Complete genome sequence of the sulfur compounds oxidizing chemolithoautotroph Sulfuricurvum kujiense type strain (YK-1^T)

Abstract

Sulfuricurvum kujiense Kodama and Watanabe 2004 is the type species of the monotypic genus Sulfuricurvum, which belongs to the family Helicobacteraceae in the class Epsilonproteobacteria. The species is of interest because it is frequently found in crude oil and oil sands where it utilizes various reduced sulfur compounds such as elemental sulfur, sulfide and thiosulfate as electron donors. Members of the species do not utilize sugars, organic acids or hydrocarbons as carbon and energy sources. This genome sequence represents the type strain of the only species in the genus Sulfuricurvum. The genome, which consists of a circular chromosome of 2,574,824 bp length and four plasmids of 118,585 bp, 71,513 bp, 51,014 bp, and 3,421 bp length, respectively, harboring a total of 2,879 protein-coding and 61 RNA genes and is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

Introduction

Strain YK-1^T (= DSM 16994 = ATCC BAA-921 = JCM 11577) is the type strain of the species Sulfuricurvum kujiense, which is the type species of the monotypic genus Sulfuricurvum [1,2]. The genus name was derived from the Latin words 'sulfur' and 'curvus' meaning 'curved', yielding the Neo-Latin word 'Sulfuricurvum', the 'curved bacterium that utilizes sulfur' [1]. The species epithet is derived from the Neo-Latin word 'kujiense' (referring to Kuji, Iwate Prefecture, Japan, where the bacterium was isolated) [1]. Three more strains of the species S. kujiense were isolated from the same habitat and exhibited identical physiological characteristics with the type strain YK-1^T [3]. Sulfuricurvum spp. have been detected in different groundwater environments [4,5] and in oil fields [6]. Here we present a summary classification and a set of features for S. kujiense strain YK-1^T, together with the description of the complete genomic sequencing and annotation.

Classification and features

A representative genomic 16S rRNA sequence of S. kujiense YK-1^T was compared using NCBI BLAST [7,8] under default settings (e.g., considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database [9] and the relative frequencies of taxa and keywords, reduced to their stem [10], were determined, weighted by BLAST scores. The most frequently occurring genus was Sulfuricurvum (100.0%) (3 hits in total). Regarding the three hits to sequences from members of the species, the average identity within HSPs was 99.1%, whereas the average coverage by HSPs was 92.9%. No hits to sequences with (other) species names were found. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification.) The highest-scoring environmental sequence was AB030609 ('groundwater clone 1061') [11], which showed an identity of 99.7% and an HSP coverage of 96.9%. The most frequently occurring keywords within the labels of all environmental samples which yielded hits were 'spring' (9.6%), 'cave' (9.4%), 'microbi' (6.9%), 'sulfid' (5.7%) and 'mat' (5.2%) (247 hits in total). These keywords suggest that habitats for S. kujiense well-matched to that supposed in the original description [1] and other publications [3,12]. Environmental samples which yielded hits of a higher score than the highest scoring species were not found.

Figure 1 shows the phylogenetic neighborhood of S. kujiense YK-1^T in a 16S rRNA based tree. The sequences of the three 16S rRNA gene copies in the genome differ from each other by one nucleotide, and differ by up to two nucleotides from the previously published 16S rRNA sequence (AB053951).

Figure 1.

Phylogenetic tree highlighting the position of S. kujiense relative to the type strains of the type species of the other genera within the class Epsilonproteobacteria. The tree was inferred from 1,364 aligned characters [13,14] of the 16S rRNA gene sequence under the maximum likelihood (ML) criterion [15]. Rooting was done initially using the midpoint method [16] and then checked for its agreement with the current classification (Table 1). The branches are scaled in terms of the expected number of substitutions per site. Numbers adjacent to the branches are support values from 1,000 ML bootstrap replicates [17] (left) and from 1,000 Maximum-Parsimony bootstrap replicates [18] (right) if larger than 60%. Lineages with type strain genome sequencing projects registered in GOLD [19] are labeled with one asterisk, those also listed as 'Complete and Published' with two asterisks [20-24].

