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Open Access Highly Accessed Research article

Genome-wide inference of regulatory networks in Streptomyces coelicolor

Marlene Castro-Melchor1, Salim Charaniya14, George Karypis2, Eriko Takano3 and Wei-Shou Hu1*

  • * Corresponding author: Wei-Shou Hu wshu@umn.edu

  • † Equal contributors

Author Affiliations

1 Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455, USA

2 Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA

3 Department of Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands

4 Genentech, Inc, 1 Antibody Way, Oceanside, CA 92056, USA

For all author emails, please log on.

BMC Genomics 2010, 11:578  doi:10.1186/1471-2164-11-578

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2164/11/578


Received:22 June 2010
Accepted:18 October 2010
Published:18 October 2010

© 2010 Castro-Melchor et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses.

Results

In this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements.

Conclusions

Despite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.

Background

Streptomycetes are soil-living organisms with a complex life cycle that includes formation of aerial mycelia and spores. Members of this genus have large genomes and the capability of producing multiple secondary metabolites, many of which have uses as antibiotics, anti-tumor agents, and immunosuppressants [1]. The genome of Streptomyces coelicolor, the model organism for this high G+C genus, contains 7825 genes. The genome contains more than 20 secondary metabolite clusters and 965 genes encoding proteins predicted to have a regulatory role [2].

With more genes than lower eukaryotes and an unusually high number of regulators, deciphering the regulatory network of Streptomyces coelicolor remains a challenge. Regulation is a dynamic process, in which overlapping signaling cascades integrate into complex networks, linking diverse aspects of growth, morphology, and secondary metabolite production. In addition, in the case of bacteria, genes can be co-transcribed as polycistrons, and it is at this level of cistrons that regulation occurs, rather than at the level of individual genes.

Single knock-out/disruption mutations have been extensively used in this organism to try to decipher the mechanisms regulating secondary metabolite production and their link to morphological changes. The study of these mutants has made multiple advances over the years, including the characterization of the regulators of gene clusters specific to synthesis of antibiotics. These approaches have also revealed that cross-regulation among disparate pathways occur [3], and is thus desirable to explore regulation at a genome scale. Transcriptome profiles across a diverse set of conditions can be used to systematically determine regulatory interactions [4].

In this study we used functional similarity, conservation of gene order, intergenic distance, and gene expression similarity as features for refining our previously published operon predictions [5]. Gene expression data at the cistron level was then used to predict networks centered on 692 regulatory cistrons.

Among the algorithms to reconstruct whole genome regulatory networks, the information-theoretic approaches have gained support in the bioinformatics community. These approaches rely on the estimation of mutual information (MI) from expression data between pairs of genes, or cistrons, to estimate candidate interactions. MI is a correlation measure that can detect non-linear correlations that other measurements like Euclidean distance or Pearson correlation cannot identify. Among the state-of-the-art information-theoretic approaches are relevance networks [6], ARACNE [7,8], CLR [4], and MRNET [9]. Benchmark studies [10,11] comparing the accuracy of the methods have not resulted in a clear winner over all, as the performance of the algorithms is affected by the type of network, and the mutual information estimator, among others. In this work we inferred whole-genome regulatory networks with ARACNE [7]. ARACNE removes indirect interactions by using the data process inequality (DPI), a property of MI [8,12]. ARACNE has been used to identify putative transcriptional targets of the cancer related genes MYC and KLF6 [13], and to reconstruct breast, colorectal, and glial normal and cancerous tissue gene coexpression networks [14].

Methods

Microarray data compilation and processing

The transcriptome data used in this analysis was obtained from in-house generated data and the public repository databases Stanford Microarray Database, Gene Expression Omnibus (GEO), and Array Express. In addition to data used previously [5] for operon prediction, 326 transcriptome data were used. The additional data consists of 105 hybridizations performed on Affymetrix diS_div712a GeneChips [15]; 55 cDNA:cDNA hybridizations [16-20]; and 166 cDNA:gDNA hybridizations [21-24] and GEO [25] accession numbers GSE21807, GSE21808, GSE21811, GSE22398, and GSE22399. The data was divided into three datasets, according to the platform used: dataset 1 for cDNA:gDNA, dataset 2 for cDNA:cDNA, and dataset 3 for Affymetrix chips. Eight transcriptome data (cDNA: cDNA) were removed before further analysis as more than 30% of the genes in those samples were flagged absent.

Prior to analysis using ARACNE, data for genes with low expression dynamics or with a large number of absent flags was removed. It was desirable that the expression data of a gene exhibit good expression dynamics in at least one of the datasets. Thus, a criterion was established that a gene must be flagged absent in less than 20% of the samples and its expression data must have a standard deviation greater than the 25% percentile of the population. This criterion must be met in at least one of the two datasets (dataset 1 or dataset 2). In addition, the expression data of the gene must satisfy a minimum passing criterion in the other dataset - flagged absent in less than 50% of the samples. Also, a probeset corresponding to that gene must be present on the Affymetrix diS_div712a GeneChip to ensure that Affymetrix gene expression data for that gene was available. The following Boolean logical criterion was used for gene selection:

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(1)

where,

a = Fraction of absent flags in dataset 1

b = Standard deviation in dataset 1 (25th percentile of the standard deviations is 0.50)

c = Fraction of absent flags in dataset 2

d = Standard deviation in dataset 2 (25th percentile of the standard deviations is 0.43)

e = Presence of a probeset for that gene on Affymetrix diS_div712a GeneChip

In all, transcript profiles of 6225 genes, corresponding to 4399 cistrons, were used. The k-nearest neighbor method [26] was used to estimate any missing values, as ARACNE requires a complete expression matrix. For each of the three datasets the expression data for each gene was z-standardized to an average of 0 and a standard deviation of 1.

Features used in operon prediction

Functional similarity was estimated based on the protein classification scheme available at the Welcome Trust Sanger Institute [27] and on Gene Ontology (GO) terms. In the case of the protein classification scheme, functional similarity was determined for adjacent genes if both genes were assigned to one of the 140 protein classes. A score of 1 was assigned when both genes belonged to the same functional class and -1 when they belonged to different classes.

