Global picture of the AppY regulon

No phenotype has ever been described for a strain deleted of appY, making it difficult to determine under which conditions AppY regulates its target genes or in which physiological process(es) it is involved. Therefore, we chose to overproduce AppY in order to define its regulon. As mentioned above, AppY overproduction stabilizes the master regulator RpoS, which itself regulates more than 500 genes in E. coli [27,28]. To avoid the identification of genes under RpoS control in our experiments, we overproduced AppY from an inducible plasmid in E. coli str. K12 substr. MG1655 strain deleted for rpoS (ΔrpoS). The same strain bearing the empty vector was used as a control. RNA-Seq experiments allowed us to identify more than 200 genes whose expressions were significantly modulated by at least a factor 4 upon AppY overproduction. In this study, we decided to focus on the 66 genes whose differential expressions are highly affected (at least 10-fold) (S1 Table). An overview of the AppY regulon is presented in Fig 1A where these 66 genes were classified based on their Gene Ontology numbers into 11 distinct biological processes. Among these genes, 43% belong to two main categories, i.e. response to stress (pH) and bacterial motility. About 75% of these 66 genes are upregulated and among those that are downregulated 77% are related to bacterial motility. This global picture indicates that the AppY regulon mainly concerns bacterial pH stress response as well as bacterial motility in addition to its previously known involvement into bacterial respiration [15,16,22,23,29].

In a closer view depicted in Fig 1B, we detailed the relative expression of genes involved in these three biological processes. We included genes below the 10-fold differential expression threshold (dotted lines) that belong to the same operon as genes differentially expressed above the 10-fold threshold. Firstly, we observed a massive induction of the two known AppY targets involved in respiration, the hya and app operons (containing 6 and 4 genes respectively, grey bars), therefore validating our strategy to identify the AppY regulon [30]. Secondly, 14 genes involved in acid stress resistance via the glutamate-dependent acid resistance system 2 (AR2), the major acid resistance pathway in E. coli [31], are upregulated (Fig 1B, pink bars). Finally, 24 genes, all of which are downregulated, are involved in flagella synthesis (Fig 1B, light green bars). Overall, these data suggest two unexpected roles for AppY in addition to its known involvement into respiration: it contributes to bacterial adaptation under low pH by inducing the glutamate-dependent response and it negatively regulates bacterial motility by down-regulating flagellar genes.

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Fig 1. Identification of AppY regulon by RNA-Seq experiments.

MG1655 ΔrpoS strain transformed with plasmids pQE80L or pQE80L-appY were grown to an OD₆₀₀ ∼0.6 and appY expression was induced with 1 mM IPTG for 1 hour. RNA was purified, reverse-transcribed and sequenced. The data shown are representative of three independent experiments. A. Global overview of genes regulated at least 10 times by AppY. Genes up-regulated (dark red) and down-regulated (light blue) at least 10 times (Log2 Fold-Change ≥ 3.2) when AppY was overproduced were classified according to their biological functions. Categories were defined using the Gene Ontology numbers listed in S1 Methods. B. AppY mainly regulates genes involved in aerobic respiration (grey), acid resistance (pink) and flagellar synthesis (green). The dashed lines indicate 10-fold induction or repression.

https://doi.org/10.1371/journal.pgen.1010672.g001

Identification of genes directly regulated by AppY

RNA-Seq experiments provided a global overview of the physiological changes occurring in the cell when AppY was overproduced but did not provide information on AppY direct targets. To identify the genes that are directly regulated by AppY, ChIP-Seq experiments were performed. Briefly, the AppY protein with a 3Flag-tag at its C-terminal end was produced from an inducible plasmid in the ΔrpoS strain. Note that the 3Flag-tag did not affect AppY functionality as the flagged protein still induced the expression of appC and hyaA as measured with the transcriptional gfp fusions P_appC-gfp and P_hyaA-gfp (S1A Fig). After stabilization of the AppY-3Flag/DNA complexes using formaldehyde, chromosomal DNA was fragmented and cells lysed by sonication. AppY-3Flag/DNA complexes were then recovered by affinity purification using the 3Flag-tag and bound DNA was sequenced (see methods section for more details). To detect DNA-binding loci enrichment, the experiment was performed in parallel with three different controls: the empty vector, the untagged AppY and an AppY-3Flag mutant designed to affect its DNA binding capacity. Mutants affected in their DNA binding function have been well-characterized in other AraC-transcriptional regulators [32]. Based on sequence alignments, we identified a lysine residue at position 170 as potentially involved in AppY DNA-binding (S1B Fig). We used site-directed mutagenesis to generate the AppY_K170E mutant. AppY_K170E production was similar to that of the wild-type protein (S1C Fig) and did not allow the expression of the two transcriptional fusions used as positive controls P_appC-gfp and P_hyaA-gfp (S1A Fig). Furthermore, we observed that ChIP-Seq enrichment profiles were essentially identical with the empty vector or the vector bearing the appY_K170E-3Flag construct. We thus confirmed that the K170 residue is critical for DNA binding.

Peaks calling by MACS resulted in a list of 690 enriched DNA-binding loci (fold-change between 1.2 and 65.7) in the AppY-3Flag overproducing conditions compared to the control experiment (S2 Table). This list is identical whether the enrichment is computed with the untagged or the AppY_K170E-3Flag mutant as a control, further confirming that the K170E mutant has indeed lost its capacity to bind DNA. We identified 22 significantly enriched chromosomal loci (at least 4 times) (Table 1). 86% of these binding regions are situated in known promoters or intergenic regions. Among them, 11 are upregulated at least 10 times when AppY is overproduced (highlighted in blue in Table 1). The detection of AppY binding in the promoter regions of hyaA and appC was in accordance with the RNA-Seq data and to our knowledge demonstrated for the first time that AppY directly regulates these operons (Fig 2A). Strikingly, no AppY binding site was identified in the promoter region of the genes involved in flagella synthesis that were heavily repressed in RNA-Seq experiments (Fig 1B, light green bars), suggesting an indirect regulation of these gene expression by AppY (Table 1). In contrast, AppY was clearly able to bind five promoter regions of genes involved in acid stress resistance: slp, gadE, gadY, adiC and glsA (Fig 2B and S2 Fig). Among them, slp, gadY and gadE are located in the acid fitness island and involved in the AR2 pathway. GadE is the master regulator of this pathway, suggesting that AppY directly regulates gadE expression with expected broad consequences on the AR2-dependent acid response [33–35]. In addition to its role in acid stress, the non-coding RNA GadY is also involved in biofilm formation [36–38]. We identified another direct AppY target involved in biofilm synthesis, the transcriptional activator NhaR [39]. These results suggest a supplementary regulatory role for AppY in biofilm formation. Overall, the ChIP-Seq results combined with those of the RNA-Seq suggest that AppY plays a key role in acid stress adaptation, biofilm formation and motility inhibition. In the following sections we explored the role of AppY in each of these pathways.

