- Research article
- Open Access
Evolutionary conservation of essential and highly expressed genes in Pseudomonas aeruginosa
© Dötsch et al; licensee BioMed Central Ltd. 2010
- Received: 8 October 2009
- Accepted: 9 April 2010
- Published: 9 April 2010
The constant increase in development and spread of bacterial resistance to antibiotics poses a serious threat to human health. New sequencing technologies are now on the horizon that will yield massive increases in our capacity for DNA sequencing and will revolutionize the drug discovery process. Since essential genes are promising novel antibiotic targets, the prediction of gene essentiality based on genomic information has become a major focus.
In this study we demonstrate that pooled sequencing is applicable for the analysis of sequence variations of strain collections with more than 10 individual isolates. Pooled sequencing of 36 clinical Pseudomonas aeruginosa isolates revealed that essential and highly expressed proteins evolve at lower rates, whereas extracellular proteins evolve at higher rates. We furthermore refined the list of experimentally essential P. aeruginosa genes, and identified 980 genes that show no sequence variation at all. Among the conserved nonessential genes we found several that are involved in regulation, motility and virulence, indicating that they represent factors of evolutionary importance for the lifestyle of a successful environmental bacterium and opportunistic pathogen.
The detailed analysis of a comprehensive set of P. aeruginosa genomes in this study clearly disclosed detailed information of the genomic makeup and revealed a large set of highly conserved genes that play an important role for the lifestyle of this microorganism. Sequencing strain collections enables for a detailed and extensive identification of sequence variations as potential bacterial adaptation processes, e.g., during the development of antibiotic resistance in the clinical setting and thus may be the basis to uncover putative targets for novel treatment strategies.
- Quorum Sense
- Essential Gene
- Conditional Independence
- Core Genome
- Nonsynonymous Substitution
In the face of the global emergence of multi-drug resistant bacterial pathogens, the search for new classes of antimicrobial agents is one of the most important challenges of modern medicine. Novel potential anti-bacterial drugs have mainly been discovered by conventional screening methods. These methods involved the testing of natural products or synthetic chemicals for growth inhibition or killing of wild-type test organisms, with the specific mode of action being worked out later [1–4]. However, recent advances in in silico genomic approaches have provided an opportunity to specifically highlight potential drug targets and have facilitated a paradigm shift from direct antimicrobial screening programs toward rational target-based strategies, where drug discovery starts at the level of the gene [4–7]. Fundamental improvements of genome-based technologies such as whole genome expression- and protein-profiling as well as whole genome sequencing has lead to further changes in the drug discovery process. This is due to the fact that large amounts of relevant biological information have become available to address highly complex biological questions [8–10].
As essential genes provide perfect potential drug targets, it has been claimed that an important task of rational target validation would be the identification of the essentiality of the genes within the genome of one organism . There are several techniques to identify essential genes. First, experimental genetic inactivation of a potential target can be accomplished by gene disruption , either in a case-to-case approach  or in a high throughput mode [13, 14] in order to provide a genome-wide assessment of essential genes in an organism. When interpreting genetic inactivation data it should, however, be recognized that the inability to isolate a viable stain under standard laboratory conditions is generally judged as evidence of essentiality, albeit these conditions might not reflect the growth conditions in, e.g., the host environment. In addition to the experimental validation of gene essentiality, information can be received by applying comparative genomics which involves the comparison of multiple fully sequenced genomes in order to identify a minimal genome set necessary to support bacterial viability [15, 16]. This bioinformatic strategy assumes that bacteria accomplish essential functions through common mechanisms and that the genes encoding these functions would be highly conserved. An alternative approach for the prediction of gene essentiality would be to draw information from the sequence of the gene itself. The detailed bioinformatic analysis of the genomic makeup of one organism might give all the information necessary to classify a gene.
Although it was suggested that there is no difference between the rates of evolution for essential and non-essential genes in eukaryotes, Jordan et al.  demonstrated that essential genes appeared to be more conserved than non-essential genes, based on the analysis of multiple complete genomic sequences and experimental knock out data of three bacterial species. However, other studies have suggested an important direct influence of the expression levels on the rate of nonsynonymous substitutions in bacteria and demonstrated that when a control for this variable was included, essentiality played no significant role in the rate of protein evolution .
