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. 2018 May 1;84(10):e00267-18. doi: 10.1128/AEM.00267-18

Reconstituting the History of Cronobacter Evolution Driven by Differentiated CRISPR Activity

Haiyan Zeng a,#, Jumei Zhang a,#, Qingping Wu a,✉, Wenjing He a, Haoming Wu a, Yingwang Ye b, Chengsi Li a, Na Ling a, Moutong Chen a, Juan Wang c, Shuzhen Cai a, Tao Lei a, Yu Ding d, Liang Xue a
Editor: Edward G Dudleye
PMCID: PMC5930372  PMID: 29523551

ABSTRACT

Cronobacter strains harboring the CRISPR-Cas system are important foodborne pathogens causing serious neonatal infections. However, the specific role of the CRISPR-Cas system in bacterial evolution remains relatively unexplored. In this study, we investigated the impact of the CRISPR-Cas system on Cronobacter evolution and obtained 137 new whole-genome Cronobacter sequences by next-generation sequencing technology. Among the strains examined (n = 240), 90.6% (193/213) of prevalent species Cronobacter sakazakii, Cronobacter malonaticus, and Cronobacter dublinensis strains had intact CRISPR-Cas systems. Two rare species, Cronobacter condimenti (n = 2) and Cronobacter universalis (n = 6), lacked and preserved the CRISPR-Cas system at a low frequency (1/6), respectively. These results suggest that the presence of one CRISPR-Cas system is important for a Cronobacter species to maintain genome homeostasis for survival. The Cronobacter ancestral strain is likely to have harbored both subtype I-E and I-F CRISPR-Cas systems; during the long evolutionary process, subtype I-E was retained while subtype I-F selectively degenerated in Cronobacter species and was even lost by the major Cronobacter pathovars. Moreover, significantly higher CRISPR activity was observed in the plant-associated species C. dublinensis than in the virulence-related species C. sakazakii and C. malonaticus. Similar spacers of CRISPR arrays were rarely found among species, suggesting intensive change through adaptive acquisition and loss. Differentiated CRISPR activity appears to be the product of environmental selective pressure and might contribute to the bidirectional divergence and speciation of Cronobacter.

IMPORTANCE This study reports the evolutionary history of Cronobacter under the selective pressure of the CRISPR-Cas system. One CRISPR-Cas system in Cronobacter is important for maintaining genome homeostasis, whereas two types of systems may be redundant and not conducive to acquiring beneficial DNA for environmental adaptation and pathogenicity. Differentiated CRISPR activity has contributed to the bidirectional divergence and genetic diversity of Cronobacter. This perspective makes a significant contribution to the literature by providing new insights into CRISPR-Cas systems in general, while further expanding the roles of CRISPR beyond conferring adaptive immunity and demonstrating a link to adaptation and species divergence in a genus. Moreover, our study provides new insights into the balance between genome homeostasis and the uptake of beneficial DNA related to CRISPR-based activity in the evolution of Cronobacter.

KEYWORDS: Cronobacter, CRISPR-Cas system, adaptive evolution, genome homeostasis, genetic diversity

INTRODUCTION

Cronobacter (formerly Enterobacter sakazakii) is a newly described genus that includes seven species: Cronobacter sakazakii, Cronobacter malonaticus, Cronobacter turicensis, Cronobacter dublinensis, Cronobacter muytjensii, Cronobacter universalis, and Cronobacter condimenti (1, 2). These species are opportunistic foodborne pathogens of humans that can cause infections in all age groups (3). Notably, these organisms can cause rare but life-threatening diseases in neonates and immunocompromised infants, including meningitis, necrotizing enterocolitis, and septicemia (4, 5). The bacterium has been isolated from the environment, food, and clinical sources (2, 6–13). To date, three species, C. sakazakii, C. malonaticus, and C. turicensis, have been reported to cause clinical infection (14). Previous pangenome analyses of Cronobacter spp. suggest a species level bidirectional divergence pattern driven by niche adaptation (15). One cluster, consisting of C. dublinensis and C. muytjensii, is characterized by the presence of several accessory genomic regions that are important for survival in a plant-associated environmental niche, and the other cluster, comprising C. sakazakii, C. malonaticus, C. universalis, and C. turicensis, harbors more virulence-related genetic traits associated with pathogenicity (15, 16).

CRISPR-Cas systems provide bacteria with sequence-specific, acquired defense against phages and plasmids as an adaptive immune system (17, 18). Recent evidence indicates that CRISPR-Cas systems also play critical functional roles beyond immunity, such as endogenous transcriptional control, bacterial physiology, and pathogenicity (19, 20). At the same time, CRISPR immunity has recently been reported to be associated with speciation events (21, 22). Streptococcus equi is believed to have evolved from an ancestral strain of Streptococcus zooepidemicus, and CRISPRs are postulated to influence their evolution, balancing the need for gene gain over genome stability (21). However, the specific role of the CRISPR-Cas system in bacterial evolution remains rather unclear.

A multilocus sequence typing (MLST) scheme has been established for the entire Cronobacter genus on the basis of seven housekeeping genes (23). Ogrodzki et al. (24) recently reported the existence of one subtype I-E CRISPR-Cas system and many CRISPR arrays in C. sakazakii sequence type 1 (ST1), ST4, ST8, and ST12 strains, which are associated with outbreaks of neonatal meningitis and necrotizing enterocolitis infections. In a previous study, we further classified all of the CRISPR arrays into five types according to their conserved locations in the genome of C. sakazakii and found that the frequent gain/loss of prophages and spacers in CRISPR loci was a likely driver of the rapid evolution of the species (25). In a recent study, Ogrodzki et al. first detected the subtype I-F CRISPR-Cas system in C. dublinensis and C. muytjensii, and interestingly, two C. dublinensis ST80 strains had different types of CRISPR-Cas systems (26). These findings raised two important questions. What is the role of the CRISPR-Cas system in the adaptive evolution of Cronobacter? Why do the same ST strains have different types of CRISPR-Cas systems?

