extfieldsofstudy:
Medicine
Computer Science

text:
Monitoring single-cell gene regulation under dynamically controllable conditions with integrated microfluidics and software

Much is still not understood about how gene regulatory interactions control cell fate decisions in single cells, in part due to the difficulty of directly observing gene regulatory processes in vivo. We introduce here a novel integrated setup consisting of a microfluidic chip and accompanying analysis software that enable long-term quantitative tracking of growth and gene expression in single cells. The dual-input Mother Machine (DIMM) chip enables controlled and continuous variation of external conditions, allowing direct observation of gene regulatory responses to changing conditions in single cells. The Mother Machine Analyzer (MoMA) software achieves unprecedented accuracy in segmenting and tracking cells, and streamlines high-throughput curation with a novel leveraged editing procedure. We demonstrate the power of the method by uncovering several novel features of an iconic gene regulatory program: the induction of Escherichia coli’s lac operon in response to a switch from glucose to lactose.

Condition
Duration  Table 1 of the main article), the duration of the experiment (in hours), the total number of growth-channels analyzed, the total number of cell observations, and the number of full cell cycles that were recorded.

Supplementary Note 2: Performance of MoMA on datasets from other labs
A problem that is often encountered with image analysis methods that have been developed using data from a pecific system and laboratory is that they fail to generalize well to data produced with other setups in other labs. Because MoMA uses a novel algorithmic approach in which segmentation and tracking are solved as a joint optimization problem, we were confident that the approach should be highly robust and generalize well to data from other settings, but we wanted to check this directly. A challenge is that, currently, virtually no raw time-lapse imaging data from microfluidic devices with mother machine like designs are publically available. However, since the MoMA software had become publically available, a small user-community has been building and we approached several groups from this community to ask whether they would be willing to share some raw time-lapse data produced in their labs, to test MoMA's performance on datasets from other setups. Several researchers graciously agreed to share some of their data and we obtained datasets from three different labs: A dataset obtained by Benjamin Sellner from the laboratory of Urs Jenal (Biozentrum, Basel), a dataset obtained by Lydia Robert from the Laboratoire Jean Perrin (UPMC university, Paris), and one dataset obtained by Christian Schwall from the laboratory of James Locke (University of Cam-bridge, UK). Each dataset uses a different microfluidic device, a different strain of bacteria (a different species in on case), and also different growth conditions. Relevant experimental parameters and summary statistics describing each dataset are in Table 2. For reference we provide the same data for our own measurements. Supplementary Table 2: Information about the experimental setup, strains, and growth conditions of the 3 datasets from other labs that we analyzed. Each column corresponds to a dataset, with the name of the person that obtained the data indicated at the top. For reference we also show the corresponding information for our own dataset (Kaiser).
Since experimental conditions were kept constant for all of the 3 datasets, cells were expected to exhibit exponential growth. We processed all data with MoMA and curated the datasets using its interactive graphical interface. Below we characterize the performance of MoMA on these 3 datasets by statistics on the number of curation interactions that were required, as well as by analysis of the quality of the resulting growth curves for individual cells.

