Characterizing the interplay between multiple levels of organization within bacterial sigma factor regulatory networks

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1 Supplementary Information Characterizing the interplay between multiple levels of organization within bacterial sigma factor regulatory networks Yu Qiu 1, Harish Nagarajan 1, Mallory Embree 1, Wendy Shieu 1, Elisa Abate 1, Katy Juárez 2, Byung-Kwan Cho 1, James G. Elkins 1, Kelly P. Nevin 3, Christian L. Barrett 1, Derek R. Lovley 3, Bernhard O. Palsson 1, and Karsten Zengler 1,* 1 Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA. 2 Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62271, Mexico. 3 Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA. * Correspondence should be directed to K.Z. (kzengler@ucsd.edu). 1

2 Supplementary Figures sigw sigv sigy sigx sigk ylac sigm sige sigg sigl siga sigb sigd sigf sigi sigo sigh Supplementary Figure S1. Sigma factor network for the Gram-positive bacterium Bacillus subtilis. The network is based on data from BsubCyc 19 and the recently published network 18. SigA, SigH and SigL have similar roles in B. subtilis as RpoD, RpoS and RpoN in E. coli respectively. Blue nodes indicate sporulation related sigma factors, yellow nodes indicate sigma factors with extracytoplasmic functions. The grey edges in the network indicate data obtained from Nicolas et al. 18, and the blue edges indicate interaction data from BsubCyc 19. 2

3 Supplementary Figure S2. The interaction network of sigma factors with the metabolic genes in E. coli. An in silico gene essentiality screen was carried out using the ijo1366 genome-scale model 45 under four different conditions to assess the effect of genetic perturbations on the sigma factor network operation. Genes determined to be essential under all four conditions are shown by red nodes; other non-essential genes are shown in gold. 3

4 A B C A rpod B rpos C rpon Supplementary Figure S3. The invariant sigma factor binding profiles between the divergent growth conditions. ChIP-chip binding profiles of G. sulfurreducens sigma factor σ D, σ S and σ N were plotted along the genome. Planktonic fumarate-reducing cells are shown in orange and biofilm-forming cells on the electrode were shown in blue. 4

5 Supplementary Figure S4. Analysis of RpoS protein concentrations during growth of G. sulfurreducens. (a) Samples were collected from wild type G. sulfurreducens on anoxic medium containing acetate as electron donor and fumarate as an electron acceptor. (b) Proteins were separated using electrophoresis on a 10% polyacrylamide gel. RpoS protein concentrations during exponential phase and stationary phase were determined using Western blot analysis. RpoS (~42 kda in size) concentrations remained at fairly constant levels across all the five time points (P1-5). 5

6 Supplementary Figure S5. RNA-seq analysis of G. sulfurreducens wild type and ΔrpoS strain in exponential growth. (a) Comparison of the gene expression level (log 2 (FPKM)) between these two strains. Genes are highlighted based on binding profiles of sigma factors in their promoters. (b) The distribution of gene expression difference (log 2 (Fold change)) of genes only regulated by σ S, and genes regulated by σ S and at least another sigma factor. 6

7 Supplementary Figure S6. Binding motifs of the four major sigma factors in G. sulfurreducens. 60 bp regions from the promoters with unique sigma factor bindings sequence logo were generated with were used for motif discovery with MEME 39 and BioProspector 40 Entropy (bit) was calculated and the sequence logo was generated using Weblogo 46. 7

8 Supplementary Figure S7. Comparison of the gene expression level (log 2 (FPKM)) of the G. sulfurreducens wild type strain grown in exponential phase and early stationary phase. Genes are highlighted based on binding profiles of sigma factors in their promoters. 8

9 Supplementary Figure S8. Sigma factor - Transcription factor interactions. (a) Geobacter sulfurreducens and (b) Escherichia coli. E.coli transcription factors information and sigma factor binding information was from Ecocyc 47 and RegulonDB 44. 9

10 Supplementary Tables Supplementary Table S1. Transcription levels (log2 Signal) of G. sulfurreducens sigma factors under different growth conditions. Sigma Factor Locus_tag Growth conditions Asp Electrode FCA FWAF Biofilm Gln Heatshock N 2 fixation σ H GSU σ E GSU σ S GSU σ N GSU σ D GSU

11 Supplementary Table S2. Transcription levels (FPKM) of G. sulfurreducens sigma factors in ΔrpoS strain. Sigma Log phase Stationary phase Locus_tag Factor Wild type ΔrpoS Wild type ΔrpoS σ H GSU σ E GSU σ S GSU σ N GSU σ D GSU