To explore the phylogenetic distribution and role of the CRISPR-Cas system in the evolution of Cronobacter, in the present study, we performed comparative genomic analyses with 137 new whole-genome sequences of Cronobacter strains and 103 publicly available genome sequences. We characterized the phylogenetic distribution of CRISPR-Cas systems in Cronobacter and constructed CRISPR spacer libraries that were used to track species divergence. We found that the CRISPR-Cas system might play a vital role in the maintenance of genome homeostasis, and the patterns of differentiated CRISPR activity against foreign elements were found to be related to the bidirectional divergence and speciation of Cronobacter. These findings provide a valuable resource for gaining further understanding of the diversity, environmental adaptation, and pathogenic role of the CRISPR-Cas system in the evolution of Cronobacter strains.

RESULTS

Distribution of CRISPR-Cas subtypes among 240 Cronobacter strains.

In our earlier study, we found five types of CRISPR arrays in the conserved site of C. sakazakii strains, but only CRISPR1 and CRISPR2 neighbored the cas genes of the subtype I-E CRISPR-Cas system (25). In this study, we further found that a “complete” cas gene cluster of subtype I-F and CRISPR6 (an additional CRISPR array besides the previous five) were integrated in the conserved region adjacent to CRISPR3 (25). The repeat sequence of CRISPR6 was the same as that of CRISPR3 (25). CRISPR3 and CRISPR6 combined with full cas genes formed the subtype I-F CRISPR-Cas system. As shown in Table 1, CRISPR1 (82.1%, 197/240) and CRISPR2 (96.7%, 232/240) were prevalent in Cronobacter strains. CRISPR3 (33.8%, 81/240) and CRISPR6 (13.3%, 32/240) were found mainly in C. sakazakii, C. malonaticus, C. dublinensis, and C. muytjensii strains. The frequency of CRISPR3 arrays (n = 60) in C. sakazakii strains was higher than that of CRISPR6 arrays (n = 15). However, CRISPR5 (n = 18) and CRISPR4 (n = 1) were found only in C. sakazakii strains.

TABLE 1.

Frequencies of CRISPR arrays and intact CRISPR-Cas systems in Cronobacter strains

Species No. of strains with:
 Totalb
CRISPR1 CRISPR2 CRISPR3 CRISPR6 CRISPR4 CRISPR5 Subtype I-E Subtype I-F Subtypes I-E + I-Fa
C. sakazakii 126 136 60 15 1 18 128 15 15 139
C. malonaticus 33 38 4 2 0 0 33 2 2 39
C. dublinensis 30 34 9 9 0 0 30 9 7 35
C. turicensis 4 7 0 0 0 0 4 0 0 7
C. muytjensii 3 11 6 6 0 0 3 6 0 12
C. universalis 1 6 0 0 0 0 1 0 0 6
C. condimenti 0 0 2 0 0 0 0 0 0 2
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a

Strains that had both subtype I-E and I-F CRISPR-Cas systems.

b

The total number of strains of each species is shown.

An intact CRISPR-Cas system must have both a “complete” cas gene cluster and one CRISPR array (27). Since it is not known whether these CRISPR-Cas system variants with the loss of one or more cas genes exhibit activity to protect strains from phage and plasmid attacks, we considered only intact CRISPR-Cas systems in this study. Overall, 86.3% (207/240) of the Cronobacter strains had intact CRISPR-Cas systems; among these, 82.9% (199/240), 13.3% (32/240), and 10.0% (24/240) of the strains harbored subtype I-E, subtype I-F, and both subtype I-E and I-F CRISPR-Cas systems, respectively. Strains with two CRISPR-Cas systems were found only in three prevalent species: C. sakazakii, C. malonaticus, and C. dublinensis. Unlike subtype I-E, which was commonly detected among the Cronobacter strains, subtype I-F CRISPR-Cas was significantly more frequently found in the plant-associated species C. dublinensis than in the human virulence-related species C. sakazakii (P = 0.005) and C. malonaticus (P = 0.009). However, C. condimenti (n = 2) lacked the CRISPR-Cas system.

Phylogenetic relationship between CRISPR-Cas system type and ST in Cronobacter strains.

As shown in Fig. 1, the core genome maximum-likelihood (ML) phylogenetic tree showed clear clustering of the various Cronobacter species within the genus. In accordance with a previous study, Cronobacter clearly diverged into two large phylogenetic clusters, supporting the species level bidirectional divergence pattern (15). At the same time, C. sakazakii ST4 strains clustered with strains of other STs such as ST15, ST267, ST283, and ST266; all STs belonged to the same clonal complex (CC), CC4. Other ST strains belonging to CC8, CC1, and CC7 also grouped into one phylogenetic cluster, implying that the phylogenetic relationship of CC was more stable than that of ST (Fig. 1). Moreover, the core genome ML phylogenetic tree distinguished the same ST strains into smaller evolutionary units, showing more powerful discrimination. For example, C. sakazakii ST4 strains were divided into several small distinct clusters, and one cluster grouped with ST15.

FIG 1.

FIG 1

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ML phylogenetic tree based on the core genome sequences of Cronobacter strains. We concatenated all core gene sequences to construct the ML tree. The ST, O serotype, four major CRISPR arrays, cas genes of the CRISPR-Cas system, and source information are listed next to the corresponding strains. The locations of two CRISPR-Cas systems are shown in the upper left. The order and orientation of cas genes and CRISPR arrays in the Cronobacter genome were determined according to those of C. sakazakii ATCC BAA894. The newly obtained whole-genome sequences of strains in this study are marked with a red pentagram. The bootstraps corresponding to the nodes of the ML tree with a value of 1.0 are marked with red circles. L, AT-rich leader sequence region; black diamonds, repeats; squares, spacers.

Twenty-four strains of C. sakazakii, C. malonaticus, and C. dublinensis harbored both subtype I-E and I-F CRISPR-Cas systems, including 13 STs. Strains with two CRISPR-Cas systems were likely to be associated with STs. Taking C. sakazakii ST148 as an example, seven out of eight isolates have two CRISPR-Cas systems. Other STs with two strains, like ST17, ST607, and ST549, were also found to have two CRISPR-Cas systems. Major pathovars such as C. sakazakii CC4, ST12, CC8, and CC1, which were associated with outbreaks of neonatal meningitis and necrotizing enterocolitis infections, and C. malonaticus CC7 strains, which were associated with adult infections, all lacked the subtype I-F CRISPR-Cas system. Except for the C. sakazakii CC4 and C. malonaticus CC7 strains, other STs had preserved CRISPR3 arrays in the genome. This can explain why previous studies on the major STs associated with clinical infection did not detect the subtype I-F CRISPR-Cas system (24, 25). Of particular note, 83.3% (15/18) of the CRISPR5 arrays were found in C. sakazakii CC8. However, this finding does not rule out the possibility of the existence of a temporary CRISPR-Cas system at some point in the history of this lineage, which should be explored in future studies.

