Comparative genomics of Streptomyces avermitilis, Streptomyces cattleya, Streptomyces maritimus and Kitasatospora aureofaciens using a Streptomyces coelicolor microarray system

DNA/DNA microarray hybridization was used to compare the genome content of Streptomyces avermitilis, Streptomyces cattleya, Streptomyces maritimus and Kitasatospora aureofaciens with that of Streptomyces coelicolor A3(2). The array data showed an about 93% agreement with the genome sequence data available for S. avermitilis and also showed a number of trends in the genome structure for Streptomyces and closely related Kitasatospora. A core central region was well conserved, which might be predicted from previous research and this was linked to a low degree of gene conservation in the terminal regions of the linear chromosome across all four species. Between these regions there are two areas of intermediate gene conservation by microarray analysis where gene synteny is still detectable in S. avermitilis. Nonetheless, a range of conserved genes could be identified within the terminal regions. Variation in the genes involved in differentiation, transcription, DNA replication, etc. provides interesting insights into which genes in these categories are generally conserved and which are not. The results also provide target priorities for possible gene knockouts in a group of bacteria with a very large numbers of genes with unknown functions compared to most bacterial species.
Introduction
Streptomyces are a group of aerobic high %G+C Gram positive bacteria that undergo complex differentiation to form filamentous mycelium, aerial hyphae and spores. In addition, they produce a broad range of secondary metabolites including antibiotics, antiparasitic agents, herbicides, anti-cancer drugs and various enzymes of industrial importance. Two Streptomyces species have had their complete genome sequences published, namely the model organism Streptomyces coelicolor (%G+C = 72.1) and avermictin producer Streptomyces avermitilis (%G+C = 70.7) (Bentley et al. 2002; Ikeda et al. 2003). Two important aspects of the genomes structures of Streptomyces were supported by sequence data. Firstly, that the genome size of Streptomyces is large compared to other bacteria; 8,667,507 basepairs for S. coelicolor (7,825 protein coding genes) and 9,025,608 bp (7,577 protein coding genes) for S. avermitilis. Secondly, that the genomes of these two species are linear and both ends contain unique terminal inverted repeats that probably covalently bind a terminal protein. Terminal inverted repeats and covalently bound terminal proteins are not found in the limited number of other bacteria that have linear chromosomes such as Borrelia burgdorferi and Agrobacterium tumefaciens and, up to the present, seem to be unique to the Streptomyces and perhaps other Actinobacteria (Lin et al. 1993; Chen et al. 2002; Goodner et al. 1999; Huang et al. 2004). Over 2,500 Streptomyces strains are present in the Ribosomal Database Project (http://www.rdp.cme.msu.edu), over 1,500 are available at the American Type Culture Collection (http://www.atcc.org/) and many more are held in both public and private culture collections throughout the world. Analysis of the small subunit ribosomal RNA gene sequences of Streptomyces confirms that they form a monophyletic clade, but one with considerable diversity. In addition, there is significant gene diversity at the interspecies level across the genomes of both completely sequenced Streptomyces with 2,291 gene unique to S. avermitilis and 2,307 genes unique to S. coelicolor.. This makes them particularly interesting targets for comparative genomic studies. In this study we chose four species to begin an analysis of the genomic diversity of the Streptomyces. S. avermitilis was chosen because of the availability of the complete genome sequence of this species, while Streptomyces maritimus was chosen because of its intermediate position in terms of phylogeny within the Streptomyces. Streptomyces cattleya was chosen because, based on small subunit ribosomal RNA sequence, this species is phylogenetically quite divergent from S. coelicolor and branches near the root of the Streptomyces clade. Streptomyces cattleya is a β-lactam producing species. Finally, Kitasatospora aureofaciens was chosen as this genus is very closely related to the Streptomyces.
The availability of two microarrays for S. coelicolor (Lum et al. 2004; Huang et al. 2001; Vinciotti et al. 2005; http://www.surrey.ac.uk/SBMS/Fgenomics/Microarrays/index.html) makes possible a comparative genomic analysis of Streptomyces species. The genes that make up the genome of S. coelicolor have been classified based on scheme of Riley and colleagues for E. coli and modified for S. coelicolor (http://www.sanger.ac.uk/Projects/S_coelicolor/scheme.shtml). A microarray analysis of the genomes of these Streptomyces using the S. coelicolor microarray is able to provide a wide ranging comparative analysis of the conserved genome content of these Streptomyces. This type of approach, where a heterologous microarray is used to analyze the genome content of a range of strains or species, has been successfully used in a wide range of organisms (Akman and Aksoy 2001; Akman et al. 2001; Behr et al. 1999; Chan et al. 2003; Cho and Tiedje 2001; Dorrell et al. 2001; Dziejman et al. 2002; Fitzgerald et al. 2001; Gill et al. 2002; Leonard et al. 2003; Murray et al. 2001; Porwollik et al. 2002; Salama et al. 2000; Israel et al. 2001; Rajashekara et al. 2004). The strains analyzed using this approach range from intraspecies comparisons such as Campylobacter jejuni, Vibrio cholerae and Staphylococcus aureus (Dorrell et al. 2001; Dziejman et al. 2002; Fitzgerald et al. 2001) to interspecies comparisons such as Sodalis glossinidiusversus an Escherichia coli array, Salmonella bongori versus a Salmonella enterica array, Shewanella species versus Shewanella oneidensis and E. coli arrays and Brucella species versus a Brucella melitensis array (Akman et al. 2001; Chan et al. 2003; Murray et al. 2001; Rajashekara et al. 2004).
In this study, we used both versions of the S. coelicolor genome microarrays to compare the gene complements of the three Streptomyces species and one Kitasatospora species. The genus Kitasatospora is closely related to the genus Streptomyces in terms of morphology, chemical taxonomy and small subunit ribosomal RNA sequence analysis. Thus, the choice of a species from this genus acts as potential outgroup in terms of overall genome structure. In terms of genes that are conserved, the types of genes of particular interest include genes involved in secondary metabolism, genes involved in chromosome replication, genes in the terminal regions of the chromosome, sigma factors, genes involved in differentiation and hypothetical genes. In terms of gene absence, the distribution of such genes along the chromosome and the apparent absence of any major housekeeping genes in a specific species are of interest. This information provides insights into genes that make up the core complement for a member of the Streptomyces and into which genes are central to defining a Streptomyces species.
Materials and methods
16S phylogeny
This was carried out on selected small subunit 16S ribosomal RNA gene sequences obtained from Ribosomal Database Project-II Release 9 (http://www.rdp.cme.msu.edu/index.jsp) and aligned using CLUSTALX (Thompson et al. 1997). The analysis was carried out using Neighbor-Joining algorithm from the same program. In the case of S. maritimus, the taxonomy of the strain was confirmed by DNA sequencing of the 16S ribosomal RNA gene.
Arrays
Two series of arrays that cover about 97% of the complete genome of Streptomyces coelicolor A3(2) (Lum et al. 2004; http://www.surrey.ac.uk/SBMS/Fgenomics/Microarrays/index.html) were used in this study. Both arrays are PCR arrays, but from different sources, namely Stanford University, USA and the University of Surrey, UK and made up of different PCR products. The Stanford array as used in this study contained sequences covering 7603 open reading frames. The Surrey microarray is made up of 7,758 unique PCR amplified sequences, 7,563 from the chromosome and 195 from SCP1. There are an additional 376 non-unique, alternative and cross-hybridizing sequences that are also spotted on to the array together with no probe spots and control spots. The two types of arrays were used to improve validation with a system using heterologous hybridization; however, only the University of Surrey array was hybridized and analyzed in duplicate. The major difference between the two arrays was that the Surrey array did not include a number of transposition element related genes, although there were other overlap differences. The sequences of the PCR products are not available for either array due to intellectual property protection requirements.
Strains and growth conditions
S. coelicolor A3(2) (SCP1+) 104, S. avermitilis ATCC 31267, S. cattleya ATCC 35852, S. maritimus Yang-Ming and K. aureofaciens ATCC 10762 were used in these studies. Fresh spores were collected and mycelium cultured in TSB liquid medium with 0.5% glycine at 30°C overnight.
Preparation of labeled DNA
Genomic DNA from a stationary phase culture was purified by the salting out procedure (Pospiech and Neumann, 1995) and had been sonicated to < 2 Kb. Four to six micrograms of sonicated genomic DNA were used as template and this was denatured in the presence of 12 μg of 72%-GC-content random hexamers in a total volume of 25 μl at 100°C for 10 min. The mixture was then snap-cooled on ice before adding the remaining reaction components: 1.5 μl of Cy3-dCTP or Cy5-dCTP (Amersham Pharmacia Biotech), 4μl Klenow fragment (NEB #212), 5μl Klenow buffer, 0.5 μl dNTP (4 mM dATP, 4 mM dTTP, 10 mM dGTP, and 0.2 mM dCTP), and 14 μl ddH2O. The random primed labeling reaction was carried out for 2–3 h at 37°C. Buffer exchange, purification and concentration of the DNA products was accomplished by three cycles of diluting the reaction mixture in 0.5 ml TE buffer (10 mM Tris and 1 mM EDTA pH 8.0) and filtering though a Microcon-30 microconcentrators (Millipore).
Microarray hybridization and data analysis
The two DNA pools to be compared were mixed and applied to an array in a hybridization mixture that contained 3.68 × SSC, 0.18% SDS, and 1 μg yeast tRNA (total 16.3 μl), which had been heated at 100°C for 5 min before being applied to array. Hybridization took place under a glass coverslip sealed by glue in a humidified Omnislide (Thermo Hybaid) at 60°C for 12–14 h. The slides were washed, dried and scanned for fluorescence using a GenePix TM 4000B scanner (Axon instruments). Average signal intensity and local background measurements were obtained for each spot on each array using GenePixPro software. The dataset was screened for aberrant spots and these were eliminated from the analysis after manual checking. Most genes are present in duplicate on the two arrays and the signal from each pair of spots was inputted into the computer program available from ScanAlyze (Eisen et al. 1998; Gollub et al. 2003). The data was then processed into a mean log2 Cy3/Cy5 ratio format. The dataset was normalized for each array separately and outputted to Excel where after checking the alignment of the datasets from each array, a mean signal for each common gene was calculated. Genes that were absent from either array, mostly transposon related genes in the University of Surrey array, were not included in the analysis. Based on Bentley et al. 2002, the mean signal and standard deviation for the core region of genes from SCO2050 to SCO5800 was calculated. The standard deviation was used to set a cut-off for gene absence at 2SD below the core mean. The microarray data is presented relative to the S. coelicolor standard in two ways. This is either as a color plot of the genes where green presents a negative hybridization signal, black represents an equal hybridization signal and red indicates a positive hybridization signal using the program Treeview (Eisen et al. 1998) or as numeric values for the signal from each gene. The microarray data for the four species described here and additional unpublished species can be accessed via rkirby@ym.edu.tw.
Comparison of the microarray dataset for S. avermitilis with the complete genome sequence
The nucleotide sequences for all the identified open reading frame from the S. avermitilis genome sequence (Ikeda et al. 2003) were compared with the genome sequence of S. coelicolor using blastn limiting the output to the best match. This E value dataset for the genes was then aligned with the S. avermitilis microarray dataset and a comparison plotted as a scatterplot. Genes showing disagreement between the two datasets were identified based on a 2 Standard Deviation (SD) cutoff for the microarray dataset and a E-10 cutoff for the blast value.
Analysis of gene presence across the chromosome
A graphical display was created by counting the number of gene detected as present from the signal based on the 2SD cutoff from each normalized microarray dataset using a moving window of 10 genes in steps of one.
