Skip to main content
Genome Announcements logoLink to Genome Announcements
. 2017 Aug 31;5(35):e00625-17. doi: 10.1128/genomeA.00625-17

Whole-Genome Sequence of the 1,4-Dioxane-Degrading Bacterium Mycobacterium dioxanotrophicus PH-06

Ya He a, Kangfei Wei b, Kaiwei Si b, Jacques Mathieu a, Mengyan Li c,, Pedro J J Alvarez a,
PMCID: PMC5578833  PMID: 28860235

ABSTRACT

We report here the complete genome sequence of Mycobacterium dioxanotrophicus PH-06, which is capable of using 1,4-dioxane as a sole source of carbon and energy. The reported sequence will enable the elucidation of this novel metabolic pathway and the development of molecular biomarkers to assess bioremediation potential at contaminated sites.

GENOME ANNOUNCEMENT

Mycobacterium dioxanotrophicus PH-06 was isolated in South Korea from river sediment that had been contaminated with 1,4-dioxane (dioxane) for more than one decade (1). PH-06 utilizes dioxane, a groundwater contaminant of emerging concern, as its sole carbon and energy source. However, it does not harbor the well-studied monooxygenase gene cluster thmADBC that codes for the initiation of dioxane biodegradation in Pseudonocardia dioxanivorans CB1190 (24). Therefore, the genome sequence of PH-06 furthers our capability to discern novel dioxane biodegradation pathways and facilitates the development of biomarkers to assess dioxane bioremediation potential. Additionally, knowledge of the PH-06 genome broadens our understanding of the genus Mycobacterium and enables an assessment of PH-06 survival and performance in bioaugmentation applications.

PH-06 was grown in ammonium mineral salts medium (5) amended with 500 mg/L of dioxane and incubated at 30°C while shaking at 150 rpm. Cells were harvested during exponential growth, and genomic DNA was extracted using the UltraClean microbial DNA isolation kit (Mo Bio, Inc.). Whole-genome sequencing was conducted using both the PacBio RS II (Yale Center for Genome Analysis, http://ycga.yale.edu) and Illumina HiSeq 4000 (Beijing Genomic Institute [BGI], http://www.genomics.cn) platforms. The whole genome was assembled and annotated in collaboration with BGI as follows. First, prior to assembly, k-mer analysis was used to evaluate genome size, heterogeneity, and repeat information based on the data obtained by Illumina sequencing (6). Second, PacBio RS II reads were assembled using the RS_HGAP assembly of SMRT Analysis version 2.3.0 (https://github.com/PacificBiosciences/SMRT-Analysis) to obtain the main contig with a length close to the estimated genome size, and Illumina reads were used to correct and optimize the assembly results. Third, the contig’s bases were corrected with Quiver, Pilon, SOAPsnp, SOAPindel (http://soap.genomics.org.cn), and GATK (http://www.broadinstitute.org/gatk). Fourth, contig circle analysis was completed by verifying overlap regions. Fifth, Glimmer (79), TRF (10), RNAmmer version 1.2 (11), tRNAscan-SE version 1.3.1 (12), Infernal (13), Rfam (14), and BLAST were used to predict genes, repeat sequences, rRNAs, tRNAs, and noncoding RNAs (ncRNAs). Finally, predicted genes were analyzed against the GO (15), KEGG (1619), COG (20), NR, Swiss-Prot (21), PHI (22), VFDB (23), ARDB (24), and CAZy (25) databases to annotate gene function and identify metabolic pathways, pathogenicity, and drug resistance.

