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Article

Role of the Bacterial Amyloid-like Hfq in Fluoroquinolone Fluxes

by
Florian Turbant
1,2,3,
Emeline Esnouf
2,
Francois Rosaz
2,
Frank Wien
1,
Grzegorz Węgrzyn
3,
Hugo Chauvet
1 and
Véronique Arluison
2,4,*
1
Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin BP48, 91192 Gif-sur-Yvette, France
2
Laboratoire Léon Brillouin LLB, UMR12 CEA CNRS, CEA Saclay, 91191 Gif-sur-Yvette, France
3
Department of Molecular Biology, Faculty of Biology, University of Gdansk, Wita Stwosza 59, 80-308 Gdansk, Poland
4
UFR SDV, Université Paris Cité, 75013 Paris, France
*
Author to whom correspondence should be addressed.
Microorganisms 2024, 12(1), 53; https://doi.org/10.3390/microorganisms12010053
Submission received: 29 November 2023 / Revised: 22 December 2023 / Accepted: 26 December 2023 / Published: 28 December 2023
(This article belongs to the Special Issue Antimicrobial Resistance Mechanisms in Bacteria)
Browse Figures
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Abstract

:
Due to their two-cell membranes, Gram-negative bacteria are particularly resistant to antibiotics. Recent investigations aimed at exploring new target proteins involved in Gram-negative bacteria adaptation helped to identify environmental changes encountered during infection. One of the most promising approaches in finding novel targets for antibacterial drugs consists of blocking noncoding RNA-based regulation using the protein cofactor, Hfq. Although Hfq is important in many bacterial pathogens, its involvement in antibiotics response is still unclear. Indeed, Hfq may mediate drug resistance by regulating the major efflux system in Escherichia coli, but it could also play a role in the influx of antibiotics. Here, using an imaging approach, we addressed this problem quantitatively at the single-cell level. More precisely, we analyzed how Hfq affects the dynamic influx and efflux of ciprofloxacin, an antibiotic from the group of fluoroquinolones that is used to treat bacterial infections. Our results indicated that the absence of either whole Hfq or its C-terminal domain resulted in a more effective accumulation of ciprofloxacin, irrespective of the presence of the functional AcrAB-TolC efflux pump. However, overproduction of the MicF small regulatory RNA, which reduces the efficiency of expression of the ompF gene (coding for a porin involved in antibiotics influx) in a Hfq-dependent manner, resulted in impaired accumulation of ciprofloxacin. These results led us to propose potential mechanisms of action of Hfq in the regulation of fluoroquinolone fluxes across the E. coli envelope.

1. Introduction

Antibiotic crisis is defined as the appearance of bacterial strains resistant to most, if not all, known antimicrobial compounds, thus making infections caused by microorganisms lethal for hosts [1]. After the era of the extensive use of antibiotics, it is now estimated that in the United States, 2.8 million infections with bacteria resistant to antibiotics occur each year, causing about 35,000 human deaths. Furthermore, it is assessed that the number of multi-drug resistance (MDR)-caused deaths will increase to about 700,000 worldwide annually by 2050 if no novel therapies are developed [1]. New ways to fight bacterial infections are thus of utmost importance.
Currently, about 15 families of antibiotics are used. Their actions fall within three main mechanisms involving the inhibition of peptidoglycan synthesis (β-lactams), protein synthesis (aminoglycosides, macrolides or tetracyclines), and DNA metabolism and repair (quinolones) [2]. The mechanisms of bacterial resistance are diverse and include mutations causing specific changes in antibiotic targets, production of enzymes that destroy antibiotics, or export of antibiotics across cell membranes [3]. For instance, fluoroquinolones (FQ) are broad-spectrum antibiotics that are used for the treatment of severe infections, and resistance to FQ is typically due to mutations in genes encoding the target enzymes, DNA gyrase and topoisomerase IV, or genes involved in efficiency of the drug fluxes [4].
The bacterial cell membrane is indeed the first layer of defense against antimicrobial agents. Because Gram-negative bacteria contain two cellular membranes, i.e., an inner membrane (IM) and outer membrane (OM), their resistance to antibiotics is generally more pronounced and difficult to combat relative to Gram-positive species [5]. Thus, regulation of OM components strongly influences the antibiotic resistance of Gram-negative bacteria.
Bacteria can modulate their gene expression profile and adapt their metabolism to resist toxic agents [6,7]. This regulation operates at two levels, either transcriptional or post-transcriptional [5,7]. The former allows for the precise control of gene activity with minimal loss of cellular energy, while the latter makes possible a very quick response to rapidly changing environmental conditions. Targeting post-transcriptional regulation thus provides one of the most promising approaches to fight resistance to antibacterial agents. This approach may consist of blocking small noncoding RNA (sRNA)-based regulation [8]. Many sRNAs regulate mRNA translation via base-pairing in Gram-negative bacteria [9]. Nevertheless, because sRNA:mRNA annealing is imperfect, the Hfq protein is needed for this regulatory process [10]. Specifically, Hfq is required as a cofactor to facilitate sRNA:mRNA annealing [11]. sRNA usually negatively regulates mRNA expression by sequestering the ribosome-binding site (rbs) and/or the AUG start codon, thus blocking mRNA translation and subsequently accelerating the degradation of naked (i.e., ribosome-free) mRNA [12,13]. Translation enhancement may also occur but it is less frequent [14]. Due to the diversity of their targets, sRNAs and Hfq are involved in numerous bacterial pathways, including virulence and pathogenicity [15,16]. For this reason, Hfq has attracted considerable attention [17,18,19].
Structurally, Hfq consists of two regions. The N-terminal region (NTR, ~65 amino acid residues) forms a toroidal-shaped homohexamer that has at least two RNA-binding sites on each face of the torus [20,21,22]. These two binding sites tend to be specific for A- or U-rich RNA [20,22,23]. Additionally, Hfq possesses an amyloid C-terminal region (referred to as CTR, ~38 amino acid residues) [24]. The role of this C-terminal region remains poorly understood. While some studies indicated that Hfq-CTR is dispensable for riboregulation, other evidence indicated that it could play a role in sRNA:mRNA annealing [25,26,27]. Furthermore, it could play a role in DNA metabolism [28].
The efficiency of antibiotic efflux that decreases antibiotic intracellular concentration is an important parameter in one mechanism of antibiotic resistance. This mechanism results from the activity of transmembrane efflux pumps that recognize and expel a broad spectrum of antibiotics [29,30]. The Resistance Nodulation Division (RND) efflux pump family is the best characterized among them [31]. In particular, the major E. coli RND efflux pump AcrAB-TolC plays a critical role in the emergence of multidrug-resistant phenotypes [32]. This pump is composed of three proteins: AcrB is an active drug transporter that functions with the proton-motive force across the IM; TolC forms an OM channel; and AcrA is a periplasmic linker [33,34,35]. Two studies have shown that Hfq plays a role in regulating acrB expression, and therefore, Hfq may contribute to antibiotic resistance [36,37]. Furthermore, Hfq modulates antibiotic import. Indeed, the expression of mRNAs encoding various outer membrane proteins is controlled by sRNA and Hfq. These include several genes coding for porins [38,39]. Nevertheless, the relative impact of Hfq on influx and efflux processes remains unknown. Here, we addressed this question using a quantitative analysis of fluoroquinolone accumulation both at the single cell and whole population level [40,41]. Fluoroquinolones (FQ) represent a set of the most-used antibiotics to treat infections due to their broad-spectrum activity, tissue penetration, and low toxicity [42] Different E. coli variants were analyzed to decipher the role of Hfq on FQ (represented here by ciprofloxacin, CIP) influx and efflux.

2. Materials and Methods

2.1. Chemicals

All chemicals, except otherwise stated, were purchased from Sigma-Aldrich (Saint Louis, MO, USA).