Next, we carefully examined Cronobacter strains with incomplete CRISPR-Cas systems. As shown in Fig. 2, reference strain C. sakazakii ATCC BAA894 had an intact subtype I-E CRISPR-Cas system but lacked cas genes of the subtype I-F CRISPR-Cas system. In line with a previous study, C. dublinensis ST80 strains were found to have different types of CRISPR-Cas systems (26). More importantly, we found that although C. dublinensis ST80 strains 582 and LMG23824 only had the intact subtype I-F CRISPR-Cas system, strain 582 had intact cas genes of the subtype I-E CRISPR-Cas system and strain LMG23824 had subtype I-E-associated CRISPR2. Meanwhile, another C. dublinensis ST80 strain, 1556, with the intact subtype I-E CRISPR-Cas system, also harbored the subtype I-F-associated CRISPR3 array and three cas genes. These results implied that ST80 ancestral strains had two CRISPR-Cas systems but diverged into different sublineages in the evolutionary process. Most of the C. sakazakii and C. malonaticus strains had intact I-E CRISPR-Cas systems, but we also found some strains to have incomplete ones. For example, C. sakazakii ES713 lost the cas3 gene, and C. sakazakii ATCC 29544 and C. malonaticus cro695B2 only had the cas3 gene. A few incomplete CRISPR-Cas systems in Cronobacter strains might be due to incomplete genome sequencing, e.g., C. sakazakii strain 978 (Fig. 1). C. universalis NCTC9529 and C. condimenti LMG26250 lost all cas genes but preserved the CRISPR2 and CRISPR3 arrays, respectively.

FIG 2.

FIG 2

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Locus architecture and gene organization of representative CRISPR-associated genomic regions. The arrow means the genome sequence orientation of this strain is the reverse complement of that of reference strain ATCC BAA894. We show the corresponding genomic architecture schematic of this strain after turning the reverse complement to the primary sequence. Gene names are based on the current nomenclature and classification.

CRISPR spacer variability among CRISPR-Cas types from Cronobacter species.

CRISPR arrays contain a series of sequence-specific conserved repeats flanking unique spacers, which are derived from invading mobile genetic elements (18). Given the limited number of genome sequences of other species, we performed comparative analyses among only C. sakazakii, C. malonaticus, and C. dublinensis. As shown in Fig. 3, there were significantly more spacers detected from subtype I-E than from subtype I-F in C. dublinensis, suggesting that the former system might have higher activity. At the same time, there were significantly more spacers from the subtype I-E CRISPR-Cas system of C. dublinensis than in those of C. sakazakii and C. malonaticus, indicating that subtype I-E in C. dublinensis had higher activity. Combined with the previous result, the plant-associated species C. dublinensis, with a higher proportion of subtype I-F and higher activity of subtype I-E, appears to exhibit a CRISPR activity level higher than that of the virulence-related species C. sakazakii and C. malonaticus.

FIG 3.

FIG 3

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CRISPR spacer variability of subtype I-E and I-F CRISPR-Cas systems in Cronobacter strains. The differences between spacers from the subtype I-E CRISPR-Cas systems of C. sakazakii, C. malonaticus, and C. dubliniensis and those between the subtype I-E and I-F CRISPR-Cas systems of C. dubliniensis are statistically significant. **, P < 0.001 (two-tailed t test).

The full spacer libraries of different species were created by orienting (judging by the repeat sequence and AT-rich leader sequence) and extracting sequences based on the type of CRISPR arrays. The redundant spacers from one species were removed from the spacer libraries. Finally, 1,012, 221, 178, 48, 1,131, and 149 unique CRISPR1 and CRISPR2 spacers were identified in C. sakazakii, C. malonaticus, C. turicensis, C. universalis, C. dublinensis, and C. muytjensii, respectively. In addition, 254, 37, 213, 70, and 3 unique CRISPR3 and CRISPR6 spacers were detected in C. sakazakii, C. malonaticus, C. dublinensis, C. muytjensii, and C. condimenti, respectively. When the spacers from all CRISPR arrays were combined within one species, 1,265, 258, 178, 1343, 219, 48, and 3 spacers were identified in C. sakazakii, C. malonaticus, C. turicensis, C. dublinensis, C. muytjensii, C. universalis, and C. condimenti, respectively. One similar spacer was detected in two CRISPR-Cas system-related CRISPR arrays of C. sakazakii and C. dublinensis, respectively. Although there were more C. sakazakii and C. malonaticus strains than C. dublinensis strains (Table 1), the latter species had more spacers. This is consistent with the previous result demonstrating that C. dubliniensis exhibits higher CRISPR activity. As shown in Fig. 4, except for the few similar spacers detected between species, the spacer contents showed strong species specificity overall. These results implied that the CRISPR arrays of seven species were subject to intensive change through adaptive acquisition during the evolutionary process.

FIG 4.

FIG 4

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In silico CRISPR target prediction. The unique spacers of all CRISPRs from seven Cronobacter species and the same spacers shared within two species are shown at the top left. The pie chart denotes the percentage of 3,299 unique spacers in Cronobacter with complementarity to a given type of target sequence. The hosts of some targeted plasmids and phages are shown.

