Pan-genome analysis of six Paracoccus type strain genomes reveal lifestyle traits

Jacqueline Hollensteiner; Dominik Schneider; Anja Poehlein; Thorsten Brinkhoff; Rolf Daniel

doi:10.1371/journal.pone.0287947

Abstract

The genus Paracoccus capable of inhabiting a variety of different ecological niches both, marine and terrestrial, is globally distributed. In addition, Paracoccus is taxonomically, metabolically and regarding lifestyle highly diverse. Until now, little is known on how Paracoccus can adapt to such a range of different ecological niches and lifestyles. In the present study, the genus Paracoccus was phylogenomically analyzed (n = 160) and revisited, allowing species level classification of 16 so far unclassified Paracoccus sp. strains and detection of five misclassifications. Moreover, we performed pan-genome analysis of Paracoccus-type strains, isolated from a variety of ecological niches, including different soils, tidal flat sediment, host association such as the bluespotted cornetfish, Bugula plumosa, and the reef-building coral Stylophora pistillata to elucidate either i) the importance of lifestyle and adaptation potential, and ii) the role of the genomic equipment and niche adaptation potential. Six complete genomes were de novo hybrid assembled using a combination of short and long-read technologies. These Paracoccus genomes increase the number of completely closed high-quality genomes of type strains from 15 to 21. Pan-genome analysis revealed an open pan-genome composed of 13,819 genes with a minimal chromosomal core (8.84%) highlighting the genomic adaptation potential and the huge impact of extra-chromosomal elements. All genomes are shaped by the acquisition of various mobile genetic elements including genomic islands, prophages, transposases, and insertion sequences emphasizing their genomic plasticity. In terms of lifestyle, each mobile genetic elements should be evaluated separately with respect to the ecological context. Free-living genomes, in contrast to host-associated, tend to comprise (1) larger genomes, or the highest number of extra-chromosomal elements, (2) higher number of genomic islands and insertion sequence elements, and (3) a lower number of intact prophage regions. Regarding lifestyle adaptations, free-living genomes share genes linked to genetic exchange via T4SS, especially relevant for Paracoccus, known for their numerous extrachromosomal elements, enabling adaptation to dynamic environments. Conversely, host-associated genomes feature diverse genes involved in molecule transport, cell wall modification, attachment, stress protection, DNA repair, carbon, and nitrogen metabolism. Due to the vast number of adaptive genes, Paracoccus can quickly adapt to changing environmental conditions.

Citation: Hollensteiner J, Schneider D, Poehlein A, Brinkhoff T, Daniel R (2023) Pan-genome analysis of six Paracoccus type strain genomes reveal lifestyle traits. PLoS ONE 18(12): e0287947. https://doi.org/10.1371/journal.pone.0287947

Editor: Gabriel Moreno-Hagelsieb, Wilfrid Laurier University, CANADA

Received: April 21, 2023; Accepted: November 15, 2023; Published: December 20, 2023

Copyright: © 2023 Hollensteiner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The whole-genome sequence project has been deposited at DDBJ/ENA/GenBank. Data are available for: P. aesturarii DSM 19484T under BioProject PRJNA689385, BioSample SAMN17208934, accession numbers (CP067169-CP067178) and raw reads have been deposited in the NCBI SRA database (SRR23190560 and SRR23190561). P. alcaliphilus DSM 8512T under BioProject PRJNA689372, BioSample SAMN17207692, accession numbers (CP067124-CP067128) and raw reads have been deposited in the NCBI SRA database (SRR23190624 and SRR23190625). P. fistulariae KCTC 22803T under BioProject PRJNA689404, BioSample SAMN17209169, accession numbers (CP067136- CP067139) and raw reads have been deposited in the NCBI SRA database (SRR23190620 and SRR23190621). P. saliphilus DSM 18447T under BioProject PRJNA689405, BioSample SAMN17209211, accession numbers (CP067140-CP067141) and raw reads have been deposited in the NCBI SRA database (SRR23190618 and SRR23190619). P. seriniphilus DSM 14827T under BioProject PRJNA689377, BioSample SAMN17208906, accession numbers (CP067129-CP067133) and raw reads have been deposited in the NCBI SRA database (SRR23190622 and SRR23190623). P. stylophorae LMG 25392T under BioProject PRJNA689381, BioSample SAMN17208933, accession numbers (CP067134-CP067135) and raw reads have been deposited in the NCBI SRA database (SRR23190562 and SRR23190563). All numbers are depicted in S1 Table.