Functional similarity based on GO terms was based on biological process and molecular function, two of the three organizing principles of Gene Ontology, and assessed with two metrics: the Czekanowski-Dice score [28] and the information theoretic metric available in the R package GoSim [29]. The Czekanowski-Dice score was calculated using the formula:

<a onClick="popup('http://www.biomedcentral.com/1471-2164/11/578/mathml/M2','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/11/578/mathml/M2">View MathML</a>

(2)

where a and b are the number of GO terms associated with each gene, and c is the number of GO terms common to both genes.

Conservation of gene order was estimated by the number of bacterial genomes in which the orthologs of a pair of adjacent genes are present in the same order. The number of orthologs was obtained from OperonDB [30,31] and it is included in additional file 1. Intergenic distance was calculated from data downloaded from StrepDB [32]. Pearson correlation (r), calculated between pairs of adjacent genes, was used as gene expression similarity measure.

Additional file 1. Conserved pairs. Column 1 and 2 contain the pair of genes, column 3 the probability, and column 4 the number of genomes in which the pair is conserved.

Format: CSV Size: 13KB Download fileOpen Data

Supervised classification for operon prediction

Supervised classification models for the prediction of operons were obtained using SVMlight [33]. Classifiers were assessed by a 10-fold cross-validation scheme. Recall, false positive rate and area under receiver operating characteristic (ROC) curves were used to assess the performance of classifiers as previously described [5].

Positive and negative classes were defined as known operon pairs (KOP) and non-operon pairs (NOP), respectively, as described previously [5]. The positive training set consisted of 425 KOPs. Of these KOPs, 149 were used in our previous study [5]. An additional 266 gene pairs were experimentally verified to be co-transcribed in the same study. Also, eleven pairs were identified from six recently reported operons: nikABCDE [34], devAB [35], nrdABS and nrdRJ [36], znuACB [37], and rpmG3-rpmJ2 [38]. This last pair, rpmG3-rpmJ2 had also been verified in our previous study [5]. The negative training set consisted of 131 NOPs. Of these NOPs, 119 gene pairs were retained from our previous study comprising 122 NOPs. The three pairs that were removed were verified to be co-transcribed in the previous report. Twelve additional NOPs were obtained from the six recently reported operons mentioned above. The list of positive and negative training sets is given in additional file 2.

Additional file 2. Training set. Column 1 and 2 contain the pair of genes, and column 3 its status as KOP or NOP.

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Transcriptional network prediction using ARACNE

Transcriptional networks were predicted on the whole genome using ARACNE [7,8]. The input to ARACNE consisted of a matrix containing the gene expression data at the cistron level and a list of regulators. The gene expression matrix consisted of 4399 rows, corresponding to cistrons, and 524 columns, corresponding to microarrays. A p-value of 1.0 × 10-9 was used as threshold for mutual information. A DPI tolerance of 0.05 was used as criteria to remove possible indirect interactions. Predicted networks were visualized in Cytoscape [39] within ARACNE.

Network modules with functional enrichment and consensus sequences

Fisher's exact test was used to identify network modules in which a significant fraction of genes are involved in the same biological pathway or function, as defined by the protein classification scheme [27] and GO terms. Those network modules with a p-value less than 1.0 × 10-4 were considered significantly enriched. The R package qvalue [40] was used to calculate the corresponding q-values using the bootstrap option [41]. All network modules reported as significantly enriched were significant at an FDR = 0.01.

The upstream regions (300 bp) of the cistrons belonging to the same network module were examined for the presence of consensus sequences using MEME version 3.5.7 [42,43]. The zero order background Markov model used in MEME (A: 0.153; C: 0.351; G: 0.347; and T: 0.149) was determined by calculating the fraction of each base in the upstream region of all 5346 predicted cistrons. To reduce the probability that the reported motifs are not statistically significant, motifs were determined for the same sequences but after randomly shuffling the sequence letters. To make this criterion stricter, this was repeated five times. An E-valuethreshold was set for each network module as the minimum of five E-values determined when the upstream cistron sequences were randomly shuffled. A consensus sequence was considered present in a network if the E-value was less than the E-valuethreshold. Consensus sequence images were generated with WebLogo [44].

Results

Operon prediction refinement

Building upon the whole genome operon map developed previously [5] we employed additional features for operon prediction: functional similarity of adjacent genes, and conservation of gene order. The training set used in this work consisted of literature reported operons, and 266 experimentally verified pairs predicted from our previous work. The positive training set thus consisted of 425 known operon pairs (KOPs), while the negative training set consisted of 131 non-operon pairs (NOPs). The compiled transcriptome dataset comprised a total of 524 cell samples, substantially larger than the 206 samples used in the previous predictions.

Features for operon prediction

Genes which are part of an operon are often involved in the same biological function or pathway. Functional similarity was assessed for the positive and negative training sets based on a protein classification scheme available at the Welcome Trust Sanger Institute [27] and on Gene Ontology (GO) terms. Functional similarity assessment requires that both genes in a pair have a category assigned, thus not all KOPs and NOPs could be tested for functional similarity. Functional similarity based on the protein classification scheme revealed that a high percentage of KOPs (72%) corresponded to pairs in which both genes belonged to the same protein class, whereas for NOPs the percentage was low (10%). Functional similarity based on GO was calculated using the Czekanowski-Dice score (see Methods) and an information theoretic metric available in the R package GoSim [29]. A Czekanowski-Dice score greater than 0.6 was calculated for 33% of the KOPs, but for none of the NOPs. Based on the information theoretic metric, adjacent genes in 79% of the KOPs have functional similarity greater than 0.6, while only 37% of the NOPs have a similarity greater than 0.6. All these functional similarity metrics indicate that adjacent genes in the same operon have a high-likelihood of being involved in the same biological function. Therefore, these similarity metrics can be used for operon prediction.

Genes in the same operon are often conserved across multiple genomes. Conservation of gene order has been previously used for operon prediction in prokaryotes [31]. The number of bacterial genomes in which the orthologs of adjacent Streptomyces coelicolor genes are present in the same order was thus used as a feature for operon prediction. Also, KOPs have shorter intergenic distance compared to NOPs, and therefore, this feature was also used for operon predictions.