The PH-06 genome consists of 4 contigs, including the chromosome (circular, 7.6 Mb), Plasmid_1 (circular, 156 kb), Plasmid_2 (circular, 153 kb), Plasmid_3 (linear, 106 kb), and Plasmid_4 (linear, 70 kb), and has an average G+C content of 66.46%. A total of 7,339 protein-encoding genes, 83 tRNAs, 9 rRNAs, and 4 ncRNAs are present. KEGG database analysis revealed genes encoding the complete citric acid and pentose phosphate pathways. Furthermore, 1,071 genes appear to be involved in the metabolism of xenobiotics. Pathogenicity analysis indicates that PH-06 harbors no known toxins or pathogenicity islands, suggesting it may be safe for bioaugmentation (2224).

One gene cluster encoding putative propane monooxygenase is located on Plasmid_3. This gene cluster has high similarity to genes in (hydrocarbon-degrading) Rhodococcus wratislaviensis IFP2016 (89%) and Mycobacterium chubuense NBB4 (86%) (26, 27). Further studies are needed to determine the role of this gene cluster in dioxane biodegradation.

Accession number(s).

The whole-genome sequence of M. dioxanotrophicus PH-06 has been deposited in GenBank under the accession numbers CP020809 to CP020813.

ACKNOWLEDGMENTS

This work was funded by SERDP (grant no. ER-2301).

We declare no competing financial interest.

We thank Yoon-Seok Chang for providing M. dioxanotrophicus PH-06.

Footnotes

Citation He Y, Wei K, Si K, Mathieu J, Li M, Alvarez PJJ. 2017. Whole-genome sequence of the 1,4-dioxane-degrading bacterium Mycobacterium dioxanotrophicus PH-06. Genome Announc 5:e00625-17. https://doi.org/10.1128/genomeA.00625-17.