Funding: This study was partly supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the collaborative research center TRR51 Roseobacter awarded to TB and RD. We acknowledge support by the Open Access Publication Funds of the Göttingen University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The genus Paracoccus is the type genus of the Paracoccaceae [1], formerly member of the Rhodobacteraceae, which is known for a broad spectrum of ecologically relevant metabolic traits and high abundance in many marine environments [2]. Paracoccus seems to be a biogeographic cosmopolite due to its distribution all over the world and inhabiting a variety of different ecological niches [2]. Nevertheless, despite its wide distribution, their role in the marine biogeochemical cycles and their ability to inhabit a wide variety of different environments remains to be elucidated. In addition, different lifestyles are described for Paracoccus, such as free-living, host-associated, pathogenic, or symbiotic [3–5]. Furthermore, high potential for genomic adaptability is a prerequisite for their global omnipresence. Therefore, genomic analysis of Paracoccus should focus on horizontal gene transfer (HGT) and mobile genetic elements (MGEs) including plasmids, genomic islands (GIs), insertion sequences (IS) elements, transposases and prophages since these are drivers of adaptation and evolution in prokaryotes [6]. So far 84 Paracoccus species were validly published (https://www.ezbiocloud.net/search?tn=paracoccus accessed 2022-09-22). However, the majority of newly announced Paracoccus species are taxonomically classified by 16S rRNA gene analysis, which is known to be insufficient for a robust species classification and often lead to unreliable or even wrong taxonomic assignment [7]. Correspondingly, several genera and species within the family Rhodobacteraceae, were reclassified [8]. Furthermore, a discrepancy in the taxonomic framework was detected by core-pan genome analysis of the genus Paracoccus [9]. Notably, 64 Paracoccus type strains genomes are available, but only 15 have a complete status. Complete genome sequences are critical for all downstream analysis including comparative genomics, phylogenetic analysis, and functional annotation or inference of biological relationships. Thus, more complete high-quality Paracoccus genomes are required to provide a solid basis for robust taxonomic assignment of new Paracoccus isolates. To widen our understanding of how Paracoccus is able to adapt to various environments and develop different lifestyles, we provide six high-quality Paracoccus genomes of the type strains P. aestuarii DSM 19484^T (= B7^T, = KCTC 22049^T) [10], P. alcaliphilus DSM 8512^T (TK 1015^T = JCM 7364^T) [11], P. fistulariae KCTC 22803^T (= 22-5^T, = CGUG 58401^T) [12], P. saliphilus DSM 18447^T (= YIM 90738^T, = CCTCC AB 206074^T) [13], P. seriniphilus DSM 14827^T (= MBT-A4^T, = CIP 107400^T) [14], and P. stylophorae LMG 25392^T (= KTW-16^T, = BCRC 80106^T) [15]. Strains were chosen to represent different habitats including tidal flat sediment [10], soil [11], bluespotted cornetfish Fistularia commersonii [12], saline-alkaline soil [13], marine bryozonan Bugula plumosa [14], and the reef-building coral Stylophora pistillata [15], respectively. The six strains can be assigned to the two lifestyles free-living and host associated. All genomes were sequenced using short-read (Illumina) and long-read (Oxford Nanopore) technology. Subsequently, genomes were de novo hybrid assembled and phylogenetically grouped into the genus Paracoccus by using all genomes available in the RefSeq database [16]. Thereby, the quality of all genomes was evaluated, followed by downstream analyses that involved determination of the pan-genome, functional annotation, and genome comparison. The latter included identification and comparison of plasmids, IS elements, GIs, transposases, and (pro)phages. This detailed comparative investigation of six Paracoccus genomes and their functional repertoire will help shading light on how Paracoccus is able to distribute globally and adapt to ecological niches.

Results and discussion

Genome features of Paracoccus type strains

As of September 2022, 185 Paracoccus genomes (64 type/representative strains) were available in the genome databases of which only 15 had the quality status “complete”. We increased the number of complete high-quality type strain genomes to 21 by sequencing and analyzing the genomes of six Paracoccus type strains. The strains originate from various habitats including soils, sediments, and associated to diverse hosts. All following genome statistics including short- and long-read sequencing statistics are summarized in S1 Table. Complete genomes were hybrid assembled from Oxford Nanopore and Illumina reads. The sequencing led to a 148–1,320x genome coverage and resulted in one circular chromosome per type strain and multiple extrachromosomal circular elements ranging from 1 to 9. (Table 1; S1 Table).

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Table 1. Genomic features of the Paracoccus strains presented in this study.

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The total genome sizes ranged from 3.64 to 4.77 Mbp, with an average GC content of 61.5 to 67.8%. Each strain encoded one tm-tRNA, harbors 6–9 rRNA and 47–56 t-RNA molecules. The quality of the genomes was assessed and evaluated with BUSCO v.5.4.5 [17] (Fig 1) and initial phylogenetic classification was performed using the Genome Taxonomy Database Toolkit (GTDB-Tk v2.1.1) [18] summarized in S2 Table.

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Fig 1. Genome assembly quality assessment and evaluation of the six Paracoccus type strains.

The chart was generated with BUSCO v.5.4.5 showing the relative completeness of each strain’s genome.

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BUSCO has identified all isolates to the family Rhodobacteraceae, now reclassified to a separate family Paracoccaceae [1]. The initial phylogenetic classification assigned all genomes to their correct species group (S2 Table). However, none of the bioinformatic tools accounted the new family level Paracoccaceae [8]. Nevertheless, all genomes included almost all single copy genes (> 97.48%).

Phylogeny of Paracoccus

Quality control of all available Paracoccus genomes including our six novel genomes (n = 182) revealed that all type strains have a completeness >97% with a contamination rate <2% excluding P. mutanolyticus RSP-02 which was therefore excluded (S3 Table). Moreover, 21 Paracoccus strains were excluded because they did not meet the aforementioned completeness or contamination threshold. In total, 160 genomes were implemented in the phylogenetic analysis of the genus Paracoccus including our six type strain genomes (Fig 2 and S4 Table).

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Fig 2. Phylogenetic analysis of the genus Paracoccus.

Type strains and the 8 largest Paracoccus clusters are highlighted in red and black. Detailed output of all available Paracoccus genomes including ANI values is summarized in S4 Table. Modifications were performed with Inkscape v 1.2. [19] (1. Inkscape Project. Inkscape [Internet]. 2020. Available from: https://inkscape.org).

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We detected 16 novel classifications and five misclassifications within the genus Paracoccus (Table 2 and S4 Table). Some of the misclassification were already detected by Puri et al, 2021 [9]. However, this analysis highlights the importance of high-quality reference genomes to establish a robust taxonomic classification as novel Paracocci were misclassified at the end of 2022 (Table 2, underlined) or for new subjected Paracocci genomes a classification was simply not performed.

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Table 2. Reclassification of Paracoccus sp. and wrongly assigned Paracoccus.