Results and discussion
Comparison of S. avermitilis, S. cattleya, S. maritimus and K. aureofaciens with the S. coelicolor genome
In total, after spot and data validation, a total of 7,083 open reading frames were included in this analysis as presence on both types of array and giving analyzable signal on all three arrays. Validity in this study was initially obtained by using microarrays from two sources that presumably use different PCR products to create the arrays. In addition, the University of Surrey array was hybridized and analyzed in duplicate. In terms of gene absence based on two standard deviations as described in the “Materials and methods" section, the agreement between the Stanford array and the duplicated University of Surrey array was about 95%, while the agreement between the two University of Surrey arrays was about 98%. In order to minimize the effect of divergent individual array spots, the signal mean for each gene from the three arrays was used throughout this study.
In this study, the genomic content of three Streptomyces species and one Kitasatospora species with divergent taxonomy, antibiotic production and SSU rRNA sequence are compared using two different S. coelicolor microarrays. It is clear that there are inherent limitations to this approach. Firstly, only gene absence or divergence rather than the presence of new genes can be identified. Secondly, it is not possible to clearly separate the absence of a gene from the presence of a divergent homologue of the same gene. Finally, although the order of the genes in S. coelicolor and S. avermitilis are known from their complete genome sequences and are well conserved, this does not mean that the synteny of most of them is conserved in other Streptomyces species. However, the detection of synteny across Actinobacteria including Mycobacterium tuberculosis, Corynebacteriun glutamicum and other species (Bentley et al. 2002 and unpublished data) supports a conserved central core structure to the genomes of the Actinomycetes and a priori most Streptomyces. Thus, although major chromosomal reorganizations in the central core region cannot be detected by microarray data, a basic chromosomal structure can be assumed as a first approximation; namely, a linear chromosome with variable terminal regions and a relatively well conserved core region.
When the pooled data from the two arrays for the four species was analyzed using Cy-3 labeled S. coelicolor A(3)2 chromosomal DNA compared to heterologous Cy-5 labeled chromosomal DNA, a wide range of signal variation could be noted and this is shown in Supplementary Fig. 1. The SSU rRNA tree places the divergence of these four strains from S. coelicolor as S. cattleya > K. aureofaciens > S. maritimus > S.avermitilis (Fig. 1). Gene differences were present in the order S. cattleya > K. aureofaciens > S. avermitilis > S. maritimus based on −2SD cutoff below the mean signal for the core region genes. The microarray data thus shows general agreement with S. cattleya and K aureofaciens being more divergent and the other two species being relatively closer. It is interesting to note that the Kitasatospora species used in this study, K. aureofaciens, shows the same general structure as the Streptomyces species. This is not unexpected and confirms the close relationship between Kitasatospora and Streptomyces and agrees with the SSU rRNA tree data.Fig. 1SSU rRNA phylogenetic tree of selected Streptomyces species and other Actinomycetes that have known complete genome sequences. The species analyzed by microarray are indicated in bold
Further support of the reliability of the data comes from a comparison of the blastn E values for all genes and the microarray data as shown in the Fig. 2 scatterplot. This indicated 232 out of 6,832 genes show gene absence by microarray when they seem to be present by blastn and 268 out of 6,832 gene show gene presence by microarray when they seem to be absent by blastn; these results are both based on cutoffs of −2SD for the microarray data and −10 for the E value. This gives an overall reliability for S. coelicolor compared to S. avermitilis of 93%. Potential errors factors include in the case of the former type of error, poor spotting of the array at that point and choice of the PCR product sequence (the comparison is with the whole gene, as the PCR products are not available) and in the latter case cross-hybridization between multiple gene copies or a unreliable hybridization signal due to poor washing in that area. However, the results for S. avermitilis clearly support the reliability of the genome comparisons produced by this study.Fig. 2Scatterplot comparing gene presence/absence based on the microarray data and gene presence/absence based in blastn between Streptomyces coelicolor and Streptomyces avermitilis. See “Material and methods" for details. Box A and Box C includes genes identified as absent in S. avermitilis by the microarray dataset but present using blastn and genes present in S. avermitilis using blastn, but identified as absent by the microarray dataset. Box B includes genes that are correctly identified as absent by the microarray dataset
Distribution of gene differences across the complete chromosome of S. coelicolor for all four other Streptomyces species
The whole chromosome microarray dataset supports the following structure for the Streptomyces chromosome. Based on Fig. 3 and Supplementary Fig. 1, there is a central core of conserved probably syntenous genes that can be found across many Actinomycetes and in the S. coelicolor genome this reach from about SCO2050 to SCO5800 (Bentley et al. 2002). The regions between SCO1100 and SCO2050 and between SCO5800 and SCO7600 are also quite well conserved between the Streptomyces studied here as well as being syntenous between the S. coelicolor and S. avermitilis genome sequences. However they are not present when the genomes of these two species are compared bioinformatically to other divergent Actinomycetes. These two regions seem to be two genus specific areas. Figure 3 also clearly shows that gene conservation drops off dramatically in the terminal region. The regions from the left terminus to SCO1100 and from SCO7600 to the right terminus show much higher gene divergence that the rest of the chromosome. This agrees with the results for the S. ambofaciens sequencing studies of that species’ terminal regions (Choulet et al. 2006a, b). The gene conservation levels averaged across the four species are as follows: left terminal region (SCO0001–SCO1100) 40.9%; left genus specific region (SCO1101–SCO2050) 84.8%; core region (SCO2050–SCO5800) 79.4%; right genus specific region (SCO5801–SCO7600) 69.6% and right terminal region (SCO7601–SCO7845) 50.3%. It is noticeable that neither the size nor the distribution of conserved genes is symmetrical between the two terminal regions or the two genus specific regions. Notably, the genus specific region actually has a higher frequency of gene conservation than the core regions as a whole and that the left terminal region is much larger than the right terminal region. This possibly represents horizontal exchange of terminal regions by recombination between strains/species that involves only one terminal region. Such an event would give rise to asymmetric gene conservation similar to that detected here.Fig. 3Analysis of “gene presence” across the four species. Created using a moving window of 10 genes and counting the number of genes with a microarray signal >2SD below the mean for the core region genes. The Y axis is the count for “gene presence”
In the Karoonuthaisiri et al. (2005) study of regional gene expression in S. coelicolor, the boundaries for higher transcript levels during vegetative growth were placed at 1.5 Mb for the left arm and 2.3 Mb for the right arm. The former is midway across the left genus specific region and the latter approximately agrees with the boundary between the core and the right genus specific region. As the core region boundaries are also defined in terms of synteny with the Mycobacterium and Corynebacterium genomes as well as the data presented here, this supports the idea that the S. coelicolor chromosome structure is asymmetrical with respect to both gene conservation and gene function. It should be noted that because we are using only S. coelicolor as the source of the array data, the results do not imply that the genomes of S. cattleya, S. maritimus and K. aurefaciens are asymmetric. However, it should be noted that the S. avermitilis genome is also asymmetric (Ikeda et al. 2003).
Notably, there are 22 identifiable regions where all four species show a significant degree of concurrent gene absence outside of the terminal regions (Table 1). The regions of high gene divergence are shown in Supplementary Fig. 2 in detail. Previously, Bentley et al. identified 14 regions in the S. coelicolor chromosome that were potentially laterally acquired regions. This analysis pinpoints all of these regions and quite accurately, usually to within one or two open reading frames. This suggests that other eight regions are probably quite robust when designated as potential lateral transfer regions. It also supports the usefulness of the microarray approach. All 22 regions were analyzed using Frame Plot (Artemis v7.1) and except for region B, they show abnormalities for at least some of the open reading frames compared to the G+C bias expected for the 1st, 2nd and 3rd codon positions of Streptomyces genes. Eight regions, A, B, F, I, M, O, Q and T contain transposon related genes near to or within the region. Four regions, H, N, P and R are flanked by highly conserved genes such as a ribosomal protein or sigma factor genes, which could encourage interspecific recombination. Finally, five regions consist largely of hypothetical proteins with no known similarity to any known protein as yet; these regions are G, J, L, S and W. Region L is particularly interesting as there is a central core of conserved gene flanked by two subregions that are highly not conserved. One of these genes is a putative spore septum determining protein, while the rest have unknown functions. Taken as a whole, the results suggests that S. coelicolor may have recently acquired all these regions either by transposition or by interspecific/intraspecific recombination (Wolf et al. 2002; Zhang et al. 2002). It is also unlikely that they were acquired from any of the four species studied here. There are other regions that could potentially be identified as lateral transfer positions using less stringent criteria and a wider screening of genomes might help to support these additional regions as being involved in hotizontal transfer. In addition, such a wider screen might allow the identification of possible origins of these regions in other species.Table 1Areas of the Streptomyces coelicolor genome identified as potentially horizontally transferred regions based on microarray parallel gene absence in all four speciesRegionArea of chromosomeGenes missinga%aSignificant featuresRegion ASCO0996–SCO101017/2959Integrase, insertion sequenceRegion BSCO2860–SCO287953/7669Rifampin ribosyl transferaseRegion CSCO3249–SCO328894/15660Integrase, excisionaseRegion DSCO3471–SCO3538198/26873AgaraseRegion ESCO3584–SCO359930/6050Region FSCO3929–SCO393722/3268Integrase/recombinase, fstK-likeRegion GSCO3980–SCO400156/6488Hypothetical proteinsRegion HSCO4052–SCO4066132/14492Boundary dnaZ geneRegion ISCO4210–SCO422337/5469Region JSCO4247–SCO425721/3658Hypothetical proteinsRegion KSCO4340–SCO435434/4085Integrase, DNA invertaseRegion LSCO4509–SCO4547106/14474Hypothetical proteinsRegion MSCO4613–SCO463140/6859Integrase, excisionaseRegion NSCO4686–SCO470024/4455Boundary ribosomal proteins operonRegion OSCO5323–SCO535157/8071Integrase, excisionaseRegion PSCO5605–SCO562046/6472Boundary sigma factor whiGRegion QSCO5632–SCO564440/4491Integrase, korSARegion RSCO5715–SCO573557/7279Boundary ribosomal protein, bldBRegion SSCO5906–SCO592428/5650Hypothetical proteins, xylanaseRegion TSCO6372–SCO640682/10082RecombinaseRegion VSCO6607–SCO664862/12052HelicaseRegion WSCO6806–SCO695373/13355Hypothetical proteinsaThis is calculated from the available normalized gene dataset from the two microarrays
Gene conservation in the terminal regions of the four Streptomyces species
As has been mentioned earlier, the two regions at either terminus are much less well conserved than the central core region; these extend from SCO0001 to about SCO1100 on the left arm of the chromosome and from about SC7600 to SCO7845 on the right arm. The boundaries of these regions are not absolutely clear-cut, but what is clear is that as one moves towards the centre of the genome, gene conservation increases beyond these points. This can be clearly seen in Fig. 3 where the gene conservation is plotted using a moving window for the four species, but it is also clear that the lack of conservation is not uniform across the terminal regions and that areas of higher gene conservation can be identified.
The significant interest in the terminal regions arises because the genomes of all Streptomyces that have been examined are linear and the problem of how the termini of such a molecule replicate is of particularly importance. Recent studies have indicated that two genes in particular, tpgA (SCO7734) and tapA (SCO7733), are involved in this process (Yang et al. 2002; Bao and Cohen 2001). tpgA encoding the terminal protein that covalently binds to the termini of many linear Streptomyces replicons is conserved across all four species. In S. avermitilis this is also true based on sequence data and, further more, there are multiple copies of tpgA unlike S. coelicolor. The signal level of the S. avermitilis gene at +1.2 supports the presence of these multiple copies. The signal levels for the other three species are between about −0.3 and −0.1, which supports a single slightly diverging copy of this gene in these species. However, if two copies are present then the sequence divergence may be higher. Furthermore, tapA is also conserved except for S. maritimus, which seems to be more divergent at −0.8. It should be noted that the presence of these two genes is not a criteria for defining a genome with a linear topology, but the presence of one or both is certainly suggestive (Dary et al. 2000; Wang et al. 1999; Huang et al. 1998; Lin and Chen 1997). Finally, ttrA is known to be involved in chromosomal transfer and is found very close to the telomere of S. coelicolor and S. avermitilis. This is also conserved in all four species suggesting the genetic exchange is highly important in Streptomyces and related species.
