Skip to main content
NCBI home page
Microbiology Resource Announcements logoLink to Microbiology Resource Announcements
. 2019 Apr 11;8(15):e00086-19. doi: 10.1128/MRA.00086-19

“Candidatus Colwellia aromaticivorans” sp. nov., “Candidatus Halocyntiibacter alkanivorans” sp. nov., and “Candidatus Ulvibacter alkanivorans” sp. nov. Genome Sequences

Mariana E Campeão a,b, Jean Swings a,b,c, Bruno Sergio Silva a,b, Koko Otsuki a,b, Fabiano L Thompson a,b,✉, Cristiane C Thompson a,✉
Editor: Catherine Putontid
PMCID: PMC6460022  PMID: 30975799

Unplanned oil spills during offshore production are a serious problem for the industry and the marine environment. Here, we present the genome sequence analysis of three novel hydrocarbon-degrading bacteria, namely, “Candidatus Colwellia aromaticivorans” sp.

ABSTRACT

Unplanned oil spills during offshore production are a serious problem for the industry and the marine environment. Here, we present the genome sequence analysis of three novel hydrocarbon-degrading bacteria, namely, “Candidatus Colwellia aromaticivorans” sp. nov., “Candidatus Halocyntiibacter alkanivorans” sp. nov., and “Candidatus Ulvibacter alkanivorans” sp. nov.

ANNOUNCEMENT

Metagenomic data obtained from microcosm experiments with oil incubation in seawater from the Foz do Amazonas and Barreirinhas basins indicated the biodegradation potential of deep-sea microbial communities (1). Here, we present the metagenome assembled genome (MAG) sequences of three novel hydrocarbon-degrading bacteria obtained from these previous microcosm experiments, as detailed in our previous work (1). First, read trimming was obtained with prinseq v0.20.4 (-trim_qual_right/trim_qual_left 20, -trim_qual_type min, -trim_qual_rule lt, -trim_qual_window 5, -trim_qual_step 1, -min_len 35, -min_qual_mean 20, -trim_left/-trim_right 10, -out_bad null, -derep 1, and -custom_params\“CTGTCTCTTATACACATCT 1;CTGTCTCTTATACACATCTGACGCTGCCGACGA 1;CTGTCTCTTATACACATCTCCGAGCCCACGAGAC 1\) (2). Subsequently, a binning approach was performed through cross-assembly using eight metagenomes from Foz do Amazonas and eight metagenomes from Barreirinhas with SPAdes v3.6.2 (3); using k-mers of 21, 33, 55, 77, 99, and 127; mapping reads against assembled scaffolds with Bowtie 2 v2.2.5 (4) (–very-fast, -a, –no-unal, –no-discordant, and –no-mixed); converting to bam format with SAMtools v1.7 (5); coverage estimating with jgi_summarize_bam_contig_depths (–outputDepth); and binning with MetaBAT v0.25.4 (–specific, -v, -m 2500, and –minSamples 5) (6). RefineM v0.0.23 (scaffold_stats, outliers, and filter_bins commands) (7) was used to remove contamination, and CheckM v1.0.11 (lineage_wf workflow) (8) was used to assess quality stats. Genomic taxonomy was performed as described previously (9–12). These three novel MAGs showed less than 98.7% 16S rRNA sequence identity with the closest species. The three novel lineages had <95% amino acid identity (AAI) and <70% DNA-DNA hybridization (DDH) toward the closest phylogenetic neighbors, suggesting that the recovered genomes are new species, namely, “Candidatus Colwellia aromaticivorans” sp. nov., “Candidatus Halocyntiibacter alkanivorans” sp. nov., and “Candidatus Ulvibacter alkanivorans” sp. nov. (Table 1). “Candidatus Ulvibacter alkanivorans” sp. nov. and “Candidatus Halocyntiibacter alkanivorans” sp. nov. possess an alkane monooxygenase, the specific enzyme for alkane degradation, that was lacking in “Candidatus Colwellia aromaticivorans” sp. nov. “Candidatus Colwellia aromaticivorans” has a phenol 2-monooxygenase gene involved in toluene biodegradation and harbors two monooxygenases and nine oxidoreductases. Oxygenases, a class of oxidoreductases, are key enzymes in hydrocarbon degradation (13).