Curation statistics
For each growth-channel of each of the 3 datasets (as well as for our own data) we counted the ratio between the total number of curations and the total number of cell observations and denote this as the curation rate of this growth-channel. We then calculated, for each dataset, the reverse-cumulative distribution of curation rate across the growth-channels that were analyzed ( Supplementary Fig. 1). For our own and the Sellner data there is a large fraction of growth-channels that require essentially no curation and the fraction of frames requiring curation is always below 1%. For the Schwall and Robert datasets most growth-channels do require some interaction, although the fraction is still low, i.e. about 2.5%. Factors that likely affect the curation rate are the number of images per cell cycle (more is better), the extent to which cells can move laterally within growth channels (less is better), and the appearance of the phase contrast images, i.e. whether there is much variability in phase contrast intensity within cells (less is better).
Quality of the growth curves and growth rates of individual cell cycles After the 3 datasets had been curated, we extracted all full cell cycles and fitted exponential growth curves by fitting the logarithm of cell-size as a function of time. Cell size was taken as height of the bounding-box of the cell as reported by MoMA. We then calculated, for each dataset, the distribution of Pearson squared correlation coefficients of the fits. Supplementary Figure 2 shows that, for all datasets, almost all cell cycles show excellent fits to exponential growth curves, i.e. r 2 > 0.95 for all but a very small percentage of the cells, and median r 2 of at least 0.99. This not only shows that cells are growing exponentially in all these datasets, it also shows that MoMA obtains accurate size estimates in all these datasets. Notably, for the large majority of cell cycles, the squared correlation for the Robert and Sellner datasets are even higher than for our own dataset.
We used the fitted exponential growth curves for individual cell cycles to calculate the distribution of growth rates for each of these data-sets ( Supplementary Fig. 3).
We see that growth rates vary substantially across the datasets, with the slowest growth rates for our data with cells in M9 minimal media (0.7 doublings per hour on average) and the fastest growth for the cells in LB (2.8 doublings per hour on average). The distribution of growth rates is a simple unimodal distribution for all data-sets. Interestingly, the size of the fluctuations in single-cell growth rates varies substantially across datasets. The coefficient of variation of growth-rates, i.e. standard-deviation divided by mean, is 0.17 for our data, 0.11 for the Robert and Sellner data, and 0.1 for the Schwall data. We also observe that there is some variability in the accuracy of the growth rate estimates across the datasets, with typical relative errors of 1 − 3% for our data, 1 − 5% for the data of Sellner, 1 − 7% for the data of Robert, and 2 − 10% for the data of Schwall. These differences in accuracy of the growth rate estimates do not stem from differences in the accuracy of the individual size measurements. Indeed, Supplementary Fig. 2 showed that r 2 values of the exponential fits do not substantially differ between datasets. However, as indicated by equation (17) in the main article, the accuracy of the growth rate scales with the inverse of the square-root of the number of observations in the cell cycle. Since there are only around 5 observations per cell cycle for the Robert and Schwall datasets (Supplementary Table 2), the accuracy of the growth-rate estimates is lower for these datasets. This also explains why the Sellner data has slightly higher errors on the growth-rate estimates. In conclusion, if very accurate estimates of the instantaneous growth rates of cells are required, it pays to take phase contrast images frequently.
Supplementary Note 3: Estimating lag times to lac operon induction and shut down In Fig. 2 of the main article, we show results from experiments where the carbon source is alternated between glucose and lactose every 4h with the LacZ-GFP strain. As expected, the expression of LacZ-GFP is induced in each phase of the experiment where lactose is present. To estimate the time until the lac operon is induced after a glucose-to-lactose switch in a given cell, we proceeded as follows. First, we checked visually that no cell induces its lac operon within the first 9 minutes in all three switches, and used the average of the estimated GFP levels in the first 3 time points after the switch as a measure of the cell's pre-induction GFP level. Second, we also observed that, as soon as the lac operon is induced, GFP levels rise quickly and monotonically. We thus defined the induction time of a cell as the first time point where the GFP level had increased significantly beyond fluctuations that could be caused by the cell's auto-fluorescence (which we conservatively took as a fluorescence equivalent of 200 GFP molecules, because the standard-deviation of auto-fluorescence fluctuations in the wild-type MG1655 is equivalent to the fluorescence of 30 GFP molecules). Thus, in order to successfully observe an induction, we need to observe a cell for at least 4 time points after the switch (3 for estimating the pre-induction GFP level, and at least 1 more to observe induction, i.e. an increase of fluorescence by the equivalent 200 GFP molecules). Of the 1051 cells that were present at the first time point after a switch, 998 were observed for at least 4 time points, and 53 were discarded because they left the channel within the first 4 time points. In order to successfully estimate a lag for as many cells as possible, if a cell divided before inducing, the total GFP of its two daughters was summed and their cell traces considered until the induction threshold was passed, or one of the two daughters was lost (because it left the channel). All in all, the induction lag was successfully quantified for 955 out of 998 cells (the 43 remaining cells left the growth-channel before reaching the induction threshold). We note that the induction time will include the maturation time of the LacZ-GFP fusion proteins.

