Gene discovery by genome-wide CDS re-prediction and microarray-based transcriptional analysis in phytopathogen Xanthomonas campestris
- Lian Zhou†1,
- Frank-Jörg Vorhölter†2,
- Yong-Qiang He3,
- Bo-Le Jiang3,
- Ji-Liang Tang3,
- Yuquan Xu1,
- Alfred Pühler2Email author and
- Ya-Wen He1Email author
© Zhou et al; licensee BioMed Central Ltd. 2011
Received: 28 January 2011
Accepted: 12 July 2011
Published: 12 July 2011
One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward.
This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of confirmed that Xcc_CDS002 encoding a novel SIR2-like domain protein is involved in virulence and Xcc_CDS1553 encoding a ArsR family transcription factor is involved in arsenate resistance.
Despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.
KeywordsXanthomonas campestris CDS re-prediction microarray analysis new CDS
Over the past two decades, we have witnessed the publication of more than 1,000 complete microbial genome sequences (http://www.ncbi.nlm.nih.gov/genomes/). The trend towards genome sequencing is expected to continue or even accelerate in the near future. The wealth of sequence information has greatly enhanced our understanding of bacterial physiology and biological processes underlying the very organization of life. One of the major tasks of the post-genomic era is "reading" genomic sequences in order to extract all the biological information contained in them. An essential step in this quest is the identification of protein-coding genes, with subsequent functional annotation of the corresponding gene products . A number of gene-finding methods have been developed to address this problem from different points of view. Generally, these gene-finding methods are divided into two broad categories . "Extrinsic" methods take into account information derived from similarity search procedures . "Intrinsic" methods, which deal with DNA sequence only, use statistic or pattern recognition algorithms to find genes in DNA through detection of specific motifs or global statistical patterns. For example, GeneMark employs a hidden Markov model (HMM) to find genes [4–6] while GLIMMER employs an interpolated Markov model [7–9]. Although a wide variety of techniques is used to solve the gene finding problem and a number of prokaryotic gene-finding software are available, gene recognition in bacteria is far from being always straightforward and there are still a lot of wrong or inaccurately annotated genes and missing genes in the published genomes [1, 10–14]. A major reason for this situation may be that genes can be tightly packed in prokaryotes, resulting in frequent overlap. Thus, detection of translation initiation sites and/or selection of the correct coding regions remain difficult . In addition, it is now well known that all microbial genomes contain an abundance of short genes [11, 15]. For statistical reasons, the longer the sequences, the easier it is to detect the codon bias. The short length of these genes probably affects both pillars of CDS prediction, namely intrinsic and extrinsic approaches [11, 16].
The Xanthomonas genus is one of the most ubiquitous groups of plant-associated bacterial pathogens. Members of this genus have been shown to infect at least 124 monocotyledonous and 268 dicotyledonous plant species . Xanthomonas campestris pv. campestris (Pammel) Dowson (Xcc hereafter) is the causal agent of black rot of crucifers, which is possibly the most important disease of crucifers worldwide . So far, genomes of the three Xcc strains ATCC 33913, 8004, and B100 have been sequenced [14, 19, 20]. The genome of Xcc strain ATCC33913 comprises a circular chromosome of 5,076,187 bp encoding a total of 4181 predicted CDSs . The genome of Xcc strain 8004 resides on a single circular chromosome of 5,148,708 bp, which encodes 4273 predicted CDSs . Although the majority of the genes encoded by the two genomes were identical, a total of 108 and 62 CDSs unique to Xcc 8004 and Xcc ATCC33913 were respectively identified . In particular, analysis of the genome of Xcc strain 8004 identified a total of 87 CDSs that have homologs in Xcc ATCC33913, but were not annotated by da Silva et al. . Similarly, annotation of the recent sequenced genome of Xcc B100 identified more than 200 additional CDSs that were not annotated in the other two Xcc strains . Although these newly identified CDSs need to be further verified, the findings suggest that there is still room for improvement in the state of gene identification of Xcc genomes.
