cas immunity in bacteria. Annu Rev Genet. 54:93-120. doi: 10.1146/annurev-genet-022120- 112523. Palazzotto, E., Tong, Y., Lee, S. Y., & Weber, T. (2019). Synthetic biology and metabolic engineering of actinomycetes for natural product discovery. Biotechnology Advances, 37(6), 107366. https://doi.org/10.1016/j.biotechadv.2019.03.005 Pánek, J., Bobek, J., Mikulík, K., Basler, M., & Vohradský, J. (2008). Biocomputational prediction of small non-coding RNAs in Streptomyces. BMC Genomics, 9(1), 217. https://doi.org/10.1186/1471-2164-9-217 Papenfort, K., & Melamed, S. (2023). Small RNAs, large networks: posttranscriptional regulons in gram-negative bacteria. Annu. Rev. Microbiol. 77, 23–43. https://doi.org/10.1146/annurev-micro-041320-025836. Patiño, L. F. (2024). Improving clavulanic acid biosynthesis: A comparative transcriptome analysis of Streptomyces clavuligerus for identifying potential metabolic limitations. Universidad de Antioquia. Ponath, F., Hör, J., & Vogel, J. (2022) An overview of gene regulation in bacteria by small RNAs derived from mRNA 3′ Ends. FEMS Microbiol. Rev. 46, fuac017. https://doi.org/10.1093/femsre/fuac017. Ramirez-Malule, H., Junne, S., López, C., Zapata, J., Sáez, A., Neubauer, P., & Rios-Estepa, R. (2016). An improved HPLC-DAD method for clavulanic acid quantification in fermentation broths of Streptomyces clavuligerus. Journal of Pharmaceutical and Biomedical Analysis, 120, 241–247. https://doi.org/10.1016/j.jpba.2015.12.035 Ribeiro, R. M. M. G. P., Esperança, M. N., Sousa, A. P. A., Neto, Á. B., & Cerri, M. O. (2021). Individual effect of shear rate and oxygen transfer on clavulanic acid production by Streptomyces clavuligerus. Bioprocess and Biosystems Engineering, 44(8), 1721–1732. https://doi.org/10.1007/s00449-021-02555-1 Richards, G. R., & Vanderpool, C. K. (2011). Molecular call and response: The physiology of bacterial small RNAs. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1809(10), 525–531. https://doi.org/10.1016/j.bbagrm.2011.07.013 Salwan, R., & Sharma, V. (2020, January 1). Molecular and biotechnological aspects of secondary metabolites in actinobacteria. Microbiological Research. Elsevier GmbH. https://doi.org/10.1016/j.micres.2019.126374 Saudagar, P. S., & Singhal, R. S. (2007). Optimization of nutritional requirements and feeding strategies for clavulanic acid production by Streptomyces clavuligerus. Bioresource Technology, 98(10), 2010–2017. https://doi.org/10.1016/j.biortech.2006.08.003 Schnoor, S.B.; Neubauer, P.; Gimpel, M. (2024). Recent Insights into the World of Dualfunction Bacterial. WIREs RNA.15, e1824. https://doi.org/10.1002/wrna.1824. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303.metabolite Sridhar, J., & Gunasekaran, P. (2013). Computational small RNA prediction in bacteria. Bioinformatics and Biology Insights, 7, 83–95. https://doi.org/10.4137/BBI.S11213 Subramanian, D., Bhasuran, B., & Natarajan, J. (2019) Genomic analysis of RNA-Seq and sRNA-seq data identifies potential regulatory sRNAs and their functional roles in Staphylococcus aureus. Genomics. 111, 1431–1446. https://doi.org/10.1016/j.ygeno.2018.09.016. Swiercz, J. P., Hindra, Bobek, J., Haiser, H. J., Di Berardo, C., Tjaden, B., & Elliot, M. A. (2008). Small non-coding RNAs in Streptomyces coelicolor. Nucleic Acids Research, 36(22), 7240–7251. https://doi.org/10.1093/nar/gkn898 Tezuka, T., Hara, H., Ohnishi, Y., & Horinouchi, S. (2009). Identification and gene disruption of small noncoding RNAs in Streptomyces griseus. Journal of Bacteriology, 191(15), 4896– 4904. https://doi.org/10.1128/JB.00087-09 Thébault, P., Bourqui, R., Benchimol, W., Gaspin, C., Sirand-Pugnet, P., Uricaru, R., & Dutour, I. (2014). Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks. Briefings in Bioinformatics, 16(5), 795– 805. https://doi.org/10.1093/bib/bbu045 Thorpe, H. A., Bayliss, S. C., Sheppard, S. K., & Feil, E. J. (2018). Piggy: A rapid, large-scale pangenome analysis tool for intergenic regions in bacteria. GigaScience, 7(4), 1–11. https://doi.org/10.1093/gigascience/giy015 Tjaden, B. (2015). De novo assembly of bacterial transcriptomes from RNA-seq data. Genome Biology, 16(1), 1. https://doi.org/10.1186/s13059-014-0572-2 Tjaden, B. (2023) Escherichia Coli transcriptome assembly from a compendium of RNA-seq data sets. RNA Biol. 20, 77–84. https://doi.org/10.1080/15476286.2023.2189331. Tsai, C. H., Liao, R., Chou, B., Palumbo, M., & Contreras, L. M. (2015). Genome-wide analyses in bacteria show small-RNA enrichment for long and conserved intergenic regions. Journal of Bacteriology, 197(1), 40–50. https://doi.org/10.1128/JB.02359-14 Uguru, G. C., Mondhe, M., Goh, S., Hesketh, A., Bibb, M. J., Good, L., & Stach, J. E. M. (2013). Synthetic RNA silencing of actinorhodin biosynthesis in Streptomyces coelicolor A3(2). PLoS ONE, 8(6). https://doi.org/10.1371/journal.pone.0067509 Vasilyev, N., Gao, A., & Serganov, A. (2019). Noncanonical features and modifications on the 5′-end of bacterial sRNAs and mRNAs. WIREs RNA. 10, e1509. https://doi.org/10.1002/wrna.1509. Vockenhuber, M.