Estimating the distribution of lac induction lags at the first switch
To calculate the probability distribution with error bars for the lag times in the first switch ( Fig. 2C of the main article), we proceeded as follows. Our data consisted of N = 326 cells that were either observed to induce within 4 hours (312) or that had not induced by the end of the 4 hour lactose phase (14). In addition, there were M = 24 cells that were pushed out of the channel before they were observed to induce. We denote by n k the number of cells that were observed to induce at 3k minutes (the cells are imaged every 3 minutes) and, for notational simplicity, we denote the 14 non-inducing cells by n 80 = 14. We will denote by m k the number of cells exiting the channel (before inducing) at 3k minutes. We want to estimate the probabilities ρ k for a cell to induce at 3k minutes with, again for notational simplicity, denoting the probability for a cell not to induce by 240 = 80 × 3 minutes by ρ 80 . Formally, the probability P (D| ρ) of the observations given a vector of probabilities ρ is given by with B = 80 the number of 3 minute bins. Using a Dirichlet prior P ( ρ) ∝ k (ρ k ) λ−1 , the posterior estimates and credible intervals for the ρ k would in principle be obtained by integrals over P (D| ρ)P ( ρ). However, these integrals are not easily analytically tractable because of the exiting cells. Since the number M = 24 of exiting cells is relatively small, i.e. 7% of the data, we decided to use the following approximation. We first determined the maximum likelihood vector of probabilities ρ * by maximizing equation (1). We then replaced our counts n k and m k with new 'counts' That is, we find the maximum ρ k * of the exact likelihood function, and replace our original data with a new dataset in which the M exiting cells are spread over the bins k in proportion with the maximum likelihood probabilities ρ * k . The likelihood in terms of these new counts takes on the simple monomial form Because the total number of observations is relatively small, we further smoothed the probability distribution ρ by averaging counts over 9 minute intervals, i.e. 3 consecutive time points. That is, we define s k =ñ k−1 +ñ k +ñ k+1 . Using this, we estimate the fraction f k of cells with a lag of 3k minutes as where we chose λ = 1/2 for the parameter of the Dirichlet prior. Using equation (3), the error-bar e k on f k is given by Similarly to estimating the time to lac induction, we can also estimate the time until the GFP signal decreases at the lactose-to-glucose switches by selecting cells that are born before the switch, have been induced during the lactose episode (defined as having at least 400 GFP molecules at the switch back to glucose), and remain in the channel for at least 9 minutes after the switch. Again, if a switching cell divides before the next switch, the total GFP of its two daughters is summed and the cell trace considered until one of the two daughters is lost.
For each cell we measured the delay between the switch and the time at which the GFP level reaches its maximum. We interpret this time to correspond to the sum of the times that it takes to shut down transcription of the lac operon, degrade the remaining mRNA, and the maturation time of the last translated LacZ-GFP proteins. Supplementary Figure 4 shows that the delay times until GFP starts to decrease is short for all cells, i.e. between 10 and 20 minutes, which may well be dominated by the maturation time of the GFP. This strongly suggests that the lac operon is quickly shut down in all cells upon the switch to glucose.