2.2. Bacterial Strains

All strains used in this study (Table 1) are derivatives of E. coli AG100 (argE3 thi-3 rpsL xyl mtl supE44) [43]. The reference strain AG100 is referred to as a wild-type (WT) throughout this manuscript. The AG100A strain devoid of the AcrB efflux pump (AG100 acrB::kan) was described previously [44]. The strains mutated in the hfq gene are AG100 hfq::cm (devoid of the functional Hfq protein) and AG100-∆ctr, producing a truncated form of the protein with only the first 72 amino acid residues that correspond to Hfq-NTR [28]. These strains were constructed by P1 transduction of hfq alleles (hfq::cm or ∆ctr = hfq72-cm) into the AG100/AG100A background. The hfq gene regions of the strains were sequenced after transduction to confirm the presence of the hfq alleles (hfq+, hfq::cm or ∆ctr). For simplicity, hfq::cm and hfq72-cm are referred to as ∆hfq and ∆ctr throughout the manuscript. To reduce the expression of the ompF gene, encoding the OmpF porin, hfq+ bacteria were transformed with the pBRpLac-MicF plasmid (Ampr), bearing the gene for expression of the MicF sRNA [45]. Expression of micF was induced by adding 1 mM isopropyl-β-d-thiogalactopyranoside (IPTG) at 0.4 OD600 and cultivation until OD600 0.6. The effect of MicF overproduction using this plasmid was quantified previously using a reporter strain bearing an ompF-mCherry fusion and MicF overproduction drastically reduced ompF expression compared with the pBRpLac empty plasmid [46]. Here, we confirm by dot-blotting using an anti-OmpF antibody (from rabbit) that the amount of OmpF is reduced by ~70–80% in our conditions of growth. Note that the regulation of ompF expression by MicF requires the presence of Hfq [47].

2.3. Preparation of the Bacterial Suspension

Strains (WT and mutants) were grown at 37 °C in LB medium supplemented with chloramphenicol (Cm), ampicillin (Ap), and/or kanamycin (Kan) when necessary. Overnight cultures were diluted 1:100 in fresh LB medium and bacteria were grown until OD600 0.6. The cells were then collected by centrifugation at 6000× g for 10 min at room temperature.

2.4. Analysis of Ciprofloxacin Accumulation in Bulk by Spectrofluorimetry

Cells from cultures at OD600 0.6 were concentrated 10-fold in NaPi-Mg buffer (50 mM sodium phosphate, 0.5 mM MgCl2, pH 7.0). Bacteria were then incubated for 5 and 15 min in the presence of ciprofloxacin (CIP) at 5 µM. The Ciprofloxacin concentration was chosen considering AG100 and AG100A strains’ minimum inhibitory concentration (MICs), reported previously [41,48]. Controls consisted of bacteria incubated without the antibiotic. Then, bacterial suspensions (800 µL) were centrifuged at 6000× g for 10 min at 4 °C to eliminate CIP. Pellets were lysed overnight at 4 °C in 500 μL of 0.1 M glycine-HCl at pH 3. The supernatants were collected and natural fluorescence of CIP was measured in bulk using an excitation λexc = 275 nm and an emission spectrum λem from 320 to 500 nm [40]. All measurements were made simultaneously in a 96-well plate using a fluorescence plate reader (Tecan Life Science, Männedorf, Switzerland). Results were normalized to the tryptophan signal and CIP concentrations in lysates and calculated according to the calibration curves (see Vergalli et al. for details [40]). As the tryptophan signal may slightly change in the ∆hfq strain [49], fluorescent units were normalized to 1 using the WT AG100 strain after 5 min of incubation with CIP as a reference. All data are presented as the average of at least quadruplicate analyses made with independent cultures. Results were obtained from n = 4 to 9 independent cultures. The t-test was used to assess significative differences between CIP concentrations to compare the strains. The t-test can be applied to a small sample size as long as the population is normally distributed [50], which is the case here (see Supplementary Figure S1).

2.5. Single Cell Micro-Spectrofluorimetry

Bacterial cells for spectrofluorimetry were prepared in the same way as for bulk fluorimetry, except that cells were resuspended in NaPi-Mg buffer at an OD600 of 4.8 (120 µL) and distributed on a 1 M sucrose cushion (165 μL). The suspensions were centrifuged at 9000× g for 10 min at 4 °C. The pellets were stored on ice up to 2 h. The pellets were then resuspended in 40 µL of NaPi-Mg buffer just before imaging, in the presence or absence of CIP. Next, 0.5 µL of resuspended pellets were squeezed between two quartz coverslips and analyzed using a DUV fluorescence automatized inverted microscope called TELEMOS on the DISCO beamline at the SOLEIL synchrotron (proposal 20210406). For quantification, a series of acquisitions using an excitation wavelength of λexc = 275 nm were taken. Two sets of bandpass filters were used, one with λem ranging from 329 to 351 nm for tryptophan and the other ranging from 420 to 480 nm for the CIP drug fluorescence [40]. The images were analyzed with Image J and with a Python script developed on the DISCO beamline (https://gitlab.synchrotron-soleil.fr/disco-beamline/bacteria-drug-uv-analysis, accessed on 25 December 2023) [51]. For each condition, two different localizations with approximately 20–30 bacterial cells per field of view were recorded and averaged. Furthermore, at least three independent cultures were analyzed for each strain. The one-way ANOVA analysis of variance followed by a Tukey’s post hoc test was used to compare the means between the groups at the last measurement time.

2.6. Analysis of Bacterial Survival by Fluorescence Imaging

To evaluate bacterial survival, the standard staining microscopy method using the kit LIVE/DEADTM BacLightTM (Thermofisher, Waltham, MA, USA) was used. This staining test allowed the direct differentiation between the total and dead bacteria using two fluorescent probes: Syto 9 which stains total bacteria as it penetrates both membranes of living and dead cells, and propidium iodide which is selective to the damaged membrane of dead cells. Staining was performed according to the manufacturer’s protocol. The quantitative assessment of cell viability based on live/dead staining has been proven using a comparison with flow cytometry [52]. This lower viability was confirmed by plating both strains after 7 min of CIP exposure.

3. Results

3.1. The Absence of Hfq Increases Antibiotic Accumulation Independently of AcrB

As Hfq could simultaneously regulate both FQ influx and efflux, we first focused our analysis on CIP influx using a strain devoid of the AcrB efflux pump. The AcrAB-TolC drug-efflux complex consists of AcrA, AcrB, and TolC polypeptides that work together. If only one of these subunits is absent or damaged (here AcrB) the activity of the whole pump is affected, limiting the efflux of FQs and increasing accumulation.
As shown in Figure 1, at the population level CIP accumulated more efficiently in the strain devoid of both Hfq and AcrB (AG100A ∆hfq) than in the strain only devoid of AcrB (AG100A). This is observed at 5 and 15 min. The accumulation ratio (e.g., the ratio of ciprofloxacin in AG100A hfq+/AG100A ∆hfq) was ~1.3 at 5 and 15 min. This effect was also observed for the ∆ctr strain, where Hfq devoid of its CTR-amyloid region is present, but the difference was less pronounced (accumulation ratio ~1.2), and the significance of the difference observed between ∆hfq and ∆ctr in the ∆acrB genetic background was not clear (p < 0.25). To complement and confirm measurements at the population level, we performed measurements at the single-cell level.
The effects observed in bulk were confirmed by an analysis at the single-cell level (Figure 2). Compared with bulk measurements, this methodology offers the opportunity (i) to examine the time course accumulation without the need for bacterial synchronization; (ii) to follow the kinetics of accumulation in exactly the same bacteria through time; (iii) to observe the variability of accumulation within the same strain population; (iv) to monitor bacteria morphology during antibiotic accumulation.
As shown in Figure 2 and Figure 3, we confirmed that in the absence of Hfq, more CIP accumulated in bacterial cells relative to the hfq+ strain, similar to bulk measurements. We also confirmed that this effect was observed, even if less pronounced, for the ∆ctr compared with the ∆hfq strain, as suspected in bulk analysis.