-P., Sharma, C. M., Statt, M. G., Schmidt, D., Xu, Z., Dietrich, S., … Suess, B. (2011). Deep sequencing-based identification of small non-coding RNAs in Streptomyces coelicolor. RNA Biology, 8(3), 468–477. https://doi.org/10.4161/rna.8.3.14421 Vockenhuber, M. P., Heueis, N., & Suess, B. (2015). Identification of metE as a second target of the sRNA scr5239 in Streptomyces coelicolor. PLoS ONE, 10(3), 1–15. https://doi.org/10.1371/journal.pone.0120147 Wade, J. T., & Grainger, D. C. (2014). Pervasive transcription: Illuminating the dark matter of bacterial transcriptomes. Nature Reviews Microbiology, 12(9), 647–653. https://doi.org/10.1038/nrmicro3316 Wadler, C. S., & Vanderpool, C. K. (2007). A dual function for a bacterial small RNA: SgrS performs base pairing-dependent regulation and encodes a functional polypeptide. Proceedings of the National Academy of Sciences, 104(51), 20454–20459. https://doi.org/10.1073/pnas.0708102104 Wagner E.G.H., & Romby, P. (2015) Small RNAs in bacteria and archaea: who they are, what they do, and how they do it. Adv Genet. 90:133-208. https://doi.org/10.1016/bs.adgen.2015.05.001 Wang, M., Fleming, J., Li, Z., Li, C., Zhang, H., Xue, Y., … Bi, L. (2016). An automated approach for global identification of sRNA-encoding regions in RNA-Seq data from Mycobacterium tuberculosis. Acta Biochimica et Biophysica Sinica, 48(6), 544–553. https://doi.org/10.1093/abbs/gmw037 Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews. Genetics, 10(1), 57–63. https://doi.org/10.1038/nrg2484 Waters, L. S., & Storz, G. (2009). Regulatory RNAs in Bacteria. Cell, 136(4), 615–628. https://doi.org/10.1016/j.cell.2009.01.043 Watkins, D., & Arya, D.P. (2019). Regulatory roles of small RNAs in prokaryotes: parallels and contrast with eukaryotic miRNA. Non-Coding RNA Investig. 3, 28. https://doi.org/10.21037/ncri.2019.10.02. Weinberg, Z., Lünse, C. E., Corbino, K. A., Ames, T. D., Nelson, J. W., Roth, A., … Breaker, R. R. (2017). Detection of 224 candidate structured RNAs by Comparative analysis of specific subsets of intergenic regions. Nucleic Acids Research, 45(18), 10811–10823. https://doi.org/10.1093/nar/gkx699 Weinberg, Z., Wang, J. X., Bogue, J., Yang, J., Corbino, K., Moy, R. H., & Breaker, R. R. (2010). Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes. Genome Biology, 11(3). https://doi.org/10.1186/gb-2010-11- 3-r31 Xiong Z. Q., Lv Z. X., Song X., Liu X. X., Xia Y. J. and Ai L. Z. (2021) Recent research advances in small regulatory RNAs in Streptococcus. Curr Microbiol. 78:2231-2241. doi: 10.1007/s00284-021-02484-y. Yepes-García, J., Caicedo-Montoya, C., Pinilla, L., Toro, L. F., & Ríos-Estepa, R. (2020). Morphological differentiation of Streptomyces clavuligerus exposed to diverse environmental conditions and its relationship with clavulanic acid biosynthesis. Processes, 8(9). https://doi.org/10.3390/pr8091038 Zhang, Z., & Wang, W. (2014). RNA-Skim: A rapid method for RNA-Seq quantification at transcript level. Bioinformatics, 30(12), 283–292. https://doi.org/10.1093/bioinformatics/btu288 Zhu, D. Q., Liu, F., Sun, Y., Yang, L. M., Xin, L., & Meng, X. C. (2015). Genome-wide identification of small RNAs in Bifidobacterium animalis subsp. lactis KLDS 2.0603 and their regulation role in the adaption to gastrointestinal environment. PLoS ONE, 10(2), 1–18. https://doi.org/10.1371/journal.pone.0117373 |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.none.fl_str_mv |
94 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.program.none.fl_str_mv |
Doctorado en Ingeniería Química |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.branch.none.fl_str_mv |
Medellín, Colombia |
| institution |
Universidad de Antioquia |
| bitstream.url.fl_str_mv |
https://bibliotecadigital.udea.edu.co/bitstreams/076e91bd-532e-47f1-a3ba-92d75ebce1c1/download https://bibliotecadigital.udea.edu.co/bitstreams/837dcbb6-8d59-45c9-b1e4-15a25fbbe980/download https://bibliotecadigital.udea.edu.co/bitstreams/cce5c7df-4011-4684-8b4b-d85a81928ef8/download https://bibliotecadigital.udea.edu.co/bitstreams/2ce7c9dc-ea58-4586-812e-169a3ced40b2/download |
