1. Ecology
  2. Microbiology and Infectious Disease
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Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance

  1. Andrés Aranda-Díaz
  2. Benjamin Obadia
  3. Ren Dodge
  4. Tani Thomsen
  5. Zachary F Hallberg
  6. Zehra Tüzün Güvener
  7. William B Ludington  Is a corresponding author
  8. Kerwyn Casey Huang  Is a corresponding author
  1. Department of Bioengineering, Stanford University, United States
  2. Department of Molecular and Cell Biology, University of California, Berkeley, United States
  3. Department of Embryology, Carnegie Institution of Washington, United States
  4. Department of Plant and Microbial Biology, University of California, Berkeley, United States
  5. Department of Microbiology and Immunology, Stanford University School of Medicine, United States
  6. Chan Zuckerberg Biohub, United States
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Cite this article as: eLife 2020;9:e51493 doi: 10.7554/eLife.51493

Abstract

Predicting antibiotic efficacy within microbial communities remains highly challenging. Interspecies interactions can impact antibiotic activity through many mechanisms, including alterations to bacterial physiology. Here, we studied synthetic communities constructed from the core members of the fruit fly gut microbiota. Co-culturing of Lactobacillus plantarum with Acetobacter species altered its tolerance to the transcriptional inhibitor rifampin. By measuring key metabolites and environmental pH, we determined that Acetobacter species counter the acidification driven by L. plantarum production of lactate. Shifts in pH were sufficient to modulate L. plantarum tolerance to rifampin and the translational inhibitor erythromycin. A reduction in lag time exiting stationary phase was linked to L. plantarum tolerance to rifampicin, opposite to a previously identified mode of tolerance to ampicillin in E. coli. This mechanistic understanding of the coupling among interspecies interactions, environmental pH, and antibiotic tolerance enables future predictions of growth and the effects of antibiotics in more complex communities.

Introduction

Decades of investigations have described detailed and precise molecular mechanisms of antibiotic action using model organisms such as Escherichia coli grown in monoculture. However, unlike in laboratory monocultures, the vast majority of bacteria live in diverse communities. In environments such as the human intestine, antibiotics impact communities in many ways, ranging from the loss of diversity (Dethlefsen et al., 2008; Jernberg et al., 2007) to the evolution of multidrug-resistant pathogens (Santajit and Indrawattana, 2016). Hence, there is a pressing need for new frameworks that predict how antibiotics affect bacterial communities in ways that cannot be predicted from simple monocultures.

Synthetic microbial communities provide the opportunity to perform controlled experiments that uncover mechanistic principles of microbial ecology (Momeni et al., 2011; Zuk and Travisano, 2018). Such communities have been used to demonstrate the evolution and consequences of microbial cooperation (Harcombe, 2010; Momeni et al., 2013), reveal the role of interactions on spatial patterning (Momeni et al., 2013), and to link physiology and metabolism to mutualistic relationships (Ponomarova et al., 2017). Interspecies interactions can occur through specific mechanisms involving members of a community (e.g. cross-feeding or competition for resources) and through global environmental variables modified by bacterial activity. An example of the latter is pH, which has recently been shown to drive community dynamics in an artificial laboratory system of decomposition bacteria (Ratzke and Gore, 2018).

A natural system with low diversity provides a consortium of species with a common evolutionary and ecological history from which to build synthetic communities (Ponomarova et al., 2017). The gut microbiota of Drosophila melanogaster fruit flies is such a low diversity assemblage in which all members can be cultured in vitro (Obadia et al., 2017), making it amenable to the systematic dissection of bacterial interactions. This community consists of ~5 species predominantly from the Lactobacillus and Acetobacter genera (Wong et al., 2011) (Figure 1A). The metabolic lifestyles of species from these genera in isolation have been largely uncovered. Lactobacillus spp. produce lactic acid (Makarova et al., 2006), while Acetobacter spp. produce acetic acid and are distinguished by their ability to oxidize lactate to carbon dioxide and water (Yamada et al., 1997).

Figure 1 with 2 supplements see all
Interspecies interactions in synthetic communities derived from the fruit fly gut microbiome induce rifampin tolerance.

(A) Synthetic community construction scheme. Relative abundances of the dominant species in the D. melanogaster gut microbiome determined from 16S rRNA sequencing. All reads mapped to two Lactobacillus species: L. plantarum (Lp) and L. brevis (Lb), and to three Acetobacter species: A. pasteurianus (Ap), A. tropicalis (At), and A. aceti (Aa). Values are mean ± standard deviation (S.D.), n = 18. Mean and S.D. were weighed by the total number of reads for each fly. Strains were isolated in agar plates and grown in liquid culture. Synthetic communities were then built by co-culturing individual species in liquid culture. (B) When grown with Ap, Lp survived after 24 h at rifampin concentrations above the MIC. Viable cell plating counts of Lp after growth in rifampin for 24 h normalized to the counts at the start of the experiment (t = 0, 3 × 105 and 4 × 105 CFU/mL in monoculture and co-culture, respectively). Dashed line indicates where the population at t = 24 h would have the same size as at t = 0. Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference of the co-culture from the monoculture (*: p<4×10−3, **: p<8×10−4, ***: p<8×10−5, equivalent to *: p<0.05/n, **: p<0.01/n, ***: p<1×10−3/n, where n = 12 is the number of comparisons; NS: not significant). (C) Protection of Lp at supra-MIC concentrations of rifampin is elicited by all Acetobacter species tested. CFU/mL of Lp grown in monoculture (Lp) or in co-culture with Ap, AtAorientalis (Ao), A. indonesiensis (Ai), and Aa, and then treated with 20 µg/mL rifampin for 24 h normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference from the monoculture (*: p<0.01, equivalent to *: p<0.05/n, where n = 5 is the number of comparisons; NS: not significant). (D) Ap-mediated survival of Lp at rifampin concentrations above the MIC is history-dependent, requiring co-culturing before exposure as compared with mixing. CFU/mL of Lp grown in monoculture, in co-culture with Ap (Co.), or mixed with Ap without subsequent growth in the absence of antibiotic (Mix), and treated with 20 µg/mL rifampin for 24 h normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. p-vValues are from a Student’s two-sided t-test of the difference from the monoculture (**: p<5×10−3, equivalent to **: p<0.01/n, where n = 2 is the number of comparisons; NS: not significant). (E) The time to killing of Lp under rifampin treatment is extended in the presence of an Acetobacter. CFU/mL of Lp grown in monoculture and co-cultured with Ap, and treated with 50 µg/mL rifampin, normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference from the monoculture at the corresponding timepoint (**: p<2×10−3, equivalent to **: p<0.01/n, where n = 5 is the number of comparisons; : measurement below limit of detection; NS: not significant). Values off the graph were below the limit of detection of the assay.

Bacteria can survive antibiotics through (i) resistance mutations, which counteract the antibiotic mechanism and increase the minimum inhibitory concentration (MIC); (ii) tolerance, whereby the entire population enters an altered physiological state that prolongs survivability without changing the MIC of the antibiotic, leading to an increase in the time required to kill a given fraction of the population; (iii) heteroresistance, whereby a subset of the population has a higher MIC and grows at concentrations that would otherwise kill the population; and (iv) persistence, whereby a subset of the population survives treatment for a longer period (Balaban et al., 2019; Brauner et al., 2016). Members of multispecies communities, such as biofilms and models of urinary tract infections, can display altered sensitivity to antibiotics in the community context (Adamowicz et al., 2018; de Vos et al., 2017; Nicoloff and Andersson, 2016; Sanchez-Vizuete et al., 2015). Previous studies have used synthetic communities to uncover the interplay between interspecies interactions and antibiotic efficacy; for example, the exoproducts of Pseudomonas aeruginosa affect the survival of Staphylococcus aureus through changes in antibiotic uptake, cell-wall integrity, and intracellular ATP pools (Radlinski et al., 2017). In genetically modified communities, intracellular antibiotic degradation affords cross-species protection against chloramphenicol (Sorg et al., 2016). Additionally, metabolic dependencies within synthetic communities can lower the viability of bacteria when antibiotics eliminate providers of essential metabolites, leading to an apparent change in the MICof the dependent species (Adamowicz et al., 2018). However, we still lack understanding of how metabolic interactions between bacteria affect the physiological processes targeted by antibiotics and the resulting balance between growth inhibition (bacteriostatic activity) and death (bactericidal activity). For example, the intimate relationship between bacterial metabolism and environmental pH could also lead to changes in antibiotic efficacy, as previously shown in monocultures (Aagaard et al., 1991; Argemi et al., 2013; Kamberi et al., 1999; Karslake et al., 2016; Yang et al., 2014). The interplay of all these processes in complex communities will provide new ways to combat pathogen survival and resistance evolution, particularly in cases involving tolerance, an important and understudied aspect of antibiotic susceptibility that can be elicited by diverse mechanisms and can facilitate the evolution of resistance (Levin-Reisman et al., 2017).

In this study, we interrogated how interspecies interactions affect growth, pH, and antibiotic susceptibilities. We used high-throughput assays to measure and compare these parameters in monocultures and co-cultures. We found that Lactobacillus plantarum (Lp) exhibited antibiotic tolerance (delay in death Brauner et al., 2016) in the presence of Acetobacter species. Lactate accumulation by Lp in monocultures acidified the media, inhibiting growth during stationary phase. Acetobacter-mediated lactate consumption released this inhibition by increasing pH, leading to a shorter Lp lag while exiting stationary phase. This reduced lag exiting stationary phase was correlated with the antibiotic tolerance of Lp that we observed. We determined that changes in pH elicited by Acetobacter activity were sufficient to modulate tolerance of Lp to both rifampin and erythromycin. Taken together, our findings indicate that simple changes to the environment can drive complex physiological behaviors and antibiotic responses within bacterial communities.

Results

Interspecies interactions induce tolerance to rifampin in a synthetic community

To determine the composition of the gut microbiota in our laboratory fruit flies, we performed 16S rRNA sequencing from 18 individual dissected guts (Materials and methods). We identified five species belonging to seven unique operational taxonomic units (OTUs) by clustering the sequences at 99% identity: L. plantarum (Lp), L. brevis (Lb), Acetobacter pasteurianus (Ap), A. tropicalis (At), and A. aceti (Aa) (Figure 1A). We then isolated the species in culture (Supplementary file 1a) and determined the antibiotic sensitivities of the four major fly gut inhabitants (Lp, Lb, Ap, and At; Figure 1A) in vitro grown in Man, Rogosa, and Sharpe (MRS) medium.

We tested 10 antibiotics representing a wide variety of classes using plate-based growth assays (Materials and methods). For many drugs, some of the isolates were resistant (detectable growth) at least up to the highest concentrations tested. Rifampin was the only drug for which all four species exhibited sensitivity (Supplementary file 1b) and it is bactericidal (Walsh and Wencewicz, 2016), hence there is the opportunity to study survival as well as sensitivity.

