Genome-wide inference of regulatory networks in Streptomyces coelicolor
- Marlene Castro-Melchor†1,
- Salim Charaniya†1, 4,
- George Karypis2,
- Eriko Takano3 and
- Wei-Shou Hu1Email author
© Castro-Melchor et al; licensee BioMed Central Ltd. 2010
Received: 22 June 2010
Accepted: 18 October 2010
Published: 18 October 2010
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.
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.
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.
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 . 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 .
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 , 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 .
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 . 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 , ARACNE [7, 8], CLR , and MRNET . 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 . 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 , and to reconstruct breast, colorectal, and glial normal and cancerous tissue gene coexpression networks .
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  for operon prediction, 326 transcriptome data were used. The additional data consists of 105 hybridizations performed on Affymetrix diS_div712a GeneChips ; 55 cDNA:cDNA hybridizations [16–20]; and 166 cDNA:gDNA hybridizations [21–24] and GEO  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.
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  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  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.
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 . Pearson correlation (r), calculated between pairs of adjacent genes, was used as gene expression similarity measure.
Supervised classification for operon prediction
Supervised classification models for the prediction of operons were obtained using SVM light . 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 .
Positive and negative classes were defined as known operon pairs (KOP) and non-operon pairs (NOP), respectively, as described previously . The positive training set consisted of 425 KOPs. Of these KOPs, 149 were used in our previous study . 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, devAB, nrdABS and nrdRJ, znuACB, and rpmG3-rpmJ2. This last pair, rpmG3-rpmJ2 had also been verified in our previous study . 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.
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  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  and GO terms. Those network modules with a p-value less than 1.0 × 10-4 were considered significantly enriched. The R package qvalue was used to calculate the corresponding q-values using the bootstrap option . 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 .
Operon prediction refinement
Building upon the whole genome operon map developed previously  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  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 . 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 . 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
Comparison of the AUC of different classifiers.
a. Protein classification scheme-based
b. Czekanowski-Dice score
c. Information theoretic metric
Conservation of gene order
Gene expression similarity
2.8 × 10-2
AUCIV - AUCIII = 0
1.6 × 10-4
AUCV - AUCIV = 0
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.
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.
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 ) and other organisms (TrcRS , SenX3-RegX3 , PrrAB ).
Supporting evidence for predicted network modules
Network modules containing known edges
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 . 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.
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.
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 . 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 . 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.
Functionally coherent network modules including a consensus sequence
Network modules enriched and with consensus sequences
Protein class enriched
GO terms enriched
ATPase activity, coupled to transmembrane movement of substances
Hydrolase activity, hydrolyzing O-glycosyl compounds
Sigma factor activity
Electron transporter activity
Mitochondrial electron transport, NADH to ubiquinone
Fatty acid and phosphatidic acid biosynthesis
NADH dehydrogenase (ubiquinone) activity
Nitrate reductase activity
Nitrate reductase complex
Sigma factor activity
Adaptations, atypical conditions
Structural molecule activity
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 . 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 , metabolic reactions , and protein-protein interactions .
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. 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 . 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 .
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.  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.
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.
We would like to thank Govind Chandra for guidance in querying StrepDB.