Supplementary Note 4: Instantaneous growth rates after lac induction
Visual inspection of the growth curves of cells that just induced their lac operon and emerge from growth arrest suggest that, instead of a phase where cells grow significantly slower while LacZ-GFP levels are still low, many cells immediately grow at high rates. To quantify this observation we took all cell cycles that fall in the first lactose phase and estimated instantaneous growth rates in a sliding window of 7 consecutive time points (21 minutes of growth) for all points after the lac induction. These instantaneous growth rates for 21 minute windows were then stratified based on the time from the middle of the window to the time at which the cell (or its ancestor) induced the lac operon. We divided these times into 15 minute bins and calculated the distribution of instantaneous growth rates in each bin as follows.
For each window w we estimate an instantaneous growth rate a w and an error-bar on this growthrate σ w as described in the Methods of the main article. For each bin, we determined the empirical distribution of estimated growth rates a w (shown as violin plots in Supplementary Fig. 5, left panel) and the standard-deviation of the estimated growth rates (grey lines in Supplementary Fig. 5, left panel). Note that, because each estimated growth-rate a w has only finite accuracy, part of the observed variation in growth-rates a w in a bin is due to measurement error. We estimated the 'true' variation in growth-rates in a bin, i.e. correcting for variation due to measurement error, using the following Bayesian procedure. Let α be the true average growth-rate for the windows in the bin, σ the true standard-deviation of the growth-rates, and α w the true growth-rate of window w. Using a Gaussian prior, the probability that window w will have true growth-rate α w is given by The probability to estimate a growth-rate a w for window w, when the true rate was α w and the measurement error standard-deviation σ w is given by Integrating over the unknown true growth-rate α w we then obtain for the probability P (a w |α, σ, σ w ) to measure growth-rate a w given the true average rate α, the true variation σ and the measurement error σ w : .
The probability of all the data D for a bin is then given by the product of this expression over all windows w in the bin P (D|α, σ) = w P (a w |α, σ, σ w ).
Finally, using a uniform prior over both α and σ, we obtain maximum posterior estimates α * and σ * by maximizing P (D|α, σ) with respect to α and σ. The estimated true means α * plus and minus σ * are shown as the black dots and whiskers in the left panel of Supplementary Fig. 5. The results in the left panel of Supplementary Fig. 5 show that, in the first hour after induction, there is a small but still noticeable reduction in growth rates. Notice however that the shift in growth rates is small relative to their variability. For example, almost a quarter of the cells grow faster than the average growth rate in constant lactose already in the first 15 minutes after their induction. By 60-75 minutes after the switch, the growth rates have assymptoted back to what is observed in constant lactose.
It is reasonable to assume that the small reduction in growth rates in the first hour after induction is due to the fact that LacZ-GFP levels are too low to support growth at full speed. To test this, we again gathered instantaneous growth rates in 21 minute windows, but now stratified by the LacZ-GFP concentration rather than by the time since induction. The LacZ-GFP concentration for a window was estimated as follows. For each time point in the window, LacZ-GFP concentration was defined as the estimated total number of LacZ-GFP molecules divided by the length of the cell, as estimated by MoMA. The average LacZ-GFP concentration in the window was then taken as the average over all time points in the window.
The right panel of Supplementary Fig. 5 shows instantaneous growth rates as a function of LacZ-GFP concentration, calculated in the same way as described above for instantaneous growth-rates as a function We see that, below 1000 molecules per µm, there is a small but noticable reduction in growth-rates, and a substantial decrease in growth-rate when levels are more than 10-fold below steady-state levels (i.e. the first bin in Supplementary Fig. 5,  right panel). Still, growth-rates vary considerable among cells with similar LacZ-GFP concentration, and many cells exhibit high growth rates even at low LacZ-GFP levels. This shows that cells can sustain high growth rates with LacZ-GFP levels that are up to 10-fold below steady-state levels.

Memory of lac induction at subsequent switches
One unique feature of our setup is that we can trace entire lineages of cells and investigate how the response of a given cell to a change in environment depends on the behavior of all its ancestors. For example, for our particular model system, we can investigate the relationship of the behavior of cells at the second switch from glucose to lactose to the behavior of their ancestor at the first switch.
Supplementary Figure 6 shows a scatter plot with the lag times of cells at the second switch versus the lag time of their ancestor at the first switch 8 hours in the past. We see that all cells that show a long lag time at the second switch (i.e. longer than 45 minutes) are descendants of cells that either never induced, or that induced near the end of the first lactose phase. That is, their ancestors never produced substantial amounts of LacZ-GFP. This strongly suggests that, for all cells with ancestors that produced a substantial amount of LacZ-GFP during the first phase in lactose, even after 4 hours of dilution in glucose, the remaining low levels of lac operon expression are sufficient to ensure a short lag at the second switch. That is, lineages of single cells retain a memory of growth in lactose that lasts for at least 4 hours. This memory has previously been observed at the population level [2], but we here show that this is highly reproducible at the single cell level, i.e. all descendants of cells that previously grew in lactose retain memory of that growth.

