Aminoglycosides are broad-spectrum antibiotics whose mechanism of action is under debate. It is widely accepted that membrane voltage potentiates aminoglycoside activity, which is ascribed to voltage-dependent drug uptake. In this paper, we measured the response of Escherichia coli treated with aminoglycosides and discovered that the bactericidal action arises not from the downstream effects of voltage-dependent drug uptake, but rather directly from dysregulated membrane potential. In the absence of voltage, aminoglycosides are taken into cells and exert bacteriostatic effects by inhibiting translation. However, cell killing was immediate upon re-polarization. The hyperpolarization arose from altered ATP flux, which induced a reversal of the F1Fo-ATPase to hydrolyze ATP and generated the deleterious voltage. Heterologous expression of an ATPase inhibitor completely eliminated bactericidal activity, while loss of the F-ATPase reduced the electrophysiological response to aminoglycosides. Our data support a model of voltage-induced death, and separates aminoglycoside bacteriostasis and bactericide in E. coli.
aminoglycosidesvoltageribosomeE. coliATPResearch organismE. colihttp://dx.doi.org/10.13039/100000011Howard Hughes Medical InstituteGilliam Fellowship for advanced studyBruniGiancarlo Noehttp://dx.doi.org/10.13039/100005665Kinship FoundationSearle Scholar AwardKraljJoelhttp://dx.doi.org/10.13039/100000057National Institute of General Medical SciencesT32GM065103BruniGiancarlo Noehttp://dx.doi.org/10.13039/100000057National Institute of General Medical Sciences1DP2GM123458KraljJoelThe funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.Author impact statementAminoglycosides are a class of antibiotics that can kill Escherichia coli by building up internal voltage through disrupting the normal consumption of ATP.Introduction
Aminoglycosides are a potent class of translation inhibitor antibiotics with a broad activity spectrum. Despite a long history in the clinic (Krause et al., 2016), their exact mechanism of action remains unclear (Ezraty et al., 2013; Keren et al., 2013; Kohanski et al., 2007). In Gram-negative bacteria, aminoglycosides must cross the outer membrane and plasma membrane (Taber et al., 1987), into the cytoplasm where they can exert their bactericidal effect which requires binding to the ribosome (Davis, 1987). The kinetics of uptake into the cytoplasm have been extensively studied and occur in three steps (Taber et al., 1987). An ionic interaction between the polycationic aminoglycosides and the outer membrane of the bacterial cell induces a disruption of the outer membrane (Hancock et al., 1981), and allows the aminoglycoside to ionically associate with the inner membrane (Bryan and Van Den Elzen, 1977). The next step is known as the energy-dependent phase I (EDP-I) and occurs almost instantaneously upon aminoglycoside treatment (Muir et al., 1984). This portion is noted as energy dependent because both respiration inhibitors (Leviton et al., 1995) and differential carbon sources (Nichols and Young, 1985) reduced uptake. EDP-I is thought to be the step at which the aminoglycoside enters the cytoplasm (Taber et al., 1987; Nichols and Young, 1985), is concentration dependent (Bryan and Van Den Elzen, 1977), and occurs in cells that are resistant to or tolerant of aminoglycosides (Ezraty et al., 2013; Bryan and Van den Elzen, 1976). Following EDP-I is EDP-II, which only occurs in aminoglycoside-sensitive cells (Taber et al., 1987; Bryan and Van den Elzen, 1976), is thought to be essential for the bactericidal activity of aminoglycosides, and requires respiration (Bryan and Van den Elzen, 1976). Throughout these early studies, uptake of the aminoglycosides was often treated as synonymous with bactericidal activity.
Proposed bactericidal mechanisms all stem from this consensus theory of aminoglycoside uptake (Ezraty et al., 2013; Kohanski et al., 2007; Leviton et al., 1995; Kohanski et al., 2008; Davis et al., 1986). Once aminoglycosides are inside the cell, several competing theories exist to explain bactericidal activity including membrane breakdown from mistranslated protein (Davis et al., 1986; Busse et al., 1992), reactive oxygen species (Kohanski et al., 2007) (ROS), and a positive feedback of drug uptake (Ezraty et al., 2013; Leviton et al., 1995), although there is debate around each (Ezraty et al., 2013; Kohanski et al., 2008; Fraimow et al., 1991). Despite this debate, there is broad agreement upon two important points. The first is that the uptake mechanism, and therefore the resulting bactericidal activity, is voltage dependent (Damper and Epstein, 1981). That is, bactericidal activity occurs after uptake, and that uptake is intrinsically tied to membrane potential (Ezraty et al., 2013; Taber et al., 1987; Davis et al., 1986). This makes sense given the ample evidence of broken respiration protecting bacteria from aminoglycosides (Ezraty et al., 2013; Nichols and Young, 1985; Lobritz et al., 2015; McCollister et al., 2011). The second point is that this voltage induced uptake is responsible for mistranslation of protein upon aminoglycoside binding, which in turn creates the membrane breakdown essential for bactericidal activity. These pores, or the ROS produced in their occurrence, are thought to be responsible for the bactericidal activity of aminoglycosides (Davis et al., 1986; Kohanski et al., 2008). New techniques offer the ability to study the effects of aminoglycosides and perhaps resolve some debated aspects of their mechanism of action.
Single cell, fluorescent imaging offers a means to shed light on the effects of antibiotic exposure with high resolution in space and time. Improvements in microscope hardware enable automated live cell imaging while resolving the responses of individual bacteria. This hardware can be coupled with genetically encoded, or chemical fluorescent sensors that report bacterial voltage (Kralj et al., 2011; Prindle et al., 2015; Stratford et al., 2019), calcium (Bruni et al., 2017), and ATP (Tantama et al., 2013; Yaginuma et al., 2015), providing a lens to explore the long-term effects of antibiotic exposure. Recently, live cell voltage imaging of Bacillus subtilis revealed the importance of membrane potential in response to translation inhibitors (Lee et al., 2019). These new tools highlight the importance of membrane potential controlling bacterial physiology, and our ability to now study electrophysiology at the single-cell level.
Despite the debate on the bactericidal mechanism of aminoglycosides, there is broad agreement that bacterial membrane potential plays a critical role. In this paper, we sought to investigate the influence of membrane potential in mediating bactericide upon treatment with aminoglycosides. We used live cell microscopy to maintain high spatial and temporal resolution while also resolving any heterogeneity within the population. We found that lethal concentrations of aminoglycosides-induced voltage hyperpolarization leading to large fluctuations in cytoplasmic calcium that persisted for >48 hr after treatment. We found these transients were correlated with the inability of cells to regrow, giving us a technique to measure the onset of cell death in real time at the single-cell level. We found evidence that the transients arise from decreased ribosomal consumption of ATP leading to a reversal of the F1Fo-ATPase. The voltage hyperpolarization, in tandem with mistranslated proteins in the membrane, induced the bactericidal action. Our model proposes a new mechanism which links the chemical energy state of the cell with membrane potential dysregulation that can lead to death.
ResultsVoltage is not necessary for aminoglycoside uptake or inner membrane pore formation in <italic>E. coli</italic> but is required for bactericidal activity
The proton ionophore cyanide m-chlorophenyl hydrazine (CCCP) dissipates voltage gradients, and is known to protect E. coli against the bactericidal activity and EDP-II uptake of aminoglycosides (Taber et al., 1987; Davis, 1987). A colony-forming unit (CFU) assay was performed using a glucose minimal medium (PMM, see Materials and method) in the presence of aminoglycosides. These measurements showed cells continued to grow in PMM in the presence or absence of CCCP (Figure 1A). Treatment of cells with aminoglycosides alone caused a rapid reduction in CFUs. In contrast aminoglycoside treatment of cells pre-treated with CCCP showed bacteriostatic activity (Figure 1A).
Voltage is not necessary for aminoglycoside uptake or inner membrane pore formation in <italic>E. coli</italic> but is required for bactericidal activity.
(A) Colony forming units (CFUs) of untreated cells (blue) over four time points compared to cells treated with 50 µM CCCP (yellow), 100 µg/mL kanamycin (orange), and 50 µM CCCP + 100 µg/mL kanamycin (purple). Each curve averages three biological replicates, with mean and standard deviation plotted for each time point. (B) Ribosomal sucrose gradient depth plotted against 254 nm absorbance from LB grown E. coli treated with vehicle (blue), 100 µg/mL kanamycin (orange). The 30S, 50S, and 70S peaks are labeled. (C) Ratio of the area under the curve for the 30S + 50S to 70S peaks from E. coli in PMM pH 7.5, +50 μM CCCP, or pH 6 in the presence or absence of kanamycin. (D) Propidium iodide (3.75 µM in PMM) fluorescence in cells that were untreated (blue), 50 µM CCCP (yellow), 100 µg/mL kanamycin (orange), and 50 µM CCCP + 100 µg/mL kanamycin (purple) treated. The curve is the mean (solid) and standard deviation (shaded) for three biological replicates.
Aminoglycosides enter cells and induce ribosomal dissociation in the abscence of membrane voltage.
(A) Ribosomal sucrose gradient depth plotted against 254 nm absorbance from E. coli in treatment conditions from Figure 1C. (B) Ratio of the area under the curve for the 30S + 50S to 70S peaks from nuoA::kanR and nuoH::kanR E. coli strains in the absence and presence of gentamicin. (C) The uptake of 3.75 µM propidium iodide (PI) was measured by microscopy in cells that were 100 μg/mL kanamycin (orange), 100 μg/mL kanamycin + 50 µM CCCP (yellow), and 100 μg/mL chloramphenicol (green) treated. The mean (line) and standard deviation (shaded) are plotted over time. (D) Texas red fluorescence of a population of cells treated with 10 μg/mL gentamicin Texas red (GTTR) in cells pretreated with 0 μM CCCP (orange) or 50 μM CCCP (purple). The solid line indicates the mean, and the shaded error bars indicate the standard deviation at each time point.
To more carefully examine the contrasting data that CCCP-treated cells were growth inhibited in the presence of aminoglycoside, and the evidence that voltage is necessary for aminoglycoside uptake, a polysome analysis was used to assess ribosomal assembly in these conditions (Figure 1B; Qin and Fredrick, 2013). Untreated cells showed a majority of 70S particles, while addition of aminoglycosides caused a large fraction of ribosomes to split into 30S and 50S subunits (Zhang et al., 2015). Unexpectedly, ribosomes in aminoglycoside-treated cells showed equal dissociation in the presence or absence of CCCP (Figure 1C, Figure 1—figure supplement 1), despite the dramatic difference in drug activity. Aminoglycoside treatment at pH 6, which also has reduced membrane potential (see Materials and methods), showed bacteriostatic activity and ribosomal dissociation (Figure 1C). In addition to chemical perturbations, naturally occurring mutations in bacterial populations can lead to protection against aminoglycosides arising from a decrease in membrane potential (Ezraty et al., 2013; Damper and Epstein, 1981). These mutations often occur in the electron transport chain and reduce aminoglycoside uptake while concomitantly increasing survival (Ezraty et al., 2013). Mutations of genes in the nuo operon have reduced uptake and death (Ezraty et al., 2013), but have equivalent aminoglycoside-induced ribosomal dissociation (Figure 1—figure supplement 1B). Although uptake of aminoglycosides in the absence of membrane potential has been observed (Fraimow et al., 1991), the equivalent effect on ribosomal fraction abundance in E. coli, independent of voltage, had not been observed previously to our knowledge.
The clear uptake of aminoglycosides in the absence or alteration of membrane voltage suggested mistranslated proteins that induce membrane pores (Kohanski et al., 2008; Davis et al., 1986) could also occur. We measured the uptake of propidium iodide (PI), a membrane-impermeable DNA-binding fluorescent dye, in the presence of aminoglycosides. The aminoglycoside-treated population showed increasing PI fluorescence as compared to untreated cells (Figure 1D), indicating a loss of membrane integrity which correlated with the kinetics of cell death when measured by CFUs. Pre-treating cells with CCCP, however, showed a similar aminoglycoside-induced increase in PI fluorescence, despite the switch from bactericidal to bacteriostatic activity. Chloramphenicol, a bacteriostatic translation inhibitor, induced only small increases in PI fluorescence (Figure 1—figure supplement 1C). Fluorescently labeled gentamicin texas-red (GTTR) also showed an increase in concentration in the presence or absence of CCCP (Figure 1—figure supplement 1D), although the increases after 1 hr could be due to a destabilized membrane, similar to the results with PI. These data suggested that protein mistranslation and membrane destabilization occur in the absence of membrane potential and are not sufficient to cause bactericidal activity. Given the discrepancy between CFUs, ribosomal dissociation, and PI uptake, we hypothesized voltage led to bactericide through mechanisms other than drug uptake. We therefore considered if bactericidal activity could arise through a combination of the mistranslated protein-induced pore formation and membrane hyperpolarization. In order to test this hypothesis, we turned to single-cell measurements of bacterial electrophysiology.