TABLE 1.

Genomic features of metagenome-associated genomesa

Genome feature Data for strain:
“Candidatus Colwellia aromaticivorans” sp. nov. “Candidatus Halocynthiibacter alkanivorans” sp. nov. “Candidatus Ulvibacter alkanivorans” sp. nov.
Coverage (×) 97 19 19
Completeness (%) 97.4 98.7 93.1
Contamination (%) 1.8 2.15 0.2
Size (Mbp) 3.71 4.7 2.7
No. of contigs 101 171 117
N50 value (bp) 72,862 55,069 47,750
No. of ORFsb 3,278 4,540 2,625
16S rRNA similarity with closest relative (%) Colwellia rossensis S51-W(gv)1T (98.5) Halocynthiibacter arcticus PAMC 20958T (97.2) Ulvibacter antarcticus IMCC3101T (96.2)
GGDc with closest relative (%) Colwellia rossensis S51-W(gv)1T (ND)d Halocynthiibacter arcticus PAMC 20958T (19.4) Ulvibacter antarcticus DSM 23424 (17.8)
AAI with closest relative (%) Colwellia rossensis S51-W(gv)1T (ND) Halocynthiibacter arcticus PAMC 20958T (65.4) Ulvibacter antarcticus DSM 23424 (71.8)
Open in a new tab
a

16S rRNA sequences recovered were retrieved with RNAmmer (14) and compared with BLAST (15) against the Silva database (16, 17). The closest were selected to align through ssu-align v0.1.1 (18, 19) and perform phylogeny through IQ-TREE (20–22). The 16S rRNA similarity was calculated with TaxonDC (23). AAI and DDH values were performed with CompareM (https://github.com/dparks1134/CompareM) and GGDC (http://ggdc.dsmz.de/), respectively. The genome of Ulvibacter antarcticus DSM 23424 was used for AAI and DDH analysis. In silico-predicted phenotype analysis reveals that “Candidatus Colwellia aromaticivorans” sp. nov. is positive for chitin hydrolysis, propionate utilization, and dl-lactate utilization. “Candidatus Halocynthiibacter alkanivorans” sp. nov. is positive for acid production from d-ribose, sucrose, and glycerol; acetate utilization; indole production; and nitrate reduction. “Candidatus Ulvibacter alkanivorans” sp. nov. is positive for dl-lactic acid utilization.

b

ORFs, open reading frames.

c

GGD, genome-to-genome distance.

d

ND, not determined.

“Candidatus Colwellia aromaticivorans” (ar.o.ma’ti.ca. N.L. adj. aromaticivorans, aromatic, referring to the property of utilizing aromatic compounds).

In silico-predicted phenotypes indicate that the novel species hydrolyzes chitin, utilizes propionate and dl-lactate, and does not utilize d-fructose, l-arabinose, glycerol, citrate, or l-malate. The G+C content is 38%.

“Candidatus Halocyntiibacter alkanivorans” (al.ka.ni’vo.rans. M.L. n. alkanum saturated aliphatic hydrocarbon; L. v. vorare to eat; L. adj. alkanivorans alkane-devouring).

In silico-predicted phenotypes indicate that the novel species produces acid from d-ribose, sucrose, and glycerol. Acetate is utilized, nitrate is reduced, and indole is produced. The G+C content is 59%.

“Candidatus Ulvibacter alkanivorans” (al.ka.ni’vo.rans. M.L. n. alkanum saturated aliphatic hydrocarbon; L. v. vorare to eat; L. adj. alkanivorans alkane-devouring).

In silico-predicted phenotypes indicate that the novel species utilizes dl-lactate and does not hydrolyze starch or carboxymethyl cellulose (CM-cellulose). It is negative for the utilization of d-mannitol, cellobiose, d-fructose, l-fucose, d-galactose, myo-inositol, d-sorbitol, sucrose, trehalose, and propionic acid. The G+C content is 40%.

Data availability.

This whole-genome shotgun project has been deposited in GenBank under the BioProject accession no. PRJNA478776.

ACKNOWLEDGMENTS

We thank CNPQ, CAPES, and FAPERJ for the support.