3.2. The Absence of Hfq Increases Antibiotic Accumulation Also in the Presence of AcrB

Using a bulk analysis (Figure 4), the absence of Hfq resulted in more effective CIP accumulation relative to the WT strain, bearing the functional efflux pump. The effect was similar to that observed for bacteria devoid of the efflux pump AG100A (see Section 3.1 and Figure 1). We observed for AG100A an accumulation ratio ~1.3, and this effect was also less pronounced with the ∆ctr strain than with full hfq deletion (accumulation ratio ~1.2 vs. 1.3, respectively). Here, a significant difference was observed between ∆hfq and ∆ctr mutants in the WT genetic background, in particular at 15 min (p < 0.01).
This effect was then confirmed using measurement of accumulation at the single-cell level (Figure 5). First, we noted that, due to the absence of a functional efflux pump and as expected, the level of CIP was higher in AG100A (devoid of AcrB), than in AG100 (bearing active AcrB, compare values in Figure 3 and Figure 5, respectively). This was expected as no CIP efflux occurs in the absence of the efflux pump, and thus the concentration of CIP was expected to be higher in AG100 ∆acrB than in AG100.
One main difference between single-cell and bulk measurements involves the AG100 ∆hfq strain. In the single-cell analysis, we observe that this strain poorly accumulates CIP. The CIP accumulation is the same for AG100 and AG100 ∆ctr for ~5 to 7 min, and then AG100 ∆hfq cells stopped accumulating the drug (Figure 5). One explanation could be due to bacterial death after the long exposure to the antibiotic. Note that hfq deletion may induce UV sensitivity in the strain [53] during image acquisition. This eventuality was tested by increasing the time delay between acquisition of the same bacteria (one set of images acquired at 5 min and a second one at 15 min). In this experiment, with a lower exposure to UV, the accumulation of AG100 ∆hfq strain was similar to the one shown on the orange curve of Figure 5 (Supplementary Figure S2).
Using the imaging set up, we observed an unusual shape for AG100 ∆hfq cells, as compared with ∆ctr and WT strains (Figure 6). We also noted a very slow growth rate when cultured. In parallel, we evaluated bacterial survival using the LIVE/DEADTM BacLightTM staining method. Survival was around 99% for AG100 hfq+ strain, while it was only ~85% for the AG100 ∆hfq strain, confirming a lower viability of this strain compared with others (Supplementary Figure S3). This lower viability was confirmed by plating both strains after 7 min of CIP exposure; more precisely, we observed 20.6% less colonies for ∆hfq strain, a value in agreement with LIVE/DEAD staining viability measurement (Supplementary Figure S3).
Next, as Hfq may modulate FQ import by an sRNA-dependent negative regulation of mRNA encoding outer membrane proteins (Omps) [54], we tested the effect of a lower abundance of porins on CIP accumulation. Among the porins regulated by sRNA and Hfq [38,39], the outer membrane OmpF protein is clearly involved in the influx of antibiotics, including FQ, and decreased amounts of OmpF caused resistance to multiple antibiotics, especially FQ [55,56]. To test the effect of MicF on CIP accumulation, we used a plasmid overproducing the MicF sRNA to reduce the expression of ompF by ~80% [46,57,58]. Under these conditions, accumulation of CIP was comparable to that of the WT strain in bulk analysis (Figure 4). In the single-cell analysis, the fluorescence increases slowly with time but remains significantly below the level observed in WT bacteria (Figure 5). The differences between results obtained in the bulk and single-cell experiments might arise from different time courses of the experiments, as the results appear comparable at short times. Furthermore, these differences may be too small to be seen in bulk given the variability of bacterial population, while this difference can be evidenced using the single-cell analysis. Note that we did not find a significant cell mortality or abnormal cellular shape in the case of impaired expression of the ompF gene, conversely to the AG100 ∆hfq strain (Figure 6).

4. Discussion

Previous studies reported that Gram-negative bacteria, E. coli or Aeromonas veronii, devoid of Hfq are more sensitive to antibiotics [36,37]. Furthermore, deleting the hfq gene results in an increase in antibiotic accumulation [37]. The same study evidenced that deletion of the acrB gene, a component of the RND efflux pump, attenuates the effect of the hfq deletion on bacterial antibiotic sensitivity [37]. The level of the AcrB protein in the ∆hfq E. coli strain was greatly reduced compared with the WT strain, without affecting the activity of the acrAB operon promoter, indicating that Hfq regulates the expression of acrB at the post-transcriptional level [37]. The results of this study are thus compatible with the previous analysis, indicating an increase in the accumulation of antibiotics in the absence of Hfq [37]. Furthermore, here we quantify this effect, an aspect that was missing in Yamada et al., 2010 [37]. Our results indicate that Hfq has little or no influence on the accumulation of ciprofloxacin depending on the presence or absence of AcrB. This was quantified by calculating the CIP accumulation ratio of AG100 hfq+/AG100A hfq+ and that of AG100 ∆hfq/AG100A ∆hfq, and both are around 1.3. The difference in accumulation between AG100 and AG100 ∆hfq was explained by the fact that Hfq negatively regulates the expression of acrB, hence, the difference in accumulation between AG100 and AG100∆hfq. Here we show that there is still an effect of the deletion of hfq in AG100A; one explanation might be that Hfq influences the expression of proteins other than AcrB.
We rule out the possibility that Hfq may regulate other RND pumps, as previous results have repeatedly shown that the accumulation of FQs was identical in AG100A and in AG100 grown in the presence of Carbonyl Cyanide m-ChloroPhenyl hydrazone (CCCP) (M. Masi, personal communication). CCCP proton pumps are known to inactivate all RND pumps by dissipating the transmembrane potential needed by the efflux pumps [41]. Another possibility would be the intervention of an ABC transporter such as MacAB-TolC, even if such a transporter has a poor affinity for FQs [59].
We thus propose that the main effect of Hfq on FQ accumulation is due to its role in the regulation of the outer membrane porin OmpF production. This porin has previously been identified as a key player in FQ antibiotics influx, and impaired expression of ompF indeed induced resistance to FQ [55,56].
An intriguing result was that in the presence of CIP, altered shapes of cells were observed in the mutant devoid of the Hfq protein only in the presence of the AcrAB pump, not in its absence. Indeed, we observed that these ∆hfq cells lose their natural shape to become more spherical or strongly deformed in the presence of CIP. The mechanism of this phenomenon remains to be elucidated. However, significant changes in bacterial cell morphology might be related to either disruption of the integrity of the cell envelope or dysregulation of DNA replication [60,61,62]. Moreover, Hfq is a direct or indirect regulator of expression of many genes, including morphogenes (mreB, bolA) [62]. Therefore, it is likely that in the absence of Hfq, cells might have difficulty in maintaining their shape as a result of the dysregulation of the networks of gene-expression events. On the other hand, as the effects on the cell shape in Hfq-deficient cells subjected to CIP action were evident only in the presence of AcrAB, it is likely that AcrAB is involved in the specific Hfq-dependent mechanism(s). One might speculate that Hfq could positively regulate expression of acrB, thus, in the mutant with deletion of this gene, the effects of the absence of Hfq on the cell shape are negligible, as the AcrAB pump would not be produced efficiently anyway. If it remains to be elucidated why CIP is able to cause changes in the ∆hfq cell shape only in the presence of the pump, it is likely that there are interactions between ArcAB and CIP, which in the presence of Hfq leads to an effective efflux of the antibiotic, but in the absence of Hfq become toxic to cells. If this is true, it would be tempting to speculate that Hfq might control the expression of gene(s) whose product(s) are involved in the effective transportation of CIP outside the cell through the AcrAB system.
Concerning OmpF regulation, the expression of ompF is regulated at both the transcriptional and post-transcriptional levels. For the latter, the translation of ompF is inhibited by an antisense sRNA called MicF. The 5′ end of MicF RNA (~25 nucleotides) anneals to the ompF 5′ untranslated region of mRNA and forms a duplex (Figure 7). In this duplex, MicF sRNA sequesters the ribosome-binding site (rbs, Shine-Dalgarno sequence) and the AUG start codon of the ompF mRNA, thereby inhibiting its translation [63]. This repression fully depends on Hfq, as the MicF:ompF duplex is imperfect and proper annealing requires the protein cofactor [64].
Under normal conditions of growth, the production of MicF sRNA occurs at a low level, but the presence of antibiotics increases transcription of micF [56]. The expression of micF is indeed increased by the MarA transcriptional activator in the presence of CIP, resulting in impairment of the ompF expression [65,66]. Reduced expression of ompF thus favors the resistance to CIP. Conversely, strains devoid of Hfq are more sensitive to FQ as no negative regulation of ompF by MicF occurs [37].
Note that other factors controlled by Hfq may also be necessary for the regulation of ompF expression. For instance, a transcriptional regulator H-NS regulates micF transcription [70] and Hfq negatively regulates the expression of hns at the post-transcriptional level through DsrA sRNA [71]. Thus, OmpF levels are decreased in an hns mutant, but increased in an hfq mutant, as H-NS translation is no longer repressed by Hfq [72].
Additionally, we also reported that the presence of a truncated form of Hfq devoid of its C-terminus region results in a more effective accumulation of CIP (in both acrB+ and acrB- backgrounds). The effect of the ctr deletion on CIP accumulation is intermediate between that in the hfq+ and ∆hfq strains. Previous studies indicated that Hfq-CTR may be dispensable for riboregulation [25]. However, here we confirm the results of other studies suggesting that the CTR region of Hfq may play a role in sRNA-based regulation [26,27]. Furthermore, this region of the protein is amyloid-like [24] and Hfq, due to its CTR region, may affect membrane integrity (both IM and OM) [68,69]. Therefore, one can imagine that the Hfq protein could also directly influence CIP fluxes independently of its role in sRNA-based regulation (Figure 7). Additionally, crosstalk between Hfq effects on DNA metabolism [28], and that of FQ that acts on DNA-gyrase and inhibits DNA supercoiling relaxation, may also occur (Figure 7).