Voltage and calcium exhibit altered electrophysiological flux in response to aminoglycosides
Fluorescent sensors of voltage and calcium have been used to monitor electrophysiology in bacteria at the single-cell level with high time resolution (Stratford et al., 2019; Bruni et al., 2017; Lee et al., 2019; Sirec et al., 2019). We used the genetically encoded sensor, PROPS, to measure voltage dynamics after 2 hr of treatment with kanamycin. The aminoglycoside-treated cells had larger fluorescent transients as compared to untreated cells (Figure 2—figure supplement 1A), but the high light intensities required prohibited long-term monitoring of single cells. GCaMP6, a fluorescent calcium indicator, is bright and sensitive enough to monitor live cells over hours or days, and we previously established calcium spikes were intrinsically linked to voltage fluctuations (Bruni et al., 2017). Individual E. coli expressing a fusion of GCaMP6f (calcium sensor) and mScarlet (spectrally independent control) were imaged upon exposure to 0 µg/mL or 100 µg/mL kanamycin and were monitored for 8 hr. Cells treated with antibiotic ceased growth and after ~2 hr showed large, non-oscillatory fluctuations which were uncoordinated between neighboring cells and not seen in untreated cells (Figure 2A, Video 1). Untreated E. coli had few cells that exhibited transients compared to drug-treated cells, and untreated cells grew and divided normally which indicated the transients were not a phototoxic effect (Figure 2—figure supplement 1B,C, Video 2). These drug-induced transients were larger than previously observed mechanically induced fluctuations (Bruni et al., 2017). At a concentration of 30 µg/mL kanamycin >99.99% of cells cannot form colonies after 6 hr, yet we saw transients > 48 hr after kanamycin treatment at that concentration (Figure 2—figure supplement 2D,E). The delay between antibiotic exposure and the appearance of calcium transients varied across the population with a mean time of 1.64 hr after treatment (Figure 2—figure supplement 2F). The fraction of cells showing transients increased with increasing concentrations of kanamycin (Figure 2B). These data showed that aminoglycosides induced large electrophysiological effects that arise at similar timescales to cell death measured by CFUs.
Voltage and calcium exhibit altered electrophysiological flux in response to aminoglycosides.
(A) Time traces of GCaMP6 fluorescence from single cells treated with 0 µg/mL (blue shades) and 100 µg/mL (orange shades) kanamycin. Individual cells display non-oscillatory transients. (B) The fraction of cells in a population of GCaMP6F expressing cells E. coli experiencing the transients in A at different concentrations of Kanamycin. The mean (line) and standard deviation (error bars) are shown for three biological replicates. (C) The average (solid line) and standard deviation (shading) of the moving GCaMP6f standard deviation (SD) over time from 0 µg/mL (blue) and 100 µg/mL kanamycin (orange) treated cells. (D) TMRM fluorescence from untreated (blue) or kanamycin treated (orange) (100 µg/mL, 2 hr) E. coli measured by cytometry. The average (line) and standard deviation (error bars) of three biological replicates are plotted. (E) The average (solid line) and standard deviation (shading) of the moving GCaMP6f Standard Deviation over time from 100 µg/mL kanamycin-treated cells in the absence (orange) or presence (purple) of 50 µM CCCP.
Kanamycin induces voltage and calcium transients.
(A) A histogram of the fraction of cells with a given standard deviation of PROPS fluorescence in the absence (blue) and presence (orange) of 100 μg/mL kanamycin. (B,C) Untreated cells have substantially fewer large calcium transients. (B) Fraction of cells exhibiting calcium transients as a function of time for untreated (yellow, purple) compared to treated (red, blue) cells. Each trace represents the mean of biological replicates of > 200 individual cells. Untreated cells do not show any coordinated transients. (C) Strip chart showing the growth of untreated cells. Unlike antibiotic-treated cells under identical imaging conditions, these E. coli cells were able to grow and divide until filling the entire field of view. The time is shown in (HH:MM) format. (D) Survival of cells in liquid culture upon increasing kanamycin concentration in µg/mL, measured by CFUs at indicated dose and time. Each curve averages three biological replicates. (E) Kanamycin-treated cells are capable of exhibiting transients for at least 48 hr. Time traces of GCaMP6 fluorescence from individual cells. Each color represents the mean fluorescence from a single cell. The black dotted line represents the addition of 30 µg/mL kanamycin. The majority of calcium transients occur within the first 12 hr after treatment, but individual cells still show activity even up to 48 hr after treatment. (F) Histogram shows the time of the first transient for cells that had transients. The population fit to a Gaussian with a peak of 1.64 hr after kanamycin addition.
Calculating moving standard deviation from treated cells.
(A) Top - GCaMP6 intensity traces from single cells marked by different colors under no external antibiotic concentrations. Bottom – Moving standard deviation traces from the same cells in the top, where the same color indicates the standard deviation from that cell. The fluorescence changes were transformed to the GCaMP6f moving standard deviation by first normalizing by dividing by a 45 min moving median. Then the moving standard deviation was calculated from the normalized trace using a 30 min sliding window, the output of which is shown here. (B) The same as in part A, except cells were treated with 100 μg/mL kanamycin. (C) Mean of the moving standard deviations from the untreated (blue) or kanamycin-treated (orange) populations. The solid line indicates the mean, and the shaded error bars indicate the standard deviation at each time point.
Only aminoglycosides induce calcium transients.
(A) All aminoglycosides tested (kanamycin, streptomycin, apramycin, gentamicin) exhibit calcium transients in a concentration dependent fashion. For each compound tested, the fraction of cells exhibiting transients increased, while the time of transient onset decreased with increasing concentration of aminoglycoside treatment. Each three panel set of the compounds; Kanamycin, Streptomycin, Apramycin, and Gentamicin: (left) representative GCaMP6 time traces at 100 µg/mL compound, (middle) the mean GCaMP6 standard deviation of the population, and (right) the mean of the Gaussian fit for the onset of transients for a population as a function of treatment concentration. (B) Antibiotics that are not aminoglycosides did not induce catastrophic calcium transients in any fashion. For trimethoprim, cyclohexamide, chloramphenicol, and erythromycin, representative traces of individual cells are shown at a treatment of 100 µg/mL (Left). The mean GCaMP6 standard deviation for the population is shown across a titration of each antibiotic (right). Each trace is the mean of two biological replicates. (C) Single-cell traces over time of pHuji (green) + GCaMP6f (blue) expressing cells treated with 30 μg/mL kanamycin. (D) Mean moving standard deviation plots of populations of cells represented in C. Dashed line represents addition of aminoglycoside treatment.
High pH is necessary to induce calcium transients.
(A) The average of the moving SD from a population of cells at pH 5.95 and pH 7.99, either treated with 0 or 100 µg/mL kanamycin. Each curve averages three biological replicates. Shading around the line is the standard deviation. (B) Mean (line) and standard deviation (error bars) of TMRM fluorescence of cells over time measured by cytometry in 200 nM TMRM in PMM. E. coli in pH 6.0 in the absence (green) or presence (magenta) of 10 µg/mL gentamicin were compared to E. coli at pH 7.5 in the absence (blue), and presence (orange) of 10 µg/mL gentamicin. (C) The average of the moving SD from a population of cells treated with 10 µg/mL gentamicin that were WT (blue), nuoA::kanR (orange), nuoB::kanR (yellow), and nuoI::kanR (purple) strains. Each curve averages three biological replicates. Shading around the line is the standard deviation.
Video of <italic>E. coli</italic> expressing GCaMP6f-mScarlet upon treatment with 100 µg/mL kanamycin.
The movie was taken using 488 nm excitation and a 40x air objective imaged onto an sCMOS camera. The movie was taken at a sampling rate of 1 image per minute for 16 hr. This movie has been corrected for uneven illumination, XY drift, and background as mentioned in the Materials and methods. The time indicated represents HH:MM.
Video of <italic>E. coli</italic> expressing GCaMP6f-mScarlet with no kanamycin addition.
The video was taken using 488 nm excitation and a 40x air objective imaged onto an sCMOS camera. The movie was taken at a sampling rate of 1 image per minute for 16 hr. This movie has been corrected for uneven illumination, XY drift, and background as mentioned in the Materials and methods. The time indicated represents HH:MM.
In order to compare the kinetics of the aminoglycoside response of populations of cells across treatment conditions, we needed a metric that would encompass the fluorescent dynamics across many cells. To visualize the transients across a population, a moving standard deviation was calculated for each cell, and then averaged across all cells. This mean of the moving standard deviation (taken from 30 to 500 cells) was considered one biological replicate, and the average and standard deviation of three biological replicates is then plotted (Figure 2C, Figure 2—figure supplement 2). This metric will depend strongly on the microscope system used, and thus requires relative comparisons of treated versus control under otherwise identical imaging conditions. We defined a drug-induced calcium transient as any cell that showed a moving standard deviation (SD) >7 fold above untreated cells for >40 min. The GCaMP moving SD metric can separate treated and untreated populations of E. coli. All aminoglycosides tested exhibited a concentration-dependent onset of calcium transients, as well as significantly increased GCaMP SD, but other bacteriostatic or bactericidal antibiotics had neither (Figure 2—figure supplement 3A,B). Our measurements do not rule out the possibility of other ions moving across the membrane (Dubin and Davis, 1961), and indeed we see that proton concentrations as measured by the red fluorescent pH indicator, pHuji (Shen et al., 2014) also show transients, but their initial amplitude is much smaller than the calcium transients (Figure 2—figure supplement 3C,D). A lack of sufficient sensors prohibited us from measuring other ions at these temporal and spatial scales.
Given the observation that CCCP and low pH eliminated the calcium transients, we hypothesized that these large fluorescent changes were a product of a more polarized membrane potential, which would be consistent with the positive feedback of drug uptake model (Ezraty et al., 2013; Bryan and Van Den Elzen, 1977; Davis et al., 1986). Tetramethylrhodamine methylester (TMRM), a membrane permeable fluorescent voltage reporter, accumulates in polarized mitochondria (Zorova et al., 2018) and E. coli (Kralj et al., 2011; Lo et al., 2007). Untreated E. coli showed no change in intracellular TMRM levels over 2.5 hr (Figure 2D). Cells treated with kanamycin showed a sharp increase in TMRM fluorescence after 80 min, corresponding to a change of −72 mV after 2.5 hr (see Materials and methods). Assuming a resting potential of −150 mV the treated cells would have a membrane voltage of −222 mV. This observation is consistent with an aminoglycoside-induced change in membrane potential occurring at the same time as the calcium transients.
If aberrant voltage induced the calcium transients, dissipating the voltage would eliminate the transients. Cells expressing GCaMP6 were treated with CCCP and compared to kanamycin exposure alone (Figure 2E). CCCP-treated cells showed no increase in GCaMP6f SD, or individual calcium transients. Cells treated at pH 6 also showed no increase in calcium transients (Figure 2—figure supplement 4A) and showed no hyperpolarization measured by TMRM (Figure 2—figure supplement 4B). Knockouts of the nuo operon show altered kinetics in the onset of the GCaMP6f SD, as well as a lower amplitude in response to aminoglycoside treatment (Figure 2—figure supplement 4C). Together, these data show that aminoglycosides-induced hyperpolarization and large ionic fluctuations only in the presence of membrane voltage, and that chemical or genetic alterations of membrane voltage affect the GCaMP6 response.
The onset of voltage hyperpolarization, calcium transients, and cell death as measured by CFUs suggested the observed fluorescent calcium traces could be a good technique to measure bactericide at the single-cell level. Fluorescence measurements were taken under continuous flow during the addition, then removal, of kanamycin. As expected, antibiotic exposure induced large calcium transients in many cells. After 4 hr of kanamycin exposure, medium without drug was added, and ~2% of cells reinitiated cell division (recovered cells, 35/1727 cells, Figure 3A, Video 3). Of the 35 recovered cells, none exhibited drug-induced calcium transients during or after antibiotic exposure (Figure 3B), and the population of recovered cells had lower calcium fluctuations as compared to arrested cells (Figure 3C). Recovered cells were not genetically resistant, as a second exposure to kanamycin stopped growth and induced calcium transients in daughter cells (Figure 3—figure supplement 1A–C). Finally, within an untreated population, a small fraction of cells exhibited transients (22 of 1544), where each cell with transients did not divide (Figure 3—figure supplement 1D–F). In all cases, tested calcium transients correlated with reduced population viability; conditions with fewer calcium transients increased CFUs, and any cell that exhibited transients did not regrow. This data provided a technique to measure one hallmark of single-cell death in E. coli in real time as all observations of these transients indicated that a cell experiencing them was rendered unable to divide, although we are not able to definitively say that the transients caused cell death.
(A) Strip chart of cells expressing GCaMP6f. Cells were imaged in PMM alone for 5 hr, then exposed to 10 µg/mL kanamycin for 4 hr. After 4 hr, PMM alone was flowed in for an additional 26 hr. The blue arrow indicates a cell that was able to divide after treatment with kanamycin, 5 µm scale bar. Time is shown in (HH:MM) format. (B) Individual GCaMP6 time traces from cells that regrow after treatment compared to a random selection of cells that do not regrow within 24 hr. (C) The average (line) and standard deviation (shaded region) of the moving SD from all cells that regrow (blue) vs those that do not regrow (red). (D) Strip chart showing GCaMP6 fluorescence (top), propidium iodide fluorescence (middle), and the merge. Cells were treated with 100 µg/mL kanamycin at time t = 0. Time is shown in (HH:MM) format. (E) The mean GCaMP6 standard deviation for the population is shown in blue. Yellow shows the population average of the PI fluorescence. (F) Time traces of individual cells showing the GCaMP6 fluorescence (blue) and the PI fluorescence (yellow) on the same cells. The PI fluorescence was not correlated with the onset of transients, although many cells did uptake PI during the course of the experiment.
