A Bayesian inference method for the analysis of transcriptional regulatory networks in metagenomic data
- Elizabeth T. Hobbs†1,
- Talmo Pereira†1,
- Patrick K. O’Neill1 and
- Ivan Erill1Email authorView ORCID ID profile
© The Author(s) 2016
Received: 26 January 2016
Accepted: 30 June 2016
Published: 8 July 2016
Metagenomics enables the analysis of bacterial population composition and the study of emergent population features, such as shared metabolic pathways. Recently, we have shown that metagenomics datasets can be leveraged to characterize population-wide transcriptional regulatory networks, or meta-regulons, providing insights into how bacterial populations respond collectively to specific triggers. Here we formalize a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes by determining the probability of regulation of orthologous gene sequences. We assess the performance of this approach on synthetic datasets and we validate it by analyzing the copper-homeostasis network of Firmicutes species in the human gut microbiome.
Assessment on synthetic datasets shows that our method provides a robust and interpretable metric for assessing putative regulation by a transcription factor on sets of promoter sequences mapping to an orthologous gene cluster. The inference framework integrates the regulatory contribution of secondary sites and can discern false positives arising from multiple instances of a clonal sequence. Posterior probabilities for orthologous gene clusters decline sharply when less than 20 % of mapped promoters have binding sites, but we introduce a sensitivity adjustment procedure to speed up computation that enhances regulation assessment in heterogeneous ortholog clusters. Analysis of the copper-homeostasis regulon governed by CsoR in the human gut microbiome Firmicutes reveals that CsoR controls itself and copper-translocating P-type ATPases, but not CopZ-type copper chaperones. Our analysis also indicates that CsoR frequently targets promoters with dual CsoR-binding sites, suggesting that it exploits higher-order binding conformations to fine-tune its activity.
We introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data that enables inference of meta-regulons in a systematic and interpretable way. Validation of this method on the CsoR meta-regulon of gut microbiome Firmicutes illustrates the usefulness of the approach, revealing novel properties of the copper-homeostasis network in poorly characterized bacterial species and putting forward evidence of new mechanisms of DNA binding for this transcriptional regulator. Our approach will enable the comparative analysis of regulatory networks across metagenomes, yielding novel insights into the evolution of transcriptional regulatory networks.
KeywordsTranscription factor Regulatory network Regulon Metagenomics Bayesian inference Copper homeostasis Metal resistance Stress response CsoR
The advent of next-generation sequencing methodologies has enabled the study of bacterial populations through direct sampling of their genetic material . Metagenomics techniques allow the detailed investigation of bacterial communities, their shared metabolic pathways and their interaction with environment and hosts [2–7], but they also pose many challenges regarding data standardization, processing and analysis [8, 9]. To date, most analyses of metagenomics datasets have focused on the phylogenetic composition of metagenomes and the relative contribution of different bacterial clades to metabolic pathways [3, 9–12]. However, metagenomics data also constitute a powerful resource for the direct analysis of transcriptional regulatory networks, or regulons, in natural environments. Such analyses can be used to characterize the contribution of non-culturable bacteria and mobile genetic elements to global regulatory networks, to analyze the changes in a population’s regulatory program in response to interventions or habitat adaptation, and to quantify the relative importance of genetic elements in the makeup of known regulatory systems. Comparative research on multiple metagenomes has revealed that regulatory potential, measured as the local density of putative transcription factor (TF)-binding sites, correlates with processes involved in the response to stimuli present in specific environments [13, 14]. Recently, we provided proof of concept that TF-binding motifs can be effectively leveraged to analyze the genetic makeup of known transcriptional regulatory networks using metagenomic data, providing insights into the function of such networks in specific microbiomes . In this work we formalize an inference method to analyze transcriptional regulatory networks in metagenomics datasets. The Bayesian inference approach we put forward provides a consistent framework for the study of regulatory networks using metagenomics datasets, facilitating the interpretation of results, standardizing the outcome of analyses to facilitate comparison and allowing users to selectively adjust sensitivity. We validate the novel inference framework on the Integrated Reference Catalog of the Human Gut Microbiome , analyzing the regulation of copper-homeostasis in gut microbiome Firmicutes through the recently characterized copper-responsive repressor CsoR . Our results reveal an inferred copper-homeostasis network congruent with that reported in studies on model organisms, outlining the core elements of this regulatory system and highlighting specific features of the human gut CsoR meta-regulon.