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Paracoccus denitrificans (formerly known as Micrococcus denitrificans) was the first described Paracoccus species and represent the largest cluster (cluster VIII; Fig 2) [20]. Cluster III (P. marcusii) and cluster IV (P. yeeii) are represented by nine strains. Notably, most of the unclassified Paracoccus sp. strains were associated to cluster III (Fig 2, Table 2, S4 Table). Moreover, as suggested by Leinberger et al. (2021) [21] the species P. haeundaenis should be resolved into the species P. marcusii which was first described in 1998 [22]. P. haeundaensis was reported in 2004 [23] as novel species based on 16S rRNA gene profiling. Often 16S rRNA gene sequencing is inconclusive for species level classification, especially for members of the family Rhodobacteraceae [8] which recently lead to a new family announcement of Paracoccacea [1]. Cluster IV (P. yeeii) and Cluster I (P. sanguinis) comprise strains described as opportunistic human pathogens, which is a unique feature within the genus Paracoccus [24, 25]. Two closely related clusters are cluster VI (P. versutus) and cluster VII (P. pantotrophus). Both type strains showed an identity value of 93.4%, which is close to the species boundary of ~94–96% which was proposed by Richter et al 2009 [26] and Konstantinidis et al. 2005 [27]. Considering that even the average nucleotide identity value between these two Paracoccus species is so close, it becomes clear that the usage of the 16S rRNA gene would be insufficient and error-prone for new strain classifications. Already misclassified strains such as P. pantotrophus DSM 1403^T had an ANI value of 98.6% to the type strain P. versutus DSM 582^T while, only 93.6% to the type strain P. pantrotophus DSM 2944^T [9]. In the future, valid classification of new genomes will become even more important as data generated by metagenome sequencing allows the assembly of metagenome assembled genomes (MAGs), which could lead to rapid error propagation within databases due to omitted or insufficient performed phylogeny.

Pan-genome analysis of Paracoccus

The pan- and core-genome of the six Paracoccus type strains followed the general results of the genus Paracoccus [9] for which an open pan-genome is proposed. The fitting least-squares curve based on Heaps’ Law indicates an open pan-genome (α = 0,3750183) (Fig 3A and 3B).

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Fig 3. Pan-core analysis of six Paracoccus type strain genomes.

A) Pan- and core size accumulation, and B) Cloud and new genes accumulation in the analyzed Paracoccus genomes. C) Distribution of core (center) and cloud (ellipses) gene clusters in pan-genome. The distribution of cloud gene clusters located on the chromosome or extrachromosomal elements is listed under each type strain (chromosome/extrachromosomal element). D) Bar charts of the pan-genome functional classification annotated in COG databases. The function was plotted either according to core (light red, left), shell (light green) and cloud (light blue, green) and according to their genomic localization, chromosomal or extrachromosomal.

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The core size accumulation curve saturates after two genomes, which suggests a stable minimum core. In our analysis of protein-encoding genes, we found that the Paracoccus pan-genome consisted of a total of 13,819 gene clusters. Interestingly, most of these gene clusters were present in the cloud genome (10,634 gene clusters, 76,95%) compared to the core (1,221 gene clusters, 8,84%) and shell (1,964 gene clusters, 14.21%) genome (Fig 3D, S5 Table). These results emphasize the highly adaptive genome and non-conserved number of genes across the Paracoccus genus.

To assess the level of functional diversity within the Paracoccus type strain core, shell and cloud genomes, COG analysis was employed (Fig 3D). The COG category analysis revealed clusters in the Paracoccus core genome were involved in a high amount of class (S) function unknown, (E) amino acid transport and metabolism, (L) replication, recombination, and repair, (J) translation ribosomal structure and biogenesis, and (C) energy production and conversion nearly exclusively encoded on the chromosome. Genes composing the shell genome were mainly from class E (amino acid transport and metabolism), followed by class (G) carbohydrate transport and metabolism, (G) carbohydrate transport and metabolism, (L) replication, recombination, and repair, (P) inorganic ion transport and metabolism, (K) transcription, and (C) energy production and conversion, where ~20% are encoded by extra-chromosomal elements. The functional composition of the cloud is not particularly different from that of the core with the main groups (E, L, G, K P). This is in line with previous studies, which showed same functional key groups for core and cloud of Paracoccus yeei CCUG 32053 [5]. However, the analysis of chromosomal and extra-chromosomal core or cloud genes revealed that the functional classes are generally similar, but the key groups are specific for the chromosome and extra-chromosomal elements. Additionally, as illustrated in Fig 3C and 3D, a substantial portion of the genes (up to58.82% for P. seriniphilus DSM 14827^T) are located on extra-chromosomal elements. These genes are involved in processes such as amino acid transport and metabolism, replication, recombination and repair, carbohydrate transport and metabolism, transcription, and inorganic ion transport and metabolism (Fig 3D). These results highlighting the role of specific functions gained by transmission through horizontal gene transfer that enable adaptation and survival in a specific ecological niche also supported by a recent study that showed a significant enrichment of functional groups specific for chromosomes versus extra-chromosomal elements in the human gut [28]. However, to further investigate genetic differences between the free-living and host-associated Paracoccus genomes we inspected shared genes specific for each lifestyle (S6 Table). In total 22 specific in free-living (FL) and 38 genes specific for host-associated (HA) genomes were identified (S6 Table). Notably, 50% of FL genes are trb-genes and genes involved in type IV secretion system (T4SS). Genes of this cluster are known for enabling transfer of genetic material especially plasmids [29]. In context of a free-living lifestyle where the environment and nutrient availabilities change fast a maintenance of a system for genetic material exchange for acquisition of new genetic traits is plausible. P. aestuarii DSM19484^T and P. alcaliphilus DSM8512^T encode those genes chromosomally, in contrast in P. saliphilus DSM18447^T these genes are found on the only plasmid (S6 Table, extra-chromosomal location highlighted in green). Besides, two genes (nasE, nasD) involved in nitrite assimilation and conversion were identified indicating a role in the nitrogen cycle and energy metabolism. Nitrite reductases are enzymes catalyzing the reduction of nitrite to ammonia [30]. In combination, a gene involved in the transport of glutamine (glnQ) was identified which is in general described with glutamate and ammonium as preferred source for nitrogen since the conversion of nitrate to ammonium is a high-energetic process [31]. Our data reveal an increased genomic equipment and resulting traid for biochemical cycling of nitrogen of free-living Paracocci.