Cells that did not experience calcium transients are not genetically resistant.
(A–C) Cells that regrow after 1 treatment of kanamycin are not genetically resistant. (A) Strip chart of E. coli with 0 µg/mL kanamycin (t = 0–2 hr), 10 µg/mL kanamycin (t = 2–6 hr), 0 µg/mL kanamycin (t = 6–23 hr), and 10 µg/mL kanamycin (t = 23–30 hr). Time is shown at each frame in (HH:MM) format. The red arrow indicates a cell that regrows after the first kanamycin exposure. (B) Image from A at t = 27.5 hr. Individual cells were manually highlighted and shown with three different colors. Each cell has it’s GCaMP6 time trace shown in (C) for just the time period of the second kanamycin addition. (C) GCaM6f fluorescence over time of individual cells in B. (D–F) Untreated cells that exhibit transients do not divide. (D) Strip chart of E. coli imaged via the GCaMP6 fluorescence under PMM without aminoglycoside treatment. The blue and red arrow mark cells that do not divide. The time is shown in (HH:MM) format. (E) Image taken after 10 min of imaging with individual cells segmented manually. Each color corresponds to the GCaMP6 fluorescent trace shown in (F). Any cell that exhibited transients in the untreated conditions did not divide within the movie, which ended with a fully overgrown field of view.
Video of <italic>E. coli</italic> expressing GCaMP6f-mScarlet switching the medium from PMM (0–5 hr), PMM + 10 µg/mL kanamycin (5–9 hr), PMM (9–35 hr).
The movie was taken at a sampling rate of 1 image per minute for 29 hr. This movie has been corrected for uneven illumination, XY drift, and background as mentioned in the Materials and methods. The time indicated represents HH:MM.
Spectrally separating PI and GCaMP enabled us to study the kinetics between catastrophic calcium transients and pore formation in single cells. The mistranslation that causes pore formation was previously measured to occur within a half hour of aminoglycoside treatment (Davis et al., 1986). We hypothesized that mistranslated proteins in the plasma membrane created an ionic imbalance in polarized cells leading to the observed calcium transients. To test our hypothesis, we incubated GCaMP6 expressing E. coli with PI in the presence of aminoglycoside (Figure 3D). The population average showed a smoothly increasing level of PI uptake upon aminoglycoside exposure (Figure 3E), similar to our earlier data. However, the GCaMP6 moving SD increased well before appreciable PI uptake. Individual cells showed calcium transients preceded PI entry into the cytoplasm, and that PI often increased in very large bursts (Figure 3F). Thus, pores large enough to accommodate PI occurred after aminoglycoside-induced hyperpolarization and catastrophic calcium transients, suggesting bactericidal activity occurred prior to pore formation.
Voltage toggles between bactericidal and bacteriostatic activity in aminoglycoside-treated cells
The data above showed that aminoglycoside uptake, ribosome dissociation, and mistranslated protein can occur without membrane potential. Aminoglycosides in the absence of a voltage exhibited a bacteriostatic effect, but voltage induced bactericide. We therefore sought to explore the requirements of voltage as the bactericidal keystone in E. coli by using the calcium transients as a real time marker of permanent cell cycle arrest, while controlling the chemical environment to actuate membrane voltage.
Treating cells with aminoglycoside-induced calcium transients (Figure 4A top, Figure 4—figure supplement 1A top) as expected. However, removing the voltage either through addition of CCCP or lowering pH immediately ceased all transients at the single cell and population levels (Figure 4A,B, Figure 4—figure supplement 1A,B), although no cells re-initiated cell division. Thus, voltage was necessary for the calcium transients to occur. Conversely, E. coli was incubated with kanamycin in the presence or absence of CCCP for 4 hr and showed calcium transients only in the cells without CCCP as expected (Figure 4C top). Removal of kanamycin and CCCP initiated transients within 7 min, much faster than the appearance of transients from aminoglycoside treatment without CCCP (Figure 4C,D). Similar results were seen exchanging pH 6 with pH 7.5 to reestablish a membrane voltage (Figure 4—figure supplement 1C,D). The rapid onset showed that aminoglycosides can exert bactericidal activity immediately upon reestablishment of membrane voltage, and that in the conditions tested, voltage is sufficient to induce catastrophic calcium transients which were correlated with cell death.
Voltage toggles between bactericidal and bacteriostatic activity in aminoglycoside-treated cells.
(A) Single-cell traces of GCaMP6f intensity over time upon treatment with kanamycin (blue bar, top), or with kanamycin followed by CCCP (yellow bar, bottom). (B) Mean GCaMP6f moving SD of biological replicates over time. The population traces are the mean of the single-cell experiments in B, with kanamycin (blue, corresponds to A top) or kanamycin +CCCP-treated cells (orange, corresponds to A bottom). (C) Single-cell traces of GCaMP6f intensity over time after kanamycin (indicated with a blue bar) was flowed across the cells at 2 hr that were pretreated with vehicle (top) or CCCP (bottom, indicated with a yellow bar). Kanamycin and CCCP were then flowed out of the chamber at 6 hr. (D) Mean GCaMP6f moving SD of biological replicates over time. The population traces are the mean of the single-cell experiments in C, with kanamycin (blue, corresponds to C top) or kanamycin +CCCP-treated cells (orange, corresponds to C bottom). (E) Cells treated with 10 μg/mL gentamycin with CCCP added at t = 0 hr (blue line), or CCCP added at t = 1 hr (orange line). CFUs taken at 1 hr were counted before addition of CCCP, which increased the number of surviving cells at t = 2 hr. *p<0.05.
Catastrophic calcium transients require a membrane potential as indicated by low pH protection.
(A–D) Catastrophic calcium transients require a membrane potential as indicated by low pH protection. (A) Random single-cell traces of GCaMP6f intensity over time upon treatment with kanamycin (pink bar, top) at pH 7.5 (lilac bar, top), or with kanamycin and normal to low pH transition (lilac to blue bar, bottom). (B) Mean GCaMP6f standard deviation of biological replicates plotted over time. The population traces correspond to the mean of the single-cell experiments in A, with kanamycin (purple, A-top, pH 7.5 only) or kanamycin +low pH-treated cells (green, A-bottom, pH 7.5- > 6.0). (C) Random single cell traces of GCaMP6f intensity over time after kanamycin was flowed on top of the cells at 2 hr that were pretreated with PMM at pH 7.5 (top, lilac bar) or PMM at low pH (bottom, blue to lilac bar). Kanamycin and low pH were then flowed out of the chamber at 6 hr (pH was restored to 7.5, lilac bar). (D) Mean GCaMP6f standard deviation of biological replicates plotted over time. The population traces correspond to the mean of the single-cell experiments in C, with kanamycin at constant pH 7.5 (purple, C-top, pH 7.5 only) or kanamycin +low pH transition-treated cells (green, C-bottom, pH 6.0- > 7.5).
If voltage hyperpolarization induced cell death, a prediction is that chemically removing voltage before the onset of transients would protect cells, even if the cells are maintained in the presence of aminoglycoside. If cells were treated with aminoglycoside, followed by CCCP addition, there would be an increase in the number of surviving cells compared to the removal of antibiotic, even if those cells were maintained in the antibiotic for a longer period of time. To test this prediction, E. coli were treated with 10 µg/mL gentamicin and CFUs were counted at 60 min. At that time, CCCP was added to the medium, and cells were incubated for another 60 min with aminoglycoside and CCCP. After 2 hr, CFUs were counted again, and there was a 22x increase in CFUs as compared to the 1 hr time point (Figure 4E). This data shows that the conditions for cell death had been established at 1 hr and that cells then plated onto LB would still die. However, cells treated with CCCP at 1 hr avoided the hyperpolarization-induced calcium transients and had a correspondingly higher survival rate.
ATP dysregulation precedes voltage-induced bactericidal killing
Published evidence suggests that metabolic dysfunction correlates with translation inhibitor efficacy (Levin et al., 2017; Allison et al., 2011; Lopatkin et al., 2019). This was hypothesized to be associated with bacterial energetic investment in protein production (Nieß et al., 2019). Furthermore, a reduction in ribosome concentration has been annotated as a means to protect persister cells (Cho et al., 2015). We reasoned that the sudden change in energetic demand from the loss of a large fraction of 70S translating ribosomes could free up ATP and GTP to be used in other processes. To connect this shift in energetics to aminoglycoside-induced voltage dysregulation, we considered how E. coli generate a membrane voltage in aerobic environments. In the presence of glucose, E. coli use glycolysis to power the NADH dehydrogenase assembly (Complex I) and induce a proton motive force (PMF). The F1Fo-ATPase then depletes the PMF to generate ATP. However, the F1Fo-ATPase can be run in reverse, using ATP hydrolysis to generate a membrane voltage, which occurs in anaerobic conditions to power flagellar rotation (Yasuda et al., 1998). We hypothesized that aminoglycosides increased cellular ATP flux through non-ribosomal sinks, leading to hyperpolarization via the combined activity of the NADH dehydrogenase and a reversed F1Fo-ATPase.
We initially measured ATP concentration in E. coli using a ratiometric fluorescent ATP sensor, mRuby-iATPSnFR1.0 (Lobas et al., 2019). Gentamicin treatment increased the 488/561 nm fluorescence ratio by 50% within 2 hr of treatment (Figure 5A). Cells at low pH or in the presence of CCCP also showed ATP increases expected from ribosome dissociation (Figure 4—figure supplement 1A,B). Other non-aminoglycoside translation inhibitors which exhibit bacteriostatic activity also showed increasing ATP (Figure 5—figure supplement 1C). Consistent with our observation that recovered cells did not exhibit calcium transients, cells that recovered after 4 hr of kanamycin treatment had lower ATP compared to arrested cells (Figure 5B). We attempted to quantify the absolute change in ATP concentration in populations of cells, as our single-cell data indicated that ATP levels were increased when cells were treated with aminoglycosides. Using a luminescence-based assay, we determined that steady state levels of ATP in gentamicin-treated E. coli were significantly lower than untreated controls (Figure 5—figure supplement 1D) in the first half hour of treatment, which was inconsistent with our iATPSnFR single-cell data. This data is, however, consistent with an increased ATP flux through consumers other than ribosomes, such as the F1Fo-ATPase. We suspected that the genetically encoded ATP sensor can act as a buffer absorbing some of this ATP flux from a loss of translation, while the luminescence-based assay measures absolute values after the cells are permeabilized. This interpretation is consistent with recent results which show an increase in an alarmone with an ATP precursor after aminoglycoside treatment (Ji et al., 2019). Collectively, these data suggest that there may be a change in metabolic flux in the system and are consistent with prior observations of aminoglycoside-treated cells, which were found to leak NTPs (Davis, 1987) and increase respiration (Lobritz et al., 2015). This change in ATP flux is consistent with a number of other observations in the field correlating metabolism with translation inhibitor efficacy (Levin et al., 2017; Allison et al., 2011; Lopatkin et al., 2019; Greulich et al., 2015).
ATP dysregulation precedes voltage-induced bactericidal killing.
(A) iATPSnFR ratios from E. coli treated with vehicle (blue) or 10 µg/mL gentamicin (orange). The ratio of iATPSnFR (488 nm) to mRuby (561 nm) indicates ATP concentration. Each trace averages two biological replicates. (B–F) Cells treated with 10 µg/mL gentamicin. (B) iATPSnFR ratios from gentamicin-treated cells that do (blue) or do not (orange) regrow. The star represents a significance of < 0.05 tested at 2 hr after treatment using a student t-test with unequal variance. (C) Normalized CFUs of gentamicin-treated knockouts of components of the F1Fo-ATPase compared to WT. Each data point is in biological triplicate. (D) Mean moving GCaMP6f SD for gentamicin-treated F1Fo-ATPase component knockouts compared to WT. Each curve averages four biological replicates. (E) Mean moving GCaMP6f SD for gentamicin-treated E. coli strain DK8, missing all components of the F1Fo-ATPase compared to WT. Each curve averages four biological replicates. (F) Normalized CFUs of gentamicin-treated mgtC expressing E. coli compared to WT.
ATP and membrane potential measurements are consistent with ATP dysregulation.