Genes which are part of an operon and are co-transcribed have similar expression profiles. Pearson correlation (r) was used as measure of gene expression similarity between the transcript profiles of pairs of adjacent genes. A correlation r > 0.7 was observed for 35% of the adjacent gene pairs in the KOPs. In contrast only 2% of the adjacent gene pairs in the NOPs had a correlation r > 0.7. The sharp discrimination between the two classes strongly indicates the importance of transcriptome data for predicting operons.

Classifiers to differentiate KOPs and NOPs

Binary support vector machine (SVM) classifiers were constructed for differentiating KOPs and NOPs using the individual features described in the previous section. A classifier combining all the features was also constructed. The performance of the constructed classifiers was compared by using a 10-fold cross-validation scheme and receiver operating characteristic (ROC) curves (Figure 1). Table 1 shows a comparison of the area under ROC curves (AUC) for all the classifiers. The null hypothesis was tested by comparing the AUC of ten ROC graphs for each classifier by one-tailed paired t-test. The best single feature classifier is that based on gene expression similarity with an AUC of 0.87, which is better than the intergenic distance-based classifier (p-value = 2.8 × 10-2, paired t-test). The radial SVM model based on all the features, which has an AUC of 0.97, outperforms all the classifiers based on single features, including the gene expression similarity classifier (p-value = 1.6 × 10-4, paired t-test).

thumbnailFigure 1. Comparison of different SVM classifiers by 10-fold cross-validation and ROC graphs. False positive rate is the percentage of NOPs misclassified as operon pairs. Recall is the percentage of KOPs correctly classified as operon pairs. Error bars indicate ± 1 standard deviation (n = 10).

Table 1. Comparison of the AUC of different classifiers.

Whole genome identification of transcription units

The operon status of same-strand pairs in the genome was predicted using the SVM classifier based on all the features. The SVM model assigns a score to each same-strand gene pair. A positive score indicates that the adjacent genes are predicted to be co-transcribed. Adjacent gene pairs with positive score were grouped into operons. A total of 5346 transcription units were predicted (additional file 3). Among these, 1389 transcription units are polycistronic, containing two or more genes.

Additional file 3. Operon predictions. Column 1 contains genes, and column 2 contains the operon into which the gene was predicted.

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Whole genome regulatory network prediction using ARACNE

Gene expression regulation occurs in prokaryotes at the level of cistrons instead of individual genes. The predicted cistrons were used as the basis to infer regulatory networks using ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) [7,8]. The interactions predicted with ARACNE were of the type "cistron A regulates target cistron B". Cistrons containing at least one gene encoding a regulatory protein were categorized as "cistron A". The regulatory proteins belong to families such as sigma factors, transcription factors, DNA-binding proteins, two-component systems, defined-family regulators, and repressors. ARACNE was used to compare the expression of every combination of two cistrons to identify the pairs with statistically significant and high mutual information. ARACNE infers regulatory interactions when pairs exhibit a high degree of expression dependency or correlation. Indirect interactions are eliminated by using the data processing inequality (DPI).

ARACNE predicts interactions based on a matrix of expression values and a list of regulators. To generate the matrix of expression values the profiles of all 7825 genes from the 524 transcriptome samples were examined and those with low dynamic expression profiles were removed. In all, the expression profiles of 6225 genes, corresponding to 4399 cistrons, were used for network prediction. The expression values for cistrons were obtained by averaging the expression values of adjacent genes in the same predicted cistron over 524 transcriptome samples. Of the 4399 cistrons, 692 contain at least one gene encoding a putative regulator. These 692 cistrons constituted the input list of regulators to ARACNE.

Using a p-value of 1.0 × 10-9 as the threshold for mutual information (MI) and a data processing inequality (DPI) tolerance of 0.05, a total of 7170 interactions between 3527 cistrons were identified by ARACNE. For each of the 692 cistrons encoding one or more regulatory proteins, a "network module" was predicted. A network module is comprised of cistrons predicted to be transcriptionally controlled by the regulatory protein, also referred to as the "hub" of the module. The complete predicted network containing 692 interconnected network modules is shown in Figure 2. Each node represents one cistron and edges between two nodes represent a potential interaction. The detailed resulting matrix for the complete network is given on additional file 4, in which the MI scores between interacting cistrons are given. The global connectivity properties of the network can be described by a power-law relationship given by p = 46.6 × k -2.71 where, p is the probability that a regulatory node has k interactions. This is indicative of a scale-free network structure. A small fraction of the regulatory nodes are highly connected and they account for a large number of interactions. Each of the top four hubs (SCO0588, SCO3063, SCO3986, and SCO5454-SCO5455) interacts with more than 50 cistrons. Interestingly, three (SCO0588, SCO3986, and SCO5454-SCO5455) of these four hubs encode two-component systems that regulate gene expression by sensing environmental cues.

thumbnailFigure 2. Predicted transcriptional regulatory network for S. coelicolor. Each node corresponds to a cistron and every edge represents a regulatory interaction between two nodes. The entire network comprises 3527 nodes and 7170 edges.

Additional file 4. Network predictions. Rows represent network elements, columns indicate network modules and the cistron containing the regulatory gene. Numbers are MI values, higher MI values indicate higher correlation.

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This result is highly encouraging, as the mode of action of two-component systems involves phosphorylation and not only interactions at transcript level. Nevertheless some interactions can be inferred from transcript levels. For two-component systems such interaction could be the effect of autoregulation, as has been reported in Streptomyces (AbsA [45]) and other organisms (TrcRS [46], SenX3-RegX3 [47], PrrAB [48]).

Supporting evidence for predicted network modules

Network modules containing known edges

The predicted interactions include known interactions that have been reported in previous studies, giving credence to this prediction. Among the known interactions retrieved are those between cdaR [49,50], actII-ORF4 [51] and their corresponding putative clusters (CDA and actinorhodin, respectively). The multilevel regulatory mechanism involving RedZ activating redD [52], in turn activating the RED biosynthetic cluster was also retrieved in our results. The network modules containing these interactions are shown in Figure 3. Known interactions involving two-component systems were also predicted, for example the two-component system AbsA1-AbsA2 acting on the CDA cluster [53], and VanRS acting on vanKHAX [54]. Another known interaction retrieved in our predictions is that between ramR and ramCSAB [55], which are required for production of aerial hyphae. The network modules containing these interactions are shown in Figure 4.

thumbnailFigure 3. Some network modules with known edges. a) cdaR and the CDA cluster; b) actII-ORF4 and the ACT cluster; c) redD and redZ, and redZ and the RED cluster. Green edge lines indicate known interactions. Node shapes and colors are as indicated in Figure 2.

thumbnailFigure 4. Additional network modules with known edges. a) The two-component system (TCS) operon absA1A2 and the CDA cluster; b) The TCS operon vanRS and the vancomycin resistance operon vanKHAX; c) ramR and ramCSAB. Green edge lines indicate known interactions. Node shapes and colors are as indicated in Figure 2.