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Fig 2. Identification of AppY binding sites by ChIP-Seq experiments.

MG1655 ΔrpoS strain containing the plasmids pQE80L, pQE80L-appY-3Flag or appY_K170E-3Flag were grown at 37°C to an OD₆₀₀∼0.2 and appY expression was induced with 0.05 mM IPTG for 1 hour. The genetic loci, the ChIP-Seq data obtained with AppY-3Flag and AppY_K170E-3Flag and the gene expression profiles obtained by RNA-Seq with AppY or the vector control are shown from the top to the bottom. In the genetic loci, genes with plain colour are located immediately downstream of a promoter; other genes are hatched. Peaks observed in ChIP-Seq with AppY_WT (blue) or AppY_K170E (black) are superimposed for comparison; the intensity of the red color in RNA-Seq panels represents the number of raw counts per gene. The data are representative of three independent experiments. A. Regulation of the hya and app operons. B. Regulation of the acid fitness island. The promoter regions where AppY binds are represented by blue arrows. gadE expression depends on four different promoters; AppY binds to the P3 promoter, the farthest from gadE start codon.

https://doi.org/10.1371/journal.pgen.1010672.g002

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Table 1. AppY direct targets.

Gene name, biological process, peak enrichment and peak absolute summit localization of AppY binding region on E. coli str. K12 substr. MG1655 chromosome (accession number U00096.3). In parenthesis are gene synonyms. "-" between two gene names indicates an intergenic binding locus. Biological processes follow the categories depicted in Fig 1A or one GO number description attached to the gene in Ecocyc when not included in Fig 1A. The differential gene expression computed in the RNA-seq experiment is indicated in the last column. When the fold change is ≥ 10 (log2 FC ≥ 3.2), genes are highlighted in blue (S1 Table). N.A means that the biological process cannot be attributed because the peak summit is between two genes.

https://doi.org/10.1371/journal.pgen.1010672.t001

AppY directly and indirectly induces the expression of genes from the AR2 system

The majority of the genes positively regulated by AppY are part of the acid fitness island and contribute to the AR2 pathway (Figs 1 and 2; Table 1, S1 and S2 Tables) [34,35,40]. The regulation of this pathway is complex and involves a large number of regulators, including three transcriptional regulators from the AraC/XylS family: GadW, GadX and YdeO [31,34,41,42]. These three regulators modulate the expression of gadE, the master regulator of the AR2 pathway. The identification of gadE as a direct AppY target suggests that the induction of several genes from the AR2 pathway observed in the RNA-Seq experiment was probably GadE-dependent. To test this hypothesis, we used gfp-transcriptional fusions in strains deleted of rpoS only or both rpoS and gadE (Fig 3A). For genes in an operon, we used fusions containing the promoter region of the first gene of the operon (Fig 2B, plain pink genes in the genetic loci). Background levels of fluorescence were measured in strains containing the control plasmid (no promoter upstream of gfp) (Fig 3A, “cont” bars), or containing the fusions of interest in the absence of AppY overproduction (Fig 3A, hatched bars). The appC fusion was used as a positive control since we observed a direct AppY binding in its promoter region (Figs 2A and 3A). When AppY was overproduced in the strain deleted of rpoS, fluorescence levels were considerably increased for most of the fusions (from 4 to 34-fold; Fig 3A, grey bars). These results are consistent with the RNA-Seq data showing induction of these genes in the presence of AppY (Fig 1B). Only a mild effect was seen for hdeA and gadX (1.6 and 1.3-fold increase, respectively) whereas they were significantly upregulated in the RNA-seq experiments (181 and 9-fold, respectively). One possible explanation is that the increased level of hdeA and gadX mRNA measured with the RNA-Seq experiment in the presence of AppY is due to a post-transcriptional regulation. Indeed, in case this regulation involves sequences other than the promoter region or a small portion at the beginning of the coding sequence, it would be missed with the gfp-fusions. For gadX, this regulation could be dependent on the small non-coding RNA GadY that we identified as a direct AppY target and which is known to stabilize gadX mRNA by base-pairing with its 3’UTR (Table 1 and Fig 2B) [36]. In the strain deleted of rpoS and gadE, AppY overproduction led to the induction of appC, slp, gadE and gadY, the direct AppY targets we identified by ChIP-Seq (Fig 3A). However, AppY overproduction failed to induce the expression of hdeD, gadB and gadA in this genetic background. These results confirm that AppY directly induces gadE expression, which in turn activates its own regulon including hdeD, gadB and gadA. Altogether, these experiments identify AppY as a new player leading both directly and indirectly to the activation of the AR2 pathway.

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Fig 3. AppY contributes to acid stress response.

A. gadE-dependent AppY targets. MG1655 strains deleted of rpoS (ΔrpoS, grey) or both rpoS and gadE (ΔrpoSΔgadE, pink) and containing the pQE80L empty vector (pCont, hatched) or pQE80L-appY (pAppY, plain) were transformed with the indicated transcriptional gfp fusions (93). Relative fluorescence intensities were measured after ON growth at 37°C in LB plus 0.05 mM IPTG to induce appY expression. Data are means +/- standard deviation (n = 3). A.U., arbitrary units. Statistical significance calculations were performed using a two-tailed unpaired Student’s t test (ns, not significant; *, p-value < 0.05; ***, p-value < 0.001). B. Induction of appY expression under acid stress conditions. Left: Schematic representation of the appY-lacZ translational fusion. The genomic coordinates indicate the beginning and the end of the appY sequence fused to lacZ including the 9 first codons of appY. Right: The MG1655 strain carrying the chromosomal appY-lacZ translational fusion was cultured ON in LBK pH = 7, diluted 1:1000 into LB at pH 8.5 (light blue) or 5.7 (dark blue) and incubated at 37°C under aerobic conditions until OD₆₀₀∼0.4. The activity of the appY fusion was determined using the Miller assay (89). The mean from three replicates is presented; the error bars represent the standard deviation (SD). Statistical significance calculations were performed using a two-tailed unpaired Student’s t test. (***, p-value < 0.001). C. AppY overproduction confers resistance to acid stress. WT (blue), ΔrpoS (grey), ΔgadE (pink) or ΔgadEΔrpoS (brown) strains transformed with pQE80L (dotted), pQE-appY_WT (plain) or pQE-appY_K170E (hatched) were grown to OD₆₀₀ = 1 in LB (pH 7.0) with 1 mM IPTG. Cells were diluted 1:200 into LB (pH 2.5) and incubated for 1 h at 37°C. Cells were spotted on plates to evaluate the number of cells that survive acid stress compared to the initial number of cells. Data are means +/- standard deviation (n = 3). Statistical significance calculations were performed using one-way ANOVA with Tukey’s multiple comparisons test (ns, not significant; ***, p-value <0.001). D. Complementation of MG1655 ΔgadE with gadE expressed under its own promotor. Strains MG1655 (blue) and ΔgadE (pink) were co-transformed with pQE80L (-, dotted) or pQE-appY_WT (WT, plain) and pACYC184 (-) or pACYC184-gadE. The experiment was carried out as described in 3.C. Data are means +/- standard deviation (n = 3). Statistical significance calculations were performed using one-way ANOVA with Tukey’s multiple comparisons test (ns, not significant; ***, p-value <0.001).