(A,B) Low pH and CCCP maintain the rise in the iATPSnFR1.0/mRuby ratio upon gentamicin treatment when compared to controls. (A) ATP measured in PMM pH 7.5 (blue) or low pH (orange) upon treatment with 10 µg/mL gentamicin. Each line represents the average of three biological replicates. (B) ATP measured in PMM (blue) or PMM + 50 µM CCCP (yellow) upon treatment with 10 µg/mL gentamicin. Each line represents the average of four biological replicates. Even under protective conditions of CCCP or pH 6, 10 µg/mL gentamicin still induced a rise in ATP, but was not coupled with cell death. (C) Some bacteriostatic antibiotics have similar iATPSnFR1.0 ratios compared to gentamicin-treated cells. (D) ATP concentration was quantified with a BacTiter Glo kit in the absence (blue boxplots) and presence (orange boxplots) of 10 µg/mL gentamicin at 0, 30, and 120 min. Simultaneously the optical density at 600 nm was read out, and the proportion of ATP to OD was taken to normalize the amount of ATP across the different time points and treatment conditions. The only significant difference between conditions is at 30 min with a p-value of 0.0036. (E,F) Compared to WT (black). (E) ATP concentration of 10 µg/mL gentamicin-treated F1Fo-ATPase component knockouts. Each curve averages four biological replicates. (F) A bar plot of the ratio of the mean TMRM fluorescence from gentamicin treated (2 hr) to untreated cells measured by flow cytometry. Error bars are 95% confidence interval.
Basal membrane potential measured in strains tested that protect against aminoglycosides does not explain the protective effect.
(A) Violin plots of individual clonal populations of E. coli treated with 200 nM TMRM measured by flow cytometry, with additional strain and treatment indicated on the x-axis. CCCP concentration is 50 µM. All strains are grown to mid-log. (B) Violin plots of individual clonal populations of E. coli treated with 200 nM TMRM measured by flow cytometry, with additional strain and treatment indicated on the x-axis. CCCP concentration is 50 µM. All strains are grown to mid-log. (C) Violin plots of individual clonal populations of E. coli treated with 1 µM DiOC6(3) measured by cytometry, with additional strain and treatment indicated on the x-axis. CCCP concentration is 50 µM. All strains are grown to mid-log.
Proposed model of aminoglycoside-induced cell death in <italic>E. coli</italic>.
(1) Uptake of aminoglycosides has been extensively studied and is not shown in detail here, although our data indicates that aminoglycoside uptake can occur in the absence of membrane potential. (2) Aminoglycosides bind to ribosomes and (3) in the process create mistranslated protein and inhibit translation for a majority of translating ribosomes. These mistranslated proteins are implicated in creating pores in the inner membrane that occur regardless of polarization. Pore formation appears to be an essential step in aminoglycoside-mediated bactericide that distinguishes it from other translation inhibitors. With the loss of translation, (4) ATP is more readily available to other cellular processes. This in turn causes (5) ATPases (including the F1Fo ATPase) to increase their activity given the greater accessibility of ATP, causing the F1Fo ATPase to hyperpolarize cells. This hyperpolarization is required for the (6) bactericidal activity to occur in our observations. Cellular processes including the electron transport chain’s dehydrogenase activity, as well as the energetic state of the cell help determine the degree to which this bactericidal activity occurs. Specifically, higher respiration and carbon sources that contribute the most ATP to the cell (glucose) are the most detrimental prior states E. coli can experience before aminoglycoside treatment. Red circles indicate processes that have data supporting their essentiality in mediating aminoglycoside cell death that do not involve the direct binding of ribosomes.
If aminoglycosides-induced ATP hydrolysis and hyperpolarization via the F1Fo-ATPase, then pump component knockouts should reduce calcium transients, show increased CFUs compared to WT, yet also show increased ATP due to the absence of hydrolysis. Knockouts from the proton conducting Fo domain (atpB, atpE, atpF) as well as atpG had increased CFUs and reduced calcium transients compared to WT (Figure 5C,D top), and all tested ATPase knockouts showed gentamicin-induced ATP accumulation (Figure 5—figure supplement 1E). Interestingly knockouts of atpC (ε-subunit), which has increased gentamicin sensitivity (Brynildsen et al., 2013), and atpH (δ-subunit) both decreased the time to calcium transient onset and reduced CFUs faster than WT (Figure 5C,D bottom). AtpC biases the motor in the direction of ATP production (Guo et al., 2019), while atpH acts as a filter for proton conduction through the Fo domain (Engelbrecht and Junge, 1990), thus the knockouts of these proteins would improve proton conduction through the F1Fo-ATPase thereby increasing membrane potential that can be generated by this pump, which are consistent with knockouts showing more rapid cell death. Furthermore, gentamicin-treated Fo domain knockouts reduced hyperpolarization while, as their function predicts, atpC and atpH increased hyperpolarization relative to WT (Figure 5—figure supplement 1F). Completely eliminating the F1Fo-ATPase (∆unc operon – strain DK8) (Klionsky et al., 1984) also showed reduced calcium transients as compared to a strain with intact F1Fo-ATPase activity (Figure 5E). Finally, expression of a virulence factor from Salmonella, mgtC, eliminated the bactericidal activity of aminoglycosides in E. coli (Figure 5F). MgtC is an inhibitor of the F1Fo-ATPase (Lee et al., 2013), and aids in Salmonella infection and survival at low magnesium (Blanc-Potard and Groisman, 1997; Pontes et al., 2016). To confirm the protective effects of the mgtC and strain DK8 were not due to a depolarized membrane potential, we measured basal membrane potential with TMRM and observed both were significantly more polarized as compared to WT (Table 1, Figure 5—figure supplement 2). Based on our model and previous data, hyperpolarization enhanced aminoglycoside killing in the absence of other protective effects, yet both of these strains show protected phenotypes, indicating that the loss or inhibition of the F-ATPase protected these strains relative to wildtype populations. These data were all consistent with aminoglycosides inducing membrane hyperpolarization from ATP hydrolysis via the F1Fo-ATPase, ultimately leading to cell death (Figure 5—figure supplement 3).
Measurements of basal membrane voltage for protective strains
Strain + treatment
Fluorescent dye
Meanemission (AU)
SD ofemission (AU)
p-value (T-test)
Voltage estimates (mV)
Voltage estimate variance
BW+CCCP
TMRM
399.86
104.09
0.02
−133.66
−34.79
BW+None
TMRM
716.67
47.76
1
−150
−10
DK8+None
TMRM
1123.41
218.67
0.08
−162.59
−31.65
mgtC118+None
TMRM
1062.52
202.43
0.09
−161.03
−30.68
BW+CCCP
TMRM
380.74
23.37
<0.01
−130.17
−7.99
BW+pH6.0
TMRM
535.72
24.2
<0.01
−139.73
−6.31
BW+pH7.5
TMRM
773.04
18.5
1
−150
−3.59
DK8+pH7.5
TMRM
1674.41
8.67
<0.01
−171.64
−0.89
BW+CCCP
DiOC6
17.43
2.55
0.01
−57.69
−8.45
BW+None
DiOC6
471.07
62.41
1
−150
−19.87
DK8+None
DiOC6
724.67
162.21
0.1
−162.06
−36.27
Discussion
Aminoglycosides are well established to bind and exert pleiotropic effects on ribosomes (Taber et al., 1987; Borovinskaya et al., 2007; Mehta and Champney, 2002), and numerous reports highlighted the importance of maintaining a membrane potential in aminoglycoside activity. This evidence included voltage-dependent aminoglycoside uptake (Leviton et al., 1995) and cell death correlated with the citric acid cycle and carbon source (Allison et al., 2011; Su et al., 2018; Meylan et al., 2017). Metabolic changes can likewise induce changes in membrane voltage and the overall proton motive force. The relationship between metabolism, proton motive force, and membrane potential has been typically seen as being requisite to the uptake of aminoglycosides, which was synonymous with cell death (Taber et al., 1987; Nichols and Young, 1985). Our work has shown that membrane voltage is not essential for drug uptake, but rather the voltage is required to initiate the bactericidal mechanism after ribosome dissociation. Although we show a correlation between the ionic imbalance (calcium and pH transients) and cell death, we did not definitively prove they cause cell death, but rather they provide a convenient metric for cell death at the single-cell level. The GCaMP signal in our hands is certainly more accurate than PI uptake. Our data also does not preclude a mechanism of voltage enhanced aminoglycoside uptake (Ezraty et al., 2013; Taber et al., 1987). Rather our work suggests that the uptake of aminoglycosides in the absence of a membrane potential is sufficient to create intracellular conditions, including ribosome dissociation, metabolic dysfunction, and pore formation, that allow the presence of a membrane potential to exert bactericidal effects. Our data is also consistent with other translation inhibitors hyperpolarizing membrane potential correlated with subsequent cell death (Lee et al., 2019). We provide evidence that one mechanism by which this hyperpolarization can occur is through F1Fo-ATPase activity. We observed enhanced aminoglycoside killing in the strain atpC::kanR, which is missing the F1Fo-ATPase ε-subunit that typically biases the rotor in the direction of ATP synthesis. This observation suggests that F1Fo-ATPases with a higher likelihood of ATP hydrolysis enhance aminoglycoside killing, which would stem from the already ribosome-related dysregulation of metabolism. We observed similar enhanced aminoglycoside killing in the strain atpH::kanR that encodes the δ-subunit of the F1Fo-ATPase, which is able to block proton conduction (Engelbrecht and Junge, 1990) and ATP hydrolysis (Xiao and Penefsky, 1994). Together, these data suggest that the difference between bactericidal activity of aminoglycosides compared to the bacteriostatic activity of other translation inhibitors may be the lack of the mistranslated membrane proteins causing pore formation. We hypothesize this mechanism kills bacteria by eliminating ion homeostasis in the presence of a membrane potential and pores that can leak ions. However, we currently lack tools to be able to induce the calcium transients in the absence of aminoglycosides, although perhaps channel rhodopsins will be able to mimic these effects.
One fascinating facet that remains to be explored is the period after aminoglycoside treatment that cells cannot divide but remain metabolically active for at least 2 days. If these arrested cells can still export quorum-sensing molecules, they could send paracrine signals to untreated cells, and influence their behavior. This observation became clear by using sensitive genetically encoded fluorescent proteins, and these tools open up a new avenue to study the long-term effects of antibiotic treatment on cells and mixed cultures. Another curious corollary is the observation that protonophores enhance aminoglycoside killing in Pseudomonas biofilms (Maiden et al., 2018; Maiden et al., 2019), which stands in opposition to our observation that protonophores protect planktonic E. coli. The differences driven by these species specific and context-dependent observations will hopefully add to a more complete picture of aminoglycoside activity in multiple bacterial species.
The model of aminoglycoside-induced death proposed from this work is consistent with evidence from other groups previous work, requires the presence of membrane pores and membrane potential to drive aminoglycoside bactericidal activity (Figure 5—figure supplement 3). Aminoglycosides enter the cell through an unknown mechanism, possibly through channels such as mscL (Wray et al., 2016), which occurs long before a loss of membrane integrity. Once aminoglycosides enter the cell they bind ribosomes, disrupt a majority of translating 70S particles and cause mistranslation of protein (Kohanski et al., 2008; Dubin and Davis, 1961). As soon as ribosome disruption occurs, respiration (Lobritz et al., 2015) and metabolism (Levin et al., 2017) go through a substantial shift in flux. This disruption of metabolism enables non-canonical generators of membrane potential, such as the F1Fo-ATPase to drive changes in membrane potential. Why voltage is so toxic in the presence of the mistranslated membrane proteins remains to be explored; however, this shift in understanding the role voltage plays in aminoglycoside lethality will hopefully provide a necessary rethinking of how these antibiotics function so much more effectively than other translation inhibitors. The difference between these mechanisms of bactericide and stasis could lead to novel antibiotics that impinge on the aminoglycoside mechanism of action.
Plasmids generated in this study are available on Addgene. Knockout strains from the Keio collection are available through Dharmacon due to an MTA. Other reagents are available upon request, and will be fulfilled by the Lead Contact, Joel Kralj (joel.kralj@colorado.edu).
Experimental model and subject details<italic>E. coli</italic> strains
E. coli strain BW25113 was acquired from the Yale Coli Genetic Stock Center and was used as the control, except experiments where specifically noted. Knockout strains were acquired from the Keio collection purchased from Dharmacon (#OEC4988). E. coli strain DK8 1100∆(uncB-uncC)ilv::TnlO, which is deficient of the F-ATPase was a generous gift from the Rubinstein lab.
Cell growth
Strains were grown in LB with antibiotics dependent on growth conditions. For GCaMPmScarlet expressing cells, clones transformed with the plasmid were grown overnight with carbenicillin (100 µg/mL). Carbenicillin was used for overnight cultures to maintain the plasmid but was not present for any experiments. For knockout strains from the keio collection kanamycin (50 µg/mL) was also added to any overnight cultures. Strain DK8 was grown overnight in the presence of tetracycline (30 µg/mL). Glycerol stocks were streaked onto plates bearing the appropriate antibiotics, and individual colonies were picked and grown in 5 mL culture tubes, or in 24-well plates, or in 50 mL Erlenmeyer flasks. All cells were grown overnight at 37°C with shaking between 150 and 200 rpm with the appropriate antibiotic if required for plasmid or strain selection. Knockouts from the Keio collection were plated on LB plates with kanamycin and carbenicillin to ensure maintenance of the knockout cassette, but overnight liquid cultures that were to be used for imaging were grown only in the presence of carbenicillin to avoid any potential effects of protein translation inhibition on sensor expression.