In contrast, in host-associated genomes the majority of genes are associated to various transporters (oppF, dppD, opuE, hmuU, dctM, dctQ, tsaT, vexB), regulators (gmuR, hpaR), in attachment and motility (fliC, flaF, ufaA), as well as carbon/nitrogen cycling (formamidase), indicating adaptations to the host by cell adhesion, substrate exchange, and host interaction. Broadly, an elevated abundance of genes observed in HA genomes predominantly comprises transporter genes including members of the ABC family [32], which may be pivotal in mediating substrate exchange and host interactions [5]. Notably, transporters have been also predicted in the opportunistic pathogen P. yeei, as virulence factor [5]. This is particularly significant in contexts involving HA nutrient scarcity, competition, and host-specific nutrient acquisition. Furthermore, transporters could serve dual roles, including functioning as efflux pumps to fortify the bacterium’s defense mechanisms within the host environment [33]. Other adaptation to hosts could be seen in cell wall modification or optimization of attachment. In HA genomes, biotine carboxylase was found which participates in fatty acid biosynthesis which could be essential for cell membrane modification [34] as well as tuberculostearic acid methyltransferase (ufaA) which modifies fatty acids and may help to adhere to the host. HA bacteria are often faced with stresses coursed by the host. An adaptation of HA Paracocci may be the lysine decarboxylase to regulate intracellular pH, by building cadaverine, which are known from Paracoccus and other marine bacteria to tolerate changes in pH and salinity [35–37]. Additionally, thiamine-phosphate synthase and thymidylate synthase 2 were found. The thiamine-phosphate synthase is involved in vitamin B1 biosynthesis, by the conversion of 4-Amino-5-hydroxymethyl-2-methylpryriminide diphosphate into thiamine phosphate. Vitamin B1 is an essential cofactor for bacteria [38] so HA environments may lead to limitations which are circumvented by de novo synthesis. In the same way, thymidylate synthase 2 (thyX) ensures the synthesis, replication and repair of DNA by the conversion of dUMP to dTMP [39]. Lastly, a formamidase was identified in all HA genomes which hydrolysis formamide into formate an ammonia. Paracoccus is known as a formate-utilizing bacterium an both products are either used as carbon or nitrogen source [40].

Comparative genomics of the genus Paracoccus

Genome reduction was once thought to be a distinctive feature of endosymbiotic bacteria that live inside host cells, either as mutualists or obligate pathogens [41]. However, it has been observed in various free-living bacteria as well [42]. We observed a similar trend for our investigated strains where in general free-living strains tend to have larger genomes. However, P. aestuarii DSM 19484^T comprised the smallest genome but the highest number of extrachromosomal elements (Table 1). To gain insights into the adaptability and evolutionary potential of Paracoccus, the six type strain genomes were screened for mobile genetic elements (MGEs) such as genomic islands (GI), insertion sequences (IS), transposases, and prophages (P), as well as genes involved in the synthesis of secondary metabolites (SMs). All genomes contained a diverse array of MGEs and SM gene clusters (Tables 3 and 4; detailed output in S7 Table), highlighting their genomic plasticity and potential for adaptation and evolution in various environments.

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Table 3. Number of detected MGEs and SMs in the analyzed Paracoccus genomes.

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Table 4. Detailed summary of detected fitness and virulence associated genes located in

, P–prophage, and extrachromosomal.

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Each type strain presented here represents a snapshot of adaptation to a specific ecological niche. Three strains can be grouped based on their lifestyles as either free-living (Table 3, grey) or host-associated. The free-living strains possess a higher number of GIs (14–23) and IS elements (81–247) compared to host-associated strains (GI: 9–13; IS: 45–78). This is consistent with a study by Newton and Bordenstein [43], who found that extracellular (free-living) bacteria tend to have a higher number of MGEs compared to obligate intracellular (host-associated) bacteria, likely due to the more variable and challenging environments they encounter. This trend was also observed in Paracoccus, although it should be considered that the here presented host-associated strains are able to survive free-living and therefore do not completely correspond to the definition of an obligate intracellular organism.

Genomic islands and prophages have been identified in several species of Paracoccus suggesting genome adaption by conferring fitness or virulence to their hosts [5, 44]. While it has been established that prophages can facilitate the acquisition of antibiotic resistance by their hosts [45], the frequency of such events is lower than initially anticipated during the recent surge in phage research [46]. To date, no phages have been identified in Paracoccus that carry genes associated with either resistance or virulence but potential genes contributing to fitness are documented [44]. We conducted an analysis of all the genes present in the genomic islands and putative prophage regions of the six type strains, focusing on their potential association in conferring either fitness or virulence (Table 4).

The majority of genes identified in the genomic islands of the six type strains were categorized as hypothetical or were characteristic with other mobile genetic elements such as IS sequences and transposases. Nevertheless, each strain harbored genes associated with increased fitness, including those encoding type secretion systems (TSS), toxin-antitoxin systems, antibiotic resistance, and heavy metal transporters. Additionally, genes associated with hemolysin and phospholipase were also identified, indicating the potential for virulence in these bacteria [47, 48]. The presence of T1SS and T4SS genes in Paracoccus implies their significance in enhancing substrate and toxin transport and facilitating exchange of DNA and proteins between bacteria. In addition, the identification of multiple Type II and Type IV toxin-antitoxin (TA) system genes suggests their significance in conferring survival under stressful conditions, such as nutrient deprivation, exposure to antibiotics, or phage infection [44]. Moreover, bacterial populations can be stabilized through the elimination of cells carrying low amounts of MGE [49]. The identification of β-lactam and streptogramin antibiotic resistance genes, as well as various heavy metal transporters, such as cadmium, mercury, and arsenate, highlights their importance for the uptake and detoxification of heavy metals in specific contaminated ecological niches. Especially in the genus of Paracoccus, phages seem to be species specific and confer metal resistances tailored to the habitat [44]. The identification of methyltransferases (MTases) and restriction modification (RM I or RM III) associated genes in all Paracoccus strains emphasizes the diverse array of mechanisms available to Paracoccus to protect against foreign DNA, plasmids, and phage attacks or to enhance fitness by acquiring new restriction enzymes for utilizing DNA from a new source [50]. Additionally, methyltransferases facilitate the modification of own DNA, thereby enabling better survival in a specific environment. Our analysis showed no clear effect of the lifestyle on MGE equipment. However, a higher number of intact prophages was observed in the host-associated strains P. seriniphilus DSM 14827^T and P. stylophorae LMG 25392^T (S7C Table). One possible explanation is that Paracocci that are associated with a host are generally subjected to unique selective pressures specific to the host, which can lead to a higher frequency and maintenance of prophages spreading virulence and fitness factors to their bacterial host [51]. In contrast, prophages can be easily acquired and lost by free-living bacteria that are not subjected to host-specific pressures [51]. However, these bacteria must also contend with the varying and often unfavorable environmental conditions of the open ocean [51]. Furthermore, the bacterial adaptation mechanism prioritizes the fixation of phage genes that confer benefits to the host, while genes that lack selective value gradually decay over time, accounting for the incomplete and uncertain detection of phages [51]. More IS elements were detected in type strains which are free-living (Table 3, Fig 4).