| bitstream.checksum.fl_str_mv |
53797ad57f67466043c265988859285b b76e7a76e24cf2f94b3ce0ae5ed275d0 79e783a75f33ba936ba945254b64570c bbb3eb67999b6ecbbc9744a4ae4939cd |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional de la Universidad de Antioquia |
| repository.mail.fl_str_mv |
aplicacionbibliotecadigitalbiblioteca@udea.edu.co |
| _version_ |
1851052113094770688 |
| spelling |
Ríos Estepa, RigobertoCaicedo Montoya, Carlos AndrésBioprocesos2025-04-11T13:36:15Z2025C.A. Caicedo Montoya “Predicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces Clavuligerus”, Tesis doctoral, Doctorado en Ingeniería Química, Universidad de Antioquia, Medellín, Antioquia, Colombia, 2024.https://hdl.handle.net/10495/45690ABSTRACT : Commercial strains of Streptomyces clavuligerus, such as ATCC 27064, produce clavulanic acid in low quantities. Increasing the production of this metabolite is essential for decreasing the cost of clavulanic acid (CA) prescriptions, thereby promoting successful treatments against resistant bacterial infections and making them accessible to poor communities. Understanding the regulatory pathways for clavulanic acid production is crucial for developing new genetically modified strains with enhanced metabolic capabilities for CA production. Recently, several studies have revealed the pivotal role of small non-coding RNAs in regulating various metabolic processes in both prokaryotic and eukaryotic organisms. However, knowledge about small RNAs (sRNAs) in Streptomyces clavuligerus is scarce. This thesis aimed to use cutting-edge bioinformatics tools and a compendium of RNA-seq data to predict the potential coding of sRNAs that might be present in the genome of Streptomyces clavuligerus ATCC 27064. In the genome of Streptomyces clavuligerus 606 intergenic regions (IGRs) are conserved, and 272 possess a highly thermodynamically stable and conserved secondary structure, indicating the presence of non-coding RNA in these regions. The transcriptome assembly of S. clavuligerus showed that the genome is completely functional, as all the annotated genes are expressed under the conditions analyzed. From this assembly, transcripts originated from IGRs were labeled as putative sRNAs, and their differential expression during the growth curve of S. clavuligerus for clavulanic acid production was established. The interactome of these differentially expressed (DE) RNAs displayed the sRNAs as global regulators, as they can have multiple mRNA targets. Functional annotation of the target genes of DE sRNAs demonstrated that they are directly involved in secondary metabolite production, as this was one of the most enriched categories in KEGG and COG annotations. Specifically, two sRNA have the genes of the biosynthetic gene cluster of CA as targets. Thus, these molecules add an additional layer to the regulatory cascade for CA biosynthesis, and we propose them as targets for metabolic engineering to increase CA production.Acknowledgements........................................................................................................... II Thesis abstract..................................................................................................................III. List of figures .......................................................................................................................V. List of tables ......................................................................................................................VI. List of Symbols and Abbreviations...........................................................................IX. Introduction: The Role of Small non-coding RNAs in Secondary Metabolite Biosynthesis...................................................................................................1 Chapter 2: Pan-genome of the genus Streptomyces and prioritization of biosynthetic gene clusters with potential to produce antibiotic compounds ............................................................................................................................9 Chapter 3: Identification of Small RNAs in Streptomyces clavuligerus using High-Resolution Transcriptomics and Expression Profiling during Clavulanic Acid Production.......................................................................................... 43 Conclusions and Outlook ...................................................................................... 