5. Conclusions

The main result of the current study is that Hfq has little or no influence on the accumulation of ciprofloxacin depending on the presence or absence of the AcrB-containing efflux pump. We indeed confirm results of previous reports [37], but show additionally that there is an effect of the deletion of hfq in AG100A (devoid of AcrB), Hfq also significantly influences the expression of proteins other than AcrB involved in quinolone fluxes. We present evidence using a strain expressing MicF sRNA, that the OmpF porin could be one of these proteins. Nevertheless, no final conclusions about the role of OmpF (and MicF) can be drawn as the MicF-based regulation imposes the presence of Hfq, and the analysis of the OmpF effect in the AG100 ∆hfq strain would also be needed to draw this conclusion. In addition, Hfq may influence the expression of other outer-membrane porins [38,39]. We thus plan to analyse a series of mutants deleted of various omp genes, including those encoding OmpF but also OmpC or OmpD [73], to exert a full repression of the Omps in the absence of Hfq that may reveal new mechanisms of regulation of FQ fluxes by sRNA and Hfq.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms12010053/s1, Supplementary Figure S1: Normal distribution of bacterial populations based on single-cell analysis; Supplementary Figure S2: CIP accumulation at the single-cell level with lower exposure to UV; Supplementary Figure S3: Evaluation of AG100A ∆hfq strain survival using a Live/Dead test and plating after CIP exposition; Supplementary Table S1: Ciprofloxacin accumulation values at the single-cell level in bacteria.

Author Contributions

Conceptualization, V.A.; Formal analysis, F.T., E.E., F.W., H.C. and V.A.; Funding acquisition, F.W., G.W. and V.A.; Investigation, F.T. and H.C.; Methodology, F.T., F.W. and H.C.; Project administration, V.A.; Resources, V.A.; Software, F.T., F.R., H.C. and V.A.; Supervision, F.W., G.W. and V.A.; Validation, E.E., F.R., H.C. and V.A.; Visualization, F.T., H.C. and V.A.; Writing—original draft, F.T., E.E., G.W. and V.A.; Writing—review and editing, F.W., G.W., H.C. and V.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by CNRS, CEA (VA), and Synchrotron SOLEIL (HC, FW). This study contributes to the IdEx Université Paris Cité ANR-18-IDEX-0001 (VA). This work was also supported by a public grant overseen by the French National Research Agency (ANR) as part of the «Investissements d’Avenir» program, through the “ADI 2021” project funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02 (FT), and by the National Science Center Poland (FT), grant no. 2016/21/N/NZ1/02850, and the University of Gdansk (task grant no. 531-D020-D242-23 (GW)).

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding authors.