Identification of consensus sequences

Operons which are part of the same regulon (i.e., operons activated or repressed by a common regulatory protein), often have a consensus sequence in their upstream region. Consensus sequences have been used not only for regulon prediction, but for operon prediction [56]. For each network module, the upstream regions (300 bp) of the cistrons in that module were examined for the presence of consensus sequences using MEME [42,43]. Consensus sequences in 414 network modules were identified. In 84 of those network modules, the consensus sequence appeared in the upstream region of all the network module elements. Additional file 5 lists the consensus sequences found in each network module.

Additional file 5. Summary of the consensus sequences found in the networks. For each network the values E-valuethreshold and E-value are given. The number of motifs detected and if applicable its consensus sequence are given, together with the total number of elements in the module and the number of elements in which the consensus sequence was detected.

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Network modules containing known consensus sequences

Several previous reports on Streptomyces coelicolor have identified the upstream consensus binding site of regulatory proteins. The consensus sequences discovered in this study were compared with previously reported binding sites. Overlaps between discovered consensus sequences and previously reported binding sites strengthen the evidence for the validity of our predicted network modules. Some of the commonalities between the sequences discovered in this study and those previously reported, are presented next. We also report the presence of these consensus sequences in additional network module members.

An ARG box has been reported in the upstream region of the Streptomyces clavuligerus genes argG and argC [57]. A consensus sequence was discovered in the upstream region of some elements of network module 151, centered on argRDBJC. The consensus sequence was identified not only on argG and argC, but also on argH and SCO1086. The previously reported ARG box is shown aligned with the consensus sequence found for network module 151(also shown) in Figure 5.

thumbnailFigure 5. Arginine related network module. Network module 151 centered on argRDBJC. The discovered consensus sequence (top) is shown aligned to the previously described ARG box (bottom). Node shapes and colors are as indicated in Figure 2.

An ScbR binding motif has been reported in the intergenic region of scbA-scbR, and the upstream region of kasO [58]. The reconstructed network module 544 centered on kasO contains eight interactions, including interactions with scbA and scbR. A consensus sequence was found in the upstream region of all nine elements of this network module. The previously reported ScbR binding consensus sequence is shown aligned with the consensus sequence found for network module 544 (also shown) in Figure 6. Also in the figure are partial gene expression profiles for the network module members along 100 microarray samples (number of microarrays limited for plotting purposes only). The strong correlation between the regulatory hub (kasO) and the eight module elements is evident.

thumbnailFigure 6. TIPK related network module. a) Network module 544 centered on kasO. The discovered consensus sequence (top) is shown aligned to the previously described ScbR binding motif (bottom). b) Partial expression profile along 100 microarrays for the 9 cistrons belonging to network 544 (Number of microarrays limited for plotting purposes). Green edge lines indicate known interactions. Node shapes and colors are as indicated in Figure 2.

Additional commonalities between consensus sequences in other network modules and previously reported motifs were found. For example, the sigU-dependent promoter sequence TGA[AG]C[AG][N16-17]CGTA [59] is similar to the consensus sequence identified in the sigU-centered network module 240. An overlap was also detected for CIRCE (Controlling Inverted Repeat of Chaperone Expression) [60], known to be present in the upstream region of groEL2, and the consensus sequence discovered in all the upstream regions of the groEL2 containing network module 239. These network modules and the discovered consensus sequence aligned to previously reported motifs are shown in Figure 7.

thumbnailFigure 7. Additional network modules with known consensus sequences. a) Network module 240 centered on sigU. The discovered consensus sequence (top) is shown aligned to the previously described sigU binding motif (bottom); b) Network module 239. The discovered consensus sequence (top) is shown aligned to CIRCE (middle) and the upstream region of groeL2 (bottom). Green edge lines indicate known interactions. Node shapes and colors are as indicated in Figure 2.

Identification of biologically enriched network modules

A functional module is a group of components and their interactions that can be attributed a specific biological function [61]. We investigated which network modules represented functionally coherent modules. Fisher's exact test was used to identify the network modules in which a significantly larger number of members were associated with a protein class or a GO term than would be associated by chance. The protein classification used was that at the Welcome Trust Sanger Institute [27]. At a p-value threshold of 1.0 × 10-4, 146 network modules were enriched in 33 different protein classes. Twenty-five network modules were enriched in the secondary metabolism protein class. Additionally, 16 network modules were enriched in the polyketide synthase protein class. For the classification using GO terms, at a p-value threshold of 1.0 × 10-4, 115 network modules were enriched in 67 GO terms. The term that appeared as enriched in the most number of network modules (13) was NADH dehydrogenase (ubiquinone) activity. The complete list of 188 unique enriched network modules can be found in additional file 6.

Additional file 6. Enriched networks. Worksheet 1 contains the list of network modules enriched according to the protein classification scheme, and worksheet 2 those enriched according to GO terms. For each of the enriched network modules the p-value, q-value and the enriched class or term is given. All network modules are significant at an FDR = 0.01.

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Functionally coherent network modules including a consensus sequence

Functionally coherent network modules that also contain a consensus sequence are highly probable to indicate true interactions. Thus, in the above functionally coherent modules, those containing a consensus sequence were identified. A total of 20 network modules contain a consensus sequence in the upstream region of all of its members and present biological enrichment (additional file 7). Of those network modules, 8 were identified as enriched in both a protein class and a GO term (Table 2).

Additional file 7. Details of the 20 network modules enriched in both a protein class and a GO term. The number of cistrons in the network module and the consensus sequence detected in all its members is given.