Correlation of lac induction lag with physiological state at the switch
We investigated whether the lac induction lag depended on the physiological state of the cell at the time of the switch from glucose to lactose. For each cell present at the switch we determined its initial LacZ-GFP level, its growth rate, and its cell cycle phase at the time of the switch. Here we defined the cell cycle phase as the ratio of time since birth to the average total cell cycle duration. Supplementary Figure 7 shows scatter plots of the observed dependencies between LacZ-GFP level, growth rate, and cell cycle phase and the lac induction lag for the first, second, and third switch. The Pearson squared correlation coefficients for the association between each of these variables and lag time are all very small showing that none of these physiological variables is predictive for lag time. The only exception is a small but significant correlation between lag at the second switch and LacZ-GFP level. As explained further in the discussion of Supplementary Fig. 6 below, this correlation results from the fact that the only cells that show long lags in the second switch are descendants of cells that failed to induce in the first switch.
These results are in particular relevant for the first switch, which shows a wide and multi-modal distribution of lag times. The multi-modal distribution of lag times strongly suggests that there is a pre-existing heterogeneity among cells that determines their lag times. However, the results in Supple-mentaryFig. 7 show that none of the variables we investigated can account for this heterogeneity. It is important to note, however, that with the sensitivity of our setup, we can reliably detect cells that have 100 or more LacZ-GFP molecules, but we cannot reliably detect the differences between a cell with zero or, say, 10 molecules of LacZ-GFP.
It is visually striking in timelapse movies (see e.g. Supplementary movie 1 [1]) that the lac induction dynamics is more correlated within a growth channel than between growth channels; in other words, once one cell has started to express LacZ-GFP in one growth channel, other cells of the same channel are very likely to also do so before cells of neighbouring channels where induction has not started. This suggests that the non-genetic determinants of the lac induction lag are heritable. In order to quantify heritability, we compared the strength of the correlation between the lags in cell pairs with different genealogical relationships: for each pair of related cells undergoing a glucose-lactose switch, pairs are stratified by genealogical relationship and the lag of one cell is plotted against the lag of the other ( Supplementary  Fig. 8). We see that lag times show high heritability. Not only is the correlation of lag times in sisters very high (r 2 = 0.85), even second cousins, i.e. cells that derive from the same great-grandmother, show highly significant correlation in their lag times. This strongly suggests that lag times are dependent on a heritable epigenetic factor, and that the presence (or absence) of this factor is correlated over multiple generations.
In order to investigate how remarkable the heritability of lag times is, we calculated correlation coefficients between sisters, first cousins, and second cousins, for a number of other physiological traits when growing in constant conditions. These include a number of traits, such as length of the cell at birth, and LacZ-GFP concentration at birth, that we know must be highly correlated, at least in sisters, because they are directly transmitted from the mother cell. That is, when the mother divides, both daughters are approximately half the size of the mother, and also the LacZ-GFP molecules are approximately equally divided between the two daughters. Indeed, we find that these traits are significantly correlated between sisters (r 2 ≈ 0.5 − 0.65), and also the growth rates of the individual cell cycles in sisters are clearly correlated (r 2 ≈ 0.5) (Supplementary Fig. 9). Notably, however, these correlation coefficients are still considerably lower than the correlation of lag times between sisters (r 2 ≈ 0.85). Even more remarkably, none of these physiological traits show clear correlations between cousins or second cousins. That is, correlations in LacZ-GFP concentration and cell size decay within 2 generations. In contrast, lag times show substantial correlations even among second cousins (r 2 = 0.51). In summary, lag time to lac induction is more strongly correlated among genealogically related cells than any other trait we measured, including ones that we know are directly inherited from the mother, and this correlation lasts for significantly more generations that the correlations of other traits.  Figure 9: Heritability of growth rate (α), cell size at birth (l birth ) and at division (l div ), and LacZ-GFP concentration at birth (c birth ) and at division (c div ) for cells grown in constant conditions. In addition, the heritability of lac induction lag is shown for comparison (lag). Noticeably, the correlation between sisters is lower at division than at birth, and almost inexistent for LacZ-GFP when its expression is repressed (in glucose).