In this study, putative protein coding sequences in the two genomes of the Xcc strains 8004 and ATCC33913 were re-predicted using the latest version of three gene-prediction programs. A total of additional 2850 putative new CDSs were identified. Based on the results of similarity searching, transcriptional pattern analysis and functional analysis, a reliable list of 306 new CDSs was obtained from this data set. The function of two newly identified genes was further confirmed by gene deletion and subsequent phenotype analysis.
CDS re-prediction and identification of putative new CDSs
Validation of new CDS by extrinsic evidence
The set of 2850 putative new CDSs was probably contaminated by pseudogene fragments and false-prediction artifacts because all the 3 gene finders are entirely based on intrinsic evidence. To find true CDS, the next strategy used in this study was to get support by extrinsic evidence. All the putative new CDSs were blasted for similar entries within the NCBI non-redundent database by means of BLASTP. Based on the three criteria described in Materials and Methods, a total of 220 putative new CDSs were found to be significantly similar to other protein sequences in the database (Additional file 1).
More recently, the genome sequence of Xcc strain B100 has been published and the genome contained 496 additional CDSs . About half of the these CDSs that were identified by the combined use of the gene finders GISMO  and REGANOR  were also present in the genomes of Xcc strains 8004 and ATCC33913, but have not been annotated . Comparing the 2850 putative new CDSs identified in this study with the 496 additional CDSs in Xcc strain B100, we found an overlapping 72 CDSs (Additional file 1). Among them, 14 CDSs had more than one homologs in non-redundant database and have been included in the 220 putative new CDSs identified by similarity searching; the remaining 58 CDSs had no homologs in non-redundant database except in Xcc strain B100 and were also regarded as new CDSs in this study (Additional file 1). Taken together, a total of 278 CDSs were screened out of 2850 putative new CDSs by extrinsic evidence.
Transcription analysis for new CDS
In order to go further in the validation of our microarray-based method for selecting true CDS, and as we are more interested in DSF signal-regulated CDSs, we chose the 15 DSF signal-regulated CDSs for further transcriptional analysis by reverse transcription PCR. The products of 11 CDSs could be amplified by using total RNAs extracted from cell culture at OD600 = 2.0 (Figure 4B). The resultant RT-PCR products were further verified by sequencing analysis (data not shown). RT-PCR analysis also verified the transcriptional difference of the 11 new CDSs between wild type and rpfF deletion mutant (Figure 4B).
Total new CDSs identified by similarity searching and transcriptional analysis
Xcc _CDS002 encodes a SIR2-like domain protein and is associated with virulence on Chinese cabbage
Xcc _CDS1553 is associated with arsenate resistance in Xcc strain 8004
In this study, we used a combined strategy for CDS prediction. GLIMMER is a computational gene-finding system and the technical underpinning of the system is an interpolated Markov model (IMM), a generalization of Markov chain methods . The GeneMark program is an ab initio gene finder, which employs inhomogeneous (three-periodic) Markov chain models describing protein-coding DNA and homogeneous Markov chain models describing non-coding DNA . ZCURVE is a system for recognizing protein-coding genes in bacterial genome, which uses the "Z-transformation" of DNA as information source for classification . The results showed that 99.7% of the CDSs (4168 of 4181) in the existing annotations of strain ATCC33913 and 99.5% of the CDSs (4254 of 4273) of strain 8004 could be predicted by the combined strategy (Figure 2A), suggesting that the combined gene finding strategy works well for finding currently annotated genes in Xcc genomes. In addition to the CDSs in the existing annotations of Xcc genomes, a total of 2850 putative new CDSs were identified in the two Xcc genomes by the combined gene prediction strategy. Among them, 306 reliable new CDSs were further confirmed by subsequent analysis based on extrinsic similarity or/and transcript detection, suggesting that the combined gene finding strategy could be used for finding new CDS in bacterial genomes. Considering the number of putative CDSs predicted and those having been confirmed by extrinsic evidence and/or microarray analysis (Figure 2B), GLIMMER seems more powerful than GeneMark and ZCURVE in new CDS prediction.