- Paradkar A, Trefzer A, Chakraburtty R, Stassi D: Streptomyces genetics: a genomic perspective. Crit Rev Biotechnol. 2003, 23 (1): 1-27. 10.1080/713609296.PubMedView ArticleGoogle Scholar
- Bentley SD, Chater KF, Cerdeno-Tarraga AM, Challis GL, Thomson NR, James KD, Harris DE, Quail MA, Kieser H, Harper D: Complete genome sequence of the model actinomycete Streptomyces coelicolor A3(2). Nature. 2002, 417 (6885): 141-147. 10.1038/417141a.PubMedView ArticleGoogle Scholar
- Huang J, Shi J, Molle V, Sohlberg B, Weaver D, Bibb MJ, Karoonuthaisiri N, Lih CJ, Kao CM, Buttner MJ: Cross-regulation among disparate antibiotic biosynthetic pathways of Streptomyces coelicolor. Mol Microbiol. 2005, 58 (5): 1276-1287. 10.1111/j.1365-2958.2005.04879.x.PubMedView ArticleGoogle Scholar
- 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): e8-10.1371/journal.pbio.0050008.PubMedView ArticleGoogle Scholar
- 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. 10.1093/nar/gkm501.PubMedView ArticleGoogle Scholar
- Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000, 418-429.Google Scholar
- 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. 10.1038/ng1532.PubMedView ArticleGoogle Scholar
- 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): S7-10.1186/1471-2105-7-S1-S7.PubMedView ArticleGoogle Scholar
- Meyer PE, Kontos K, Lafitte F, Bontempi G: Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol. 2007, 79879-Google Scholar
- Haynes BC, Brent MR: Benchmarking regulatory network reconstruction with GRENDEL. Bioinformatics. 2009, 25 (6): 801-807. 10.1093/bioinformatics/btp068.PubMedView ArticleGoogle Scholar
- 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-Google Scholar
- Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A: Reverse engineering cellular networks. Nat Protoc. 2006, 1 (2): 662-671. 10.1038/nprot.2006.106.PubMedView ArticleGoogle Scholar
- Sole X, Hernandez P, de Heredia ML, Armengol L, Rodriguez-Santiago B, Gomez L, Maxwell CA, Aguilo F, Condom E, Abril J: Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility. BMC Genomics. 2008, 9: 12-10.1186/1471-2164-9-12.PubMedView ArticleGoogle Scholar
- Torkamani A, Schork NJ: Identification of rare cancer driver mutations by network reconstruction. Genome Res. 2009, 19 (9): 1570-1578. 10.1101/gr.092833.109.PubMedView ArticleGoogle Scholar
- 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): R161-10.1186/gb-2007-8-8-r161.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1365-2958.2006.05304.x.PubMedView ArticleGoogle Scholar
- 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. 10.1101/gad.943401.PubMedView ArticleGoogle Scholar
- 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. 10.1128/AEM.01888-06.PubMedView ArticleGoogle Scholar
- 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. 10.1101/gad.264403.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1365-2958.2005.04761.x.PubMedView ArticleGoogle Scholar
- 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: 56-10.1186/1471-2164-9-56.PubMedView ArticleGoogle Scholar
- 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: 229-10.1186/1471-2164-8-229.PubMedView ArticleGoogle Scholar
- Lian W: Genome-wide analysis of regulatory networks controling secondary metabolism in Streptomyces species. PhD thesis. 2005, University of Minnesota, Department of Chemical Engineering and Materials ScienceGoogle Scholar
- Mersinias V: DNA microarray-based analysis of gene expression in Streptomyces coelicolor A3(2) and Streptomyces lividans. PhD thesis. 2004, University of Manchester, Department of Biomolecular SciencesGoogle Scholar
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA: NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009, D885-890. 10.1093/nar/gkn764. 37 Database
- 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. 10.1093/bioinformatics/17.6.520.PubMedView ArticleGoogle Scholar
- Protein Classification Scheme. [http://www.sanger.ac.uk/Projects/S_coelicolor/scheme.shtml]
- 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): R101-10.1186/gb-2004-5-12-r101.PubMedView ArticleGoogle Scholar
- 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: 166-10.1186/1471-2105-8-166.PubMedView ArticleGoogle Scholar
- Pertea M, Ayanbule K, Smedinghoff M, Salzberg SL: OperonDB: a comprehensive database of predicted operons in microbial genomes. Nucleic Acids Res. 2009, D479-482. 10.1093/nar/gkn784. 37 Database
- Ermolaeva MD, White O, Salzberg SL: Prediction of operons in microbial genomes. Nucleic Acids Res. 2001, 29 (5): 1216-1221. 10.1093/nar/29.5.1216.PubMedView ArticleGoogle Scholar
- StrepDB - The Streptomyces Annotation Server. [http://strepdb.streptomyces.org.uk/cgi-bin/dc3.pl?accession=AL645882&start=4291472&end=4302043&iorm=map&width=900]