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Fig 4. Distribution of IS-families identified in the analyzed Paracoccus type strains.

The elements are members of 20 IS families represented by a different color. Free-living associated strains strains are displayed in grey. Family IS481, IS4, IS1380, IS200/IS605, and ISKRA4 are unique to P. aestuarii DSM19484^T, P. alcaliphilus DSM 8512^T, P. fistulariae KCTC 22803^T, P. saliphilus DSM 18447^T, and P. seriniphilus DSM14827^T, respectively.

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Our analysis revealed 612 insertion sequence (IS) elements representing 20 IS families (Fig 4). Most abundant families were IS5 and IS3 while other IS families are unevenly distributed in Alphaproteobacteria [52] and in the ISfinder database [53]. Notably, Dziewit et al. 2012 described only 10 IS families in the mobilome of a Paracoccus spp. lacking families IS1, IS3, IS91 and IS110 which are common in other Alphaproteobacteria [52], which might be due to the rapid increase of new identified IS families [54]. In P. yeei only seven IS families (IS3, IS5, IS30, IS66, IS256, IS110 and IS1182) were identified [5]. Our strains include eight up to 14 different IS elements where IS110, IS21, IS66 and IS256 made the majority, whereas ISAs1 described as rare in Paracoccus were not identified [52]. Additionally, we identified rare strain specific IS elements such as IS481 (P. aestuarii DSM 19484T), IS4 (P. alcaliphilus DSM 8512^T); IS1380 (P. fistulariae KCTC 22803^T); IS200/IS605 (P. saliphilus DSM 18477^T), and ISKRA4 (P. seriniphilus DSM 14827^T) (Fig 4). Host associated strains had the lowest amount of IS which is in line with the theory that a restricted environment for example on a host act as a bottleneck, where the specific nutrient richness of a host can lead to a genome streamlining. However, in free-living bacteria IS elements thought to be more beneficial in fluctuating environments which allow a higher degree of genetic plasticity for rapid acquisition of new traits [53]. Since all Paracoccus strains studied can survive under free-living conditions, it is not surprising that all strains comprise such a diversity of IS elements.

All strains were also screened for genes involved in secondary metabolite biosynthesis using the antiSMASH6.0 database [55]. SM play a key role in mediating competitive or cooperative interactions [56] and can act as weapon against competitors, signaling molecules, agents of symbiosis or as differentiation effectors that could influence niche adaptation [57]. Moreover, all strains were screened for glycine, betaine and trehalose since there are known stress modulators in Paracoccus for thermal endurance, salt tolerance, and osmolytic stress [58, 59]. In the six type strains, 50 potential SM clusters were identified (Table 3; S7D Table).

The ectoine cluster, which was found in all strains with 100% similarity, is typically present in bacteria that can endure extreme environmental conditions, such as high salinity, high temperatures, or low water availability [60]. Ectoine serves as a protective molecule that helps organisms combat osmotic stress, UV radiation, and acts as an antioxidant [60, 61]. Besides ectoine, commonly observed solutes including trehalose, betaine-glycine, glycerol, heat shock proteins, and chaperones have been identified as regulators of stress responses under conditions of elevated osmolarity, thermal stress, and desiccation [58, 62, 63]. We summarized all identified genes in S8 Table. All investigated strains can produce and transport trehalose for accumulation by utilizing the conserved TPP/TPS pathway or using the TreY/TreZ pathway except P. fistulariae KCTC22803^T (S8 Table). None is using the TS pathway which was described additionally for P. sp. AK26 [59]. In contrast, to production, trehalose transporters (sugA-sugC) were found in all strains. Glycogen operon genes (glgA-glgC, and glgX) have been detected in all genomes except P. alcaliphilus DSM8512^T, which, unlike trehalose, is more important for desiccation [63]. It is possible that the higher pH in the alkaline environment affected the stability and functionality of these enzymes for glycogen metabolism, leading to a loss of this pathway. Vice versa, P. aestuari DSM 19484^T isolated from tidal flat sediment in South Korea [10] comprises a higher number of glycogen genes. This habitat is dry twice a day at ebb tide and is accordingly exposed to strong fluctuations (temperature, UV, humidity), where an increased protection against desiccation could be beneficial. In addition, betaine is synthesized via the betA-betB, betI operon. A variety of transporters for organic osmolytes such as glycine-betaine clusters (opuAA, opuAB, opuD, proV, proX yehW-Z, and ousW) to combat salt and osmolytic stress have been found. Notably, proW and betT transporters were not detected compared to P. sp. AK26 [59]. P. fisulariae KCTC22803^T lacks, besides the trehalose pathways also all yeh-group glycine-betaine transporter. This could indicate environmental and host-associated genome adaptations. The strain was isolated from the intestine of the bluespotted cornetfish and therefore maybe don’t need to produce high amounts of trehalose for accumulation and glycine-betaine because of more stable environmental conditions inside of the host. Another explanation could be that trehalose is provided by the host because transporters are found. Besides, also P. alcaliphilus DSM8512^T revealed an increased number of genes for glycine-betaine and trehalose production which could indicate as already mentioned an adaption to the extreme alkaline environment with higher pH.