Human gut metagenomics data was obtained from the Integrated Reference Catalog of the Human Gut Microbiome service (http://meta.genomics.cn/) . The dataset contains 1267 gut metagenomes, totaling 6.4 Tb. To ensure consistency, here we restricted the analysis to 401 samples from healthy European individuals obtained in the MetaHIT project. This subset contains 5,133,816 predicted genes, with roughly half of them (2,579,737) functionally annotated with eggNOG/COG identifiers from the eggNOG v4.0 database . The bacterial population in these 401 samples is dominated by two bacterial orders [Bacteroidales (58.51 %) and Clostridiales (32.11 %)] belonging to two major bacterial phyla [Bacteroidetes (59.29 %) and Firmicutes (34.97%)]. A CsoR-binding motif was compiled by combining experimentally-validated and computationally inferred Firmicutes CsoR-binding sites available in the CollecTF and RegPrecise databases [19, 20].
The mixing parameter α corresponds to the probability of observing a functional binding site in a regulated promoter, which can be estimated from known instances of TF-binding sites in their genomic context. For CsoR, we expect on average one binding site in a regulated promoter of length 300 bp, so α is defined to be 1/300 [23, 24].
The priors P(R) and P(B) can be inferred from genomic data. P(R) and P(B) can be approximated by the fraction of annotated operons in a genome that are known and not known, respectively, to be regulated by the TF. Using B. subtilis as a reference genome for CsoR, we obtain P(R) = 3/1811 and P(B) = 1 − P(R).
Sensitivity adjustment and determination of putatively regulated eggNOG/COGs
The large size of metagenomics datasets poses challenges for the efficient computation of the posterior probabilities outlined above. It is known that a large fraction of the eggNOG/COG identifiers will not be regulated by the TF. The computation may therefore be simplified by defining a score threshold to exclude operons with promoters that show no evidence of regulation . This strategy has the added benefit of compensating for heterogeneity in eggNOG/COG clustering, which may assign distant orthologs to the same eggNOG/COG identifier, potentially diluting the contribution of a regulated ortholog to the eggNOG/COG posterior probability.
Similarly, the priors P(R) and P(B) must be renormalized by multiplying the observed number of regulated and non-regulated operons in a reference genome by (1 − U B ) and (1 − U R ), respectively, in order to account for the fact that thresholding alters the base rate at which regulated promoters are observed.
The permutation test therefore defines an alternative statistic to assess putative regulation of an eggNOG/COG based on the distribution of scores in the promoters mapping to it.
Validation of the Bayesian inference pipeline on synthetic datasets
Analysis of the copper-homeostasis CsoR regulon in the human gut microbiome
To evaluate the proposed inference method in a real life setting, we analyzed the copper-homeostasis regulon controlled by CsoR in the human gut microbiome. Together with CopY and CueR, CsoR-family members are well-characterized copper-responsive regulators that detect and modulate the abundance of copper ions in the cell . CsoR provides a suitable target for analysis, because it is presumed to be the sole regulator of copper homeostasis in Clostridiales, the second most abundant bacterial order in the IGC MetaHIT project dataset, while being noticeably absent in the most abundant order (Bacteroidales) [17, 26]. We analyzed the CsoR regulon by running the Bayesian inference pipeline on operons containing genes mapping to the Firmicutes. Computation was sped up by adjusting sensitivity with θ = 6.65 (6 standard deviations below the CsoR motif mean). This substantially decreased the number of processed promoters while increasing the prior for regulation P(R) only to 0.01 (Fig. 2). We established a mean probability of regulation of 0.9 for the set of putatively regulated eggNOG/COGs and required that they had at least 5 promoters mapping to them at the established θ value.