https://doi.org/10.1371/journal.pgen.1010672.g003

appY induction contributes to E. coli survival in acidic environment

Since AppY directly regulates gadE leading to the activation of the AR2 pathway, we could expect that appY itself is expressed under acidic conditions. Looking at the literature, we found two global studies reporting an induction of appY expression during a shift from pH = 8.5 to pH = 5.7; appY expression was further increased in the absence of oxygen [43,44]. Using a strain containing a chromosomal appY-lacZ translational fusion, we followed appY expression through beta-galactosidase assays under the growth conditions used in these studies. We measured a 2.8-fold increase in the activity of the fusion when the strain was grown at pH = 5.7 compared to pH = 8.5, validating the fact that appY expression was induced in acidic environment (Fig 3B). However, we did not detect a higher induction in the absence of oxygen (S3A Fig). The two-component system EvgA/EvgS has been shown to be involved in the sensing of acidic environment and activation of the AR2 system [45]. To determine whether EvgA/S and AppY belong to the same regulatory circuit, we measured the activity of the appY-lacZ fusion at pH = 5.7 or 8.5 in a strain deleted of evgA (S3B Fig). The absence of evgA did not affect appY induction at pH = 5.7, leading to the conclusion that appY is part of a regulatory pathway independent of the two-component system EvgA/S.

We then assessed the contribution of AppY to acid resistance under conditions known to activate the AR2 system. This system requires glutamate to function: in the absence of glutamate, the AR2 system is not active and the cell cannot survive at pH = 2.2 [33,46,47]. As a control, we used a strain deleted of gadC that codes for the transporter allowing glutamate entry in the cells. As expected, a strain deleted of gadC did not grow at pH = 2.2, with or without glutamate (S3C Fig). The WT and the ΔappY strains were unable to grow in the absence of glutamate but grew when glutamate was added without any significant difference (S3C Fig), indicating that in this experimental setup AppY is not essential for AR2 activation. We then postulated that if AppY was indeed involved in acid stress management, the benefit of AppY overproduction for the host could be an increased cell survival at low pH. To test this hypothesis, AppY wild-type or the K170E mutant was overproduced in the wild-type MG1655 strain; the cultures were then shifted from pH = 7 to pH = 2.5 for one hour and cell survival assayed by plating. Acid stress severely affected survival of the wild-type strain containing an empty vector after one hour at pH = 2.5 (Fig 3C, blue bars and S3D Fig). Strikingly, AppY overproduction massively increased cell survival by at least 100-fold under the same conditions. The increase in acid stress resistance was not observed with the AppY_K170E mutant that does not bind DNA (Fig 3C, hatched blue bars, and S3D Fig). These results confirm that AppY confers acid resistance and that this effect depends on its transcriptional regulator function. The effect of AppY overproduction was also tested in a ΔrpoS genetic background. In this context, we found that AppY overproduction still increased cell survival but 10-fold less than in a WT background (Fig 3C, compare WT (blue bars) with ΔrpoS (grey bars), and S3D Fig). This difference can be attributed to a higher sensitivity to acid stress of the strain lacking rpoS. To definitively link the positive effect of AppY on survival to the induction of the AR2 system, we repeated the same experiments in strains deleted of gadE. As expected, AppY overproduction did not increase bacterial survival in this genetic context (Fig 3C, pink and brown bars, and S3D Fig). However, when gadE was cloned together with its promoter region and co-transformed with pQE80L or pQE80L-appY_WT in the ΔgadE strain, it restored acid stress survival only in the presence of AppY (Fig 3D, pink bars, and S3E Fig). Overall, the data presented here demonstrate that AppY is involved in E. coli survival to acid stress, mainly by inducing gadE expression, hence the AR2 system.

AppY overproduction increases biofilm formation via the simultaneous induction of gadY and nhaR

Out of the 11 direct AppY targets identified by ChIP-Seq, gadY and nhaR have been shown to be involved in biofilm formation through the up-regulation of the pgaABCD operon, responsible for the synthesis of the adhesin poly-ß-1,6-N-acetyl-D-glucosamine [38,39]. In order to test whether AppY overproduction led to biofilm formation, MG1655 ΔrpoS strain was transformed either with the pQE80L empty vector or with the same vector containing appY WT or the K170E mutant affected in DNA-binding. Cells were grown for 24 hours at 30°C and biofilm formation was quantified with crystal violet staining followed by OD₅₅₀ measurement. This quantification clearly showed that AppY overproduction led to a 3-fold increase in biofilm formation (Fig 4A, grey bars and S4A Fig). AppY_K170E overproduction had no effect on biofilm showing that the observed phenotype is linked to the regulatory function of AppY (Fig 4A, grey hatched bars and S4A Fig).

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Fig 4. AppY favors biofilm formation through nhaR and gadY induction.