Microarrays traditionally have been used to analyze the expression behavior of large numbers of annotated genes in bacteria. In this study, microarray analysis, applied together with CDS prediction, was used to find new genes, which was further validated by RT-PCR analysis. Compared to other transcript detection methods, microarray analysis is more sensitive and suitable for highthroughput analysis. So far, a similar strategy has only been reported for Escherichia coli. Selinger et al.  introduced a high-density oligonucleotide probe array for E. coli that not only carries strand-specific probes for all mRNA, tRNA, and rRNA regions, but also covers intergenic regions of >40 bp. Using E. coli RNA from cells grown on different media, over 1100 transcripts corresponding to intergenic regions were identified. Further classification revealed 317 novel transcripts with unknown function .
SIR2 proteins are found in organisms ranging from bacteria to humans . In eukaryotes, SIR2 proteins regulate transcriptional repression, recombination, the cell division cycle, microtubule organization, cellular responses to DNA-damaging agents and aging [28, 29]. A phylogenetically conserved NAD+-dependent protein deacetylase activity has been demonstrated in Sir2 family proteins in eukaryotes [34–36]. So far very limited evidence is available regarding the function of SIR-2 proteins in bacteria. The only reported case was from Salmonella typhimurium, where the gene cobB is involved in the biosynthesis of cobalamin and the catabolism of propionate . Further analysis revealed that the recombinant SIR2 protein CobB had NAD-dependent ADP-ribosyltransferase activity in vitro. The demonstration that the ribosyltransferase and NAD+-dependent protein deacetylase activities are both dependent on an acetylated substrate confirms the fundamental link between the two activities . The true enzymatic activity of Sir2x and how Sir2x is involved in the regulation of virulence in Chinese cabbage remains to be dissolved. The involvement of sir2x in virulence of Xcc strain XC1 is in good agreement with previous findings that transposon insertion in the promoter region of XC4281 encoding a phage-related regulatory protein cII led to a complete loss of virulence of Xcc strain 8004 on radish . As shown in Figure 6, XC4281 and the newly identified sir2x are within the same operon and they share a common promoter. Transposon insertion in the promoter region probably disrupts not only the expression of XC4281, but also the expression of sir2x. The roles of Sir2x in Xcc virulence remains to be dissolved.
Arsenic, a toxic metalloid, is currently and has always been ranked first on the Superfund List of Hazardous Substances (available on the World Wide Web), in part because of its environmental ubiquity. As a consequence, many bacterial species have genes that confer resistance to arsenic. Environmental arsenic is sensed by members of the ArsR/SmtB family of metalloregulatory transcriptional repressors [30, 39], which represses the expression of operons involved in the uptake, efflux, sequestration, or detoxification of metal ions . This study identified an ArsR family repressor and found that the XC2294-XC2295-arsR operon is involved in arsenate resistance in Xcc strain 8004. Since no ArsR homologs were found in Xcc strains ATCC33913, B100 and XC1, we propose that the arsR may have been acquired by Xcc strain 8004 in a lateral gene transfer event.
This study reported a thorough search for new CDS in the two published Xcc genomes. In the first, putative CDSs encoded in the two genomes were re-predicted using three gene finders, resulting in the identification of 2850 putative new CDSs. In the second, similarity searching was conducted and 278 CDSs were found to have homologs in other bacterial species. In the third, oligonucleotide microarray and RT-PCR analysis identified 147 CDSs with detectable mRNA transcripts. Finally, in-frame deletion and subsequent phenotype analysis of the two newly identified CDSs confirmed their functionality. Our results showed that, despite sophisticated approaches available for genome annotation, many cellular transcripts have remained unidentified so far in Xcc genomes. Through a combined strategy involving bioinformatic, postgenomic and genetic approaches as demonstrated in this study, a reliable list of 306 new CDSs was identified and a more thorough understanding of some cellular processes was gained.