Inferred human gut Firmicutes CsoR meta-regulon
eggNOG / COG
eggNOG 4.0 annotation
Operons for analysis
Operon with COG1937
Operon with COG2217
IPR006121, IPR023214, IPR008250
Predicted membrane protein
IPR006121, IPR003834, IPR018758
A Bayesian inference pipeline for metagenomics analysis of regulatory networks
The increasing availability of large metagenomics datasets prompts and enables the development of algorithms to interrogate novel aspects of these heterogeneous sequence repositories. Here we formalize and validate a Bayesian inference framework to analyze the composition of transcriptional regulatory networks in metagenomes. Comparative genomics analyses have long established that the study of bacterial regulons benefits significantly from the availability of genomic data. Enrichment in TF-binding sites upstream of orthologous genes provides the means to curb the false positive rate of in silico methods for detecting these regulatory signals and to identify the key components of a regulatory network [29–32]. Leveraging the clusters of orthologous groups defined in the eggNOG database, here we define a conceptually similar approach to analyze bacterial regulons in metagenomic samples. We apply Bayesian inference to compute the probability that an eggNOG/COG is regulated by a TF with a known binding motif. To facilitate computation, the method assumes independence among the scores over a sequence and a normal distribution for site scores, which may be replaced by the exact distribution . Beyond these assumptions, the method relies only on the availability of priors for site density (α) and operon regulation P(R), which can be estimated from reference genomes. The method also provides the means to speed up computation by restricting the set of promoter sequences to be analyzed in a principled manner.
Our results on synthetic datasets show that the method performs as expected, assigning higher posterior values to sequences containing better-scoring sites (Fig. 1a) and to eggNOG/COGs with a larger number of sequences containing putative sites mapping to them (Fig. 2a). These results also illustrate some interesting properties of the approach. The assumption of positional independency provides a simple yet effective method to integrate the contribution of multiple sites in a promoter sequence. This is an important component for the analysis of bacterial regulons, since many bacterial transcriptional regulators exploit cooperative binding between multiple sites to modulate their activity at specific promoters [34–37]. Another element to take into account in metagenomics analysis is the presence of multiple instances of a clonal sequence mapping to an eggNOG/COG. These sequences occur frequently in metagenomic datasets and may carry multiple instances of a putative TF-binding site. The explicit modeling of regulated promoters with a mixture distribution results in lower posterior probabilities for such sequence sets (Fig. 1b), minimizing their assessment as false positives. Sequence sets carrying instances of a site with average score, such as the sequences mapping to NOG109008 (Table 1), may still be assigned high posterior probabilities. Given enough sample size, such false positives can be addressed by the introduction of heuristics based on the variance of scores for high-confidence sites in sequences mapping to an eggNOG/COG.
The proposed approach also provides a method to adjust the sensitivity and speed of the analysis by removing sequences with no evidence of regulation. This method is formally integrated within the Bayesian inference framework by the introduction of a score threshold (θ) and the corresponding normalization of priors and likelihoods. In combination with taxonomic filtering (i.e. preserving only sequences mapping to the clade of interest), sensitivity adjustment allows users to focus their analysis on those sequences most likely to contribute relevant information on the regulatory system under analysis. Sensitivity adjustment may hence allow detecting evidence of regulation in eggNOG/COGs with a relatively small percentage of putatively regulated sequences (Fig. 2b). This may be advantageous when assessing regulation in large heterogeneous COGs, where only a small subset of the mapping genes are regulated orthologs, but the progressive refinement of orthologous groups in the eggNOG database will soon address such concerns. Moreover, sensitivity adjustment should be used with caution, since it alters the prior for regulation P(R) and can therefore complicate the interpretation of results (Fig. 2b). There is no well-established method to determine what constitutes an acceptable prior when reporting posterior probabilities. As a conservative rule of thumb, one may require that the magnitude of the prior (φ′) be of the same order as the complement of the average posterior probability to be reported (1 − φ). Nonetheless, the adjusted prior should always be clearly stated when reporting adjusted posterior probabilities to facilitate their assessment. As shown in Fig. 3, the Bayesian framework also performs better as a predictor of eggNOG/COG regulation than a more conventional approach based on permutation tests. This is primarily due to the influence of the Bayesian priors on the posterior probability computation, which greatly reduces the chances of generating false positives in non-regulated eggNOG/COGs. Furthermore, the ability to infer regulation without the need for permuted models decreases run-time and provides consistency across multiple runs.