When all of the spacers from the Cronobacter strains were pooled, 3,299 unique spacer sequences were identified. To better understand the types of genetic elements being blocked by the CRISPR-Cas system, we compared the unique spacer library obtained from all of the Cronobacter strains analyzed against the ACLAME database and the new Cronobacter pangenomes. There were 3.30% (109/3,299), 5.24% (173/3,299), and 12.12% (400/3,299) spacers targeting sequences from plasmids, phages, and Cronobacter pangenomes (i.e., protospacers), respectively. In addition, 48.25% (193/400) of the spacers targeted with Cronobacter genomes were also matched with publicly available plasmids or phages; these spacers were only counted once within plasmids or phages (Fig. 4). This result implies that the intact CRISPR-Cas system in Cronobacter strains is likely active and is further useful to determine putative mobile genetic elements in the Cronobacter genome (28). Unfortunately, 85.18% (2,810/3,299) spacers had not found the target. The lack of identified phages and plasmids in public databases might be one reason, while critical functional roles beyond the immunity of the CRISPR-Cas system could be another reason. We further carefully examined the hosts of the targeted plasmids and phages (or prophages). Since there was no significant difference in the hosts of targeted mobile elements between subtype I-E and I-F spacers, we considered them as a whole. Most of these plasmids and phages have been found in human-pathogenic bacteria belonging to the family Enterobacteriaceae. Given that the virulence genes in Cronobacter are still not well defined, these identified spacer targets might be useful for exploring pathogenic factors in the future. Some of the targeted plasmids and phages were found to belong to bacteria that are isolated primarily from water or soil, as well as some plant-associated bacteria. The results support speculations that Cronobacter spp. are waterborne and plant associated (29, 30).

Evolutionary histories of Cronobacter strains under CRISPR pressure.

We used a Bayesian phylogenetic approach to provide estimates of the nucleotide substitution rates and divergence times of Cronobacter. We estimated the genome-wide substitution rate of Cronobacter strains at 3.6 × 10−7 substitutions site−1 year−1 (95% credible interval [CI] = 1.2 × 10−7 to 5.0 × 10−7), similar to Escherichia coli and Shigella dysenteriae (31, 32), which places the likely most recent common ancestor of this genus 521.6 (95% CI, 341.8 to 1,394.4) thousand years ago (Fig. 5). The species level bidirectional divergence occurred approximately 469.8 (95% CI, 255.8 to 1,048.9) thousand years ago. In this evolutionary process, we found higher CRISPR activity in the plant-associated species C. dublinensis than in the virulence-related species C. sakazakii and C. malonaticus. The speciation of all Cronobacter species occurred 14.5 to 51.5 thousand years ago. This estimate is later than a previous date calculated by MLST with respect to the difference in the substitution rate and calculation method (23). The CRISPR spacer contents in Cronobacter species have clearly been subjected to intensive change within this long evolutionary process, showing obvious species specificity (Fig. 5; see Fig. S1 in the supplemental material). Within one species, unique CRISPR spacer contents were also detected in every ST.

FIG 5.

FIG 5

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Hypothesized history of Cronobacter evolution driven by differentiated CRISPR activity. A timed phylogeny of Cronobacter isolates in the MCC tree, measured in thousands of years before the present, is shown on the left. Major nodes of the MCC tree with posterior probability equal to 1.0 are marked by black circles. A heat map illustrating the CRISPR array spacer content of the subtype I-E CRISPR-Cas system associated with the phylogeny is shown on the right. The presence of every unique spacer sequence in each CRISPR-positive strain is denoted by a blue square. The species reported to cause human infection are marked with red pentagrams.

DISCUSSION

Horizontal gene transfer is an important evolutionary process in the bacterial life cycle, and some mobile elements often provide an accessory pool of genes that can enhance the environmental adaptation or pathogenicity of host strains (33). However, the gain of mobile genetic elements can also exert a fitness cost, and a trade-off between the benefit of new traits against genome stability may ensue (22). To maintain genome integrity and survive a phage attack, bacterial populations have evolved an array of immune system responses, including restriction-modification systems, receptor switching, and abortive phage mechanisms; however, the recently identified CRISPR-Cas system is the only adaptive immune system characterized to date (34). Different bacteria may choose different mechanisms as major defense systems to avoid phage and plasmid attacks, and the CRISPR-Cas system is not the only choice. Hence, opposite phenomena define the roles of the CRISPR-Cas system in the dissemination of antibiotic resistance in bacteria like staphylococci and E. coli (35, 36).

In this study, we examined the phylogenetic distribution of the CRISPR-Cas system in 240 Cronobacter strains, including 137 new strains isolated from food sources in China. The high proportion of Cronobacter strains with the CRISPR-Cas system implied that this system had an important impact on the evolution of this genus. Unlike the common subtype I-E CRISPR-Cas system, subtype I-F CRISPR-Cas was significantly more frequently found in the plant-associated species C. dublinensis than in the human virulence-related species C. sakazakii and C. malonaticus. Moreover, all of the major pathovars, like C. sakazakii CC4, CC8, CC1, and ST12 and C. malonaticus CC7, lacked subtype I-F. These findings raise the following question. Is subtype I-F a degenerated immune system or a newly acquired system in the evolutionary process of Cronobacter? In our study, all intact subtype I-E and I-F CRISPR-Cas systems from all Cronobacter species had the same composition and were conservatively located in the same region of the genome by comparative genomic analyses (Fig. 1). Taking C. sakazakii as an example, except 15 strains containing the intact subtype I-F CRISPR-Cas system, 44 other strains without cas genes of subtype I-F still harbored CRISPR3 arrays (Table 1). In C. malonaticus, strains cro913A2 and cro695B2 lacking cas genes of subtype I-F also had the CRISPR3 array. Although no cas gene was detected in C. condimenti (n = 2), we also found CRISPR3 from C. condimenti in the same region. Looking at the partial cas gene regions of Cronobacter strains was highly informative. Intact subtype I-E, incomplete subtype I-F, incomplete subtype I-E, and intact subtype I-F in different C. dublinensis ST80 strains were compelling evidence (Fig. 2). All of these results support the notion that the subtype I-E and I-F CRISPR-Cas systems might have coexisted in the ancestral Cronobacter strains before the divergence of the seven species. However, in our study, only 13.75% (33/240) of Cronobacter strains harbored the subtype I-F CRISPR-Cas system. These results strongly supported the idea that subtype I-F was indeed a degenerating CRISPR-Cas system in Cronobacter strains.

The potential impact of the CRISPR-Cas system on the Cronobacter evolutionary process is summarized in Fig. 5. Integrating our present results with previous reports (8, 29, 30, 37), the ancestral Cronobacter strain harboring the subtype I-E and I-F CRISPR-Cas systems likely originated from water approximately 521.6 thousand years ago. Approximately 51.8 thousand years later, this ancestral strain underwent species level directional divergence into two lineages under environmental selective pressure (15, 23). In previous investigations, C. sakazakii, C. malonaticus, and C. dublinensis were demonstrated to be prevalent species, whereas C. universalis and C. condimenti were rarely detected (7–10, 12, 38, 39). In this study, 90.6% (193/213) of C. sakazakii, C. malonaticus, and C. dublinensis strains had intact CRISPR-Cas systems. More than half of the strains of C. turicensis (4/7) and C. muytjensii (9/12), which are neither prevalent species nor rare species, had intact CRISPR-Cas systems. However, two rare species, C. condimenti (n = 2) and C. universalis (n = 6), lacked and preserved the CRISPR-Cas system at a low frequency (1/6), respectively. Thus, the presence of one CRISPR-Cas system in Cronobacter is important for the species to maintain genome homeostasis for survival.