Bacterial strains and growth conditions
Xcc strains XC1 and 8004 were grown at 30°C with shaking (250 rpm/min) in YEB, LB or NYG medium as described by He et al. . E. coli strains were grown at 37°C in LB medium. Antibiotics were added at the following concentrations when required: kanamycin, 100 μg/ml, rifampicin, 25 μg/ml, and tetracycline, 10 μg/ml.
Nucleotide sequence source, gene prediction and domain analysis
Complete genome records of the Xcc strains ATCC33913 and 8004 [19, 20] were downloaded from the NCBI Microbial genome database (http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi?view=1). Gene prediction was conducted by the gene finders GLIMMER 2.03 , GeneMark  and ZCURVE . For the prediction, the minimum length of CDS was set as 90 bp. BLASTN (http://blast.ncbi.nlm.nih.gov/Blast.cgi) was used to find the locations of all the putative new CDSs in the genomes of Xcc strain 8004 and ATCC33913. Multiple sequence alignment analysis was performed using CLUSTAL W (1.83) (http://sbcr.bii.a-star.edu.sg/clustalw/). Domain architecture analysis was performed using the SMART database application (http://smart.embl-heidelberg.de/). The nucleic acid sequences of two well-studied regulator sir2x and arsR have been deposited in the NCBI GeneBank database and the accession numbers are JF966390 and JF966391.
Screening new CDS by extrinsic evidence
The amino acid sequences of all 2850 putative new CDSs were submitted for BLASTP analysis. Homologs in the nr database were selected on the basis of the following three criteria. Firstly, only the subjects with E-values lower than 10-4 were considered hits. Secondly, the subjects should have similar sizes as the queries. Thirdly, for each query there should be more than one matched subject unless the E-value is very low (less than 10-30).
Design and synthesis of CDS-specific oligonucleotides, and preparation of Xcc oligo microarray chip
Based on the annotated genome sequences of the Xcc strains ATCC33913 and 8004 [19, 20], we used a CDS-specific oligonucleotide selection algorithm  to successfully design unique 50-mer oligonucleotides for 1724 putative new CDSs. The majority of these CDSs were more than 300 bps in length. As specificity controls, 50-mer oligonucleotides were also designed based on the sinat5 (NCBI No.: AF480944) and nac1 (NCBI No.: AF198054) genes of Arabidopsis thaliana, and the genes rag1 (NCBI No.: NM_131389) of zebrafish and the olf1 (NCBI No.:U56420) of Homo sapiens. Thus, a total of 5770 CDS-specific oligonucleotides representing 4042 annotated CDS , plus 1724 putative new CDSs, and 4 specificity controls were used for the oligonucleotide microarray chip preparation. Oligonucleotides were synthesized at a 50 nmol scale by Operon Technologies (Alameda, CA, USA). The protocol employed for constructing the oligo-chip has been previously described . Briefly, all oligos were dissolved in saline sodium citrate buffer (3 × SSC) to a final concentration of 40 μM. Oligo samples were arrayed with Pixsys 5500XL Arrayer (Cartesian) to poly-L-Lysine-coated microscope slides. DNA samples were fixed by rehydration, snap-drying and UV cross-linking. The remaining poly-L-Lysine on the slides was rendered non-reactive by treatment with blocking solution (150 mM succinic anhydride in 1-methy-2-pyrrolidinone, buffered with 85 mM sodium borate, pH 8.0) for 30 min. After washing with water, the array plates were rinsed with 95% ethanol and dried.
