- Open Access
Non-contiguous finished genome sequence of plant-growth promoting Serratia proteamaculans S4
Standards in Genomic Sciences volume 8, pages 441–449 (2013)
Serratia proteamaculans S4 (previously Serratia sp. S4), isolated from the rhizosphere of wild Equisetum sp., has the ability to stimulate plant growth and to suppress the growth of several soil-borne fungal pathogens of economically important crops. Here we present the non-contiguous, finished genome sequence of S. proteamaculans S4, which consists of a 5,324,944 bp circular chromosome and a 129,797 bp circular plasmid. The chromosome contains 5,008 predicted genes while the plasmid comprises 134 predicted genes. In total, 4,993 genes are assigned as protein-coding genes. The genome consists of 22 rRNA genes, 82 tRNA genes and 58 pseudogenes. This genome is a part of the project “Genomics of four rapeseed plant growth-promoting bacteria with antagonistic effect on plant pathogens” awarded through the 2010 DOE-JGI’s Community Sequencing Program.
The genus Serratia is a diverse and widely dispersed group of Gammaproteobacteria [1,2]. Some of these have beneficial effects on ecologically and economically important plants [3–4] and others are known as opportunistic pathogens of humans and other organisms . Plant-associated Serratia spp. are of considerable agricultural interest and several strains of S. plymuthica have recently been studied in relation to their possible use as biocontrol agents in agriculture [3–4].
Serratia proteamaculans S4 (previously Serratia sp. S4) was isolated from the rhizosphere of naturally growing Equisetum plants in 1980 from Uppsala, Sweden. The bacterium is able to enhance the growth of rapeseed plants and inhibit the growth of different fungal pathogens such as Verticillium dahliae, and Rhizoctonia solani. Sequencing the S. proteamaculans S4 genome will therefore assist in the identification of genetic traits underlying its potential and its beneficial effects on plant growth. Here we present the non-contiguous finished genome sequence of S. proteamaculans S4.
Classification and features
A representative 16S rRNA gene sequence of S. proteamaculans S4 was subjected to comparison with the most recently released databases in GenBank. The NCBI BLAST  tool was used under the default settings (i.e. by considering only the high-scoring segment pairs (HSP’s) from the best 250 hits). The most frequently matching genus was Serratia (almost 50% of total matches). When considering high score, coverage and identity – S. proteamaculans 568 was the first match with 100% identity and 100% coverage. Other Serratia species with maximum identity were other S. proteamaculans strains (10%) with maximum identity 99%, S. fonticola (2%) with maximum identity 98%, S. grimesii (3.2%) with maximum identity 99%, S. liquefaciens (4.4%) with maximum identity 99%, S. plymuthica (3.2%) maximum identity 98–99% and unclassified Serratia sp. (22%) with maximum identity 98–99%. Remaining matches were with Rahnella sp. (2%) with maximum identity 98–99% and other uncultured bacterial clones (40%) with maximum identity 98–99%.
Figure 1 shows the phylogenetic proximity of S. proteamaculans S4 to S. proteamaculans 568 (CP000826) as well as its distinct separation from other members of the Enterobacteriaceae. Its phylogenetic relationship was further confirmed by digital DNA-DNA hybridization  values above 70% with the genome sequence of the S. proteamaculans 568 using the GGDC web-server .
Serratia proteamaculans S4, a Gram-negative, rod shaped, non-sporulating and motile bacterium measuring 1–2 µm in length and 0.5–0.7 µm in width [Figure 2], was isolated from Equisetum roots. The bacterium is a pale yellow colored, facultative aerobe and easily grows on a broad spectrum of organic compounds including carbon sources such as glucose, sucrose, succinate, mannitol, inositol, sorbitol, arabinose, trehalose, and melibiose. The optimal temperature for its growth is 28 °C and it can grow in the pH range 4–10 [Table 1].
Genome sequencing information
Serratia proteamaculans S4 was selected for sequencing because of its biological control potential and plant growth enhancing activity in rapeseed crops. The genome sequence is deposited in the Genomes On Line Databases . Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI). A summary of the project information is shown in Table 2 together with associated MIGS identifiers .