Since Lb grew significantly more slowly and had a much longer lag phase than Lp (Figure 1—figure supplement 1), we focused on Lp and its interactions with the Acetobacter species, particularly Ap, which is more abundant in the fly gut than the other Acetobacter species (Figure 1A). We grew Lp and Ap separately for 48 h in test tubes, combined them in test tubes at an optical density at 600 nm (henceforth OD) of 0.02 each, and co-cultured them in MRS for 48 hr. We then diluted this co-culture and 48 h monocultures of Lp and Ap into fresh MRS at ~5 × 105 colony-forming units/mL (CFU/mL) in 96-well plates over a range of rifampin concentrations. To determine whether the co-culture still contained both species, we measured the percentage of survival and the fraction of each species at various rifampin concentrations by plating on selective media and exploiting species-specific colony morphologies (Supplementary file 1a). In the co-culture, Ap died off at a similar concentration of rifampin as during growth in a monoculture (1.25 µg/mL, Figure 1—figure supplement 2A). For Lp, the MIC was similar in co-culture as in monoculture (1.25 µg/mL, Figure 1B), but at concentrations above the MIC, significantly more Lp cells survived in co-culture versus monoculture (Figure 1B). This effect could not be explained by small differences in the initial inoculum, as increasing cell densities up to 100-fold did not change the MIC or survival of Lp in monoculture (Figure 1—figure supplement 2B,C). Because of the change in Lp survival, we focused herein on this phenotype.

To determine whether Lp’s increased survival was specific to co-culturing with Ap, we co-cultured Lp with each of the Acetobacter species, including a wild fly isolate of A. indonesiensis (Ai), and lab fly isolates of A. orientalis (Ao) and Aa, the fifth member of the microbiota of our flies (Figure 1A). We then diluted each co-culture to an initial Lp cell density of ~5 × 105 CFU/mL into fresh MRS with 20 µg/mL rifampin (16X MIC) and let the cells grow for 24 hr. Co-culturing with any of the Acetobacter species increased survival by approximately one order of magnitude (Figure 1C). To determine whether this increased survival requires co-culturing prior to rifampin treatment (rather than the presence of Acetobacter species being sufficient), we grew Lp and Ap separately for 48 h and mixed and diluted them at the time of addition of 20 µg/mL rifampin. After 24 h of incubation, the number of CFU/mL was significantly lower in mixed culture than in co-culture (Figure 1D), indicating a history dependence to increased survival.

To determine whether co-culturing slows killing by the drug, we examined the survival of Lp over time at a high drug concentration (50 µg/mL, 40X MIC). Similar to the experiments above, we compared Lp CFU/mL in a monoculture with that in a co-culture with Ap. In monoculture, Lp rapidly died, with CFU/mL becoming undetectable within 18 hr; by contrast, Lp survived >30 h after co-culturing (Figure 1E). To test whether the increased time to death was due to a disruption in the balance of growth and death, we performed single-cell imaging of mono- and co-cultures on MRS agarose with 50 µg/mL rifampin. We observed no growth in either mono- or co-cultures over 3 h of imaging (n = 568 and n = 236 cells, respectively; Figure 1—figure supplement 2D), indicating that survivability is due to protection from death, as opposed to increased growth. Because the MIC of Lp was unchanged in co-cultures compared with monocultures (no resistance), and the delay in killing was observed in the bulk population in co-cultures, these results indicate that co-culturing Lp with Ap induces tolerance of Lp to rifampin (Balaban et al., 2019; Brauner et al., 2016).

Co-culturing leads to growth of Lp in stationary phase

To investigate possible environmental factors that could be linked to Lp tolerance to rifampin when co-cultured with Acetobacter species (Figure 1C,E), we first inquired whether the total amount of growth of the co-culture was larger or smaller than expected from the yield of the monocultures. We grew Lp and each of the Acetobacter species separately for 48 hr, diluted the monocultures to OD = 0.04, combined the Lp monoculture 1:1 with each Acetobacter monoculture, and grew the co-cultures for 48 h in a plate reader. We then diluted the saturated cultures 1:30 in phosphate-buffered saline (PBS) to accurately measure the final OD, and computed an interaction score based on an additive model:

(1) α=ODco-(ODLp+ODA)ODLpODA

where ODco and ODA are the final ODs of the co-culture and the Acetobacter monoculture, respectively. With this metric, α>0 indicates synergy and α<0indicates antagonism. Ap showed a strong (positive) interaction with Lp, whereas At, Ai, and Aa had an interaction score closer to 0 (Figure 2—figure supplement 1A). Only the Lp-Ao co-culture was significantly antagonistic (Figure 2—figure supplement 1A). We also performed this measurement in cultures grown in test tubes, where we only observed a significant positive interaction of Lp with Ap (Figure 2—figure supplement 1A). We then determined the total carrying capacity of each species in the co-cultures by counting CFUs. The Lp CFU/mL values for 48-h co-cultures with Ap, At, and Ai were higher than the Lp monoculture (Figure 2A); Ap showed the strongest effect. Aa and Ao did not significantly increase Lp CFU/mL (Figure 2A). At and Ao reached lower CFU/mL in co-cultures with Lp than in monocultures, while Ap, At, and Aa did not change significantly (Figure 2—figure supplement 1C). Thus, Lp benefits from growth with certain Acetobacter species including Ap, and it has negative or neutral effects on the Acetobacter species (Figure 2—figure supplement 1A, B).

Figure 2 with 3 supplements see all
Lp growth during stationary phase in Acetobacter co-cultures is associated with an increase in pH and a decrease in lactate concentration.

(A) Co-culturing Lp with Ap, At, or Ai resulted in increased Lp cell density after 48 hr. Co-culturing with Ao or Aa did not significantly increase Lp cell density by 48 hr. Error bars are standard deviation (S.D.) for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference from the monoculture (*: p<0.01, **: p<2×10−3, equivalent to *: p<0.05/n, **: p<0.01/n, where n = 5 is the number of comparisons; NS: not significant). (B) Co-culturing Lp with Ap resulted in higher Lp cell density in stationary phase, as well as faster growth and shorter lag for Ap. Shaded regions indicate S.D., n = 3. Inset: zoom-in on region inside dashed box highlighting increase in carrying capacity in co-culture. (C) L- and D-lactate accumulated to much higher levels in Lp monocultures than in co-cultures after 48 h of growth, suggesting that Acetobacter spp. consumed Lp-produced lactate. Lactate concentration was measured enzymatically from culture supernatants at 48 hr. Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference from the monoculture (**: p<2×10−3, ***: p<2×10−4, equivalent to **: p<0.01/n, ***: p<1×10−3/n, where n = 5 is the number of comparisons; : measurement below limit of detection). (D) The increase in Lp cell density in stationary phase is associated with an Acetobacter-dependent increase in pH early in stationary phase. pH was measured with the pH-sensitive dye 2’,7-bis-(2-carboxyethyl)−5-(and-6)-carboxyfluorescein over time (Materials and methods). Shaded regions indicate S.D., n = 3. Data points were taken every 8.3 min.

To determine when the additional Lp growth took place, we monitored CFU/mL values for Lp and Ap in co-culture throughout a 48 h time course starting from an initial combined cell density of ~5 × 105 CFU/mL. Initially, Lp accounted for the bulk of the growth in the co-culture (Figure 2B). Interestingly, Ap in liquid monoculture showed little to no growth in most replicates after 40 h (Figure 2B); by contrast, in co-culture Ap started to grow after ~20 h and reached saturation by ~40 h (Figure 2B), indicating that Ap also benefited from growth as a co-culture. Thus, a mutualism exists between Lp and Ap driven by growth during stationary phase.

Lactate metabolism leads to changes in pH in stationary phase co-cultures

Interestingly, after 30 hr, Lp displayed a significant (~2X) increase in CFU/mL in the Lp-Ap co-culture that did not occur in the monoculture (Figure 2B), indicating that the increase in final yield occurs late in stationary phase. Since this increase occurs generally (Figure 2B), we hypothesized that Lp has a common metabolic interaction with each of the Acetobacter species. An obvious candidate is cross-feeding, since Lp produces lactate and the Acetobacter species consume it. We measured lactate levels in the supernatants of Lp monocultures and co-cultures of Lp with each of the Acetobacter species individually, after 48 h of growth. As expected, the Lp monoculture accumulated L- and D-lactate to high levels (>100 mM; Figure 2C). All co-cultures had significantly lower concentrations of both isomers than the monoculture (<2 mM, Figure 2C). The Lp-Ao co-culture harbored higher levels of L-lactate than any other co-culture and Lp-Aa had the highest concentration of L-lactate of the co-cultures (Figure 2C). Lp-Ap, Lp-Ao, and Lp-Aa co-cultures all accumulated lactate to >10 mM by 20 h (Figure 2—figure supplement 2A). Taken together, these data suggest that Lp metabolism leads to an initial accumulation of lactate and that the Acetobacter species consume it, although Aa and Ao are less efficient at consuming L-lactate than the other species.

Since lactate is a short-chain fatty acid with a pKa of 3.86, we suspected that lactate production would affect the pH of the culture. We first monitored the pH dynamics of monocultures of Lp and of each Acetobacter species using the pH-sensitive fluorophore 2’,7-bis-(2-carboxyethyl)−5-(and-6)-carboxyfluorescein (BCECF) (James-Kracke, 1992). In the Lp monoculture, pH decreased from pH = 6.75 to below four during growth (Figure 2D); more precisely, we measured a final supernatant pH = 3.77 using a pH meter (Figure 2—figure supplement 2A). For monocultures of all Acetobacter species except Ap, the medium first acidified down to pH ~5, and then increased back to pH = 6–7 (Figure 2D).

To test whether Acetobacter species reverse the pH decrease due to the accumulation of lactate produced by Lp, we measured the pH of co-cultures over time using BCECF. Co-cultures with Ap, At, and Ai followed similar trajectories in which the pH mimicked that of the Lp monoculture for the first 20 hr, after which the pH increased up to a final value of ~7 (Figure 2D). The Lp-Aa co-culture experienced a ~ 10 h delay in the pH increase, while the co-culture with Ao showed only a slight pH increase by 48 h (Figure 2D). The slight pH increase in the Lp-Ao co-culture is consistent with lower L-lactate consumption by Ao (Figure 2C). Using a pH meter for validation, we measured final pH values of 5.9, 5.8, 4.6, 5.4, and 4.8 in co-cultures with Ap, At, Ao, Ai, and Aa, respectively (Figure 2—figure supplement 2B). Thus, lactate metabolism dictates dramatic shifts in environmental pH that are related to physiological changes in antibiotic tolerance (Figure 1C).

Given the strong acidification of the medium in Lp monoculture but not in Lp-Ap co-culture (Figure 2D), we hypothesized that intracellular pH decreases in monoculture and increases in co-culture. To measure intracellular pH, we transformed our Lp strain with a plasmid expressing pHluorin (a GFP variant that acts as a ratiometric pH sensor Martinez et al., 2012) under the control of a strong constitutive promoter (Rud et al., 2006). The two absorbance peaks, which we measured at 405 and 475 nm, are sensitive to pH and the ratio of the emission (at 509 nm) at these two excitation wavelengths can be used to estimate intracellular pH. We grew this strain in monoculture and in co-culture with Ap and measured fluorescence over time in a plate reader. Because of the high autofluorescence of the medium at 405 nm (data not shown), we could only track changes in fluorescence at an excitation wavelength of 475 nm. We observed an initial increase in signal as the Lp cells started to proliferate (Figure 2—figure supplement 3A). After the cultures saturated (t ~ 20 hr), we detected a decrease in the signal down to the levels of medium autofluorescence in the monoculture (Figure 2—figure supplement 3A). In the co-culture, in which the extracellular pH was raised by the metabolic activity of Ap, fluorescence did not decrease over time (Figure 2—figure supplement 3A), suggesting that intracellular pH decreases over time in monoculture but not in co-culture.