Supplementary Note 6: Lac induction lags with IPTG
We hypothesized that long lag times occur when cells have zero LacY permease and/or zero LacZ-GFP at the time of the switch, because this would prohibit cells either from importing lactose or from metabolizing it into allolactose (which is the molecule that binds the LacI repressor and inhibits its DNA binding). If this were the case, then adding an artificial inducer such as IPTG, which can diffuse into the cell without LacY permease and binds directly to LacI, should abolish the occurrence of long lags.
We tested this by adding 500µM IPTG to the lactose media. Supplementary Figure 10 shows that, with the added artificial inducer, indeed no more long lags are observed. It is interesting to note that the lags with IPTG during the first switch are a bit longer than those seen at the second and third switch with pure lactose. We hypothesize that this is because, at the first switch, most cells have no LacY permease, and thus the IPTG needs to enter the cell by diffusion, which is considerably slower than when LacY permease is available to pump IPTG into the cell. In contrast, at the second and third switch with pure lactose there are still LacY permeases left, and these cause the lactose to be pumped into the cell more quickly. Note also that at the second switch with IPTG, the lag times are even a bit shorter than in the second and third switch with pure lactose. We believe that this speed-up is due to the fact that IPTG is imported at higher rate by LacY than by other non specific transporters. Additionally, the fact that IPTG directly binds LacI, thus circumventing the need of LacZ-GFP metabolizing lactose into allolactose, might also contribute to the shortening of lags.

Supplementary Note 7: LacZ-GFP expression in glucose
Our hypothesis that cells with long lag times correspond to cells that had zero lac operon expression at the time of the switch predicts that, while growing in glucose, a substantial proportion of cells (but not all) should have zero lac operon expression. To estimate the expression of LacZ-GFP while growing in lactose we obtained the distribution of measured GFP levels for both the strain carrying LacZ-GFP, and the wild-type strain that does not carry GFP ( Supplementary Fig. 11).
We see that the median estimated number of GFP molecules in wild-type cells is close to zero, as it should be, given that we have corrected for the average amount of cell auto-fluorescence. In addition, we see that the auto-fluorescence fluctuates from cell to cell with a standard-devation equivalent to about 30 GFP molecules.
The distribution of measured GFP molecules per cells is clearly shifted to slightly higher values in the experiments with the ASC662 strain carrying GFP. We assume that the distribution of the total fluorescence in this strain is a convolution of the distribution of auto-fluorescence of the cells and the distribution of fluorescence originating from GFP molecules. Assuming that these two variables are independent, the mean and variance of the total fluorescence is given by the sums of the means and variances of GFP molecules and auto-fluorescence. We can thus estimate the mean and variance of the distribution of the number of GFP molecules by subtracting, from the mean and variance of total fluorescence, the mean and variance of the auto-fluorescence distribution. Using this approach we estimate that the average number of GFP molecules is n = 14, and that its standard-deviation is sd(n) = 10.4 molecules. Given that the mean is only about one standard-deviation above zero, this would indeed predict that a substantial number of cells have zero LacZ-GFP molecules during growth in glucose.


extfieldsofstudy: Chemistry Medicine

text:
Spittlebugs produce foam as a thermoregulatory adaptation

Insects have evolved multiple mechanisms to adapt to variations in environmental temperatures, including postural control of solar input, variations in diurnal activity, external morphological structures and selecting/generating microhabitats. Foam produced by Mahanarva fimbriolata nymphs (also known as root spittlebugs) was found to aid in creating a constant thermal microhabitat despite environmental temperature fluctuations. The temperature within the foam was found to be similar to that of soil during the day and remained constant despite fluctuating external temperatures. In chemically analysing the composition of the foam, palmitic and stearic acids, carbohydrates and proteins were detected. These substances have previously been shown to act as a surfactant to stabilize and modulate foams. Since the immature ancestor of the spittlebug developed below ground, it is speculated that the foam may function as an ‘extension’ of the soil and, thus, may have enabled the spittlebug to emerge from the soil and adopt an epigean lifestyle.