Aquaporin was detected in P. alcaliphilus DSM8512^T, P. fisulariae KCTC22803^T, P. saliphilus DSM18447^T, and P. stylophorae LMG25392^T which are described as a regulator for osmotic stress in water movement [59]. P. seriniphilus DSM14827^T comprise no aquaporin indicating that in the specific ecological niche of marine bryozoan Bugula plumosa minor water movements are needed. From another bryozoan Flustra foliaceae it is known that bacteria colonize the host surface by development of biofilms [64]. Biofilms consist of EPS which could create a physical barrier and reduce water movements. P. aestuari DSM 19484^T with an assumed free-living lifestyle also don’t encode aquaporin, which may be due to the tidal (ebb and flow) habitat, where aquaporins and the lifestyle are not useful.

For thermal endurance genomes were screened for chaperones and heat-shock proteins. As described previously for Paracoccus, no Hsp90 heat shock or RNA polymerase sigma E factor which is known as heat stress management factor was found [62]. Instead, chaperones (hslO, grpE, groS, groL, ibpA, dnaK and dnaJ), heat shock proteins (hspQ, hslR), the transcriptional repressor (hrcA), and the RNA polymerase sigma 32 factor (rpoH) were identified in all genomes which were also found in some mesophilic Paracoccus species [62].

The presence of the ectoine, trehalose, betaine-glycine, glycogen clusters and chaperones in all strains can again be explained by the facultative free-living lifestyle and specific ecological habitats.

At last, to our knowledge, it was not investigated yet, if there is a correlation between bacterial lifestyles and numbers of encoded secondary metabolites. The regulation and production of SM depends on the complexity and dynamics of the environment where bacteria tend to produce a higher diversity and number of SM than bacteria that live in more stable environments [65, 66]. We observed a similar effect in free-living strains, where more clusters to known, substances were identified compared to host-associated strains. In general, we identified similarity to genes encoding for substances that provide fitness advantages, such as carotenoids [67], pyoverdine [68], and bacillibactin [69], as well as compounds that mediate interactions or confer competitive advantages between bacteria and other organisms in their environment, including desotamide [70], bacteriocin [71], sunshinamide [72], lysocin [73], asukamycin [74], formicamiycins [75], fengycin [76], and chejuenolide [77]. Many of these substances have not been previously described in Paracoccus (S7D Table).

Finally, we examined whether MGEs, in terms of genomic flexibility, cluster together in evolutionary hotspots or are evenly distributed throughout the chromosomes. We compared all identified MGEs (excluding IS elements, S7A Table) of the six Paracoccus chromosomes (Fig 5).

Download:

Fig 5. Comparison of six Paracoccus type strain chromosomes.

Concentric colored rings represent Blast matches according to a percentage identity of (100, 90 and 70%). As reference the largest chromosome was chosen (P. saliphilus DSM 25392^T) colored in yellow most inner ring. Putative prophage regions of all investigated strains are depicted in black (3 outer ring, P-), putative Genomic Islands in purple (2^nd outer ring, GI-), and secondary metabolite clusters in grey (outer ring, A-). The GC content is shown in black as most inner ring, followed by the GC Skew. The visualization was modified with Inkscape v 1.2. [19].

https://doi.org/10.1371/journal.pone.0287947.g005

The chromosome comparison shows that each strain contains a high number of MGEs in addition to the partially high plasmid content (Table 1; up to nine—P. aestuarii DSM 19484^T). Therefore, we assume that these strains contain a high genomic flexibility, which is one reason for their ability to adapt to different habitats as horizontal gene transfer (HGT) enabled by MGE is the main driver of prokaryotic evolution and adaptation [6]. The regions of differences in all strains were caused by potential phages, genomic islands or SMs and the genes they transfer. Studies with E. coli proposed an increasing gradient of integration of prophages along the ori to ter axis [78]. Our data did not support this trend, which may be due to the analysis of six different Paracocci belonging to six different species, resulting in a high number of variables, (six different ecological niches, two potential lifestyles) that cannot be accounted for.

Conclusion

In summary, we increased the number of available closed high-quality Paracoccus genomes from 15 to 21 (09.2022). We were able to classify the six type strains carefully phylogenetically within the genus Paracoccus. Furthermore, 16 Paracoccus spp. were assigned at species level and five misclassifications were uncovered. Pan-genome analysis of six Paracoccus genomes representing six ecological niches and two lifestyles revealed an open minimal core which is predominantly encoded by the chromosome. Extra-chromosomal cloud genes displayed a specific functional pattern for each of the strains, but all comprised genes associated to adaptational processes, increasing our understanding of the evolution, ecology, and adaptational potential of the genus Paracoccus. With respect to lifestyle adaptations, FL genomes shared specific genes involved in genetic exchange via T4SS, which for the genus of Paracoccus which are known for comprising a vast majority of extrachromosomal elements, make sense to adapt to the rapid changing environmental conditions. In contrast, in HA genomes genes of diverse function were identified involved in transporting molecules, cell wall modification, attachments, protection against stress, DNA repair and carbon and nitrogen metabolism were identified. Moreover, in-depth comparative genomics of MGEs indicated that free-living Paracocci comprised more MGE (GI and IS elements) compared to host-associated strains. In conclusion, complete bacterial type strain genomes are essential as a comprehensive reference for bacterial species and their classification. Pan-genome and comparative MGE analysis guide the understanding of ecological and evolutionary dynamics of bacteria, especially in terms of environmental adaptation. Future studies may benefit from a reduced dataset focusing on a single Paracoccus species, such as P. marcusii (Fig 2, Cluster 3-P. marcusii) with an increased number of strains, to investigate species specific key genes and functional pattern involved in evolution or adaptation effects.