Growth conditions and DNA isolation
Serratia proteamaculans S4 was grown on Luria Broth (LB) medium for 12 hours at 28 °C. The DNA was extracted from the cells by using a standard CTAB protocol for bacterial genomic DNA isolation, which is available at JGI .
Genome sequencing and assembly
The draft genome of S. proteamaculans S4 was generated using a combination of Illumina and 454 sequencing platforms. The details of library construction and sequencing are available at the JGI . The sequence data generated from Illumina GAii (4,232 Mb) were assembled with Velvet  and the consensus sequence was computationally shredded into 1.5 kb overlapping fake reads. The sequencing data generated from 454 pyrosequencing (89.5 Mb) were assembled with Newbler and consensus sequences were computationally shredded into 2 kb overlapping fake reads. The initial draft assembly contained 50 contigs in 2 scaffolds. The 454 Newbler consensus reads, the Illumina Velvet consensus reads and the read pairs in the 454 paired end library were integrated using parallel Phrap [28,29]. The software, Consed  was used for the subsequent finishing process. The software Polisher  was used to correct the base errors and increase the consensus quality. Possible mis-assemblies were corrected with gapResolution (, unpublished), Dupfinisher  or by sequencing cloned bridging PCR fragments with subcloning. The gaps between contigs were closed by editing in the software Consed , by PCR and by Bubble PCR (J.-F. Chang, unpublished) primer walks. A total of 95 additional reactions was necessary to close gaps and to raise the quality of the finished sequence. The final assembly is based on 47 Mb of 454 draft data which provides an average 8.7 × coverage of the genome and 4,143.8 Mb of Illumina draft data, which provides an average 767.4 × coverage of the genome.
The S. proteamaculans S4 genes were identified using Prodigal  as part of the DOE-JGI annotation pipeline  followed by a round of manual curation using the JGI GenePRIMP pipeline . The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) non-redundant database, UniProt, TIGRFam, Pfam, PRIAM, KEGG, COG, and InterPro databases. These data sources were combined to assert a product description for each predicted protein. Non-coding genes and miscellaneous features were predicted using tRNAscan-SE , RNAmmer , Rfam , TMHMM , and signalP . Additional gene prediction analysis and manual functional annotation was performed within the Integral Microbial Genomics-Expert Review (IMG-ER)  platform developed by the Joint Genome Institute, Walnut Creek, CA, USA.
The genome includes a circular chromosome of 5,324,944 bp (55% GC content) along with a circular plasmid of 129,797 bp (50% GC content). The chromosome comprises 5,008 predicted genes while the plasmid comprises 137 predicted genes. In total 4,993 genes are assigned as protein-coding genes. About 85% of the protein-coding genes were assigned to a putative function with the remaining annotated as hypothetical proteins. The genome consists of 22 rRNA genes, 82 tRNA genes and 58 pseudogenes. The properties and the statistics of the genome are summarized in Tables 3 and 4 and Figures 3a and 3b.
The genome contains genes arranged in several gene clusters encoding secondary metabolites such as siderophores (enterobactin and aerobactin) and antibiotics (pyrrolnitrin). These compounds can contribute indirectly to plant growth enhancement by suppressing growth of pathogens. The genome also includes genes for the production of plant growth hormones such as indole-3-acetic acid (IAA), which can be directly involved in plant growth. Further studies of the biochemical properties of additional secondary metabolites and regulation of their production using functional genomics will elucidate the detailed mechanisms underlying plant growth promotion by S. proteamaculans S4.