The two terminal regions encompass the major areas that are prone to deletion in many Streptomyces species and are therefore not essential except for linear terminal replication and genetic exchange. Given the relatively high lack of conservation of genes in this region, genes that are present in all four species represent an interesting class. A full list of all genes conserved in all four species in the terminal regions is provided in Tables 2a and 2b. There are 36 hypothetical genes that show high similarity in the two terminal regions. Analysis of these groups of conserved genes using Artemis v7 (The Sanger Institute) identifies a total of five groups of genes that may make up possible single transcriptional units. These are SCO0551–SCO0552, SCO0705–SCO0710, SCO1021–SCO1024, SCO7677–SCO7680 and SCO7682–SCO7688. In addition to TpgA and TapA, it is possible that there are other genes involved in terminal replication and these may be among the conserved genes present in the terminal regions. Although possible candidates can be deduced from a direct comparison of the two known Streptomyces genome sequences, they are many in number. Using the microarray analysis of the Actinomycetes in this study, the candidates can be reduced significantly. From candidates in Tables 2a and 2b, two possible transcriptional units seem to be potential candidates for involvement in terminal replication; these are SCO1021–SCO1024 (hypothetical proteins), and SCO7677–SCO7689 (including hypothetical proteins, an AMP-binding ligase and membrane proteins). Gene knockout studies may be able to identify possible functions for these and other gene candidates, especially the other hypothetical proteins that are conserved in these four species.Table 2Genes from the (a) left terminal, (b) right terminal region of Streptomyces coelicolor showing microarray conservation in all four species(a)SCO0002 ttrASCO0800 putative TetR-family transcriptional regulatory proteinSCO0142 hypothetical proteinSCO0802 hypothetical proteinSCO0150 hypothetical proteinSCO0810 putative ABC transporter permeaseSCO0201 putative integral membrane proteinSCO0830 putative penicillin-binding proteinSCO0232 hypothetical proteinSCO0839 putative transmembrane transport proteinSCO0415 hypothetical proteinSCO0840 putative marR-family transcriptional regulatorSCO0443 hypothetical proteinSCO0854 hypothetical proteinSCO0452 putative SIR2-like regulatory proteinSCO0883 polypeptide deformylaseSCO0466 araC family transcriptional regulatorSCO0887 putative TetR-family transcriptional regulatorSCO0471 putative araC family transcriptional regulatorSCO0894 putative membrane proteinSCO0496 putative iron-siderophore permease transmembrane proteinSCO0895 RNA polymerase principal sigma factor HrdCSCO0536 hypothetical proteinSCO0900 putative transmembrane efflux proteinSCO0538 probable sugar transporter sugar binding lipoproteinSCO0905 putative membrane proteinSCO0544 hypothetical secreted proteinSCO0907 putative dehydrogenaseSCO0546 pyruvate carboxylaseSCO0925 putative lysR-family transcriptional regulatorSCO0551 putative histidine kinase proteinSCO0926 hypothetical proteinSCO0552 putative response regulatorSCO0931 putative secreted proline-rich proteinSCO0565 putative polyprenyl synthetaseSCO0942 putative RNA polymerase sigma factorSCO0584 putative cytochromeSCO0943 hypothetical proteinSCO0591 putative lysozyme precursorSCO0947 putative integral membrane proteinSCO0592 hypothetical proteinSCO0949 hypothetical proteinSCO0614 hypothetical proteinSCO1011 conserved hypothetical proteinSCO0619 putative membrane proteinSCO1015 hypothetical proteinSCO0637 hypothetical proteinSCO1018 putative isomeraseSCO0690 possible oxidoreductaseSCO1021 hypothetical proteinSCO0695 hypothetical proteinSCO1022 hypothetical proteinSCO0701 hypothetical proteinSCO1024 hypothetical proteinSCO0707 putative branched-chain amino acid ABC transport permeaseSCO1034 putative tetR-family regulatory proteinSCO0708 putative branched-chain amino acid ABC transport proteinSCO1036 putative phosphotriesterase-family proteinSCO0709 putative branched-chain amino acid transport ATP-binding proteinSCO1040 putative DNA repair proteinSCO0710 putative branched-chain amino acid transport ATP-binding proteinSCO1041 hypothetical proteinSCO0765 secreted endoglucanaseSCO1043 putative transcriptional regulatory proteinSCO0779 conserved hypothetical proteinSCO1044 putative secreted proteinSCO0788 hypothetical proteinSCO1046 putative metal transporter ATPaseSCO0790 putative hydrolase(b)SCO7649 putative two-component system sensor kinaseSCO7677 putative secreted solute-binding proteinSCO7678 putative metal transport integral membrane proteinSCO7679 putative transport system integral membrane proteinSCO7680 putative ABC transporter ATP-binding proteinSCO7681 putative AMP-binding ligaseSCO7682 putative non-ribosomal peptide synthaseSCO7684 conserved hypothetical proteinSCO7685 conserved hypothetical proteinSCO7687 putative thioesteraseSCO7688 hypothetical proteinSCO7689 putative ABC transporter ATP-binding proteinSCO7718 hypothetical proteinSCO7720 hypothetical proteinSCO7724 hypothetical proteinSCO7734 Tpg proteinBold indicates groups of consecutive genes that may form a single transcriptional unit
Conservation of functional groups of genes across the four Streptomyces species
One approach to analyzing genetic variation across these four Streptomyces species is to look at the functional groupings of genes. Such an approach should allow the identification of strain versus genus specific genes especially when there are large numbers of genes with related functions such as sigma factors or where there are two copies of a gene, such as ftsK. However, because microarray data paints a broad picture across a whole genome, it is essential that once a gene or genes has been targeted based on microarray data, that experimental verification by other means is carried out. However, it is hoped that this dataset will be able to help researchers prioritize their gene targets better. The genes of the S. coelicolor chromosome have been grouped based on the scheme of M. Riley and colleagues for E. coli (ecocyc.org) modified for S. coelicolor (http://www.sanger.ac.uk/Projects/S_coelicolor/scheme.shtml) and we used this classification. The genes involved in ribosomal proteins synthesis and modification should be highly conserved and the results indicate that almost all of them are present in all four species (Table 3; Supplementary Fig. 4). The only exceptions are SCO0436, SCO0509 SCO3430 and SCO3909 in S. avermitilis and SCO4716 and SCO5514 in K. aureofaciens. Of these genes, SCO0436, SCO0509 and SCO5514 represent duplicate genes in the S. coelicolor genome and therefore the choice of the microarray sequence will have had a significant effect on the heterologous hybridization. There is no obvious explanation for the failure to hybridize of the other two genes, but as a whole, this dataset supports the integrity of the array system for analysis of genome content as these genes are scattered across the whole Streptomyces genome.Table 3Microarray data for ribosomal proteins from the four species S. avermitilisS. cattleyaS. maritimusK. aureofaciensSCO0436 probable 50S ribosomal protein−0.35−0.130.44−0.29SCO0569 putative 50S ribsomomal protein fragment−0.750.63−0.420.27SCO1150 50S ribosomal protein L310.56−0.47−0.240.14SCO1505 30S ribosomal protein S40.36−0.350.76−0.31SCO1598 50S ribosomal protein L201.500.630.890.34SCO1599 50S ribosomal protein L350.230.410.49−0.51SCO1998 30S ribosomal protein S11.390.770.990.91SCO2563 30s ribosomal protein S20−0.34−0.340.770.33SCO2596 50S ribosomal protein L271.010.590.080.82SCO2597 ribosomal protein L210.270.080.64−0.18SCO3124 ribosomal L25p family protein0.390.31−0.23−0.89SCO3427 putative 50S ribosomal protein L310.240.370.220.60SCO3428 putative 50S ribosomal protein L330.150.280.540.09SCO3429 putative 50S ribosomal protein L280.680.160.550.45SCO3430 putative 30S ribosomal protein S14−0.800.10−0.17−0.19SCO3880 putative 50S ribosomal protein L341.020.130.240.71SCO3906 putative 30S ribosomal protein S60.700.941.11−0.18SCO3909 putative 50S ribosomal protein L91.300.010.87−1.27SCO4648 50S ribosomal protein L111.670.430.900.47SCO4649 50S ribosomal protein L10.620.53−0.250.92SCO4652 50S ribosomal protein L100.42−0.430.64−0.35SCO4653 50S ribosomal protein L7/L121.221.030.790.45SCO4659 30S ribosomal protein S120.740.650.70−0.53SCO4660 30S ribosomal protein S70.57−0.230.680.12SCO4701 30S ribosomal protein S101.191.171.16−0.21SCO4702 50S ribosomal protein L30.920.020.840.49SCO4703 50S ribosomal protein L41.160.910.590.23SCO4704 50S ribosomal protein L230.851.441.240.36SCO4705 50S ribosomal protein L20.85−0.120.840.22SCO4706 30S ribosomal protein S190.060.240.32−0.26SCO4707 50S ribosomal protein L220.960.690.640.15SCO4708 30S ribosomal protein S31.150.380.781.07SCO4709 50S ribosomal protein L160.520.671.091.26SCO4710 50S ribosomal protein L290.33−0.06−0.190.41SCO4711 30S ribosomal protein S170.590.920.51−0.13SCO4712 50S ribosomal protein L141.050.350.480.82SCO4713 50S ribosomal protein L241.090.880.630.64SCO4714 50S ribosomal protein L51.240.780.771.03SCO4715 30S ribosomal protein S140.390.030.190.12SCO4716 30S ribosomal protein S81.18−0.110.64−0.76SCO4717 50S ribosomal protein L60.960.820.79−0.02SCO4718 50S ribosomal protein L180.090.300.570.74SCO4719 30S ribosomal protein S51.560.661.02−0.09SCO4720 50S ribosomal protein L300.130.390.640.26SCO4721 50S ribosomal protein L151.790.420.800.84SCO4726 50S ribosomal protein L360.46−0.130.35−0.10SCO4727 30S ribosomal protein S130.63−0.230.62−0.17SCO4728 30S ribosomal protein S111.120.550.870.27SCO4730 50S ribosomal protein L170.690.270.860.51SCO4734 50S ribosomal protein L13−0.270.450.500.40SCO4735 30S ribosomal protein S90.36−0.020.00−0.01SCO5359 50S ribosomal protein L310.860.221.400.46SCO5564 putative 50S ribosomal protein L280.600.280.210.51SCO5591 30S ribosomal protein S160.440.030.57−0.60SCO5595 50S ribosomal protein L190.770.781.54−0.03SCO5624 30S ribosomal protein S21.160.481.520.30SCO5736 30S ribosomal protein S150.700.410.79−0.46Mean hybridization score for ribosomal protein genes0.670.350.610.18Bold values indicate that the signal for that gene is more than 2SD below the mean core signal for that species and such a value is suggestive of either gene absence or very low similarity