During or after the bidirectional divergence process, the subtype I-E CRISPR-Cas system was retained but subtype I-F was selectively degenerated in all species. There was no difference in the type of spacer targets for the two types of CRISPR-Cas systems in Cronobacter, suggesting that these two systems in a given strain may be somewhat redundant. On the one hand, the CRISPR-Cas system provides bacteria with sequence-specific acquired defense against phages and plasmids to maintain genome integrity (17, 18). On the other hand, the benefits inherent to maintaining genome homeostasis also come at a cost of reduced uptake of beneficial DNA, which in turn affects the emergence, drug resistance, and virulence of human bacterial pathogens (40–42). Two types of these systems in Cronobacter were probably not conducive to obtaining new beneficial nucleotide sequences to facilitate both environmental adaptation and pathogenicity. This might explain the selective degeneration of one CRISPR-Cas system in the evolutionary process of Cronobacter. Moreover, significantly more spacers were detected in the subtype I-E CRISPR-Cas system than in the subtype I-F system in C. dublinensis, suggesting that the former system might have higher activity. This might also explain why subtype I-E appears to have been selected in the majority of Cronobacter strains to protect them from harmful phage attacks. Overall, we have provided new evidence toward gaining a better understanding of the specific costs and benefits of CRISPR during bacterial adaptive evolution.

Moreover, higher activity of the CRISPR-Cas system was also found in the plant-associated species C. dublinensis than in the virulence-related species C. sakazakii and C. malonaticus, which might be the product of survival under environmental pressure. According to previous publications, C. muytjensii is a highly divergent species not associated with virulence. Instead, its strains appear to extrapolate from C. dublinensis with respect to plant-associated species (2, 15, 16). The distribution of the CRISPR-Cas system in C. muytjensii was also divergent (Table 1 and Fig. 1). However, owing to the limited availability of sequences from this species, we did not investigate this aspect in detail. We hope to address this in the future, when sufficient genome sequences are available. After Cronobacter speciation, further strain divergence might have occurred through the acquisition and loss of specific spacers in the CRISPR array. Such differentiated CRISPR activity could contribute to the species level bidirectional divergence and diversity of Cronobacter. The CRISPR spacer targets of plasmids and phages from bacteria are not only helpful for understanding the origin of these organisms but are also useful to explore the potential pathogenic factors of Cronobacter (28). Because spacers are added in a polarized manner, cracking the “spacer code” from the ancestral end to the most recently acquired sequences can help unfold a complete story of strain divergence and relatedness (22). CRISPR represents a key system for determining the unique microbial fingerprint in microbiomes, in terms of both diversity and dynamics. Accordingly, the diversity of CRISPR spacer contents in Cronobacter strains identified in this study can provide a powerful tool for tracking the lineages of genetically similar strains within an outbreak (24). In the next step of this research, we will try to collect more Cronobacter strains to examine the application of this new method more comprehensively.

This study makes a significant contribution to a better understanding of the bacterial adaptive evolutionary process under the pressure of the CRISPR-Cas system. Besides providing novel insights into CRISPR-Cas systems in general, these results further expand the roles of CRISPR beyond conferring adaptive immunity and demonstrate a link to adaptation and species divergence in a genus. This study not only supplies a valuable resource for gaining further understanding of the environmental adaptation and pathogenic role of the CRISPR-Cas system in the evolution of Cronobacter strains but also provides new insights into the balance between genome homeostasis and the uptake of beneficial DNA, relating CRISPR activity to Cronobacter evolution.

MATERIALS AND METHODS

Bacterial strains and genome sequencing.

A total of 137 Cronobacter strains, including 77 C. sakazakii, 27 C. malonaticus, 30 C. dubliniensis, and 3 C. turicensis strains, were isolated from several types of food in China. Detailed information about these strains, including STs and O serotypes, is provided in Data Set S1. A 500-bp sample library was subjected to 400-bp single-end sequencing on an Ion S5 instrument or to 2× 250-bp paired-end sequencing with the Illumina HiSeq 2500 instrument with 100-fold coverage. The raw data for each bacterium were error corrected and assembled with SPAdes 3.6.2 (43). The final assemblies were filtered to contain ≥200-bp contigs. Genome annotation was performed with Prokka 1.11 (44).

Identification of cas genes and CRISPR arrays.

We downloaded genome sequences of Cronobacter strains from the NCBI genome database and the Cronobacter MLST database. For the publicly available strains, we selected just one isolate (clone) from an outbreak as a representative according to the primary paper; other isolates were deleted from our data sets as described in our previous paper (25). We selected 103 public genome sequences (Data Set S2), incorporated them in our local database, and reannotated them with Prokka 1.11 for the analyses in this study (44). The CRISPR array was detected with CRISPRFinder (45), and the spacers were also collected on the basis of the conserved direct repeat motif. All cas genes were identified as described previously (25, 27). The Wilcoxon rank sum test was used to examine the distribution of the subtype I-F CRISPR-Cas system among different Cronobacter species; significance was assessed by using multiple hypothesis-adjusted P values. The statistical significance of the differences in the number of spacers of one CRISPR-Cas system between species or two CRISPR-Cas systems in one species was determined by the two-tailed t test.

Core genome phylogenetic analyses.

Pangenome analysis of all genome sequences was performed with GET_HOMOLOGUES software as described previously (25, 46, 47). The results of pangenome analyses of Cronobacter species strains showed 563 single-copy core genes. All of these core genes were aligned with BioEdit v7.2.6 software and cut to the same length manually. Every core gene sequence was subjected to recombinant analysis with SimPlot 3.5.1 software to rule out the possibility of sequences with recombination. We concatenated all of these core gene sequences (287,220 nucleotides) to construct the ML phylogenetic tree by FastTree (48). The tree was viewed and adjusted in Figtree v1.4.3 software.