Acknowledgments

We are Grateful to Maude Guillier (IPBC, Paris) for the gift of pBRpLac-MicF plasmid, and Sylwia Bloch and Bożena Nejman-Falenczyk (University of Gdansk) for their help in construction of bacterial strains. We thank Margot Draveny (Synchrotron SOLEIL) for her help in bulk fluorescence measurements and for the gift of anti-OmpF antibodies; we also thank Muriel Masi (AMU, Marseilles, France) for many fruitful discussions, for critical reading of the manuscript, and for the gift of AG100 original strains. We are finally grateful to Richard R. Sinden (SDMT, SD, USA) for critical reading of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cook, M.A.; Wright, G.D. The past, present, and future of antibiotics. Sci. Transl. Med. 2022, 14, eabo7793. [Google Scholar] [CrossRef] [PubMed]
  2. Peleg, A.Y.; Hooper, D.C. Hospital-acquired infections due to gram-negative bacteria. N. Engl. J. Med. 2010, 362, 1804–1813. [Google Scholar] [CrossRef] [PubMed]
  3. Wesseling, C.M.J.; Martin, N.I. Synergy by Perturbing the Gram-Negative Outer Membrane: Opening the Door for Gram-Positive Specific Antibiotics. ACS Infect. Dis. 2022, 8, 1731–1757. [Google Scholar] [CrossRef] [PubMed]
  4. Redgrave, L.S.; Sutton, S.B.; Webber, M.A.; Piddock, L.J. Fluoroquinolone resistance: Mechanisms, impact on bacteria, and role in evolutionary success. Trends Microbiol. 2014, 22, 438–445. [Google Scholar] [CrossRef] [PubMed]
  5. Le Berre, D.; Reverchon, S.; Muskhelishvili, G.; Nasser, W. Relationship between the Chromosome Structural Dynamics and Gene Expression-A Chicken and Egg Dilemma? Microorganisms 2022, 10, 846. [Google Scholar] [CrossRef] [PubMed]
  6. Sharma, P.; Bano, A.; Nadda, A.K.; Sharma, S.; Varjani, S.; Singh, S.P. Crosstalk and gene expression in microorganisms under metals stress. Arch. Microbiol. 2022, 204, 410. [Google Scholar] [CrossRef] [PubMed]
  7. Ponath, F.; Hor, J.; Vogel, J. An overview of gene regulation in bacteria by small RNAs derived from mRNA 3′ ends. FEMS Microbiol. Rev. 2022, 46, fuac017. [Google Scholar] [CrossRef]
  8. Desgranges, E.; Marzi, S.; Moreau, K.; Romby, P.; Caldelari, I. Noncoding RNA. Microbiol. Spectr. 2019, 7, 10–1128. [Google Scholar] [CrossRef]
  9. Papenfort, K.; Melamed, S. Small RNAs, Large Networks: Posttranscriptional Regulons in Gram-Negative Bacteria. Annu. Rev. Microbiol. 2023, 77, 23–43. [Google Scholar] [CrossRef]
  10. Vogel, J.; Luisi, B.F. Hfq and its constellation of RNA. Nat. Rev. Microbiol. 2011, 9, 578–589. [Google Scholar] [CrossRef]
  11. Santiago-Frangos, A.; Woodson, S.A. Hfq chaperone brings speed dating to bacterial sRNA. Wiley Interdiscip. Rev. RNA 2018, 9, e1475. [Google Scholar] [CrossRef] [PubMed]
  12. Aiba, H. Mechanism of RNA silencing by Hfq-binding small RNAs. Curr. Opin. Microbiol. 2007, 10, 134–139. [Google Scholar] [CrossRef] [PubMed]
  13. Morita, T.; Maki, K.; Aiba, H. RNase E-based ribonucleoprotein complexes: Mechanical basis of mRNA destabilization mediated by bacterial noncoding RNAs. Genes Dev. 2005, 19, 2176–2186. [Google Scholar] [CrossRef] [PubMed]
  14. Majdalani, N.; Cunning, C.; Sledjeski, D.; Elliott, T.; Gottesman, S. DsrA RNA regulates translation of RpoS message by an anti-antisense mechanism, independent of its action as an antisilencer of transcription. Proc. Natl. Acad. Sci. USA 1998, 95, 12462–12467. [Google Scholar] [CrossRef] [PubMed]
  15. Bojer, M.S.; Jakobsen, H.; Struve, C.; Krogfelt, K.A.; Lobner-Olesen, A. Lack of the RNA chaperone Hfq attenuates pathogenicity of several Escherichia coli pathotypes towards Caenorhabditis elegans. Microbes Infect. 2012, 14, 1034–1039. [Google Scholar] [CrossRef] [PubMed]
  16. Kendall, M.M.; Gruber, C.C.; Rasko, D.A.; Hughes, D.T.; Sperandio, V. Hfq virulence regulation in enterohemorrhagic Escherichia coli O157:H7 strain 86-24. J. Bacteriol. 2011, 193, 6843–6851. [Google Scholar] [CrossRef]
  17. Sittka, A.; Pfeiffer, V.; Tedin, K.; Vogel, J. The RNA chaperone Hfq is essential for the virulence of Salmonella typhimurium. Mol. Microbiol. 2007, 63, 193–217. [Google Scholar] [CrossRef]
  18. Turbant, F.; Partouche, D.; El Hamoui, O.; Trepout, S.; Legoubey, T.; Wien, F.; Arluison, V. Apomorphine Targets the Pleiotropic Bacterial Regulator Hfq. Antibiotics 2021, 10, 257. [Google Scholar] [CrossRef]
  19. Wang, Y.; Teng, Y.; Geng, J.; Long, J.; Yang, H.; Duan, G.; Chen, S. Involvement of RNA chaperone hfq in the regulation of antibiotic resistance and virulence in Shigella sonnei. Res. Microbiol. 2023, 174, 104047. [Google Scholar] [CrossRef]
  20. Brennan, R.G.; Link, T.M. Hfq structure, function and ligand binding. Curr. Opin. Microbiol. 2007, 10, 125–133. [Google Scholar] [CrossRef]
  21. Dimastrogiovanni, D.; Frohlich, K.S.; Bandyra, K.J.; Bruce, H.A.; Hohensee, S.; Vogel, J.; Luisi, B.F. Recognition of the small regulatory RNA RydC by the bacterial Hfq protein. Elife 2014, 3, e05375. [Google Scholar] [CrossRef] [PubMed]
  22. Link, T.M.; Valentin-Hansen, P.; Brennan, R.G. Structure of Escherichia coli Hfq bound to polyriboadenylate RNA. Proc. Natl. Acad. Sci. USA 2009, 106, 19292–19297. [Google Scholar] [CrossRef] [PubMed]
  23. Schumacher, M.A.; Pearson, R.F.; Moller, T.; Valentin-Hansen, P.; Brennan, R.G. Structures of the pleiotropic translational regulator Hfq and an Hfq- RNA complex: A bacterial Sm-like protein. EMBO J. 2002, 21, 3546–3556. [Google Scholar] [CrossRef] [PubMed]
  24. Fortas, E.; Piccirilli, F.; Malabirade, A.; Militello, V.; Trepout, S.; Marco, S.; Taghbalout, A.; Arluison, V. New insight into the structure and function of Hfq C-terminus. Biosci. Rep. 2015, 35, e00190. [Google Scholar] [CrossRef] [PubMed]
  25. Olsen, A.S.; Moller-Jensen, J.; Brennan, R.G.; Valentin-Hansen, P. C-Terminally truncated derivatives of Escherichia coli Hfq are proficient in riboregulation. J. Mol. Biol. 2010, 404, 173–182. [Google Scholar] [CrossRef] [PubMed]
  26. Santiago-Frangos, A.; Kavita, K.; Schu, D.J.; Gottesman, S.; Woodson, S.A. C-terminal domain of the RNA chaperone Hfq drives sRNA competition and release of target RNA. Proc. Natl. Acad. Sci. USA 2016, 113, E6089–E6096. [Google Scholar] [CrossRef] [PubMed]
  27. Turbant, F.; Wu, P.; Wien, F.; Arluison, V. The Amyloid Region of Hfq Riboregulator Promotes DsrA:rpoS RNAs Annealing. Biology 2021, 10, 900. [Google Scholar] [CrossRef]
  28. Malabirade, A.; Partouche, D.; El Hamoui, O.; Turbant, F.; Geinguenaud, F.; Recouvreux, P.; Bizien, T.; Busi, F.; Wien, F.; Arluison, V. Revised role for Hfq bacterial regulator on DNA topology. Sci. Rep. 2018, 8, 16792. [Google Scholar] [CrossRef]
  29. Alenazy, R. Drug Efflux Pump Inhibitors: A Promising Approach to Counter Multidrug Resistance in Gram-Negative Pathogens by Targeting AcrB Protein from AcrAB-TolC Multidrug Efflux Pump from Escherichia coli. Biology 2022, 11, 1328. [Google Scholar] [CrossRef]
  30. Nikaido, H.; Pages, J.M. Broad-specificity efflux pumps and their role in multidrug resistance of Gram-negative bacteria. FEMS Microbiol. Rev. 2012, 36, 340–363. [Google Scholar] [CrossRef]
  31. Venter, H.; Mowla, R.; Ohene-Agyei, T.; Ma, S. RND-type drug efflux pumps from Gram-negative bacteria: Molecular mechanism and inhibition. Front. Microbiol. 2015, 6, 377. [Google Scholar] [CrossRef] [PubMed]