81COL0023715DoctoradoDoctor en Ingeniería Química94 páginasapplication/pdfengPredicting, Identifying, and Characterizing small non-coding RNAs Implicated in Regulating Clavulanic Acid Production in Streptomyces ClavuligerusTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06http://purl.org/redcol/resource_type/TDTexthttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/draftDoctorado en Ingeniería QuímicaFacultad de IngenieríaMedellín, ColombiaAlam, K., Islam, M. M., Islam, S., Hao, J., Abbasi, M. N., Hayat, M., … Li, A. (2023). Comparative genomics with evolutionary lineage in Streptomyces bacteria reveals high biosynthetic potentials. World Journal of Microbiology and Biotechnology, 39(2), 1–12. https://doi.org/10.1007/s11274-022-03433-yÁlvarez-Álvarez, R., Rodríguez-García, A., Santamarta, I., Pérez-Redondo, R., Prieto Domínguez, A., Martínez-Burgo, Y., & Liras, P. (2014). Transcriptomic analysis of Streptomyces clavuligerus ΔccaR:: Tsr: Effects of the cephamycin C-clavulanic acid cluster regulator CcaR on global regulation. Microbial Biotechnology, 7(3), 221–231. https://doi.org/10.1111/1751-7915.12109Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq-A Python framework to work with highthroughput sequencing data. Bioinformatics, 31(2), 166–169. https://doi.org/10.1093/bioinformatics/btu638Aramaki, T., Blanc-Mathieu, R., Endo, H., Ohkubo, K., Kanehisa, M., Goto, S., … Valencia, A. (2020). KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics, 36(7), 2251–2252. https://doi.org/10.1093/bioinformatics/btz859Bailey, T. L., Johnson, J., Grant, C. E., & Noble, W. S. (2015). The MEME Suite. Nucleic Acids Research, 43(W1), W39–W49. https://doi.org/10.1093/nar/gkv416Bauer, J. S., Fillinger, S., Förstner, K., Herbig, A., Jones, A. C., Flinspach, K., … Apel, A. K. (2017). dRNA-seq transcriptional profiling of the FK506 biosynthetic gene cluster in Streptomyces tsukubaensis NRRL18488 and general analysis of the transcriptome. RNA Biology, 14(11), 1617–1626. https://doi.org/10.1080/15476286.2017.1341020Bilyk, O., & Luzhetskyy, A. (2016). Metabolic engineering of natural product biosynthesis in actinobacteria. Current Opinion in Biotechnology, 42, 98–107. https://doi.org/10.1016/j.copbio.2016.03.008Blin, K., Shaw, S., Augustijn, H. E., Reitz, Z. L., Biermann, F., Alanjary, M., … Weber, T. (2023). AntiSMASH 7.0: New and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Research, 51(W1), W46–W50. https://doi.org/10.1093/nar/gkad344Boisset, S.; Geissmann, T.; Huntzinger, E.; Fechter, P.; Bendridi, N.; Possedko, M.; Chevalier, C.; Helfer, A.C.; Benito, Y.; Jacquier, A.; et al (2007). Staphylococcus aureus RNAIII coordinately represses the synthesis of virulence factors and the transcription regulator rot by an antisense mechanism. Genes Dev. 21, 1353–1366. https://doi.org/10.1101/gad.423507Brantl, S., & Müller, P (2021). Cis- and trans-encoded small regulatory RNAs in Bacillus subtilis. Microorganisms. 9, 1865. https://doi.org/10.3390/microorganisms9091865Bushell, M. E., Kirk, S., Zhao, H. J., & Avignone-Rossa, C. A. (2006). Manipulation of the physiology of clavulanic acid biosynthesis with the aid of metabolic flux analysis. Enzyme and Microbial Technology, 39(1), 149–157. https://doi.org/10.1016/j.enzmictec.2006.01.017Bustamante, M. C. C., Costa, C. L. L., Esperança, M. N., Mazziero, V. T., Cerri, M. O., & Badino, A. C. (2024). Effect of impeller type on cellular morphology and production of clavulanic acid by Streptomyces clavuligerus. Brazilian Journal of Microbiology, 55(2), 1167–1177. https://doi.org/10.1007/s42770-024-01306-0Caicedo-Montoya, C., Manzo-Ruiz, M., & Rios-Estepa, R. (2021). Pan-genome of the genus Streptomyces and prioritization of biosynthetic gene clusters with potential to produce antibiotic compounds. Frontiers in Microbiology, 12(September). https://doi.org/10.3389/fmicb.2021.677558Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560Chevez-Guardado, R., & Peña-Castillo, L. (2021). Promotech: a general tool for bacterial promoter recognition. Genome Biology, 22(1), 1–16. https://doi.org/10.1186/s13059- 021-02514-9Desnoyers, G., Bouchard, M. P., & Massé, E. (2013). New insights into small RNA-dependent translational regulation in prokaryotes. Trends in Genetics, 29(2), 92–98. https://doi.org/10.1016/j.tig.2012.10.004Dutta, T., & Srivastava, S. (2018). Small RNA-mediated regulation in bacteria: A growing palette of diverse mechanisms. Gene, 656, 60–72. https://doi.org/10.1016/j.gene.2018.02.068Galperin, M. Y., Wolf, Y. I., Makarova, K. S., Alvarez, R. V., Landsman, D., & Koonin, E. V. (2021). COG database update: Focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Research, 49(D1), D274–D281. https://doi.org/10.1093/nar/gkaa1018Georg, J., & Hess, W. R. (2018). Widespread antisense transcription in prokaryotes. Microbiology Spectrum, 6(4). https://doi.org/10.1128/microbiolspec.rwr-0029-2018Gottesman, S., & Storz, G. (2011). Bacterial small RNA regulators: Versatile roles and rapidly evolving variations. Cold Spring Harbor Perspectives in Biology, 3(12), 1–16. https://doi.org/10.1101/cshperspect.a003798Groher, F., & Suess, B. (2014). Synthetic riboswitches - A tool comes of age. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms. https://doi.org/10.1016/j.bbagrm.2014.05.005 /doi.org/10.1093/nar/gkx428Gruber, A. R., Findeiß, S., Washietl, S., Hofacker, I. L., & Stadler, P. F. (2010). RNAz 2.0: improved noncoding RNA detection. In Pacific Symposium on Biocomputing 2010 (pp. 69–79). https://doi.org/10.1142/9789814295291_0009Helmann, J.D. (2019), Where to begin? Sigma factors and the selectivity of transcription initiation in bacteria. Mol Microbiol, 112: 335-347. https://doi.org/10.1111/mmi.14309Heueis, N., Vockenhuber, M.-P., & Suess, B. (2014). Small non-coding RNAs in Streptomycetes. RNA Biology, 11(5), 464–469. https://doi.org/10.4161/rna.28262Hindra, Moody, M. J., Jones, S. E., & Elliot, M. A. (2014). Complex intra-operonic dynamics mediated by a small RNA in Streptomyces coelicolor. PloS One, 9(1), e85856. https://doi.org/10.1371/journal.pone.0085856Hwang, S., Lee, N., Choe, D., Lee, Y., Kim, W., Jeong, Y., … Cho, B.-K. (2021). Elucidating the regulatory elements for transcription termination and posttranscriptional processing in the Streptomyces clavuligerus genome. MSystems, 6(3). https://doi.org/10.1128/msystems.01013-20Jackson, L.A., Day, M., Allen, J., Scott, E. & Dyer, D.W (2017). Iron-regulated small RNA expression as Neisseria gonorrhoeae FA 1090 transitions into stationary phase growth. BMC Genom. 18, 317. https://doi.org/10.1186/s12864-017-3684-8.Jeong, Y., Kim, J.-N., Kim, M. W., Bucca, G., Cho, S., Yoon, Y. J., … Cho, B.-K. (2016). The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2). Nature Communications, 7, 11605. https://doi.org/10.1038/ncomms11605Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., … Petrov, A. I. (2021). Rfam 14: Expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research, 49(D1), D192–D200. https://doi.org/10.1093/nar/gkaa1047Kang, Y. J., Yang, D. C., Kong, L., Hou, M., Meng, Y. Q., Wei, L., & Gao, G. (2017). CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Research, 45(W1), W12–W16. https://doi.org/10.1093/nar/gkx428Katz, K., Shutov, O., Lapoint, R., Kimelman, M., Rodney Brister, J., & O’Sullivan, C. (2022). The Sequence Read Archive: A decade more of explosive growth. Nucleic Acids Research, 50(D1), D387–D390. https://doi.org/10.1093/nar/gkab1053Kingsford, C. L., Ayanbule, K., & Salzberg, S. L. (2007). Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake. Genome Biology, 8(2). https://doi.org/10.1186/gb-2007-8-2-r22Kopylova, E., Noé, L., Touzet, H. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24): 3211-3217.Kurt, A., Álvarez-Álvarez, R., Liras, P., & Özcengiz, G. (2013). Role of the cmcH-ccaR intergenic region and ccaR overexpression in cephamycin C biosynthesis in Streptomyces clavuligerus. Applied Microbiology and Biotechnology, 97(13), 5869–5880. https://doi.org/10.1007/s00253-013-4721-4Langmead, B., & Salzberg, S. L. (2013). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359. https://doi.org/10.1038/nmeth.1923.FastLarkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., Mcgettigan, P. A., McWilliam, H., … Higgins, D. G. (2007). Clustal W and Clustal X version 2.0. Bioinformatics, 23(21), 2947– 2948. https://doi.org/10.1093/bioinformatics/btm404Lee, N., Hwang, S., Lee, Y., Cho, S., Palsson, B., & Cho, B. K. (2019). Synthetic biology tools for novel secondary metabolite discovery in Streptomyces. Journal of Microbiology and Biotechnology, 29(5), 667–686. https://doi.org/10.4014/jmb.1904.04015Lee, Y., Lee, N., Hwang, S., Kim, W., Cho, S., Palsson, B. O., & Cho, B. K. (2022). Genome-scale analysis of genetic regulatory elements in Streptomyces avermitilis MA-4680 using transcript boundary information. BMC Genomics, 23(1). https://doi.org/10.1186/s12864-022-08314-0Lee, Y., Lee, N., Jeong, Y., Hwang, S., Kim, W., Cho, S., … Cho, B. K. (2019). The transcription unit architecture of Streptomyces lividans TK24. Frontiers in Microbiology, 10. https://doi.org/10.3389/fmicb.2019.02074Leonard, S., Meyer, S., Lacour, S., Nasser, W., Hommais, F., & Reverchon, S. (2019). APERO: A genome-wide approach for identifying bacterial small RNAs from RNA-Seq data. Nucleic Acids Research, 