PSP: rapid identification of orthologous coding genes under positive selection across multiple closely related prokaryotic genomes
© Su et al.; licensee BioMed Central Ltd. 2013
Received: 24 July 2013
Accepted: 26 December 2013
Published: 27 December 2013
With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure.
Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/.
PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.
With the next-generation sequencing data “tsunami” in our midst, sets of closely related prokaryotic genomes suitable for comparative evolutionary studies have been available . To date, well established cases of gene selection have been rare . Big data mining of bacterial genomes has shown that positive selection is more widespread at the molecular level than expected under a restrictive interpretation of the neutral theory . Genome-wide molecular selection analyses, designed to assess selection pressure across the entire genomes of different strains, have attempted to address the role of gene selection in the process of microevolution [4, 5], especially in host-pathogen interactions, and metabolic adaptation to a changing environment (stress, antibiotic). Positive selection studies on model bacteria, such as the species Escherichia coli[4, 6] and Listeria, or the genera Streptococcus[8, 9] and Campylobacter have revealed that positive selection is an essential part of natural selection to fix advantageous mutations, and improves the adaptability of bacteria in a wide range of environmental conditions.
A number of methods have been proposed for detecting positive selection in DNA or protein sequences . The most common approach is to integrate evolutionary features into codon-based models, and to use probability-based theory to estimate the ratio (ω) of nonsynonymous (d N ) and synonymous (d S ) substitutions, such as implemented in the PAML  and FitModel . Estimating the ratio ω gives a measure of selective pressure, indicating neutral evolution (ω = 1), purifying selection (ω < 1) and positive selection (ω > 1). In the model of neutral evolution, the likelihood that a nonsynonymous mutation would go to fixation is the same as that for a synonymous mutation. Purifying selection can result in stabilizing selection through the purging of deleterious variations that arise. Positive selection pressure serves to maintain a given set of adaptive traits that aids in survival.
Several nice tools are currently available, such as IDEA , JCoDA  and WSPMaker . However, they are not set up specifically to examine prokaryotic genomes, and they exhibit two major deficiencies: (i) the evolution selection analysis could be difficult to configure on a local computer for most biologists who are not familiar with phylogenetic or evolutionary theory, and (ii) excessively long computing times for analyzing several genomes at once are prohibitive. In this study, we present an open access web server called PSP (Positive Selection analysis for Prokaryotic genomes) to identify orthologous coding genes under positive selection across closely related prokaryotic genomes. It provides several core functions for in-depth analysis of evolutionary selection: retrieving the orthologous groups, generating codon-delimited and un-gapped alignments, removing recombination, building phylogenetic trees, and estimating ω under different models used by PAML/FitModel. Remarkably, PSP is able to facilitate efficient exploration of the identified orthologous genes at the metabolic pathway level by assignments and enrichments of KO (KEGG Orthology), GO (Gene Ontology) or COG (Clusters of Orthologous Groups) terms. Results are presented in a user-friendly web interface, which provides an efficient visualization of positive selection pressure on each orthologous groups.
Rapid identification of orthologous groups across multiple prokaryotic genomes
The identification of orthologs is an important problem in the field of phylogenetic analyses. Basically, there are three types of relationships between orthologous genes, one-to-one, one-to-many and many-to-many . In the study of Adrian et al., OrthoMCL shows a balanced performance, such as the accuracy, the number of genomes analyzed and usability of the web-interface. Therefore, PSP integrates OrthoMCL to quickly identify the many-to-many orthologous relationship. PSP allows users to select or upload complete sequences and annotation details of closely related genomes for comparison. Users can also simultaneously upload thousands of annotated protein-coding genes as Multi-Fasta formatted files. With the default settings, OrthoMCL recognizes co-ortholog relationships with a BLASTp E-value cutoff of 1e-5 and a minimum of 50% coverage. Then PSP performs homolog grouping using the Markov Cluster algorithm with an inflation value of 1.5. Interestingly, PSP runs on a high-performance server and can accept up to thirty comparator bacterial genomes simultaneously. Moreover, one-to-one orthologous relationship of genes could be identified using reciprocal BLAST best hit, which is the first and most widely used method for automatically establishing orthologous relationships .
Optimization of multiple sequence alignment for automated phylogenetic analysis
The protein-coding genes of the individual orthologous groups can be aligned by using MUSCLE or MAFFT. In connection with the automated phylogenetic analysis, PSP improves the coding sequence alignments by using the two following processes: (i) generation of codon-delimited alignments with ad hoc Perl scripts; (ii) maximization of the un-gapped alignment area by using MaxAlign to remove non-homologous sequences .
