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Central metabolism is a key player in E. coli biofilm stimulation by sub-MIC antibiotics [1]

['Luke N. Yaeger', 'Department Of Biochemistry', 'Biomedical Sciences', 'The Michael G. Degroote Institute For Infectious Disease Research', 'Mcmaster University', 'Hamilton', 'Ontario', 'Shawn French', 'Eric D. Brown', 'Jean Philippe Côté']

Date: 2023-12

Exposure of Escherichia coli to sub-inhibitory antibiotics stimulates biofilm formation through poorly characterized mechanisms. Using a high-throughput Congo Red binding assay to report on biofilm matrix production, we screened ~4000 E. coli K12 deletion mutants for deficiencies in this biofilm stimulation response. We screened using three different antibiotics to identify core components of the biofilm stimulation response. Mutants lacking acnA, nuoE, or lpdA failed to respond to sub-MIC cefixime and novobiocin, implicating central metabolism and aerobic respiration in biofilm stimulation. These genes are members of the ArcA/B regulon–controlled by a respiration-sensitive two-component system. Mutants of arcA and arcB had a ‘pre-activated’ phenotype, where biofilm formation was already high relative to wild type in vehicle control conditions, and failed to increase further with the addition of sub-MIC cefixime. Using a tetrazolium dye and an in vivo NADH sensor, we showed spatial co-localization of increased metabolic activity with sub-lethal concentrations of the bactericidal antibiotics cefixime and novobiocin. Supporting a role for respiratory stress, the biofilm stimulation response to cefixime and novobiocin was inhibited when nitrate was provided as an alternative electron acceptor. Deletion of a gene encoding part of the machinery for respiring nitrate abolished its ameliorating effects, and nitrate respiration increased during growth with sub-MIC cefixime. Finally, in probing the generalizability of biofilm stimulation, we found that the stimulation response to translation inhibitors, unlike other antibiotic classes, was minimally affected by nitrate supplementation, suggesting that targeting the ribosome stimulates biofilm formation in distinct ways. By characterizing the biofilm stimulation response to sub-MIC antibiotics at a systems level, we identified multiple avenues for design of therapeutics that impair bacterial stress management.

Treatment of bacterial pathogens with subinhibitory concentrations of many different types of antibiotics can have the perverse effect of increasing biofilm formation, making infections more difficult to treat and providing opportunities for development of other forms of resistance. The physiological changes that connect subinhibitory antibiotic treatment to increased biofilm are poorly understood. Using E. coli as a model, we did a genome-wide screen for mutants that failed to make more biofilm when treated with subinhibitory concentrations of three chemically- and mechanistically-distinct antibiotics, looking for commonalities that might highlight key pathways. We identified genes in central metabolism and respiratory pathways, which implicates metabolic changes leading to oxidative stress as important for stimulation, and showed that provision of the alternate electron acceptor nitrate could suppress stimulation for some antibiotics. Bacteriostatic compounds that inhibited translation were uniquely insensitive to nitrate suppression, suggesting that suppression of metabolism can also stimulate biofilm formation. Therefore, divergent perturbations of central metabolism are associated with a similar phenotype of increased biofilm. This work suggests that metabolic changes under antibiotic stress are protective rather than maladaptive as previously thought.

Funding: This work was funded by Natural Sciences and Engineering Research Council of Canada ( www.nserc-crsng.gc.ca ) grants RGPIN-2021-04237 to LLB and RGPIN-2019-07090 to EDB, and by infrastructure funding from Canada Foundation for Innovation and the Ontario Research Fund (ORF-RE09-047). LLB and EDB hold Tier I Canada Research Chairs in Microbe-Surface Interactions and Microbial Chemical Biology, respectively. LNY holds an NSERC PGS-D award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