47(15). https://doi.org/10.1093/nar/gkz485Levine, E., & Hwa, T. (2008). Small RNAs establish gene expression thresholds. Current Opinion in Microbiology, 11(6), 574–579. https://doi.org/10.1016/j.mib.2008.09.016Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352Li, R., & Townsend, C. A. (2006). Rational strain improvement for enhanced clavulanic acid production by genetic engineering of the glycolytic pathway in Streptomyces clavuligerus. Metabolic Engineering, 8(3), 240–252. https://doi.org/10.1016/j.ymben.2006.01.003Liras, P., & Martín, J. F. (2021). Streptomyces clavuligerus: The omics era. journal of industrial Microbiology and Biotechnology, 48(9–10). https://doi.org/10.1093/jimb/kuab072Liu, W. B., Shi, Y., Yao, L. L., Zhou, Y., & Ye, B. C. (2013). Prediction and characterization of small non-coding RNAs related to secondary metabolites in Saccharopolyspora erythraea. PLoS ONE, 8(11), 1–11. https://doi.org/10.1371/journal.pone.0080676Llorens-Rico, V., Cano, J., Kamminga, T., Gil, R., Latorre, A., Chen, W.-H., … Lluch-Senar, M. (2016). Bacterial antisense RNAs are mainly the product of transcriptional noise. Science Advances, 2(3), e1501363–e1501363. https://doi.org/10.1126/sciadv.1501363Lorenz, R., Bernhart, S. H., Siederdissen, C. H. zu, Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011). ViennaRNA Package 2.0. Algorithms for molecular biology, 6(26). https://doi.org/10.1186/1748-7188-6-26Mann, M., Wright, P. R., & Backofen, R. (2017). IntaRNA 2.0: Enhanced and customizable prediction of RNA-RNA interactions. Nucleic Acids Research, 45(W1), W435–W439. https://doi.org/10.1093/nar/gkx279Martín-Sánchez, L., Singh, K. S., Avalos, M., Van Wezel, G. P., Dickschat, J. S., & Garbeva, P. (2019). Phylogenomic analyses and distribution of terpene synthases among Streptomyces. Beilstein Journal of Organic Chemistry, 15, 1181–1193. https://doi.org/10.3762/bjoc.15.115Melamed, S., Peer, A., Faigenbaum-Romm, R., Gatt, Y. E., Reiss, N., Bar, A., … Margalit, H. (2016). Global Mapping of small RNA-target interactions in bacteria. Molecular Cell, 63, 884–897. https://doi.org/10.1016/j.molcel.2016.07.026Menard, G., Silard, C., Suriray, M., Rouillon, A., & Augagneur, Y. (2022) Thirty years of sRNAmediated regulation in Staphylococcus Aureus: From initial discoveries to in vivo biological implications. Int. J. Mol. Sci. 23, 7346. https://doi.org/10.3390/ijms23137346.Mentz, A., Neshat, A., Pfeifer-Sancar, K., Pühler, A., Rückert, C., & Kalinowski, J. (2013). Comprehensive discovery and characterization of small RNAs in Corynebacterium glutamicum ATCC 13032. BMC Genomics, 14, 714. https://doi.org/10.1186/1471-2164- 14-714Mingyar, E., Mühling, L., Kulik, A., Winkler, A., Wibberg, D., Kalinowski, J., … Stegmann, E. (2021). A regulator based “semi-targeted” approach to activate silent biosynthetic gene clusters. International Journal of Molecular Sciences, 22(14). https://doi.org/10.3390/ijms22147567Moody, M. J., Young, R. A., Jones, S. E., & Elliot, M. A. (2013). Comparative analysis of noncoding RNAs in the antibiotic-producing Streptomyces bacteria. BMC Genomics, 14(1), 558. https://doi.org/10.1186/1471-2164-14-558Muzellec, B., Teleńczuk, M., Cabeli, V., & Andreux, M. (2023). PyDESeq2: a python package for bulk RNA-seq differential expression analysis. Bioinformatics, 39(9). https://doi.org/10.1093/bioinformatics/btad547Nawrocki, E. P., & Eddy, S. R. (2013). Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics, 29(22), 2933–2935. https://doi.org/10.1093/bioinformatics/btt509Nussenzweig P. M., and Marraffini L. A. (2020). Molecular mechanisms of CRISPR-Cas immunity in bacteria. Annu Rev Genet. 54:93-120. doi: 10.1146/annurev-genet-022120- 112523.Palazzotto, E., Tong, Y., Lee, S. Y., & Weber, T. (2019). Synthetic biology and metabolic engineering of actinomycetes for natural product discovery. Biotechnology Advances, 37(6), 107366. https://doi.org/10.1016/j.biotechadv.2019.03.005Pánek, J., Bobek, J., Mikulík, K., Basler, M., & Vohradský, J. (2008). Biocomputational prediction of small non-coding RNAs in Streptomyces. BMC Genomics, 9(1), 217. https://doi.org/10.1186/1471-2164-9-217Papenfort, K., & Melamed, S. (2023). Small RNAs, large networks: posttranscriptional regulons in gram-negative bacteria. Annu. Rev. Microbiol. 