Functional characterization of bacterial sRNAs using a network biology approach

Abstract

Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

Fig. 1.

Small RNA regulatory network in E. coli. We inferred highly significant regulatory interactions for Hfq-dependent sRNAs (shown in red) by applying the CLR algorithm to a compendium of E. coli microarrays. Genes in the seven sRNA subnetworks are colored if they are involved in the functional process assigned to the subnetwork by enrichment (i.e., green for DNA repair genes, yellow for protection and adaptation genes, blue for iron metabolism genes, orange for amino acid metabolism genes, purple for extracellular transport genes, gray for chemotaxis and motility genes, and pink for pH adaptation genes). Genes in white have functions that are not associated with an enriched process. Genes in brown encode transcriptional regulators.

Fig. 2.

A functional role for IsrA and GlmZ in the DNA damage response. (A) Inferred network connections for IsrA and GlmZ. Of the identified interactions, 18 are involved in DNA damage pathways. Approximately 50% of these DNA repair genes are members of the LexA regulon. (B) Representative micrographs of MG1655 (Left) and ΔisrAΔglmZ MG1655 (Right) before (100× objective) and during DNA damage treatment (40× objective). Images show cells during norfloxacin treatment (125 ng/mL, T = 3 h), MMC treatment (2 μg/mL, T = 2 h), and repeated UV exposure (100 J/m², T = 1.5 h). See Materials and Methods for treatment details and Fig. S3 for full micrograph images. (C) Log change in colony-forming units per milliliter (CFU/mL) during DNA damage exposure. Survival of MG1655 (blue diamonds) and ΔisrAΔglmZ MG1655 (red squares) following exposure to norfloxacin (125 ng/mL), MMC (2 μg/mL), and repeated UV exposure (100 J/m²). In this and all other figures, error bars represent ± SE. (D) Basal mutation rate (mutations per cell per generation) for MG1655 (blue) and ΔisrAΔglmZ MG1655 (red) using a rifampicin-based selection method. Wild-type mutation rate is similar to that previously reported (28).

Fig. 3.

A functional role for GcvB in amino acid availability. (A) Inferred network connections for GcvB. From our CLR results and GO term enrichment, GcvB shows a large number of interactions (51%) with genes involved in amino acid metabolism and transport, including the transcriptional regulator Lrp. Approximately 28% of these amino acid-related genes are members of the Lrp regulon. (B) Doubling times for MG1655 + pZA31-null (white bars), ΔgcvB MG1655 + pZA31-null (black bars), and ΔgcvB MG1655 + pZA31-gcvB (gray bars) in M9 minimal media, calculated using OD₆₀₀ values. Leucine and phenylalanine were supplemented at 2 mM, and serine and threonine were supplemented at 1 mM. No growth was observed for ΔgcvB MG1655 + pZA31-gcvB in serine-supplemented media. Asterisks represent significant (P < 0.05) differences in growth rate compared with MG1655 + pZA31-null.

Fig. 4.

Mutual inhibitory relationship of GcvB and Lrp. (A) GFP translational fusions for dppA and lrp (Left) and lrp and lrp-mut (Right). dppA::gfp (plasmid pSK-015) was grown in EZ rich media to model conditions in which it was originally tested (11). lrp::gfp and lrp-mut::gfp were grown in M9 minimal media to assess the effects of gcvB expression in nutrient-limiting conditions. Unregulated target fusion specific fluorescence (expressing control vector) is shown in gray, and regulated target fusion specific fluorescence (expressing gcvB) is shown in orange. See Materials and Methods and SI Materials and Methods for details on fluorescence measurements and calculations. Asterisks represent significant (P < 0.05) differences between unregulated and regulated target fusion specific fluorescence. (B) Fold-difference in gcvB expression in ΔgcvA and Δlrp relative to wild-type during growth in M9 minimal media. Blue bars represent relative expression when exogenous glycine was absent, and red bars represent relative expression when glycine (300 μg/mL) was added to the media. Error bars represent propagated error measures. (C) GcvB-Lrp regulatory subnetwork resulting from translational fusion, expression data, and known regulatory interactions. Unsequestered GcvA activates gcvB expression when glycine is present. GcvB directly represses Lrp and Lrp directly or indirectly represses gcvB.