Analysis of the human gut Firmicutes CsoR meta-regulon
The analysis of the human gut Firmicutes CsoR meta-regulon reported here provides a first glimpse at the genetic organization of this copper homeostasis regulon in its natural setting. The Firmicutes CsoR meta-regulon is dominated by two putatively regulated COGs that map to two major components of the canonical CsoR regulon (csoR and copA). These two COGs comprise more than 90 % of the putatively CsoR-regulated promoters, suggesting that these two elements are the sole defining features of the CsoR regulon in the Firmicutes species that populate the human gut. The absence of eggNOG/COG identifiers mapping to the third canonical CsoR regulon member (copZ) is noteworthy, since the copZ gene codes for a copper chaperone that binds copper ions and transfers them to copper ATPases [26, 38]. Members of several putatively regulated eggNOG/COGs harboring a HMA domain (COG2836, NOG218972 and NOG81268; Table 1) appear to be distant orthologs of B. subtilis CopZ, and some might therefore function as copper chaperones. However, the COG associated to B. subtilis CopZ (COG2608) obtains a very low posterior probability of regulation in our analysis (9.76 · 10−15; Additional file 7). BLAST analysis with B. subtilis and Staphylococcus aureus CopZ against complete genomes reveals that only one (Clostridium) of the ten most abundant Clostridiales genera in the human gut microbiome encodes a CopZ homolog (Additional file 8). Furthermore, in reference genomes the Clostridium copZ homolog is not in the vicinity of copA, does not display a putative CsoR-binding site and appears to be associated with an ArsR-family transcriptional regulator, which may be capable of sensing copper . Together, these data convincingly identify CsoR as a transcriptional regulator of copper homeostasis through a canonical CsoR-binding motif in the gut microbiome Firmicutes. Furthermore, they indicate that the CsoR meta-regulon comprises CsoR and a P-type ATPase (CopA), but not a CopZ-type chaperone, and that the contribution of other heavy-metal-associated domain proteins to CsoR-directed copper homeostasis is comparatively small . The absence of copZ from bacterial genomes has been noted before [26, 38], and it has been suggested that the short length of this gene may hinder its detection . Our analysis, however, indicates that, even when present, copZ is not regulated by CsoR in the gut microbiome Firmicutes.
Beyond identifying and quantifying the components of a transcriptional regulatory network, our results show that metagenomics analysis of bacterial regulons can also shed light into the wiring of the network and the regulatory mode of the transcription factor. In the species where it has been experimentally described, the CsoR regulon displays a notable variety of genetic arrangements, ranging from single csoR-copA-copZ and copZ-csoR-copA operons in L. monocytogenes and Thermus thermophilus, to independent regulation of csoR and copZA operons in B. subtilis, S. aureus or Streptomyces lividans [23, 24, 27, 28]. Our analysis indicates that CsoR regulation in human gut Firmicutes follows this broad pattern, with independent regulation of csoR and copA being the norm and a relatively small fraction of COG1937 and COG2217 instances associated in putative operons. Similarly, experimental reports of CsoR regulated promoters have documented to date CsoR binding to individual binding sites located at distances ranging from −20 to −180 bp upstream of the predicted translational start site of regulated genes [17, 23, 24, 27]. In contrast, our analysis reveals that 44 % of the sequences mapping to regulated COG1937 and COG2217 instances possess two high-scoring sites separated by three well-defined spacing classes (26, 36–38 and 56 bp; Table 1; Additional file 6). There are currently three available structures for CsoR [17, 28, 40], showing CsoR to form either homodimers (M. tuberculosis) or tetramers (S. lividans and T. thermophilus), based on a three α-helix bundle. However, in the absence of co-crystals and of a canonical DNA-binding fold, the exact mechanism by which CsoR recognizes DNA remains elusive [25, 28]. It has been proposed that CsoR tetramers bind each dyad of the CsoR-binding motif through extensive exposure of DNA to the α1–α2 face of the bundle . In this model the α3 helices of each tetramer may interact and contribute to enhance DNA binding by stabilizing an octameric conformation of CsoR on DNA . Crucially, the ability of α3 helices to interact could be restricted by copper binding, triggering de-repression. Such a model is compatible with the adoption of hexadecameric conformations through extended α3 contacts. In this light, the location of CsoR-binding site relative to the TLS and the spacing distances observed for site pairs in our analysis are reminiscent of promoter architectures that leverage multiple sites to induce DNA bending [34, 35]. This suggests that higher-order conformations of DNA-bound CsoR may be exploited by gut microbiome Firmicutes and other species to fine-tune the cellular response to excess copper ions.