Method detailsPlasmids
Expression of GCaMP6f-mScarlet was carried out with a constitutive promoter (118, iGem biobrick) assembled in an ampicillin-resistant plasmid similar to earlier work (Bruni et al., 2017). The mScarlet amino acid sequence was taken from the original publication (Bindels et al., 2017) and purchased as a gBlock (IDT). The plasmid was double digested with Pme1/Nco1 and assembled using Gibson assembly. The mRuby-iATPSnFR1.0 construct was created by obtaining the amino acid sequence directly from the publication (Lobas et al., 2019) and codon optimizing it in a single gBlock ordered from IDT, then Gibson cloned into the same constitutive promotor backbone as GCaMP6f-mScarlet. Expression of these constructs was carried out in the 118 plasmid. Expression of GCaMP6f alone was carried out using a similar constitutive promoter (100, iGem biobrick) in the same backbone. The mgtC over expression plasmid was created by obtaining the amino acid sequence directly from salmonella on a gBlock, and Gibson cloned into the 118 biobrick backbone used above. GCaMP6f tethered to pHuji was purchased on a gBlock and Gibson cloned into the same constitutive biobrick 118 promoter used previously. All novel plasmids and sequences have been deposited on Addgene. All plasmids were transformed into their respective genetic background strain using Transfer Storage Solution transformation protocol.
Imaging media and fluorescent dyes
Unless otherwise noted, all imaging experiments were conducted in PMM at pH 7.5. The PMM recipe used is: 1x M9 salts (Sigma), 0.2% glucose (Sigma), 0.2 mM MgSO4, 10 µM CaCl2, 1x MEM amino acids (Gibco). Experiments were conducted at pH 7.5 unless otherwise noted in the text, and NaOH or HCl was used to change the pH to the final value. Given the critical importance of pH in aminoglycoside response, all PMM media with additional chemicals was pH adjusted to 7.5 before imaging. At more basic pH, and higher concentrations of Mg, precipitate forms in this media over time. For oxygen free microscopy experiments, Oxyrase for Broth was added to the media pads during the pre-imaging incubation time to 10% v:v, then sealed to have oxygen removed.
Propidium iodide (Life Tech) was dissolved in water in a stock concentration, and added to a final concentration of 3 µg/mL. PI was imaged with a 561 nm laser in a flow experiment, and was added at the same time as 30 µg/mL kanamycin.
Gentamicin Texas Red (GTTR) was synthesized using a previously described protocol (Saito et al., 1986). Texas Red-succinimidyl ester (Invitrogen) was dissolved in anhydrous N,N-dimethylformamide on ice to final concentration of 20 mg/ml. Gentamicin was dissolved in 100 mM K2CO3, pH 8.5, to a final concentration of 10 mg/ml. On ice, 10 µL of Texas Red was slowly added to 350 µL gentamicin solution to allow a conjugation reaction. The gentamicin-Texas Red product from this reaction was used for the imaging experiments. Gentamicin uptake was measured by incubating gentamicin-Texas Red (final concentration of 10 µg/ml) simultaneously with GCaMP6f in a flow experiment.
TMRM (ThermoFischer) was dissolved in DMSO in a 1 mM stock solution, and diluted in PMM to 8 µM, then added to a final concentration of 200 nM to cell suspensions. TMRM was measured as described below in flow cytometry.
DiOC6(3) was dissolved in Ethanol to a 10 mM stock solution, and diluted in PMM to 1 mM, then added to a final concentration of 1 µM to cell suspensions. DiOC6(3) was measured as described below in flow cytometry.
Preparing cells for imaging
All imaging of cells took place under agarose pads which were composed of PMM at the appropriate pH and 2% low melt agarose.
For experiments using flow, the agarose was melted in PMM buffer and cast between 2 pieces of glass covering a silicone mold. The silicone was 3/16’ as the final thickness, and was cut by hand. The pads were diced into small squares using an exacto knife to fit into the flow chambers (~2 mm x 2 mm). Cells from an overnight culture were placed directly on to the agarose pad (1.0 µL) and left for ~5 min. The agarose pads were then placed with the cells down onto a 24 mm x 50 mm glass coverslip (thickness 1.5) with a silicone flow chamber. The apparatus was then sealed with a custom glass slide with holes drilled to enable flow.
Experiments involving drug titrations or knockouts were prepared onto 96-well glass bottom dishes (Brooks Automation, MGB096-1-2-LG-L). A custom 96-well mold was created and 3D printed using a commercial service. The mold was designed to hold a volume of 200 µL per well (Shapeways), with a separate piece designed to press the agarose pads into the coverslip in 8-, 12-, or 96-well format. The 3D printed pieces are available at the Kralj Lab store on Shapeways (https://www.shapeways.com/shops/kraljlab) and the. stl files are available to researchers upon request. The bottom of the agarose mold was sealed with a 4’ x 6’ piece of glass (McMaster Carr), and liquid agarose was added to the desired wells. A second piece of glass (3’ x 5’) was used to seal both sides, and the agarose was left to cast for >1 hr. The glass piece was then removed, and cells were added to each pad individually (2 µL) and left for 10 min for the liquid to absorb into the agarose. The cells were then pressed out into the 96-well plate using the custom 3D printed press. For all experiments, cells were left in the pad for ~1 hr before imaging. Any chemical treatments were then added to the top of the pad. A 5 µL drop of a solution at 40x final concentration was added on to the pad and left to diffuse throughout. In house measurements with a small fluorescent dye showed compounds diffuse to the glass in ~5 min.
Imaging
Flow experiments were conducted using a Nikon TiE base with perfect focus, running Elements software, with a custom laser illumination with high angle illumination. A 488 nm (Obis 150 LX, Coherent) or 561 nm (Obis 50 LS) were combined, expanded, and focused onto the back aperture to create a widefield illumination. A mirror located 1 f away from the widefield lens was used to control the illumination angle. Imaging took place with a 100x NA 1.45 objective with intensities (at the sample) of 130 mW/cm2 488 nM and 1050 mW/cm2 561 nm light. A quad band emission filter (Semrock) was used for reflecting the illumination light, and no emission filter was needed. The light was imaged onto an Andor EMCCD (iXon 888 Ultra) using an exposure time of 200 ms. Images were acquired sequentially (561 nm, then 488 nm) once per minute over the entire experiment (6–48 hr). These illuminations showed no evidence of phototoxicity compared to unilluminated cells as measured by growth rate.
Flow was controlled with two identical syringe pumps (Harvard Apparatus). Flow rates were set to 20 µL/minute which was sufficient to fully exchange the medium in the chamber within 2.5 min. Each syringe pump was loaded with the appropriate medium and was programmed to turn on or off at the desired time. A typical experiment involved 2 hr of PMM alone, followed by switching to PMM+Kan using the second pump. Tubing from multiple syringes was connected with a T-connector with a dead volume of ~20 µL. At all times during flow cell experiments, the specified media was flowed through the chamber.
Imaging 96-well glass bottom plates took place using a Nikon Ti2 inverted microscope running the Elements software package. Fluorescent excitation was achieved with a Spectra-X LED source (Lumencor). A 40x, NA 0.95 air objective was used to both illuminate and image the cells onto 2- Flash 4 v2 sCMOS cameras (Hamamatsu) using a custom splitter to image two colors simultaneously (Thorlabs). Illumination was achieved by simultaneous excitation with 470/26 and 554/20 band pass LED illumination for a 200 ms exposure. Measured light intensities at the sample were 330 mW/cm2 (470 nm) and 2050 mW/cm2 (554 nm). Typical sampling rates were one frame per minute, unless noted in the text.
CFU measurements
CFUs were measured by plating-treated cells onto LB-agarose without antibiotic and counting growing colonies. CFU measurements were conducted trying to mimic the experiments performed via microscopy. Briefly, cells were grown overnight in LB and diluted 1:20 in 5 mL PMM. These cultures were grown at room temperature and shaking for 2 hr (t = 0) followed by the addition of antibiotic. At each time point, the culture was removed from the shaker, and 100 µL was removed. A 10x series dilution was then conducted by removing 20 µL and adding to 180 µL LB alone in a 96-well plate. The 10-fold dilution was performed seven times, leading to the original concentration to a dilution of 107. From each of the 10x dilution series, 3 µL was plated onto an LB agar pad and left to dry (one colony = 333 cells/mL, lower end of our dynamic range). After an entire experiment (typically 5 hr), the agar was placed into an incubator and grown overnight. Colonies were then manually counted the next morning.
Cell cytometry
A 5 ml of PMM media was seeded with 50 µL of overnight BW25113 cells, or the respective knockout strain tested. When the cells reached ~0.4 OD, 100 µg/ml kanamycin, 10 µg/ml gentamicin, or PMM alone was added. After 30 min of antibiotic or mock treatment, TMRM or DiOC6(3) was added to the suspension at a final concentration of 0.2 mM or 1 µM, respectively. Two hours later, 1 mL of cell suspension was transferred to a 1 mL Falcon polystyrene round-bottom tube. Cells were quantified for their TMRM incorporation by counting 100,000 events per condition using a BDFACSCellesta Flow Cytometer with the following Voltage settings: FSC at 700, SSC at 350, with 561 nm laser D585/15 at 500, C610/20 at 500 and B670/30 at 481. Emission for each event was collected at the 585/15 nm wavelengths. Cells were quantified for their DiOC6(3) incorporation by counting 100,000 events per condition using a BDFACSCellesta Flow Cytometer with the following Voltage settings: FSC at 700, SSC at 350, with 488 nm laser B 530/30 at 350. Emission for each event was collected at the 530/30 nm wavelengths.
Bactiter glo ATP analyses
ATP per optical density unit was quantified using Promega’s BacTiter-Glo kit coupled with a BioTek Synergy plate reader. BacTiter-Glo reagents and standards were prepared as described in the manual. Briefly, exponentially growing cultures of E. coli were treated according to the experimental parameters for the times indicated. When the time of treatment was reached 100 µL of culture, blank, or standard, was added to a black walled clear bottom 96-well plate. This was done in technical triplicate for each condition, blank, or standard, which had at least three biological replicates. Once the plate was prepared 100 µL of BacTiter-Glo Reagent was added to each well, and shaken in an orbital shaker for 1 min at room temperature, then left on the benchtop for 5 min. Luminescence was recorded using the BioTek Synergy plate reader, set to auto scaling and 1 s integration time. Simultaneously with BacTiter-Glo plate preparation, an optical density plate was created with the same cultures, and the absorbance of the culture was read on the same plate reader at 600 nm. ATP per OD unit was calculated by the average of the three biological replicates (which were averaged from the technical replicates), which were then divided by the obtained OD values.
Polysome analyses
Sucrose gradients were prepared in Beckman Coulter Ultra-Clear Tubes (14 × 89 mm) Reorder No. 344059. Media recipes and protocol is from Qin and Fredrick, 2013. Roughly 6 ml of 10% sucrose was layered on the bottom of the tube, then a large needle was used to add 40% sucrose below the 10% layer up to a 6 ml marker on the outside of the tube. If a clear meniscus between the two layers was not visible the tube was discarded. Tubes were placed in a MagnaBase tube holder (sku B105-914A-I/R), and short caps were placed on top to eliminate all air from the tube. The tube holder was then placed on the gradient maker. A 10–40% gradient was then established using a BIOCOMP Gradient Station ip gradient maker with the following settings: Short cap, Sucrose, 10–40%wv, 81°, 1:48 min:sec. Caps were then removed, and gradient tubes were stored no longer than 1 hr at 4 °C until lysate supernatant was prepared.
Ribosomes and ribosomal subunits were characterized using a slightly adapted protocol, due to differences in available equipment, from Qin and Fredrick, 2013. Briefly 50 mL cultures were grown to ~0.35–0.45 OD. Antibiotic, or a mock treatment was then added, and these cultures were allowed to grow for another 1 (LB, Figure 1B) or 1.5 (PMM, Figure 1C). Aminoglycosides enter cells and induce ribosomal dissociation in the abscence of membrane voltage.; Figure 1—figure supplement 1. Aminoglycosides enter cells and induce ribosomal dissociation in the abscence of membrane voltage.B1-S1BF1F1-S1,) hr. Optical Density was taken at time of collection, when 37.5 ml of culture was then transferred to Nalgene Oak Ridge Centrifuge Tubes (Cat. 3119–0050) on ice. Cells were then pelleted in a chilled Sorvall SA-600 rotor in a Sorvall RC 5C Plus Centrifuge at 10,000 rpm for 5 min at 4 °C. Culture media was decanted and aspirated. Cell pellets were then resuspended in 500 µL lysis buffer (750 µL for anaerobic conditions), and flash frozen in liquid nitrogen. Frozen suspensions were thawed in a 5–10°C water bath, then flash frozen again, and either stored at −80 °C or thawed in the same manner and treated as follows. Lysis was completed by adding 15 µL of 10% sodium deoxycholate to freeze-fractured pellet resuspensions and mixed by inversion. Lysate was then separated by centrifugation at 4 °C at 10,000 rpm in a chilled Eppendorf FA45-30-11 rotor in an Eppendorf 5804R Centrifuge for 10 min at 4 °C. Lysate supernatant was collected in chilled microfuge tubes. Then 300 µL of the 10–40% gradient was removed from the top of the sucrose gradient columns and replaced with 300 µL of lysate supernatant.