We view biofilm stimulation as a defensive response to stressors [ 17 ] and consistent with this hypothesis, the major E. coli stress response pathways Cpx, Rcs, and OmpR influence E. coli biofilm formation [ 26 – 28 ]. There is also significant input from the stress-responsive sigma factor RpoS and oxidative stress responses in biofilm regulation [ 14 , 29 , 30 ]. To better understand the mechanism underlying the response to sub-MIC antibiotics, we applied a systems genetics approach to identify genes involved in biofilm stimulation. Here we describe a 1536-colony density, high-throughput screen of the Keio collection–a single gene knockout library of E. coli K12 [ 31 ]–that used uptake of the matrix-binding dye Congo Red as a readout to identify genes important for biofilm stimulation in response to sub-MIC antibiotics of three different classes with three different targets: cefixime (CEF), novobiocin (NOVO), and tetracycline (TET), to identify common pathways. We identified multiple genes in central metabolism and respiration as important for biofilm stimulation in response to each of those drugs. Follow-up experiments also implicated the two-component system ArcA/B in biofilm stimulation, suggesting that oxidative stress from electron transport chain perturbations was a key trigger. We showed that increased respiratory activity spatially colocalizes with and follows treatment by sub-inhibitory antibiotic concentrations. Provision of nitrate in aerobic growth conditions inhibited biofilm stimulation by certain sub-MIC antibiotics, suggesting that an alternative electron acceptor could relieve the oxidative stress that drives stimulation by those compounds. Finally, we linked this observation to nitrate’s role as an electron acceptor using nitrate respiration mutants and a nitrate reduction assay. These results suggest that perturbations of metabolism and respiratory chain activity resulting from exposure to sub-MIC antibiotics drives biofilm stimulation.

Treatment with sub-minimal inhibitory concentrations (sub-MIC) of antibiotics stimulates biofilm formation [ 17 ]. Biofilms are stimulated by multiple classes of antibiotics, in many different organisms [ 18 ], suggesting there are conserved responses to antibiotic stress that converge on increased biofilm formation. The diverse stressors that induce biofilm formation extend beyond antibiotics to type six secretion system effectors, bacteriocins, bacteriophages, and biocides [ 19 – 22 ]. This list of triggers informed the idea of ‘competition sensing’, where cells are proposed to detect damage or kin cell death and respond to protect themselves [ 23 – 25 ]. The induction of biofilms by sub-MIC antibiotics and the ability of biofilms to tolerate multiple stressors suggests that this phenotype falls under the umbrella of competition sensing. Despite the importance of antibiotics, interspecies interactions, and biofilm formation in E. coli ecology, how antibiotic stress generates a physiological signature that is relayed into increased biofilm formation remains unclear.

Biofilms are adherent bacterial communities and a common form of bacterial growth [ 1 , 2 ]. They also play a significant role in infection and antibiotic tolerance [ 3 – 5 ]. Biofilms consist of bacterial cells surrounded by a self-produced matrix of exopolysaccharides, extracellular DNA (eDNA), and proteinaceous adhesins that provide protection against physical and chemical threats [ 6 ]. The composition of the Escherichia coli biofilm matrix varies among strains, but generally includes a combination of curli fimbriae (an amyloid), eDNA, and the exopolysaccharides colanic acid, cellulose, and poly-N-acetyl glucosamine (PNAG) [ 7 – 10 ]. Curli, PNAG, the autotransporter antigen 43, and type 1 fimbriae all contribute to structural integrity of E. coli biofilms through cell-cell and cell-surface adhesion [ 7 , 11 – 13 ]. While specific pro-biofilm components drive the emergent properties of biofilms, the production of these components is influenced by an array of central pathways in E. coli [ 14 ]. The E. coli biofilm life cycle is well-studied, involving reversible and irreversible attachment, maturation, and dispersal [ 15 ]. Although biofilms are a common mode of growth, environmental conditions can negatively or positively influence biofilm formation [ 16 ].

Results

Development of a high-throughput 1536-colony density biofilm stimulation screen To identify genes and pathways involved in the E. coli biofilm stimulation response, we took a forward genetic approach. First, we used a 96-well peg-lid assay [32] to identify a set of antibiotics that elicited a robust biofilm stimulation response at subinhibitory concentrations. The polystyrene peg lid provides a substrate for cell attachment and biofilm formation. Removing the peg lid after incubation allows for separation of adherent from planktonic cells and biofilm quantification. Staining of the peg lids with crystal violet allows for quantification of adhered biomass as a measurement of total biofilm. As expected, biofilm formation peaked at concentrations below those that decreased planktonic growth (Fig 1A). From the panel of antibiotics tested (S1 Fig), we selected CEF (cefixime), NOVO (novobiocin), and TET (tetracycline) as model antibiotics with distinct mechanisms of action (targeting peptidoglycan synthesis, DNA replication, and protein synthesis, respectively) that stimulated robust biofilm formation. Although the peg-lid assay is a reproducible method for quantifying biofilms, its relatively low throughput is less amenable to large-scale genetic screens. PPT PowerPoint slide

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TIFF original image Download: Fig 1. A high throughput screen for genes involved in biofilm stimulation. a) (top) An illustration of the biofilm stimulation response, where tuning the levels of antibiotics to just below the MIC stimulates biofilm formation. (bottom) A 96-well peg lid assay to quantify biofilm stimulation in a dose response for E. coli K12 BW25113. Values are shown as a percent of the vehicle control and each point of a technical triplicate are shown with the bars showing the mean and standard error of the mean shown with lines. Percent of control indicates the Growth (OD 600 ) or Biofilm (Abs 600 ) values for treatment by a given condition divided by the Growth or Biofilm of the matched vehicle control multiplied by 100. Yellow bars show planktonic growth and purple bars show biofilm. The data are representative of at least 3 biological replicates. The mean Abs 600 raw values are shown above their respective biofilm bar. A one-way ANOVA followed by Dunnett’s multiple comparisons test was used to compare biofilm formation between the untreated control and CEF treated wells; ** = p value<0.01, **** = p value<0.0001. b) Measuring biofilm formation using Congo Red on solid 50:50 agar plates with and without sub-MIC CEF. Colonies are shown for the WT K12 and the csgA mutant from the Keio collection c) Screening workflow for identifying mutants deficient in biofilm stimulation. d) Screening results for CEF shown in a replica plot with extremely high stimulation data points (above 4) removed to help visualize the low stimulation hits. The red box indicates the cut-off for hits, where points within the box indicate hits. Histograms show the distribution of values across each replicate shown on their respective axes. https://doi.org/10.1371/journal.pgen.1011013.g001 To create a colony-based biofilm assay for high-throughput screening, we leveraged the ability of the dye Congo Red (CR) to bind the E. coli extracellular matrix components curli, cellulose, and PNAG [33,34]. In this assay, the abundance of matrix components serves as a proxy for biofilm formation. We spotted the parent strain of the Keio collection, E. coli K12 BW25113, on 50:50 LB:PBS agar + CR supplemented with sub-MIC (1/4 to 1/2 MIC) CEF, NOVO, or TET, and grew the plates for 24 h at 37°C–generally considered a suppressive media and temperature for curli expression and CR binding [35,36]. While the resulting colonies on antibiotic-free control plates showed no colour change, those on sub-MIC antibiotic-supplemented plates bound CR and turned red (Fig 1B). This result indicates that treatment with sub-MIC antibiotics allows E. coli to overcome the suppressive effects of the growth conditions on CR binding. Interestingly, curli are likely not solely responsible for antibiotic-induced CR binding, as a csgA mutant still showed increased staining upon exposure to sub-MIC antibiotics (Fig 1B). Other CR-binding E. coli biofilm components include cellulose and PNAG; however, K12 strains are unable to produce cellulose [33]. Therefore, the only known CR binding polymer remaining in the csgA mutant is PNAG, an exopolysaccharide reported previously to be important for translation inhibitor-induced biofilm formation [37]. We then endeavoured to identify genes important for the physiological changes that precede production of biofilm components. As outlined in Fig 1C, the Keio collection was arrayed in 1536-density on CR-supplemented agar plates, first without and with sub-MIC NOVO to optimize the assay conditions, then in a second round of screening, with NOVO, CEF, or TET (below). The plates were grown for 24 h at 37°C, then imaged and analyzed with Fiji [38]. Growth was quantified by measuring colony density; then, the colour channels for each image were separated to isolate the red channel, and the red pixel intensity measured to quantify CR binding. To correct for plate positioning effects, CR binding and growth were normalized by the interquartile mean for each colony’s respective column and row [39]. We generated a CR enrichment value by dividing the normalized CR binding by the normalized growth, and plotted the enrichment values (i.e. normalized stimulation) for both replicates (Fig 1D). Mutants with enrichment values at least two standard deviations below the mean in both replicates were classified as hits in our initial NOVO screen. We also identified mutants with enrichment values well above average. These high values could indicate hyper-biofilm stimulation mutants or result from mutations causing small colony sizes, high basal CR binding, or slight changes in antibiotic sensitivity that decreased colony size or impacted the relative MIC to result in more biofilm without affecting a central stress response pathway. For these reasons, we did not further investigate the high enrichment-value mutants as they were more liable to be false positives.