Material and methods

Isolation, growth conditions and genomic DNA extraction

In total, six type strain genomes of the genus Paracoccus were sequenced in this study all received from type strain collections (S1 Table). All strains except P. alcaliphilus DSM 8512^T were grown in bacto marine broth (MB) medium (DSM 514, https://www.dsmz.de/collection/catalogue/microorganisms/culture-technology/list-of-media-for-microorganisms) for 1–2 days at 20°C and 100 rpm in the dark. P. alcaliphilus DSM 8512^T was grown under same conditions in Paracoccus alcaliphilus medium (DSM 772, https://www.dsmz.de/collection/catalogue/microorganisms/culture-technology/list-of-media-for-microorganisms). Genomic DNA was extracted by using the MasterPure complete DNA and RNA purification kit (Epicentre, Madison, WI) as described by the manufacturer. A pre-lysis step was performed using lysozyme (5 mg, Serva, Heidelberg, Germany) for 30 min at 180 rpm (Infors HT, INFORS AG, Bottmingen, Switzerland) and 37°C. The quality of the extracted DNA was assessed as described by Hollensteiner et al. 2020 [79].

Genome sequencing, assembly, and annotation

Illumina paired-end sequencing and Oxford Nanopore sequencing was performed as described by Hollensteiner et al. [79]. Nanopore sequencing was performed for 72 h using a MinION device Mk1B and a SpotON Flow Cell R9.4.1 as recommended by the manufacturer (Oxford Nanopore Technologies) using MinKNOW software (v19.06.8-v20.06.05) for sequencing. For demultiplexing and basecalling Guppy v4.0.15+5694074 (https://community.nanoporetech.com) was applied. Illumina reads were quality-filtered using fastp version 0.20.1 [80] by applying base error correction, removing adapters, read filtering using a quality value (Phred score) >20, with mean quality of 20, and minimum read length of > 50bp. Additionally, a mean quality of 20 was set and all front and ends trimmed below the mean value using a window size of 4. Remaining phiX contaminations were removed with bowtie2 v.2.4.0 [81]. Long reads were quality filtered with fastp version 0.20.1 [80] read filtering using a quality value of 10, and minimum read length of > 1000 bp. Additionally, a mean quality of 10 was set and all front and ends trimmed below the mean value using a window size of 10. Remaining adapters were removed with Porechop v0.2.4 (https://github.com/rrwick/Porechop.git). FastQC v.0.11.8 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was applied for general inspection of read quality. Unicycler version 0.4.8 [82] was used for a de novo hybrid assembly in normal mode using the following parameter—no_correct—start_gene_id 70.0—start_gene_cov 70.0. Coverages were calculated for long as well as for short reads using QualiMap v2.2.2 [83]. For mappings minimap2 v2.17 [84] and bowtie2 v.2.4.0 [81] were applied, respectively. For data conversion from.sam to.bam and data sorting SAMtools v.1.9 [85] was used. The overall mean for each genome was calculated. N₅₀/N₉₀ for long reads and N₅₀/L₉₀ values for the assembly were calculated and summarized in S1 Table. Default parameters were used for all software unless otherwise specified. Genome annotation was performed employing the Prokaryotic Genome Annotation Pipeline (PGAP) v 5.0, accessed 2021-01-04 [86]. BUSCO v5.4.5 was used to evaluate the quality of genomes [17, 87] by using the bacteria_odb10 dataset (OrthoDBv10; 2023-03-03).

Genome data acquisition and quality control

For an overall taxonomic analysis of the whole genus Paracoccus the EzBio list of species was used. The genus comprised over 84 validly published genomes (https://www.ezbiocloud.net/search?tn=paracoccus accessed 2022-09-22). Genomes were downloaded from the National Center for Biotechnology Information (NCBI) database (accessed 2022-09-22). In total, 185 genomes were downloaded of which 64 were Paracoccus type strains or representative genomes. To eliminate redundancy, identical genomes which were independently sequenced by different laboratories with a lower final status (contigs, scaffolds) were excluded (GCA_014656455.1, GCA_003633525.1, GCA_019633685.1, GCA_014164625.1, GCA_000763885.1) (n = 180). Moreover, four of our sequenced strains, namely (P. aestuarii DSM 19484^T; P alcaliphilus DSM 8512^T, P. saliphilus DSM 18447^T, and P seriniphilus DSM 14827^T were already sequenced but all in draft status and replaced by our high-quality finished genomes (n = 180) (detailed genome comparisons are summarized in S9 Table. Finally, we deposited two new genome sequences of the type strains P. fistulariae KCTC 22803^T and P. stylophorae LMG 25392^T which resulted in a dataset of 182 genomes. Those (n = 182) were further quality checked for completeness and contamination by using checkM v.1.2.0 [88] (S3 Table) and an initial phylogenetic classification was performed using the Genome Taxonomy Database Toolkit (GTDB-Tk v2.1.1) [18] (S2 Table). Statistics of genomes such as number of sequences, size in bp, GC-content (%) were calculated with the perl script gaas_fasta_statistics.pl v1.2.0 from the GAAS toolkit (https://github.com/NBISweden/GAAS). All Paracoccus genomes with a completeness >85% and a contamination rate <5% were used for phylogenetic analysis (n = 160). Genomes that did not meet these quality criteria were removed from the analysis (S3 Table, completeness <85% = grey; contamination >5% orange; remaining duplicates = purple).

Average nucleotide identity analysis

For an initial taxonomic grouping of the six type strains in the genus Paracoccus the Type Strain Genome Server (TYGS) [89] was applied based on 16S rRNA level (S1A Fig). Afterwards, a phylogenetic tree of the whole genomes was performed by the comparison of average nucleotide similarity to all available type strain genomes (n = 64, S1B Fig, S10 Table) and all quality filtered Paracoccus genomes (n = 160, Fig 2, S4 Table). The average nucleotide identity (ANI) was calculated with pyANI python module v.0.2.11 [90] based on MUMmer [91] using the ANIm mode (S4 Table).