Grimont PA, Grimont F. The genus Serratia. Annu Rev Microbiol 1978; 32:221–248. PubMed http://dx.doi.org/10.1146/annurev.mi.32.100178. 001253
Grimont PD, Grimont F, Starr M. Serratia species isolated from plants. Curr Microbiol 1981; 5:317–322. http://dx.doi.org/10.1007/BF01567926
Kalbe C, Marten P, Berg G. Strains of the genus Serratia as beneficial rhizobacteria of oilseed rape with. Microbiol Res 1996; 151:433–439. PubMed http://dx.doi.org/10.1016/S0944-5013(96)80014-0
Kurze S, Bahl H, Dahl R, Berg G. Biological control of fungal strawberry diseases by Serratia plymuthica HRO-C48. Plant Dis 2001; 85:529–534. http://dx.doi.org/10.1094/PDIS.2001.85.5.529
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997; 25:3389–3402. PubMed http://dx.doi.org/10.1093/nar/25.17.3389
Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, et al. Clustal W and Clustal X version 2.0. Bioinformatics 2007; 23:2947–2948. PubMed http://dx.doi.org/10.1093/bioinformatics/btm404
Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 2011; 28:2731–2739. PubMed http://dx.doi.org/10.1093/molbev/msr121
Pattengale N, Alipour M, Bininda-Emonds OP, Moret BE, Stamatakis A. How many bootstrap replicates are necessary? Lect Notes Comput Sci 2009; 5541:184–200. http://dx.doi.org/10.1007/978-3-642-02008-7_13
Liolios K, Chen IMA, Mavromatis K, Tavernarakis N, Hugenholtz P, Markowitz VM, Kyrpides NC. The Genomes On Line Database (GOLD) in 2009: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res 2010; 38:D346–D354. PubMed http://dx.doi.org/10.1093/nar/gkp848
Auch AF, von Jan M, Klenk HP, Göker M. Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci 2010; 2:117–134. PubMed http://dx.doi.org/10.4056/sigs.531120
Auch AF, Klenk HP, Göker M. Standard operating procedure for calculating genome-to-genome distances based on high-scoring segment pairs. Stand Genomic Sci 2010; 2:142–148. PubMed http://dx.doi.org/10.4056/sigs.541628
Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ, Angiuoli SV, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol 2008; 26:541–547. PubMed http://dx.doi.org/10.1038/nbt1360
Woese CR, Kandler O, Wheelis ML. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc Natl Acad Sci USA 1990; 87:4576–4579. PubMed http://dx.doi.org/10.1073/pnas.87.12.4576
Garrity GM, Bell JA, Liburn T. Phylum XIV. Proteobacteria phy nov. In: Garrity GM, Brenner DJ, Krieg NR, Staley JT (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 2, Part B, Springer, New York, 2005, p. 1.
Garrity GM, Bell JA, Liburn T. Class III. Gammaproteobacteria class nov. In: Garrity GM, Brenner DJ, Krieg NR, Staley JT (eds), Bergey’s Manual of Systematic Bacteriology, Second edition, Volume 2, Part B. New York: Springer; 2005. p 1.
Validation of publication of new names and new combinations previously effectively published outside the IJSEM. Int J Syst Evol Microbiol 2005; 55:2235–2238. http://dx.doi.org/10.1099/ijs.0.64108-0
Garrity GM, Holt JG. Taxonomic Outline of the Archaea and Bacteria. In: Garrity GM, Boone DR, Castenholtz RW (eds), Bergey’s Manual of Systematic Bacteriology, Second Edition, Volume 1, Springer, New York, 2001, p. 155–166.
Skerman VBD, McGowan V, Sneath PHA. Approved lists of bacterial names. Int J Syst Bacteriol 1980; 30:225–420. http://dx.doi.org/10.1099/00207713-30-1-225
Rahn O. New principles for the classification of bacteria. Zentralbl Bakteriol Parasitenkd Infektionskr Hyg 1937; 96:273–286.
Judicial Commission. Conservation of the family name Enterobacteriaceae, of the name of the type genus, and designation of the type species OPINION NO. 15. Int Bull Bacteriol Nomencl Taxon 1958; 8:73–74.
Sakazaki R. Genus IX. Serratia Bizio 1823, 288. In: Buchanan RE, Gibbons NE (eds), Bergey’s Manual of Determinative Bacteriology, Eighth Edition, The Williams and Wilkins Co., Baltimore, 1974, p. 326.