Sub-MIC bactericidal antibiotics cause a spike in metabolic activity To visualize the effects of sub-MIC antibiotics on respiration, we used a disc diffusion assay to generate a gradient of antibiotic concentrations. We used a tetrazolium dye, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), that is reduced intracellularly by NADH into a crystalline formazan dye, to monitor changes in cellular respiration (Fig 4A) [61]. Formazan dye deposition (indicated by a dark ring and drop in the pixel intensity) clearly localized to the periphery of the zone of inhibition for CEF and GENT, but not TET (Fig 4B and 4C). Thus, the bactericidal antibiotics CEF and GENT increase respiration in the sub-MIC zone, whereas the bacteriostatic antibiotic TET does not, consistent with our biofilm data. We also used the two-plasmid in vivo NADH reporter system developed by Liu et al. to quantify NADH concentration in live cells growing in liquid culture (Fig 4D) [62]. As predicted, the bactericidal antibiotics CEF and tobramycin (used in place of GENT as pB-Rex has a GENT resistance cassette) increased NADH levels while TET did not (Fig 4E). PPT PowerPoint slide

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TIFF original image Download: Fig 4. Measuring increased metabolism with sub-MIC antibiotic treatment. a) Reduction of the tetrazolium salt MTT into a formazan dye by cellular metabolism. b) Images of the zone of inhibition on the MTT agar plates for TET, CEF, and GENT that were used for quantification. c) Pixel intensity as a function of distance (in centimeters) from the edge of an antibiotic disk. The zone of inhibition (>MIC) is marked by a dashed lines and extends from the edge of the disk to the point where growth became visible. The sub-MIC zone (<MIC) is marked by solid lines and extends to the edge of the measured region. Data for TET, CEF, and GENT are indicated by purple, red, and green lines, respectively. A scale bar representing 50 units of pixel intensity is shown. The graphs are representative of three biological replicates. d) A diagram depicting the synthetic reporter system for in vivo NADH detection. Rex is constitutively expressed from pB-Rex and represses expression of GFP from p-ROP-PP-GFP by binding the B-Rex operator site (perfect palindrome). An increase in intracellular NADH leads to more NADH-bound B-Rex and relieves repression of GFP. mCherry is divergently expressed from a constitutive reporter on pROP-PP-GFP to allow for normalization. e) Changes in in vivo NADH levels as measured by GFP fluorescence are shown as bar graphs for CEF, TOB, or TET treatment. The bars, error bars, and circles represent the technical triplicate mean, standard error of the mean, and individual data points, respectively. A one-way ANOVA followed by Dunnett’s multiple comparisons test was used to compare the untreated and antibiotic treated conditions; ns = not significant, *** = p value <0.001 **** = p value<0.0001. https://doi.org/10.1371/journal.pgen.1011013.g004

Controlling biofilm stimulation with the terminal electron acceptor nitrate Our data suggest that ETC activity is central to the biofilm stimulation response. Notably, the redox state of ubiquinone/ubiquinol, and the ratio of ubiquinone to menaquinone, which all shuttle electrons through the ETC control ArcA/B activity [53]. We reasoned that if sub-MIC antibiotics were inducing aerobic respiration changes that influenced ArcA/B activity, then supplementing the growth medium with an additional terminal electron acceptor could change the abundance or redox state of quinones to suppress biofilm stimulation. Through these quinones, the ArcA/B system is responsive to the availability of nitrate as an electron acceptor when oxygen availability is restricted [54]. As predicted, potassium nitrate suppressed biofilm stimulation in a dose-dependent manner but without affecting the CEF MIC (Fig 5A). To rule out the effect of potassium cations, we tested sodium nitrate, which also inhibited biofilm stimulation (S4 Fig). Notably, nitrate also suppressed biofilm stimulation by NOVO but not TET (S5 Fig), which was further explored below. E. coli dissimilative nitrate respiration follows the ammonification pathway, where nitrate is reduced to nitrite, then ammonia, although the latter step is less frequent due to repression of nitrite reductase by nitrate and efflux of nitrite [63]. Since aerobic conditions such as those in our experiments generally repress nitrate reduction, we controlled for nitrate’s potential ability to inhibit biofilm stimulation through pathways other than respiration. We tested whether a mutant lacking NarG, a subunit of the major nitrate reductase [64], had an altered biofilm stimulation response upon nitrate supplementation. Consistent with our hypothesis that nitrate inhibits biofilm stimulation through its role as an electron acceptor, sub-MIC CEF stimulated biofilm formation by the narG mutant, regardless of nitrate addition (Fig 5B). We also directly tested if sub-MIC CEF stimulates nitrate reduction using an orthogonal, nitrite-driven diazotization reaction that produces an azo dye (Fig 5C) and identified a significant increase in nitrate reduction at ½ MIC (Fig 5D) [65,66]. These results are in line with reports of upregulation of anaerobic respiration genes by sub-MIC antibiotics in Listeria monocytogenes [67]. PPT PowerPoint slide

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TIFF original image Download: Fig 5. Nitrate respiration suppresses biofilm stimulation. a) A dose-response assay showing the effect of nitrate on biofilm stimulation. The data were taken from CEF dose-response peg-lid assays and the maximum biofilm from each nitrate concentration is shown. One representative experiment from three similar biological replicates is shown. A one-way ANOVA followed by a Dunnett’s multiple comparisons test was performed in GraphPad Prism to compare biofilm formation to the no nitrate control (** = p value <0.01, and **** = p value <0.0001) b) Effects of nitrate on a nitrate reductase mutant. An illustration of nitrate to nitrite reduction is shown at the top of the panel. Plus and minus signs indicate the presence of 50 mM nitrate in 50:50 broth. The maximum biofilm stimulation from a dose response assay for each strain and condition is shown. Percent of control is relative to the untreated control for each strain and condition. For a) and b), percent of control indicates the Growth (OD 600 ) or Biofilm (Abs 600 ) values for treatment by a given condition divided by the Growth or Biofilm of the matched vehicle control multiplied by 100. As well, the mean Abs 600 raw values are shown above their respective biofilm bar, where the bottom value indicates the raw values from the untreated control and the top value indicates the raw values from the antibiotic treated condition. The graph is representative of two biological replicates. A one-way ANOVA followed by Tukey’s multiple comparisons test was performed in GraphPad Prism to compare biofilm formation between the -/+ nitrate wells for the WT and narG (** = p value <0.01, and ns = not significant). c) The reaction scheme for detecting nitrite from nitrate reduction using a diazotization assay. Nitrite is converted to nitrous acid with acetic acid, which reacts with 4-aminobenzenesulfonic acid to create a diazonium salt that reacts with 1-napthylamine to produce a dye. Chemical structures were created in ChemDraw (PerkinElmer). d) Using the diazotization assay from c) to measure nitrate reduction after growth of WT (circles) or narG (triangles) in increasing sub-MIC CEF concentrations. Planktonic growth is plotted on the left y-axis as the sterility control-subtracted OD 600 values, and nitrate reduction is shown as the absorbance at 546 nm with sterility control subtracted, divided by the growth of the corresponding well, and plotted on the right y-axis. The graph is representative of two biological replicates. A one-way ANOVA followed by Dunnett’s multiple comparisons test was used to compare nitrate reduction between the treated wells and untreated control for each strain. There was no significant change for any condition except the nitrate reduction for the 0.63μM CEF treated WT and narG (* = p value <0.05, ** = p value <0.01). For all graphs, bars indicate the triplicate mean and error bars show the standard error of the mean. https://doi.org/10.1371/journal.pgen.1011013.g005

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