Pan-genome analysis

The pangenome of the six type strains, and the identification of core and cloud genes were determined by using Roary v3.13.3 [92] with default settings (minimum BLASTP sequence identity of 70% with paralog splitting). To reduce the bias of annotation at date of analysis all genomes were reannotated with the latest version of the Rapid prokaryotic genome annotation pipeline v1.14.6 [93]. Generated protein GFF3 output files produced by prokka were used for downstream analysis using roary [92]. To estimate whether the pangenome was open or closed, we used Heaps’ Law: η = κ * N^−α [94], implemented in the R package “micropan” v.2.1 [95], in which η is the expected number of genes for a given number of genomes (N), and κ and α are constants to fit the specific curve. The exponent α is an indicator for the status of the pangenome open (α <1) or closed (α > 1). Additionally, gene presence and absence tables were loaded in R Studio 2022.12.0+353 [96] and pan-genome figures plotted using the script create_pan_genome_plots.R. For functional bar charts the gene presence absence output table (S4 Table) were filtered according to core, shell, and cloud and by genomic localization chromosomal or extra-chromosomal. Proteins were functional annotated with eggNOGmapper v.2.1.9 [97] using default parameter. Visualization was performed in R Studio using “ggplot2”v.3.4.1 [98] and “viridis” v. 0.6.2 [99]. Inkscape v. 1.2.1 was used for modifications [19].

Comparative genome analysis and detection of mobile genetic elements

To identify genomic islands IslandViewer 4 was employed [100]. Genomic islands were accounted by a size > 8 kb, a different GC content compared to the remaining genome and the detection of common mobile-related elements such as integrases and transposases. To determine insertion sequences and transposases, ISEscan v1.7.2.3 [54] was applied with default parameters. To determine putative prophage regions PHASTER [101] was used. AntiSMASH 6.0 [55] was chosen to detect potential biosynthetic gene clusters encoding for secondary metabolites with relaxed parameters using all extra features. Genome comparison of all six sequenced Paracoccus type strains genomes was performed via BRIG [102]. BLAST matches are shown as concentric viridis colored rings on a sliding scale according to percentage identity (100%, 90%, or 70%). Mobile genetic elements such as, genomic islands, phages and potential secondary metabolites were highlighted manually added as tab-separated file.

Supporting information

S1 Fig. Phylogenetic tree of investigated six Paracoccus type strains.

The trees were reconstructed based on (A) 16S rRNA comparison using TYGS, and (B) WGS comparison with all 64 available Paracoccus genomes using ANI. Presented Paracoccus genomes used in this study are highlighted in red. Complete genomes are highlighted in blue.

https://doi.org/10.1371/journal.pone.0287947.s001

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S1 Table. Sequencing statistics and genomic features of the Paracoccus strains.

https://doi.org/10.1371/journal.pone.0287947.s002

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S2 Table. Quality assessment of Paracoccus genomes.

https://doi.org/10.1371/journal.pone.0287947.s003

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S3 Table. Initial phylogenetic classification of Paracoccus genomes using GTDB-TK.

https://doi.org/10.1371/journal.pone.0287947.s004

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S4 Table. Average nucleotide identity values of Paracoccus genomes.

https://doi.org/10.1371/journal.pone.0287947.s005

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S5 Table. Pan genome analysis of six Paracoccus type strains.

https://doi.org/10.1371/journal.pone.0287947.s006

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S6 Table. Genes associated to lifestyle of Paracoccus genomes.

https://doi.org/10.1371/journal.pone.0287947.s007

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S7 Table. Mobile genetic elements and secondary metabolite gene clusters detected in Paracoccus genomes.

https://doi.org/10.1371/journal.pone.0287947.s008

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S8 Table. Identified genes in Paracoccus genomes for thermal endurance, salt tolerance and stress response.

https://doi.org/10.1371/journal.pone.0287947.s009

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S9 Table. Comparison of six Paracoccus type strains to previous sequencing results.

https://doi.org/10.1371/journal.pone.0287947.s010

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S10 Table. Average nucleotide identity values of all available Paracoccus type strain/representative genomes.

https://doi.org/10.1371/journal.pone.0287947.s011

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Acknowledgments

We thank Janina Leinberger for strain acquisition, raising and shipment. We thank Melanie Heinemann and Mechthild Bömeke for technical support.

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Figures

Abstract

Introduction

Results and discussion

Genome features of Paracoccus type strains

Phylogeny of Paracoccus

Pan-genome analysis of Paracoccus

Comparative genomics of the genus Paracoccus

Conclusion

Material and methods

Isolation, growth conditions and genomic DNA extraction

Genome sequencing, assembly, and annotation

Genome data acquisition and quality control

Average nucleotide identity analysis

Pan-genome analysis

Comparative genome analysis and detection of mobile genetic elements

Supporting information

S1 Fig. Phylogenetic tree of investigated six Paracoccus type strains.

S1 Table. Sequencing statistics and genomic features of the Paracoccus strains.

S2 Table. Quality assessment of Paracoccus genomes.

S3 Table. Initial phylogenetic classification of Paracoccus genomes using GTDB-TK.

S4 Table. Average nucleotide identity values of Paracoccus genomes.

S5 Table. Pan genome analysis of six Paracoccus type strains.

S6 Table. Genes associated to lifestyle of Paracoccus genomes.

S7 Table. Mobile genetic elements and secondary metabolite gene clusters detected in Paracoccus genomes.

S8 Table. Identified genes in Paracoccus genomes for thermal endurance, salt tolerance and stress response.

S9 Table. Comparison of six Paracoccus type strains to previous sequencing results.

S10 Table. Average nucleotide identity values of all available Paracoccus type strain/representative genomes.

Acknowledgments

References