In this study we aimed to address the main issue of whether essential genes are more evolutionarily conserved than nonessential ones in bacteria and analyzed the impact of the gene expression rate and subcellular localization of the encoded proteins as additional factors that are linked to protein evolution. A remarkable collection of genomic data is already available for the opportunistic pathogen Pseudomonas aeruginosa . Sequence information of six strains has been published [20–22, 24], and there is a large collection of transcriptional profiles that have been recorded under various environmental conditions . Furthermore, comprehensive experimental knock out libraries of two P. aeruginosa strains have been established [13, 14].
In this study we amended the existing collection of P. aeruginosa genomic data with whole-genome sequence data from 36 clinical P. aeruginosa strains in order to accurately and definitively revisit the interdependence of gene essentiality, rate of nonsynonymous substitutions, gene expression and subcellular localization.
Pooled sequencing of 36 clinical P. aeruginosa isolates
Overview of sequencing results.
Sequence variation in P. aeruginosa
The median sequence variation (for protein coding genes in all 36 strains) was calculated to be ~0.47%, which is consistent with previous reports that found sequence variation in P. aeruginosa to be about one order of magnitude lower than in other γ-proteobacteria [26, 27]. Nucleotide substitutions in genes coding for proteins can be either synonymous (do not change the amino acid sequence, also called silent substitutions), or non-synonymous (change the amino acid sequence). The median number of non-synonymous differences dN for all protein coding genes was 2.1 × 10-3 with a ratio of nonsynonymous to synonymous (dS) substitutions of dN:dS = 0.19, indicating that variations of amino acid sequences are generally suppressed by selection.
It is well known that the P. aeruginosa core genome is highly conserved and there are only few exceptions of highly variable genes (e.g., pilA or the pyoverdin cluster, ), while inter-strain variation is mostly restricted to the accessory genome including pathogenicity islands and prophages . Consequently, sequence variation among the 36 clinical P. aeruginosa strains was markedly increased (dN = 3.5 × 10-3) for genes belonging to these regions of genomic plasticity (RGPs, ).
Essential genes in P. aeruginosa
Genes missing in the sequenced clinical isolates for which no transposon mutants are availablea.
normalized read depthb
Codon adaptation index in P. aeruginosa
Gene essentiality and expression level are correlated with genetic variability
Furthermore, essential genes showed a significantly increased expression level in P. aeruginosa. If the gene expression data are grouped by essentiality it becomes clear, that essential genes show a significant increased proportion of highly expressed genes (Figure 4B). With the aim of testing whether the observed correlation of gene essentiality and dN is the result of this overrepresentation of high expression rates among the essential genes or whether there is also an independent effect, we performed a statistical test for conditional independence. Therefore, the dN-values of the non-essential genes were normalized in order to compensate for their lower expression rates. The null hypothesis for the test for conditional independence was that the distributions of the dN-values are identical for essential and non-essential genes after this normalization has been performed. The null hypothesis of conditional independence had to be rejected for the averaged data set of all 36 strains (p = 0.0169). This means that, besides the expression level, essentiality also accounts for dN in P. aeruginosa, although the statistical significance is not very high. Apparently, the effect of gene expression on protein evolution rates is dominant in P. aeruginosa, but still an independent (though weaker) effect of essentiality could be observed.
Extracellular proteins evolve at a faster rate
Other parameters that have been identified to correlate with slow protein evolution include protein localization (with extracellular proteins being more variable than cytoplasmic ones) [32–34], evolutionary age  and protein connectivity .
Highly conserved non-essential genes
As described above and depicted in Figure 2, essential genes were shown to be generally more conserved than non-essential ones in our collection of P. aeruginosa strains. Vice versa, many highly conserved genes were essential according to the standard definition (experimentally essential) because no transposon mutants are available in either of the two comprehensive libraries [13, 14]. However, we also found a large subset of genes that are experimentally dispensable but highly conserved. Genes that are dispensable, but conserved throughout distantly related bacteria, have been termed persistent non-essential genes and are proposed to be regarded as truly essential from an evolutionary point of view . These genes might be dispensable for short term survival and growth under laboratory conditions but are in fact essential for successful survival of the population under varying environmental conditions in its native habitat.
Interestingly, overall as many as 980 protein encoding genes (only 124 of which were experimentally essential) did not exhibit any variations in the protein sequence (dN = 0, completely conserved genes) in the 36 clinical isolates and 79 were even identical at the nucleotide level (dS = 0). While this is consistent with the generally high conservation of the P. aeruginosa core genome, these genes could furthermore be interpreted as being 'evolutionarily essential' for the species P. aeruginosa in a sense similar to the aforementioned persistent non-essential genes .