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Table 2. Network modules enriched and with consensus sequences

Among the network modules with a consensus sequence in all of their members and biological enrichment in both a protein class and a GO term are network modules 56, 49, and 431. Network module 56 (Figure 8a) is the network module with the highest number of connections (53). Network module 49 (Figure 8b) is centered on a putative MarR family transcriptional regulator. A third of the 24 cistrons of network module 49 contain regulatory genes, including some that can coordinate cellular responses to external signals (e. g., MarR regulator, two-component system, and extracytoplasmic function sigma factors). It is possible that network module 49 is involved in responding to an environmental change that is currently unknown. Network module 431 (Figure 8c) is centered on a putative deoR family transcriptional regulator. More than one third of the 20 cistrons in network module 431 include regulatory genes, like the morphology-related bldN. This network includes conservon cvnABCDE9, a probable membrane-associated complex which may connect to the bld cascade [62]. Network module 431 could thus be involved in detecting nutritional limitation and the transition from substrate to aerial mycelia. Other network modules that present a consensus sequence in all their members and enrichment in a protein class and a Gene Ontology term appear in additional file 8.

thumbnailFigure 8. Network modules enriched and with a consensus sequence in all their members. Network modules are enriched in both a protein class and a GO term. a) Network module 56 centered on SCO0588, b) Network module 49 centered on SCO0487, c) Network module 431 centered on SCO4920. Node shapes and colors are as indicated in Figure 2.

Additional file 8. Additional network modules enriched and with consensus sequence. Network modules 20, 45, 636, and 691 enriched in a protein class and a GO term and containing a consensus sequence in all of its members (see Table 2 and additional file 7).

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Discussion

In this study, we integrate large scale transcriptome data with genomic features to predict operons in the antibiotic producer Streptomyces coelicolor. The transcriptome data, at the cistron level, was then used to infer the whole genome regulatory network of this organism. The network modules, centered on cistrons containing genes encoding regulatory proteins, contain potential interactions between genes encoding regulatory proteins and their targets. Some of the interactions in the network modules correspond to experimentally known interactions. In addition, the network modules were analyzed for functional enrichment and the presence of consensus sequences. Some of the consensus sequences overlap previously described binding sequences and motifs.

Improved operon prediction by using an expanded transcriptome set

The inclusion of additional predictive features and the expansion of the training set and the transcriptome dataset resulted in an improvement in the operon predictability of the classifiers developed in this work. The performance of the classifier based on gene expression similarity, as determined by area under ROC graph, improved from 0.81 in the previous work to 0.87 in this study. Even though the classifiers based on functional similarity performed poorly, most likely due to the lack of GO term assignment for many genes, they contributed to improve the performance of the classifier including all features. The area under ROC graph increased from 0.91 in the previous work to 0.97 in this study. Comparison of our current operon predictions to our previous predictions indicated a good agreement between the two sets. Of the 4965 same strand pairs, 4439 (89.4%) retained the same prediction. Of the 526 differences, 422 correspond to adjacent genes predicted to be co-transcribed in the current prediction, but not in the previous one. Only 104 differences corresponded to adjacent genes not predicted as co-transcribed in the current work and predicted as co-transcribed in the previous work. Most of these pairs had a low expression correlation in the expanded dataset.

The training set for the current predictions consisted of 425 KOPs and 131 NOPs. The KOPs consist of literature-reported operons as well as those experimentally verified in our previous study [5]. Thus, the training set contains more than three-fold higher KOPs compared to NOPs creating the possibility of an imbalance between the positive and the negative training sets in the operon model. However, as noted above, with an 89.4% overlap, there is a high degree of consistency between the prediction of the previously-reported model and the current predictions. The previous predictions employed a more balanced training set (149 KOPs and 122 NOPs) and the prediction results were experimentally verified. Thus, the consistency between the two predictive models gives credence to the results of the current predictions.

Reverse engineering transcriptional network prediction

An advantage of the algorithm employed in this study (ARACNE) is that unlike clustering algorithms (such as k-means, or self-organizing maps) where cistrons or genes are assigned to mutually exclusive groups, a cistron can participate in multiple network modules, thus linking them and allowing a cistron to engage in different biological functions. ARACNE identified a Streptomyces coelicolor transcriptional network with scale-free connectivity distribution. Scale-free architecture has been noted in other networks derived from transcriptional interactions [7], metabolic reactions [63], and protein-protein interactions [64].

Additional considerations for regulatory predictions

An implicit assumption of most reverse engineering approaches based on microarray data is that all the network components are fully observed. However gene interactions are not static and additional layers of gene regulation exist. Although on a global level, mRNA abundance correlates with the protein levels of the corresponding genes, discrepancies between mRNA and protein profiles have been noted for several genes in Streptomyces coelicolor [65]. Moreover, due to post-translational modifications (e.g., phosphorylation of two-component systems), the active protein levels cannot be reliably estimated from transcript levels. These uncertainties introduce hidden variables which are not observed in transcriptomic studies. Due to this limitation of partial observability, it may be impossible to identify all the direct interactions and eliminate those interactions that arise due to indirect statistical dependencies [66]. It is conceivable that many of these regulatory predictions can be substantiated and improved by combining gene expression data with other genomic data sources such as functional annotation, associations discovered by text-mining biomedical literature, and protein-protein interactions. In addition, approaches that detect dependencies between genes at different time delays are starting to emerge, see for example [67].

Combination of network modules with annotation and consensus sequence presence

A vast majority of the 7170 interactions predicted in this study are novel and not yet experimentally verified. Because the network prediction was based only on transcriptome data at the cistron level, other interaction types involving proteins and even protein modifications would most likely not be captured with this methodology. It is encouraging though that known protein-DNA interactions were obtained in several network modules. Techniques such as EMSA, ChIP-chip and even ChIP-Seq can be used to experimentally verify these predictions. However, genome-scale experimental data for protein-DNA interactions in Streptomyces coelicolor is at the moment almost non existing. Prioritization of these predicted inferences will undoubtedly assist any future attempts to further analyze or verify these interactions. In this study a total of twenty network modules (additional file 7) presented functional enrichment and the presence of a consensus sequence in all of its members. These modules represent promising candidates for further analysis and experimental verification.