- Joachims T: Making large-scale support vector machine learning practical. Advances in kernel methods. Edited by: Scholkofp B, Burges CJC, Mika S. 1999, MIT Press, 169-184.Google Scholar
- 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. 10.1111/j.1365-2958.2006.05065.x.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.00307-06.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.188.7.2512-2520.2006.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.01851-06.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.01901-06.PubMedView ArticleGoogle Scholar
- 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. 10.1101/gr.1239303.PubMedView ArticleGoogle Scholar
- 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. 10.1073/pnas.1530509100.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1467-9868.2004.00439.x.View ArticleGoogle Scholar
- Bailey TL, Williams N, Misleh C, Li WW: MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 2006, W369-373. 10.1093/nar/gkl198. 34 Web Server
- Bailey TL, Gribskov M: Combining evidence using p-values: application to sequence homology searches. Bioinformatics. 1998, 14 (1): 48-54. 10.1093/bioinformatics/14.1.48.PubMedView ArticleGoogle Scholar
- Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: a sequence logo generator. Genome Res. 2004, 14 (6): 1188-1190. 10.1101/gr.849004.PubMedView ArticleGoogle Scholar
- 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. 10.1046/j.1365-2958.2001.02240.x.PubMedView ArticleGoogle Scholar
- Haydel SE, Benjamin WH, 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. 10.1128/JB.184.8.2192-2203.2002.PubMedView ArticleGoogle Scholar
- 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.PubMedView ArticleGoogle Scholar
- Ewann F, Locht C, Supply P: Intracellular autoregulation of the Mycobacterium tuberculosis PrrA response regulator. Microbiology. 2004, 150 (1): 241-246. 10.1099/mic.0.26516-0.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.184.3.794-805.2002.PubMedView ArticleGoogle Scholar
- 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.View ArticleGoogle Scholar
- 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. 10.1016/0092-8674(91)90120-N.PubMedView ArticleGoogle Scholar
- 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.PubMedGoogle Scholar
- 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. 10.1128/JB.00305-07.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1365-2958.2004.04032.x.PubMedView ArticleGoogle Scholar
- 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. 10.1046/j.1365-2958.2002.03004.x.PubMedView ArticleGoogle Scholar
- 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: 79-10.1186/1471-2164-9-79.PubMedView ArticleGoogle Scholar
- 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. 10.1046/j.1365-2958.1997.4511815.x.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1365-2958.2005.04543.x.PubMedView ArticleGoogle Scholar
- 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. 10.1128/JB.01759-07.PubMedView ArticleGoogle Scholar
- Servant P, Mazodier P: Negative regulation of the heat shock response in Streptomyces. Arch Microbiol. 2001, 176 (4): 237-242. 10.1007/s002030100321.PubMedView ArticleGoogle Scholar
- Ulitsky I, Shamir R: Identification of functional modules using network topology and high-throughput data. BMC Syst Biol. 2007, 1: 8-10.1186/1752-0509-1-8.PubMedView ArticleGoogle Scholar
- 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. 10.1111/j.1365-2958.2006.05461.x.PubMedView ArticleGoogle Scholar
- Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL: The large-scale organization of metabolic networks. Nature. 2000, 407 (6804): 651-654. 10.1038/35036627.PubMedView ArticleGoogle Scholar
- Yook SH, Oltvai ZN, Barabasi AL: Functional and topological characterization of protein interaction networks. Proteomics. 2004, 4 (4): 928-942. 10.1002/pmic.200300636.PubMedView ArticleGoogle Scholar
- 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): e2097-10.1371/journal.pone.0002097.PubMedView ArticleGoogle Scholar
- Margolin AA, Califano A: Theory and limitations of genetic network inference from microarray data. Ann N Y Acad Sci. 2007, 1115: 51-72. 10.1196/annals.1407.019.PubMedView ArticleGoogle Scholar
- 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): 154-10.1186/1471-2105-11-154.PubMedView ArticleGoogle Scholar
- Nieselt K, Battke F, Herbig A, Bruheim P, Wentzel A, Jakobsen OM, Sletta H, Alam MT, Merlo ME, Moore J: The dynamic architecture of the metabolic switch in Streptomyces coelicolor. BMC Genomics. 2010, 11: 10-10.1186/1471-2164-11-10.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.