Abstract
Bacterial phylogenies have become one of the most important challenges for microbial ecology. This field started in the mid-1970s with the aim of using the sequence of the small subunit ribosomal RNA (16S) tool to infer bacterial phylogenies. Phylogenetic hypotheses based on other sequences usually give conflicting topologies that reveal different evolutionary histories, which in some cases may be the result of horizontal gene transfer events. Currently, one of the major goals of molecular biology is to understand the role that horizontal gene transfer plays in species adaptation and evolution. In this work, we compared the phylogenetic tree based on 16S with the tree based on dszC, a gene involved in the cleavage of carbon-sulfur bonds. Bacteria of several genera perform this survival task when living in environments lacking free mineral sulfur. The biochemical pathway of the desulphurization process was extensively studied due to its economic importance, since this step is expensive and indispensable in fuel production. Our results clearly show that horizontal gene transfer events could be detected using common phylogenetic methods with gene sequences obtained from public sequence databases.
1. Introduction
The discussion concerning bacteria phylogenies has become one of the most important aspects of microbial ecology. In the mid-1970s, Woese and his collaborators proposed and began assembling a significant database of sequence information based on small subunit ribosomal RNA (SSU rRNA 16S). The current universal tree is based on this [1–6], since it is easily sequenced (±1,500 nucleotides) and widely available in sequence databases (Gen-Bank, EMBL) [7, 8]. However, phylogenetic hypotheses based on several other genes result in conflicting topologies and reveal different evolutionary histories. In many cases, especially within bacteria, these may be the result of horizontal gene transfers (HGTs) [9, 10], which are regarded as a crucial mechanism of increasing genetic variability among bacteria [11–13]. Currently, one of the major goals of molecular biology is to understand the role that HGT plays in species adaptation and evolution [10, 14, 15]. The presence of HGT in bacteria has been reported for several years, suggesting that for some genes the tree of life becomes a net [16]. HGT is dominant among various groups of genes in prokaryotes such as antibiotic resistance, carbon source utilization, organic contaminant degradation, and freeze tolerance genes [12, 13]. However, there is some evidence of HGT in housekeeping genes such as those for replication, transcription, and translation as well [10, 17, 18].
Sulphur is the third most abundant element in petroleum (after carbon and hydrogen), and its release contributes to air pollution by causing acid rain [19, 20]. For this reason, sulphur regulations have continued to become more stringent and it is necessary to remove sulphur oxides from fossil fuels during the refining process. Most inorganic and simple organic sulphur can be removed by hydrodesulphurization, the technique currently used by most petroleum refineries, but, in petroleum, the majority of sulphur is found in dibenzothiophene (DBT) and its derivatives, which can only be removed through a specific biological mechanism called biodesulphurization [21]. Several studies have investigated the development of aerobic microbial desulphurisation pathways [22–24].
Some bacteria can desulphurize DBT to 2-hydroxybiphenyl (2-HBP) through the sulphur-specific degradation pathway (4S pathway) without destroying the hydrocarbon skeleton [22, 24–26]. In natural environments, the cleavage of carbon-sulfur bonds in molecules such as DBT liberates sulfur, making it available as a nutrient to support the growth of bacteria in environments poor in mineral sulfur [27]. These bacteria have been assigned to a number of genera including Rhodococcus [21, 28], Acinetobacter, and Pseudomonas [24]. Species of other bacteria genera, such as Brevibacterium sp. strain DO [29], strains identified as Arthrobacter spp. [30], and Gordonia sp. strain CYKS1 [31], are also able to use this pathway.
The pSOX plasmid [28], or dsz genes [32, 33], responsible for the sulfur oxidation in DBT, have been cloned, sequenced, and studied, generating considerable knowledge of these pathway enzymes [28, 32, 34]. The 4S pathway consists of three genes designated dsz A, B, and C. Studies have shown that the product of dszC directly converts DBT to DBTO2 and the products of dszA and dszB act together to convert DBTO2 to 2-HBP. The operon dsz occupies a 4 kb gene locus in a 120 kb linear plasmid in bacteria Rhodococcus erythropolis strain IGTS8 [28, 32, 35–37]. The plasmid nature of the dsz genes increases the probability of successful transfers, and the availability of the dszC sequences in GenBank allows the construction of phylogenetic hypothesis based on this gene, in order to compare it with the 16S.