As one of the families selected for Figure 1, Nautiliaceae (comprising the genera Caminibacter, Lebetimonas, Nautilia, Nitratifractor, Nitratiruptor and Thioreductor) did not appear as monophyletic in the tree, we conducted both unconstrained heuristic searches for the best tree under the maximum likelihood (ML) [15] and maximum parsimony (MP) criteria [18] as well as searches constrained for the monophyly of all families (for details of the data matrix see the figure caption). Our own re-implementation of CopyCat [25] in conjunction with AxPcoords and AxParafit [26] was used to determine those leaves (species) whose placement significantly deviated between the constrained and the unconstrained tree. The best-known ML tree had a log likelihood of -8,012.83, whereas the best trees found under the constraint had a log likelihood of -8,014.70. The significantly (α = 0.05) distinctly placed species were Hydrogenimonas thermophila ('Hydrogenimonaceae'), Nitratifractor salsuginis and Thioreductor micantisoli (Nautiliaceae). However, the constrained tree was not significantly worse than the globally best one in the Shimodaira-Hasegawa test as implemented in RAxML [15] (α = 0.05). The best-known MP trees had a score of 1,290, whereas the best constrained trees found had a score of 1,295 and were not significantly worse in the Kishino-Hasegawa test as implemented in PAUP* [16] (α = 0.05). (See, e.g. chapter 21 in [27] for an in-depth description of such paired-site tests.) Accordingly, the current classification of Campylobacterales (Campylobacteraceae, Helicobacteraceae, 'Hydrogenimonaceae') and Nautiliales (Nautiliaceae) is not in significant disagreement with the 16S rRNA data.

The cells of strain YK-1^T are curved rods of 0.4 × 1-2 µm length (Figure 2) [1]. Spiral cells are also observed in the exponential growth phase [1]. S. kujiense cells stain Gram-negative and non spore-forming (Table 1). The organism is described as motile with one polar flagellum (not visible in Figure 2). Motility-related genes account for 5.3% of total genes in the genome (COG category N). The organism is a facultatively anaerobic chemolithoautotroph [1,3]. S. kujiense can grow only under NaCl concentrations below 1% [1,3]. A low-ion-strength medium (MBM) has been developed for growing S. kujiense [1,3]. The organism also grows in solid medium containing 1.5% Bacto-agar [1,3]. The temperature range for growth is between 10°C and 35°C, with an optimum at 25°C [1,3]. The pH range for growth is 6.0-8.0, with an optimum at pH 7.0 [1,3]. S. kujiense grows autotrophically on carbon dioxide and bicarbonate [1,3]. The organism does not utilize organic acids such as acetate, lactate, pyruvate, malate, succinate, or formate nor does it utilize methanol, glucose or glutamate [1,3]. S. kujiense is not able to ferment phenol, octane, toluene, benzene, benzoate or ascorbate [1,3]. S. kujiense uses sulfide, elemental sulfur, thiosulfate and hydrogen as electron donors, and nitrate as well as small amounts of molecular oxygen (1% in gas phase) as electron acceptors [1,3]. It does not utilize nitrite [1,3]. S. kujiense shows oxidase activity, but is catalase-negative [1,3]. The organism is of ecological interest because of its ability to utilize different sulfur species and nitrate [1,3].

Figure 2.

Scanning electron micrograph of S. kujiense YK-1^T

Table 1. Classification and general features of S. kujiense YK-1^T according to the MIGS recommendations [28] and the NamesforLife database [29].