Network module overlap with coherent clusters from an independent study

In a recent study by Nieselt et al. [68] the metabolic switch of Streptomyces coelicolor was studied by clustering of temporal transcriptome profiles, which resulted in several biologically coherent clusters, dominated by a few large operons. The eight clusters discussed in that study were compared to our predicted network modules and a considerable overlap was identified in all of them. This overlap includes the clusters associated with synthesis and regulation of the cryptic type I polyketide, and the RED and ACT antibiotics, which have several genes in common with the biologically-relevant networks identified in this study. Additionally, the ribosomal gene cluster from Nieselt et al. includes 46 genes, 23 of which appear in our network module 570, which is enriched in the protein class "Ribosomal proteins - synthesis, modification" and the GO cellular component "Ribosome". Similarly, the nitrogen metabolism cluster from Nieselt et al. includes five genes, three of which appear in our network modules 213 and 488. The network module 213 is enriched in the GO terms "nitrate reductase activity" and "nitrate reductase complex" whereas the network module 488 is enriched in the GO term "nitrogen compound metabolic process", which indicates that both networks may be involved in nitrogen metabolism. Some of the genes up regulated by phosphate depletion also appear in our network modules 370 and 371, which are centered on phoU and phoRP, respectively. Thus, the overlaps between the predicted network modules and the coherent gene clusters from an independent study further indicates the importance of combining global and temporal gene expression datasets with physiological information such as gene functions and consensus sequences.

Conclusions

Here, we implement a systematic approach for mining large volumes of transcriptome data to predict the transcription regulatory network of Streptomyces coelicolor. The network comprises more than 7000 direct associations between putative transcription factors and more than 3500 predicted cistrons in Streptomyces coelicolor. The network displays a scale-free architecture with a small-world property observed in several biological networks in bacteria as well as higher organisms. A substantial percentage of these interactions comprise network modules with coherency of biological function. Further attempts to integrate diverse genomic dataset will seek to improve the sensitivity and specificity of these network predictions. Such integrative efforts substantiated with experimental validation present a highly promising systems approach for elucidating the regulatory determinants of secondary metabolism.

Authors' contributions

MCM performed network interpretations, consensus sequence analysis, and manuscript writing. SC performed operon and network prediction, and assisted in manuscript writing. GK reviewed results and participated in critical discussions. ET participated in discussions. WSH supervised the study, reviewed results and assisted in manuscript preparation. All authors have read and approved the final manuscript.

Acknowledgements

We would like to thank Govind Chandra for guidance in querying StrepDB.

References

  1. Paradkar A, Trefzer A, Chakraburtty R, Stassi D: Streptomyces genetics: a genomic perspective.

    Crit Rev Biotechnol 2003, 23(1):1-27. PubMed Abstract | Publisher Full Text OpenURL

  2. Bentley SD, Chater KF, Cerdeno-Tarraga AM, Challis GL, Thomson NR, James KD, Harris DE, Quail MA, Kieser H, Harper D, et al.: Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2).

    Nature 2002, 417(6885):141-147. PubMed Abstract | Publisher Full Text OpenURL

  3. Huang J, Shi J, Molle V, Sohlberg B, Weaver D, Bibb MJ, Karoonuthaisiri N, Lih CJ, Kao CM, Buttner MJ, et al.: Cross-regulation among disparate antibiotic biosynthetic pathways of Streptomyces coelicolor.

    Mol Microbiol 2005, 58(5):1276-1287. PubMed Abstract | Publisher Full Text OpenURL

  4. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS: Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

    PLoS Biol 2007, 5(1):e8. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. Charaniya S, Mehra S, Lian W, Jayapal KP, Karypis G, Hu WS: Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor.

    Nucleic Acids Res 2007, 35(21):7222-7236. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  6. Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements.

    Pac Symp Biocomput 2000, 418-429. PubMed Abstract OpenURL

  7. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A: Reverse engineering of regulatory networks in human B cells.

    Nat Genet 2005, 37(4):382-390. PubMed Abstract | Publisher Full Text OpenURL

  8. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.

    BMC Bioinformatics 2006, 7(Suppl 1):S7. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  9. Meyer PE, Kontos K, Lafitte F, Bontempi G: Information-theoretic inference of large transcriptional regulatory networks.

    EURASIP J Bioinform Syst Biol 2007, 79879. PubMed Abstract | PubMed Central Full Text OpenURL

  10. Haynes BC, Brent MR: Benchmarking regulatory network reconstruction with GRENDEL.

    Bioinformatics 2009, 25(6):801-807. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  11. Olsen C, Meyer PE, Bontempi G: On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.

    EURASIP J Bioinform Syst Biol 2009, 308959. PubMed Abstract | PubMed Central Full Text OpenURL

  12. Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A: Reverse engineering cellular networks.

    Nat Protoc 2006, 1(2):662-671. PubMed Abstract | Publisher Full Text OpenURL

  13. Sole X, Hernandez P, de Heredia ML, Armengol L, Rodriguez-Santiago B, Gomez L, Maxwell CA, Aguilo F, Condom E, Abril J, et al.: Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility.

    BMC Genomics 2008, 9:12. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  14. Torkamani A, Schork NJ: Identification of rare cancer driver mutations by network reconstruction.

    Genome Res 2009, 19(9):1570-1578. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  15. Hesketh A, Chen WJ, Ryding J, Chang S, Bibb M: The global role of ppGpp synthesis in morphological differentiation and antibiotic production in Streptomyces coelicolor A3(2).

    Genome Biol 2007, 8(8):R161. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  16. San Paolo S, Huang J, Cohen SN, Thompson CJ: rag genes: novel components of the RamR regulon that trigger morphological differentiation in Streptomyces coelicolor.

    Mol Microbiol 2006, 61(5):1167-1186. PubMed Abstract | Publisher Full Text OpenURL

  17. Huang J, Lih CJ, Pan KH, Cohen SN: Global analysis of growth phase responsive gene expression and regulation of antibiotic biosynthetic pathways in Streptomyces coelicolor using DNA microarrays.

    Genes Dev 2001, 15(23):3183-3192. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  18. Fong R, Vroom JA, Hu Z, Hutchinson CR, Huang J, Cohen SN, Kao CM: Characterization of a large, stable, high-copy-number Streptomyces plasmid that requires stability and transfer functions for heterologous polyketide overproduction.