Determination of spacer matches.

The similarity search of the identified spacer sequences was performed by blastn. We considered matches spacers that showed at least 84% (27/32) similarity, as described previously (25). The unique spacers were assembled as a global spacer library. Comparison of these unique spacers to previously studied elements within the ACLAME database (49) was performed to identify potential targets. We then performed the same blast analyses of spacers within our pangenome sequences to find more targets. By mapping the presence or absence of every unique spacer sequence found in the nonredundant spacer library for each strain harboring a CRISPR array, we identified strains harboring similar spacer contents. The heat map was generated in RStudio with the pheatmap package.

Core genome temporal analysis.

We used Bayesian evolutionary analysis by sampling trees (BEAST) version 1.8.4 to date the important nodes (50). The analyses were conducted with a subsample of 93 isolates (one sequence per ST) selected across the core genome ML tree. The concatenated core genome alignments of these 93 strains were subjected to BEAST analyses with Bayesian skyline population size change models in different combinations with a strict molecular clock and a relaxed clock, in line with previous studies (31). Three independent chains of 100 million generations each were run to ensure convergence, with sampling every 1,000 iterations. Convergence and effective sample size values were inspected with Tracer version 1.6 (50). The marginal-likelihood estimation was then used to determine which model gave the best fit, by calculating the Bayes factor. The strict clock model, together with the Bayesian skyline demographic model, proved a much better fit for the data in this study. Maximum clade credibility (MCC) trees were generated with TreeAnnotator version 1.8.4 on the combined files and visualized with FigTree version 1.4.3 (48). Estimates are reported as median values with the 95% highest posterior density.

Accession number(s).

The GenBank accession numbers of the 137 whole-genome sequences of Cronobacter strains reported here are shown in Data Set S1. They ranged from NRJK00000000 to NRJZ00000000, NRKA00000000 to NRKZ00000000, NRLA00000000 to NRLZ00000000, NRMA00000000 to NRMZ00000000, NRNA00000000 to NRNZ00000000, and NROA00000000 to NROQ00000000.

Supplementary Material

Supplemental material
supp_84_10_e00267-18__index.html (1.3KB, html)

ACKNOWLEDGMENTS

We thank Luhao Huang (Field Bioinformatics Scientist of Thermo Fisher Scientific) for supplying valuable script to process the data.

This work was supported by grants from National Natural Science Foundation of China (31601571 and 31371780), the Natural Science Foundation of Guangdong Province (2016A030310315), and GDAS' Special Project of Science and Technology Development (2017GDASCX-0201).

All of us read and approved the final manuscript. None of us have any conflict of interest to declare.

Footnotes

Supplemental material for this article may be found at https://doi.org/10.1128/AEM.00267-18.