77, 23–43. https://doi.org/10.1146/annurev-micro-041320-025836.Patiño, L. F. (2024). Improving clavulanic acid biosynthesis: A comparative transcriptome analysis of Streptomyces clavuligerus for identifying potential metabolic limitations. Universidad de Antioquia.Ponath, F., Hör, J., & Vogel, J. (2022) An overview of gene regulation in bacteria by small RNAs derived from mRNA 3′ Ends. FEMS Microbiol. Rev. 46, fuac017. https://doi.org/10.1093/femsre/fuac017.Ramirez-Malule, H., Junne, S., López, C., Zapata, J., Sáez, A., Neubauer, P., & Rios-Estepa, R. (2016). An improved HPLC-DAD method for clavulanic acid quantification in fermentation broths of Streptomyces clavuligerus. Journal of Pharmaceutical and Biomedical Analysis, 120, 241–247. https://doi.org/10.1016/j.jpba.2015.12.035Ribeiro, R. M. M. G. P., Esperança, M. N., Sousa, A. P. A., Neto, Á. B., & Cerri, M. O. (2021). Individual effect of shear rate and oxygen transfer on clavulanic acid production by Streptomyces clavuligerus. Bioprocess and Biosystems Engineering, 44(8), 1721–1732. https://doi.org/10.1007/s00449-021-02555-1Richards, G. R., & Vanderpool, C. K. (2011). Molecular call and response: The physiology of bacterial small RNAs. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1809(10), 525–531. https://doi.org/10.1016/j.bbagrm.2011.07.013Salwan, R., & Sharma, V. (2020, January 1). Molecular and biotechnological aspects of secondary metabolites in actinobacteria. Microbiological Research. Elsevier GmbH. https://doi.org/10.1016/j.micres.2019.126374Saudagar, P. S., & Singhal, R. S. (2007). Optimization of nutritional requirements and feeding strategies for clavulanic acid production by Streptomyces clavuligerus. Bioresource Technology, 98(10), 2010–2017. https://doi.org/10.1016/j.biortech.2006.08.003Schnoor, S.B.; Neubauer, P.; Gimpel, M. (2024). Recent Insights into the World of Dualfunction Bacterial. WIREs RNA.15, e1824. https://doi.org/10.1002/wrna.1824.Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303.metaboliteSridhar, J., & Gunasekaran, P. (2013). Computational small RNA prediction in bacteria. Bioinformatics and Biology Insights, 7, 83–95. https://doi.org/10.4137/BBI.S11213Subramanian, D., Bhasuran, B., & Natarajan, J. (2019) Genomic analysis of RNA-Seq and sRNA-seq data identifies potential regulatory sRNAs and their functional roles in Staphylococcus aureus. Genomics. 111, 1431–1446. https://doi.org/10.1016/j.ygeno.2018.09.016.Swiercz, J. P., Hindra, Bobek, J., Haiser, H. J., Di Berardo, C., Tjaden, B., & Elliot, M. A. (2008). Small non-coding RNAs in Streptomyces coelicolor. Nucleic Acids Research, 36(22), 7240–7251. https://doi.org/10.1093/nar/gkn898Tezuka, T., Hara, H., Ohnishi, Y., & Horinouchi, S. (2009). Identification and gene disruption of small noncoding RNAs in Streptomyces griseus. Journal of Bacteriology, 191(15), 4896– 4904. https://doi.org/10.1128/JB.00087-09Thébault, P., Bourqui, R., Benchimol, W., Gaspin, C., Sirand-Pugnet, P., Uricaru, R., & Dutour, I. (2014). Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks. Briefings in Bioinformatics, 16(5), 795– 805. https://doi.org/10.1093/bib/bbu045Thorpe, H. A., Bayliss, S. C., Sheppard, S. K., & Feil, E. J. (2018). Piggy: A rapid, large-scale pangenome analysis tool for intergenic regions in bacteria. GigaScience, 7(4), 1–11. https://doi.org/10.1093/gigascience/giy015Tjaden, B. (2015). De novo assembly of bacterial transcriptomes from RNA-seq data. Genome Biology, 16(1), 1. https://doi.org/10.1186/s13059-014-0572-2Tjaden, B. (2023) Escherichia Coli transcriptome assembly from a compendium of RNA-seq data sets. RNA Biol. 20, 77–84. https://doi.org/10.1080/15476286.2023.2189331.Tsai, C. H., Liao, R., Chou, B., Palumbo, M., & Contreras, L. M. (2015). Genome-wide analyses in bacteria show small-RNA enrichment for long and conserved intergenic regions. Journal of Bacteriology, 197(1), 40–50. https://doi.org/10.1128/JB.02359-14Uguru, G. C., Mondhe, M., Goh, S., Hesketh, A., Bibb, M. J., Good, L., & Stach, J. E. M. (2013). Synthetic RNA silencing of actinorhodin biosynthesis in Streptomyces coelicolor A3(2). PLoS ONE, 8(6). https://doi.org/10.1371/journal.pone.0067509Vasilyev, N., Gao, A., & Serganov, A. (2019). Noncanonical features and modifications on the 5′-end of bacterial sRNAs and mRNAs. WIREs RNA. 10, e1509. https://doi.org/10.1002/wrna.1509.Vockenhuber, M.-P., Sharma, C. M., Statt, M. G., Schmidt, D., Xu, Z., Dietrich, S., … Suess, B. (2011). Deep sequencing-based identification of small non-coding RNAs in Streptomyces coelicolor. RNA Biology, 8(3), 468–477. https://doi.org/10.4161/rna.8.3.14421Vockenhuber, M. P., Heueis, N., & Suess, B. (2015). Identification of metE as a second target of the sRNA scr5239 in Streptomyces coelicolor. PLoS ONE, 10(3), 1–15. https://doi.org/10.1371/journal.pone.0120147Wade, J. T., & Grainger, D. C. (2014). Pervasive transcription: Illuminating the dark matter of bacterial transcriptomes. Nature Reviews Microbiology, 12(9), 647–653. https://doi.org/10.1038/nrmicro3316Wadler, C. S., & Vanderpool, C. K. (2007). A dual function for a bacterial small RNA: SgrS performs base pairing-dependent regulation and encodes a functional polypeptide. Proceedings of the National Academy of Sciences, 104(51), 20454–20459. https://doi.org/10.1073/pnas.0708102104Wagner E.G.H., & Romby, P. (2015) Small RNAs in bacteria and archaea: who they are, what they do, and how they do it. Adv Genet. 