  32. Eicher, T.; Brandstatter, L.; Pos, K.M. Structural and functional aspects of the multidrug efflux pump AcrB. Biol. Chem. 2009, 390, 693–699. [Google Scholar] [CrossRef] [PubMed]
  33. Du, D.; Wang, Z.; James, N.R.; Voss, J.E.; Klimont, E.; Ohene-Agyei, T.; Venter, H.; Chiu, W.; Luisi, B.F. Structure of the AcrAB-TolC multidrug efflux pump. Nature 2014, 509, 512–515. [Google Scholar] [CrossRef] [PubMed]
  34. Puzari, M.; Chetia, P. RND efflux pump mediated antibiotic resistance in Gram-negative bacteria Escherichia coli and Pseudomonas aeruginosa: A major issue worldwide. World J. Microbiol. Biotechnol. 2017, 33, 24. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, M.; Shi, X.; Yu, Z.; Fan, G.; Serysheva, I.I.; Baker, M.L.; Luisi, B.F.; Ludtke, S.J.; Wang, Z. In situ structure of the AcrAB-TolC efflux pump at subnanometer resolution. Structure 2022, 30, 107–113.e3. [Google Scholar] [CrossRef] [PubMed]
  36. Wang, D.; Li, H.; Ma, X.; Tang, Y.; Tang, H.; Huang, D.; Lin, M.; Liu, Z. Hfq Regulates Efflux Pump Expression and Purine Metabolic Pathway to Increase Trimethoprim Resistance in Aeromonas veronii. Front. Microbiol. 2021, 12, 742114. [Google Scholar] [CrossRef]
  37. Yamada, J.; Yamasaki, S.; Hirakawa, H.; Hayashi-Nishino, M.; Yamaguchi, A.; Nishino, K. Impact of the RNA chaperone Hfq on multidrug resistance in Escherichia coli. J. Antimicrob. Chemother. 2010, 65, 853–858. [Google Scholar] [CrossRef] [PubMed]
  38. Guillier, M.; Gottesman, S.; Storz, G. Modulating the outer membrane with small RNAs. Genes Dev. 2006, 20, 2338–2348. [Google Scholar] [CrossRef]
  39. Guillier, M.; Gottesman, S. Remodelling of the Escherichia coli outer membrane by two small regulatory RNAs. Mol. Microbiol. 2006, 59, 231–247. [Google Scholar] [CrossRef]
  40. Vergalli, J.; Dumont, E.; Pajovic, J.; Cinquin, B.; Maigre, L.; Masi, M.; Refregiers, M.; Pages, J.M. Spectrofluorimetric quantification of antibiotic drug concentration in bacterial cells for the characterization of translocation across bacterial membranes. Nat. Protoc. 2018, 13, 1348–1361. [Google Scholar] [CrossRef]
  41. Vergalli, J.; Chauvet, H.; Oliva, F.; Pajovic, J.; Malloci, G.; Vargiu, A.V.; Refregiers, M.; Ruggerone, P.; Pages, J.M. A framework for dissecting affinities of multidrug efflux transporter AcrB to fluoroquinolones. Commun. Biol. 2022, 5, 1062. [Google Scholar] [CrossRef] [PubMed]
  42. King, D.E.; Malone, R.; Lilley, S.H. New classification and update on the quinolone antibiotics. Am. Fam. Physician 2000, 61, 2741–2748. [Google Scholar] [PubMed]
  43. George, A.M.; Levy, S.B. Amplifiable resistance to tetracycline, chloramphenicol, and other antibiotics in Escherichia coli: Involvement of a non-plasmid-determined efflux of tetracycline. J. Bacteriol. 1983, 155, 531–540. [Google Scholar] [CrossRef] [PubMed]
  44. Okusu, H.; Ma, D.; Nikaido, H. AcrAB efflux pump plays a major role in the antibiotic resistance phenotype of Escherichia coli multiple-antibiotic-resistance (Mar) mutants. J. Bacteriol. 1996, 178, 306–308. [Google Scholar] [CrossRef] [PubMed]
  45. Mandin, P.; Gottesman, S. Integrating anaerobic/aerobic sensing and the general stress response through the ArcZ small RNA. EMBO J. 2010, 29, 3094–3107. [Google Scholar] [CrossRef] [PubMed]
  46. Chen, J.; To, L.; de Mets, F.; Luo, X.; Majdalani, N.; Tai, C.H.; Gottesman, S. A fluorescence-based genetic screen reveals diverse mechanisms silencing small RNA signaling in E. coli. Proc. Natl. Acad. Sci. USA 2021, 118, e2106964118. [Google Scholar] [CrossRef]
  47. Storz, G.; Opdyke, J.A.; Zhang, A. Controlling mRNA stability and translation with small, noncoding RNAs. Curr. Opin. Microbiol. 2004, 7, 140–144. [Google Scholar] [CrossRef]
  48. Maira-Litran, T.; Allison, D.G.; Gilbert, P. An evaluation of the potential of the multiple antibiotic resistance operon (mar) and the multidrug efflux pump acrAB to moderate resistance towards ciprofloxacin in Escherichia coli biofilms. J. Antimicrob. Chemother. 2000, 45, 789–795. [Google Scholar] [CrossRef]
  49. Andrade, J.M.; Dos Santos, R.F.; Chelysheva, I.; Ignatova, Z.; Arraiano, C.M. The RNA-binding protein Hfq is important for ribosome biogenesis and affects translation fidelity. EMBO J. 2018, 37, e97631. [Google Scholar] [CrossRef]
  50. de Winter, J.C.F. Using the Student’s t-test with extremely small sample sizes. Pract. Assess. Res. Eval. 2013, 18, 10. Available online: https://scholarworks.umass.edu/pare/vol18/iss1/10 (accessed on 25 December 2023).
  51. Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671–675. [Google Scholar] [CrossRef] [PubMed]
  52. Kummrow, A.; Frankowski, M.; Bock, N.; Werner, C.; Dziekan, T.; Neukammer, J. Quantitative assessment of cell viability based on flow cytometry and microscopy. Cytometry A 2013, 83, 197–204. [Google Scholar] [CrossRef] [PubMed]
  53. Tsui, H.C.; Leung, H.C.; Winkler, M.E. Characterization of broadly pleiotropic phenotypes caused by an hfq insertion mutation in Escherichia coli K-12. Mol. Microbiol. 1994, 13, 35–49. [Google Scholar] [CrossRef] [PubMed]
  54. Chapman, J.S.; Georgopapadakou, N.H. Routes of quinolone permeation in Escherichia coli. Antimicrob. Agents Chemother. 1988, 32, 438–442. [Google Scholar] [CrossRef] [PubMed]
  55. Kishii, R.; Takei, M. Relationship between the expression of ompF and quinolone resistance in Escherichia coli. J. Infect. Chemother. 2009, 15, 361–366. [Google Scholar] [CrossRef]
  56. Jaktaji, R.P.; Heidari, F. Study the Expression of ompf Gene in Esherichia coli Mutants. Indian J. Pharm. Sci. 2013, 75, 540–544. [Google Scholar] [PubMed]
  57. Andersen, J.; Delihas, N. micF RNA binds to the 5′ end of ompF mRNA and to a protein from Escherichia coli. Biochemistry 1990, 29, 9249–9256. [Google Scholar] [CrossRef]
  58. Delihas, N. Discovery and characterization of the first non-coding RNA that regulates gene expression, micF RNA: A historical perspective. World J. Biol. Chem. 2015, 6, 272–280. [Google Scholar] [CrossRef]
  59. Li, B.; Zhang, J.; Li, X. A comprehensive description of the TolC effect on the antimicrobial susceptibility profile in Enterobacter bugandensis. Front. Cell. Infect. Microbiol. 2022, 12, 1036933. [Google Scholar] [CrossRef]
  60. Reshes, G.; Vanounou, S.; Fishov, I.; Feingold, M. Cell shape dynamics in Escherichia coli. Biophys. J. 2008, 94, 251–264. [Google Scholar] [CrossRef]
  61. Ghosh, A.S.; Melquist, A.L.; Young, K.D. Loss of O-antigen increases cell shape abnormalities in penicillin-binding protein mutants of Escherichia coli. FEMS Microbiol. Lett. 2006, 263, 252–257. [Google Scholar] [CrossRef] [PubMed]
  62. Cayrol, B.; Fortas, E.; Martret, C.; Cech, G.; Kloska, A.; Caulet, S.; Barbet, M.; Trepout, S.; Marco, S.; Taghbalout, A.; et al. Riboregulation of the bacterial actin-homolog MreB by DsrA small noncoding RNA. Integr. Biol. 2015, 7, 128–141. [Google Scholar] [CrossRef] [PubMed]
  63. Vogel, J.; Papenfort, K. Small non-coding RNAs and the bacterial outer membrane. Curr. Opin. Microbiol. 2006, 9, 605–611. [Google Scholar] [CrossRef] [PubMed]
  64. Corcoran, C.P.; Podkaminski, D.; Papenfort, K.; Urban, J.H.; Hinton, J.C.; Vogel, J. Superfolder GFP reporters validate diverse new mRNA targets of the classic porin regulator, MicF RNA. Mol. Microbiol. 2012, 84, 428–445. [Google Scholar] [CrossRef] [PubMed]