MIGS ID	Property	Term	Evidence code
	Current classification	Domain Bacteria	TAS [30]
		Phylum Proteobacteria	TAS [31]
		Class Epsilonproteobacteria	TAS [32,33]
		Order Campylobacterales	TAS [32,34]
		Family Helicobacteraceae	TAS [32,35]
		Genus Sulfuricurvum	TAS [1]
		Species Sulfuricurvum kujiense	TAS [1]
		Type strain YK-1	TAS [1]
	Gram stain	negative	TAS [1]
	Cell shape	curved rods	TAS [1]
	Motility	motile	TAS [1]
	Sporulation	none	TAS [1]
	Temperature range	10°C–35°C	TAS [1]
	Optimum temperature	25°C	TAS [1]
	Salinity	below 1% NaCl; best without NaCl	TAS [1]
MIGS-22	Oxygen requirement	anaerobic, microaerobic	TAS [1]
	Carbon source	carbon dioxide, bicarbonate	TAS [1]
	Energy metabolism	chemolithoautotroph	TAS [1]
MIGS-6	Habitat	groundwater	TAS [3,11]
MIGS-15	Biotic relationship	free-living	NAS
MIGS-14	Pathogenicity	none	NAS
	Biosafety level	1	TAS [36]
	Isolation	drain water from an underground crude-oil storage cavity	TAS [3,11]
MIGS-4	Geographic location	Kuji in Iwate, Japan	TAS [1]
MIGS-5	Sample collection time	March 1999	TAS [3,11]
MIGS-4.1	Latitude	40.19	NAS
MIGS-4.2	Longitude	141.78	NAS
MIGS-4.3	Depth	not reported
MIGS-4.4	Altitude	sea level	NAS

Evidence codes - NAS: Non-traceable Author Statement (i.e., not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project [37].

Genome sequencing and annotation

Genome project history

This organism was selected for sequencing on the basis of its phylogenetic position [38], and is part of the Genomic Encyclopedia of Bacteria and Archaea project [39]. The genome project is deposited in the Genomes On Line Database [19] and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI). A summary of the project information is shown in Table 2.

Table 2. Genome sequencing project information.

MIGS ID	Property	Term
MIGS-31	Finishing quality	Finished
MIGS-28	Libraries used	Three genomic libraries: one 454 pyrosequence standard library, one 454 PE library (8.7 kb insert size), one Illumina library
MIGS-29	Sequencing platforms	Illumina GAii, 454 GS FLX Titanium
MIGS-31.2	Sequencing coverage	357.4 × Illumina; 51.1 × pyrosequence
MIGS-30	Assemblers	Newbler version 2.3, Velvet, phrap version SPS - 4.24
MIGS-32	Gene calling method	Prodigal 1.4, GenePRIMP
	INSDC ID	CP002355 (chromosome)       CP002356-9 (plasmids SULKU01-04)
	Genbank Date of Release	October 7, 2011 (chromosome and plasmids)
	GOLD ID	Gc01552
	NCBI project ID	43399
	Database: IMG-GEBA	649633097
MIGS-13	Source material identifier	DSM 16994
	Project relevance	Tree of Life, GEBA

Growth conditions and DNA isolation

S. kujiense strain YK-1^T, DSM 16994, was grown anaerobically in DSMZ medium 1020 (MBM medium) [40] at 25°C. DNA was isolated from 0.5-1 g of cell paste using MasterPure Gram-positive DNA purification kit (Epicentre MGP04100) following the standard protocol as recommended by the manufacturer with modification st/DL for cell lysis as described in Wu et al. 2009 [39]. DNA is available through the DNA Bank Network [41].