Results
Foam as a thermal microhabitat. To determine whether the internal temperature of spittlebug foam changes with fluctuations in external temperature, several local temperatures were monitored including: outside the foam, ground temperature near the foam and inside the foam. These temperature recordings were performed during summer in a sugarcane field where nymphs reside. While external temperatures range from 24.43 ± 0.44 °C to 29.20 ± 1.66 °C, the internal foam temperature was found to vary to a much smaller degree (Fig. 1). In the middle of the day (11h00-13h00) when the external temperature was 29.20 ± 1.66 °C, the foam temperature was observed to be significantly lower at a temperature of 25.18 ± 0.63 °C (Fig. 1, one-way ANOVA followed by a Bonferroni post hoc test, n = 25, F (1,100) = 88.763, P < 0.0001) (Detailed values of means ± SD corresponding to Fig. 1 are shown in Supplementary Table S1). Indeed, despite fluctuating external temperatures, a uniform foam temperature was observed throughout the day (i.e. 25 ± 0.78 °C (mean ± SD)). Specifically a previous investigation found that 25 °C resulted in the greatest nymph viability 26 . In monitoring over 10 hours during the day, the surface temperature difference between the foam and the soil was ≤ 0.2 °C while the maximum difference between foam and the external temperature was ≥ 4.0 °C. This indicates that the temperature of the soil and the foam are similar (Fig. 1, one-way ANOVA followed by a Bonferroni post hoc test, n = 25, F (1,100) = 0.008, P = 0.928). Using thermograms obtained from an infrared camera, the difference in temperature near the foam can be visualized (Fig. 2).
To more rigorously examine the thermocapacity of the foam, nymphs were evaluated in a fitotron in which the temperature was controlled and elevated above normal field conditions. When the fitotron temperature was raised to 32.29 ± 0.58 °C (mean ± SD) for 30 minutes, foam temperature remained at 30.41 ± 1.01 °C (mean ± SD) which is approximately 2 °C below the air temperature (Fig. 3, one-way ANOVA followed by a Bonferroni post  hoc test, n = 25, F (1,25) = 57.220, P < 0.0001). These results indicate that the foam acts as a thermoregulator at 32 °C, which has previously been shown to be lethal for nymphs 26 . The combined field and laboratory data indicate that nymph foam production (Supplementary Movie 1) results in a relatively constant internal temperature at a wide range of external air temperatures that creates a suitable thermal microhabitat for nymph survival.
Foam chemical composition. Using gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) two major peaks were identified as palmitic acid and stearic acid; co-injection with commercial standards confirmed their identity. Amounts of palmitic and stearic acid in the foam were 2.54 ± 0.88 and 2.78 ± 0.97 µg ml −1 foam (means ± SE), respectively. Total carbohydrates were quantified by means of the phenol-sulfuric acid method 27 , with 0.579 ± 0.05 µg ml -1 foam (mean ± SE). Based on the Bradford method 28 total protein was 320 ± 50 µg ml -1 foam. These components are recognized as important substances in the formation and stabilization of foam bubbles [29][30][31][32] , and as such are likely necessary for maintaining a stable bubble layer around nymphs.