12 Supplementary Note Bacteria selectively regulate the relative abundance of different sigma factors in response to environmental stimuli. Typically, the usage of anti-sigma factors or sophisticated protease cascades 2,49 can lead to a dynamic sigma factor regulatory network in which different portions of the network are switched on or off depending on the environment stimulus. However, this reconstructed sigma factor regulatory network in G. sulfurreducens substantially differs from this model with a near-static network state between diverse physiological conditions. This was further manifested in the shared regulation of ~40% of the genes in the network by at least two sigma factors, providing a regulation redundancy that guarantees robust expression of these genes regardless of environmental stimuli. Most genes critical for cellular functions, such as ATP synthase, were under the regulation of σ D and at least one alternative sigma factor. Certain condition specific essential genes, however, were regulated solely by alternative sigma factors. For instance, all genes encoding key enzymes for nitrogen assimilation (GDH, GS, and GOGAT) and the essential outer membrane porin (ompa) are transcribed by σ N -dependent promoters. Therefore, while it has been possible to delete σ S in G. sulfurreducens without affecting its viability 48, σ N deletion has not produced a viable phenotype 14. Even when σ S was deleted, the changes in the transcriptomic level of genes under shared regulation between σ S and other sigma factors were found to be much less significant compared to the genes solely regulated by σ S (Supplementary Fig. S5b). The mechanisms controlling the levels of alternative sigma factors and how they compete with σ D for the core RNAP under different conditions needs further investigation. It is known for E. coli and some other bacteria that the σ D -sequester Rsd plays an important role in modulating σ D availability and therefore facilitates the recruitment of alternative sigma factors. However, a homolog of this protein and other known anti-sigma factors could not be identified in G. sulfurreducens. Additionally, the G. sulfurreducens genome does not encode for the ClpXP protease cascade (including RssB and SprE) 50 to regulate the stability of σ S, allowing the accumulation of stable σ S during exponential phase (Supplementary Fig. S4). This further highlights the difference in the modulation of the sigma factor network of G. sulfurreducens and E. coli. Deletion of the rpos gene in G. sulfurreducens resulted in a modest up-regulation of rpoh and rpoe transcription (~2-fold) during exponential phase, while both of them as well as rpod were highly induced in early stationary phase (38-, 33- and 3-fold, respectively) (Supplementary Table S2). This genetic alteration also yielded a drastic change in the transcriptome and may have partially resulted from the induction of the three sigma factors (Supplementary Data 5). This suggests that G. sulfurreducens could potentially utilize other sigma factors to compensate for the loss of σ S. 12

13 Also, it is known that ppgpp could target the RNAP holoenzyme and modulate its activity on promoters 51. Monitoring intracellular levels of ppgpp in G. sulfurreducens revealed that rel Gsu (RelA homolog) is involved in stress response such as stationary phase growth 52. To evaluate the physiological role of sigma factors in response to such stress conditions, it would be informative to investigate the intracellular ppgpp levels in a double mutant of G. sulfurreducens (ΔrpoS::rel Gsu ). Additionally, post-transcriptional regulation, or trans-acting transcription factors might be deployed instead to tune gene transcription in response to environmental changes in G. sulfurreducens. 13

14 Supplementary References 45 Orth, J. D. et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism Mol Syst Biol 7, 535 (2011). 46 Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res 14, (2004). 47 Keseler, I. M. et al. EcoCyc: a comprehensive view of Escherichia coli biology. Nucleic Acids Res 37, D (2009). 48 Yan, B. et al. Computational prediction of RpoS and RpoD regulatory sites in Geobacter sulfurreducens using sequence and gene expression information. Gene 384, (2006). 49 Campbell, E. A., Westblade, L. F. & Darst, S. A. Regulation of bacterial RNA polymerase sigma factor activity: a structural perspective. Curr Opin Microbiol 11, (2008). 50 Battesti, A., Majdalani, N. & Gottesman, S. The RpoS-mediated general stress response in Escherichia coli. Annu Rev Microbiol 65, (2011). 51 Dalebroux, Z. D. & Swanson, M. S. ppgpp: magic beyond RNA polymerase. Nat Rev Microbiol 10, (2012). 52 DiDonato, L. N. et al. Role of RelGsu in stress response and Fe(III) reduction in Geobacter sulfurreducens. J Bacteriol 188, (2006). 14