The indicated strains were grown in LB with 0.5 mM IPTG at 30°C without shaking for 24 hours. Biofilm formation was visualized using crystal violet staining and quantified by measuring the optical density at 550 nm (OD₅₅₀). The mean from three replicates is presented; the error bars represent the standard deviation (SD). Statistical significance calculations were performed using a two-tailed unpaired Student’s t test (ns, not significant; **, p-value < 0.01). A. Biofilm formation dependent on AppY, GadY and NhaR. MG1655 ΔrpoS (grey), MG1655 ΔrpoS ΔgadY (pink) or MG1655 ΔrpoS ΔnhaR (purple) were transformed with the pQE80L empty vector (-, dotted) or containing appY (WT, plain) or appY mutant (K170E, hatched). B. Complementation of MG1655 ΔrpoS ΔgadY with gadY and nhaR expressed under their own promoters. MG1655 ΔrpoS ΔgadY strain was co-transformed with the following plasmids: pQE80L empty vector (-, dotted), or containing appY (WT, plain) or appY mutant (K170E, hatched) and a pACYC184 construct (empty vector, -, grey), containing gadY (pink) or nhaR (purple). C. Complementation of MG1655 ΔrpoS ΔnhaR with gadY and nhaR expressed under their own promoters. MG1655 ΔrpoS ΔnhaR strain was co-transformed with a pQE80L construct (empty vector, -, dotted line), containing appY (WT, plain) or appY mutant (K170E, hatched) and a pACYC184 construct (empty vector, -, grey), containing gadY (pink) or nhaR (purple).

https://doi.org/10.1371/journal.pgen.1010672.g004

To confirm that AppY acted on biofilm through GadY and NhaR, we deleted the corresponding genes in the MG1655 ΔrpoS background. AppY overproduction in ΔrpoS ΔgadY or in ΔrpoS ΔnhaR strains did not lead to biofilm formation suggesting that GadY and NhaR are both essential in this pathway (Fig 4A pink and purple bars respectively and S4A Fig). In order to quantify the contribution of these two regulators for biofilm formation, we cloned gadY and nhaR under their own promoters and co-transformed them with either pQE80L, pQE80L-appY_WT or pQE80L-appY_K170E in strains deleted of gadY or nhaR. In the presence of AppY, nhaR and gadY should be expressed from the plasmids and complement the biofilm formation defect of these two strains. In the MG1655 ΔrpoS ΔgadY strain, the plasmid expressing gadY showed no effect on biofilm when co-transformed with the control vector (Fig 4B, pink dotted bar and S4B Fig). However, the production of AppY led to the expression of gadY and restored biofilm formation (Fig 4B, plain pink bar and S4B Fig). As expected, no biofilm was observed with AppY_K170E. Interestingly, in this genetic background, we did not observe an increase in biofilm with the plasmid expressing nhaR, even in the presence of AppY (Fig 4B, purple bars and S4B Fig). Altogether, this first set of experiments shows that NhaR alone is not sufficient to induce biofilm formation and that AppY contributes to biofilm formation through gadY induction. We then performed the same experiments in a MG1655 ΔrpoS ΔnhaR strain. In this background, GadY alone was not sufficient to induce biofilm formation, even when AppY was overproduced (Fig 4C, pink bars and S4C Fig). In the presence of nhaR and the control vector we observed a slight increase in biofilm formation; the same result was obtained with AppY_K170E (Fig 4C and S4C Fig). However, biofilm was fully restored only when both AppY and NhaR were produced at the same time (Fig 4C, plain purple bar). Overall, these data confirm that AppY favors biofilm formation by activating simultaneously the expression of the transcriptional regulator gene nhaR and the small RNA GadY.

Characterization of the interaction between AppY and its DNA targets

After demonstrating the physiological relevance of AppY positive regulation on some of its direct targets, we aimed at identifying a binding motif for AppY. We therefore subjected the DNA sequences of the 22 peaks enriched by at least four-fold identified in our ChIP-seq experiments (201 bp centered on the predicted absolute summit) to MEME Suite analyses. Although the binding site for most AraC/XylS family transcriptional regulators is 15 to 20 bp in length, we identified the 8 bp ATGSCWGM enriched motif in 20 out the 22 submitted sequences with a satisfactory E-value 6.8e-3 (Fig 5A). This motif was not found only in the enriched peaks upstream acrZ and yiiS (Table 1 and S2 Table). We noticed that the exact same motif overlapped the -35 sequence of P3, one of the four gadE promoters (blue arrow in Fig 2A situated downstream hdeD) and the -35 sequence of the gadY promoter. However, the distance between the identified motif and the -35 region was different between these two genes. Looking more closely at the gadE P3 promoter sequence, we identified a -35 element optimally positioned 17 bp upstream of the -10 hexamer and closer from the consensus than the one previously assigned [48]. Considering this new -35 element, the 8 bp motif was now located at the same position in gadE and gadY promoter regions, suggesting a unique AppY-dependent mechanism to induce transcription (Fig 5A). To confirm the role of this sequence in gadE and gadY AppY-dependent regulation, we mutated 4 of the 8 nucleotides in gfp-transcriptional fusions (labelled gadE* and gadY*) and monitored their activities (Fig 5B). Both mutated fusions displayed a lower activity than the WT fusions in the presence of AppY (decrease of a factor 2 and 2.6 for gadE* and gadY* respectively). This result shows that the identified sequence contributes to AppY-dependent regulation. To prove that AppY interacts with this sequence, we performed a pull-down assay (Fig 5C). Briefly, in a first step, AppY_WT-3Flag or AppY_K170E-3Flag were purified on anti-Flag beads. Then, PCR amplified Cy5-labeled promoter regions of gadE and gadY were incubated with the beads loaded with AppY WT or the K170E mutant. The same experiment was performed in parallel with the hyaA promoter region as a positive control and the malEK promoter region as a negative control since it was not identified as a direct AppY target. Moreover, the different DNA fragments were also all incubated together with beads in the absence of AppY to check for non-specific interactions. Using this technique, the hyaA DNA was retained on the beads in presence of AppY_WT whereas only a tiny amount was retained with AppY_K170E (Fig 5C, compare lines 1 and 2 and S5 Fig). As expected, no interaction was detected between AppY and the malEK promoter or with the different DNA fragments incubated with the beads only (Fig 5C, lines 3 to 5 and S5 Fig). An interaction was detected between AppY_WT and the promoter regions of gadE and gadY (Fig 5C, lines 6 and 8). This interaction is abolished in the presence of the K170E mutation, formally demonstrating that this mutant is affected in DNA binding (Fig 5C, lines 7 and 9). The same experiment was repeated with gadE* and gadY* DNA fragments mutated in the putative AppY binding motif. Mutation of this motif abolishes AppY interaction with the promoter region of gadE but not of gadY (Fig 5C, lines 10 to 12). The different sensibility of AppY to a mutation in the identified sequence suggests that this sequence is involved in AppY binding but does not represent the full AppY binding motif.