Isolation of total RNA and microarray analysis
Bacterial cells were collected by centrifugation at 4°C for 5 min at 10,000 rpm. Total RNA samples were prepared by using RNeasy midi columns following the manufacturer's instructions (Qiagen). RNA integrity was confirmed by electrophoresis using a 1.3% formaldehyde agrose gel. The quality of DNA-free RNA was monitored by PCR and RT-PCR analysis of at least two known genes. Cy3- or Cy5-labeled cDNA was generated by using random hexamers as primers for reverse transcription (Invitrogen). cDNA labeling, purification and hybridization against the microarray were conducted as previously described . Slides were scanned for the fluorescent intensity using a ScanArray 5000 laser scanner. The signal intensities were quantified by using the software ImaGene 5 (BioDiscovery). Hybridization signals were normalized using the scale normalization procedure previously described . Each treatment was repeated three times and the data presented were the means of two representative replicates. The fold changes were then calculated from the normalized log ratios.
Screening new CDS by statistical analysis of microarray hybridization signal intensity
In this study, oligonucleotide microarray analysis was used to detect transcription, so as to confirm the functionality of the putative new CDSs. The putative CDSs with detectable transcript was identified using the normalized signal median of the corresponding probe. To calculate the normalized signal median, firstly the average signal median S0 of 8 negative control probes representing 4 Arabidopsis and zebrafish genes  was determined by using the following formula: S0 = ∑(SAZ-BAZ)/8, where SAZ indicates the signal median of the negative control probe and BAZ indicates the corresponding background signal median. Secondly, the normalized signal median (S) of the putative new CDSs was calculated following the formula: S = SCDS - BCDS -S0, where SCDS indicates the signal median of the putative new CDS and BCDS indicates the background median of the putative new CDSs. Finally, if S >0, it is regarded as CDS with detectable transcript.
Reverse transcription (RT) PCR analysis
RT-PCR analysis was conducted using a QIAGEN®OneStep RT-PCR Kit following the manufacturer's instructions. The primers used for RT-PCR analysis are listed in Additional file1. Total RNAs were extracted from bacterial culture grown in YEB medium at OD600 = 2.0 and a total of 200 ng of total RNA was used for reverse transcription. The cycle number differed in the amplification of different CDS products.
Generation of in-frame deletion mutants and complementation analysis
Spontaneous rifampicin-resistant derivatives of strain XC1 or 8004 were used as parental strains for generation of deletion mutants. In-frame deletion of Xcc _CDS002 (sir2x) and Xcc _CDS1553 (arsR) was conducted using the primers listed in Additional file 1 following the methods described previously . For complementation analysis, the coding regions of sir2x and arsR respectively were amplified by PCR using the primers listed in Additional file 1 and cloned under the control of lac promoter in expression vector pLAFR3. The resultant constructs were transferred into Xcc strains through triparental mating.
Quantitative determination of extracellular enzyme activity, EPS production and virulence test
The extracellular cellulase and protease activity and EPS production in the culture supernatants of Xcc strains at OD600 = 2.3 were measured according to the methods described previously . The virulence of Xcc to Chinese cabbage was determined following the scissors-clipping method described previously . Fifteen plants were inoculated for each bacterial strain and the experiment was repeated three times.
Arsenate resistance assay
Sodium arsenate (SIGMA) was added in the following final concentrations (mM): 0.10, 0.25, 0.50, 0.75 and 1.00. Fifty microliters of fresh culture of Xcc strain 8004 were inoculated into 5 ml of NYG liquid media with rifampicin (25 μg/ml) and sodium arsenate at different concentrations and grown at 28°C with shaking (250 rpm/min) for overnight. Bacterial growth was indicated by measuring the optical density at 600 nm.
This work was supported by a Research Foundation for Returned Scholars, Shanghai Jiao Tong University (WS3107208008 to YWH). We thank Mr. Jianli Wang at Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore for mass blasting analysis, and Prof. Lian-Hui Zhang and Prof. Byrappa Venkatesh at IMCB for technical support.