Among the 980 completely conserved genes (additional file 2) we found many encoding ribosomal proteins, genes important for energy production (respiratory chain, ATP synthase) or DNA replication and repair, e.g., dnaN (encoding for a DNA polymerase III subunit) or the helicase ruvA/ruvB. Many of them also belong to the class of persistent non-essential genes in γ-Proteobacteria .
Remarkably, among the set of completely conserved genes we also found the two paralogous operons phzA1-G1 and phzA2-G2 that encode enzymes necessary for phenazine synthesis . Both operons are almost identical, not only within one strain (with only a few dissimilarities between genes phzA1/2, phzB1/2 and phzC1/2 in PAO1) but furthermore no sequence variation could be detected for the whole collection of 36 clinical strains (with the only exceptions of phzA1 and phzB2) - in most cases not even at the nucleotide level (dS = 0). Phenazines including pyocyanin, which is responsible for the well known blue-green color of P. aeruginosa cultures, and which is a major virulence factor , have been shown to mediate extracellular electron transfer under microaerophilic conditions . Other genes involved in phenazine synthesis and regulation (phzM, mexG, pqsE) and many regulatory genes - vfr, algU, rsmA, gacA, phoP, mvaT, algR, and phoB, all of which play an important role in the environmental versatility of P. aeruginosa including virulence [39, 40] - also showed no protein variations.
Additionally, we identified the quorum sensing (QS) genes lasI, rhlR and rsaL to be fully conserved at the protein level. It has been reported that QS is frequently impaired in clinical isolates, especially those isolated from long term chronically infected cystic fibrosis (CF) patients [41–43]. Thereby, loss of QS is mainly caused by disruption of lasR that also showed considerable variation in this study, including the identification of premature stop-codons in a fraction of strains (not shown). While the high conservation of the above described genes of the las and rhl system underlines the general importance of QS for P. aeruginosa, loss of QS - preferably by disruption of lasR - might be an important adaptive strategy to more specific habitats such as the chronically infected CF lung.
Highly conserved genes linked to antibiotic resistance
Assuming that high conservation of genes is positively correlated with their evolutionary importance, the 980 highly conserved genes described above might be potential novel drug targets. To further examine this potential, we compared the list of genes with the results of three recently published screens for resistance determinants in P. aeruginosa [44–46]. In total, of the 980 genes 27 were identified in at least one of the three screens as being positively linked to antibiotic resistance (additional file 2).
Genes completely conserved on the protein level that are specific to the genus Pseudomonas
Thanks to recent advances in sequencing technologies it has been possible to analyze many genomes with little expense within a short space of time. Because whole-genome sequence data and bioinformatics provide potential for large scale comparative genomics and evolutionary inference, they will be an important sector in the future global approaches for deciphering the genomic make-up of an organism and for rational drug discovery programs. In the presented work, we have demonstrated the combined use of short read sequencing and bioinformatic methods to analyze a whole collection of clinical P. aeruginosa strains. The genome sequence data of 36 pooled bacterial strains combined with the availability of more than 200 gene expression profiles and the experimental genome-wide assessment of essential genes, gave the unique opportunity to revisit the central question of the interdependency of the effects of various factors on sequence variation in an accurate and definite manner. Here, we have confirmed that the rate of expression is a major determinant of how rapidly a protein evolves. We were furthermore able to demonstrate an independent correlation of gene essentiality with protein evolution rates in P. aeruginosa, although essential genes on average also showed an increased expression. Additionally, we could demonstrate increased substitution rates for genes encoding for extracellular proteins.
Furthermore, our comparison of whole genome sequence data of 36 P. aeruginosa strains revealed a subset of 980 proteins that were fully conserved, and did not show any variation in the amino acid sequence. The full conservation of these protein sequences may indicate their evolutionary importance for P. aeruginosa as a species, comparable to the persistent nonessential genes on the inter-species level. Among the completely conserved proteins, we found many that are involved in the central cellular mechanisms like energy production, replication and protein synthesis, many of which were also identified by Fang et al. as persistent nonessential genes, or that are coding for regulatory and virulence factors (additional file 2). In accordance with the lifestyle of P. aeruginosa as a versatile environmental organism and opportunistic pathogen, this demonstrates a) the high level of optimization of the genome (which is also obvious in the codon bias, Figure 1) and b) the evolutionary importance of these genes for its survival in various habitats.