To verify that the decrease in fluorescence in monoculture was due to a drop in intracellular pH, as opposed to a decrease in protein concentration, we sampled cells after 48 h of growth, centrifuged them, resuspended them in PBS to measure pHluorin signal at both its excitation wavelengths, and measured fluorescence within 1 min of resuspension. The pHluorin signal ratio was significantly higher in Lp-Ap co-culture than in an Lp monoculture (Figure 2—figure supplement 3B). Taken together, these data indicate that the intracellular pH of Lp cells is significantly lower in monoculture than in co-culture with Acetobacter species.

Low pH inhibits the growth of Lp and extends lag phase

The growth rates of lactic acid bacteria such as Lp are known to be affected by pH (Stecka and Grzybowsii, 2000). Since Ap growth causes a large increase in the extracellular pH of an Lp-Ap co-culture, we sought to determine the general dependence of Lp growth properties on pH. We diluted a 48 h culture of Lp cells grown in MRS at starting pH = 6.75 to a starting OD = 0.02 in MRS adjusted to starting pH ranging from 3 to 8 (Materials and methods). We then measured growth and BCECF fluorescence (Figure 3—figure supplement 1A,B). For lower starting pH values, the carrying capacity was lower (Figure 3A) and varied over a large OD range from <0.02 to>2. For all starting pH values, Lp cells reduced the pH to a common final value of ~3.7 (Figure 3A). The bulk growth rate approached zero (Figure 3B) as the pH approached its final value, explaining the differences in yield. Interestingly, the maximum growth rate was also pH-dependent (Figure 3C), with the highest growth rate at starting pH = 7.

Figure 3 with 1 supplement see all
An increase in extracellular pH in stationary phase releases growth inhibition in Lp monocultures and shortens lag phase.

(A) Lp growth is inhibited by low pH. Logarithm of OD (blue) and pH measured using BCECF (orange) after 48 h of growth in MRS at various starting pH values. Error bars are standard deviation (S.D.), n = 4. (B) Instantaneous growth rate in MRS is strongly linked to pH. Each curve was initialized at a different starting pH and represents 48 h of growth. Arrowheads indicate direction of time. Shaded regions are S.D., n = 4. Data points were taken every 8.3 min. (C) Maximal growth rate in MRS increases with increasing initial pH. Error bars are S.D., n = 4. (D) Increasing the pH of a saturated, spent Lp culture from 3.77 to 7 allows growth, although not as much as fresh MRS. Error bars are S.D., n = 3. Data points were taken every 8.3 min. (E) Increasing the pH of an Lp monoculture at t = 30 h from 4 to 7 to mimic the pH increase in Lp-Ap co-culture (top) leads to a shorter lag phase (bottom). Lag time was calculated by fitting growth curves to the Gompertz equation. Error bars are S.D., n = 3. p-values are from a Student’s two-sided t-test of the difference from the control (***: p<5×10−4, equivalent to ***: p<1×10−3/n, where n = 2 is the number of comparisons; NS: not significant).

Given these findings, we hypothesized that the inhibition of growth in stationary phase of an Lp monoculture is due to the decreased intracellular and extracellular pH, and that Ap releases Lp’s growth inhibition by raising intracellular and extracellular pH. To test this hypothesis, we inoculated a 48 h culture of Lp to an initial OD = 0.02 into the supernatant of a 48 h Lp culture at pH = 3.77 or manually adjusted to pH = 7. Almost no growth took place in supernatant starting from pH = 3.77, but we observed substantial growth (~20 fold increase in OD) of the bulk culture in supernatant raised to pH = 7 (Figure 3D). As expected, the maximal growth rate was lower than in fresh MRS, presumably due to the partial depletion of nutrients (Figure 3D); addition of glucose to the conditioned medium supported faster growth, but only starting from neutral pH (Figure 3—figure supplement 1C). We also hypothesized that the accumulation of lactate by Lp would allow growth of Ap in Lp-conditioned medium even at low starting pH. When we diluted a saturated Ap culture in Lp-conditioned medium generated as above, Ap achieved an even higher OD than in fresh MRS (Figure 3—figure supplement 1D).

These findings suggested that Ap’s effects on Lp growth might be due primarily to the pH changes that Ap initiates because Acetobacter species can grow at low pH and consume lactate. Thus, we first increased the pH of an Lp monoculture to 7 after 30 h of growth, which is when the pH increased most rapidly in the Lp-Ap co-culture (Figure 3E). We then incubated the cells for an additional 18 hr. We did not observe a significant increase in CFUs/mL from this pH-adjusted culture versus controls that simply grew for 48 h or were subjected to all washes required for pH adjustment and then returned to the same supernatant (Figure 3—figure supplement 1E), possibly due to the high cell density and the transient nature of pH neutralization. We then assessed if increasing the pH at 30 h resulted in a decrease in the duration of lag phase by diluting the monoculture to OD = 0.0375 into fresh MRS after an additional 18 h of growth. Indeed, the lag phase was shorter for the pH-adjusted cells compared with a control culture (Figure 3E). Thus, pH is a driver of the growth advantages of Lp in lag phase even in the absence of a net increase in cell number.

Co-culturing Lp with Acetobacter species reduces lag time

Canonical antibiotic tolerance in E. coli results from a decrease in growth rate or an increase in lag phase that protects cells through metabolic inactivity (Brauner et al., 2016). To measure growth rate and lag phase, we co-cultured Lp with each of the Acetobacter species individually for 48 hr, diluted the culture to a common OD of 0.0375, and monitored growth in a plate reader. The maximum growth rate was the same for the Lp monoculture and co-cultures with Ap, At, and Ai, and slightly higher for co-cultures with Ao and Aa (Figure 4—figure supplement 1). We previously observed for Lp monocultures when shifting the pH that the stimulation of growth in stationary phase was connected with a shorter lag phase (Figure 3E). In agreement with these data, there was a significant decrease in bulk lag time for all the Acetobacter co-cultures (Figure 4A,B). Surprisingly, this shorter lag phase was linked to Lp tolerance to rifampin, opposite to the longer phase linked to E. coli tolerance to ampicillin (Brauner et al., 2016). Ap, At, and Ai co-cultures had the largest lag decreases. The Aa and Ao co-cultures had smaller, although still significant, decreases (Figure 4A,B); interestingly, Aa and Ao were also less efficient at consuming lactate than the other Acetobacter species (Figure 2C). These data indicate that interspecies interactions can change the physiology of community members, and that differences across the Acetobacter species constitute an opportunity to probe the underlying cause of the lag phenotype.

Figure 4 with 2 supplements see all
Co-cultures of Lp and Acetobacter species undergo shorter lag phases.

(A) Calculating the logarithm of OD normalized by OD at t = 0 reveals that co-cultures of Lp and various Acetobacter species (Ax) experience more rapid transitions from stationary phase to exponential growth than monocultures of Lp. Shaded regions indicate standard deviation (S.D.), n = 5. Inset: zoom-in of region inside dashed box highlighting lag differences. Data points were taken every 8.3 min. (B) Co-culture lag times are significantly shorter than Lp monoculture lag times. Lag times were obtained by fitting the growth curves in (A) to the Gompertz equation. Error bars are S.D. for each condition, n = 5. p-values are from a Student’s two-sided t-test of the difference from the monoculture (***: p<2×10−4, equivalent to ***: p<1×10−3/n, where n = 5 is the number of comparisons). (C) Mixing Lp monocultures with Ap monocultures (Mix) yields growth curves with a similar lag phase than those of Lp monocultures. Shaded regions indicate S.D., n = 5. Inset: zoom-in on region inside dashed box highlighting lag differences. Data points were taken every 8.3 min. (D) Mixed Lp-Ap cultures do not experience significantly shorter lag times than Lp monocultures. Lag times were obtained by fitting the curves in (C) to the Gompertz equation. Error bars are S.D. for each condition, n = 5. p-values are from a Student’s two-sided t-test of the difference from the monoculture (***: p<5×10−4, equivalent to ***: p<1×10−3/n, where n = 2 is the number of comparisons; NS: not significant). (E) Single-cell microscopy demonstrates that a decrease in the duration of lag phase of Lp was responsible for the lag-time decrease in co-culture. Representative phase microscopy images of Lp in monoculture and co-cultured with Ap on an MRS agar pad. The only Ap cell visible in these images is indicated with an arrow. Size bar = 5 µm. (F) The instantaneous elongation rate of single Lp cells increases faster in co-culture than in monoculture. Phase-contrast images were segmented and cells were classified as Lp or Ap based on their aspect ratio. Lines are the mean and shaded regions are the standard error for an Lp monoculture (nLp,0 h = 465, nLp,9.5 h = 27,503) or a co-culture with Ap (nLp,0 h = 448, nLp,9.5 h = 58,087, nAp,0 h = 47, nAp,9.5 h = 146). Images were taken every 5 min.

As with Lp tolerance to antibiotics (Figure 1B), the shortened lag phase of the Lp-Ap co-culture depended on the inoculum coming from a co-culture. When we simply mixed independent 48 h cultures of Lp and Ap, the resulting bulk culture had the same lag time as an Lp monoculture (Figure 4C,D). To determine which of the two species was responsible for the decrease in lag, we performed time-lapse microscopy (Materials and methods) to monitor the initiation of growth at the single-cell level (Figure 4E). Lp and Ap were clearly distinguishable based on morphology (Figure 4—figure supplement 2): Lp cells are longer (2.46 ± 0.78 µm vs. 1.66 ± 0.38 µm) and thinner (0.72 ± 0.12 µm vs. 0.90 ± 0.09 µm) than Ap cells. Therefore, we used the aspect ratio (length/width; 3.41 ± 0.91 for Lp and 1.87 ± 0.46 for Ap) to distinguish single cells from each species in co-culture. We validated this strategy on co-cultures of fluorescently tagged strains of the same two species and measured a 10% error rate in classification (Figure 4—figure supplement 2D–F). In co-culture, most Lp cells were observed to have grown by 1 h after spotting, but in Lp monoculture, few cells were growing even after 2 h (Figure 4E,F). Ap cells did not grow during the time of imaging (Figure 4F), indicating that the reduced lag time was due to Lp’s growth, in agreement with CFU/mL measurements from liquid cultures (Figure 2B).

Growth status and pH are drivers of antibiotic tolerance

Since pH changes shortened lag phase (Figure 3E) and changes in lag time were linked to antibiotic tolerance (Figures 1 and 4), we tested whether shortening lag phase was sufficient to induce tolerance. Increased time in starvation leads to changes in physiology, including an increase in the duration of lag phase (Levin-Reisman et al., 2010). To determine the relationship between time spent in stationary phase and lag time for Lp, we grew Lp cultures for varying amounts of time in stationary phase then used these cultures as inocula for growth in a plate reader. Incubating the Lp monocultures for more than 48 h resulted in a dramatic increase in the duration of lag phase, while reducing the culturing time shortened lag phase (Figure 5A).

Tolerance to rifampin is modulated by pH.