Bizio B. Lettera di Bartolomeo Bizio al chiarissimo canonico Angelo Bellani sopra il fenomeno della polenta porporina. Biblioteca Italiana o sia Giornale di Letteratura. [Anno VIII]. Scienze e Arti 1823; 30:275–295.
Grimont PAD, Grimont F, Starr MP. Serratia proteamaculans (Paine and Stansfield) comb. nov., a senior subjective synonym of Serratia liquefaciens (Grimes and Hennerty) Bascomb et al. Int J Syst Bacteriol 1978; 28:503–510. http://dx.doi.org/10.1099/00207713-28-4-503
BAuA. 2010, Classification of bacteria and archaea in risk groups. http://www.baua.de TRBA 466, p. 200.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25:25–29. PubMed http://dx.doi.org/10.1038/75556
DOE Joint Genome Institute. http://www.jgi.doe.gov.
Zerbino DR, Birney E. Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829. PubMed http://dx.doi.org/10.1101/gr.074492.107
Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using Phred. I. accuracy assessment. Genome Res 1998; 8:175–185. PubMed http://dx.doi.org/10.1101/gr.8.3.175
Ewing B, Green P. Base-calling of automated sequencer traces using Phred. II. error probabilities. Genome Res 1998; 8:175–185. PubMed http://dx.doi.org/10.1101/gr.8.3.175
Gordon D, Abajian C, Green P. Consed: a graphical tool for sequence finishing. Genome Res 1998; 8:195–202. PubMed http://dx.doi.org/10.1101/gr.8.3.195
Lapidus A, LaButti K, Foster B, Lowry S, Trong SEG. POLISHER: An effective tool for using ultra short reads in microbial genome assembly and finishing. AGBT, Marco Island, FL, 2008.
Han C, Chain P. Finishing repeat regions automatically with Dupfinisher. In: Proceeding of the 2006 international conference on bioinformatics & computational biology. Arabina HR, Valafar H (eds), CSREA Press. June 26–29, 2006: 141–146.
Hyatt D, Chen GL, LoCascio P, Land M, Larimer F, Hauser L. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119. PubMed http://dx.doi.org/10.1186/1471-2105-11-119
Mavromatis K, Ivanova N, Chen A, Szeto E, Markowitz V, Kyrpides NC. Standard operating procedure for the annotations of microbial genomes by the production genomic facility of the DOE JGI. Stand Genomic Sci 2009; 1:63–67. PubMed http://dx.doi.org/10.4056/sigs.632
Pati A, Ivanova NN, Mikhailova N, Ovchinnikova G, Hooper SD, Lykidis A, Kyrpides NC. GenePRIMP: a gene prediction improvement pipeline for prokaryotic genomes. Nat Methods 2010; 7:455–457. PubMed http://dx.doi.org/10.1038/nmeth.1457
Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997; 25:955–964. PubMed
Lagesen K, Hallin P, Rødland EA, Stærfeldt HH, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108. PubMed http://dx.doi.org/10.1093/nar/gkm160
Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. Rfam: an RNA family database. Nucleic Acids Res 2003; 31:439–441. PubMed http://dx.doi.org/10.1093/nar/gkg006
Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol 2001; 305:567–580. PubMed http://dx.doi.org/10.1006/jmbi.2000.4315
Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of signal peptides: SignalP 3.0. J Mol Biol 2004; 340:783–795. PubMed http://dx.doi.org/10.1016/j.jmb.2004.05.028
Markowitz VM, Mavromatis K, Ivanova NN, Chen IMA, Chu K, Kyrpides NC. IMG ER: a system for microbial genome annotation expert review and curation. Bioinformatics 2009; 25:2271–2278. PubMed http://dx.doi.org/10.1093/bioinformatics/btp393
The work conducted by the US Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
About this article
Cite this article
Neupane, S., Goodwin, L.A., Högberg, N. et al. Non-contiguous finished genome sequence of plant-growth promoting Serratia proteamaculans S4. Stand in Genomic Sci 8, 441–449 (2013). https://doi.org/10.4056/sigs.4027757
- Facultative aerobe