Table 4 shows genes identified as possible sigma factors, anti-sigma factors and ant-sigma factor antagonists. The genes found in the central core region are more conserved. As would be expected, the major sigma factors such as hrdA, hrdB, hrdC and hrdD are conserved as well as many of the other studied sigma factors of S. coelicolor such as are sigA, sigE, sigF, sigG, sigR, sigT and whiG. Overall, fewer regulation genes from this group (anti-sigma factors and anti-anti-sigma factors) are conserved than sigma factors themselves. This analysis allows the identification of new candidate sigma factors for further study outside of the well studied ones, but within S. coelicolor and in other species. Overall, the results support the hypothesis that there is a core of sigma factors essential to keeping protein synthesis in Streptomyces running smoothly. The functionality of the rest may vary and include complete silence of some gene fragments, duplication of function, involvement in specific secondary metabolic activities and species/genus specific functions.Table 4Conservation across the four species of genes annotated as sigma factors or related proteins in Streptomyces coelicolor S.avermitilisS.cattleyaS. maritimusK. aureofaciens SCO0037 putative sigma factor−1.04−0.90−0.88−1.49SCO0159 putative ECF sigma factor−1.26−0.652.05−0.60SCO0194 putative sigma factor−0.87−0.35−0.61−0.52SCO0255 putative transcriptional regulator−0.64−0.37−0.99−0.46SCO0414 putative RNA polymerase sigma factor−0.05−0.28−0.15−0.22ConservedSCO0598 putative anti anti sigma factor0.110.50−0.080.57ConservedSCO0599 putative regulator of sig8−1.41−1.07−0.84−1.08SCO0632 putative RNA polymerase sigma factor−0.140.19−0.820.11SCO0672 putative anti-sigma factor antagonist−0.10−0.40−0.190.12SCO0781 putative anti sigma factor antagonist−0.79−0.86−1.06−0.83SCO0803 putative RNA polymerase sigma factor−0.25−0.09−0.51−0.01SCO0864 probable ECF-family sigma factor−0.74−0.86−1.04−0.50SCO0866 probable ECF-family sigma factor−0.130.19−0.280.23ConservedSCO0869 putative anti-sigma factor antagonist−0.59−0.90−1.23−0.90SCO0895 RNA polymerase principal sigma factor HrdC0.520.431.120.34ConservedSCO0942 putative RNA polymerase sigma factor0.340.810.610.45ConservedSCO1263 putative ECF-sigma factor−0.17−0.260.080.35ConservedSCO1276 RNA polymerase ECF sigma factor−1.22−0.550.60−0.88SCO1564 putative RNA polymerase sigma factor1.04−0.331.34−0.47SCO1723 putative RNA polymerase sigma factor0.19−0.39−0.150.52ConservedSCO1876 putative RNA polymerase sigma factor−1.01−0.93−0.77−0.50SCO2465 RNA polymerase principal sigma factor0.760.740.970.64ConservedSCO2639 putative RNA polymerase sigma factor0.740.220.010.27ConservedSCO2954 putative RNA polymerase sigma factor1.12−0.510.94−0.46SCO3066 putative regulator of Sig150.530.290.880.31ConservedSCO3067 putative anti anti sigma factor−0.74−1.250.89−0.42SCO3068 putative RNA polymerase sigma factor0.33−0.061.07−0.41ConservedSCO3202 RNA polymerase principal sigma factor0.980.291.400.19ConservedSCO3323 putative RNA polymerase sigma factor0.760.491.080.27ConservedSCO3356 ECF sigma factor 37−0.050.110.730.46ConservedSCO3450 putative RNA polymerase sigma factor (ECF subfamily)0.16−0.09−0.79−0.23SCO3548 putative anti-sigma factor−0.570.500.49−0.06SCO3549 bldG putative anti-sigma factor antagonist−0.03−0.160.21−0.20ConservedSCO3613 putative RNA polymerase sigma factor0.570.150.080.46ConservedSCO3692 putative anti-sigma factor antagonist0.140.64−0.270.13ConservedSCO3709 putative ECF sigma factor0.020.06−0.210.61ConservedSCO3715 putative ECF sigma factor0.450.92−0.28−0.27ConservedSCO3736 putative RNA polymerase ECF sigma factor−0.06−0.290.28−0.09ConservedSCO3892 putative RNA polymerase sigma factor0.680.210.80−0.27ConservedSCO4027 putative anti sigma factor antagonist−0.041.11−0.590.58SCO4034 putative RNA polymerase sigma factor0.981.271.260.22ConservedSCO4035 RNA polymerase sigma factor (fragment)1.041.270.880.45ConservedSCO4146 putative ECF subfamily sigma factor−0.230.530.580.16ConservedSCO4409 putative RNA polymerase sigma factor−0.100.120.100.64ConservedSCO4410 putative anti anti sigma factor−0.890.070.16−0.81SCO4452 putative sigma factor−0.17−0.21−0.080.27ConservedSCO4769 ECF sigma factor0.09−0.380.680.61SCO4864 putative ECF sigma factor0.02−0.34−0.12−0.73SCO4866 putative ECF sigma factor0.120.190.090.38ConservedSCO4895 putative ECF sigma factor−0.32−1.15−0.11−0.56SCO4938 putative ECF-sigma factor0.170.430.240.64ConservedSCO4960 possible sigma factor−0.040.05−0.660.60SCO4996 putative RNA polymerase ECF sigma factor−0.54−0.040.580.48SCO5147 putative ECF-subfamily sigma factor−0.390.350.790.59SCO5217 anti-sigma factor−0.47−0.050.28−0.80SCO5244 anti-sigma factor−0.32−0.54−0.37−0.29SCO5386 putative anti-sigma factor antagonist0.150.370.00-0.07ConservedSCO5621 RNA polymerase sigma factor WhiG0.790.920.64−0.27ConservedSCO5820 hrdB, major vegetative sigma factor1.361.061.531.09ConservedSCO5934 putative sigma factor0.070.25-0.580.17SCO6239 putative sigma factor−0.92−1.27−1.84−0.74SCO6996 putative RNA polymerase sigma factor-0.340.290.00−0.04ConservedSCO7099 putative RNA polymerase sigma factor−0.20−0.380.380.29SCO7104 putative RNA polymerase sigma factor−0.70−0.02−0.790.56SCO7112 putative ECF-family RNA polymerase sigma factor−0.35−0.25−1.65−0.38SCO7144 putative ECF sigma factor−0.620.13−0.910.34SCO7314 probable RNA polymerase sigma factor−0.25−0.110.230.46ConservedSCO7323 anti-sigma factor antagonist0.30−0.010.230.31ConservedSCO7325 anti-sigma factor antagonist-0.37-0.68−0.23−1.15SCO7341 putative RNA polymerase secondary sigma factor−0.090.540.290.44ConservedSCO7573 putative anti-sigma factor antagonist−0.180.03−1.690.21SCO7619 putative anti sigma factor antagonist−0.280.37−0.97−0.64SCO7754 putative anti-sigma factor antagonist−1.20−0.51−1.890.02Mean hybridization score for ribosomal protein genes0.010.060.100.05NABold values indicate that the signal for that gene is more than 2SD below the mean core signal for that species and such a value is suggestive of either gene absence or very low similarity. A conserved gene is one that seems to be present in all four species. NA, Not applicable
All four species studied here undergo differentiation and spore formation and as such would be expected to retain most genes involved in cell division/sporulation/differentiation. This is supported by Table 5. K. aureofaciens shows greater gene divergence for certain genes when compared to the three Streptomyces species and these are specifically ftsI (SCO2090) and a putative cell division protein (SCO2968). However, in general, the same genes in all four species show a higher divergence, for example sapA, which is a protein associated with the spore surface hydrophobicity. As spore morphology varies a lot in the Streptomyces, high variability/gene loss in such a gene is not unexpected. Other genes that show higher divergence are those involved in partitioning and cell division. This suggests that the genes and thus the proteins involved in these functions may differ from species to species in order to create the variation seen in aerial mycelium and spore structure across Streptomyces species. Specifically, SCO3934, an ftsK family protein gene is less well conserved than its homologue. This suggests that SCO5750 may produce the major ftsK protein. Other Fts proteins show a similar pattern with at least one homologue being well conserved. This may well help an understanding of the relationships between the genes involved in cell division and will allow better identification of specific targets for further study. One anomaly that stands out is bldB. This gene consistently shows a low level of hybridization. A comparison of the bldB gene sequence between S. coelicolor and S. avermitilis shows a nucleotide identity of about 87%, which ought to give a signal in the region of 0.0 or better. As two different arrays are used in this study, mechanical problems with this spot can probably be eliminated as the source of the anomaly. We suggest that because this is a relatively small gene, the PCR product chosen for both arrays may be the reason for this result. This emphasizes that array data should be used with a degree of caution and needs to be backed up by other experimental evidence when specific genes are being investigated.Table 5Conservation across the four species of genes in Streptomyces coelicolor annotated as involved in cell division, sporulation and differentiation S. avermitilisS. cattleyaS. maritimus K. aureofaciensSCO0409 sapA spore-associated protein precursor−1.99−1.39−0.59−0.67SCO1454 putative amino oxidase1.000.451.18−0.17SCO1489 bldD putative DNA binding protein0.990.761.020.58SCO1772 putative partitioning or sporulation protein0.690.350.390.54SCO2082 ftsZ cell division protein1.440.890.970.95SCO2083 ftsQ sporulation protein0.320.740.060.26SCO2084 murG0.86−0.010.490.32SCO2085 fts W putative cell division protein0.820.810.590.50SCO2086 murD0.580.230.350.45SCO2087 murX0.410.050.51−0.30SCO2088 murF1.180.730.670.01SCO2089 murE0.730.470.360.31SCO2090 ftsl cell division protein0.800.010.45−0.50SCO2607 Sfr protein0.730.730.91−0.01SCO2608 penicillin binding protein−0.04−0.250.45−0.92SCO2609 mreD rod shape-determining protein0.090.070.660.43SCO2610 mreC rod shape-determining protein−0.42−0.160.17−0.52SCO2611 mreB rod shape-determining protein0.980.800.680.19SCO2620 putative cell division trigger factor0.81−0.120.500.25SCO2968 putative cell division protein0.35−0.170.34−0.39SCO2969 ftsE cell division ATP-binding protein0.37−0.430.00−0.51SCO3034 whiB sporulation regulatory protein0.230.500.810.26SCO3323 bldB putative RNA polymerase sigma factor0.760.491.080.27SCO3404 ftsH2 cell division protein ftsH homolog1.110.511.070.15SCO3549 bldG putative anti-sigma factor antagonist−0.03−0.160.21−0.20SCO3557 putative septum site determining protein0.310.45−0.190.92SCO3558 putative morphological differentiation-associated protein0.69−0.261.62−0.20SCO3846 putative FtsW/RodA/SpoVE family cell cycle protein1.110.311.070.61SCO3886 putative partitioning or sporulation protein0.00−0.83−0.43−0.98SCO3887 putative partitioning or sporulation protein−0.19−0.190.24−1.04SCO3934 ftsK/spoIIIE family protein−0.560.39−1.18−0.53SCO4014 sporulation associated protein−0.87−0.93−0.81−1.17SCO4184 mfC aerial mycelium formation0.120.170.01−0.05SCO4508 putative cell division-related protein−0.62−0.270.070.59SCO4531 putative septum determining protein−0.56−0.880.12−0.09SCO4620 traB1 putative sporulation-related protein−0.49−0.680.19−0.34SCO4621 traA1 putative sporulation-related protein−0.03−0.511.33−0.38SCO4767 putative regulatory protein0.140.001.67−0.01SCO4768 bldM putative two-component regulator1.061.000.920.61SCO5006 minD1 putative septum site-determining protein−0.31−0.040.58−0.28SCO5008 minD3 putative septum site-determining protein0.04−0.21−0.11−0.07SCO5112 BldKA−0.420.92−0.750.51SCO5114 BldKC−0.39−0.08−1.23−0.23SCO5115 BldKD0.01−0.18−0.89−0.15SCO5116 bldKE putative peptide transport system ATP-binding protein−0.04−0.22−0.70−0.03SCO5314 whiE protein VII−1.24−0.230.03−0.67SCO5315 polyketide cyclase−0.39−0.82−0.31−0.25SCO5316 acyl carrier protein−0.42−0.290.750.03SCO5318 polyketide beta-ketoacyl synthase alpha−0.03−0.130.860.43SCO5321 polyketide hydroxylase0.12−0.091.500.27SCO5587 ftsH cell division protein FtsH homolog−0.050.310.230.21SCO5621 whiG RNA polymerase sigma factor WhiG0.790.920.64−0.27SCO5723 bldB putative regulator, BldB−1.47−0.76−1.27−1.05SCO5750 ftsK homolog0.670.160.652.52SCO5819 whiH, sporulation transcription factor0.680.120.160.13SCO6029 whiI two-component regulator0.14−0.260.770.92Mean hybridization score for ribosomal protein genes0.05−0.050.27−0.02Bold values indicate that the signal for that gene is more than 2SD below the mean core signal for that species and such a value is suggestive of either gene absence or very low similarity