(A) The duration of lag phase of bulk cultures of Lp depends on the time spent in stationary phase. Lp monocultures grown for various times from OD = 0.02, and co-cultures with Ap, were diluted into fresh medium. Lag time was calculated by fitting growth curves to the Gompertz equation. Error bars are standard deviation (S.D.), n = 12. p-values are from a Student’s two-sided t-test of the difference with respect to the 48 h culture (***: p<2.5×10−4, equivalent to ***: p<1×10−3/n, where n = 4 is the number of comparisons). (B) Culturing Lp as a monoculture for a shorter time leads to higher cell survival. Viable cell plating counts of Lp after growth in 20 µg/mL rifampin for 24 h normalized to the counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference between the cultures (***: p<1×10−3). (C) Neutralization of pH in stationary phase in Lp monocultures is sufficient to induce tolerance. Increasing the pH of an Lp monoculture at t = 30 h or t = 40 h to 7 to mimic the pH increase in co-cultures of Lp with Acetobacter species (upper panel) increased cell survival (CFU/mL normalized to counts at the start of the experiment, t = 0) after treatment with 20 µg/mL rifampin for 24 h (lower panel). A 48-h-old culture with no changes in pH was used as a control (Ctrl.). Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference between the cultures (**: p<5×10−3, equivalent to **: p<0.01/n, where n = 2 is the number of comparisons). (D) Acidification of Lp co-cultures with Ap during the exponential-to-stationary phase transition or in late stationary phase sensitizes Lp to rifampin. Decreasing the pH of an Lp co-culture with Ap at t = 30 h or t = 40 h to 3.7 to mimic the pH of an Lp monoculture (upper panel) increased survival (CFU/mL normalized to counts at the start of the experiment, t = 0) after treatment with 20 µg/mL rifampin for 24 h (lower panel). Error bars are S.D. for each condition, n = 3. p-values are from a Student’s two-sided t-test of the difference between the cultures (***: p<5×10−4, equivalent to ***: p<1×10−3, where = 2 is the number of comparisons). : values below the limit of detection. (E) The dynamics of killing in Lp co-culture with Acetobacter species differs quantitatively according to species and from Lp monoculture (blue), indicating that the Acetobacter species induce rifampin tolerance to different degrees. CFU/mL of Lp in monoculture and in co-culture with Acetobacter species, and treated with 50 µg/mL rifampin, normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. (F) The timing of the pH change in Acetobacter co-culture predicts the extent of protection against 50 µg/mL rifampin. Neutralization of pH in Lp monocultures at 40 h of growth (to mimic Lp+Aa co-cultures) elicits longer protection against rifampin than neutralization at 30 hr. A small increase in pH (from 3.85 to 4.5) at 30 h (to mimic Lp+Ao co-cultures) provides protection comparable to complete neutralization. CFU/mL were normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3. (G) Lp survival to erythromycin is ~10 times higher after 24 h of treatment with erythromycin at supraMIC concentrations for 48-h-old monocultures of Lp than for 20-h-old Lp monocultures. Viable cell plating counts of Lp after growth in erythromycin for 24 h normalized to cell counts at the start of the experiment (t = 0). Error bars are S.D., n = 3. p-values are from a Student’s two-sided t-test of the difference between the two samples at a given time point (*: p<4×10−3, **: p<8×10−4 equivalent to *: p<0.05/n, **: p<0.01/n, where n = 12 is the number of comparisons; NS: not significant). (H) Shifting the pH of an Lp monoculture at 30 h to 4 or 7, followed by 18 h of growth before treatment with 2 µg/mL erythromycin, mimics the survival dynamics of a 48-h-old or 20-h-old culture in stationary phase, respectively. CFU/mL of Lp monocultures were normalized to counts at the start of the experiment (t = 0). Error bars are S.D. for each condition, n = 3.

Because lag phase in co-culture is slightly shorter than that of a 24 h monoculture (Figure 5A), we decided to match the lag time of a co-culture by growing a monoculture for 20 h from an initial OD = 0.02. We then measured Lp survival in 20 µg/mL rifampin after 24 h in cultures diluted from a 48-hr-old or a 20-hr-old culture. Culturing for 20 h resulted in a significant increase in survival (Figure 5B). We next tested whether shortening lag phase by changing the pH in stationary phase also yielded increased rifampin tolerance of Lp in monoculture. We increased the pH of an Lp monoculture to 7 after 30 and 40 hr, and grew cells for an additional 18 h and 8 hr, respectively. We then measured the change in CFU/mL upon treatment with 50 µg/mL rifampin for 24 hr. The upshift in pH at t = 30 h or 40 h resulted in increased tolerance relative to the unshifted monoculture (Figure 5C). To test whether pH changes can also decrease tolerance, we grew co-cultures of Lp and Ap for a total of 48 hr, decreasing the pH to 3.7 at t = 30 h or 40 hr. In both cases, the viability after 24 h of rifampin exposure was significantly reduced relative to an untreated monoculture (Figure 5D). Thus, pH can affect tolerance both positively and negatively.

In co-culture with Lp, Ap raised the pH earlier than did Aa, while Ao only raised the pH very slightly (Figure 2D). We hypothesized that due to these distinct pH dynamics, rifampin would also have different Lp killing dynamics in these co-cultures. We grew co-cultures of these Acetobacter species with Lp as previously, and then treated the co-cultures with 50 µg/mL rifampin. While all co-cultures had extended survival relative to Lp monoculture, the killing dynamics of Lp were indeed distinct, with Aa inducing the highest tolerance (Figure 5E). To determine the extent to which these dynamics can be explained by the time at which each species raises the pH, we measured CFU/mL at various time points after rifampin treatment for Lp monocultures grown for 48 h whose pH was raised to pH seven at t = 30 h or 40 hr, mimicking the early and late increases in pH for Ap and Aa co-cultures, respectively. The shift to pH 7 at 40 h induced higher rifampin tolerance than the shift at 30 h (Figure 5F), consistent with the increased tolerance of the Lp-Aa co-culture (Figure 5E). Moreover, shifting the pH to 4.5 at t = 30 hr, to mimic the slight increase caused by Ao, was also sufficient to increase tolerance comparable to pH neutralization at t = 30 h (Figure 5F), consistent with the similar killing dynamics of the Ap and Ao co-cultures (Figure 5E). All pH shifts induced higher tolerance compared to control cultures that underwent the same protocol but whose pH was maintained (Figure 5F). Taken together, these experiments establish that pH changes drive changes in both the exit from stationary phase and tolerance of Lp to rifampin.

Growth status and pH also modulate tolerance to a ribosome-targeting antibiotic

The robust relationships among changes in pH, lag time, and rifampin tolerance prompted us to explore how changes in pH and lag time affect survival to other antibiotics. We decided to use the ribosome-targeting macrolide erythromycin because it is bacteriostatic (in contrast to rifampin) and Lp is sensitive to it (Supplementary file 1b). We treated Lp monocultures grown for 20 h or 48 h with increasing concentrations of erythromycin for 24 h at a starting cell density of ~5 × 105 CFU/mL. Opposite to our observations with rifampin (Figure 5B), a 48 h Lp monoculture displayed tolerance to erythromycin, while a 20 h culture did not (Figure 5G). While both cultures had the same MIC in erythromycin (0.078 µg/mL, Figure 5G), at concentrations above the MIC, the 48 h culture showed no changes in CFU/mL after 24 h of erythromycin treatment; the 20 h culture had a reduction of ~10 fold in CFU/mL (Figure 5G). This result suggests that Lp cells are typically tolerant to erythromycin but killing is accelerated under conditions that make them tolerant to rifampin.

To determine whether antibiotic tolerance underlies the survival of Lp to erythromycin as well as to rifampin, we diluted 20- and 48 h cultures to a starting density of ~5 × 105 CFU/mL, exposed them to a high concentration of erythromycin (2 µg/mL, 25X MIC), and monitored CFU/mL over time. Cells from a 20 h culture died significantly more rapidly than cells from a 48-hr-old culture (Figure 5H), indicating that the differences in survival (Figure 5G) are explained by erythromycin tolerance. Further, increasing the pH of an Lp monoculture at 30 h and then exposing it after an additional 18 h of growth to 2 µg/mL erythromycin in fresh medium had an increase in the rate of killing that was similar to that achieved with a 20 h culture (Figure 5H). These results highlight that the effects of changing the growth status of a culture on antibiotic response are not limited to rifampin and—as in the case of erythromycin—can be opposite.

Discussion

Our measurements of growth behavior in synthetic communities motivated by the natural context of the fly gut indicate that interspecies interactions impact both growth and the effect of antibiotics on individual species. The pH-based mechanism underlying the tolerance of Lp induced by Acetobacter species is intrinsically connected to the metabolic capacity of each species, and hence is likely to be generally relevant in more complex communities. Moreover, these findings could have important implications for human health, for example in the context of Lactobacillus-dominated vaginal microbiotas (Boris and Barbés, 2000), hence their generality should be tested broadly in other contexts.

In this study, we observed a novel form of antibiotic tolerance. Tolerance has been defined as increased time to killing of the population as a whole (Brauner et al., 2017), as opposed to resistance (a change in the MIC), or persistence or heterotolerance whereby a subpopulation of bacteria displays increased time to killing (Balaban et al., 2019; Brauner et al., 2016). Lp cells in monoculture rapidly died, while Lp cells in co-cultures showed close to 100% survival for extended periods (Figures 1E and 5E). Furthermore, the time and magnitude of pH change affected the kinetics of killing but not the size of the surviving population (Figure 5E and F). These behaviors contrast with the stereotypical rapid decrease in viability, with a small subpopulation of surviving cells, observed in persistence. At high doses and late time points, cell counts were below our limit of detection and therefore we cannot rule out that, in addition to tolerance, pH changes in stationary phase could also modulate persistence. Tolerance to beta lactams such as ampicillin has been observed in E. coli cultures that exhibit slow growth or a long lag phase (Brauner et al., 2016), and E. coli mutants with longer lag phases can be selected through experimental evolution to match the time of treatment (Fridman et al., 2014; Levin-Reisman et al., 2017). Based on these previous studies, we were surprised to find the opposite effect with rifampin on Lp: cultures with a shorter lag phase exhibited increased tolerance (Figures 1 and 4). Moreover, although tolerance to erythromycin was associated with a longer lag phase (Figures 3E and 5A,H), killing retardation was at least an order of magnitude longer than the change in lag time (Figure 5A,H), indicating that tolerance is not determined by an elongation of lag phase alone, in contrast to the effects of ampicillin on E. coli (Fridman et al., 2014). The opposite behaviors of Lp to rifampin and erythromycin upon pH changes suggest that tolerance is more complex than previously thought, and should motivate future investigations into the physiological and genetic basis of tolerance.

Several genetic factors that increase time to killing have been identified in E. coli, including toxin-antitoxin modules such as hipBA (Rotem et al., 2010) that induce the stringent response and thus cause transient growth arrest. In Lp co-culture with Acetobacter species, metabolic interactions alter the physiological state of Lp during late stationary phase by changing the environmental pH (Figure 2). The stringent response is required to survive acid shock in Helicobacter pylori (Mouery et al., 2006) but not in Enterococcus faecalis (Abranches et al., 2009), which is in the same taxonomic order as Lp. In the case of Lp, whether the stringent response could be a major factor in the increased tolerance to rifampin is unclear due to the surprising connection with decreased lag.