Removal of gene recombination off orthologous groups
Recombination (or gene conversion) is 10–50 times more likely to cause changes in nucleotide sequence than mutation . To eliminate the influence of horizontal relationships in the positive selection detection, PSP is able to identify recombination signals among the aligned nucleotide sequences of orthologous groups. PSP performs a statistical test to identify recombination breakpoints by using GeneConv . It pre-defines the g-scale parameter as 1, which allows mismatches within a recombining fragment, and the inner fragment P-values with 10,000 random permutations. In addition, PSP also detects recombination with three other statistical tests implemented with the PhiPack package with default settings, including pairwise homoplasy index (PHI), Max χ2 and neighbor similarity score (NSS) . Finally, if a recombination signal is detected to be significant by all four tests (P-value < 0.05 in each method), the alignment would be splitted into two or more fragments.
Detection of orthologous genes under positive selection
Phylogenetic trees are built by the PHYLIP using maximum parsimony or neighbor-joining method. The trees are also able to be generated with Markov codon models by using CodonPhyML . The evolutionary selection is subsequently implemented in the program PAML or FitModel. Only orthologous groups with enough data (at least 4 protein-coding genes) are used as input to detect positive selection, due to the poor quality of Bayes predictions based on small samples . Because the lack of any methods to deal with alignment gaps properly in both programs, a cutoff of the percentages of sequence have data in PSP is used to filter the alignments column by column. In the PSP server, PAML uses three evolutionary models proposed by Yang et al. (Additional file 1: Table S1): site model, strain-specific branch model and strain-specific site-branch model. In the strain-specific analysis, the branches of selected target strains are specified and referred to as “foreground branches” and the rest as “background branches”, which is a powerful tool to detect the selection pressure during the process of environmental adaptation . The in silico detection of evolutionary selection is computationally intensive, particularly using Bayes empirical Bayes to determine posterior probabilities (PP). PSP, which runs on a high-performance server, is able to rapidly calculate the d N /d S ratios and screen orthologous coding genes under positive selection. Similarly, PSP also can apply switching Markov modulated codon models as implemented in the program FitModel to orthologous coding genes to accurately estimate the strength of selection. To most biologists who are not familiar with phylogenetic or evolutionary theory, PSP pre-defines the evolutionary models as described in Additional file 1: Table S1. PSP is also very flexible to set most key parameters in the PAML/FitModel and run strain-specific analysis freely for the evolutionary researchers. For each pair hypothesis, nested models are calculated by comparing the difference in log likelihood values to a χ2 statistic (LRT) for the detection of positive selection. If there is significant evidence for positive selection of any fragment, the orthologous genes, from which the fragment was separated, are suggested to be under positive selection. Notably, PSP also provides a user-friendly visualization tool for performing evolutionary analysis on orthologous coding genes. The embedded Java applet JalView  reports the PP values for all sites, which is helpful for users to determine nucleotide substitution at synonymous and nonsynonymous sites within protein-coding regions. Three-dimensional structural models of the protein of interest are predicted and displayed by HHpred  and Jmol . Additionally, Primer3Plus  is integrated to facilitate design of PCR primers to assay orthologous genes based on similar selection among a panel of strains isolated from the same habitat.
Functional investigation at metabolic pathway level
To explore functions of the identified orthologous coding genes under positive selection, PSP performs KO mapping and GO/COG classification. First, to assign the KO terms, PSP uses the level of sequence identity and ratio of matching length to query length cut-off obtained from BLASTp. For each query against the locally installed KEGG gene database, the simple H a -value homology score  is calculated as follows: H a = i × (l m /l q ), where i is the level of identity between protein sequences in the region with the highest Bit score expressed as a ratio between 0 and 1, l m the length of the highest scoring matching sequence (including gaps), and l q the query length. In this study, the H a -value cutoff of 0.7 was used to assign KO. PSP then enriches metabolic pathways with genes under positive selection by tracing back the hierarchical KO levels. The user can select one or more KEGG-archived closely related genomes (up to 20) as references. PSP calculates the P-value of each pathway category by using a hyper-geometric distribution method . In the same way, PSP provides enrichment analysis for COG functional terms based on RPSBLAST searches against the local CDD database, and GO slims analysis based on GOA database.
In the PSP pipeline, a large number of hypotheses are considered which could result in a high rate of Type-1 error even for a relatively stringent P-value cutoff. To reduce Type-1 errors, PSP corrects the obtained P-values using the Q-value  to produce a Q-value at level of FDR < 0.2.
Results and discussion
The PSP tool is applicable to a wide range of prokaryotic species. In this study, we applied PSP to do genome-wide positive selection analyses in two cases (Escherichia coli and Bacillus cereus) both to benchmark PSP and to illustrate its accuracy and usefulness for the exploration of data.