Genome sequencing and assembly

The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website [42]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly consisting of 18 contigs in two scaffolds was converted into a phrap [43] assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library. Illumina GAii sequencing data (788.0 Mb) was assembled with Velvet [44] and the consensus sequences were shredded into 1.5 kb overlapped fake reads and assembled together with the 454 data. The 454 draft assembly was based on 124.3 Mb 454 draft data and all of the 454 paired end data. Newbler parameters are -consed -a 50 -l 350 -g -m -ml 20. The Phred/Phrap/Consed software package [43] was used for sequence assembly and quality assessment in the subsequent finishing process. After the shotgun stage, reads were assembled with parallel phrap (High Performance Software, LLC). Possible mis-assemblies were corrected with gapResolution [43], Dupfinisher [45], or sequencing cloned bridging PCR fragments with subcloning. Gaps between contigs were closed by editing in Consed, by PCR and by Bubble PCR primer walks (J.-F. Chang, unpublished). A total of 85 additional reactions were necessary to close gaps and to raise the quality of the finished sequence. Illumina reads were also used to correct potential base errors and increase consensus quality using a software Polisher developed at JGI [46]. The error rate of the completed genome sequence is less than 1 in 100,000. Together, the combination of the Illumina and 454 sequencing platforms provided 408.5 × coverage of the genome. The final assembly contained 368,924 pyrosequence and 27,990,437 Illumina reads.

Genome annotation

Genes were identified using Prodigal [47] as part of the Oak Ridge National Laboratory genome annotation pipeline, followed by a round of manual curation using the JGI GenePRIMP pipeline [48]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes - Expert Review (IMG-ER) platform [49].

Genome properties

The genome consists of a 2,574,824 bp long circular chromosome with a G+C content of 45% and four circular plasmids of 3,421 bp, 51,014 bp, 71,513 bp and 118,585 bp length, respectively (Table 3 and Figure 3). Of the 2,879 genes predicted, 2,818 were protein-coding genes, and 61 RNAs; 20 pseudogenes were also identified. The majority of the protein-coding genes (67.9%) were assigned with a putative function while the remaining ones were annotated as hypothetical proteins. The distribution of genes into COGs functional categories is presented in Table 4.

Table 3. Genome Statistics.

Attribute	Value	% of Total
Genome size (bp)	2,819,357	100.00
DNA coding region (bp)	2,623,121	93.04
DNA G+C content (bp)	1,256,420	44.56
Number of replicons	5
Extrachromosomal elements	4
Total genes	2,879	100.00
RNA genes	61	2.12
rRNA operons	3
Protein-coding genes	2,818	97.88
Pseudo genes	20	0.69
Genes with function prediction	1,964	67.87
Genes in paralog clusters	1,264	43.90
Genes assigned to COGs	2,129	73.95
Genes assigned Pfam domains	2,100	72.94
Genes with signal peptides	926	32.16
Genes with transmembrane helices	633	21.99
CRISPR repeats	0

Figure 3.

Graphical map of the chromosome (plasmids not shown). From bottom to center: Genes on forward strand (color by COG categories), Genes on reverse strand (color by COG categories), RNA genes (tRNAs green, rRNAs red, other RNAs black), GC content, GC skew.

Table 4. Number of genes associated with the general COG functional categories.

Code	Value	%age	Description
J	154	6.4	Translation, ribosomal structure and biogenesis
A	0	0.0	RNA processing and modification
K	119	5.0	Transcription
L	126	5.3	Replication, recombination and repair
B	0	0.0	Chromatin structure and dynamics
D	33	1.4	Cell cycle control, cell division, chromosome partitioning
Y	0	0.0	Nuclear structure
V	46	1.9	Defense mechanisms
T	283	11.8	Signal transduction mechanisms
M	177	7.4	Cell wall/membrane/envelope biogenesis
N	127	5.3	Cell motility
Z	0	0.0	Cytoskeleton
W	0	0.0	Extracellular structures
U	96	4.0	Intracellular trafficking, secretion, and vesicular transport
O	102	4.3	Posttranslational modification, protein turnover, chaperones
C	168	7.0	Energy production and conversion
G	73	3.1	Carbohydrate transport and metabolism
E	129	5.4	Amino acid transport and metabolism
F	57	2.4	Nucleotide transport and metabolism
H	107	4.5	Coenzyme transport and metabolism
I	40	1.7	Lipid transport and metabolism
P	134	5.6	Inorganic ion transport and metabolism
Q	21	0.9	Secondary metabolites biosynthesis, transport and catabolism
R	229	9.6	General function prediction only
S	175	7.3	Function unknown
-	750	26.1	Not in COGs