The pH in stationary phase can affect many factors, such as the chemistry of extracellular metabolites and macromolecules as well as the surface of the cell (Peters et al., 2016). Importantly, our assays of antibiotic sensitivities were all performed at a starting pH of 7. Nonetheless, shifts in extracellular pH can lead to buffered drops in cytoplasmic pH (Choi and Groisman, 2016; Olsen et al., 2002) that can be regulated (Chakraborty et al., 2015) or result from internalization of low-pKa species such as short-chain fatty acids (Ricke, 2003). Such changes could lead to protonation of macromolecules involved in adsorption or changes in the proton motive force (Krulwich et al., 2011). How these factors affect non-polycationic antibiotics such as rifampin remains to be determined; neither of the ionizable functional groups of rifampin (pKas 1.7 and 7.9 Maggi et al., 1966) nor erythromycin (pKa 8.88 McFarland et al., 1997) have pKas in the pH range achieved in our cultures (Figure 2, Figure 3—figure supplement 1B). Protonation changes in target macromolecules could also lead to protection against antibiotics, although we would expect a subsequent change in MIC, contrary to our findings (Figures 1 and 5). Intracellular acidification by the short-chain fatty acid propionate has been proposed to lengthen lag phase in Salmonella in vitro and in the mouse gut (Jacobson et al., 2018), consistent with our finding that lag time (Figure 4) and intracellular pHluorin fluorescence (Figure 2—figure supplement 3A) are related.

Changes in intra- and extracellular pH have been shown to lead to transcriptional responses that provide cross-protection against antibiotics (Komora et al., 2017; McMahon et al., 2007; O'Sullivan and Condon, 1997), suggesting that the killing retardation due to a pH increase in stationary phase may result from a complex regulatory process. One major factor influencing the Lactobacillus-Acetobacter interaction is that these organisms form a recurrent community and may therefore have evolved to sense and benefit from each other’s presence. Further experiments are needed to uncover the molecular mechanisms that link growth state and susceptibility to antibiotics in Lactobacillus species, other non-model organisms, and microbial communities. In addition, although we consistently observed related shifts in lag phase and tolerance (Figures 3 and 5), it remains to be established whether lag time and tolerance are causally linked or coupled to some global variable. The opposite connections with rifampin and erythromycin tolerance underscore the complexity of the link between growth and antibiotic action. Changes in growth may lead to changes in the levels and activity of these antibiotics’ molecular targets. Moreover, the molecular mechanisms that lead to death downstream of the antibiotic target could be a function of the growth state of the cell. Previous work has shown that bacterial interactions can elicit changes in antibiotic sensitivity by changing cellular physiology or interfering with antibiotic action directly or indirectly (Adamowicz et al., 2018; Radlinski et al., 2017; Sorg et al., 2016).

In principle, a myriad of intra- and extracellular variables are subject to the composition and dynamics of the ecosystems that bacteria inhabit, and microbial communities within animal hosts can elicit changes in environmental variables both locally and globally. Specifically, the microaerobic and anaerobic microenvironments of the fly (Obadia et al., 2017) and human gastrointestinal tracts enable the growth of short chain fatty acid producers. Some of these short chain fatty acids, like butyrate, have been shown to play an important role on host physiology and health (Nicholson et al., 2012). The consequences of the accumulation of these short chain fatty acids and other small molecules on microenvironments, as well as their effect on bacterial physiology and antibiotic treatment efficacy in vivo, have yet to be systematically explored. Our findings linking short chain fatty acid metabolism, growth, and antibiotic action in commensal microbes from the fruit fly gut opens the door to studying these phenomena in a model organism. While the human gut microbiome comprises hundreds of bacterial species, the simplicity of the Drosophila gut microbiota (Wong et al., 2011), the genetic tractability of Drosophila, and the fact that ~ 65% of human disease-causing genes have homologs in the Drosophila genome (Ugur et al., 2016), make the fruit fly a powerful model for host-microbiome interactions (Douglas, 2018). Our results also emphasize the need to probe the action of antibiotics – as well as other drugs that are thought not to target microbial growth (Maier et al., 2018) – in complex and varied conditions (Beppler et al., 2017). Furthermore, our findings highlight the utility of studying growth physiology in co-cultures in the absence of antibiotics for uncovering novel mechanisms of community-encoded protection against antibiotics.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Strain, strain background (Drosophila melanogaster)Wolbachia-free Drosophila melanogaster Canton-SBloomingtonDrosophilaStock CenterBL64349
Strain, strain background (Escherichia coli)BW29427Carol Gross labpir+ DAP- host strain thrB1004, pro, thi, rpsL, hsdS, lacZDM15, RP4-1360 ∆(araBAD) 567 ∆dapA1341::[erm pir+], donor for conjugation with Acetobacter pasteurianus
Strain, strain background (Lactobacillus plantarum)Lp(Obadia et al., 2017)Lactobacillus plantarum (WF) wild fly (D. melanogaster) isolate
Strain, strain background (Lactobacillus brevis)Lb(Obadia et al., 2017)Lactobacillus brevis lab fly (Oregon-R) isolate
Strain, strain background (Acetobacter pasteurianus)Ap(Gould et al., 2018)Acetobacter pasteurianus lab fly (Oregon-R) isolate
Strain, strain background (Acetobacter tropicalis)At(Gould et al., 2018)Acetobacter tropicalis lab fly (Oregon-R) isolate
Strain, strain background (Acetobacter orientalis)Ao(Obadia et al., 2017)Acetobacter orientalis lab fly (Oregon-R) isolate
Strain, strain background (Acetobacter indonesiensis)Ai(Obadia et al., 2017)Acetobacter indonesiensis lab fly isolate
Strain, strain background (Acetobacter aceti)Aa(Obadia et al., 2017)Acetobacter aceti lab fly isolate
Strain, strain background (Lactobacillus plantarum)Lp mCherry(Obadia et al., 2017)Lactobacillus plantarum (WF) wild fly isolate pCD256-P11-mCherry
Strain, strain background (Lactobacillus plantarum)LppHThis studyLactobacillus plantarum (WF) wild fly isolate pCD256-P11-pHluorin
Strain, strain background (Acetobacter pasteurianus)Ap GFPThis studyAcetobacter pasteurianus lab fly (Oregon-R) isolate pCM62-Plac-sfGFP
Recombinant DNA reagentpCM62 (plasmid)(Marx and Lidstrom, 2001)Plasmid to clone sfGFP under the control of the Escherichia coli lac promoter
Recombinant DNA reagentpCD256-mCherry (plasmid)(Spath et al., 2012a)Backbone for pHluorin expression
Recombinant DNA reagentpBad-sfGFP (plasmid)AddgeneRRID:Addgene_85482Source of sfGFP
Recombinant DNA reagentpZS11-pHluorin(Mitosch et al., 2017)Source of pHluorin
Sequence-based reagentZTG109This studyPCR primersggatttatgcATGAGCAAGGGCGAGGAG
Sequence-based reagentZTG110This studyPCR primersgctttgttagcagccggatcgggcccggatctcgagTTACTTGTACAGCTCGTCCATG
Sequence-based reagentZFH064-pHluorinThis studyPCR primersATTACAAGGAGATTTTACAT ATGAGTAAAGGAGAAGAACTTTTC
Sequence-based reagentZFH065-pHluorinThis studyPCR primersgtctcggacagcggttttGGATCCTTATTTGTATAGTTCATCCATG
Commercial assay or kitCell Viability KitBD349483
Commercial assay or kitEnzyChrom L-lactate Assay KitBioAssay SystemsECLC-100, Lots BH06A30 and BI07A09
Commercial assay or kitEnzyChrom D-lactate Assay KitBioAssay SystemsEDLC-100, Lots BH0420 and BI09A07
Chemical compound, drugD-mannitolACROS OrganicsAC125345000, Lot A0292699
Chemical compound, druglactateSigmaL6661-100ML Lot MKCC6092
Chemical compound, drugTween 80ACROS OrganicsAC278632500 Lot A0375189
Chemical compound, drugNaOHEMD MilliporeSX0590, Lot B0484969043
Chemical compound, drugHClFisher ChemicalA144-500, Lot 166315
Chemical compound, drugampicillinMP Biomedicals02194526, Lot R25707
Chemical compound, drugstreptomycinSigmaS9137 Lot SLBN3225V
Chemical compound, drugchloramphenicolCalbiochem220551, Lot D00083225
Chemical compound, drugtetracyclineMP Biomedicals02103011, Lot 2297K
Chemical compound, drugerythromycinSigmaE5389-1G, Lot WXBC4044V
Chemical compound, drugciprofloxacinSigma-Aldrich17850, Lot 116M4062CV
Chemical compound, drugtrimethoprimAlfa AesarJ63053-03, Lot T16A009
Chemical compound, drugspectinomycinSigma-AldrichPHR1426-500MG, Lot LRAA9208
Chemical compound, drugrifampinSigmaR3501-5G, Lot SLBP9440V
Chemical compound, drugvancomycinSigma-AldrichPHR1732−4 × 250 MG, Lot LRAB3620
Chemical compound, drugBCECFInvitrogenB1151, Lot 1831845
Chemical compound, drugDMSOFisher BioReagentsBP231, Lot 165487
Software, algorithmMATLABMathworksRRID:SCR_01622R2018a
Software, algorithmµManager(Edelstein et al., 2010)RRID:SCR_016865v. 1.4
Software, algorithmMorphometrics(Ursell et al., 2017)
Software, algorithmSuperSegger(Stylianidou et al., 2016)v. 3
OtherMRS mediumBD288110
Otheryeast extractResearch Products InternationalY20020, Lot 30553
OtherpeptoneBD211677 Lot 7065816
OtheragarBD214530
OtherPBSGibco70011044(10X, pH 7.4)

Fruit fly stocks and gut microbiome sequencing

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Wolbachia-free Drosophila melanogaster Canton-S (BL64349) flies were obtained from the Bloomington Drosophila Stock Center, and were reared and maintained as previously described in Obadia et al. (2017). To determine the bacterial strains present in our flies, we performed culture-independent 16S amplicon sequencing targeting the V4 region on an Illumina MiSeq. Individual flies were CO2-anesthetized, surface-sterilized by washing with 70% ethanol and sterile PBS six times each. Flies were dissected under a stereo microscope and their guts were placed in 2 mL screw cap microtubes containing 200 µL of 0.1 mm sterile zirconia-silicate beads (BioSpec Products 11079101z) and 350 µL of sterile lysis buffer (10 mM Tris-HCl, pH 8, 25 mM NaCl, 1 mM EDTA, 20 mg/mL lysozyme). Samples were homogenized by bead beating at maximum speed (Mini-Beadbeater, BioSpec Products) for 1 min. Proteinase K was added at 400 µg/mL and samples were incubated for 1 h at 37°C. Sample were then centrifuged (3000 × g for 3 min) and 300 µL of the nucleic acids-containing supernatant were transferred to 1.7 mL microtubes. Genomic DNA from samples was cleaned up through a DNA Clean and Concentrator-5 column (Zymo Research D4014). Using the protocol described in Fadrosh et al. (2014) for library preparation and sequencing, we sequenced the gut contents of 18 individual flies, three flies each from six independent vials. Paired-end 250-base pair sequencing generated >10,000 reads per sample. Reads were filtered using PrinSeq as in Koch et al. (2016). The reads were then clustered into operational taxonomic units (OTUs) at 99% identity and assigned taxonomy using LOTUS (Hildebrand et al., 2014) with the following parameters: [-threads 60 -refDB SLV -highmem 1 -id 0.99 p miseq -useBestBlastHitOnly 1 -derepMin 3:10,10:3 -simBasedTaxo 1 CL 3]. Redundant strain identities were collapsed into single OTUs. Common reagent contaminant strains were then removed (Salter et al., 2014). After filtering, only five unique species were identified (Figure 1A). We isolated these species in culture and verified the taxonomic identity of our isolates using Sanger sequencing of the complete 16S rRNA gene (Gould et al., 2018). At 97% OTU clustering, only three species were found: Acetobacter sp., Lactobacillus plantarum, and Lactobacillus brevis. When less stringent FASTQ quality filtering was used, trace amounts (~0.01%) of two mammalian gut strains were identified: Blautia sp. and Bacteroides sp. Because these OTUs were eliminated by more stringent quality filtering, we speculate that they may have resulted from barcode bleed-through on the MiSeq flowcell.