The genomic analysis of multiple strains will greatly enhance our knowledge on the distribution and variation of genes and their relation to the lifestyle of the particular organism. A future expansion of this study, which is biased towards clinical isolates, would be to include soil and water isolated P. aeruginosa strains and to test whether they share the identified traits. Furthermore, the comparison of strain collections with specific virulence or resistance phenotypes will simplify the detection of genetic determinants that are responsible for the development of severe infections and/or multi-resistance and will uncover putative targets for novel anti-bacterial strategies.
Organism & Culturing
A collection of 36 clinical P. aeruginosa isolates (additional file 1) was used for sequencing. The strains were divided into 3 groups and sequenced as pooled samples. Group 1 contained fourteen strains, group 2 seven strains and group 3 fifteen strains. Out of group 1, four strains (Psae1152, Psae1747, Psae2136 and Psae2162) were selected to be also sequenced individually. Each strain was cultivated independently in 5 mL LB medium in 50 mL glass flasks at 37°C, 180 rpm over night. Cells were harvested by centrifugation of 1 mL culture (5 min, 8000 rpm) in a tabletop microcentrifuge and washed once by resuspension in 1 mL H2O and repeated centrifugation.
For sequencing of pooled samples, all 36 isolates were grown independently in 2 mL LB medium in glass tubes at 37°C, 180 rpm over night. Cells were harvested by centrifugation of 1 mL culture (5 min, 8000 rpm), the supernatant and visible extracellular matrix material were removed and the remaining pellets were resuspended in 1 mL H2O. Cell suspensions were pooled according to their allocation to one of the three groups at equal cell numbers as estimated by determination of the optical density at 600 nm using a NanoDrop photometric device (ThermoScientific). The resulting pooled suspensions were centrifuged (5 min, 8000 rpm), supernatant was removed and the pellets were stored at -70°C.
DNA preparation and sequencing
Genomic DNA was isolated from thawed pellets using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's instructions. DNA samples were further prepared and sequenced using 35 bp paired end sequencing on a Genome Analyzer II (Illumina). Libraries of 300 bp prepared according the manufacturer's instructions "Preparing Samples for Paired-End-Sequencing". Cluster generation was performed using the Illumina cluster station, sequencing for read 1 and read 2 on the Genome Analyzer followed a standard protocol. The fluorescent images were processed to sequences using the Genome Analyzer Pipeline Analysis software 1.3.2 (Illumina).
The primary sequencing results (short read data) of the four individually sequenced strains and the three pools have been deposited in the NCBI Sequence Read Archive (SRA, http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/Traces/sra/sra.cgi) as a single study with the accession number 'SRP001802'.
The sequence output (35 base pair short reads) of the Genome Analyzer II was transformed to FastQ format and mapped against the genome sequence of reference strain P. aeruginosa PAO1 downloaded from the Pseudomonas genome database  using the 'easyrun' option of the Maq software . For the four single strains, genetic variations were identified from the 'consensus.snp' output file produced by Maq. SNPs were filtered by consensus quality (≥ 30) and read depth (≥ 15) to exclude low quality reads. For the pooled sequences, genetic variations were identified using a Perl script for SNP detection from pooled DNA samples that uses the -N option of Maq to calculate frequencies with confidence intervals for all SNPs . To optimize parameter settings for P. aeruginosa sequence data, the results of the four individual samples were pooled in silico and analyzed using the aforementioned script with varying parameter sets (minimum mapping quality: 20 - 40, maximum mismatches per read: 1 - 5). The results were compared with the SNPs detected in the four individually sequenced strains. Using ≥ 25 mapping quality and ≤ 3 mismatches yielded a sensitivity of 89.8% and specificity (fraction of true positives) of 99.0%, therefore these parameters were applied as standard settings for SNP identification in the pooled sequences data.
where and are the nonsynonymous and synonymous substitution rate averaged for all 36 strains, s k is the number of strains included in data set k and dNj, kand dSj, kare the respective rate for data set k.
Information on gene essentiality in P. aeruginosa was taken from the Database of Essential Genes http://tubic.tju.edu.cn/deg/, which integrates the results of two comprehensive P. aeruginosa transposon mutant libraries [13, 14]. Gene annotation data (PseudoCAP functional category, subcellular localization, etc.) were downloaded from the Pseudomonas genome database http://www.pseudomonas.com/. Gene expression data were obtained from the NCBI Gene Expression Omnibus database . The median expression for each gene was calculated from normalized absolute log2 signals of 232 microarrays (27 independent experiments) found in the database for the GPL84 platform (Affymetrix P. aeruginosa array Pae_G1a).