In this work, we aim to demonstrate the utility of phylogenetic methods based on molecular data to help in studies of horizontal transfer of functional genes in bacteria.
2. Materials and Methods
2.1. Nucleotide Sequences
The nucleotide sequences used in this study were obtained from the National Center for Biotechnology Information-GenBank (http://www.ncbi.nlm.nih.gov). For analyses involving the dszC gene, 18 sequences were selected (Table 1), representing all genera and/or species with dszC sequences available in the database as of March 2012. We also searched for the other two genes of the operon, dszA and dszB, but they are underrepresented in GenBank and phylogenetic trees could not be constructed based on these genes. For the 16S gene, we chose 39 sequences, including at least two sequences of at least 1400 bp from each genera and/or species in the dszC tree (Table 2).
2.2. Phylogenetic Analysis
Phylogenetic analyses were performed with four different methods: neighbour joining (NJ) using the program MEGA 5.0 [38]; maximum parsimony (MP) and maximum likelihood (ML) using the program PAUP* [39]; Bayesian inference (BA) using the program MrBayes [40]. For NJ, ML, and BA, we chose the best nucleotide substitution model using the programs Mega 5.0 [38], ModelTest [41], and MrModelTest [42]. The chosen models are shown in Table 3. We used the Tree Bisection and Reconnection heuristic search method to search for the MP and ML trees. The MP tree started with a random tree, while the ML tree started with an NJ tree. To infer the tree through the BA, we run two independent analyses with four chains each (one cold and three hot chains), started with four different random trees modified through 5,000,000 generations of MCMC. We checked the likelihood of the resulting topologies and burned-out 25% of the trees (to keep those within the area of the best likelihoods) to construct the consensus tree. The robustness of each node of the tree was obtained by the bootstrap test (MV, MP, and NJ); the posterior probability was calculated by the frequency of each node in the consensus BA tree.
2.3. Phylogenetic Network Estimation of dszC Genes
Given the phylogenetic hypothesis for the dszC gene, we constructed a network using the most related haplotypes with statistical-parsimony analyses [43]. The graphic network was constructed using TCS vers. 1.21 [44]. This method starts by calculating the overall limits of parsimony for the complete data set using a statistic from neutral coalescent theory [45, 46]. Although this method has been used extensively with restriction site and nucleotide sequence data to estimate population level genealogies when divergences are low (intra-specific data) [46, 47], it also proved to be reliable at higher divergences, outperforming parsimony and parsimony with bootstrapping [48].
3. Results and Discussion
The BA hypotheses for the 16S gene are shown in Figure 1, which presents the expected pattern of species grouped within their respective genera. The different phylogenetic methods resulted in very similar tree topologies (data not shown) and robust bootstrap values for NJ, ML, MP, and BA posterior probability of the branches. The only exception was the branch containing representatives from the Rhodococcus spp. (in red), which showed low bootstrap value in MP (58%), low posterior probability value in BA (0.61), and bootstrap values for NJ and ML < 50%. Although the convergence of results using different phylogenetic methods has been considered good evidence that the correct phylogeny was obtained [49], total genome phylogenies show that different phylogenetic methods can provide incongruent phylogenies [50, 51]. However, the comparison of 16S sequences is still considered a powerful and accepted tool for deducing phylogenetic and evolutionary relationships among bacteria and is routinely used [4, 52–54]. In fact, most of bacteria systematics is based on the topologies generated by this gene [3].
Figure 2, on the other hand, did not group the species by genera. Instead, this figure presents only three branches: the first includes Mycobacterium sp. (strain G3—AB070603.1) and Bacillus subtilis (AB076745.1) sequences; the second groups two sequences of Gordonia alkanivorans (strain 1 B—AY678116.1 and strain RIPI90A—EU364831.1); the third clusters all remaining sequences belonging to all genera included in this work except for Bacillus. It is expected that molecular phylogenies based on single genes lead to apparently conflicting results with alternative branches that present low bootstrap (or posterior probability) values [50]. However, the conflicting topologies shown in Figures 1 and 2 present high bootstrap and posterior probability values in alternative branches, strongly suggesting that the dszC was indeed subjected to horizontal transfer events among these bacteria.