REFERENCES

  • 1.Iversen C, Mullane N, McCardell B, Tall BD, Lehner A, Fanning S, Stephan R, Joosten H. 2008. Cronobacter gen. nov., a new genus to accommodate the biogroups of Enterobacter sakazakii, and proposal of Cronobacter sakazakii gen. nov., comb. nov., Cronobacter malonaticus sp. nov., Cronobacter turicensis sp nov., Cronobacter muytjensii sp nov., Cronobacter dublinensis sp. nov., Cronobacter genomospecies 1, and of three subspecies, Cronobacter dublinensis subsp. dublinensis subsp. nov., Cronobacter dublinensis subsp. lausannensis subsp. nov. and Cronobacter dublinensis subsp. lactaridi subsp. nov. Int J Syst Evol Microbiol 58:1442–1447. doi: 10.1099/ijs.0.65577-0. [DOI] [PubMed] [Google Scholar]
  • 2.Joseph S, Desai P, Ji Y, Cummings CA, Shih R, Degoricija L, Rico A, Brzoska P, Hamby SE, Masood N, Hariri S, Sonbol H, Chuzhanova N, McClelland M, Furtado MR, Forsythe SJ. 2012. Comparative analysis of genome sequences covering the seven Cronobacter species. PLoS One 7:e49455. doi: 10.1371/journal.pone.0049455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Kucerova E, Joseph S, Forsythe S. 2011. The Cronobacter genus: ubiquity and diversity. Qual Assur Saf Crops Foods 3:104–122. doi: 10.1111/j.1757-837X.2011.00104.x. [DOI] [Google Scholar]
  • 4.Bowen AB, Braden CR. 2006. Invasive Enterobacter sakazakii disease in infants. Emerg Infect Dis 12:1185–1189. doi: 10.3201/eid1208.051509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Willis J, Robinson JE. 1988. Enterobacter sakazakii meningitis in neonates. Pediatr Infect Dis J 7:196–199. doi: 10.1097/00006454-198803000-00012. [DOI] [PubMed] [Google Scholar]
  • 6.Friedemann M. 2007. Enterobacter sakazakii in food and beverages (other than infant formula and milk powder). Int J Food Microbiol 116:1–10. doi: 10.1016/j.ijfoodmicro.2006.12.018. [DOI] [PubMed] [Google Scholar]
  • 7.Kandhai MC, Reij MW, Gorris LG, Guillaume-Gentil O, van Schothorst M. 2004. Occurrence of Enterobacter sakazakii in food production environments and households. Lancet 363:39–40. doi: 10.1016/S0140-6736(03)15169-0. [DOI] [PubMed] [Google Scholar]
  • 8.Ueda S. 2017. Occurrence of Cronobacter spp. in dried foods, fresh vegetables and soil. Biocontrol Sci 22:55–59. doi: 10.4265/bio.22.55. [DOI] [PubMed] [Google Scholar]
  • 9.Miranda N, Banerjee P, Simpson S, Kerdahi K, Sulaiman IM. 2017. Molecular surveillance of Cronobacter spp. isolated from a wide variety of foods from 44 different countries by sequence typing of 16S rRNA, rpoB and O-antigen genes. Foods 6:1–14. http://www.mdpi.com/2304-8158/6/5/36/htm. doi: 10.3390/foods6010001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Brandão ML, Umeda NS, Jackson E, Forsythe SJ, de Filippis I. 2017. Isolation, molecular and phenotypic characterization, and antibiotic susceptibility of Cronobacter spp. from Brazilian retail foods. Food Microbiol 63:129–138. doi: 10.1016/j.fm.2016.11.011. [DOI] [PubMed] [Google Scholar]
  • 11.Bowen A, Wiesenfeld HC, Kloesz JL, Pasculle AW, Nowalk AJ, Brink L, Elliot E, Martin H, Tarr CL. 2017. Notes from the field: Cronobacter sakazakii infection associated with feeding extrinsically contaminated expressed human milk to a premature infant—Pennsylvania, 2016. MMWR Morb Mortal Wkly Rep 66:761–762. doi: 10.15585/mmwr.mm6628a5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Alsonosi A, Hariri S, Kajsík M, Oriešková M, Hanulík V, Röderová M, Petrželová J, Kollárová H, Drahovská H, Forsythe S, Holý O. 2015. The speciation and genotyping of Cronobacter isolates from hospitalised patients. Eur J Clin Microbiol Infect Dis 34:1979–1988. doi: 10.1007/s10096-015-2440-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Liu H, Cui JH, Cui ZG, Hu GC, Yang YL, Li J, Shi YW. 2013. Cronobacter carriage in neonate and adult intestinal tracts. Biomed Environ Sci 26:861–864. doi: 10.3967/bes2013.011. [DOI] [PubMed] [Google Scholar]
  • 14.Singh N, Goel G, Raghav M. 2015. Insights into virulence factors determining the pathogenicity of Cronobacter sakazakii. Virulence 6:433–440. doi: 10.1080/21505594.2015.1036217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Grim CJ, Kotewicz ML, Power KA, Gopinath G, Franco AA, Jarvis KG, Yan QQ, Jackson SA, Sathyamoorthy V, Hu L, Pagotto F, Iversen C, Lehner A, Stephan R, Fanning S, Tall BD. 2013. Pan-genome analysis of the emerging foodborne pathogen Cronobacter spp. suggests a species-level bidirectional divergence driven by niche adaptation. BMC Genomics 14:366. doi: 10.1186/1471-2164-14-366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Grim CJ, Kothary MH, Gopinath G, Jarvis KG, Beaubrun JJ, McClelland M, Tall BD, Franco AA. 2012. Identification and characterization of Cronobacter iron acquisition systems. Appl Environ Microbiol 78:6035–6050. doi: 10.1128/AEM.01457-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Barrangou R. 2013. CRISPR-Cas systems and RNA-guided interference. Wiley Interdiscip Rev RNA 4:267–278. doi: 10.1002/wrna.1159. [DOI] [PubMed] [Google Scholar]
  • 18.Westra ER, Buckling A, Fineran PC. 2014. CRISPR-Cas systems: beyond adaptive immunity. Nat Rev Microbiol 12:317–326. doi: 10.1038/nrmicro3241. [DOI] [PubMed] [Google Scholar]
  • 19.Louwen R, Staals RH, Endtz HP, van Baarlen P, van der Oost J. 2014. The role of CRISPR-Cas systems in virulence of pathogenic bacteria. Microbiol Mol Biol Rev 78:74–88. doi: 10.1128/MMBR.00039-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Barrangou R. 2015. The roles of CRISPR-Cas systems in adaptive immunity and beyond. Curr Opin Immunol 32:36–41. doi: 10.1016/j.coi.2014.12.008. [DOI] [PubMed] [Google Scholar]
  • 21.Waller AS, Robinson C. 2013. Streptococcus zooepidemicus and Streptococcus equi evolution: the role of CRISPRs. Biochem Soc Trans 41:1437–1443. doi: 10.1042/BST20130165. [DOI] [PubMed] [Google Scholar]
  • 22.Briner AE, Barrangou R. 2016. Deciphering and shaping bacterial diversity through CRISPR. Curr Opin Microbiol 31:101–108. doi: 10.1016/j.mib.2016.03.006. [DOI] [PubMed] [Google Scholar]
  • 23.Joseph S, Sonbol H, Hariri S, Desai P, McClelland M, Forsythe SJ. 2012. Diversity of the Cronobacter genus as revealed by multilocus sequence typing. J Clin Microbiol 50:3031–3039. doi: 10.1128/JCM.00905-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Ogrodzki P, Forsythe SJ. 2016. CRISPR-Cas loci profiling of Cronobacter sakazakii pathovars. Future Microbiol 11:1507–1519. doi: 10.2217/fmb-2016-0070. [DOI] [PubMed] [Google Scholar]