Standard least squares regression is extremely sensitive to single outliers. Robust regression replaces the squared error by other error measures reducing the influence of outliers . In order to avoid artifacts introduced by outliers with respect to dN or the expression level, we have preferred robust regression over standard least squares regression. Here we have applied robust regression based on Huber's error function as it is implemented in the statistics software R in the rlm-method in the package MASS.
Statistical test for conditional independence
The random variable X is said to be conditionally independent of the random variable Y given the random variable Z if P(X|Y,Z) = P(X|Z) holds. Conditional independence means that X might be highly correlated with Y as well as Z, but the correlation between X and Y is fully explained or covered by Z. Here, we apply a Kolmogorov-Smirnov test to the distributions of the dN-values (corresponding to the random variable X) of the essential genes and the non-essential genes (essentiality/non-essentiality represents the random variable Y) where the dN-values of the non-essential genes are weighted according to the different expression levels (random variable Z) in essential and non-essential genes .
Identification of orthologous proteins
P. aeruginosa PAO1 protein sequences obtained from the Pseudomonas Genome Database  were aligned to the 'nr' database of non-redundant protein sequences obtained from the NCBI blast website  using BLASTP. All the resulting alignments with at least 50% identities and an alignment length of at least 50% of the size of the PAO1 protein were considered as orthologs.
We thank Daniel Jonas (Freiburg University Medical Centre, Freiburg, Germany) for providing us with clinical P. aeruginosa strains. AD is a recipient of a predoctoral stipend provided by the DFG-sponsored International Research Training Group 'Pseudomonas: Pathogenicity and Biotechnology'. Funding from the Helmholtz Gemeinschaft is gratefully acknowledged.
- Obrecht D, Robinson JA, Bernardini F, Bisang C, DeMarco SJ, Moehle K, Gombert FO: Recent progress in the discovery of macrocyclic compounds as potential anti-infective therapeutics. Curr Med Chem. 2009, 16: 42-65. 10.2174/092986709787002844.PubMedView ArticleGoogle Scholar
- Rasmussen TB, Givskov M: Quorum-sensing inhibitors as anti-pathogenic drugs. Int J Med Microbiol. 2006, 296: 149-161. 10.1016/j.ijmm.2006.02.005.PubMedView ArticleGoogle Scholar
- Pathania R, Brown ED: Small and lethal: searching for new antibacterial compounds with novel modes of action. Biochem Cell Biol. 2008, 86: 111-115. 10.1139/O08-011.PubMedView ArticleGoogle Scholar
- Payne DJ, Gwynn MN, Holmes DJ, Rosenberg M: Genomic approaches to antibacterial discovery. Methods Mol Biol. 2004, 266: 231-259.PubMedGoogle Scholar
- Dutta A, Singh SK, Ghosh P, Mukherjee R, Mitter S, Bandyopadhyay D: In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori. In Silico Biol. 2006, 6: 43-47.PubMedGoogle Scholar
- Taylor PL, Wright GD: Novel approaches to discovery of antibacterial agents. Anim Health Res Rev. 2008, 9: 237-246. 10.1017/S1466252308001527.PubMedView ArticleGoogle Scholar
- Pucci MJ: Novel genetic techniques and approaches in the microbial genomics era: identification and/or validation of targets for the discovery of new antibacterial agents. Drugs R D. 2007, 8: 201-212. 10.2165/00126839-200708040-00001.PubMedView ArticleGoogle Scholar
- Bielecki P, Glik J, Kawecki M, Martins dos Santos VAP: Towards understanding Pseudomonas aeruginosa burn wound infections by profiling gene expression. Biotechnol Lett. 2008, 30: 777-790. 10.1007/s10529-007-9620-2.PubMedView ArticleGoogle Scholar