Positive selection analysis of Escherichia coli
Results of positive selection scanning during the analysis pipeline
Petersen et al.
Chen et al.
Num. of orthologous groups
Num. of orthologous (groups size > = 4)
Num. of genes removed by MaxAlign
Method for detecting recombination
GenConv & PhiPack
GeneConv & Reticulate
Num. of recombination
M1a-M2a & M7-M8
M1a-M2a & Branch-Site Model
Num. of genes under positive selection in E. coli K12
List of additional genes under positive selection across the six Escherichia coli genomes using the M1a-M2a test
Bifunctional cobinamide kinase and cobinamide phosphate guanylyltransferase
Reactivating factor for ethanolamine ammonia lyase
Flagellar filament structural protein
Hsp31 molecular chaperone
Glutamyl-Q tRNA synthetase
Conserved inner membrane protein
Predicted diguanylate cyclase
Putative DNA-binding transcriptional regulator
Genes under positive selection in Bacillus cereusgenomes
Genes that show evidences for positive selection in B. anthracis group
Ribosomal large subunit pseudouridylate synthase D
Glycerol uptake facilitator protein
Protease synthase and sporulation negative regulatory protein
Sensor histidine kinase
Iron (III) transport system permease protein
Multidrug resistance protein
Alkyl hydroperoxide reductase, subunit F
Transcriptional regulator, AraC family
GMPP; mannose-1-phosphate guanylyltransferase
Putative prophage LambdaBa04, major capsid protein
RNA pseudouridine synthase family protein
Sugar-binding transcriptional regulator, LacI family
ABC transporter, permease
ABC transporter, permease
Iron complex transport system permease protein
Nucleoside permease NupC
Mass transport system is another effective way for microorganism to resist antibiotic, although it is probable that they may have other natural physiological functions . There are seven genes involved in the mass transport system, which shows the significant enrichment compared to the references (P-value = 0.0395 in the strain Ames Ancestor). The araJ gene was regarded as nonessential membrane protein of unknown function. Recently, it is believed to belong to a large class of multidrug resistance translocators and in particular to the major facilitator superfamily . The orthologous genes of ptr2, which encode a peptide transporter, present in all B. cereus genomes and are regarded as a multidrug resistance protein. In this research, we found it is under strong positive selection and may have important roles to extrude drugs. Iron acquisition genes are also important contributors to B. anthracis, while iron limitation is a component of host defense against infection . The gene fepD, which shows strong evidence for positive selection, is a virulence-associated gene and involved in iron-chelating ABC uptake systems . The gene afuB was reported to differentially express upon treatment with antibiotic, and is also very important for iron transporter . Porins are also important in interaction with the host immune system and could work as receptors for phages or antibiotics . Aquaglyceroporin glpF selectively conducts the passage of small hydrophilic across the inner membrane of B. cereus. The function of remaining two putative ABC transporters is still unknown, but one of them (COG3127) was predicted to involve in lysophospholipase L1 biosynthesis, which may be a good potential target for new antibiotics.
We also identified two transcriptional repressors (purR and araC) and a two-component system (ntrB-ntrC), which show strong evidences for positive selection. Autoregulation of purR controls the expression of many genes involved in purine biosynthetic pathway in B. subtilis. ntrB, a member of the ntrB-ntrC two-component system, encodes the signal-transducing kinase/phosphatase nitrogen regulator, on the regulated phosphatase activity involved in nitrogen regulation. These genes are known to have multiple functions in different ways, which are likely that the positive selection from the interaction of host-pathogen simultaneously improve the adaptation of microorganisms by acting on the versatile proteins, such as metabolic adaptation.
The PSP web server, which integrates a wide variety of useful analytical and functional tools, has been developed to rapidly identify orthologous coding genes under positive selection across up to thirty user-selected or user-supplied prokaryotic genomes. Hosted by a high-performance server and with easy navigation and flexible input options, we present it as a quick and comprehensive genome microevolution tool for biologists. Remarkably, PSP excludes the effect of gene recombination and incorporates functional investigation at the metabolic pathway level. In the future, we plan to improve the computing power of PSP markedly with Paralleled PAML. The upgraded version will be also able to map the positive selection sites to three-dimensional structures of proteins. We propose that a tool such as PSP will support genome-scale analysis for evolutionary selection, aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species.
Availability and requirements
Project name: PSP:Positive Selection analysis for Prokaryotic genomes.
Operating system(s): CentOS.
License: PSP is available free of charge to academic and non-profit institutions.
Any restrictions to use by non-academics: Please contact authors for commercial use.
This work was supported, in part, by the grants from National Basic Research Program of China (2013CB733901) and from Chinese National Natural Science Foundation (31230002, 31170082, and 31121064).
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