- Bocs S, Danchin A, Médigue C: Re-annotation of genome microbial coding-sequences: finding new genes and inaccurately annotated genes. BMC Bioinformatics. 2002, 3: 5-10.1186/1471-2105-3-5.PubMedPubMed CentralView ArticleGoogle Scholar
- Fickett JW: Finding genes by computer: the state of the art. Trends in genetics. 1996, 12: 316-320. 10.1016/0168-9525(96)10038-X.PubMedView ArticleGoogle Scholar
- Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25 (17): 3389-3402. 10.1093/nar/25.17.3389.PubMedPubMed CentralView ArticleGoogle Scholar
- Lukashin AV, Borodovsky M: GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 1998, 26 (4): 1107-1115. 10.1093/nar/26.4.1107.PubMedPubMed CentralView ArticleGoogle Scholar
- Besemer J, Lomsadze A, Borodovsky M: GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001, 29 (12): 2607-2618. 10.1093/nar/29.12.2607.PubMedPubMed CentralView ArticleGoogle Scholar
- Besemer J, Borodovsky M: GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 2005, W451-454. 33 Web Server
- Salzberg SL, Delcher AL, Kasif S, White O: Microbial gene identification using interpolated Markov models. Nucleic Acids Res. 1998, 26: 544-548. 10.1093/nar/26.2.544.PubMedPubMed CentralView ArticleGoogle Scholar
- Delcher AL, Harmon D, Kasif S, White O, Salzberg SL: Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999, 27: 4636-4641. 10.1093/nar/27.23.4636.PubMedPubMed CentralView ArticleGoogle Scholar
- Delcher AL, Bratke KA, Powers EC, Salzberg SL: Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics. 2007, 23 (6): 673-679. 10.1093/bioinformatics/btm009.PubMedPubMed CentralView ArticleGoogle Scholar
- Camus JC, Pryor MJ, Médigue C, Cole ST: Re-annotation of the genome sequence of Mycobacterium tuberculosis H37Rv. Microbiology. 2002, 148: 2967-73.PubMedView ArticleGoogle Scholar
- Harrison PM, Carriero N, Liu Y, Gerstein M: A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs. J Mol Biol. 2003, 333 (5): 885-892. 10.1016/j.jmb.2003.09.016.PubMedView ArticleGoogle Scholar
- Nielsen P, Krogh A: Large-scale prokaryotic gene prediction and comparison to genome annotation. Bioinformatics. 2005, 21 (24): 4322-4329. 10.1093/bioinformatics/bti701.PubMedView ArticleGoogle Scholar
- Salzberg SL: Genome re-annotation: a wiki solution?. Genome Biol. 2007, 8 (1): 102-PubMedPubMed CentralGoogle Scholar
- Vorhölter FJ, Schneiker S, Goesmann A, Krause L, Bekel T, Kaiser O, Linke B, Patschkowski T, Rückert C, Schmid J, Sidhu VK, Sieber V, Tauch A, Watt SA, Weisshaar B, Becker A, Niehaus K, Pühler A: The genome of Xanthomonas campestris pv. campestris B100 and its use for the reconstruction of metabolic pathways involved in xanthan biosynthesis. J Biotechnol. 2008, 134 (1-2): 33-45. 10.1016/j.jbiotec.2007.12.013.PubMedView ArticleGoogle Scholar
- Ibrahim M, Nicolas P, Bessières P, Bolotin A, Monnet V, Gardan R: A genome-wide survey of short coding sequences in streptococci. Microbiology. 2007, 153 (11): 3631-3644. 10.1099/mic.0.2007/006205-0.PubMedView ArticleGoogle Scholar
- Borodovsky M, Koonin EV, Rudd KE: New genes in old sequence: a strategy for finding genes in the bacterial genome. Trends in Biochemical Sciences. 1994, 19 (8): 309-313. 10.1016/0968-0004(94)90067-1.PubMedView ArticleGoogle Scholar