- Sakharkar KR, Sakharkar MK, Chow VTK: A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa. In Silico Biol. 2004, 4: 355-360.PubMedGoogle Scholar
- Sakharkar KR, Sakharkar MK, Chow VTK: Biocomputational strategies for microbial drug target identification. Methods Mol Med. 2008, 142: 1-9. full_text.PubMedView ArticleGoogle Scholar
- Arigoni F, Talabot F, Peitsch M, Edgerton MD, Meldrum E, Allet E, Fish R, Jamotte T, Curchod ML, Loferer H: A genome-based approach for the identification of essential bacterial genes. Nat Biotechnol. 1998, 16: 851-856. 10.1038/nbt0998-851.PubMedView ArticleGoogle Scholar
- Reznikoff WS, Winterberg KM: Transposon-based strategies for the identification of essential bacterial genes. Methods Mol Biol. 2008, 416: 13-26. full_text.PubMedView ArticleGoogle Scholar
- Liberati N, Urbach J, Miyata S, Lee D, Drenkard E, Wu G, Villanueva J, Wei T, Ausubel F: An ordered, nonredundant library of Pseudomonas aeruginosa strain PA14 transposon insertion mutants. Proc Natl Acad Sci USA. 2006, 103: 2833-2838. 10.1073/pnas.0511100103.PubMed CentralPubMedView ArticleGoogle Scholar
- Jacobs MA, Alwood A, Thaipisuttikul I, Spencer D, Haugen E, Ernst S, Will O, Kaul R, Raymond C, Levy R, Chun-Rong L, Guenthner D, Bovee D, Olson MV, Manoil C: Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc Natl Acad Sci USA. 2003, 100: 14339-14344. 10.1073/pnas.2036282100.PubMed CentralPubMedView ArticleGoogle Scholar
- Gustafson AM, Snitkin ES, Parker SCJ, DeLisi C, Kasif S: Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genomics. 2006, 7: 265-10.1186/1471-2164-7-265.PubMed CentralPubMedView ArticleGoogle Scholar
- Carbone A: Computational prediction of genomic functional cores specific to different microbes. J Mol Evol. 2006, 63: 733-746. 10.1007/s00239-005-0250-9.PubMedView ArticleGoogle Scholar
- Jordan IK, Rogozin IB, Wolf YI, Koonin EV: Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. 2002, 12: 962-968.PubMed CentralPubMedView ArticleGoogle Scholar
- Rocha EPC, Danchin A: An analysis of determinants of amino acids substitution rates in bacterial proteins. Mol Biol Evol. 2004, 21: 108-116. 10.1093/molbev/msh004.PubMedView ArticleGoogle Scholar
- Winsor GL, Van Rossum T, Lo R, Khaira B, Whiteside MD, Hancock REW, Brinkman FSL: Pseudomonas Genome Database: facilitating user-friendly, comprehensive comparisons of microbial genomes. Nucleic Acids Res. 2009, 37: D483-8. 10.1093/nar/gkn861.PubMed CentralPubMedView ArticleGoogle Scholar
- Winstanley C, Langille MGI, Fothergill JL, Kukavica-Ibrulj I, Paradis-Bleau C, Sanschagrin F, Thomson NR, Winsor GL, Quail MA, Lennard N, Bignell A, Clarke L, Seeger K, Saunders D, Harris D, Parkhill J, Hancock REW, Brinkman FSL, Levesque RC: Newly introduced genomic prophage islands are critical determinants of in vivo competitiveness in the Liverpool Epidemic Strain of Pseudomonas aeruginosa. Genome Res. 2009, 19: 12-23. 10.1101/gr.086082.108.PubMed CentralPubMedView ArticleGoogle Scholar
- Mathee K, Narasimhan G, Valdes C, Qiu X, Matewish JM, Koehrsen M, Rokas A, Yandava CN, Engels R, Zeng E, Olavarietta R, Doud M, Smith RS, Montgomery P, White JR, Godfrey PA, Kodira C, Birren B, Galagan JE, Lory S: Dynamics of Pseudomonas aeruginosa genome evolution. Proc Natl Acad Sci USA. 2008, 105: 3100-3105. 10.1073/pnas.0711982105.PubMed CentralPubMedView ArticleGoogle Scholar
- Lee DG, Urbach JM, Wu G, Liberati NT, Feinbaum RL, Miyata S, Diggins LT, He J, Saucier M, Dßziel E, Friedman L, Li L, Grills G, Montgomery K, Kucherlapati R, Rahme LG, Ausubel FM: Genomic analysis reveals that Pseudomonas aeruginosa virulence is combinatorial. Genome Biol. 2006, 7: R90-10.1186/gb-2006-7-10-r90.PubMed CentralPubMedView ArticleGoogle Scholar