90:133-208. https://doi.org/10.1016/bs.adgen.2015.05.001Wang, M., Fleming, J., Li, Z., Li, C., Zhang, H., Xue, Y., … Bi, L. (2016). An automated approach for global identification of sRNA-encoding regions in RNA-Seq data from Mycobacterium tuberculosis. Acta Biochimica et Biophysica Sinica, 48(6), 544–553. https://doi.org/10.1093/abbs/gmw037Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews. Genetics, 10(1), 57–63. https://doi.org/10.1038/nrg2484Waters, L. S., & Storz, G. (2009). Regulatory RNAs in Bacteria. Cell, 136(4), 615–628. https://doi.org/10.1016/j.cell.2009.01.043Watkins, D., & Arya, D.P. (2019). Regulatory roles of small RNAs in prokaryotes: parallels and contrast with eukaryotic miRNA. Non-Coding RNA Investig. 3, 28. https://doi.org/10.21037/ncri.2019.10.02.Weinberg, Z., Lünse, C. E., Corbino, K. A., Ames, T. D., Nelson, J. W., Roth, A., … Breaker, R. R. (2017). Detection of 224 candidate structured RNAs by Comparative analysis of specific subsets of intergenic regions. Nucleic Acids Research, 45(18), 10811–10823. https://doi.org/10.1093/nar/gkx699Weinberg, Z., Wang, J. X., Bogue, J., Yang, J., Corbino, K., Moy, R. H., & Breaker, R. R. (2010). Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes. Genome Biology, 11(3). https://doi.org/10.1186/gb-2010-11- 3-r31Xiong Z. Q., Lv Z. X., Song X., Liu X. X., Xia Y. J. and Ai L. Z. (2021) Recent research advances in small regulatory RNAs in Streptococcus. Curr Microbiol. 78:2231-2241. doi: 10.1007/s00284-021-02484-y.Yepes-García, J., Caicedo-Montoya, C., Pinilla, L., Toro, L. F., & Ríos-Estepa, R. (2020). Morphological differentiation of Streptomyces clavuligerus exposed to diverse environmental conditions and its relationship with clavulanic acid biosynthesis. Processes, 8(9). https://doi.org/10.3390/pr8091038Zhang, Z., & Wang, W. (2014). RNA-Skim: A rapid method for RNA-Seq quantification at transcript level. Bioinformatics, 30(12), 283–292. https://doi.org/10.1093/bioinformatics/btu288Zhu, D. Q., Liu, F., Sun, Y., Yang, L. M., Xin, L., & Meng, X. C. (2015). Genome-wide identification of small RNAs in Bifidobacterium animalis subsp. lactis KLDS 2.0603 and their regulation role in the adaption to gastrointestinal environment. PLoS ONE, 10(2), 1–18. https://doi.org/10.1371/journal.pone.0117373info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Antibióticos BetalactámicosBeta Lactam AntibioticsRNA-seqÁcido ClavulánicoClavulanic AcidPerfilación de la Expresión GénicaGene Expression ProfilingMicroARNshttps://id.nlm.nih.gov/mesh/D000097902https://id.nlm.nih.gov/mesh/D000081246https://id.nlm.nih.gov/mesh/D019818https://id.nlm.nih.gov/mesh/D020869https://id.nlm.nih.gov/mesh/D035683PublicationORIGINALCaicedoCarlos_2025_PredictingIdentifyingCharacterizingCaicedoCarlos_2025_PredictingIdentifyingCharacterizingTesis doctoralapplication/pdf5018402https://bibliotecadigital.udea.edu.co/bitstreams/076e91bd-532e-47f1-a3ba-92d75ebce1c1/download53797ad57f67466043c265988859285bMD51trueAnonymousREADLICENSElicense.txtlicense.txttext/plain; charset=utf-814837https://bibliotecadigital.udea.edu.co/bitstreams/837dcbb6-8d59-45c9-b1e4-15a25fbbe980/downloadb76e7a76e24cf2f94b3ce0ae5ed275d0MD53falseAnonymousREADTEXTCaicedoCarlos_2025_PredictingIdentifyingCharacterizing.txtCaicedoCarlos_2025_PredictingIdentifyingCharacterizing.txtExtracted texttext/plain100214https://bibliotecadigital.udea.edu.co/bitstreams/cce5c7df-4011-4684-8b4b-d85a81928ef8/download79e783a75f33ba936ba945254b64570cMD54falseAnonymousREADTHUMBNAILCaicedoCarlos_2025_PredictingIdentifyingCharacterizing.jpgCaicedoCarlos_2025_PredictingIdentifyingCharacterizing.jpgGenerated Thumbnailimage/jpeg6807https://bibliotecadigital.udea.edu.co/bitstreams/2ce7c9dc-ea58-4586-812e-169a3ced40b2/downloadbbb3eb67999b6ecbbc9744a4ae4939cdMD55falseAnonymousREAD10495/45690oai:bibliotecadigital.udea.edu.co:10495/456902025-04-12 04:00:39.003open.accesshttps://bibliotecadigital.udea.edu.coRepositorio Institucional de la Universidad de Antioquiaaplicacionbibliotecadigitalbiblioteca@udea.edu.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 |