    Appl Environ Microbiol 2007, 73(4):1296-1307. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  19. Elliot MA, Karoonuthaisiri N, Huang J, Bibb MJ, Cohen SN, Kao CM, Buttner MJ: The chaplins: a family of hydrophobic cell-surface proteins involved in aerial mycelium formation in Streptomyces coelicolor.

    Genes Dev 2003, 17(14):1727-1740. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  20. Lee EJ, Karoonuthaisiri N, Kim HS, Park JH, Cha CJ, Kao CM, Roe JH: A master regulator sigmaB governs osmotic and oxidative response as well as differentiation via a network of sigma factors in Streptomyces coelicolor.

    Molecular Microbiology 2005, 57(5):1252-1264. PubMed Abstract | Publisher Full Text OpenURL

  21. Lian W, Jayapal KP, Charaniya S, Mehra S, Glod F, Kyung YS, Sherman DH, Hu WS: Genome-wide transcriptome analysis reveals that a pleiotropic antibiotic regulator, AfsS, modulates nutritional stress response in Streptomyces coelicolor A3(2).

    BMC Genomics 2008, 9:56. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  22. Jayapal KP, Lian W, Glod F, Sherman DH, Hu WS: Comparative genomic hybridizations reveal absence of large Streptomyces coelicolor genomic islands in Streptomyces lividans.

    BMC Genomics 2007, 8:229. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  23. Lian W: Genome-wide analysis of regulatory networks controling secondary metabolism in Streptomyces species. PhD thesis. University of Minnesota, Department of Chemical Engineering and Materials Science; 2005. OpenURL

  24. Mersinias V: DNA microarray-based analysis of gene expression in Streptomyces coelicolor A3(2) and Streptomyces lividans. PhD thesis. University of Manchester, Department of Biomolecular Sciences; 2004. OpenURL

  25. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, et al.: NCBI GEO: archive for high-throughput functional genomic data.

    Nucleic Acids Res 2009, (37 Database):D885-890. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  26. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays.

    Bioinformatics 2001, 17(6):520-525. PubMed Abstract | Publisher Full Text OpenURL

  27. Protein Classification Scheme [http://www.sanger.ac.uk/Projects/S_coelicolor/scheme.shtml] webcite

  28. Martin D, Brun C, Remy E, Mouren P, Thieffry D, Jacq B: GOToolBox: functional analysis of gene datasets based on Gene Ontology.

    Genome Biol 2004, 5(12):R101. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  29. Frohlich H, Speer N, Poustka A, Beissbarth T: GOSim--an R-package for computation of information theoretic GO similarities between terms and gene products.

    BMC Bioinformatics 2007, 8:166. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  30. Pertea M, Ayanbule K, Smedinghoff M, Salzberg SL: OperonDB: a comprehensive database of predicted operons in microbial genomes.

    Nucleic Acids Res 2009, (37 Database):D479-482. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  31. Ermolaeva MD, White O, Salzberg SL: Prediction of operons in microbial genomes.

    Nucleic Acids Res 2001, 29(5):1216-1221. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  32. StrepDB - The Streptomyces Annotation Server [http:/ / strepdb.streptomyces.org.uk/ cgi-bin/ dc3.pl?accession=AL645882&start=429 1472&end=4302043&iorm=map&width=900] webcite

  33. Joachims T: Making large-scale support vector machine learning practical. In Advances in kernel methods. Edited by Scholkofp B, Burges CJC, Mika S. MIT Press; 1999:169-184. OpenURL

  34. Ahn BE, Cha J, Lee EJ, Han AR, Thompson CJ, Roe JH: Nur, a nickel-responsive regulator of the Fur family, regulates superoxide dismutases and nickel transport in Streptomyces coelicolor.

    Mol Microbiol 2006, 59(6):1848-1858. PubMed Abstract | Publisher Full Text OpenURL

  35. Hoskisson PA, Rigali S, Fowler K, Findlay KC, Buttner MJ: DevA, a GntR-like transcriptional regulator required for development in Streptomyces coelicolor.

    J Bacteriol 2006, 188(14):5014-5023. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  36. Borovok I, Gorovitz B, Schreiber R, Aharonowitz Y, Cohen G: Coenzyme B12 controls transcription of the Streptomyces class Ia ribonucleotide reductase nrdABS operon via a riboswitch mechanism.

    J Bacteriol 2006, 188(7):2512-2520. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  37. Shin JH, Oh SY, Kim SJ, Roe JH: The zinc-responsive regulator Zur controls a zinc uptake system and some ribosomal proteins in Streptomyces coelicolor A3(2).

    J Bacteriol 2007, 189(11):4070-4077. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  38. Owen GA, Pascoe B, Kallifidas D, Paget MS: Zinc-responsive regulation of alternative ribosomal protein genes in Streptomyces coelicolor involves zur and sigmaR.

    J Bacteriol 2007, 189(11):4078-4086. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  39. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks.

    Genome Res 2003, 13(11):2498-2504. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  40. Storey JD, Tibshirani R: Statistical significance for genomewide studies.

    Proceedings of the National Academy of Sciences of the United States of America 2003, 100(16):9440-9445. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  41. Storey JD, Taylor JE, Siegmund D: Strong control, conservative point estimation, and simultaneous conservatie consistency of false discovery rates: A unified approach.

    Journal of the Royal Statistical Society, Series B 2004, 66:187-205. Publisher Full Text OpenURL

  42. Bailey TL, Williams N, Misleh C, Li WW: MEME: discovering and analyzing DNA and protein sequence motifs.

    Nucleic Acids Res 2006, (34 Web Server):W369-373. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  43. Bailey TL, Gribskov M: Combining evidence using p-values: application to sequence homology searches.

    Bioinformatics 1998, 14(1):48-54. PubMed Abstract | Publisher Full Text OpenURL

  44. Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: a sequence logo generator.

    Genome Res 2004, 14(6):1188-1190. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  45. Anderson TB, Brian P, Champness WC: Genetic and transcriptional analysis of absA, an antibiotic gene cluster-linked two-component system that regulates multiple antibiotics in Streptomyces coelicolor.