The genes involved in DNA replication, repair, restriction/modification are shown in Table 6 and only about 20% of these genes are not conserved relatively well across all four species. This is to be expected as DNA replication and repair are core functions. Most of the genes that show higher levels of gene divergence are found in the terminal regions of the linear chromosome and probably are genes that perform functions that are not essential to cell survival because the terminal regions of Streptomyces chromosomes are unstable and liable to deletion without lethality. Of particular interest are SCO0183 and SCO0842 (deoxiribopyrmidine photolyases); these repair system would seem to be absent in S. lividans and S. maritimus, but a homologue is present in S. avermitilis (confirmed by the genome sequence) and in S. cattleya. This confirms the high variability found for this repair function across the Streptomyces (Kobayashi et al. 1989). A similar situation of high variability is found for the mutT homologues, potential 8 hydroxy-dGTP hydrolases. Knockout of this gene has been shown to increase the A:T to G:T mutation rate and thus it has a possible repair function (Kamiya et al. 2004). The genes for recA (SCO5769), recF (SCO3876) and recR (SCO3618) are present in all four species; however, the recX (SCO5770), is more divergent and gives a low signal for S. cattleya and S. maritimus. SCO6405, a putative DNA recombinase, is scored as absent in all four species suggesting that there is redundancy in the Streptomyces genes concerned with recombination or that this gene is transposon related. The latter is supported by low homology to S. avermitilis putative integrases/recombinases. There are four genes encoding DNA gyrases on the microarray, namely, gyrA DNA gyrase subunit A (SCO3873) and gyrB DNA gyrase subunit B (SCO3874) together with SCO5836 and SCO5822 and these may be TopIV homologues involved in resolving chromosome concatenates. All are conserved although the conservation of SCO5822 gyrB2 is lower. Thus both sets of gyrase genes would seem to be important. As expected, SCO1518, a ruvB Holliday junction protein gene and SCO1520, a ruvC crossover junction endonuclease are conserved across all the species. Unexpectedly, although probably present in all species, SCO1519 ruvA is much more divergent that the other two gene in this Holliday junction complex. This diversity is unexpected and not easily explicable except by the fact that recombination in Streptomyces may occur via a more variable mechanism than in other groups of bacteria and this is then reflected in the greater divergence of SCO1519 ruvA. All three genes annotated as a DNA polymerase 1 homologue are conserved as are four out of the five DNA polymerase III homologues, suggesting that there are roles for all of these conserved genes in Streptomyces. Two other unclassified DNA polymerase type genes, SCO4495 and SCO6084 are also conserved and thus may have important functions. There is, however, more diversity among the helicases and methylases/methyltransferases. With the helicases, three out of 14 show significant divergence and therefore most of the helicases probably have important cellular roles. Four out of nine methylases/methyltransferases show divergence. As some of these genes may be involved in the DNA modification part of restriction/modification, such diversity across strains in not unexpected. Finally, four out of six ligases show divergence, perhaps reflecting the fact that the origin of a number of these ligases might be from bacteriophages.Table 6Conservation across the four species of genes in Streptomyces coelicolor annotated as involved in DNA replication, repair, restriction and modification S. avermitilisS. cattleyaS. maritimusK. aureofaciensSCO0183 putative deoxyribodipyrimidine photolyase−1.38331−0.76404−1.15786−0.78973SCO0760 putative methyltransferase−0.23598−0.176030.079613−0.16712SCO0842 putative deoxyribodipyrimidine photolyase0.0024750.167429−0.051980.35453SCO0918 putative excinuclease ABC subunit A−0.28707−0.348130.010852−0.46861SCO0945 putative formamidopyrimidine-DNA glycosylase−0.34857−0.60698−0.4371−0.38083SCO1040 putative DNA repair protein0.0473150.5687230.4561680.367071SCO1050 putative DNA protection protein−0.474790.0932990.451487−0.92697SCO1114 uracil DNA glycosylase−0.345730.3571820.4339020.950894SCO1167 putative helicase (fragment)0.6048130.072648−0.40455−0.25074SCO1180 putative DNA polymerase III beta chain−0.347−0.21511−0.42838−0.20922SCO1202 putative DNA ligase0.3084970.202188−0.155240.096586SCO1203 putative MutT-like protein−0.17791−0.37050.324233−0.28871SCO1255 G/U mismatch-specific DNA glycosylase0.5216790.419040.3212080.429148SCO1343 uracil-DNA glycosylase0.612940.003390.3114780.036821SCO1380 putative DNA damage inducible protein0.7630640.3139480.2149310.677262SCO1395 mutT-like protein0.0682150.5725270.170710.414092SCO1475 putative primosomal protein0.0488920.6892320.535450.457655SCO1518 ruvB holliday junction DNA helicase1.1364890.6388030.9300671.045593SCO1519 ruvA holliday junction DNA helicase−0.57721−0.23275−0.153740.008274SCO1520 ruvC crossover junction endodeoxyribonuclease1.0797860.7081310.8863630.979821SCO1534 putative DNA polymerase III0.32960.371242−0.100250.090739SCO1739 putative DNA polymerase III1.1280490.4231581.1119640.326701SCO1780 putative DNA repair protein−0.14578−0.091750.258713−0.32746SCO1792 putative 3-methyladenine DNA glycosylase−0.20485−0.077550.083499−0.2824SCO1827 putative DNA polymerase III0.5597150.7112650.5128730.343849SCO1966 ABC excision nuclease subunit B0.0473820.068961.085903−0.05934SCO1969 putative DNA-methyltransferase−0.19025−0.121840.1433670.56545SCO2003 DNA polymerase I1.1721760.1884930.4981260.201411SCO2468 DNA primase0.827150.5205521.256130.69797SCO2626 putative DNA repair hydrolase (fragment)0.2899160.3373650.5677830.326821SCO2863 putative helicase−0.47935−0.68124−1.92881−0.81052SCO2952 putative helicase protein0.5139520.388640.7505170.379556SCO3109 putative transcriptional-repair coupling factor−0.87255−0.296110.759684−0.33637SCO3351 putative DNA repair protein−0.95043−0.685210.24637−0.48466SCO3352 putative DNA-binding protein0.090569−0.096020.536392−0.2275SCO3434 putative DNA polymerase I0.85541.6517680.8742461.328528SCO3510 putative DNA methylase0.402433−1.11393−2.15274−2.07607SCO3541 putative DNA polymerase0.003597−0.402890.297076−0.75003SCO3543 probable DNA topoisomerase I0.7987051.334770.878841.226661SCO3550 putative helicase0.2636440.139350.5598720.216127SCO3618 putative recomination protein0.5334040.0526230.511469−0.36893SCO3873 DNA gyrase subunit A1.293458−0.208510.985546−1.2494SCO3874 DNA gyrase subunit B0.993330.9582471.2506690.37466SCO3878 DNA polymerase III0.053750.0260530.328502−0.62343SCO3879 chromosomal replication initiator protein (fragment)1.259831.5725310.731065−0.12482SCO4092 ATP-dependent helicase−0.007520.0551940.978098−0.21188SCO4143 putative mutT-like protein0.0210350.4354710.479161−0.28423SCO4272 putative mutT-like protein−0.13520.016206−0.329680.527274SCO4351 putative DNA invertase−1.26688−0.78144−1.29284−0.68489SCO4495 putative DNA polymerase related protein−0.09624−0.759370.5824190.316259SCO4577 putative helicase0.7269620.0531961.044155−0.09189SCO4797 putative ATP-dependent DNA helicase II0.3233660.2864210.7498680.026912SCO5064 putative bifunctional protein−0.20262−0.49259−1.90763−0.29433SCO5143 DNA-3-methyladenine glycosylase I−0.865940.3424890.2766110.981316SCO5183 putative ATP-dependent DNA helicase0.2356330.1207650.7675040.591935SCO5184 putative ATP-dependent DNA helicase0.180638−0.175870.2088390.666073SCO5188 putative ATP-dependent DNA helicase0.204048−0.360050.808168−0.04087SCO5331 putative DNA methylase−1.52864−2.37483−3.76436−2.18209SCO5494 putative DNA ligase0.1247810.2884290.215946−0.0149SCO5566 putative ATP-dependent DNA helicase0.4089951.1867140.4281830.825946SCO5567 putative methylase0.338198−0.822070.596196−0.83241SCO5573 formamidopyrimidine-DNA glycosylase0.387552−0.083530.6015850.435209SCO5760 DNA glycosylase0.8469850.022350.6758760.46121SCO5770 RecX protein−0.1193−0.72104−0.580750.079675SCO5802 putative ATP-dependent helicase0.823429−0.104330.5115950.058287SCO5803 SOS regulatory protein LexA0.143322−0.78751−0.0905−0.16946SCO5805 ribonucleotide reductase0.2351820.2715140.98906−0.06823SCO5815 probable ATP-dependent DNA helicase−0.52023−0.31138−0.75254−0.71096SCO5822 gyrB2, probable DNA gyrase0.1672750.2234730.4268490.393883SCO5836 DNA gyrase-like protein0.7257080.5074980.9375310.073608SCO6084 putative DNA polymerase−0.053810.1656340.084564−0.16407SCO6151 putative methylated-DNA-protein-cysteine methyltransferase−0.88705−0.081120.3752140.207725SCO6262 putative helicase 6884138:6887071 forward MW:1039120.260624−0.288360.753508−0.08897SCO6405 putative DNA recombinase−0.16958−0.65562−1.15936−0.30768SCO6462 putative methylated-DNA-protein-cysteine methyltransferase−0.09961−0.002850.2318360.026866SCO6640 putative ATP-dependent helicase−0.56659−0.52367−1.18266−0.38994SCO6707 putative DNA ligase−0.254090.618121−0.0252−0.3333SCO6844 putative DNA methylase.0.4878060.475711−0.593870.230037SCO6907 putative DNA ligase.−0.714910.181384−0.67564−0.69734SCO7345 probable ATP-dependent DNA ligase0.1766920.2076060.3402550.085159SCO7522 putative DNA ligase−0.31874−0.0533−0.637010.470458Mean hybridization score0.1136330.0074410.153752−0.04491Bold values indicate that the signal for that gene is more than 2SD below the mean core signal for that species and such a value is suggestive of either gene absence or very low similarity
Table 7 shows the genes involved in peptidoglycan and teichoic acid synthesis. In this area of metabolism, there is also a relatively high level of conservation of genes, particularly the murA, murA2, murB, murD, murE, murF,murG and murX genes. Also conserved are the shape-determining genes SCO2609, SCO2610 and SCO2611, which may form an operon. This probably represent a core of genes together with the genes involved in biosynthesis of the cell wall that are needed to give a basic structure to the cells of any Streptomyces species. The penicillin binding proteins show a higher degree of variability, except for SCO2897, SCO4013 and SCO5301. The peptidases SCO3580, SCO3596, SCO3011 and SCO4439 and the D-alanine:D-lactate ligase SCO3595 all show a low level of gene conservation, perhaps because they are involved in relatively broad cellular functions and not under a great deal of selective pressure.Table 7Conservation across the four species of genes in Streptomyces coelicolor annotated as involved in peptidoglycan biosynthesis S. avermitilisS.cattleyaS. maritimusK. aureofaciensSCO0237 putative oxidoreductase−0.07526−1.14854−0.09821−0.57347SCO0286 putative peptidoglycan binding protein−0.9459−0.93949−1.83967−1.02755SCO0830 putative penicillin-binding protein0.2434580.0813410.5560960.08585SCO0936 putative oligosaccharide deacetylase−0.78759−1.38608−0.58892−1.00821SCO1018 putative isomerase0.3905340.5192040.3926050.600019SCO1875 putative secreted penicillin binding protein−0.448310.0395980.3203560.256976SCO2084 murG0.85602−0.006770.4859890.319639SCO2085 putative cell division protein0.8166240.807420.5922140.502565SCO2086 murD0.5805060.2253480.3476740.449973SCO2087 murX0.4057310.0496450.509959−0.3047SCO2088 murF1.1750780.7309250.6673830.006107SCO2089 murE0.7340680.4708690.3634580.308295SCO2345 putative peptidodoglycan-binding membrane protein−0.03290.0163380.1333550.069404SCO2451 putative rod shape-determining protein0.6043270.1800280.6122430.205981SCO2589 putative glycosyl transferase0.093285−0.412170.478809−0.09528SCO2590 putative glycosyltransferase0.069933−0.52938−1.18523−0.89326SCO2608 penicillin binding protein−0.0413−0.246810.445143−0.92429SCO2609 rod shape-determining protein0.0858790.067210.6636090.427758SCO2610 rod shape-determining protein−0.41695−0.157980.168038−0.52083SCO2611 rod shape-determining protein0.9798840.7964030.6752040.186331SCO2706 putative transferase0.2334230.505652−0.555440.407847SCO2707 putative transferase0.014126−0.372250.088105−0.12772SCO2897 probable penicillin-binding protein0.5766460.4093880.6932370.120551SCO2949 murA0.4183630.2090240.3351590.062475SCO3580 putative transpeptidase−0.09884−0.76666−1.133470.212152SCO3595 putative D-alanine:D-lactate ligase−0.77707−1.21402−1.61266−1.31715SCO3596 putative D-alanine:D-alanine dipeptidase−1.47294−0.65926−1.4814−0.85496SCO3811 putative D-alanyl-D-alanine carboxypeptidase−0.50714−0.586170.155077−0.78413SCO3847 putative penicillin-binding protein0.128622−0.436430.640849−0.18456SCO3901 putative penicillin-binding protein−0.59684−0.78306−0.26692−0.39905SCO4013 putative penicillin binding protein−0.09095−0.01844−0.047970.164779SCO4132 putative secreted transglycosylase0.237226−0.17562−0.03522−0.2261SCO4439 putative D-alanyl-D-alanine carboxypeptidase−0.74865−0.930490.177585−1.21329SCO4643 murB−0.01505−0.206590.197283−0.35551SCO5039 putative penicillin-binding protein0.590106−0.709260.8918190.313533SCO5301 putative penicillin-binding protein−0.19347−0.131310.547233−0.22028SCO5365 putative transferase1.11236−0.273340.569152−1.16844SCO5467 muramoyl-pentapeptide carboxypeptidase−0.154480.387477−0.49044−0.02243SCO5560 D-alanine-D-alanine ligase0.7281120.2096020.1677690.330255SCO5998 murA20.8007260.7929751.2913680.051102SCO6060 putative UDP-N-acetylmuramoyl-L-alanine ligase0.199410.3770040.8173290.527531SCO7050 putative D-alanyl-D-alanine carboxypeptidase0.6059490.702030.3438270.524521Mean hybridization score0.125637−0.107440.118866−0.14494Bold values indicate that the signal for that gene is more than 2SD below the mean core signal for that species and such a value is suggestive of either gene absence or very low similarity