Loaded gradient columns were placed in Beckman SW-41 swinging buckets and balanced to within 0.01 g of each other using the 10% sucrose solution. Loaded sucrose gradient buckets were then centrifuged using the SW-41 rotor in an LM-8 Ultracentrifuge in 4 °C at 35,000 rpm for 3 hr. Sucrose gradient columns were then removed, and fractions were then collected using the following series of machines. A BIOCOMP Gradient Station IP with settings Distance 80.00 mm, Speed 0.3 mm/s was tethered to a BIORAD Model 2110 Fraction Collector with the following settings: six drops/fraction. As fractions were collected the absorbance at 254 nm was collected from the fractions using a BIORAD Econo UV Monitor set to range 1.0 (AUFS) tethered to computer running the BIOCOMP Gradient Profiler 2.0 software. Data files for each gradient run were saved as. csv files and later analyzed in Matlab using custom scripts to integrate peaks with the trapz.m function.
Due to the nature of collection with these devices, often the beginning of the non-ribosomal RNA peaks was missed, capturing the absorbance as the non-ribosomal RNA ran through the detector midway through the peak. In all conditions tested, non-ribosomal RNA, 30S, 50S, and 70S peaks were detected. To simplify comparisons between conditions, polysomes beyond the 70S peak were ignored in the (30S+50S)/70S ratio measurements. Note that because of the nature of these experiments, different total quantities cell lysate, and therefore of total RNA, are loaded into the sucrose gradients columns. Due to this reality, comparing the 254 nm absorbance quantities between samples is unreasonable; however, comparing the ratio of the ribosome peaks should be total-RNA agnostic.
Quantification and statistical analysisImage processing
Data was stored as. ND2 files which contain the 16 bit images and the associated metadata. The BioFormats Matlab package was used to access data in the. ND2 format. All data analyses were performed using custom scripts in Matlab (available upon request).
Image processing followed the general scheme of (1) estimating the illumination profile for all experiments on a given day, (2) correcting the uneven illumination for each movie, (3) registering drift and jitter in XY, (4) subtracting an estimated background, (5) segmenting cells using a Hessian algorithm, (6) extracting time traces for individual cells, (7) processing each time trace for the onset and amplitude of calcium transients.
Estimating the illumination profile: For a given day, every movie was averaged across time, and opened using a morphological operator and blurred using a 2D Gaussian filter. Each of these experimental images were then averaged together to give an estimate of the uneven illumination. These images were smooth across the entire field of view, and varied by ~50% across the entire image.
Correcting uneven illumination. Each individual movie was then loaded into memory sequentially. Each frame of the movie was converted to a double, and then divided by the uneven illumination. This image was then multiplied by the average value of the movie and converted back into a uint16 to maintain consistent intensity values. Each frame was then reassembled into an illumination corrected movie.
Registering drift and jitter in XY: Each frame was aligned to the previous frame using a convolution of the 2D Fourier transform. Each sequential image was first estimated by applying the XY warping from the previous frame. Then, the 2DFT was taken for each image, and multiplied to the previous frame. The optimal updated XY position was then calculated and applied.
Subtracting the estimated background: The background was estimated for each frame individually using a morphological operator. A disk structured element with radius 9 µm was blurred with a Gaussian filter. This background estimation was then subtracted from the original image. To protect against potential negative values, the minimum of the entire movie was set to 50 counts.
Segmenting cells using a Hessian algorithm: To segment cells, first the foreground was estimated using Otsu’s method from the background subtracted image. The Hessian was then calculated on the background subtracted image, and then elementwise multiplied to a logical image of the foreground. Otsu’s method was again used on this modified Hessian image to identify individual cells. Hard limits were set to remove potential noise that did not fit given criteria for size or minimum intensity. We found that first increasing the size of the image using a spline interpolation gave superior segmentation results. Using this method, not all cells were identified within a microcolony, though we estimate that it can identify ~96% of the cells accurately.
Extracting time traces for individual cells: From a given identified cell, for each time point in the movie, we extracted the mean intensity using the Matlab command, regionprops. The mean intensity for both the GCaMP6f and the mScarlet were extracted using this method, or any other fluorophore the cells expressed.
Processing each time trace for the onset and amplitude of transients: For each time trace, the moving median over 45 min was divided to remove the slow baseline trends. A standard deviation was calculated from the timepoints before aminoglycoside addition, and a cell was defined as blinking if it had transients that lasted >10 min that were >7 x the pre-treatment standard deviation. From each normalized time trace, the moving standard deviation was also calculated using a 30 min sliding window. Within a given FOV, the entire population moving standard deviations was averaged, providing the average standard deviation trace shown in the figures.
During flow experiments, single frames were sometimes contaminated by bubbles that dramatically changed the contrast. To remove these features, we took the average of all extracted cells. If the differential of any single frame was initially lower, then higher than 5x the standard deviation of the whole movie, a single frame was removed. This preprocessing removed spurious catastrophic blinks that appeared to occur in every cell at the same instant.
Cytometry analysis
E. coli energize their membrane through a proton motive force (PMF) that powers their flagellar motors and several membrane pumps. The PMF is the amount of free energy gained by a proton moving from one side of the membrane to the other, and the energy can be gained either by changes in pH (proton gradient) or voltage (membrane potential). The Nernst equation sets an equivalence between changes in pH and voltage as:PMF=∆ψ*-58mV(ΔpH)
E. coli typically try to maintain a cytoplasmic pH around 7.5, so that a changing extracellular pH will induce a corresponding change in the PMF. For example, if the extracellular pH is at pH 7.5, then there is no pH difference, so all of the PMF will be carried in the voltage component, which would be accomplished by establishing ionic gradients using pumps and channels. On the other hand, if the extracellular pH is low, for example pH 6, then the PMF could have a value −87 mV without having to maintain any voltage component. The PMF could be carried entirely by the change in pH, which could drive the flagellar motors and other PMF dependent processes in the membrane.
Thus, by changing the environmental pH from 7.5 to 6, we can lower the membrane voltage by fact that the cell will utilize the pH component of PMF without the need to generate an external voltage from other ions.
Analysis of the cytometry data was achieved by fitting the data to a 1D Gaussian distribution and calculating the mean and 95% confidence interval for each of these fits for each strain tested. These values were then taken as a ratio of the gentamycin-treated cells relative to the vehicle-treated cells.
Assuming TMRM partitions according to Boltzmann’s law:CinCout=e−qkTVwhere Cin and Cout are the concentrations of the dye in and out of the cell, q is the ionic charge, k is the Boltzmann constant, and T is the temperature in kelvin. Comparing two different conditions (Vkan and VPMM), we can solve for the treated condition to yield:VKan=VPMM−28mV*ln(Cin,KanCin,PMM)if we assume the concentration of dye out of the cell is the same in both conditions. Given a large reservoir relative to the cytoplasmic volume of the cells, this is a reasonable estimate. These same assumptions and calculations were applied to the values in Table 1, as well as for the dye DiOC6(3).
Significance testing
Significant differences across populations of individual cells were tested using the unpaired t-test with unequal variance. For cytometry experiments, we used the 95% confidence interval (CI) to a single Gaussian fit.
Acknowledgements
Thanks to Annette Erbse and Keda Zhou for help with ribosomal profiling, Theresa Nahreini for help with cytometery, and Karolin Luger, Joe Falke, Tom Cech and the Biochemistry Shared Instruments Pool at the University of Colorado Boulder for equipment resources. We thank Stephanie Bueler and John Rubinstein for the DK8 strain and helpful discussion. We had helpful discussions with Corrie Detweiler, Amy Palmer, Ben Dodd, Stacey Scott, and Rose Luder. Special thanks to Thomas Yao for many helpful discussions. Funding: Searle Scholars Program and NIH New Innovator (1DP2GM123458) to JMK, T32 training grant (T32GM065103) and HHMI Gilliam Fellowship for Advanced Study to GNB Flow cytometer was acquired with an instrumentation grant (NIH S10OD021601).
Additional informationCompeting interests
No competing interests declared
Author contributions
Software, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing
Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing
Additional filesData availability
All data have been submitted on Dryad.
The following dataset was generated:
BruniGNKraljJM2020E. coli aminoglycoside treatmentDryad Digital Repository10.5061/dryad.fxpnvx0pp
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In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
Acceptance summary:
Although aminoglycosides are important, clinically relevant antibiotics; the precise mechanism by which they kill bacteria has remained unclear and somewhat controversial. This paper uses clever imaging approaches based on a voltage-sensitive reporter to provides evidence that their bactericidal activity stems from a hyperpolarization of the inner membrane of cells rather than affecting uptake, or at least rather than affecting only uptake.
Decision letter after peer review:
[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]
Thank you for submitting your work entitled "Membrane voltage dysregulation, not uptake, underlies bactericidal activity of aminoglycosides" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The reviewers have opted to remain anonymous.
Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.
The reviewers each highlighted the general importance of the work and the notion that this work may be revealing an important new angle on aminoglycoside mechanism of action. However, there were significant concerns raised about whether the calcium transients were simply correlated with cell death or truly causal. And precisely how calcium transients would, at a mechanistic level, result in cell death was unclear. These concerns are laid out in some detail in the individual reviews provided below and we hope they will be helpful to the authors.
Reviewer #1:
This paper tackles an important and interesting problem, namely why aminoglycosides are bactericidal. Relying largely on a GCaMP6 reporter that is a calcium sensor, the authors provide some data to support a claim that aminoglycosides trigger hyperpolarization of the cell, which is ultimately lethal. Although this represents an intriguing new angle on aminoglycoside mechanism of action, I thought some of the results were overinterpreted and at the end of the paper I'm still left wondering (i) how aminoglycosides lead to the purported increase in ATP that the authors think drives reverse action of the F1Fo ATPase to produce hyperpolarization and (ii) how hyperpolarization ultimately kills cells.
1) Figure 2D: It definitely seems like low pH reduces transients, but the authors should verify that there is, in fact, less killing by aminoglycosides in this low pH environment.
4) For the results in Figure 3E, the authors argue that there's no correlation between GTTR uptake and calcium transients, but there seems to be some sort of pattern in which GTTR uptake increases after a long burst of transients, i.e. toward the end of each trace shown. So, I'm not fully convinced there's no relationship.
3) "However, single cell data showed the initiation of transients and the uptake of PI were not correlated. The catastrophic transients started before dye uptake in all observed cells." But couldn't it be that catastrophic transients arise more easily than PI uptake but that both are reporting on aminoglycoside-induced membrane defects?
4) I guess I don't really understand why pH and protonophore CCCP both diminish transients and aminoglycoside efficacy. Do these treatments prevent hyperpolarization or aminoglycoside uptake or something else? I can see why CCCP likely prevents hyperpolarization by dissipating the PMF, but then shouldn't acidic pH conditions exacerbate not rescue the effects seen?
5) The increase in CFU in Figure 4E following addition of CCCP at 1 hr is not particularly large – it looks like an increase of only 2-3 fold. Given the central importance of this experiment to the model, I'm worried about overinterpretation of modest effect sizes here.
6) In Figure 5 I think it's essential for the authors to measure ATP concentration in a more direct way than their fluorescent reporter, which doesn't seem to have a particularly large dynamic range.
7) Why do large calcium transients kill cells?
8) I don't really understand why aminoglycoside increase ATP levels as hypothesized based on the results in Figure 5. If it's just a matter of inhibiting translation, then shouldn't a bacteriostatic antibiotic like chloramphenicol show a similar increase in ATP?
Reviewer #2:
In the manuscript by Bruni et al., from Kralj's lab, the authors proposed that aminoglycoside bactericidal action arises from dysregulated membrane potential. The authors used a genetically encoded calcium sensor previously developed in Bruni et al., 2017.
The Introduction is very short and could contain more information about what is known on aminoglycoside uptake (EDP-I and II, PMF, feed forward loop,… Taber et al., 1987).
The results are interesting, provocative and conceptually new. However, clarifications, controls and new experiences are needed to support their conclusions.
Essential revisions:
My main concern is that the authors consider the uptake as an aminoglycoside mechanism of action. This point of view leads to surprising conclusions as in the title of the paper:
“Membrane voltage dysregulation, not uptake, underlies bactericidal activity of aminoglycosides.”
For me, aminoglycoside act on ribosomes, so uptake is essential, without uptake no death. The authors use a fluorescent gentamicin (GTTR) to track the uptake and observe that the calcium transients appear before the drug uptake. Is GTTR fluorescence detection sufficiently sensitive for this type of experiment?
It will be interesting if the authors could use their tools (GCaMP6, GTTR) in the conditions previously used in others studies to support their conclusions: adding chloramphenicol to block translation and EDP-II step; used PMF altered strains (nuo sdh, Fe-S biosynthesis) and Gm-resistant strain.
In the same vein, the authors need to clarify the impact of low pH, anoxic medium and CCCP (subsection “Aminoglycosides induced catastrophic calcium transients”), these three conditions are known to decrease PMF (essential for aminoglycoside uptake). Instead, the authors talk about "environments that diminish aminoglycoside efficacy".
The most surprising result is that even in presence of CCCP, the ribosomes are still dissociated with aminoglycoside, leading to the conclusion that voltage is not necessary for aminoglycoside uptake. Is it possible to detect GTTR fluorescence in the presence of CCCP?