- Stover CK, Pham XQ, Erwin AL, Mizoguchi SD, Warrener P, Hickey MJ, Brinkman FS, Hufnagle WO, Kowalik DJ, Lagrou M, Garber RL, Goltry L, Tolentino E, Westbrock-Wadman S, Yuan Y, Brody LL, Coulter SN, Folger KR, Kas A, Larbig K, Lim R, Smith K, Spencer D, Wong GK, Wu Z, Paulsen IT, Reizer J, Saier MH, Hancock RE, Lory S, Olson MV: Complete genome sequence of Pseudomonas aeruginosa PA01, an opportunistic pathogen. Nature. 2000, 406: 959-964. 10.1038/35023079.PubMedView ArticleGoogle Scholar
- Roy PH, Tetu SG, Larouche A, Elbourne L, Tremblay S, Ren Q, Dodson R, Harkins D, Shay R, Watkins K, Mahamoud Y, Paulsen IT: Complete genome sequence of the multiresistant taxonomic outlier Pseudomonas aeruginosa PA7. PLoS ONE. 2010, 5: e8842-10.1371/journal.pone.0008842.PubMed CentralPubMedView ArticleGoogle Scholar
- Gene Expression Omnibus. [http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/projects/geo/]
- Spencer DH, Kas A, Smith EE, Raymond CK, Sims EH, Hastings M, Burns JL, Kaul R, Olson MV: Whole-genome sequence variation among multiple isolates of Pseudomonas aeruginosa. J Bacteriol. 2003, 185: 1316-1325. 10.1128/JB.185.4.1316-1325.2003.PubMed CentralPubMedView ArticleGoogle Scholar
- Kiewitz C, Tümmler B: Sequence diversity of Pseudomonas aeruginosa: impact on population structure and genome evolution. J Bacteriol. 2000, 182: 3125-3135. 10.1128/JB.182.11.3125-3135.2000.PubMed CentralPubMedView ArticleGoogle Scholar
- Klockgether J, Würdemann D, Wiehlmann L, Binnewies TT, Ussery DW, Tümmler B: Genome Diversity of Pseudomonas aeruginosa. Pseudomonas genomics and molecular biology. Edited by: Cornelis P. 2008, Caister Academic Press, 19-42.Google Scholar
- Zhang R, Ou H, Zhang C: DEG: a database of essential genes. Nucleic Acids Res. 2004, 32: D271-2. 10.1093/nar/gkh024.PubMed CentralPubMedView ArticleGoogle Scholar
- Sharp PM, Li WH: The Codon Adaptation Index - a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15: 1281-1295. 10.1093/nar/15.3.1281.PubMed CentralPubMedView ArticleGoogle Scholar
- Drummond DA, Wilke CO: Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell. 2008, 134: 341-352. 10.1016/j.cell.2008.05.042.PubMed CentralPubMedView ArticleGoogle Scholar
- Campanaro S, Treu L, Valle G: Protein evolution in deep sea bacteria: an analysis of amino acids substitution rates. BMC Evol Biol. 2008, 8: 313-10.1186/1471-2148-8-313.PubMed CentralPubMedView ArticleGoogle Scholar
- Julenius K, Pedersen AG: Protein evolution is faster outside the cell. Mol Biol Evol. 2006, 23: 2039-2048. 10.1093/molbev/msl081.PubMedView ArticleGoogle Scholar
- Liu J, Zhang Y, Lei X, Zhang Z: Natural selection of protein structural and functional properties: a single nucleotide polymorphism perspective. Genome Biol. 2008, 9: R69-10.1186/gb-2008-9-4-r69.PubMed CentralPubMedView ArticleGoogle Scholar
- Fang G, Rocha E, Danchin A: How essential are nonessential genes?. Mol Biol Evol. 2005, 22: 2147-2156. 10.1093/molbev/msi211.PubMedView ArticleGoogle Scholar
- Mavrodi DV, Bonsall RF, Delaney SM, Soule MJ, Phillips G, Thomashow LS: Functional analysis of genes for biosynthesis of pyocyanin and phenazine-1-carboxamide from Pseudomonas aeruginosa PAO1. J Bacteriol. 2001, 183: 6454-6465. 10.1128/JB.183.21.6454-6465.2001.PubMed CentralPubMedView ArticleGoogle Scholar
- Mahajan-Miklos S, Tan MW, Rahme LG, Ausubel FM: Molecular mechanisms of bacterial virulence elucidated using a Pseudomonas aeruginosa -Caenorhabditis elegans pathogenesis model. Cell. 1999, 96: 47-56. 10.1016/S0092-8674(00)80958-7.PubMedView ArticleGoogle Scholar