In this work we introduce and validate a method for the analysis of transcriptional regulatory networks from metagenomic data. By adopting a Bayesian inference framework, our method provides the means to infer regulatory networks from metagenomic data in a systematic and reproducible way, generating posterior probability values that facilitate the interpretation of results. The availability of robust methods for metagenomic regulon inference paves the way for the comparative analysis of regulatory networks across metagenomes, which has the potential to address fundamental questions about the evolution of bacterial regulatory networks. Validation of the method on the CsoR meta-regulon of gut microbiome Firmicutes provides convincing evidence that CsoR is a functional copper-responsive regulator of copper homeostasis in human gut. By virtue of the taxonomic composition of the human gut microbiome, our analysis also constitutes the first description of the CsoR-governed copper homeostasis regulon of a broad taxonomic group, the Clostridiales, encompassing several poorly characterized species of increasing clinical interest. Notable aspects of this putative regulatory network include the absence of CopZ-type copper chaperones and the likely use of dual CsoR-binding sites to fine-tune gene regulation.
basic local alignment search tool
clusters of orthologous groups
copper-sensitive operon repressor
domain of unknown function
evolutionary genealogy of genes: non-supervised orthologous groups
open reading frame
translation start site
integrated non-redundant gene catalog
metagenomics of the human intestinal tract
area under the curve
ETH and TP implemented the code for the computational analysis pipeline. TP gathered and standardized the metagenomic datasets. TP and IE designed the computational analysis pipeline. PKO and IE devised the Bayesian inference framework. ETH and IE benchmarked the pipeline, interpreted the results and drafted the manuscript. All authors read and approved the manuscript.
The authors wish to thank David Nicholson and Joseph Cornish for their contribution to earlier versions of the metagenomic analysis pipeline.
The authors declare that they have no competing interests.
Availability of data and materials
All code and data for this work are made openly available through the Erill Lab git repository on GitHub (https://github.com/ErillLab/CogsNormalizedPosteriorProbabilityThetas) .
This work was funded by the US National Science Foundation Division of Molecular and Cellular Biosciences award MCB-1158056, by the UMBC Office of Research through a Special Research Assistantship/Initiative Support (SRAIS) award and by the UMBC Office of Undergraduate Research through an Undergraduate Research Award (TP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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- Sleator RD, Shortall C, Hill C. Metagenomics. Lett Appl Microbiol. 2008;47:361–6.View ArticlePubMedGoogle Scholar
- Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers Y-H, Smith HO. Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004;304:66–74.View ArticlePubMedGoogle Scholar
- Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, Podar M, Short JM, Mathur EJ, Detter JC, Bork P, Hugenholtz P, Rubin EM. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.View ArticlePubMedGoogle Scholar
- Ward AC, Bora N. Diversity and biogeography of marine actinobacteria. Curr Opin Microbiol. 2006;9:279–86.View ArticlePubMedGoogle Scholar
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Hug LA, Beiko RG, Rowe AR, Richardson RE, Edwards EA. Comparative metagenomics of three Dehalococcoides-containing enrichment cultures: the role of the non-dechlorinating community. BMC Genom. 2012;13:327.View ArticleGoogle Scholar
- Segata N, Haake SK, Mannon P, Lemon KP, Waldron L, Gevers D, Huttenhower C, Izard J. Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples. Genome Biol. 2012;13:R42.View ArticlePubMedPubMed CentralGoogle Scholar
- Thomas T, Gilbert J, Meyer F. Metagenomics—a guide from sampling to data analysis. Microb Inf Exp. 2012;2:3.View ArticleGoogle Scholar
- De Filippo C, Ramazzotti M, Fontana P, Cavalieri D. Bioinformatic approaches for functional annotation and pathway inference in metagenomics data. Brief Bioinform. 2012;13:696–710.View ArticlePubMedPubMed CentralGoogle Scholar
- Warnecke F, Luginbühl P, Ivanova N, Ghassemian M, Richardson TH, Stege JT, Cayouette M, McHardy AC, Djordjevic G, Aboushadi N, Sorek R, Tringe SG, Podar M, Martin HG, Kunin V, Dalevi D, Madejska J, Kirton E, Platt D, Szeto E, Salamov A, Barry K, Mikhailova N, Kyrpides NC, Matson EG, Ottesen EA, Zhang X, Hernández M, Murillo C, Acosta LG, et al. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature. 2007;450:560–5.View ArticlePubMedGoogle Scholar
- Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R, Gordon JI. Evolution of mammals and their gut microbes. Science. 2008;320:1647–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Zheng W, Zhang Z, Liu C, Qiao Y, Zhou D, Qu J, An H, Xiong M, Zhu Z, Zhao X. Metagenomic sequencing reveals altered metabolic pathways in the oral microbiota of sailors during a long sea voyage. Sci Rep. 2015;5:9131.View ArticlePubMedPubMed CentralGoogle Scholar
- Tobar-Tosse F, Rodríguez AC, Vélez PE, Zambrano MM, Moreno PA. Exploration of noncoding sequences in metagenomes. PLoS One. 2013;8:e59488.View ArticlePubMedPubMed CentralGoogle Scholar
- Fernandez L, Mercader JM, Planas-Fèlix M, Torrents D. Adaptation to environmental factors shapes the organization of regulatory regions in microbial communities. BMC Genom. 2014;15:877.View ArticleGoogle Scholar
- Cornish JP, Sanchez-Alberola N, O’Neill PK, O’Keefe R, Gheba J, Erill I. Characterization of the SOS meta-regulon in the human gut microbiome. Bioinformatics. 2014;30:1193–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, Arumugam M, Kultima JR, Prifti E, Nielsen T, Juncker AS, Manichanh C, Chen B, Zhang W, Levenez F, Wang J, Xu X, Xiao L, Liang S, Zhang D, Zhang Z, Chen W, Zhao H, Al-Aama JY, Edris S, Yang H, Wang J, Hansen T, Nielsen HB, Brunak S, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32:834–41.View ArticlePubMedGoogle Scholar
- Liu T, Ramesh A, Ma Z, Ward SK, Zhang L, George GN, Talaat AM, Sacchettini JC, Giedroc DP. CsoR is a novel Mycobacterium tuberculosis copper-sensing transcriptional regulator. Nat Chem Biol. 2007;3:60–8.View ArticlePubMedGoogle Scholar
- Powell S, Forslund K, Szklarczyk D, Trachana K, Roth A, Huerta-Cepas J, Gabaldón T, Rattei T, Creevey C, Kuhn M, Jensen LJ, von Mering C, Bork P. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 2014;42(Database issue):D231–9.View ArticlePubMedGoogle Scholar
- Novichkov PS, Laikova ON, Novichkova ES, Gelfand, Arkin AP, Dubchak I, Rodionov DA. RegPrecise: a database of curated genomic inferences of transcriptional regulatory interactions in prokaryotes. Nucleic Acids Res. 2010;38(Database issue):D111–8.View ArticlePubMedGoogle Scholar
- Kiliç S, White ER, Sagitova DM, Cornish JP, Erill I. CollecTF: a database of experimentally validated transcription factor-binding sites in Bacteria. Nucleic Acids Res. 2014;42(Database issue):D156–60.View ArticlePubMedGoogle Scholar
- Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.View ArticlePubMedGoogle Scholar
- Haverty PM, Hansen U, Weng Z. Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification. Nucleic Acids Res. 2004;32:179–88.View ArticlePubMedPubMed CentralGoogle Scholar
- Smaldone GT, Helmann JD. CsoR regulates the copper efflux operon copZA in Bacillus subtilis. Microbiology. 2007;153(Pt 12):4123–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Baker J, Sengupta M, Jayaswal RK, Morrissey JA. The Staphylococcus aureus CsoR regulates both chromosomal and plasmid-encoded copper resistance mechanisms. Environ Microbiol. 2011;13:2495–507.View ArticlePubMedGoogle Scholar
- Rademacher C, Masepohl B. Copper-responsive gene regulation in bacteria. Microbiology. 2012;158(Pt 10):2451–64.View ArticlePubMedGoogle Scholar
- Solioz M, Abicht HK, Mermod M, Mancini S. Response of gram-positive bacteria to copper stress. J Biol Inorg Chem. 2010;15:3–14.View ArticlePubMedGoogle Scholar
- Corbett D, Schuler S, Glenn S, Andrew PW, Cavet JS, Roberts IS. The combined actions of the copper-responsive repressor CsoR and copper-metallochaperone CopZ modulate CopA-mediated copper efflux in the intracellular pathogen Listeria monocytogenes. Mol Microbiol. 2011;81:457–72.View ArticlePubMedGoogle Scholar
- Dwarakanath S, Chaplin AK, Hough MA, Rigali S, Vijgenboom E, Worrall JAR. Response to copper stress in Streptomyces lividans extends beyond genes under direct control of a copper-sensitive operon repressor protein (CsoR). J Biol Chem. 2012;287:17833–47.View ArticlePubMedPubMed CentralGoogle Scholar
- Tan K, Moreno-Hagelsieb G, Collado-Vides J, Stormo GD. A comparative genomics approach to prediction of new members of regulons. Genome Res. 2001;11:566–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Rodionov DA, Mironov AA, Gelfand MS. Conservation of the biotin regulon and the BirA regulatory signal in Eubacteria and Archaea. Genome Res. 2002;12:1507–16.View ArticlePubMedPubMed CentralGoogle Scholar
- Sanchez-Alberola N, Campoy S, Barbe J, Erill I. Analysis of the SOS response of Vibrio and other bacteria with multiple chromosomes. BMC Genom. 2012;13:58.View ArticleGoogle Scholar
- GrootKormelink T, Koenders E, Hagemeijer Y, Overmars L, Siezen RJ, de Vos WM, Francke C. Comparative genome analysis of central nitrogen metabolism and its control by GlnR in the class Bacilli. BMC Genom. 2012;13:191.View ArticleGoogle Scholar
- Rahmann S, Müller T, Vingron M. On the power of profiles for transcription factor binding site detection. Stat Appl Genet Mol Biol 2003;2:1544–6115. doi:10.2202/1544-6115.1032.
- Maddocks SE, Oyston PCF. Structure and function of the LysR-type transcriptional regulator (LTTR) family proteins. Microbiology. 2008;154(Pt 12):3609–23.View ArticlePubMedGoogle Scholar
- Minchin SD, Busby SJ. Analysis of mechanisms of activation and repression at bacterial promoters. Methods. 2009;47:6–12.View ArticlePubMedGoogle Scholar
- Pryor EE Jr, Waligora EA, Xu B, Dellos-Nolan S, Wozniak DJ, Hollis T. The transcription factor AmrZ Utilizes multiple DNA binding modes to recognize activator and repressor sequences of Pseudomonas aeruginosa Virulence Genes. PLoS Pathog. 2012;8:e1002648.View ArticlePubMedPubMed CentralGoogle Scholar
- Cournac A, Plumbridge J. DNA looping in prokaryotes: experimental and theoretical approaches. J Bacteriol. 2013;195:1109–19.View ArticlePubMedPubMed CentralGoogle Scholar
- Argüello JM, Raimunda D, Padilla-Benavides T. Mechanisms of copper homeostasis in bacteria. Front Cell Infect Microbiol. 2013;3:73.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu T, Chen X, Ma Z, Shokes J, Hemmingsen L, Scott RA, Giedroc DP. A Cu(I)-sensing ArsR family metal sensor protein with a relaxed metal selectivity profile. Biochemistry (Mosc). 2008;47:10564–75.View ArticleGoogle Scholar
- Sakamoto K, Agari Y, Agari K, Kuramitsu S, Shinkai A. Structural and functional characterization of the transcriptional repressor CsoR from Thermus thermophilus HB8. Microbiology. 2010;156(Pt 7):1993–2005.View ArticlePubMedGoogle Scholar
- Ma Z, Cowart DM, Scott RA, Giedroc DP. Molecular insights into the metal selectivity of the copper(I)-sensing repressor CsoR from Bacillus subtilis. Biochemistry (Mosc). 2009;48:3325–34.View ArticleGoogle Scholar
- Hobbs E, Erill I, Pereira T, O’Neill PK. Metagenome regulatory analysis: working release. Zenodo. 2016. doi:10.5281/zenodo.55783.Google Scholar