Conserved genes with no known function
Genes with no known function and no homologue outside of S. avermitilis that are conserved across the other three Streptomyces species should represent genes important to specifically being a myceliate Actinobacteria and the phenotype of gene knockout strains for these genes will be particularly interesting in terms of Streptomyces biology. Based on the dataset here, 936 genes can be identified as annotated as either conserved hypothetical genes or non-conserved hypothetical genes and these are shown in Supplementary Table 1. The proportion of these genes that are conserved across all four species are 9%, 20%, 13%, 16% and 12% for the left terminal region, left Streptomyces specific region, core region, right Streptomyces specific region and right terminal region, respectively. There is also a low frequency of conserved hypothetical genes in the left terminal region and right Streptomyces specific region, 0.78% and 0.96%, respectively compared to 3.4% for the left Streptomyces specific region, 1.80% for the core region and 2.11% for the right terminal region. It is clear that there is a need to further screen these genes by increasing the range of Streptomyces species analyzed by microarray hybridization. This will reduce the number to a manageable number and will allow prioritization of genes for knockout and detailed phenotypic analysis.
Another approach to the problem of identifying functionally important genes is by the pinpointing of functional groups of such genes that may form a transcriptional unit. Blocks of three or more hypothetical genes that are conserved across all species were identified and are shown in Table 8. It is possible that these groups represent conserved functional groups of genes essential to core functions that make Streptomyces different from other bacteria. They are found mostly in the area between the Streptomyces terminal regions and the central core region. There are seven groups of conserved hypothetical genes larger than five genes (SCO1407–SCO1413, SCO2362–SCO2370, SCO2911–SCO2919, SCO3846–SCO3854, SCO5536–SCO5543, SCO5762–SCO5767 and SCO6522–6528). It is likely, due to the proximity of various genes around SCO3846–SCO3854, that this complex is involved in cell division, development and DNA partitioning. The function of the others groups is unknown. Interestingly, none of these gene groups are upregulated shifting from exponential phase to stationary phase or under stress shift as indicated by Karoonuthaisiri et al. (2005).Table 8Hypothetical genes in S. coelicolor conserved as a group in the four species analyzedGenes (SCO)Operon structureaLinked function if anyb0614, 0616, 0617, 0618None–1317, 1318, 1319, 1320None–1521, 1522, 1523, 1524Possible operonRecombination1634, 1635, 1636Possible operon–1650, 1651, 1652, 1653Possible operonProteosome1788, 1789, 1790, 1791, 1794, 1795, 1796Possible operonBoth flanks of rRNA gene homologues2030, 2031, 2032Possible operon–2124, 2125, 2127, 2129, 2130Possible operonGlucose kinase2268, 2269, 2270Possible operonClose to heme oxygenase2913, 2915, 2916, 2917None–3115, 3117, 3118, 3119None–3150, 3151, 3152, 3153None–3406, 3407, 3408Possible operonPenicillin binding protein3950, 3951, 3952Possible operonOxidoreductase4028, 4029, 4030None–4801, 4803, 4804, 4805None–5307, 5308, 5309, 5310, 5312None–5600, 5601, 5602, 5603, 5604Possible operonHomology to Mycobacterium tuberculosis5762, 5763, 5764, 5765Possible operonDNA helicase6413, 6415, 6416, 6417, 6419, 6420, 6421, 6422None–6574, 6575, 6576, 6577, 6578, 6579, 6580Possible operonPossible DNA binding protein6671, 6672, 6674, 6675, 6676Possible operon–7070, 7071, 7072None–a Gene structure from Artemis v7 is compatible with an operon type structure with possible appropriate ribosome binding sitesb Inside or linked to the conserved genes is a gene(s) of known function
Conservation of genes involved in secondary metabolism and similar functions
Genes that are involved in secondary metabolism and antibiotic production are widely distributed in the Streptomyces and many if not most may have been involved in horizontal transfer. However, there is significant similarity between genes involved in similar pathways and thus significant cross-hybridization may occur between similar metabolic pathways. A large number of genes are also involved in secondary metabolism (165) and polyketide synthesis (102) in the S. coelicolor genome. These are grouped together in 23 pathway groups and are displayed in Supplementary Fig. 3. Genes identified as secondary metabolic genes but existing on their own and not in a group of secondary metabolic genes have been eliminated to simplify the analysis leaving only genes involved in these functions with two or more genes together in a group. These include specific pathways producing secondary metabolic products such as melanin, actinorhodin, CDA and Red pathway. Many of the other potential pathways have not been studied in detail and the functions of these genes are unknown. Because of evolutionary similarity, the presence of genes hybridizing to a particular pathway does not mean that the specific pathway is present, but possibly that a related one is may be present. Similarly, a high level of hybridization can mean either a very close relationship between the pathways in the two species or the presence of multiple copies of related pathways. In general terms S. maritimus shows the greatest absence of secondary metabolic pathways that are present in S. coelicolor. Interesting, S. cattleya and K. aureofaciens seems to have pathways related to many of the S. coelicolor secondary metabolic pathways present in their genomes, although they are phylogenetically more distant than S. maritimus. The actinorhodin pathway would seem to be absent from S. avermitilis (as expected from the genome data), S. cattleya and S. maritimus although some related genes do seem to be present in K. aureofaciens. The WhiE pathway is conserved in all species, but with some genes showing a very low level of hybridization in certain cases and these include whiE protein VII and the acyl carrier protein. Genes from the Red pathway show varying levels of hybridization suggesting that distantly related pathways may be present in these species. The CDA pathway is conserved in all four species and in certain cases the genes seem to be over represented suggesting multiple examples of the same type of pathway in S. cattleya and S. maritimus. The presence of similar pathways at a level of about 50% for K. aureofaciens supports the well established idea that horizontal gene transfer of secondary metabolic pathways may have played a significant role in the evolution of the Streptomyces and any related genus.
Because the natural environment of Streptomyces is the soil, they are thought to play an important role in the recycling of lignocellulose material. However, there is relatively little information on what genes are involved in this process. Interestingly, melC1 and melC2, which encode tyrosinase (monophenol monooxygenase, SCO2700) and its cofactor (SCO2701) (Leu et al. 1992) are conserved across the three Streptomyces species and probably also Kitasatospora (SCO2700 −0.76, SCO2701 0.08). On the other hand, the duplicate MelD1 (SCO2701) and MelD2 (SCO2700) genes found in S. coelicolor are not conserved and are phylogenetically distinct from MelC1 and MelC2 found in other Streptomyces (unpublished results). This perhaps represents a divergence of function between this two gene pairs. S. coelicolor does not produce a detectable amount of black melanin pigment and these results suggest that these enzymes may be involved in the metabolic conversion of lignocellulose byproducts rather than pigment formation. Evolutionary conservation of these genes to serve this function under particular conditions of induction would make more sense than retention of inducible black pigment formation. Other enzymes with a possible role in the lignocellulose cycle that are conserved across the species are shown in Table 9. These include many oxygenases that may have a role in producing oxygen radicals capable of attacking lignin, genes involved in the sensing and breaking down hydrogen peroxide, cellulose metabolism genes, cellobiose metabolism genes, etc. Those found in the terminal regions may represent gene groups that are not conserved in a syntenous manner and subject to horizontal gene transfer, while those within the core and intermediate regions may be part of the basic group of genes essential to Streptomyces in the soil environment. Lignocellulose degradation is a difficult topic to study in the Actinomycetales and therefore these candidate genes may help to solve some of the problems associated with this.Table 9Genes conserved in the four Streptomyces species that are potentially involved in lignocellulose cyclingSCO0333DioxygenaseSCO0560Catalase/PeroxidaseSCO0765EndoglucanaseSCO1187CellulaseSCO1188Cellulose binding proteinSCO1338MonooxygenaseSCO1451EndoglucanaseSCO1923DioxygenaseSCO2016MonooxygenaseSCO2267Heme oxygenaseSCO2700Tyrosinase (monophenol monooxygenase)SCO2701Tyrosinase cofactorSCO2783MonooxygenaseSCO2798Cellobiose hydrolaseSCO2838EndoglucanaseSCO3172MonooxygenaseSCO3236OxygenaseSCO4416MonooxygenaseSCO4870MonooxygenaseSCO5033Hydrogen peroxide sensing regulatorSCO5293OxygenaseSCO5390Alkanal monoxygenaseSCO5773MonooxygenaseSCO6545CellulaseSCO7223MonooxygenaseSCO7637EndoglucanaseNote that the oxygeneases included as possible enzymes that make be able to attack lignin are all unclassified yet as to their real function. The core region is in bold
Conclusions
This study confirms that within the Streptomyces analyzed here there is conservation of a core set of genes in the middle of the linear S. coelicolor/S. avermitilis chromosome structure. This is associated with a much higher diversity of gene in the terminal regions of the linear chromosome. Linking these regions are two intermediate regions where there seems to be conservation of genus specific genes and gene clusters. This study also identifies candidate genes that may be possibly involved in terminal replication and other myceliate growth related functions based on a classification of genes into conserved and none conserved groups. This study also provides insights into which genes in Streptomyces play a more significant role in the biochemical network of S. coelicolor, Streptomyces and myceliate Actinobacteria in general. Finally, the degree of gene conserved detected between the four species implies that that genome model of S. coelicolor may extent well beyond the borders of the Streptomyces. It includes at least one Kitasatospora species; furthermore, a similar structure by microarray analysis has been found for Saccharomonospora viridis and Streptosporangium roseum, but not Streptomyces rimosus ATCC10970 (unpublished data). Thus, the microarray approach to genome content analysis and exploration of genome evolution may be fairly widely applicable in the various Actinomycete genus close to Streptomyces that undergo complex morphogenesis.