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Fig 5. Identification of AppY binding sequence in vivo and in vitro.

A. Left, Logo of the AppY DNA-binding motif consensus predicted by the MEME suite (ATGSCWGM). S: C or G; W: A or T; M: A or C. Right, gadE P3 promoter region and gadY promoter region. The -10 and -35 regions are underlined and the AppY DNA-binding motif is highlighted in blue. A new -35 sequence was defined for the P3 promoter of gadE. This sequence is at an optimal distance from the -10 hexamer and has a sequence closer to the consensus than the originally predicted -35 sequence (48). The mutated nucleotides in the variant promoter regions (*) are labelled in red. These mutations have been introduced in the gfp-transcriptional fusions or in the DNA fragments used in the pull-down assay. B. Mutations of the AppY binding site decrease the AppY-dependent induction of gadE and gadY transcriptional fusions. MG1655 ΔrpoS was co-transformed with the pQE80L empty vector (-, grey) or containing appY (WT, blue) or appY mutant (K170E, brown) and the indicated gfp fusions. Relative fluorescence intensities were measured after ON growth at 37°C in LB plus 0.05 mM IPTG to induce appY expression. Data are means +/- standard deviation (n = 3). A.U., arbitrary units. Statistical significance calculations were performed using a two-tailed unpaired Student’s t test (ns, not significant; ***, p-value < 0.001). C. AppY specifically binds to the promoter region of hyaA, gadE and gadY in vitro. Schematic representation of the pull-down assay. 1) Lysates containing AppY_WT-3Flag, AppY_K170E-3Flag or, as a control, a lysate made from cells containing the empty vector (pQE80L) were incubated with anti-Flag beads for purification. 2) Beads loaded with AppY_WT-3Flag or AppY_K170E-3Flag were incubated with DNA fragments labelled with Cy5 and corresponding to the promoter regions of hyaA, gadE, gadY or malEK as a control. AppY was detected with an anti-AppY antiserum and DNA fragments by fluorescence. The two revelations have been merged into a single image.

https://doi.org/10.1371/journal.pgen.1010672.g005

AppY promotes the degradation of the master regulator FlhC, leading to a strong defect in bacterial motility

According to the RNA-Seq experiments, AppY overproduction strongly down-regulates several genes involved in flagellar formation and motility (Fig 1 and S1 Table). However, the ChIP-Seq data showed no AppY binding site upstream of these genes suggesting that this regulation was indirect (Table 1 and S2 Table). To confirm the negative effect of AppY on the expression of flagellar genes, we quantified the level of expression of the first gene of each operon when AppY was overproduced using qRT-PCR (Fig 6A; plain arrows). The appC gene was used as a positive control since we have demonstrated that it is directly and positively regulated by AppY (Figs 2B and 6A). In agreement with the RNA-Seq data, expression of all of the fli and flg genes tested was reduced between 2.5 and 16-fold in the presence of AppY. This decrease in gene expression was not observed with AppY_K170E confirming that an intact DNA binding domain is needed to exert this repression (Fig 6A). We then assayed the consequences of this repression on E. coli motility. We transformed pQE80L-appY, pQE80L-appY_K170E or the control vector pQE80L into the MG1655 ΔrpoS strain and performed a classical swimming test on soft-agar plates. In the absence of inducer, all strains were equally motile (Fig 6B, light green bars). In contrast, in the presence of IPTG, AppY overproduction inhibited cell motility (Fig 6B, dark green bars). Here again, motility inhibition was not observed with the AppY_K170E mutant indicating that the DNA binding activity is required in this process (Fig 6B).

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Fig 6. AppY indirectly reduces bacterial motility.

A. Expression of flagellar genes is down-regulated by AppY overproduction. MG1655 ΔrpoS strain was transformed with the pQE80L empty vector (-) or containing appY (WT) or appY mutant (K170E). Cultures were grown to OD₆₀₀∼0.5 and induced with 1 mM IPTG for 1 hour. RNAs were extracted from 10⁹cells and qRT-PCR experiments were performed. The genetic organization of flagellar genes is shown above the graph. Expression of the first gene of each operon (plain colour) was tested (fliALF in green, flgB in dark green, flhD in blue); appC (grey) is a control. Data are means +/- standard deviation (n = 3). B. AppY overproduction affects motility. ON cultures were adjusted to OD₆₀₀ = 1 and 1μl was spotted on plates without (light green) or with IPTG (green). Plates were incubated at 30°C during 15 to 20 h and swimming diameters were measured. A representative picture of swimming is shown below the graph. Statistical significance calculations were performed using a two-tailed unpaired Student’s t test (ns, not significant; **, p-value < 0.01; ***, p-value < 0.001). C. AppY favors FlhC degradation by the HslUV protease. MG1655 ΔrpoS (blue) and MG1655 ΔrpoS ΔhslU (yellow) strains containing flhC-SPA on the chromosome were transformed with the pQE80L empty vector (-, hatched) or containing appY (WT, plain) or appY mutant (K170E, dotted). Cultures were grown to an OD₆₀₀∼0.6 and induced with 1 mM IPTG at 37°C for 1 hour. FlhC levels were analysed by Western blotting using anti-Flag antibody. HtpG was detected as a loading control. Each value obtained for FlhC has been normalized on the loading control. Then the “Induced” samples were divided by the “Uninduced” samples for each condition. Statistical significance calculations were performed on these data using a two-tailed unpaired Student’s t test (ns, not significant; *, p-value < 0.05; **, p-value < 0.01).

https://doi.org/10.1371/journal.pgen.1010672.g006

The global downregulation of a large number of genes involved in flagella synthesis suggests that FlhDC, the master regulator of this pathway, could itself be affected by AppY overproduction. However, neither the RNA-Seq nor the qRT-PCR experiments showed any significant decrease in flhDC expression when AppY was overproduced (Fig 6A, blue bars and S1 Table). Therefore, the repression of the fli and flg operons cannot be attributed to a direct AppY effect on flhDC transcription or mRNA stability. Interestingly, FlhD and FlhC have been previously shown to be actively turned-over by the Lon and ClpXP proteases in Proteus mirabilis and Salmonella enterica Typhimurium, respectively [49,50]. In order to follow the level of FlhDC in the presence of AppY, we fused a SPA-tag to FlhD and FlhC C-terminal extremities; for unknown reasons, this tag only led to FlhC detection. However, the absence of detection for FlhD when produced alone has already been described [50]. We quantified the level of FlhC, in the presence of the empty vector and observed a constant level of FlhC with or without IPTG (Fig 6C). When AppY was overproduced, a decrease in FlhC amount was visualized; this decrease did not occur with the AppY_K170E mutant (Fig 6C, blue bars). This result shows that AppY production favors FlhC degradation. Looking at our RNA-Seq experiments, we noticed that the expression of the hslUV protease was greatly enhanced in the presence of AppY (4.7 and 6-fold for hslU and hslV, respectively). In order to determine if this protease complex was responsible for FlhC degradation, we measured the FlhC level in strains deleted for hslU. This deletion led to FlhC-SPA stabilization even in the presence of AppY (Fig 5C, light yellow bars). Overall, the data presented here suggest that AppY production results in the degradation of FlhC, which is at least partially dependent on HslUV. This active degradation leads to a severe motility defect in E. coli.