  65. Viveiros, M.; Dupont, M.; Rodrigues, L.; Couto, I.; Davin-Regli, A.; Martins, M.; Pages, J.M.; Amaral, L. Antibiotic stress, genetic response and altered permeability of E. coli. PLoS ONE 2007, 2, e365. [Google Scholar] [CrossRef]
  66. Karczmarczyk, M.; Martins, M.; Quinn, T.; Leonard, N.; Fanning, S. Mechanisms of fluoroquinolone resistance in Escherichia coli isolates from food-producing animals. Appl. Environ. Microbiol. 2011, 77, 7113–7120. [Google Scholar] [CrossRef]
  67. Lease, R.A.; Belfort, M. Riboregulation by DsrA RNA: Trans-actions for global economy. Mol. Microbiol. 2000, 38, 667–672. [Google Scholar] [CrossRef]
  68. Turbant, F.; Waeytens, J.; Campidelli, C.; Bombled, M.; Martinez, D.; Grelard, A.; Habenstein, B.; Raussens, V.; Velez, M.; Wien, F.; et al. Unraveling Membrane Perturbations Caused by the Bacterial Riboregulator Hfq. Int. J. Mol. Sci. 2022, 23, 8739. [Google Scholar] [CrossRef]
  69. Turbant, F.; Waeytens, J.; Blache, A.; Esnouf, E.; Raussens, V.; Wegrzyn, G.; Achouak, W.; Wien, F.; Arluison, V. Interactions and Insertion of Escherichia coli Hfq into Outer Membrane Vesicles as Revealed by Infrared and Orientated Circular Dichroism Spectroscopies. Int. J. Mol. Sci. 2023, 24, 11424. [Google Scholar] [CrossRef]
  70. Suzuki, T.; Ueguchi, C.; Mizuno, T. H-NS regulates OmpF expression through micF antisense RNA in Escherichia coli. J. Bacteriol. 1996, 178, 3650–3653. [Google Scholar] [CrossRef]
  71. Lease, R.A.; Cusick, M.E.; Belfort, M. Riboregulation in Escherichia coli: DsrA RNA acts by RNA:RNA interactions at multiple loci. Proc. Natl. Acad. Sci. USA 1998, 95, 12456–12461. [Google Scholar] [CrossRef] [PubMed]
  72. Lease, R.A.; Belfort, M. A trans-acting RNA as a control switch in Escherichia coli: DsrA modulates function by forming alternative structures. Proc. Natl. Acad. Sci. USA 2000, 97, 9919–9924. [Google Scholar] [CrossRef] [PubMed]
  73. Du, Y.; Wang, Y.; Geng, J.; Long, J.; Yang, H.; Duan, G.; Chen, S. Molecular Mechanism of Hfq-dependent sRNA1039 and sRNA1600 Regulating Antibiotic Resistance and Virulence in Shigella sonnei. Int. J. Antimicrob. Agents 2023, 107070. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Ciprofloxacin accumulation by bacteria devoid of the efflux pump as assessed by fluorescence bulk measurements. Blue: AG100A hfq+; green: AG100A ∆ctr; orange: AG100A ∆hfq. Note that to compare results between the different conditions and experiments, fluorescent units were normalized to 1 using WT AG100 strain after 5 min of incubation with CIP as a reference (presented on Figure 4). Quantitatively, an exposure time to ciprofloxacin of 5 min for AG100 (our reference) corresponds approximately to 60,000 molecules/bacteria. The t-test was used to determine if the differences between intracellular CIP concentration for WT and mutated Hfq strains were significant (- p < 0.25; ++ p < 0.1; +++++ p < 0.01).
Figure 1. Ciprofloxacin accumulation by bacteria devoid of the efflux pump as assessed by fluorescence bulk measurements. Blue: AG100A hfq+; green: AG100A ∆ctr; orange: AG100A ∆hfq. Note that to compare results between the different conditions and experiments, fluorescent units were normalized to 1 using WT AG100 strain after 5 min of incubation with CIP as a reference (presented on Figure 4). Quantitatively, an exposure time to ciprofloxacin of 5 min for AG100 (our reference) corresponds approximately to 60,000 molecules/bacteria. The t-test was used to determine if the differences between intracellular CIP concentration for WT and mutated Hfq strains were significant (- p < 0.25; ++ p < 0.1; +++++ p < 0.01).
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Figure 2. Time course accumulation of Ciprofloxacin at the single-cell level in bacteria devoid of the efflux pump. Kinetics were followed for 15 min at 20 °C using the bandpass filter 420–480 nm. As seen, bacteria retained their normal shape during the experiment.
Figure 2. Time course accumulation of Ciprofloxacin at the single-cell level in bacteria devoid of the efflux pump. Kinetics were followed for 15 min at 20 °C using the bandpass filter 420–480 nm. As seen, bacteria retained their normal shape during the experiment.
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Figure 3. Ciprofloxacin accumulation at the single-cell level in bacteria devoid of the efflux pump. Blue: AG100A; green: AG100A∆ctr; orange: AG100A∆hfq. The results are shown as mean values from 20–30 bacteria analysed for at least three different pre-cultures, with error bars indicating SD. The one-way ANOVA analysis of variance followed by a Tukey’s post hoc test was used to compare the means between strains AG100A vs. AG100A∆hfq, AG100A vs. AG100A∆ctr, and AG100A∆hfq vs. AG100A∆ctr at the last recorded time point, which showed that the means of all pairs are significantly different with a p-value lower than 0.01. See also Supplementary Table S1.
Figure 3. Ciprofloxacin accumulation at the single-cell level in bacteria devoid of the efflux pump. Blue: AG100A; green: AG100A∆ctr; orange: AG100A∆hfq. The results are shown as mean values from 20–30 bacteria analysed for at least three different pre-cultures, with error bars indicating SD. The one-way ANOVA analysis of variance followed by a Tukey’s post hoc test was used to compare the means between strains AG100A vs. AG100A∆hfq, AG100A vs. AG100A∆ctr, and AG100A∆hfq vs. AG100A∆ctr at the last recorded time point, which showed that the means of all pairs are significantly different with a p-value lower than 0.01. See also Supplementary Table S1.
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Figure 4. Ciprofloxacin accumulation as assessed by fluorescence bulk measurements. Blue: AG100; green: AG100 ∆ctr; orange: AG100 ∆hfq; red: AG100/pBRpLacMicF. As in Figure 1, fluorescence units were normalized to 1 using WT AG100 strain after 5 min of incubation with CIP as a reference. Quantitatively, the exposure time to ciprofloxacin of 5 min for AG100 corresponds approximately to 60,000 molecules per bacteria (our reference here in blue). The t-test has been used to determine if the differences between intracellular CIP concentration for WT and mutated Hfq strains are significant (- p < 0.25; + p < 0.2; ++ p < 0.1; +++ p < 0.05; ++++ p < 0.025; +++++ p < 0.01). Note that variances have similar values, except for the strain expressing MicF where the variance is very small, suggesting a part of the variability between cells comes from influx of CIP.
Figure 4. Ciprofloxacin accumulation as assessed by fluorescence bulk measurements. Blue: AG100; green: AG100 ∆ctr; orange: AG100 ∆hfq; red: AG100/pBRpLacMicF. As in Figure 1, fluorescence units were normalized to 1 using WT AG100 strain after 5 min of incubation with CIP as a reference. Quantitatively, the exposure time to ciprofloxacin of 5 min for AG100 corresponds approximately to 60,000 molecules per bacteria (our reference here in blue). The t-test has been used to determine if the differences between intracellular CIP concentration for WT and mutated Hfq strains are significant (- p < 0.25; + p < 0.2; ++ p < 0.1; +++ p < 0.05; ++++ p < 0.025; +++++ p < 0.01). Note that variances have similar values, except for the strain expressing MicF where the variance is very small, suggesting a part of the variability between cells comes from influx of CIP.