Electronic supplementary material
Below is the link to the electronic supplementary material
ESM (PDF 938 kb)

actinomycetales
genomotyping
linear genome


Certolizumab pegol, a monthly subcutaneously administered Fc-free anti-TNFα, improves health-related quality of life in patients with moderate to severe Crohn’s disease

Background and aims Certolizumab pegol, a polyethylene glycolated Fc-free Fab’ was efficacious and well tolerated in patients with moderate-to-severe Crohn’s disease in a previously reported randomized, placebo-controlled study. In this paper, we report the effect of certolizumab pegol on health-related quality of life (HRQoL).
Introduction
Crohn’s disease is a chronic relapsing and remitting, inflammatory bowel disease (IBD) that has a significant impact on health-related quality of life (HRQoL) [1, 2]. HRQoL has been recognized as an important health outcome and has been defined in the medical setting as “a concept encompassing a broad range of physical and psychological characteristics and limitations which describe an individual’s ability to function and derive satisfaction from doing so” [3]. Crohn’s disease frequently begins in early adulthood, causing a heavy disease burden in a relatively young patient population. Consequently, many patients will be affected for most of their adult life.
In Crohn’s disease, health status is affected as much by psychosocial factors and functional status as by disease activity [4]. For example, the need to wear an ostomy bag may adversely affect self-image, and this has been reported to be a common worry for patients with Crohn’s disease [5]. Moreover, previous studies have demonstrated a high correlation between disease severity, as measured by the Crohn’s Disease Activity Index (CDAI) [6], and HRQoL, as assessed using the Inflammatory Bowel Disease Questionnaire (IBDQ) [7, 8]. A large longitudinal study prospectively assessed the HRQoL of patients with Crohn’s disease over 1 year and determined influencing factors [9]. The following factors were associated with a negative impact on overall HRQoL: female gender, tobacco use, active Crohn’s disease, involvement of the colon, hospitalization, corticosteroid treatment, and surgery in the past 3 months (order does not indicate priority). These findings were reflected in a survey of patients with a long-term diagnosis of Crohn’s disease [10]. It is therefore important to include improvement in HRQoL as a key therapeutic goal in the treatment of patients with Crohn’s disease.
Conventional treatment options for Crohn’s disease include aminosalicylates, antibiotics, corticosteroids, immunomodulators, and surgery. However, in some patients, the disease can be refractory or unresponsive to certain pharmacologic treatments. Furthermore, corticosteroids are associated with a risk of dependency and potentially serious side effects (e.g., diabetes, osteoporosis, moon face, and acne), which are detrimental to a patient’s HRQoL and may negatively affect self-esteem [11–13]. Such treatments therefore require careful management. Additionally, it is rarely possible to achieve long-term relief from Crohn’s disease with a single surgical procedure, and as a result, patients with Crohn’s disease fear surgery so much that their HRQoL is reduced [14].
It is now well accepted that tumor necrosis factor α (TNFα) plays a pivotal role in the underlying inflammatory pathophysiology of Crohn’s disease [15]. Furthermore, neutralization of TNFα has been shown to improve the symptoms of this debilitating illness [13, 16]. Infliximab has been shown to improve HRQoL, as measured by the IBDQ and the Short-Form Health Survey (SF-36) [8, 17], but the occurrence of infusion reactions along with the risk of developing anti-infliximab antibodies, which can reduce long-term efficacy [18], could negatively impact HRQoL over time. Patients may feel that subcutaneous treatment has advantages over treatments requiring infusion; a long stay at the clinic may negatively impact a patient’s daily life. This increased freedom in patient treatment management may lead to improved treatment compliance with a consequent impact on therapeutic outcomes. Thus, there is a need for alternative long-term therapies for moderate-to-severe Crohn’s disease that have minimal negative impact on patients’ everyday life while also reducing the impact of the disease on patients’ HRQoL [19].
Certolizumab pegol is the first polyethylene glycolated (PEGylated) anti-TNFα antibody fragment (Fab’) to be studied in Crohn’s disease. Unlike conventional anti-TNF agents, certolizumab pegol lacks the crystallizable fragment (Fc) region, thus potentially avoiding unwanted effects [20]. PEGylation increases the plasma half-life and reduces the frequency of dosing required. Certolizumab pegol is administered by subcutaneous injection once every 4 weeks (more frequent dosing may be used during an induction phase). Certolizumab pegol 400 mg administered subcutaneously demonstrated efficacy and was well tolerated in patients with moderate-to-severe active Crohn’s disease in phases II [21] and III (PRECiSE 1 and PRECiSE 2 [22, 23]) studies. Certolizumab pegol has also demonstrated clinical efficacy and good tolerability in patients with rheumatoid arthritis [24, 25].
This is the first paper to report the effect of certolizumab pegol, a monthly subcutaneous treatment, on the HRQoL of patients with moderate-to-severe active Crohn’s disease in a phase II placebo-controlled study [18]. HRQoL was assessed using the IBDQ.
Materials and methods
Ethical conduct
This study was approved by the Independent Ethics Committee/Institutional Review Board for each center before initiation. It was conducted in accordance with the International Conference for Harmonization Guidelines for Good Clinical Practice and the declaration of Helsinki (revised 1996).
Written informed consent was obtained from each patient at screening before any study procedures were performed.
Patients
Adult patients (age ≥18 years) with moderate-to-severe active Crohn’s disease, as defined by a CDAI score of 220–450 points during the week before the administration of the first dose of study drug, were eligible for the study. Exclusion criteria included suspected or diagnosed abscess, a bowel perforation or evidence of non-inflammatory obstruction in 6 months before screening, extensive bowel resection, a functional colostomy or ileostomy, or a known history of tuberculosis. Concomitant medication was allowed, provided that doses were stable and could be continued for the 12-week double-blind period and the 8-week follow-up.
Study design
This was a multicenter, randomized, double-blind, placebo-controlled, 12-week study conducted at 58 international centers between February 2001 and March 2002. An 8-week follow-up period allowed collection of additional safety data. At screening, patients with moderate-to-severe active Crohn’s disease were stratified according to concomitant steroids, immunosuppressants, or long-term anti-infectives. Eligible patients were randomized (1:1:1:1) to receive certolizumab pegol (100, 200, or 400 mg) or placebo (0.9% w/v saline) by subcutaneous injection at weeks 0, 4, and 8 (Fig. 1). Before dosing, patients’ demography, medical history, and concomitant diseases were recorded.
Fig. 1Study design (showing the total numbers of patients recruited into each treatment group)
Outcomes
The primary endpoint of the study was the proportion of patients achieving a clinical response [≥100-point decrease in CDAI score or CDAI score ≤150 points (remission)] at week 12. HRQoL was a secondary endpoint evaluated at baseline (week 0, pre-injection) and at weeks 2, 4, 6, 8, 10, and 12 using the 32-item self-administered IBDQ. The IBDQ is a disease-specific HRQoL measure. The questionnaire assesses the four aspects of a patient’s life that are affected by IBD: symptoms directly related to the primary bowel symptoms, systemic symptoms, emotional function, and social function [26]. The reliability, validity, and responsiveness of the IBDQ have been demonstrated by Guyatt et al. [26, 27] and Irvine et al. [7, 28]. The IBDQ is completed using a 7-point Likert response for each question. Total IBDQ scores range from 32 to 224, with a higher score indicating better HRQoL. Remission is generally indicated by a score of at least 170 points, and a change of 16 points is regarded as clinically meaningful [7]. In addition to a total score, domain scores can be calculated for the Bowel Symptoms, Systemic Symptoms, Emotional Function, and Social Function domains. IBDQ measurements were performed in parallel with determination of CDAI score and serum concentrations of C-reactive protein (CRP). Official IBDQ versions translated into Danish, French, German, and Swedish were used. Our own translations were used for Russian, Serbian, and Afrikaans. To assess whether our own translated versions of the IBDQ provided a valid measure of HRQoL, we compared scores of the translated versions with those of the official translated versions and evaluated those scores in relation to the patient’s CDAI scores. A sensitivity analysis was also carried out to check for influence of specific item scores on the resulting total scores. The results of these analyses demonstrated that our own translated versions of the IBDQ provided a valid measure of HRQoL, comparable to the official, translated versions.
Statistical analysis
A sample size of 260 patients was calculated to give approximately 83% power to detect a true difference between treatment groups of 23% for the primary endpoint of the study, based on a placebo response rate of 12%. After screening, patients were to be randomized in a 1:1:1:1 ratio (65 patients to each of the four treatment groups). Therefore, 195 patients were to receive certolizumab pegol and 65 were to receive placebo.
Efficacy was assessed for the intent-to-treat (ITT) population, which included all patients who received at least one injection and had at least one efficacy measurement after the first injection. Patients who terminated the trial prematurely were advanced to the end-of-study visit. The last observation carried forward method was used in cases where data were missing.
Post hoc analyses of IBDQ total score and individual domain scores were performed. These were purely exploratory in nature. Relationships between CDAI and IBDQ global scores at week 12 were assessed for the ITT population using Kendall’s tau coefficient, a measure of the strength of dependence showing to what extent these two variables move in the same direction. Changes from baseline in IBDQ total score and individual domain scores for each treatment group were formally compared with the placebo group. Least-squares means, adjusted for stratum, pooled country, and baseline IBDQ score, were compared across treatment groups. Fisher’s exact test was used to compare the proportion of patients who achieved remission (defined as an IBDQ total score of ≥170 points) in each active treatment group with that for the placebo group at each week. The analyses were performed based on both the overall ITT population and in the subgroup of patients with elevated baseline CRP (≥10 mg/l). P values ≤0.05 were considered to be statistically significant.
Results
The clinical efficacy and safety assessments of this phase II dose-finding study have been reported in detail elsewhere [21]. Because the 400 mg dose was identified as being the most appropriate dose, this paper focuses on data for patients receiving certolizumab pegol 400 mg.
Patients
In total, 372 patients were screened of whom 292 patients were enrolled (CDAI score <220 points was the most common reason for screening failure). Of these 292 patients, 291 were included in the ITT population used to measure the efficacy endpoints according to CDAI scores [1 patient (400 mg group) was excluded because of missing efficacy data]. Baseline IBDQ scores were not obtained for one patient in the certolizumab pegol 100 mg treatment group. The ITT analyses for the HRQoL evaluations therefore included data for 290 patients. Seventy-five patients (25.7%) withdrew from the study by week 12. The majority of withdrawals were a consequence of lack of improvement/disease progression.