  • 25.Zeng H, Zhang J, Li C, Xie T, Ling N, Wu Q, Ye Y. 2017. The driving force of prophages and CRISPR-Cas system in the evolution of Cronobacter sakazakii. Sci Rep 7:40206. doi: 10.1038/srep40206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ogrodzki P, Forsythe SJ. 2017. DNA-sequence based typing of the Cronobacter genus using MLST, CRISPR-Cas array and capsular profiling. Front Microbiol 8:1875. doi: 10.3389/fmicb.2017.01875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA, Saunders SJ, Barrangou R, Brouns SJ, Charpentier E, Haft DH, Horvath P, Moineau S, Mojica FJ, Terns RM, Terns MP, White MF, Yakunin AF, Garrett RA, van der Oost J, Backofen R, Koonin EV. 2015. An updated evolutionary classification of CRISPR-Cas systems. Nat Rev Microbiol 13:722–736. doi: 10.1038/nrmicro3569. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.van Belkum A, Soriaga LB, LaFave MC, Akella S, Veyrieras JB, Barbu EM, Shortridge D, Blanc B, Hannum G, Zambardi G, Miller K, Enright MC, Mugnier N, Brami D, Schicklin S, Felderman M, Schwartz AS, Richardson TH, Peterson TC, Hubby B, Cady KC. 2015. Phylogenetic distribution of CRISPR-Cas systems in antibiotic-resistant Pseudomonas aeruginosa. mBio 6:e01796–15. doi: 10.1128/mBio.01796-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Hariri S, Joseph S, Forsythe SJ. 2013. Cronobacter sakazakii ST4 strains and neonatal meningitis, United States. Emerg Infect Dis 19:175–177. doi: 10.3201/eid1901.120649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Forsythe SJ. 22 December 2017. Updates on the Cronobacter genus. Annu Rev Food Sci Technol. doi: 10.1146/annurev-food-030117-012246. [DOI] [PubMed] [Google Scholar]
  • 31.Njamkepo E, Fawal N, Tran-Dien A, Hawkey J, Strockbine N, Jenkins C, Talukder KA, Bercion R, Kuleshov K, Kolinska R, Russell JE, Kaftyreva L, Accou-Demartin M, Karas A, Vandenberg O, Mather AE, Mason CJ, Page AJ, Ramamurthy T, Bizet C, Gamian A, Carle I, Sow AG, Bouchier C, Wester AL, Lejay-Collin M, Fonkoua MC, Hello SL, Blaser MJ, Jernberg C, Ruckly C, Merens A, Page AL, Aslett M, Roggentin P, Fruth A, Denamur E, Venkatesan M, Bercovier H, Bodhidatta L, Chiou CS, Clermont D, Colonna B, Egorova S, Pazhani GP, Ezernitchi AV, Guigon G, Harris SR, Izumiya H, Korzeniowska-Kowal A, et al. 2016. Global phylogeography and evolutionary history of Shigella dysenteriae type 1. Nat Microbiol 1:16027. doi: 10.1038/nmicrobiol.2016.27. [DOI] [PubMed] [Google Scholar]
  • 32.Stoesser N, Sheppard AE, Pankhurst L, De Maio N, Moore CE, Sebra R, Turner P, Anson LW, Kasarskis A, Batty EM, Kos V, Wilson DJ, Phetsouvanh R, Wyllie D, Sokurenko E, Manges AR, Johnson TJ, Price LB, Peto TE, Johnson JR, Didelot X, Walker AS, Crook DW, Modernizing Medical Microbiology Informatics G . 2016. Evolutionary history of the global emergence of the Escherichia coli epidemic clone ST131. mBio 7:e02162–15. doi: 10.1128/mBio.02162-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Bobay LM, Touchon M, Rocha EP. 2014. Pervasive domestication of defective prophages by bacteria. Proc Natl Acad Sci U S A 111:12127–12132. doi: 10.1073/pnas.1405336111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Labrie SJ, Samson JE, Moineau S. 2010. Bacteriophage resistance mechanisms. Nat Rev Microbiol 8:317–327. doi: 10.1038/nrmicro2315. [DOI] [PubMed] [Google Scholar]
  • 35.Marraffini LA, Sontheimer EJ. 2008. CRISPR interference limits horizontal gene transfer in staphylococci by targeting DNA. Science 322:1843–1845. doi: 10.1126/science.1165771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Touchon M, Charpentier S, Pognard D, Picard B, Arlet G, Rocha EP, Denamur E, Branger C. 2012. Antibiotic resistance plasmids spread among natural isolates of Escherichia coli in spite of CRISPR elements. Microbiology 158:2997–3004. doi: 10.1099/mic.0.060814-0. [DOI] [PubMed] [Google Scholar]
  • 37.Jardine JL, Abia ALK, Mavumengwana V, Ubomba-Jaswa E. 2017. Phylogenetic analysis and antimicrobial profiles of cultured emerging opportunistic pathogens (phyla Actinobacteria and Proteobacteria) identified in hot springs. Int J Environ Res Public Health 14:E1070. doi: 10.3390/ijerph14091070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Xu X, Li C, Wu Q, Zhang J, Huang J, Yang G. 2015. Prevalence, molecular characterization, and antibiotic susceptibility of Cronobacter spp. in Chinese ready-to-eat foods. Int J Food Microbiol 204:17–23. doi: 10.1016/j.ijfoodmicro.2015.03.003. [DOI] [PubMed] [Google Scholar]
  • 39.Berthold-Pluta A, Garbowska M, Stefanska I, Pluta A. 2017. Microbiological quality of selected ready-to-eat leaf vegetables, sprouts and non-pasteurized fresh fruit-vegetable juices including the presence of Cronobacter spp. Food Microbiol 65:221–230. doi: 10.1016/j.fm.2017.03.005. [DOI] [PubMed] [Google Scholar]
  • 40.Bikard D, Hatoum-Aslan A, Mucida D, Marraffini LA. 2012. CRISPR interference can prevent natural transformation and virulence acquisition during in vivo bacterial infection. Cell Host Microbe 12:177–186. doi: 10.1016/j.chom.2012.06.003. [DOI] [PubMed] [Google Scholar]
  • 41.Hatoum-Aslan A, Marraffini LA. 2014. Impact of CRISPR immunity on the emergence and virulence of bacterial pathogens. Curr Opin Microbiol 17:82–90. doi: 10.1016/j.mib.2013.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Palmer KL, Gilmore MS. 2010. Multidrug-resistant enterococci lack CRISPR-Cas. mBio 1:e00227–10. doi: 10.1128/mBio.00227-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.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, Pevzner PA. 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]
  • 44.Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069. doi: 10.1093/bioinformatics/btu153. [DOI] [PubMed] [Google Scholar]
  • 45.Grissa I, Vergnaud G, Pourcel C. 2007. CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Res 35:W52–W57. doi: 10.1093/nar/gkm360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Contreras-Moreira B, Vinuesa P. 2013. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl Environ Microbiol 79:7696–7701. doi: 10.1128/AEM.02411-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, Deboy RT, Davidsen TM, Mora M, Scarselli M, Margarit y Ros I, Peterson JD, Hauser CR, Sundaram JP, Nelson WC, Madupu R, Brinkac LM, Dodson RJ, Rosovitz MJ, Sullivan SA, Daugherty SC, Haft DH, Selengut J, Gwinn ML, Zhou L, Zafar N, Khouri H, Radune D, Dimitrov G, Watkins K, O'Connor KJ, Smith S, Utterback TR, White O, Rubens CE, Grandi G, Madoff LC, Kasper DL, Telford JL, Wessels MR, Rappuoli R, Fraser CM. 2005. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci U S A 102:13950–13955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Price MN, Dehal PS, Arkin AP. 2009. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26:1641–1650. doi: 10.1093/molbev/msp077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Leplae R, Lima-Mendez G, Toussaint A. 2010. ACLAME: a CLAssification of Mobile genetic Elements, update 2010. Nucleic Acids Res 38:D57–D61. doi: 10.1093/nar/gkp938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Drummond AJ, Rambaut A. 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol 7:214. doi: 10.1186/1471-2148-7-214. [DOI] [PMC free article] [PubMed] [Google Scholar]

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