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Figure 5. Ciprofloxacin accumulation at the single-cell level. Blue: AG100; green: AG100 ∆ctr; orange: AG100 ∆hfq; red: AG100/pBRpLacMicF. The results are shown as mean values from ~30 bacteria analysed for at least three different experiments (independent pre-cultures), with error bars indicating SD. The one-way ANOVA analysis of variance followed by a Tukey’s post hoc test was used to compare the means between strains AG100 vs. AG100∆hfq, AG100 vs. AG100∆ctr, AG100∆hfq vs. AG100∆ctr, and AG100 vs. AG100/pBRpLacMicF at the last recorded time point, which showed that they are all significantly different from one to the other with a p-value less than 0.01. See also Supplementary Table S1.
Figure 5. Ciprofloxacin accumulation at the single-cell level. Blue: AG100; green: AG100 ∆ctr; orange: AG100 ∆hfq; red: AG100/pBRpLacMicF. The results are shown as mean values from ~30 bacteria analysed for at least three different experiments (independent pre-cultures), with error bars indicating SD. The one-way ANOVA analysis of variance followed by a Tukey’s post hoc test was used to compare the means between strains AG100 vs. AG100∆hfq, AG100 vs. AG100∆ctr, AG100∆hfq vs. AG100∆ctr, and AG100 vs. AG100/pBRpLacMicF at the last recorded time point, which showed that they are all significantly different from one to the other with a p-value less than 0.01. See also Supplementary Table S1.
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Figure 6. Morphological analysis of AG100 bacteria in the presence of CIP at the single-cell level. We clearly observe that AG100 ∆hfq bacteria lose their natural shape and become more spherical or strongly deformed. This effect is observed only for this specific strain and not for other AG100 (AG100 hfq+ and AG100 ∆ctr).
Figure 6. Morphological analysis of AG100 bacteria in the presence of CIP at the single-cell level. We clearly observe that AG100 ∆hfq bacteria lose their natural shape and become more spherical or strongly deformed. This effect is observed only for this specific strain and not for other AG100 (AG100 hfq+ and AG100 ∆ctr).
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Figure 7. Network of Hfq-dependent regulations influencing FQ fluxes. MicF was previously shown to be regulated at the transcriptional level by MarA and H-NS transcription factors [65,66]. Since hns translation is repressed by DsrA and Hfq (note that the nature of DsrA:hns mRNA complex is still unclear and that it has been proposed that the 3′ UTR of hns mRNA could be required for its post-transcriptional regulation [67]), Hfq may also influence micF transcription negatively. Additionally, ompF mRNA translation is also directly repressed by MicF sRNA with the help of Hfq. In parallel, Hfq positively regulates the expression of acrAB (coding for components of the efflux pump) at the post-transcriptional level [37]. Finally, Hfq could also directly influence CIP fluxes independently of its role in the sRNA-based regulation, as it creates pores in IM and OM membranes [68,69]. Finally, interference between Hfq and DNA-gyrase or topoisomerase of DNA topology may also occur and influence DNA-related processes such as transcription. CIP is depicted as a blue circle. sRNA regulators controlling mRNAs are shown as black open arrows; Hfq is represented by a toroidal hexamer; mRNAs are depicted as thick black lines; 5′ and 3′ ends of the mRNA are depicted by a “ball and arrow head”, respectively; H-NS and MarA are shown as orange and pink ellipses, respectively; transcriptional regulations are represented by thin green lines; translational regulations are shown as thin blue lines; positive and negative regulations are indicated by red arrows and horizontal bars, respectively; dotted line symbolizes peptidoglycan (PG) between outer (OM) and inner (IM) membranes.
Figure 7. Network of Hfq-dependent regulations influencing FQ fluxes. MicF was previously shown to be regulated at the transcriptional level by MarA and H-NS transcription factors [65,66]. Since hns translation is repressed by DsrA and Hfq (note that the nature of DsrA:hns mRNA complex is still unclear and that it has been proposed that the 3′ UTR of hns mRNA could be required for its post-transcriptional regulation [67]), Hfq may also influence micF transcription negatively. Additionally, ompF mRNA translation is also directly repressed by MicF sRNA with the help of Hfq. In parallel, Hfq positively regulates the expression of acrAB (coding for components of the efflux pump) at the post-transcriptional level [37]. Finally, Hfq could also directly influence CIP fluxes independently of its role in the sRNA-based regulation, as it creates pores in IM and OM membranes [68,69]. Finally, interference between Hfq and DNA-gyrase or topoisomerase of DNA topology may also occur and influence DNA-related processes such as transcription. CIP is depicted as a blue circle. sRNA regulators controlling mRNAs are shown as black open arrows; Hfq is represented by a toroidal hexamer; mRNAs are depicted as thick black lines; 5′ and 3′ ends of the mRNA are depicted by a “ball and arrow head”, respectively; H-NS and MarA are shown as orange and pink ellipses, respectively; transcriptional regulations are represented by thin green lines; translational regulations are shown as thin blue lines; positive and negative regulations are indicated by red arrows and horizontal bars, respectively; dotted line symbolizes peptidoglycan (PG) between outer (OM) and inner (IM) membranes.
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Table 1. E. coli strains used in this study.
Table 1. E. coli strains used in this study.
StrainDescriptionEfflux PumpHfqSource
AG100AG100++[43]
AG100AAG100 acrB::kan−+[44]
AG100 ∆hfqAG100 hfq::cm+−This study
AG100 ∆ctrAG100 hfq72-cm+±This study
AG100A ∆hfqAG100 acrB::kan hfq::cm−−This study
AG100A ∆ctrAG100 acrB::kan hfq72-cm−±This study
AG100 + pMicFAG100/pBRpLacMicF++This study
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Turbant, F.; Esnouf, E.; Rosaz, F.; Wien, F.; Węgrzyn, G.; Chauvet, H.; Arluison, V. Role of the Bacterial Amyloid-like Hfq in Fluoroquinolone Fluxes. Microorganisms 2024, 12, 53. https://doi.org/10.3390/microorganisms12010053

AMA Style

Turbant F, Esnouf E, Rosaz F, Wien F, Węgrzyn G, Chauvet H, Arluison V. Role of the Bacterial Amyloid-like Hfq in Fluoroquinolone Fluxes. Microorganisms. 2024; 12(1):53. https://doi.org/10.3390/microorganisms12010053

Chicago/Turabian Style

Turbant, Florian, Emeline Esnouf, Francois Rosaz, Frank Wien, Grzegorz Węgrzyn, Hugo Chauvet, and Véronique Arluison. 2024. "Role of the Bacterial Amyloid-like Hfq in Fluoroquinolone Fluxes" Microorganisms 12, no. 1: 53. https://doi.org/10.3390/microorganisms12010053

APA Style

Turbant, F., Esnouf, E., Rosaz, F., Wien, F., Węgrzyn, G., Chauvet, H., & Arluison, V. (2024). Role of the Bacterial Amyloid-like Hfq in Fluoroquinolone Fluxes. Microorganisms, 12(1), 53. https://doi.org/10.3390/microorganisms12010053

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