In all experiments, the GCaMP6 fluorescence tends to increase even before adding drugs. Given that previous study of the same lab have shown that using agarose pad lead to a voltage-induced calcium flux via a local mechanical environment, it will be essential to represent in a same figure the control without aminoglycoside. Even though in the text (subsection “Aminoglycosides induced catastrophic calcium transients”) this comparison is made.
Reviewer #3:
Bruni and Kralj present data supporting that bactericidal activity of aminoglycosides relies on membrane voltage dysregulation. Specifically, the authors describe an interesting observation of fast calcium fluctuations in E. coli, under the addition of aminoglycosides that correlates with cellular death. Driven by this observation, they perform additional experiments and conclude that aminoglycosides increase ATP concentration inside bacterial cells, which reverses the F1Fo-ATPase activity, causing hyperpolarization and eventually cell death. Together, this work provides a better understanding of the bactericidal mechanism of antibiotics, which is very significant. In general, I found the paper meaningful, but I have major concerns regarding the main claims, and specifically regarding the proposed mechanism of action.
Essential revisions:
1) The authors claim that the increased cellular ATP reverses the activity of the F1Fo-ATPase generating a hyperpolarized state of the cell. While I agree with the authors that reverse activity of F1Fo-ATPase can cause hyperpolarization, it is unclear to me if this is the only, or even the major mechanism of hyperpolarization upon aminoglycoside treatment. The authors present two lines of evidence to argue their point: ATPase knockout mutants and mgtC expression data.
My first concern with the author's claims is that while some ATPase knockouts show a reduction of aminoglycosides action, there is no single mutant that abolishes the effect (changes in mean SD of calcium transient or recover CFUs). This is in contrast to the results where the authors truly abolish the hyperpolarization through other means such as CCCP or pH. This discrepancy suggests the possibility that the reverse activity of F1Fo-ATPase may not by the sole cause, or even the major cause of the hyperpolarization.
Second, the expression of MgtC eliminates the bactericidal activity of aminoglycosides. MgtC is a magnesium ion transporter, and thus it is also plausible to think that increased magnesium ion uptake prevents hyperpolarization.
2) Contrary to the authors claim, I could not find evidence in the manuscript to support that the catastrophic calcium transients are the direct cause of cell death. Even more, the fact that any cell that experienced calcium transients in the untreated condition did not divide, but did also not die (Introduction), provokes the question whether calcium transients are the true cause of death.
The authors would be better off, stating the relationship between calcium transients and cell death as a correlation rather than causation. However, if the authors choose to stick with their claim of causality, they have to show that calcium transients itself are sufficient to kill the cell, regardless of potential other effects related with hyperpolarization. Perhaps the authors could use a specific calcium chelator, such as BAPTA, to prevent calcium transient and therefore cellular death?
Below I detail my major concerns regarding the claimed mechanism of action:
- One of the most important results in this manuscript is that some ATPase knockouts affect calcium transients. To show the change between these mutants and WT strain, the authors measured differences in the mean GCaMP6 SD signal. My problem is that the measurements for different knockouts were done at different time points (Figure 5D top and bottom. See Table 1). I don't think that this is the proper way of comparing strains. It is unclear why the authors chose this strange comparison.
- Why is there so much variation of the GCaMP6 signal between WT replicas? For example, the same Figure 5D, and Figure 2—figure supplement 4C, Supplementary igure 5A, and 5E. In some cases, the variation between different WT replicas could be even larger than the difference between ATPase mutants and the WT strain.
- Furthermore, shouldn't the ATP concentration decrease if F1Fo-ATPase keeps hydrolyzing ATP?
- It is also not clear why the atpC knockout should have a higher membrane potential than WT. In the discussion, the authors do not mention anything about atpC.
- The authors do not mention why calcium transients (and possibly increase in ATP concentration) are not observed in other antibiotics that also impair ribosomal activity, such as chloramphenicol?
- Importantly, GCaMP6 signal doesn't report directly on the membrane potential. Therefore, to determine whether hyperpolarization induces a positive feedback on gentamycin uptake, it would be necessary to use a more direct membrane potential reporter, such as the one the authors used for their other measurements.
[Editors’ note: further revisions were suggested prior to acceptance, as described below.]
Thank you for submitting your article "Membrane voltage dysregulation driven by metabolic dysfunction underlies bactericidal activity of aminoglycosides" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Daisy Lee (Reviewer #3).
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.
Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.
This paper examines the mechanism of killing by aminoglycoside antibiotics, providing evidence that their bactericidal activity stems from a hyperpolarization of the inner membrane of cells rather than affecting uptake, or at least rather than affecting only uptake. This revised version of the manuscript was deemed substantially improved by the reviewers and they are each enthusiastic about the work and the prospect of publishing it in eLife. However, one of the reviewers raised a couple of important points about the role of ATP and the reversal of ATP synthase that the authors should address, either by providing additional data or by adjusting the language in the papers and the claims made.
1) The revised manuscript still does not include an experiment addressing aminoglycoside uptake. Did the authors have any alternative to replace GTTR?
2) It is unclear what 'by metabolic dysfunction' in the title means. Do the authors mean increased intracellular ATP? While the abstract claims that 'the hyperpolarization arose from altered ATP flux', it is hard to understand why or how F1Fo ATPase reverts its action only when the membrane potential is high enough (inside is more negative), not in the other way around (inside is not as negative – it seems to me it's better to revert the action in this case since then they can increase the membrane potential to the normal level through reverting it…).
3) Throughout the data, it seems that the membrane potential plays a critical role in exerting bactericidal effects. While ATPase mutants and the mgtC expressing strain presented here likely have altered membrane potential without any antibiotic treatment, I couldn't find data showing their basal membrane potential level compared to the WT. Without the basal membrane potential data, it is impossible to discern if the phenotypes of the mutants are due to their basal membrane potential differences or by the author's main claim in the abstract. It would be very valuable if the authors can provide the basal membrane potential level of mutants compared to WT, CCCP added case, and/or pH6.5 case in the study.
10.7554/eLife.58706.sa2Author response
[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]
[…]
Reviewer #1:
This paper tackles an important and interesting problem, namely why aminoglycosides are bactericidal. Relying largely on a GCaMP6 reporter that is a calcium sensor, the authors provide some data to support a claim that aminoglycosides trigger hyperpolarization of the cell, which is ultimately lethal. Although this represents an intriguing new angle on aminoglycoside mechanism of action, I thought some of the results were overinterpreted and at the end of the paper I'm still left wondering (i) how aminoglycosides lead to the purported increase in ATP that the authors think drives reverse action of the F1Fo ATPase to produce hyperpolarization and (ii) how hyperpolarization ultimately kills cells.
We regret having overinterpreted some of the results that we presented. We have reassessed many of our results in the context of ample previous and current evidence related to aminoglycoside induced cell death and attempted to put our results in context of the broader picture of our collective understanding of aminoglycoside’s efficacy.
We have addressed two of the points this reviewer had been left wondering about with the following:
i) We have included a more detailed contextual description of how we think aminoglycosides lead to the altered ATP flux in subsection “ATP dysregulation precedes voltage induced bactericidal killing”.
ii)A more detailed description of how hyperpolarization might be killing cells is included in the Discussion section.
1) Figure 2D: It definitely seems like low pH reduces transients, but the authors should verify that there is, in fact, less killing by aminoglycosides in this low pH environment.
There is evidence of low pH protection in the field (Taber et al., 1987; Davis, 1987; Damper and Epstein, 1981). Unfortunately, we did not have time to address this prior to the COVID related loss of lab activity. We have some data in the lab which shows single cell data correlated with continued growth for up to 16 hours at low pH in the presence of aminoglycoside that we’re happy to share to provide some evidence that growth is still possible, but we do not have the gold standard CFU assay to back it up, and will not until we return to lab.
2) For the results in Figure 3E, the authors argue that there's no correlation between GTTR uptake and calcium transients, but there seems to be some sort of pattern in which GTTR uptake increases after a long burst of transients, i.e. toward the end of each trace shown. So, I'm not fully convinced there's no relationship.
We agree that the correlation in time between GTTR uptake and calcium transients being sustained argues that there could be a relationship between the two. Some data that opposes this positive correlation is in the Materials and methods section Figure 1D, in combination with Figure 4A, B. The presence of CCCP eliminates transients but still allows large fluorescent molecules (like GTTR) to accumulate due to the pore formation aminoglycosides create. We instead focus on propidium iodide as a means to measure membrane breakdown.
3) "However, single cell data showed the initiation of transients and the uptake of PI were not correlated. The catastrophic transients started before dye uptake in all observed cells." But couldn't it be that catastrophic transients arise more easily than PI uptake but that both are reporting on aminoglycoside-induced membrane defects?
Yes. You’re absolutely right. See the Discussion section.
4) I guess I don't really understand why pH and protonophore CCCP both diminish transients and aminoglycoside efficacy. Do these treatments prevent hyperpolarization or aminoglycoside uptake or something else? I can see why CCCP likely prevents hyperpolarization by dissipating the PMF, but then shouldn't acidic pH conditions exacerbate not rescue the effects seen?
PMF is comprised of both voltage and pH differences, so as the external pH is lowered, more of the PMF is carried in the pH term, meaning that less voltage is needed. At pH 6, cells need no voltage in order to maintain ~100 mV of PMF. We have included a more detailed description in the Materials and methods section.
5) The increase in CFU in Figure 4E following addition of CCCP at 1 hr is not particularly large – it looks like an increase of only 2-3 fold. Given the central importance of this experiment to the model, I'm worried about overinterpretation of modest effect sizes here.
The axis was in log scale, but we apologize for not making the increase more apparent in the text. Please see subsection “Voltage toggles between bactericidal and bacteriostatic activity in aminoglycoside treated cells”.
6) In Figure 5 I think it's essential for the authors to measure ATP concentration in a more direct way than their fluorescent reporter, which doesn't seem to have a particularly large dynamic range.
We agree that the iATPSnFR does not have a large dynamic range and in response we did undertake new experiments to test absolute changes. In light of this new experiment, which was contradictory to the iATPSnFR data, we reformulated our explanation and found many pieces of literature that substantiated this new explanation. Please see Materials and methods section, Figure 5—figure supplement 1D, and subsection “ATP dysregulation precedes voltage induced bactericidal killing”.
7) Why do large calcium transients kill cells?
We are unable to say definitively that the calcium transients prevent future cell division, though this remains an intriguing possibility. In the current draft, we are much more specific in saying that we use the calcium transients as means to visualize death, but cannot prove they induce death. We have multiple instances reinforcing that there is no evidence that large calcium transients kill cells including subsection “Single cell calcium flux predicts cellular aminoglycoside response”.
8) I don't really understand why aminoglycoside increase ATP levels as hypothesized based on the results in Figure 5. If it's just a matter of inhibiting translation, then shouldn't a bacteriostatic antibiotic like chloramphenicol show a similar increase in ATP?
Yes. And some non-aminoglycoside translation inhibitors do have ATP increases with identical kinetics while others show a significantly different kinetics but ultimately higher fluorescence levels (the data wasn’t shown out to that time point to simplify figure presentation), Materials and methods section, Figure 5—figure supplement 1C.
Reviewer #2:
In the manuscript by Bruni et al., from Kralj's lab, the authors proposed that aminoglycoside bactericidal action arises from dysregulated membrane potential. The authors used a genetically encoded calcium sensor previously developed in Bruni et al., 2017.
The Introduction is very short and could contain more information about what is known on aminoglycoside uptake (EDP-I and II, PMF, feed forward loop,… Taber et al., 1987).
The results are interesting, provocative and conceptually new. However, clarifications, controls and new experiences are needed to support their conclusions.
We thank the reviewer for the excellent references and idea to improve our introduction. We have heeded this advice and overhauled the introduction in favor of a historically relevant context for our results. This has also altered some of the previously overstated conclusions we drew and hope that this new draft is more succinct and clearer.
Essential revisions:
My main concern is that the authors consider the uptake as an aminoglycoside mechanism of action. This point of view leads to surprising conclusions as in the title of the paper:
“Membrane voltage dysregulation, not uptake, underlies bactericidal activity of aminoglycosides.”
We have altered the title and introduction to better reflect our actual observations.
For me, aminoglycoside act on ribosomes, so uptake is essential, without uptake no death. The authors use a fluorescent gentamicin (GTTR) to track the uptake and observe that the calcium transients appear before the drug uptake. Is GTTR fluorescence detection sufficiently sensitive for this type of experiment?
We agree that uptake is essential. We have provided evidence that uptake sufficient to kill the cells in the absence of a membrane potential (determined after membrane potential was restored in the absence of aminoglycoside) can occur in these E. coli. Main text figure 1C combined with Figure 4C, D
It will be interesting if the authors could use their tools (GCaMP6, GTTR) in the conditions previously used in others studies to support their conclusions: adding chloramphenicol to block translation and EDP-II step; used PMF altered strains (nuo sdh, Fe-S biosynthesis) and Gm-resistant strain.