Sequencing data is available at the NCBI website under BioProject accession number PRJNA530819.

Bacterial growth and media

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Bacterial strains used in this study are listed in Supplementary file 1a. For culturing, all strains were grown in MRS medium (Difco Lactobacilli MRS Broth, BD 288110). MYPL medium was adapted from Moens et al. (2014), with 1% (w/v) D-mannitol (ACROS Organics AC125345000, Lot A0292699), 1% (w/v) yeast extract (Research Products International Y20020, Lot 30553), 0.5% (w/v) peptone (Bactopeptone, BD 211677 Lot 7065816), 1% (w/v) lactate (Lactic acid, Sigma L6661-100ML Lot MKCC6092), and 0.1% (v/v) Tween 80 (Polyoxyethylene(20)sorbitan monooleate, ACROS Organics AC278632500 Lot A0375189). The medium was set to pH 7 with NaOH (EMD Millipore SX0590, Lot B0484969043). All media were filter-sterilized.

Frozen stocks were streaked onto MRS agar plates (1.5% agar, Difco agar, granulated, BD 214530) and single colonies were picked to start cultures. Colonies were inoculated into 3 mL MRS in glass test tubes and grown for 48 h at 30°C with constant shaking. Unless otherwise noted, the saturated cultures were diluted to OD = 0.02 and grown in 3 mL MRS in glass test tubes for 48 h at 30°C with constant shaking before the start of the experiment. For co-cultures, we grew Lp and each of the Acetobacter species separately for 48 h from colonies as above, diluted the monocultures to OD = 0.04, combined the Lp monoculture 1:1 with each Acetobacter monoculture, and grew them in 3 mL MRS in glass test tubes for 48 h at 30°C with constant shaking before the start of the experiment. The experiments shown in the Figures were conducted at 30°C in either: 200 µL in 96-well polypropylene plates with constant shaking in a plate reader (Figure 1B, Figure 1—figure supplement 1, Figure 1—figure supplement 2, Figure 2C,D, Figure 2—figure supplement 1A, Figure 2—figure supplement 2B, Figure 3A–D, Figure 3—figure supplement 1A–D, Figure 4A–D, Figure 4—figure supplement 1, and Figure 5G–H); 3 mL in glass test tubes with constant shaking (Figure 1C–E, Figure 2A,B, Figure 2—figure supplement 1, Figure 2—figure supplement 2A, Figure 2—figure supplement 3, Figure 3E, Figure 3—figure supplement 1E, and Figure 5A–F); or on MRS agarose pads (see below, Figure 1—figure supplement 2D, Figure 4E,F, and Figure 4—figure supplement 2).

To count CFUs in cultures, aliquots were diluted serially in PBS. For cultures treated with high concentrations of antibiotics, cells were centrifuged for 1.5 min at 8000 x g and resuspended in 1X PBS pH 7.4 (Gibco 70011044) after removing the supernatants. PBS-diluted cultures were plated on MRS and MYPL because Lactobacillus species grow faster than Acetobacter species on MRS and vice versa on MYPL. Colony morphology and color enable differentiation of Lactobacillus from Acetobacter species.

Conditioned media

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Conditioned media were obtained by centrifuging cultures at 4500 x g for 5 min and filtering the supernatant with a 0.22 µm polyethersulfone filter (Millex-GP SLGP033RS) to remove cells. Conditioned media were acidified with HCl (Fisher Chemical A144-500, Lot 166315) or basified with NaOH (EMD Millipore SX0590, Lot B0484969043). Conditioned media were sterilized after adjusting pH with 0.22 µm PES filters.

MIC estimations

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To estimate the sensitivity of each species to various antibiotics, colonies were inoculated into MRS and grown for 48 h at 30°C with constant shaking. Cultures were diluted to an OD of 0.001 for Lp, Lb, and At, and 0.01 for Ap. Diluted cultures (195 µL) were transferred to 96-well plates containing 5 µL of antibiotics at 40X the indicated concentration. Antibiotics used were ampicillin (ampicillin sodium salt, MP Biomedicals 02194526, Lot R25707, stock at 100 mg/mL in milliQ H2O), streptomycin (streptomycin sulfate salt, Sigma S9137 Lot SLBN3225V, stock at 50 mg/mL in milliQ H2O), chloramphenicol (Calbiochem 220551, Lot D00083225, stock at 50 mg/mL in ethanol), tetracycline (tetracycline hydrochloride, MP Biomedicals 02103011, Lot 2297K, stock at 25 mg/mL in dimethyl sulfoxide (DMSO)), erythromycin (Sigma E5389-1G, Lot WXBC4044V, stock at 64 mg/mL in methanol), ciprofloxacin (Sigma-Aldrich 17850, Lot 116M4062CV, stock at 1.2 mg/mL in DMSO), trimethoprim (Alfa Aesar J63053-03, Lot T16A009, stock at 2 mg/mL in DMSO), spectinomycin (spectinomycin hydrochloride, Sigma-Aldrich PHR1426-500MG, Lot LRAA9208, stock at 50 mg/mL in milliQ H2O), rifampin (Sigma R3501-5G, Lot SLBP9440V, stock at 50 mg/mL in DMSO), and vancomycin (vancomycin hydrochloride, Sigma-Aldrich PHR1732−4 × 250 MG, Lot LRAB3620, stock at 200 mg/mL in DMSO:H2O 1:1). Antibiotics were diluted serially in 2-fold increments into MRS. Cultures were grown for 24 h at 30°C with constant shaking and absorbance was measured in an Epoch2 plate reader (BioTek Instruments) at 600 nm. The MIC was estimated as the minimum concentration of antibiotic with absorbance within two standard deviations of media controls.

For experiments in Figure 1B and Figure 1—figure supplement 2A–C, mono- and co-cultures were diluted to an OD of 0.001 (final cell density ~5 × 105 CFU/mL) and transferred to 96-well plates containing 5 µL of rifampin at 40X working concentration. Cultures were grown for 24 h and then serially diluted in 5-fold increments in PBS, and 3 µL of the dilutions were spotted onto MRS and MYPL rectangular plates using a semi-automated high-throughput pipetting system (BenchSmart 96, Mettler Toledo). Plates were incubated at 30°C until colonies were visible for quantification of viability.

Plate reader growth curves

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Cultures were grown from single colonies for 48 h in MRS at 30°C with constant shaking. Then, cultures were diluted to a final OD of 0.02 and 200 µL of the dilutions were transferred to clear-bottom transparent 96-well plates. Plates were sealed with transparent film pierced with a laser cutter to have ~0.5 mm holes to allow aeration in each well. Absorbance was measured at 600 nm in an Epoch2 plate reader (BioTek Instruments). Plates were shaken between readings with linear and orbital modes for 145 s each.

Growth rates and lag times were quantified using MATLAB (Mathworks, R2008a). The natural logarithm of OD was smoothed with a mean filter with window size of 5 timepoints for each condition over time, and the smoothed data were used to calculate the instantaneous growth rate d(ln(OD))/dt. The smoothed ln(OD) curve was fit to the Gompertz equation (Zwietering et al., 1990) to determine lag time and maximum growth rate.

pH measurements

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Culture pH was measured using the dual-excitation ratiometric pH indicator 2’,7-bis-(2-carboxyethyl)−5-(and-6)-carboxyfluorescein, mixed isomers (BCECF, Invitrogen B1151, Lot 1831845), which has a pKa of ~6.98. A stock solution of 1 mg/mL BCECF in DMSO (Fisher BioReagents BP231, Lot 165487) was diluted 1000-fold into MRS to a final concentration of 1 µg/mL. Cells were grown in a Synergy H1 plate reader (BioTek Instruments) following the procedure described above. In addition to absorbance, fluorescence was measured every cycle using monochromators at excitation (nm)/emission (nm) wavelength combinations 440/535 and 490/535. After subtracting the fluorescence of wells containing cells without the indicator, the ratio of the signals excited at 490 nm and 440 nm was used to calculate the culture pH using a calibration curve of MRS set to various pH values.

Culture pH after 48 h of growth was directly measured with a pH meter (sympHony, VWR) equipped with a pH combination electrode (Fisherbrand accumet 13-610-104A).

Changes in pH during growth

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To change the pH of monocultures and co-cultures in stationary phase, we obtained conditioned medium at 30 h or 40 h as described above and set the pH to the desired values. We then centrifuged 2 mL of a replicate culture for 3 min at 8000 x g, removed the supernatant, and resuspended cells in 1 mL of the corresponding medium to wash the cells. The suspension was centrifuged a second time and the pellets were resuspended in 2 mL of the corresponding medium.

Time-lapse and fluorescence microscopy

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Cells were imaged on a Nikon Eclipse Ti-E inverted fluorescence microscope with a 100X (NA 1.40) oil-immersion objective. Images were collected on a DU897 electron multiplying charged couple device camera (Andor) using µManager v. 1.4 (Edelstein et al., 2010). Cells were maintained at 30°C during imaging with an active-control environmental chamber (Haison).

Cultures grown for 48 h were diluted 100-fold into PBS and 2 µL were spotted onto a 1% (w/v) agarose MRS pad. After drying at room temperature, the pads were covered with a cover slip, sealed with a mixture of equal portions of Vaseline, lanolin, and paraffin, and transferred to the microscope. Images were taken every 2 min using µManager v. 1.4.

To quantify the morphology of cells using fluorescent strains, co-cultures were diluted 100-fold into PBS and 2 µL were spotted onto a 1% (w/v) agarose PBS pad. After drying, the pads were covered with a cover slip and transferred to the microscope. Images were acquired at room temperature using µManager v. 1.4.

Single-cell tracking and analysis

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Images were segmented and cells were tracked using the software SuperSegger v. 3 (Stylianidou et al., 2016). Further analysis of single-cell growth was performed using MATLAB. Cells with length >6 µm were removed from further analysis due to issues with segmentation. Length traces were smoothed using a mean filter of window size 5. Cells were classified as Lp or Ap if 90% of their traces were above (Lp) or below (Ap) a log10(length-to-width ratio) of 0.375. Traces with more than 15 timepoints were used for further analysis. Elongation rates d(ln L)/dt were calculated for each cell and the mean and standard error were computed for each time point.