The phylogenetic network estimation (Figure 3) of the dszC haplotypes of the most specious cluster shown in Figure 2 emphasizes the fact that Acidovorax delafieldii (Seq 1), Agrobacterium tumefaciens (Seq 2), Brevibacillus brevis (Seq 3), and Rhodococcus sp. (Seq 11) present identical sequences, which were grouped together as a square within Figure 3, while other haplotypes are displayed as ovals connected to the square by lines with black circles to indicate the maximum number of steps between each pair of haplotypes.
The results presented here, based solely on GenBank data, provide strong evidence that the dszC gene was horizontally transferred among different evolutionary lineages of bacteria. This evidence is reinforced by the fact that the dczC gene is generally found in conjugative plasmids, in the vicinity of insertion sequences, transcribed in the same direction and under the control of a single promoter [35, 37, 55, 56]. Furthermore, another evidence of dszC horizontal transfer is the significant difference of the C+G content of this gene with the C+G content of the entire chromosome of some species studied here (data not shown).
Our results reinforce the importance of public sequence repositories (such as GenBank), which result from a successful policy of requiring the inclusion of gene sequences in public databases in order to publish any research article containing sequence analyses [57, 58]. In addition to the DNA sequence of each entry, GenBank and other public databases include associated metadata, which provide relevant information about the organism whose sequence is available, generally by linking to the articles with the respective sequence [58]. However, the public databases also contain several molecular sequences submitted by researchers who have not published their results. In these cases, there is neither citation information nor any relevant data about the organisms from which the sequences were made, which in most cases makes the sequences useless for in silico works, since diverse knowledge about a given molecular sequence provides an essential first step in developing research hypotheses.
It is easy to generate new sequences and add them to the GenBank database, which contains about 150 million gene sequences as of February 2012. However, GenBank, along with its INSDC (International Nucleotide Sequence Database Collaboration) partners (EMBL & DDBJ), should be treated not only as archival stores of molecular sequence data (a task at which it has been very successful) but also as a starting point for future studies. In this context, it would be helpful if the process of submitting sequences required a minimum of information about the organism from which the sequences were made, as well as the details of the gene sequenced, in order to substantiate future research.
In this sense, our study could be improved if flanking DNA sequences of functional genes such as dszC were available in the databases, since we could then evaluate if one set of dsz genes is flanked by a particular insertion sequence while another cluster is not.
Although laboratory data that demonstrate the transfer by conjugation of plasmids containing dsz genes or transposition of these genes are scarce, their distribution in bacterial cultures strongly supports the hypothesis that these genes are commonly subject to horizontal transfer in nature as evidenced in the present work. For this reason, we conclude that phylogenetic tools can be useful for inferring horizontal transfer events of functional genes such as dszC. Phylogenetic comparisons with other genes traditionally used for this purpose, such as 16S, can provide good information about evolution and functional gene distribution.
Lateral gene transfer events provide a venue for bacterial diversification by rearranging existing capabilities. Because bacterial genomes can maintain only a finite amount of information, they are sampling rather than accumulating sequences, counterbalancing gene acquisition with gene loss. As a result, lateral gene transfer can redefine the ecological niche of a microorganism, in effect promoting bacterial speciation [58]. Although a potential result of interspecific recombination is the uncertainty of species boundaries, the increased mixing of genes and the observed phylogenetic inconsistencies show the history of a gene-transfer-mediated diversification of microorganisms.
Acknowledgments
The authors would like to thank M.S. Marcos M. D. Carneiro for help in reviewing the English of this work, M.S. Gilberto de O. Mendes for his help in editing the figures, M.S. Mateus Ferreira Santana for his help in phylogenetic network analyses, and FAPEMIG, CNPq, and CAPES for financial support. This paper is dedicated in memoriam of Aurea C. S. Pylro.