- Dietrich L, Price-Whelan A, Petersen A, Whiteley M, Newman D: The phenazine pyocyanin is a terminal signalling factor in the quorum sensing network of Pseudomonas aeruginosa. Mol Microbiol. 2006, 61: 1308-1321. 10.1111/j.1365-2958.2006.05306.x.PubMedView ArticleGoogle Scholar
- Jensen V, Löns D, Zaoui C, Bredenbruch F, Meissner A, Dieterich G, Münch R, Häussler S: RhlR expression in Pseudomonas aeruginosa is modulated by the Pseudomonas quinolone signal via PhoB-dependent and -independent pathways. J Bacteriol. 2006, 188: 8601-8606. 10.1128/JB.01378-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Venturi V: Regulation of quorum sensing in Pseudomonas. FEMS Microbiol Rev. 2006, 30: 274-291. 10.1111/j.1574-6976.2005.00012.x.PubMedView ArticleGoogle Scholar
- Smith E, Buckley D, Wu Z, Saenphimmachak C, Hoffman L, D'Argenio D, Miller S, Ramsey B, Speert D, Moskowitz S, Burns J, Kaul R, Olson M: Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc Natl Acad Sci USA. 2006, 103: 8487-8492. 10.1073/pnas.0602138103.PubMed CentralPubMedView ArticleGoogle Scholar
- Hoffman LR, Kulasekara HD, Emerson J, Houston LS, Burns JL, Ramsey BW, Miller SI: Pseudomonas aeruginosa lasR mutants are associated with cystic fibrosis lung disease progression. J Cyst Fibros. 2009, 8: 66-70. 10.1016/j.jcf.2008.09.006.PubMed CentralPubMedView ArticleGoogle Scholar
- D'Argenio DA, Wu M, Hoffman LR, Kulasekara HD, Déziel E, Smith EE, Nguyen H, Ernst RK, Larson Freeman TJ, Spencer DH, Brittnacher M, Hayden HS, Selgrade S, Klausen M, Goodlett DR, Burns JL, Ramsey BW, Miller SI: Growth phenotypes of Pseudomonas aeruginosa lasR mutants adapted to the airways of cystic fibrosis patients. Mol Microbiol. 2007, 64: 512-533. 10.1111/j.1365-2958.2007.05678.x.PubMed CentralPubMedView ArticleGoogle Scholar
- Fajardo A, Martinez-Martin N, Mercadillo M, Galan J, Ghysels B, Matthijs S, Cornelis P, Wiehlmann L, Tümmler B, Baquero F, Martinez J: The neglected intrinsic resistome of bacterial pathogens. PLoS ONE. 2008, 3: e1619-10.1371/journal.pone.0001619.PubMed CentralPubMedView ArticleGoogle Scholar
- Dötsch A, Becker T, Pommerenke C, Magnowska Z, Jänsch L, Häussler S: Genome-wide identification of genetic determinants of antimicrobial drug resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2009, 53: 2522-2531. 10.1128/AAC.00035-09.PubMed CentralPubMedView ArticleGoogle Scholar
- Breidenstein E, Khaira B, Wiegand I, Overhage J, Hancock R: A complex ciprofloxacin resistome revealed by screening a Pseudomonas aeruginosa mutant library for altered susceptibility. Antimicrob Agents Chemother. 2008, 52: 4486-4491. 10.1128/AAC.00222-08.PubMed CentralPubMedView ArticleGoogle Scholar
- Maq - Mapping and Assembly With Qualities. [http://maq.sourceforge.net/]
- Holt KE, Teo YY, Li H, Nair S, Dougan G, Wain J, Parkhill J: Detecting SNPs and estimating allele frequencies in clonal bacterial populations by sequencing pooled DNA. Bioinformatics. 2009, 25: 2074-2075. 10.1093/bioinformatics/btp344.PubMed CentralPubMedView ArticleGoogle Scholar
- Goldman N, Yang Z: A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol Biol Evol. 1994, 11: 725-736.PubMedGoogle Scholar
- Cai JJ, Smith DK, Xia X, Yuen K: MBEToolbox: a MATLAB toolbox for sequence data analysis in molecular biology and evolution. BMC Bioinformatics. 2005, 6: 64-10.1186/1471-2105-6-64.PubMed CentralPubMedView ArticleGoogle Scholar
- Huber P: Robust Statistics. 2004, New York. WileyGoogle Scholar
- Pearl J: Causality: Models, Reasoning, and Inference. 2000, New York. Cambridge University PressGoogle Scholar
- NCBI BLAST. [http://blast.ncbi.nlm.nih.gov/]
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