Cloning and transformations

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To generate the fluorescently labeled Ap strain, the sfGFP coding sequence was cloned into pCM62 (Marx and Lidstrom, 2001) under control of the Escherichia coli lac promoter. The sfGFP coding sequence was amplified from pBAD-sfGFP using primers ZTG109 (5’ ggatttatgcATGAGCAAGGGCGAGGAG) and ZTG110 (5’- gctttgttagcagccggatcgggcccggatctcgagTTACTTGTACAGCTCGTCCATG). Gibson assembly (Gibson et al., 2009) was used to insert the amplified sfGFP cassette into BglII/XhoI-digested pCM62. This construct was delivered into Ap by conjugation as previously described (Deeraksa et al., 2005). Escherichia coli BW29427 was used as a donor strain and maintained with 80 mg/mL 2,6-diaminopimelic acid (Sigma Aldrich 33240) in potato agar mating plates (Deeraksa et al., 2005). Transformed Ap was selected with 10 µg/mL tetracycline on yeast peptone glycerol agar plates (Deeraksa et al., 2005).

To generate the Lp strain harboring pHluorin, the pHluorin coding sequence was cloned into pCD256-mCherry (Spath et al., 2012a) under the control of the strong p11 promoter (Rud et al., 2006). The pHluorin coding sequence was amplified from pZS11-pHluorin (Mitosch et al., 2017) using primers ZFH064-pHluorin (5’-ATTACAAGGAGATTTTACAT ATGAGTAAAGGAGAAGAACTTTTC) and ZFH065-pHluorin (5’-gtctcggacagcggttttGGATCCTTATTTGTATAGTTCATCCATG). Gibson assembly (Gibson et al., 2009) was used to insert the amplified pHluorin cassette into NdeI/BamHI-digested pCD256-mCherry. The Lp-pHluorin strain was generated by transforming wild type Lp as previously described (Spath et al., 2012b).

Fluorescent strains were further grown in MRS with antibiotics (10 µg/mL chloramphenicol (Calbiochem 220551, Lot D00083225) for Lp, tetracycline (10 µg/mL tetracycline hydrochloride, MP Biomedicals 02103011, Lot 2297K) for Ap).

pHluorin measurements

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Cells were grown following the procedure described above. The Lp pHluorin strain was grown in MRS containing 10 µg/mL chloramphenicol for the first 48 h of growth. In addition to absorbance, fluorescence was measured every cycle using monochromators at excitation (nm)/emission (nm) wavelength combinations 405/509 and 475/509. Because the signal from excitation wavelength 405 nm was indistinguishable from signal from medium (data not shown), we also measured pHluorin signal at both excitation/emission wavelength combinations for cells in PBS. Cultures (48-h-old, 250 µL) were centrifuged at 10,000 x g for 1 min and resuspended in 1X PBS. Aliquots (200 µL) were transferred to a 96-well plate and fluorescence was measured using monochromators at excitation (nm)/emission (nm) wavelength combinations 405/509 and 475/509 within 1 min of resuspension in a Synergy H1 plate reader (BioTek Instruments).

Lactate measurements

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Colonies of Lp and Acetobacter species were inoculated into 3 mL MRS and grown for 48 h at 30°C with constant shaking. Saturated cultures were diluted to OD 0.02, mixed 1:1, and grown at 30°C with constant shaking. After mixing for 20 h and 48 hr, a 700 µL aliquot was transferred to a microcentrifuge tube and centrifuged at 10,000 x g for 4 min. Supernatant (600 µL) was transferred to a new tube and centrifuged at 10,000 x g for 4 min. Supernatant (500 µL) was transferred to a new tube and kept on ice for not longer than 1 hr, until lactate was measured.

L- and D-lactate concentrations were measured using the EnzyChrom L- (BioAssay Systems ECLC-100, Lots BH06A30 and BI07A09) and D-lactate (BioAssay Systems EDLC-100, Lots BH0420 and BI09A07) Assay Kits. Samples were diluted 10- and 100-fold in water, and absorbance was measured according to the manufacturer’s instructions in a plate reader (Tecan M200). We also included controls without lactate dehydrogenase to account for endogenous activity in the supernatants.

Statistical analyses

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To determine significance of differences, we performed pairwise Student’s two-sided t-tests throughout. To decrease Type I error, we performed Bonferroni corrections for each experiment. Significant differences are denoted in the figures: *: p<0.05/n, **: p<0.01/n, ***: p<0.001/n, where n is the number of comparisons.

The number of technical replicates are indicated in the figure legends. In all cases, technical replicates originated from a single bacterial colony, which was grown for 48 h in liquid media. The saturated culture was then split into technical replicates. Biological replicates correspond to experiments starting from a different bacterial colony, freshly streaked from frozen stocks. Biological replicates of the main conclusions of this study are present in multiple figures: co-culturing of Lp with Ap protects Lp from rifampin killing (Figure 1B,C,D); the protective effect is due to a change in killing kinetics (Figure 1E and Figure 5E); co-culturing of Lp with Ap leads to an increase in pH in stationary phase (Figure 2D and Figure 2—figure supplement 2B); and co-culturing of Lp with Ap shortens lag phase (Figure 4A,C and Figure 5A).

References

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    Antibiotics: challenges, mechanisms, opportunities 
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Decision letter

  1. Wendy S Garrett
    Senior Editor; Harvard TH Chan School of Public Health, United States
  2. Wenying Shou
    Reviewing Editor; Fred Hutchinson Cancer Research Center, United States
  3. Jianxiao Song
    Reviewer; Northwestern Polytechnical University, China
  4. Jeff Gore
    Reviewer; Massachusetts Institute of Technology, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Aranda-Diaz et al., describe how microbial interactions alter the impact of antibiotics on bacteria found in the gut of fruit flies. The authors find that a strain of Lactobacillus plantarum (Lp) is killed more slowly by rifampin when the bacteria are co-cultured with Acetobacter species also found in fly guts. In this system, L. plantarum increases the pH of growth media through the release of lactate. Acetobacter reduce acidification and reduce the lag time of L. plantarum in co-culture. It appears that the reduced impact of rifampin on L. plantarum in co-culture is driven by the impact that Acetobacter have on pH and lagtime. The reduced killing can be recapitulated by either altering the pH or the lagtime of L. plantarum in monoculture. This work adds to the growing literature demonstrating that bacterial interactions can alter the impact of antibiotics. The described mechanism of increasing tolerance by reducing lag time is novel.

Decision letter after peer review:

Thank you for submitting your article "Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance" for consideration by eLife. Your article has been reviewed by 4 peer reviewers and the evaluation was overseen by Wenying Shou as the Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Jianxiao Song (Reviewer #2); Jeff Gore (Reviewer #4).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Understanding how antibiotics impact multi-species microbial communities is an important challenge to microbiology and medicine. In this study, the authors find that antibiotic tolerance of Lactobacillus plantarum (Lp) to rifampicin increases in the presence of other members of the fruit fly microbiome, and that this increase in tolerance is mediated by pH changes of the media induced by microbial metabolism. Surprisingly, the increase in tolerance was associated with a decrease in the lag time, which is the opposite of what has been observed in E. coli with ampicillin (and also opposite to what we would expect based on the rule of thumb that non-growing cells are better able to survive antibiotics).

Essential revisions:

The reviewers liked this work. They would like to see some clarifications and more clear writing - please see the points below.

Reviewer #1:

Aranda-Diaz et al., describe how microbial interactions alter the impact of antibiotics on bacteria found in the gut of fruit flies. The described mechanism of increasing tolerance by reducing lag time is novel.

One question I have is whether the increased tolerance could be the result of growth compensating for the number of dying cells or instead is purely a matter of cells dying at a slower rate. For example, if the cells were inoculated into carbon-free media and therefore could not replicate would the change in death rate still be observed?

I also think that there is room for clarifying the writing. For example, it would be helpful to have more information on how the bacterial strains were isolated. Were all strains isolated at the same time or were strains reported in previous papers isolated at different times. Did any of the strains come from the same fly? Similarly, it seems that asterisks are not used in a consistent manner in figures making it difficult to determine which results are significant.

Reviewer #2:

1) The figures in the manuscript are titled with Fig X or Figure X…), while the figure legends are titled with Figure X. Please change them to have consistent names (either Figure X or Fig.X) across the whole manuscript including supporting information.

2) Reformulate the first sentence of subsection “Co-culturing leads to growth of Lp in stationary phase”.

3) Please clarify the implication on human gastrointestinal tracts, which is more complex by studying fly gut microbiota in the Discussion section.

4) Discussion section, please be very careful with the definition of tolerance and resistance (as well as the differences between tolerance and resistance).

5) Discussion section, “or result from” should be changed to “or resulted from”.

6) Figure 1 legend, please indicate the meaning of the dashed line the exact MIC in Figure 1B or the figure legend and to make the figure self-understandable without reading the whole manuscript. Figure 1C legend, CFUs of Lp grown in monoculture (Lp) or in co-culture with Ax-cocultures was Normalized to CFUs at t=0? The figure legends in Figure 2C are confusing. Please revise it.

Reviewer #3:

The authors should do a more thorough search for published papers on pH and antibiotics to set the proper stage. An example is Karslake et al. (2016) (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005098).

Reviewer #4:

I thought that this study illuminated a fascinating consequence of growth in the context of a multi-species community, and that many of the lessons could have broad implications. Moreover, I thought that the experiments were done with care and the conclusions were justified by the experiments and the analysis. If anything, I thought that two much space was devoted to exploring the pH dynamics and not enough to exploring the surprising finding of how/why a decrease in lag time would lead to an increase in tolerance.

We normally think of tolerance as being associated with an increase in the lag time, yet in this study the authors find the opposite. I would have appreciated some speculation in the Discussion section regarding why this might be, but I understand that the authors may not want to say anything that ends up not being true. However, in the Results section I think that the authors should be more clear about the direction of the effect and that this is a surprise (this is done in the Discussion section, but a short comment about it in the Results section may be appropriate). Also, if there were any experiments that the authors could do to illuminate this question then it would strengthen the paper.

I found the writing of the paper to be a bit dry and technical. Much of the Results section on pH interactions felt longer than it needed to be, with extended discussion of the precise values of experimental parameters and pH values. This is a stylistic question that should be left to the discretion of the authors, but the details at times obscured the central take-aways and made me lose some interest as I was reading.

Why are the percentage survival values in Figure 1C so much larger than in Fig1B? In Figure 1B at 20ug/mL it looks like only < 1 in 10^5 cells are surviving < 0.001%, whereas the survival rates in Figure 1C are 10%.

https://doi.org/10.7554/eLife.51493.sa1

Author response

Essential revisions:

Reviewer #1:

[…] One question I have is whether the increased tolerance could be the result of growth compensating for the number of dying cells or instead is purely a matter of cells dying at a slower rate. For example, if the cells were inoculated into carbon-free media and therefore could not replicate would the change in death rate still be observed?

The reviewer brings up a very interesting point. To address this question, we imaged cells from an Lp+Ap co-cultureon MRS pads with rifampin. From these experiments, we did not observe any growth taking place, so it does not appear that the increased tolerance is due to a shift in the balance of growth and death. The new data is in Figure 1—figure supplement 2E.