    Mol Microbiol 2001, 39(3):553-566. PubMed Abstract | Publisher Full Text OpenURL

  46. Haydel SE, Benjamin WH Jr, Dunlap NE, Clark-Curtiss JE: Expression, autoregulation, and DNA binding properties of the Mycobacterium tuberculosis TrcR response regulator.

    J Bacteriol 2002, 184(8):2192-2203. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  47. Himpens S, Locht C, Supply P: Molecular characterization of the mycobacterial SenX3-RegX3 two-component system: evidence for autoregulation.

    Microbiology 2000, 146(12):3091-3098. PubMed Abstract OpenURL

  48. Ewann F, Locht C, Supply P: Intracellular autoregulation of the Mycobacterium tuberculosis PrrA response regulator.

    Microbiology 2004, 150(1):241-246. PubMed Abstract | Publisher Full Text OpenURL

  49. Ryding NJ, Anderson TB, Champness WC: Regulation of the Streptomyces coelicolor calcium-dependent antibiotic by absA, encoding a cluster-linked two-component system.

    J Bacteriol 2002, 184(3):794-805. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  50. Hojati Z, Milne C, Harvey B, Gordon L, Borg M, Flett F, Wilkinson B, Sidebottom PJ, Rudd BAM, Hayes MA: Structure, Biosynthetic Origin, and Engineered Biosynthesis of Calcium-Dependent Antibiotics from Streptomyces coelicolor.

    Chemistry & Biology 2002, 9(11):1175-1187. OpenURL

  51. Fernandez-Moreno MA, Caballero JL, Hopwood DA, Malpartida F: The act cluster contains regulatory and antibiotic export genes, direct targets for translational control by the bldA tRNA gene of Streptomyces.

    Cell 1991, 66(4):769-780. PubMed Abstract | Publisher Full Text OpenURL

  52. White J, Bibb M: bldA dependence of undecylprodigiosin production in Streptomyces coelicolor A3(2) involves a pathway-specific regulatory cascade.

    J Bacteriol 1997, 179(3):627-633. PubMed Abstract | PubMed Central Full Text OpenURL

  53. McKenzie NL, Nodwell JR: Phosphorylated AbsA2 negatively regulates antibiotic production in Streptomyces coelicolor through interactions with pathway-specific regulatory gene promoters.

    J Bacteriol 2007, 189(14):5284-5292. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  54. Hong HJ, Hutchings MI, Neu JM, Wright GD, Paget MSB, Buttner MJ: Characterization of an inducible vancomycin resistance system in Streptomyces coelicolor reveals a novel gene (vanK) required for drug resistance.

    Molecular Microbiology 2004, 52(4):1107-1121. PubMed Abstract | Publisher Full Text OpenURL

  55. O'Connor TJ, Kanellis P, Nodwell JR: The ramC gene is required for morphogenesis in Streptomyces coelicolor and expressed in a cell type-specific manner under the direct control of RamR.

    Mol Microbiol 2002, 45(1):45-57. PubMed Abstract | Publisher Full Text OpenURL

  56. Laing E, Sidhu K, Hubbard SJ: Predicted transcription factor binding sites as predictors of operons in Escherichia coli and Streptomyces coelicolor.

    BMC Genomics 2008, 9:79. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  57. Rodriguez-Garcia A, Ludovice M, Martin JF, Liras P: Arginine boxes and the argR gene in Streptomyces clavuligerus: evidence for a clear regulation of the arginine pathway.

    Mol Microbiol 1997, 25(2):219-228. PubMed Abstract | Publisher Full Text OpenURL

  58. Takano E, Kinoshita H, Mersinias V, Bucca G, Hotchkiss G, Nihira T, Smith CP, Bibb M, Wohlleben W, Chater K: A bacterial hormone (the SCB1) directly controls the expression of a pathway-specific regulatory gene in the cryptic type I polyketide biosynthetic gene cluster of Streptomyces coelicolor.

    Mol Microbiol 2005, 56(2):465-479. PubMed Abstract | Publisher Full Text OpenURL

  59. Gordon ND, Ottaviano GL, Connell SE, Tobkin GV, Son CH, Shterental S, Gehring AM: Secreted-protein response to sigmaU activity in Streptomyces coelicolor.

    J Bacteriol 2008, 190(3):894-904. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  60. Servant P, Mazodier P: Negative regulation of the heat shock response in Streptomyces.

    Arch Microbiol 2001, 176(4):237-242. PubMed Abstract | Publisher Full Text OpenURL

  61. Ulitsky I, Shamir R: Identification of functional modules using network topology and high-throughput data.

    BMC Syst Biol 2007, 1:8. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  62. Komatsu M, Takano H, Hiratsuka T, Ishigaki Y, Shimada K, Beppu T, Ueda K: Proteins encoded by the conservon of Streptomyces coelicolor A3(2) comprise a membrane-associated heterocomplex that resembles eukaryotic G protein-coupled regulatory system.

    Mol Microbiol 2006, 62(6):1534-1546. PubMed Abstract | Publisher Full Text OpenURL

  63. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL: The large-scale organization of metabolic networks.

    Nature 2000, 407(6804):651-654. PubMed Abstract | Publisher Full Text OpenURL

  64. Yook SH, Oltvai ZN, Barabasi AL: Functional and topological characterization of protein interaction networks.

    Proteomics 2004, 4(4):928-942. PubMed Abstract | Publisher Full Text OpenURL

  65. Jayapal KP, Philp RJ, Kok YJ, Yap MG, Sherman DH, Griffin TJ, Hu WS: Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor.

    PLoS One 2008, 3(5):e2097. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  66. Margolin AA, Califano A: Theory and limitations of genetic network inference from microarray data.

    Ann N Y Acad Sci 2007, 1115:51-72. PubMed Abstract | Publisher Full Text OpenURL

  67. Zoppoli P, Morganella S, Ceccarelli M: TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.

    BMC Bioinformatics 2010, 11(1):154. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  68. Nieselt K, Battke F, Herbig A, Bruheim P, Wentzel A, Jakobsen OM, Sletta H, Alam MT, Merlo ME, Moore J, et al.: The dynamic architecture of the metabolic switch in Streptomyces coelicolor.

    BMC Genomics 2010, 11:10. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL