Overview of functional categories captured in the <i>R</i>. <i>sphaeroides</i> TRN.

<p>Heat map showing the most significantly enriched GO terms for 48 of the 120 clusters identified in our analysis. The predicted regulators for each cluster is shown on the right hand side of the map, while the GO categories are at the bottom. Darker shades of blue indicated greater significance.</p

PrrA target genes identified by ChIP-seq and gene expression analysis of <i>R. sphaeroides</i> cells.

a<p>Chromosomal locations of start and stop of ChIP-seq peaks.</p>b<p>Fold enrichment of PrrA-myc ChIP over control myc antibody ChIP in WT control.</p>c<p>Regulatory role of PrrA on target operons based on change in gene expression between WT and Δ<i>prrA</i> cells. +  =  positively regulated by PrrA. −  =  negatively regulated by PrrA. NA - Not applicable.</p>†<p>Number of binding sites. Some binding sites correspond to more than one operon.</p><p>*<i>dxsA</i> - 1-deoxy-D-xylulose-5-phosphate synthase; <i>dxr</i> - 1-deoxy-D-xylulose 5-phosphate reductoisomerase.</p><p>PrrA target genes identified by ChIP-seq and gene expression analysis of <i>R. sphaeroides</i> cells.</p

Global Analysis of Photosynthesis Transcriptional Regulatory Networks

<div><p>Photosynthesis is a crucial biological process that depends on the interplay of many components. This work analyzed the gene targets for 4 transcription factors: FnrL, PrrA, CrpK and MppG (RSP_2888), which are known or predicted to control photosynthesis in <i>Rhodobacter sphaeroides</i>. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identified 52 operons under direct control of FnrL, illustrating its regulatory role in photosynthesis, iron homeostasis, nitrogen metabolism and regulation of sRNA synthesis. Using global gene expression analysis combined with ChIP-seq, we mapped the regulons of PrrA, CrpK and MppG. PrrA regulates ∼34 operons encoding mainly photosynthesis and electron transport functions, while CrpK, a previously uncharacterized Crp-family protein, regulates genes involved in photosynthesis and maintenance of iron homeostasis. Furthermore, CrpK and FnrL share similar DNA binding determinants, possibly explaining our observation of the ability of CrpK to partially compensate for the growth defects of a ΔFnrL mutant. We show that the Rrf2 family protein, MppG, plays an important role in photopigment biosynthesis, as part of an incoherent feed-forward loop with PrrA. Our results reveal a previously unrealized, high degree of combinatorial regulation of photosynthetic genes and significant cross-talk between their transcriptional regulators, while illustrating previously unidentified links between photosynthesis and the maintenance of iron homeostasis.</p></div

Analysis of the PpsR regulon in <i>R</i>. <i>sphaeroides</i>.

<p>(A) Using ChIP-seq, we identified the binding sites for PpsR across the <i>R</i>. <i>sphaeroides</i> genome, with several binding sites across chromosome 1 highlighted. MochiView [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004103#pcbi.1004103.ref069" target="_blank">69</a>] was used for visualization of binding profile. (B) Heat map depicts the expression profiles of the first members of PpsR targets operons across our microarray compendium of 198 experiments conducted under aerobic respiratory (Aerobic), anoxygenic photosynthetic (Photosynthesis) and anaerobic respiratory conditions (DMSO). Expression profiles for experiments conducted on the <i>ΔprrA</i> and <i>ΔprrA-ΔppsR</i> strains are highlighted. Deletion of PpsR from <i>ΔprrA</i> results in derepession of PpsR target genes. (C) Position weight matrix logo generated for PpsR using targets identified by ChIP-seq compared to logo generated from our TRN inference analysis.</p

An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks

<div><p>Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium <i>Escherichia coli</i> showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the <i>E. coli</i> TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium <i>Rhodobacter sphaeroides</i> that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in <i>R. sphaeroides</i> and/or other bacteria. Experimental validation of predictions from this <i>R. sphaeroides</i> TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to <i>R. sphaeroides</i> TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.</p></div

Regulation of iron-dependent genes in <i>R</i>. <i>sphaeroides</i>.

<p>(A) Heat map of iron-dependent DE genes between wild-type (WT) and <i>ΔRSP_3341</i> mutant cells from global gene expression analysis. RSP_4112 (hypothetical protein), RSP_0474 (Cytochrome c’), RSP_2424 (ferredoxin II), RSP_2945 (cytochrome c-type biogenesis protein CcmE). (B) Direct binding of RSP_3341 to the <i>napEFGABC</i>, <i>cycJ</i> and <i>dnaK</i> promoters identified by ChIP-seq. (C) Predicted gene regulatory network controlling iron-homeostasis in <i>R</i>. <i>sphaeroides</i>. Both RSP_2888 and RSP_3341 are RirA like proteins with C-terminal cysteine residues potentially capable of binding Fe-S clusters and sensing oxygen. Solid lines indicate experimentally verified interactions, while dashed lines indicated predicted but as yet unverified interactions.</p

MppG target genes identified by ChIP-seq and gene expression analysis.

a<p>Chromosomal locations of start and stop of ChIP-seq peaks.</p>b<p>Fold enrichment of MppG-myc ChIP over control myc antibody ChIP in WT control.</p>c<p>Regulatory role of MppG on target operons based on change in gene expression between ΔMppG and WT or ΔMppG<i>+</i>pIND5-<i>mppG</i> cells. −  =  negatively regulated by MppG. NA - Not applicable.</p><p>MppG target genes identified by ChIP-seq and gene expression analysis.</p

The RSP_0489 regulon.

<p>(A) Heat map of metabolic genes DE between wild-type (WT) and <i>ΔRSP_0489</i> mutant cells from global gene expression analysis. Only the first members of DE operons are depicted in the heat map for brevity. RSP_0490 (carbohydrate kinase), RSP_3372 (TRAP-T family transporter), RSP_0577 (hypothetical protein), RSP_1420 (TRAP-T family transporter), RSP_1613 (TRAP-T family transporter), RSP_2401 (putative 6-aminohexanoate-cyclic-dimer hydrolase), RSP_2508 (Methylcrotonyl-CoA carboxylase beta chain), RSP_1883 (ABC polyamine/opine transporter), RSP_2506 (Isovaleryl-CoA dehydrogenase), RSP_3168 (ABC transporter), RSP_3169 (FAA-hydrolase-family protein). (B) Direct binding of RSP_0489 to the <i>uxaC</i>, RSP_0490, <i>uxuA</i>, RSP_3372 and RSP_2667 promoters identified by ChIP-seq. (C) RSP_0489 binding site motif obtained from ChIP-seq analysis compared to that obtained from phylogenetic footprinting analysis of the RSP_0489 promoter.</p

Predictions for <i>E</i>. <i>coli</i> TFs.

<p>* NA—Not applicable i.e., no predictions made by inference approach for that TF. A value of 0 indicates some predictions were made but all were inaccurate. Prec.—precision; Rec.—recall.</p><p>† TFs for which accurate predictions were made by: A—only integrated approach; B—both CLR and the integrated approach; C—all 3 inference approaches; D—only CLR and GENIE3; E—only CLR; F—only GENIE3.</p><p>Predictions for <i>E</i>. <i>coli</i> TFs.</p

Physiological and genomic analysis of MppG regulation.

<p>(A) Growth of WT, ΔMppG and ΔMppG<i>+</i>pIND5-<i>mppG</i> with increasing IPTG concentrations under photosynthetic conditions. (B) Amounts of bacteriochlorophyll produced in WT, ΔMppG and ΔMppG<i>+</i>pIND5-<i>mppG</i>. (C) Expression profiles of genes differentially expressed in response to the loss of MppG (ΔMppG) or over-expression of MppG (ΔMppG<i>+</i>pIND5-<i>mppG</i>) strains. Genes differentially expressed in the ΔMppG only are indicated with an asterisk (*). (D) ChIP-seq binding profile of MppG at the <i>mppG</i>, <i>bchF</i> and <i>appA</i> promoters.</p