- Leyns F, De Cleene M, Swings J, De Ley J: The host range of the genus Xanthomonas. Bot Rev. 1984, 50: 308-355. 10.1007/BF02862635.View ArticleGoogle Scholar
- Williams PH: Black rot: a continuing threat to world crucifers. Plant Dis. 1980, 64: 736-742. 10.1094/PD-64-736.View ArticleGoogle Scholar
- da Silva AC, Ferro JA, Reinach FC, et al: Comparison of the genomes of two Xanthomonas pathogens with differing host specificities. Nature. 2002, 417: 459-463. 10.1038/417459a.PubMedView ArticleGoogle Scholar
- Qian W, Jia Y, Ren SX, et al: Comparative and functional genomic analyses of the pathogenicity of phytopathogen Xanthomonas campestris pv. campestris. Genome Res. 2005, 15: 757-767. 10.1101/gr.3378705.PubMedPubMed CentralView ArticleGoogle Scholar
- Borodovsky M, McIninch J: GeneMark: parallel gene recognition for both DNA strands. Computers Chemistry. 1993, 17: 123-133.View ArticleGoogle Scholar
- Guo FB, Ou HY, Zhang CT: ZCURVE: a new system for recognizing protein-coding genes in bacterial and archaeal genomes. Nucleic Acids Res. 2003, 31 (6): 1780-1789. 10.1093/nar/gkg254.PubMedPubMed CentralView ArticleGoogle Scholar
- Krause L, McHardy AC, Nattkemper TW, Pühler A, Stoye J, Meyer F: GISMO-gene identification using a support vector machine for ORF classification. Nucleic Acids Res. 2007, 35 (2): 540-549.PubMedView ArticleGoogle Scholar
- Linke B, McHardy AC, Neuweger H, Krause L, Meyer F: REGANOR: a gene prediction server for prokaryotic genomes and a database of high quality gene predictions for prokaryotes. Appl Bioinformatics. 2006, 5 (3): 193-198. 10.2165/00822942-200605030-00008.PubMedView ArticleGoogle Scholar
- He YW, Xu M, Lin K, Ng YJ, Wen CM, Wang LH, Liu ZD, Zhang HB, Dong YH, Dow JM, Zhang LH: Genome scale analysis of diffusible signal factor regulon in Xanthomonas campestris pv. campestris: identification of novel cell-cell communicationdependent genes and functions. Mol Microbiol. 2006, 59: 610-622. 10.1111/j.1365-2958.2005.04961.x.PubMedView ArticleGoogle Scholar
- He YW, Boon C, Zhou L, Zhang LH: Co-regulation of Xanthomonas campestris virulence by quorum sensing and a novel two-component regulatory system RavS/RavR. Mol Microbiol. 2009, 71 (6): 1464-1476. 10.1111/j.1365-2958.2009.06617.x.PubMedView ArticleGoogle Scholar
- He YW, Ng AY, Xu M, Lin K, Wang LH, Dong YH, Zhang LH: Xanthomonas campestris cell-cell communication involves a putative nucleotide receptor protein Clp and a hierarchical signalling network. Mol Microbiol. 2007, 64: 281-292. 10.1111/j.1365-2958.2007.05670.x.PubMedView ArticleGoogle Scholar
- Frye RA: Phylogenetic Classification of Prokaryotic and Eukaryotic Sir2-like Proteins. Biochemical and Biophysical Research Communications. 2000, 273: 793-798. 10.1006/bbrc.2000.3000.PubMedView ArticleGoogle Scholar
- North BJ, Verdin E: Sirtuins: Sir2-related NAD-dependent protein deacetylases. Genome Biology. 2004, 5: 224-10.1186/gb-2004-5-5-224.PubMedPubMed CentralView ArticleGoogle Scholar
- Busenlehner LS, Pennella MA, Giedroc DP: The SmtB/ArsR family of metalloregulatory transcriptional repressors: structural insights into prokaryotic metal resistance. FEMS Microbiology Reviews. 2003, 27: 131-143. 10.1016/S0168-6445(03)00054-8.PubMedView ArticleGoogle Scholar
- Campbell DR, Chapman KE, Waldron KJ, Tottey S, Kendall S, Cavallaro G, Andreini C, Hinds J, Stoker NG, Robinson NJ, Cavet JS: Mycobacterial cells have dual nickel-cobalt sensors: sequence relationships and metal sites of metal-responsive repressors are not congruent. J Bio Chem. 2007, 282 (44): 32298-32310. 10.1074/jbc.M703451200.View ArticleGoogle Scholar
- Selinger DW, Cheung KJ, Mei R, Johansson EM, Richmond CS, Blattner FR, Lockhart DJ, Church GM: RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nat Biotechnol. 2000, 18: 1262-1268. 10.1038/82367.PubMedView ArticleGoogle Scholar
- Tjaden B, Saxena RM, Stolyar S, Haynor DR, Kolker E, Rosenow C: Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays. Nucleic Acids Res. 2002, 30: 3732-3738. 10.1093/nar/gkf505.PubMedPubMed CentralView ArticleGoogle Scholar
- Imai S, Armstrong CM, Kaeberlein M, Guarente L: Transcriptional silencing and longevity protein Sir2 is an NAD-dependent histone deacetylase. Nature. 2000, 403: 795-800. 10.1038/35001622.PubMedView ArticleGoogle Scholar
- Landry J, Sutton A, Tafrov ST, Heller RC, Stebbins J, Pillus L, Sternglanz R: The silencing protein SIR2 and its homologs are NAD-dependent protein deacetylases. Proc Natl Acad Sci USA. 2000, 97: 5807-5811. 10.1073/pnas.110148297.PubMedPubMed CentralView ArticleGoogle Scholar
- Smith JS, Brachmann CB, Celic I, Kenna MA, Muhammad S, Starai VJ, Avalos JL, Escalante-Semerena JC, Grubmeyer C, Wolberger C, Boeke JD: A phylogenetically conserved NAD+-dependent protein deacetylase activity in the Sir2 protein family. Proc Natl Acad Sci USA. 2000, 97: 6658-6663. 10.1073/pnas.97.12.6658.PubMedPubMed CentralView ArticleGoogle Scholar
- Tsang AW, Escalante-Semerena JC: cobB function is required for catabolism of propionate in Salmonella typhimurium LT2: evidence for existence of a substitute function for CobB within the 1,2-propanediol utilization (pdu) operon. J Bacteriol. 1996, 178: 7016-7019.PubMedPubMed CentralGoogle Scholar
- Frye RA: Characterization of five human cDNAs with homology to the yeast SIR2 gene: Sir2-like proteins (sirtuins) metabolize NAD and may have protein ADP-ribosyltransferase activity. Biochem Biophys Res Commum. 1999, 260: 273-279. 10.1006/bbrc.1999.0897.View ArticleGoogle Scholar
- Xu C, Rosen BP: Metalloregulation of Soft Metal Resistance Pumps. Metals and Genetics. Edited by: Sarkar B. 1999, New York, Plenum Press, 5-19.View ArticleGoogle Scholar
- Tottey S, Harvie DR, Robinson NJ: Understanding how cells allocate metals using metal sensors and metallochaperones. Accounts of Chemical Research. 2005, 38: 775-783. 10.1021/ar0300118.PubMedView ArticleGoogle Scholar
- Lin K, Liu J, Miller DL, Wong L: Genome-wide cDNA oligo design and its applications in Schizosaccharomyces pombe. The Practical Bioinformatician. Edited by: Wong L. 2004, Singapore, World Scientific Publishing, 347-358.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.