Media and growth conditions

Cells were grown in Luria-Bertani (LB) broth. For acid stress experiments, we used E medium: (10 g/L MgSO₄, 100 g/L citric acid, 500 g/L KH₂PO₄ and 175 g/L NaNH₄HPO₄ 4H₂O), EG medium (E plus 0.4% D-Glucose) or potassium-modified LB (LBK; 10 g/L tryptone, 5 g/L of yeast extract, 7.45 g/L KCl) [77,78].

Liquid cultures were grown in aerobic conditions at 37°C under shaking (180 rpm) and plates were incubated at 37°C unless otherwise stated. The usual agar concentration for solid media is 1.5%. When required, antibiotics were added at the following concentrations: 100 μg/mL ampicillin (Amp), 25 μg/mL chloramphenicol (Cam), 50 μg/mL kanamycin (Kan), 10 μg/mL tetracycline (Tet) and 25 μg/mL zeocin (Zeo).

Bacterial strains, plasmids and primers

Bacterial strains are listed in S2 Table, plasmids in S3 Table and primers in S4 Table. All strains are derivatives of E. coli str. K-12 substr. MG1655 constructed by P1 transduction, selected on the appropriate antibiotic and verified by PCR. To construct the flhC-SPA-kan strain (where SPA is a Sequential Peptide Affinity tag) or the deletion/insertion mutants of gadE, gadY and nhaR, we used the λ red recombination system [79,80] (see more details in S1 Methods). The P-appY translational fusion was constructed using the NM580 strain, which contains the mini-λ prophage and has been engineered to construct chromosomal lacZ fusions using a selection/counter-selection method [81] (see more details in S1 Methods). This fusion contains a fragment ranging from -615 bp to +24 bp relative to the ATG start codon of appY Open Reading Frame (ORF). Site-specific mutagenesis was performed to introduce the K170E mutation into pQE-AppY (primers BAΦ184/BAΦ185; codon AAA changed to AGA) [82]. Plasmids pND-671, pND-677 and pND-678 contain DNA fragments ranging from -598 pb to +14 pb relative to the gadE ATG start codon, from -245 bp to +14 bp relative to the beginning of the gadY sequence and from -210 bp to +15 bp relative to the ATG start codon for gadA. Plasmid pND-665 contains the full length gadY sequence and 245 nucleotides upstream. As we wanted to express nhaR from its own promoter region to keep the AppY-dependent induction and since it is in operon with nhaA, we amplified both genes plus a region of 482 nucleotides upstream nhaA ATG. PCR fragments were then digested with ClaI/BamHI for gadY and EcoRV/SalI for nhaR and cloned into pACYC184 digested with the same enzymes. The resulting plasmid is pND-692. Plasmid pND-807 contains gadE sequence plus 798 nucleotides upstream gadE ATG. All constructs were confirmed by Sanger sequencing.

Chromatin immunoprecipitation sequencing (ChIP-Seq)

The ChIP-Seq protocol was adapted from [42,83]. ND3 strain transformed with the pQE80L empty vector or containing untagged appY, appY-3Flag or appY_K170E-3Flag, was grown in LB/Amp medium to an OD₆₀₀ of 0.2. Cultures were then induced with 0.05 mM IPTG and further grown for 1 h. This experiment was performed with 3 independent cultures (replicates R1, R2 and R3) for each condition. Cells were fixed with 1% formaldehyde; the crosslinking reaction was stopped by adding 250 mM glycine to the cultures. After being washed with PBS, the pellets were flash-frozen in liquid nitrogen and stored at -80°C. Cellular pellets were re-suspended in 1 mL of cold lysis buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton-X100, protease inhibitor cocktail) plus 3.5 units of lysozyme (Novagen). Samples were then incubated at room temperature for 10 min on a rotating wheel (20 rpm) before shearing the crosslinked DNA by sonication at 4°C using a Bioruptor Standard Waterbath Sonicator (cycles of 30 sec ON/ 30 sec OFF, pulsing at maximum amplification for 10 min) (Diagenode). After centrifugation, 900 μL of the supernatant were mixed with 40 μL of Agarose Anti-Flag M2 gel beads (Sigma-Aldrich) and incubated overnight (ON) on a rotating wheel (20 rpm). After a gentle centrifugation step, the beads were washed sequentially with Low salt washing buffer, High salt washing buffer, LiCl washing buffer and twice with TBS buffer (see detailed composition in S1 Methods). Two elution steps of the Flag-tagged protein-DNA complexes were performed with 200 μL of Flag buffer (TBS buffer plus 100 μg/ml of 3Flag peptide from Sigma-Aldrich) and the samples were incubated 30 min at RT on a rotating wheel (20 rpm). After centrifugation, the protein-DNA crosslink was reversed by adding 25 μL of 5 M NaCl to the eluate and incubated ON at 65°C. Proteins and RNA were removed by adding sequentially proteinase K (NEB) and RNAse A (0.2 mg/ml) (Sigma). DNA was extracted with phenol/chloroform/isoamyl alcohol (25:24:1, Sigma-Aldrich) and ethanol precipitated. The DNA was finally resuspended in 30 μl of DNAse-free water, quantified with the Qubit dsDNA HS Assay kit (Invitrogen). DNA profiles were recorded with the TapeStation 4200 System (Agilent) in combination with the D5000 ScreenTape (Agilent). Libraries for high throughput DNA sequencing were prepared using the TruSeq ChIP Sample Preparation kit (Illumina) according to the manufacturer’s protocol starting with 10 ng dsDNA from the previous step. High throughput DNA sequencing was performed as described below.