We have included some of our ∆nuo experiments throughout the new text (Figure 2—figure supplement 1B). Sadly, some of the other experiments proposed were not reached prior to us leaving the lab, but we look forward to conducting them in a follow-up study. The reviewer should feel free to contact us without revealing their identity if they’d like to discuss or collaborate on these types of experiments further, as we think there is ample space to improve the current aminoglycoside death model with our new tools. Furthermore, the GTTR data became suspect when we explored our PI data further. If uptake up PI can occur in a majority of cells, even in the absence of a membrane potential, then the uptake of GTTR cannot be considered to be occurring through the typical uptake route of aminoglycoside because they can enter through pores that are not there during the initial aminoglycoside uptake. We have removed the GTTR data as a result of these conclusions.
In the same vein, the authors need to clarify the impact of low pH, anoxic medium and CCCP (subsection “Aminoglycosides induced catastrophic calcium transients”), these three conditions are known to decrease PMF (essential for aminoglycoside uptake). Instead, the authors talk about "environments that diminish aminoglycoside efficacy".
We have clarified the impact of low pH and CCCP and attempted to better explain their inhibitory effects in the supplementary discussion (Materials and methods section). We have removed the anoxic medium piece of data because we have been unable to set up anoxic conditions for all of the necessary experiments to clearly argue their protective effect throughout the text. We hope to address the differences anoxic conditions create in a future publication as we believe it is more nuanced than a decreased PMF.
The most surprising result is that even in presence of CCCP, the ribosomes are still dissociated with aminoglycoside, leading to the conclusion that voltage is not necessary for aminoglycoside uptake. Is it possible to detect GTTR fluorescence in the presence of CCCP?
This surprised us as well, and ultimately inspired a good portion of this project’s direction. We have altered the main text to highlight this point, including the ribosome dissociation in the first figure. We have removed the GTTR data because of the Propidium iodide data in main text Figure 1D: GTTR increased on the same timescale as our PI experiments, so we cannot rule out GTTR uptake through pores. Therefore, the GTTR data is suspect unless it can be detected prior to PI uptake (spectral overlap of GTTR and PI preclude us from determining this result). We have altered the text to reflect this.
In all experiments, the GCaMP6 fluorescence tends to increase even before adding drugs. Given that previous study of the same lab have shown that using agarose pad lead to a voltage-induced calcium flux via a local mechanical environment, it will be essential to represent in a same figure the control without aminoglycoside. Even though in the text (subsection “Aminoglycosides induced catastrophic calcium transients”) this comparison is made.
We have included main text Figure 2A, C to address this shortcoming.
Reviewer #3:
[…]
Essential revisions:
1) The authors claim that the increased cellular ATP reverses the activity of the F1Fo-ATPase generating a hyperpolarized state of the cell. While I agree with the authors that reverse activity of F1Fo-ATPase can cause hyperpolarization, it is unclear to me if this is the only, or even the major mechanism of hyperpolarization upon aminoglycoside treatment. The authors present two lines of evidence to argue their point: ATPase knockout mutants and mgtC expression data.
We agree that there are numerous ATPases that can affect membrane voltage, which will temper the potential importance of the F-ATPase in the context of aminoglycoside killing. Hopefully our revisions to the text have clarified that this molecular machine is merely one, albeit significant and important, contributor to aminoglycoside killing in this context. We have found that in strain with all components of the ATPase, there are still a few transients, though much reduced compared to the parent strain (Figure 5E).
My first concern with the author's claims is that while some ATPase knockouts show a reduction of aminoglycosides action, there is no single mutant that abolishes the effect (changes in mean SD of calcium transient or recover CFUs). This is in contrast to the results where the authors truly abolish the hyperpolarization through other means such as CCCP or pH. This discrepancy suggests the possibility that the reverse activity of F1Fo-ATPase may not by the sole cause, or even the major cause of the hyperpolarization.
We found the observation that no single mutant abolished the aminoglycoside effect unsettling as well and further reflected on this point. In order to address this concern, we have included a complete knockout of the F-ATPase (DK8) in main text Figure 5E. Unfortunately, due to COVID related loss of laboratory access the corresponding DK8 CFU experiments remain to be completed. Regardless of that outcome, we have adapted the text by downplaying what we initially argued was a central role for this ATPase. Instead, we now invoke a relatively well-established translation inhibitor cellular response – the dysregulation of metabolism – as the main driver of cell death. While the F-ATPase contributes to this cell death through hyperpolarization, it is not exclusively responsible given the single component knockout results.
Second, the expression of MgtC eliminates the bactericidal activity of aminoglycosides. MgtC is a magnesium ion transporter, and thus it is also plausible to think that increased magnesium ion uptake prevents hyperpolarization.
While mgtC is upregulated in response to low magnesium and aids in Mg uptake with other components of its operon, we are unaware of evidence that mgtC alone is a magnesium ion transporter. There is even evidence that the M. tuberculosis mgtC does not bind magnesium https://www.ncbi.nlm.nih.gov/pubmed/22984256, and further most of the evidence we have found for the Salmonella mgtC function appears to be one of a regulatory role in the cell. If we have grossly missed evidence to the contrary, please let us know what that is so that we can run additional experiments to verify that the function of mgtC we claim is due to F-ATPase activity inhibition rather than Mg uptake.
2) Contrary to the authors claim, I could not find evidence in the manuscript to support that the catastrophic calcium transients are the direct cause of cell death. Even more, the fact that any cell that experienced calcium transients in the untreated condition did not divide, but did also not die (Introduction), provokes the question whether calcium transients are the true cause of death.
The authors would be better off, stating the relationship between calcium transients and cell death as a correlation rather than causation. However, if the authors choose to stick with their claim of causality, they have to show that calcium transients itself are sufficient to kill the cell, regardless of potential other effects related with hyperpolarization. Perhaps the authors could use a specific calcium chelator, such as BAPTA, to prevent calcium transient and therefore cellular death?
We cannot prove calcium transients are the direct cause of cell death. We unfortunately did have a sentence that insinuated this in the prior version of the text. That has been removed. We have multiple instances reinforcing that there is no evidence that large calcium transients kill cells including subsection “Single cell calcium flux predicts cellular aminoglycoside response”.
Below I detail my major concerns regarding the claimed mechanism of action:
- One of the most important results in this manuscript is that some ATPase knockouts affect calcium transients. To show the change between these mutants and WT strain, the authors measured differences in the mean GCaMP6 SD signal. My problem is that the measurements for different knockouts were done at different time points (Figure 5D top and bottom. See Table 1). I don't think that this is the proper way of comparing strains. It is unclear why the authors chose this strange comparison.
We believe that the majority of these ATPase knockouts are affecting the onset of transients due to their differential effects on the polarization state of the cell, and therefore would exhibit altered kinetics. We are happy to run statistical testing at multiple time points if that is preferable. We can also separate different strain’s GCSD traces in the figure to improve readability if this was the main cause of concern.
- Why is there so much variation of the GCaMP6 signal between WT replicas? For example, the same Figure 5D, and Figure2—figure supplement 4EC, Supplementary figure 5A, and 5E. In some cases, the variation between different WT replicas could be even larger than the difference between ATPase mutants and the WT strain.
The standard deviation metrics will include aspects of the signal-to-noise characteristics of the GCaMP images, which are intrinsically tied to the microscope used to collect data. During our experiments, we used more than one microscope (one with a sCMOS camera and 40x air objective, and one with a EMCCD with a 100x oil objective) which have very different noise characteristics. Thus, we believe the only way to view this type of analysis is through a relative measurement of untreated or wildtype cells taken at the same time. For all data shown, the biological replicates and conditions were taken on the same day on the same microscope and used for comparison. Using these relative metrics, we can easily identify treated vs untreated conditions, as well as investigate the kinetics and amplitude of the calcium transients.
- Furthermore, shouldn't the ATP concentration decrease if F1Fo-ATPase keeps hydrolyzing ATP?
On the basis of newly included experiments, we have adapted our thinking of the ATP result. The steady state level of ATP changes very little, but the flux through non-ribosomal components increases dramatically. Please see Materials and methods section, Figure 5—figure supplement 1D, and subsection “ATP dysregulation precedes voltage induced bactericidal killing”.
- It is also not clear why the atpC knockout should have a higher membrane potential than WT. In the Discussion section, the authors do not mention anything about atpC.
Please see subsection “ATP dysregulation precedes voltage induced bactericidal killing”. We have added a brief discussion on about the roles of atpH and atpC. Discussion section.
- The authors do not mention why calcium transients (and possibly increase in ATP concentration) are not observed in other antibiotics that also impair ribosomal activity, such as chloramphenicol?
Additional experiments have confirmed that non-aminoglycoside translation inhibitors do increase ATP flux, and some even alter basal membrane potential (data not included). This new data supports a model that incorporates previously known aminoglycoside effects including mistranslated proteins and membrane pores helping to explain the difference in bactericidal activity. Hopefully this discrepancy is now clear. Also see Figure 5—figure supplement 1C.
- Importantly, GCaMP6 signal doesn't report directly on the membrane potential. Therefore, to determine whether hyperpolarization induces a positive feedback on gentamycin uptake, it would be necessary to use a more direct membrane potential reporter, such as the one the authors used for their other measurements.
We agree that the hyperpolarization could induce a positive feedback on gentamycin uptake. However, we see that even in the absence of membrane potential, enough aminoglycoside enters the cell to dissociate the ribosome and increase ATP. The hyperpolarization is required for bactericidal activity, though, which is a new way of viewing the role of voltage in the activity of aminoglycosides.
[Editors’ note: what follows is the authors’ response to the second round of review.]
[…]
1) The revised manuscript still does not include an experiment addressing aminoglycoside uptake. Did the authors have any alternative to replace GTTR?
We initially removed the GTTR data from our revised manuscript since the ribosome dissociation data is more specific indicator of aminoglycoside activity. We have added it back in to help support our hypothesis. Figure 1—figure supplement figure 1D (a new panel) shows GTTR uptake in the absence or presence of CCCP, again showing dye uptake into cells regardless of membrane voltage. Furthermore, given our data with PI showing a large non-specific uptake, the GTTR signal may be confounded with our factors, which warrants caution in interpretation.
2) It is unclear what 'by metabolic dysfunction' in the title means. Do the authors mean increased intracellular ATP? While the abstract claims that 'the hyperpolarization arose from altered ATP flux', it is hard to understand why or how F1Fo ATPase reverts its action only when the membrane potential is high enough (inside is more negative), not in the other way around (inside is not as negative – it seems to me it's better to revert the action in this case since then they can increase the membrane potential to the normal level through reverting it…).
We apologize for confusion in the presentation of the model. When we refer to metabolic dysfunction, we are referring to deleterious usage of ATP within the cell. The F1Fo ATPase can trade negative (inside) voltage for ATP. So if it makes ATP, it is moving the voltage closer to 0 mV. On the other hand, the pump can also use ATP to increase (more negative) the membrane polarization. So if the balance of ATP to voltage is tilted, the motor can shift from consuming ATP to hydrolyzing ATP. In our model, ATP is not consumed by ribosomes, and is therefore free to be consumed by other ATPases (including the F1Fo ATPase) which will then hyperpolarize the membrane, leading to the transients.
We have attempted to clarify the model in Figure 5—figure supplement 3 (new supplementary figure) showing this stepwise process. Furthermore, we revised our thinking on calling step 2 “increased cellular ATP” in lieu of our BacTiter-Glo data, showing in bulk that cells do not have more ATP. Rather we believe that there is the same amount of ATP, however the metabolic burden of translation is no longer contributing to ATP consumption. Other groups have seen similar shifts in metabolic load in the presence of aminoglycosides and other translation inhibitors (cited in the relevant parts of the paper), so we believe this is part of a more universal effect of translation inhibitors causing metabolic dysfunction. Our language in subsection “ATP dysregulation precedes voltage induced bactericidal killing” of the main text document reflect this change in thinking, however if it is not clear that we mean a shift in ATP flux, rather than an increase in bulk ATP concentrations, we will revise our language to better reflect this thinking.
3) Throughout the data, it seems that the membrane potential plays a critical role in exerting bactericidal effects. While ATPase mutants and the mgtC expressing strain presented here likely have altered membrane potential without any antibiotic treatment, I couldn't find data showing their basal membrane potential level compared to the WT. Without the basal membrane potential data, it is impossible to discern if the phenotypes of the mutants are due to their basal membrane potential differences or by the author's main claim in the abstract. It would be very valuable if the authors can provide the basal membrane potential level of mutants compared to WT, CCCP added case, and/or pH6.5 case in the study.
We agree that membrane potential plays a critical role in the aminoglycoside response, and that the protection afforded by these mutations could be due to decreased voltage. To test the basal voltage, we have included a table and figure (Figure 5—figure supplement 2) which contains the membrane potentials for individual strains BW, DK8 (∆unc – F-ATPase components are all removed), and BW+mgtC. We observed that the DK8 and BW+mgtC strain had a more polarized basal membrane potential as compared to the BW strain alone. Finally, to see if this result was due to the strain, or some effect that is specific to the strain treated with TMRM, we utilized the less sensitive Nernstian dye DiOC6 to assess if dye uptake was altered in the DK8 strain overall. We observed results similar to our TMRM data with the dye DiOC6. These data are consistent with the interpretation that protection arises from altered ATP consumption, and not depolarization.