REFERENCES

  • 1.Campeão ME, Reis L, Leomil L, de Oliveira L, Otsuki K, Gardinali P, Pelz O, Valle R, Thompson FL, Thompson CC. 2017. The deep-sea microbial community from the Amazonian basin associated with oil degradation. Front Microbiol 8:1019. doi: 10.3389/fmicb.2017.01019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Schmieder R, Edwards R. 2011. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863–864. doi: 10.1093/bioinformatics/btr026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pyshkin AV. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477. doi: 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup . 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079. doi: 10.1093/bioinformatics/btp352. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kang DD, Froula J, Egan R, Wang Z. 2015. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3:e1165. doi: 10.7717/peerj.1165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, Hugenholtz P, Tyson GW. 2017. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2:1533–1542. doi: 10.1038/s41564-017-0012-7. [DOI] [PubMed] [Google Scholar]
  • 8.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055. doi: 10.1101/gr.186072.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Amaral GRS, Dias GM, Wellington-Oguri M, Chimetto L, Campeão ME, Thompson FL, Thompson CC. 2014. Genotype to phenotype: identification of diagnostic vibrio phenotypes using whole genome sequences. Int J Syst Evol Microbiol 64:357–365. doi: 10.1099/ijs.0.057927-0. [DOI] [PubMed] [Google Scholar]
  • 10.Amaral GRS, Campeão ME, Swings J, Thompson FL, Thompson CC. 2015. Finding diagnostic phenotypic features of Photobacterium in the genome sequences. Antonie Van Leeuwenhoek 107:1351–1358. doi: 10.1007/s10482-015-0414-6. [DOI] [PubMed] [Google Scholar]
  • 11.Thompson CC, Amaral GR, Campeão M, Edwards RA, Polz MF, Dutilh BE, Ussery DW, Sawabe T, Swings J, Thompson FL. 2015. Microbial taxonomy in the post-genomic era: rebuilding from scratch? Arch Microbiol 197:359–370. doi: 10.1007/s00203-014-1071-2. [DOI] [PubMed] [Google Scholar]
  • 12.De Vos P, Thompson F, Thompson C, Swings J. 2017. A flavor of prokaryotic taxonomy: systematics revisited, p 29–44. In Kurtböke I. (ed), Microbial resources: from functional existence in nature to applications. Elsevier B.V, Amsterdam, Netherlands. [Google Scholar]
  • 13.Vandecasteele JP, Jones T. 2008. Petroleum microbiology: concepts, environmental implications, industrial applications. Editions Technips, Paris, France. [Google Scholar]
  • 14.Lagesen K, Hallin P, Rødland EA, Stærfeldt H-H, 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]
  • 15.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol 215:403–410. doi: 10.1016/S0022-2836(05)80360-2. [DOI] [PubMed] [Google Scholar]
  • 16.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glöckner FO. 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35:7188–7196. doi: 10.1093/nar/gkm864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. doi: 10.1093/nar/gks1219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Nawrocki EP, Kolbe DL, Eddy SR. 2009. Infernal 1.0: inference of RNA alignments. Bioinformatics 25:1335–1337. doi: 10.1093/bioinformatics/btp157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Cannone JJ, Subramanian S, Schnare MN, Collett JR, D'Souza LM, Du Y, Feng B, Lin N, Madabusi LV, Müller KM, Pande N, Shang Z, Yu N, Gutell RR. 2002. The Comparative RNA Web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics 3:2. doi: 10.1186/1471-2105-3-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. 2015. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Mol Biol Evol 32:268–274. doi: 10.1093/molbev/msu300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. 2018. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol 35:518–522. doi: 10.1093/molbev/msx281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. 2017. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587–589. doi: 10.1038/nmeth.4285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tarlachkov SV, Starodumova IP. 2017. TaxonDC: calculating the similarity value of the 16S rRNA gene sequences of prokaryotes or ITS regions of fungi. J Bioinform Genom 3:1–4. doi: 10.18454/jbg.2017.3.5.1. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

This whole-genome shotgun project has been deposited in GenBank under the BioProject accession no. PRJNA478776.

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

RESOURCES