We included the following text:

“To test whether the increased time to death was due to a disruption in the balance of growth and death, we performed single-cell imaging of mono- and co-cultures on MRS agarose with 50 µg/mL rifampin. We observed no growth in either mono- or co-cultures over 3 h of imaging (n=568 and n=236 cells, respectively; Figure 1—figure supplement 2D), indicating that survivability is due to protection from death, as opposed to increased growth. Because the MIC of Lp was unchanged in co-cultures compared with monocultures (no resistance), and the delay in killing was observed in the bulk population in co-cultures, these results indicate that co-culturing Lp with Ap induces tolerance of Lp to rifampin (Balaban et al., 2019; Brauner et al., 2016).”

I also think that there is room for clarifying the writing. For example, it would be helpful to have more information on how the bacterial strains were isolated. Were all strains isolated at the same time or were strains reported in previous papers isolated at different times. Did any of the strains come from the same fly?

We appreciate the reviewer’s insight into whether strains that are isolated together have more interactions. The strains used in this study were isolated from pools of flies and hence we cannot identify whether any came from the same animal. We note that these are the same strains that we have used in previous publications (Obadia et al.,2017 and Gould et al.,2018). This deep question about strain-level changes to interactions goes beyond the scope of this study, but we are actively investigating this idea.

Similarly, it seems that asterisks are not used in a consistent manner in figures making it difficult to determine which results are significant.

We apologize for the confusion. We have now corrected any inconsistencies and clarified any multiple hypothesis corrections.

Reviewer #2:

1) The figures in the manuscript are titled with Fig X or Figure X…), while the figure legends are titled with Figure X. Please change them to have consistent names (either Figure X or Fig.X) across the whole manuscript including supporting information.

We have made these names consistent by using “Figure” throughout.

2) Reformulate the first sentence of subsection “Co-culturing leads to growth of Lp in

stationary phase”.

We have rewritten these lines. They now read:

“To investigate possible environmental factors that could be linked to Lp tolerance to rifampin when co-cultured with Acetobacter species (Figure 1C,E), we first inquired whether the total amount of growth of the co-culture was larger or smaller than expected from the yield of the monocultures.”

3) Please clarify the implication on human gastrointestinal tracts, which is more complex by studying fly gut microbiota in the Discussion section.

We have clarified the implications of our results on the human GI tract by adding the following text:

“Our findings linking short chain fatty acid metabolism, growth, and antibiotic action in commensal microbes from the fruit fly gut opens the door to studying these phenomena in a model organism. While the human gut microbiome comprises hundreds of bacterial species, the simplicity of the Drosophila gut microbiota (Wong et al., 2011), the genetic tractability of Drosophila, and the fact that ~65% of human disease-causing genes have homologs in the Drosophila genome (Ugur et al., 2016), make the fruit fly a powerful model for host-microbiome interactions (Douglas, 2018).”

4) Discussion section, please be very careful with the definition of tolerance and resistance (as well as the differences between tolerance and resistance).

We appreciate the reviewer’s point and have been very careful about the definitions in our manuscript by reviewing a recent review by Balaban et al., that seeks to clarify definitions of persistence and tolerance. We have modified the following paragraph in the Introduction:

“Bacteria can survive antibiotics through (i) resistance mutations, which counteract the antibiotic mechanism and increase the minimum inhibitory concentration (MIC); (ii) tolerance, whereby the entire population enters an altered physiological state that prolongs survivability without changing the MIC of the antibiotic, leading to an increase in the time required to kill a given fraction of the population; (iii) heteroresistance, whereby a subset of the population has a higher MIC and grows at concentrations that would otherwise kill the population; and (iv) persistence, whereby a subset of the population survives treatment for a longer period (Balaban et al., 2019; Brauner et al., 2016).”

The lines mentioned by the reviewer now read:

“In this study, we observed a novel form of antibiotic tolerance. Tolerance has been defined as increased time to killing of the population as a whole (Brauner et al., 2017), as opposed to resistance (a change in the MIC), or persistence or heterotolerance whereby a subpopulation of bacteria displays increased time to killing (Balaban et al., 2019; Brauner et al., 2016).”

5) Discussion section, “or result from” should be changed to “or resulted from”.

We have made this change.

6) Figure 1 legend, please indicate the meaning of the dashed line the exact MIC in Figure 1B or the figure legend and to make the figure self-understandable without reading the whole manuscript.

We apologize for the confusion, we have added a label to the dashed line to the figure and clarified the legend.

Figure 1C legend, CFUs of Lp grown in monoculture (Lp) or in co-culture with Ax-cocultures was Normalized to CFUs at t=0?

The reviewer is correct, and we have explained the normalizations in all legends more clearly.

The figure legends in Figure 2C are confusing. Please revise it.

We have revised this legend.

Reviewer #3:

I have already provided my critiques during the pre-submission stage. The authors should do a more thorough search for published papers on pH and antibiotics to set the proper stage. An example is Karslake et al. (2016) (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005098).

We appreciate all of the reviewer’s feedback. We have included the reference above, and others to set the stage for our discussion of pH and antibiotics. The following paragraph was modified in the Introduction:

“For example, the intimate relationship between bacterial metabolism and environmental pH could also lead to changes in antibiotic efficacy, as previously shown in monocultures (Aagaard et al., 1991; Argemi et al., 2013; Kamberi et al., 1999; Karslake et al., 2016; Yang et al., 2014). The interplay of all of these processes in complex communities will provide new ways to combat pathogen survival and resistance evolution, particularly in cases involving tolerance, an important and understudied aspect of antibiotic susceptibility that can be elicited by diverse mechanisms and can facilitate the evolution of resistance (Levin-Reisman et al., 2017).”

Reviewer #4:

I thought that this study illuminated a fascinating consequence of growth in the context of a multi-species community, and that many of the lessons could have broad implications. Moreover, I thought that the experiments were done with care and the conclusions were justified by the experiments and the analysis. If anything, I thought that two much space was devoted to exploring the pH dynamics and not enough to exploring the surprising finding of how/why a decrease in lag time would lead to an increase in tolerance.

We normally think of tolerance as being associated with an increase in the lag time, yet in this study the authors find the opposite. I would have appreciated some speculation in the Discussion section regarding why this might be, but I understand that the authors may not want to say anything that ends up not being true.

We appreciate the reviewer’s comments on our study, and we have added some speculation about the link between lag time and tolerance, while emphasizing that our work highlights the inherent complexity of the tolerance phenotype.

We have added the following text to the Discussion section:

“The opposite connections with rifampin and erythromycin tolerance underscore the complexity of the link between growth and antibiotic action. Changes in growth may lead to changes in the levels and activity of these antibiotics’ molecular targets. Moreover, the molecular mechanisms that lead to death downstream of the antibiotic target could be a function of the growth state of the cell.Previous work has shown that bacterial interactions can elicit changes in antibiotic sensitivity by changing cellular physiology or interfering with antibiotic action directly or indirectly (Adamowicz et al., 2018; Radlinski et al., 2017; Sorg et al., 2016).”

However, in the Results section I think that the authors should be more clear about the direction of the effect and that this is a surprise (this is done in the Discussion section, but a short comment about it in the Results section may be appropriate).

We have added this point to the Results section in the following paragraph:

“We previously observed for Lp monocultures when shifting the pH that the stimulation of growth in stationary phase was connected with a shorter lag phase (Figure 3E).In agreement with these data, there was a significant decrease in bulk lag time for all of the Acetobacter co-cultures (Figure 4A,B). Surprisingly, this shorter lag phase was linked to Lp tolerance to rifampin, opposite to the longer phase linked to E. colitolerance to ampicillin (Brauner et al., 2016).”

Also, if there were any experiments that the authors could do to illuminate this question then it would strengthen the paper.

I found the writing of the paper to be a bit dry and technical. Much of the Results section on pH interactions felt longer than it needed to be, with extended discussion of the precise values of experimental parameters and pH values. This is a stylistic question that should be left to the discretion of the authors, but the details at times obscured the central take-aways and made me lose some interest as I was reading.

We appreciate the reviewer’s interest, which we share. We were not able to come up with any experiments to illuminate this question, but we agree that it is a very exciting future research direction.

Why are the percentage survival values in Figure 1C so much larger than in Figure 1B? In Figure 1B at 20ug/mL it looks like only < 1 in 10^5 cells are surviving < 0.001%, whereas the survival rates in Figure 1C are 10%.

We apologize for the confusion. The numbers in Figure 1B are measured relative to the initial inoculum, and for 20 µg/mL the total number of viable counts (CFUs) is ~0.1 or ~0.01 of the initial number of cells for Lp+Ap and Lp alone, respectively(Figure 1B). These numbers are in good agreement with the data in Figure 1C, which shows that 10% and 1% survival for Lp+Ap and Lp, respectively.

https://doi.org/10.7554/eLife.51493.sa2

Article and author information

Author details

  1. Andrés Aranda-Díaz

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0566-4901
  2. Benjamin Obadia

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3286-3236
  3. Ren Dodge

    Department of Embryology, Carnegie Institution of Washington, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  4. Tani Thomsen

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Zachary F Hallberg

    Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Resources, Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Zehra Tüzün Güvener

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  7. William B Ludington

    1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    2. Department of Embryology, Carnegie Institution of Washington, Baltimore, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Project administration
    For correspondence
    ludington@carnegiescience.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9637-4493
  8. Kerwyn Casey Huang

    1. Department of Bioengineering, Stanford University, Stanford, United States
    2. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, United States
    3. Chan Zuckerberg Biohub, San Francisco, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Project administration
    For correspondence
    kchuang@stanford.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8043-8138

Funding

National Institutes of Health (DP2OD006466)

  • Kerwyn Casey Huang

National Science Foundation (MCB-1149328)

  • Kerwyn Casey Huang

Paul G. Allen Family Foundation

  • Kerwyn Casey Huang

National Institutes of Health (DP5OD017851)

  • William B Ludington

Howard Hughes Medical Institute (International Student Research Fellowship)

  • Andrés Aranda-Díaz

Chan Zuckerberg Biohub

  • Kerwyn Casey Huang

Stanford University (Bio-X Graduate Fellowship)

  • Andrés Aranda-Díaz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Vivian Zhang for technical support, Elizabeth Skovran for kindly providing the pCM62 plasmid for Acetobacter spp., and Kazunobu Matsushita for providing the A tropicalis SKU1100 control strain in the initial conjugation experiments. We also thank the Huang and Ludington labs for fruitful discussions. This work was supported by NIH Director’s New Innovator Awards DP2OD006466 (to KCH), NSF CAREER Award MCB-1149328 (to KCH), the Allen Center for Systems Modeling of Infection (to KCH), and NIH Director’s Early Independence Award DP5OD017851 (to WBL). KCH is a Chan Zuckerberg Biohub Investigator. AA-D is a Howard Hughes Medical Institute International Student Research fellow and a Stanford Bio-X Bowes fellow.

Senior Editor

  1. Wendy S Garrett, Harvard TH Chan School of Public Health, United States

Reviewing Editor

  1. Wenying Shou, Fred Hutchinson Cancer Research Center, United States

Reviewers

  1. Jianxiao Song, Northwestern Polytechnical University, China
  2. Jeff Gore, Massachusetts Institute of Technology, United States

Publication history

  1. Received: August 30, 2019
  2. Accepted: January 28, 2020
  3. Accepted Manuscript published: January 29, 2020 (version 1)
  4. Version of Record published: February 17, 2020 (version 2)

Copyright

© 2020, Aranda-Díaz et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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