REFERENCES

  • 1.Kim YM, Jeon JR, Murugesan K, Kim EJ, Chang YS. 2009. Biodegradation of 1,4-dioxane and transformation of related cyclic compounds by a newly isolated Mycobacterium sp. PH-06. Biodegradation 20:511–519. doi: 10.1007/s10532-008-9240-0. [DOI] [PubMed] [Google Scholar]
  • 2.Sales CM, Mahendra S, Grostern A, Parales RE, Goodwin LA, Woyke T, Nolan M, Lapidus A, Chertkov O, Ovchinnikova G, Sczyrba A, Alvarez-Cohen L. 2011. Genome sequence of the 1,4-dioxane-degrading Pseudonocardia dioxanivorans strain CB1190. J Bacteriol 193:4549–4550. doi: 10.1128/JB.00415-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Grostern A, Sales CM, Zhuang WQ, Erbilgin O, Alvarez-Cohen L. 2012. Glyoxylate metabolism is a key feature of the metabolic degradation of 1,4-dioxane by Pseudonocardia dioxanivorans strain CB1190. Appl Environ Microbiol 78:3298–3308. doi: 10.1128/AEM.00067-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Sales CM, Grostern A, Parales JV, Parales RE, Alvarez-Cohen L. 2013. Oxidation of the cyclic ethers 1,4-dioxane and tetrahydrofuran by a monooxygenase in two Pseudonocardia species. Appl Environ Microbiol 79:7702–7708. doi: 10.1128/AEM.02418-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Parales RE, Adamus JE, White N, May HD. 1994. Degradation of 1,4-dioxane by an actinomycete in pure culture. Appl Environ Microbiol 60:4527–4530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Liu B, Shi Y, Yuan J, Hu X, Zhang H, Li N, Li Z, Chen Y, Mu D, Fan W. 2013. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. arXiv:13082012 https://arxiv.org/abs/1308.2012. [Google Scholar]
  • 7.Delcher AL, Harmon D, Kasif S, White O, Salzberg SL. 1999. Improved microbial gene identification with GLIMMER. Nucleic Acids Res 27:4636–4641. doi: 10.1093/nar/27.23.4636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Salzberg SL, Delcher AL, Kasif S, White O. 1998. Microbial gene identification using interpolated Markov models. Nucleic Acids Res 26:544–548. doi: 10.1093/nar/26.2.544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Delcher AL, Bratke KA, Powers EC, Salzberg SL. 2007. Identifying bacterial genes and endosymbiont DNA with glimmer. Bioinformatics 23:673–679. doi: 10.1093/bioinformatics/btm009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Benson G. 1999. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27:573–580. doi: 10.1093/nar/27.2.573. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Lagesen K, Hallin P, Rødland EA, Staerfeldt HH, Rognes T, Ussery DW. 2007. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 35:3100–3108. doi: 10.1093/nar/gkm160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Lowe TM, Eddy SR. 1997. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 25:955–964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Nawrocki EP. 2014. Annotating functional RNAs in genomes using infernal. Methods Mol Biol 1097:163–197. doi: 10.1007/978-1-62703-709-9_9. [DOI] [PubMed] [Google Scholar]
  • 14.Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, Bateman A. 2009. Rfam: updates to the RNA families database. Nucleic Acids Res 37:D136–D140. doi: 10.1093/nar/gkn766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. 2000. Gene ontology: tool for the unification of biology. Nat Genet 25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. 2004. The KEGG resource for deciphering the genome. Nucleic Acids Res 32:D277–D280. doi: 10.1093/nar/gkh063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, Katayama T, Araki M, Hirakawa M. 2006. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354–D357. doi: 10.1093/nar/gkj102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Tatusov RL, Koonin EV, Lipman DJ. 1997. A genomic perspective on protein families. Science 278:631–637. doi: 10.1126/science.278.5338.631. [DOI] [PubMed] [Google Scholar]
  • 19.Kanehisa M. 1997. A database for post-genome analysis. Trends Genet 13:375–376. doi: 10.1016/S0168-9525(97)01223-7. [DOI] [PubMed] [Google Scholar]
  • 20.Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, Rao BS, Smirnov S, Sverdlov AV, Vasudevan S, Wolf YI, Yin JJ, Natale DA. 2003. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4:41. doi: 10.1186/1471-2105-4-41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Magrane M, UniProt Consortium . 2011. UniProt Knowledgebase: a hub of integrated protein data. Database 2011:bar009. doi: 10.1093/database/bar009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Vargas WA, Martín JMS, Rech GE, Rivera LP, Benito EP, Díaz-Mínguez JM, Thon MR, Sukno SA. 2012. Plant defense mechanisms are activated during biotrophic and necrotrophic development of Colletotricum graminicola in maize. Plant Physiol 158:1342–1358. doi: 10.1104/pp.111.190397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Chen L, Xiong Z, Sun L, Yang J, Jin Q. 2012. VFDB 2012 update: toward the genetic diversity and molecular evolution of bacterial virulence factors. Nucleic Acids Res 40:D641–D645. doi: 10.1093/nar/gkr989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Liu B, Pop M. 2009. ARDB—antibiotic resistance genes database. Nucleic Acids Res 37:D443–D447. doi: 10.1093/nar/gkn656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. 2009. The carbohydrate-active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37:D233–D238. doi: 10.1093/nar/gkn663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Coleman NV, Yau S, Wilson NL, Nolan LM, Migocki MD, Ly MA, Crossett B, Holmes AJ. 2011. Untangling the multiple monooxygenases of Mycobacterium chubuense strain NBB4, a versatile hydrocarbon degrader. Environ Microbiol Rep 3:297–307. doi: 10.1111/j.1758-2229.2010.00225.x. [DOI] [PubMed] [Google Scholar]
  • 27.Auffret MD, Yergeau E, Labbé D, Fayolle-Guichard F, Greer CW. 2015. Importance of Rhodococcus strains in a bacterial consortium degrading a mixture of hydrocarbons, gasoline, and diesel oil additives revealed by metatranscriptomic analysis. Appl Microbiol Biotechnol 99:2419–2430. doi: 10.1007/s00253-014-6159-8. [DOI] [PubMed] [Google Scholar]

Articles from Genome Announcements are provided here courtesy of American Society for Microbiology (ASM)

RESOURCES