RNA sequencing (RNA-Seq)

The ND3 strain transformed with pQE80L or the pQE80L-appY was grown in LB/Amp medium to OD₆₀₀ = 0.6 at 37°C under shaking (180 rpm). 1 mM IPTG was then added and cells grown for one additional hour. Total RNAs were then purified as described above. This experiment was performed with 3 independent cultures (replicates R1, R2 and R3) for each condition. Total RNAs samples (3 μg) were depleted from ribosomal RNA using the bacteria Ribo-Zero rRNA Removal kit (Illumina) according to the manufacturer’s protocol. The cDNA libraries were then prepared from the depleted RNA samples obtained during the previous step using the GoScript Reverse transcriptase (Promega) to synthesize the first strand cDNA and the TruSeq Stranded mRNA Library Prep kit (Illumina) according to the manufacturers’ protocols. High throughput DNA sequencing was performed as described below.

RNA preparation and Reverse Transcription

For RNA-Seq and qRT-PCR experiments, 5.10⁹ cells were harvested and cellular pellets were flash-frozen in liquid nitrogen and stored at -80°C. Total RNAs were isolated from the pellet using the Maxwell 16 LEV miRNA Tissue Kit (Promega) according to the manufacturer’s instructions and an extra TURBO DNase (Invitrogen) digestion step to eliminate the contaminating DNA. The RNA quality was assessed by TapeStation 4200 system (Agilent). RNA was quantified spectrophotometrically at 260 nm (NanoDrop 1000; Thermo Fisher Scientific). For cDNA synthesis, 1 μg total RNA and 0.5 μg random primers (Promega) were used with the GoScript Reverse transcriptase (Promega) according to the manufacturer instructions.

High throughput DNA sequencing

Prior to sequencing, RNA-Seq and ChIP-Seq libraries were quantified with the Qubit dsDNA HS Assay kit and their size distribution profiles recorded with the TapeStation 4200 System (Agilent) in combination with the D5000 DNA ScreenTape System (Agilent). Libraries were then diluted at 4 nM. Paired-end (2 x 75 bp) DNA sequencing was performed on the in-lab MiSeq sequencer hosted at the Institute Transcriptomic and Genomic facility with a MiSeq v3 (150-cycles) flow cell according to Illumina’s protocol. S5 Table summarizes the different sequencing runs performed during this study and the corresponding data yield for each sample.

Raw sequencing reads (FASTQ files trimmed from their Illumina adaptors) were submitted to the NCBI Sequence Read Archive under the BioProject accession number PRJNA751735.

High throughput sequencing data analysis

The FASTQ files generated by the MiSeq sequencer were trimmed and clipped for quality control with Trimmomatic [84] using the following parameters: ILLUMINACLIP:TruSeq3-SE:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. For subsequent analyses we retained only the paired-reads FASTQ files generated by Trimmomatics.

RNA-Seq analysis

Trimmed paired-reads were mapped on Escherichia coli str. K12 substr. MG1655 reference genome (accession number U00096.3) and the raw read count per genomic feature computed with Rockhopper [85] using the following parameters: Orientation of mate-pair reads = rf, Max bases between paired reads = 500, Allowed mismatches = 0.15, Minimum seed length = 0.33, Min reads mapping to transcript = 10, Min transcript length = 50, Min count to seed a transcript = 50, Min count to extend a transcript = 5. Rockhopper mapping statistics are summarized in S6 Table. We observed fairly high variations in the efficiency of the ribo-depletion with as much as 36% of the reads mapping to rRNA in one sample. In order to obtain a proper data normalization in downstream analyses, we manually cured the read count matrix generated by Rockhopper from remaining rRNA read counts (16S-, 23S- and 5S- rRNA); the resulting trimmed matrix was then use as input for the differential gene expression analysis performed with Voom/Limma method. The latter is embedded in DEGUST (https://degust.erc.monash.edu/), an interactive web-tool for RNA-Seq analysis (https://doi.org/10.5281/zenodo.3258932), that allows to compare 3 different methods for differential gene expression analysis as well as easy data visualization, browsing and data export. All the RNA-Seq results are accessible on NCBI Sequence Read Archive under the BioProject accession number PRJNA751735. The RNA-Seq data are summarized in S1 Table.

ChIP-Seq analysis

Illumina pair-end short reads were aligned on Escherichia coli str. K12 substr. MG1655 reference genome (accession number U00096.3) with Bowtie2 using default parameters [86]. The alignments were visualized and compared with IGV [87]. Detection and visualization of enriched loci along the genome were done with the SeqMonk software (https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). For DNA binding motive identification, BAM files generated by Bowtie2 were filtered to retain alignments with a minimum MAPQ quality of 20. Peak calling on the datasets obtained in the condition where 3Flag-tagged AppY is overproduced was then performed on a Galaxy platform (https://usegalaxy.org) with MACS (version 2.1.1.20160309) with the following parameters: Build model = create_model, Lower mfold bound = 3, Band width for picking regions to compute fragment size = 300, Peak detection based on qvalue, Minimum FDR (q-value) cutoff for peak detection = 0.05. Datasets obtained with the untagged AppY or the AppY_K170E-3Flag overexpression served as ChIP-Seq control file. In order to predict an AppY DNA-binding sequence consensus, we used MEME and CentriMo from the MEME Suite [88]. As an input we used 201 bp DNA sequences centered on the absolute summit of each peak computed by MACS.

Measure of expression with transcriptional gfp fusions

Strains ND3 or BAPHI089 were co-transformed with a plasmid carrying a gfpmut2 transcriptional fusion and the pQE80L vector producing AppY wild-type (WT) or AppY_K170E. ON cultures were diluted 60-fold into fresh LB medium supplemented with Amp, Kan and IPTG (0.05 mM) into a black 96-well plate with transparent bottom (Greiner). The plate was incubated into a Tecan Spark plate reader at 37°C with shaking (180 rpm) during 10 h. Every 10 min, the optical density at 600 nm (OD₆₀₀) as well as the fluorescence intensity (excitation: 480 nm; emission: 535 nm) were measured. The transcriptional gfp fusion expression levels were standardized by dividing the fluorescence intensity by the OD₆₀₀. Mean values and Standard deviation (SD) were computed on a minimum of three independent experiments.

appY induction under acid conditions

This assay was carried out as previously described [43,44,77]. ON cultures of the BAPHI018 strain carrying the appY translational fusion were diluted 1:1000 in LBK medium buffered with 100 mM piperazine-N, N’-bis-2-(ethanesulfonic acid) and adjusted at pH 5.7 or 8.5 using KOH.