Baseline CRP measurements were obtained for 290 of the 291 patients in the ITT population. Elevated baseline CRP levels (≥10 mg/l) were recorded for 119 patients (41.0%). One of these patients was the patient in the certolizumab pegol 100 mg treatment group who was excluded from analyses as a result of baseline IBDQ scores not being obtained. The subgroup analyses of IBDQ by baseline CRP status were therefore performed on data from 118 patients with an elevated baseline CRP concentration.
The four treatment groups were generally well matched in terms of demographic and clinical characteristics (including baseline IBDQ total score and concomitant medication profile; Table 1). In the ITT population, 21.6% of patients had received previous anti-TNFα therapy, and 15.5% had concomitant medication with both immunomodulators and steroids.
Table 1Baseline demographic and clinical characteristics (intent to treat population used to measure efficacy endpoints according to Crohn’s Disease Activity Index scores) Placebo(n = 73)Certolizumab pegol100 mg (n = 74)200 mg (n = 72)400 mg (n = 72)Mean CDAI score (range)291.5 (206–448)299.2 (194–520)310.7 (184–446)304.5 (204–461)Mean IBDQ score (range)a122.9 (61–190)132.2 (66–189)b122.9 (74–189)126.5 (71–177)Geometric mean plasma CRP, mg/l (range)7.268 (0.27–86.10)6.231 (0.15–141.00)b6.475 (0.17–127.00)7.738 (0.35–128.28)CRP ≥ 10 mg/l, n (%)28 (38.4)31 (42.5)b28 (38.9)32 (44.4)Concomitant medications, n (%)  Aminosalicylates29 (39.7)37 (50.0)32 (44.4)28 (38.9)  Anti-infectives7 (9.6)6 (8.1)7 (9.7)6 (8.3)  Antidiarrheals10 (13.7)19 (25.7)16 (22.2)12 (16.7)  Steroids29 (39.7)24 (32.4)29 (40.3)22 (30.6)  Codeine and derivatives6 (8.2)5 (6.8)5 (6.9)2 (2.8)Immunomodulators  Azathioprine17 (23.3)13 (17.6)23 (31.9)22 (30.6)  6-Mercaptopurine4 (5.5)9 (12.2)2 (2.8)2 (2.8)  Methotrexate5 (6.8)4 (5.4)4 (5.6)3 (4.2)CDAI Crohn’s Disease Activity Index; CRP C-reactive protein; IBDQ Inflammatory Bowel Disease Questionnaire; n number of patientsaMaximum score is 224 points.bData missing for one patient.
Clinical response
The onset of effect (clinical response) of certolizumab pegol was evident at week 2, when certolizumab pegol treatment produced a statistically significant improvement in CDAI score compared with placebo (certolizumab pegol 400 mg 33.3% vs placebo 15.1%; P = 0.010). Clinical response rates were highest in the certolizumab pegol 400 mg group at all time points, with maximal response at week 10 (certolizumab pegol 400 mg 52.8% vs placebo 30.1%; P = 0.006) [21]. Lower CDAI score (representing lower disease severity) was associated with a higher IBDQ global score (representing better HRQoL), as reflected by a high negative Kendall’s tau value (−0.48; P < 0.0001) Clinical response was observed irrespective of baseline CRP level.
Improvement in HRQoL
Patients demonstrated HRQoL improvements from baseline, as measured by change in IBDQ total score, by the first assessment point, week 2 [certolizumab pegol 100 mg, 16.6 points; certolizumab pegol 200 mg, 21.8 points (P ≤ 0.05 vs placebo); certolizumab pegol 400 mg, 22.8 points (P ≤ 0.05 vs placebo); placebo, 10.6 points] (Fig. 2). Patients receiving certolizumab pegol 400 mg experienced statistically significantly greater improvements from baseline in IBDQ total score at all time points (P ≤ 0.05) compared with those receiving placebo. The greatest increase in IBDQ total score was recorded at week 10 (certolizumab pegol 400 mg 32.2 points vs 18.6 points for placebo; P ≤ 0.05). Approximately half of the patients treated with certolizumab pegol 400 mg demonstrated clinically meaningful improvements in IBDQ total score (≥16-point increase) at week 2 (52.8%) and at week 12 (66.7%).
Fig. 2Mean change from baseline in Inflammatory Bowel Disease Questionnaire (IBDQ) total score during 12 weeks’ subcutaneous treatment with either certolizumab pegol or placebo in patients with moderate-to-severe Crohn’s disease (intent-to-treat population); least-squares means (adjusted for stratum, pooled country, and baseline score)
As early as week 2, patients receiving certolizumab pegol 400 mg demonstrated statistically significant improvements in the Bowel Symptoms and Emotional Function domains of the IBDQ relative to placebo (Fig. 3). Improvement in the Systemic Symptoms domain score approached statistical significance at week 2 (P = 0.054). These improvements were generally dose-related (Figs. 3a−d). Interestingly, patients in the certolizumab pegol 200 mg group achieved statistically significantly greater improvements than those receiving placebo in IBDQ total score and all four IBDQ domains at week 2 (Figs. 2 and 3a–d).
Fig. 3Mean change from baseline in Inflammatory Bowel Disease Questionnaire (IBDQ) domain scores during 12 weeks subcutaneous treatment with either certolizumab pegol or placebo in patients with moderate-to-severe Crohn’s disease (intent-to-treat population); least-squares means (adjusted for stratum, pooled country, and baseline score): a Bowel Symptoms domain, b Systemic Symptoms domain, c Emotional Function domain, and d Social Function domain
Emotional Function and Systemic Symptoms were the domains that patients reported as being most improved by certolizumab pegol treatment. At every assessment, patients receiving certolizumab pegol 400 mg showed statistically significantly (P ≤ 0.05) greater improvements in Emotional Function domain scores than those receiving placebo (Fig. 3c). They also demonstrated statistically significantly greater improvements in Systemic Symptoms domain scores than those receiving placebo at weeks 4, 8, 10, and 12 (Fig. 3b). The improvements patients experienced in Social Function domain scores approached statistical significance (P = 0.054) at week 4 (Fig. 3d).
The remission rate in the certolizumab pegol 400 mg group was highest at week 10. This was statistically significantly greater than in the placebo group (Table 2). The proportion of patients achieving remission in the certolizumab pegol 400 mg group was also statistically significantly greater than in the placebo group at weeks 6, 8, and 12. The subgroup of patients with elevated CRP showed similar improvements in IBDQ scores to those observed in the ITT population.
Table 2Proportion of patients achieving remission defined by Inflammatory Bowel Disease Questionnaire total score (≥170 points)Patients (%), intent-to-treat population (n = 290)WeekPlacebo (n = 73)Certolizumab pegol100 mg (n = 73)200 mg (n = 72)400 mg (n = 72)217.832.919.427.8419.237.0a*26.433.3617.835.6*30.638.9*821.935.629.238.9*1027.443.836.147.2*1223.338.423.638.9*aThe higher remission rate at week 2 in the certolizumab pegol 100 mg group compared with higher-dose groups may reflect the slightly higher mean Inflammatory Bowel Disease Questionnaire (IBDQ) score at baseline in this group. However, certolizumab pegol 400 mg resulted in the greatest mean change in IBDQ at all time points.*p ≤ 0.05 vs placebo
Discussion
Patients with Crohn’s disease often have poor HRQoL [29], with disease-related concerns, such as worry about needing surgery, being the main causal factor [10]. It therefore follows that treatments relieving the symptoms of Crohn’s disease and/or reducing the risk of surgery should improve HRQoL in patients with Crohn’s disease.
Certolizumab pegol administered subcutaneously every 4 weeks is a rapid and clinically effective treatment of moderate-to-severe Crohn’s disease with the 400 mg dose (highest dose tested) inducing the highest response rates [21, 22, 23]. This analysis indicates that such clinical findings are accompanied by improvements in HRQoL.
Certolizumab pegol 400 mg appears to exert a rapid, consistently positive effect on HRQoL in patients with moderate-to-severe Crohn’s disease. Patients who received certolizumab pegol 400 mg demonstrated statistically significantly greater improvements in IBDQ total score than those in the placebo group as early as week 2 and at all subsequent time points. As a reflection of the lower baseline IBDQ scores in the 400 mg group, patients in this group experienced the greatest improvement, despite the remission rate (which is the proportion of patients reaching a pre-defined score) being higher in the 100 mg group. Improvements in HRQoL were not dependent on baseline CRP level. Higher baseline CRP concentration was associated with greater improvement in clinical variables in this phase II study [21], but such a relationship has not been evident in phase III studies [30]. In addition, patients receiving certolizumab pegol 200 mg showed a statistically significantly greater change from baseline in IBDQ total score than those receiving placebo at weeks 2 and 4. Collectively, these findings suggest that certolizumab pegol has an early treatment effect in patients with moderate-to-severe Crohn’s disease. The benefits of certolizumab pegol on HRQoL were also apparent in the phase III studies [22, 23, 31, 32].
Improvements in emotional well-being (e.g. depression, anxiety, irritability, and anger resulting from bowel problems) and systemic symptom domains were particularly marked with certolizumab pegol treatment. It appears that the improvements in these two domains of the IBDQ are largely responsible for the reported beneficial effect of certolizumab pegol on HRQoL. Overall, these findings suggest that certolizumab pegol alleviates the psychological symptoms associated with Crohn’s disease and improves a patient’s general well-being, in addition to providing clinical benefits in terms of CDAI response.
HRQoL provides an integral picture of the interactions between a patient’s disease, their treatment, and quality of life—positive effects resulting from the efficacy of a treatment and negative effects caused by adverse events are evaluated. Few trials have examined the effects of conventional agents upon HRQoL in patients with Crohn’s disease. While corticosteroids are effective in controlling acute Crohn’s disease and are reported to improve IBDQ scores [33], the potentially serious adverse events associated with corticosteroids may negate any positive effects on HRQoL gained through control of symptoms. Additionally, corticosteroids are not suitable for maintenance treatment. Improvements in HRQoL have been observed with methotrexate treatment in a large, randomized, double-blind, placebo-controlled study [34]. However, 17% of patients (16/94) receiving methotrexate were withdrawn from the study because of adverse events compared with 2% of placebo-treated patients (1/47).
The improvements in HRQoL observed with certolizumab pegol in this study appear to be similar to those reported in patients with Crohn’s disease after treatment with infliximab [8, 17, 35]. Similarly, natalizumab has been shown to improve HRQoL in patients with Crohn’s disease [36].
Given the detrimental effects of Crohn’s disease on physical and psychosocial functioning, additional research is needed to assess the impact of currently available and newer therapies on HRQoL. Although HRQoL was evaluated using the self-administered IBDQ, which has proved to be a valid and reliable disease-specific instrument for determining health status in patients with Crohn’s disease [7], the analyses presented in this paper were purely exploratory in nature, and firm conclusions cannot be drawn from the results described. However, clinical benefit was also accompanied by improved HRQoL in the phase III studies [22, 23, 31, 32]. Further benefits may be gained through the good tolerability of certolizumab pegol [37] and convenience of subcutaneous dosing with certolizumab pegol once every 4 weeks.
Conclusions
This study demonstrates that the clinical efficacy of certolizumab pegol in patients with moderate-to-severe Crohn’s disease, which is evident within 2 weeks of injection, is paralleled by improvements in HRQoL. The benefits of certolizumab pegol were most pronounced at the highest dose tested (400 g). Improvements in emotional well-being and systemic symptoms appear to be largely responsible for the observed beneficial effect of certolizumab pegol on HRQoL.

quality of life
crohn’s disease
inflammatory bowel disease
immunotherapy