Anaerobic cultures were performed in screw-caped test tubes with a slow agitation on a rotating wheel (8 rpm); aerobic cultures were performed in 14 ml aerated polypropylene tubes in an orbital water bath (200 rpm). At OD₆₀₀ = 0.4, samples were taken for β-galactosidase assay using the method described by Miller [89]. The pH of the cultures was verified at the end of the experiment to ensure that the values were maintained.

Acid resistance assay

Acid resistance assays were performed as described previously [34]. Briefly, ON cultures were diluted 1:200 in 20 mL of LB at pH 7 with Amp and IPTG (1 mM) and grown at 37°C with aeration (180 rpm). At OD₆₀₀ ≈ 1, 50 μL samples of culture were serially diluted in PBS and spotted on LB plates to count the colony forming cells (expressed in Colony Forming Unit or CFU). The resulting CFU represents the initial number of live cells before stress. In parallel, 50 μL of culture were transferred into 2 ml of preheated LB pH 2.5 (pH adjusted with HCl) and incubated for one hour at 37°C. 50 μL samples were serially diluted in PBS and 10 μL were spotted on LB plate. The CFU obtained here represents the cells having survived to the acid stress. The survival percentages were calculated by dividing the final CFU number by the initial CFU number. Each experiment was performed at least three times.

Quantitative Real-Time-PCR for Transcriptional analysis

Quantitative real-time PCR (qPCR) and the corresponding analysis were performed on a CFX96 Real-Time System (Bio-Rad). The reaction volume was 15 μL and the final concentration of each primer was 0.5 μM. The cycling parameters of the qRT-PCR were 98°C for 2 min, followed by 45 cycles of 98°C for 5 s, 55°C for 10 s, 72°C for 1s. A final melting curve from 65°C to 95°C was performed to determine the specificity of the amplification. To determine the amplification kinetics of each product, the fluorescence derived from the incorporation of EvaGreen into the double-stranded PCR products was measured at the end of each cycle using the SsoFast EvaGreen Supermix 2X Kit (Bio-Rad, France). The results were analyzed using the Bio-Rad CFX Manager software, version 3.0 (Bio-Rad, France). The 16S RNA gene was used as a reference for normalization. For each point a technical duplicate was performed. The amplification efficiencies for each primer pairs were comprised between 80 and 100%. All of the primer pairs used for qRT-PCR are reported in the S4 Table.

Pull down of AppY-3Flag with its DNA

Pull down we carried out using fluorescently Cy5-labbelled DNA fragments of hyaA, gadE gadY and MalEK promoter regions amplified by PCR. Fragments of 160 pb were amplified for hyaA and gadE, 200 pb for gadY and 442 pb for MalEK. Strain ND3 transformed with the pQE80L empty vector or containing appY-3Flag or appY_K170E-3Flag was grown at 37°C in 100 ml LB medium plus Amp to an OD₆₀₀ around 0.2. Cultures were then induced with 0.5 mM IPTG and grown for 1 hour. Cells were pelleted and store at -20°C. Pellets were resuspended in 4 ml of cold lysis buffer containing 50 mM Tris-HCL pH 7.5, 150 mM NaCl, 1 mM EDTA and protease inhibitor cocktail (Sigma). Samples were sonicated at 4°C using Branson Sonifier 450 (two cycles of 1 min with an output control of 2 and a duty control of 80%). After centrifugation, 600 μl of lysates were incubated with 50 μl of Agarose Anti-Flag M2 gel beads (Sigma-Aldrich) during 1 hour at 4°C on a rotating wheel to fix AppY-3Flag or AppY_K170E-3Flag onto the beads. After a gentle centrifugation, beads were washed three times with 1 ml of lysis buffer. A mix containing 210 μl of Lysis buffer and 500 ng of target DNA tagged with Cy5 was made; 10 μl were kept as a loading control (input) and 100 μl were incubated with beads during 1 hour at room temperature on a rotating wheel. A sample of 10 μl of the supernatant was kept to observe the non-retained fraction (flowthrough). Input and flowthrough samples were loaded on a 1.5% TBE gel. Beads were washed three times. The elution step was done by incubating the beads with 20 μl of 2X loading buffer SDS at 25°C with agitation during 30 min. Eluates were loaded onto a gel 20% bis-acrylamide. The amount of eluted DNA, was detected by scanning gels with a FLA500 (Fuji) scanner (excitation wavelength: 635 nm (800 V scanning intensity) and emission wavelength: 665 nm). The amount of eluted protein was detected by western-blot using an anti-AppY antiserum.

Motility assays

Motility assays were performed as described previously [34] on 0.5% Tryptone and 0.5% NaCl plates supplemented with 0.3% agar plus Amp, with or without 0.5 mM IPTG. ON cultures in LB plus Amp were standardized to an OD₆₀₀ = 1 and 1 μl of each culture were spotted on plates. Plates were incubated at 30°C and after 15 to 20 h, the diameter of the swimming zone was measured using the ImageJ software [90]. All strains were tested in triplicate and each experiment was independently performed three time.

Biofilm quantification assay

Biofilm quantification was performed according to [91]. ON cultures were diluted 1:1000 in fresh medium plus 0.5 mM IPTG in 24-well plate and incubated at 30°C without agitation for 24 h. Cultures were removed and the plate was washed two times with water before adding 0.1% crystal violet (Sigma-Aldrich) for 10 min. The plate was washed three times with water, let dry ON and samples were solubilized by using glacial acetic acid at 30% (Carlo Erba). After 15 min incubation, the OD₅₅₀ was measured to quantify biofilm formation. Standard deviations are based on a minimum of three independent experiments.

Protein electrophoresis and Western blotting

Samples were analyzed using 12% lab-made standard SDS-polyacrylamide gels for AppY detection and using 12% Precast Hepes-Tris gels (WSHT) for other proteins. Semidry electrophoretic transfer onto nitrocellulose membranes was performed using Trans-Blot Turbo Transfer System (BioRad). Membranes were probed with an anti-AppY antiserum (GenScript—4.4:10000 dilution), or a Flag antibody (SIGMA—1:10000 dilution) for FlhC-SPA detection and with HtpG antibody [92] (1:100000 dilution) as a loading control. The blots were developed with Immobilon Western (Millipore) using a chemiluminescence image analyzer (ImageQuant Las 4000, GE Healthcare).

Statistical methods

Statistical analyses were done using the GraphPad Prism 8 software. Statistical significance calculations were analyzed using unpaired Student’s t test or one-way or two-way ANOVA with Tukey’s multiple comparisons test. P-values when differences are significant are provided on graphs and in figure legends.