Discussion
Insects have evolved complex mechanisms to regulate their body temperature within a remarkably narrow range for successful survival and reproduction [33][34][35] . Here, we showed that M. fimbriolata nymphs produce and cover themselves with foam as a thermoregulatory adaptation that enable spittlebugs to maintain their body temperature within the optimal range for development 26 . The temperature measured inside the foam was similar to the soil even though the air temperature in both the field and fitotron varied to a much greater extent (Figs 1 and 3). Since spittlebug and some below ground insects are thought to share a common ancestor 7,9 , insect-produced foam may serves as an 'extension' of the soil and enable immature spittlebugs to exploit food sources for above ground feeding. Without such a domestic protection, delicate cuticles would leave spittlebug nymphs vulnerable to adverse abiotic epigean environmental factors, such as high temperature and low humidity 19 .
Lipids, carbohydrates, and proteins were detected in M. fimbriolata foam similar to the foam composition of other spittlebug species 11,14,15,36,37 . Proteins were detected in most foams analysed 11,14,15,36 whereas lipids are much less common being observed only in the foam of Japanese spittlebugs 37 . While the chemical analysis has not been shown to directly provide thermal protection, lipids, carbohydrates and proteins that are present in spittlebug foam has previously been shown to function as a surfactant to stabilize the foam thereby reducing surface tension and modulating the size and distribution of bubble [29][30][31] . Lipids, including palmitic and stearic acids, are critical for the formation and stability of foams because of their elastic forces 32 . For example, palmitic, linolenic and pentadecanoic acid have been positively correlated with the height of cider-type beverage foams 24 . Proteins are involved, especially in the formation of film that reduce interfacial tension and increase the viscosity and elasticity of a foam [38][39][40] , which allows the foam to breathe and secure around the insect. Although carbohydrates have no direct effect on the air−water interface, they promote interactions among proteins, which create a stable film that stiffens and stabilizes the foam 41 .
While the mechanism behind the observed thermal protection is proposed to be due to thermal insulation the extent in which evaporative cooling may play a role in controlled temperature conditions still needs to be investigated. Evaporative cooling has been demonstrated for hemipterans that feed on xylem and do not produce foam, such as the cicada Okanagodes gracilis, that regulate their body temperature by water loss through pores in the  33 . Interestingly, for foam-producing Aphrophora saratoga nymphs, water evaporation from foam was demonstrated be lower than from free-water surface, making the foam an uncertain protection against desiccation for this species 42 . In future studies, by measuring temperature of dried cercopid foam differentiating these two mechanisms experimentally should be possible.
In summary, nymph-produced foam forms a microhabitat for the thermoregulation of M. fimbriolata nymphs. Future investigations on physical properties of the foam, especially optical reflection and heat dissipation will provide further insights into the phenomenon reported here.

Methods
Thermal microhabitat. To determine whether the foam covering M. fimbriolata nymphs has a thermoregulatory role, we conducted a field bioassay during the summer of 2015 in a sugarcane field in Piracicaba, São Paulo, Brazil (22°42′06″S, 047°33′50″W). The sugarcane plants were approximately 2 m tall, with 1 m between rows. We selected 25 sites inhabited by foam-covered fourth-and fifth-instar nymphs of M. fimbriolata, maintaining a minimum distance of 10 m between each site. Using a type K thermocouple (RDXL4SD, Omega Engineering, Stamford, CT, USA), we measured the temperature inside the foam, 0.1 m from foam and ground, 2.5 m and 1.5 m above ground, and 0.1 m below ground level at five time intervals that represented the natural variation in temperature during the day: 07h00-09h00, 09h00-11h00, 11h00-13h00, 13h00-15h00 and 15h00-17h00 (Fig. 1). We constructed a thermogram using an infrared camera (SC640 FLIR Systems, Boston, MA, USA) and analysed the temperature of the thermographic images using the ThermaCAM Researcher 2.9 software (FLIR Systems, Boston, MA, USA).
To investigate if the thermoregulation occurs at higher environmental temperatures than those achieved in the field experiments, we also evaluated the thermophysiology of the foam under controlled conditions using a fitotron growth chamber (ELETROLAB, São Paulo, SP, Brazil). Fourth and fifth-instar nymphs of M. fimbriolata were collected from the same field and carefully transferred to the roots of sugarcane plants aged [25][26][27][28][29][30] days growing in pots (200 ml) containing organic substrate (Golden-Mix, Ananindeua, PA, Brazil), with one nymph per plant, for a total of 25 replicates. The plants and insects were initially equilibrated at room conditions (25 ± 2 °C, 70 ± 10% UR) for 30 min before nymphs began to produce foam. Next, they were arranged within the growth chamber at a temperature of approximately 32 ± 0.11 °C. After 30 min of acclimation and temperature stabilization, we recorded the temperatures in the chamber, 1 cm below the surface of the soil in the pots, and inside the foam using the type K thermocouple.

Fatty acid analysis.
To analyse the fatty acids present in the foam, five foam samples were collected in the same sugarcane field cited above, placed in glass vials (10 ml) using a glass pipette and stored at −30 °C until analysis. Following an extraction sequence, 1 ml of each sample was derivatised through the application of ethyl chloroformate 43 . At the end of the derivatisation process, each sample was adjusted to 0.5 ml with cyclohexane as the solvent. Each sample received 5 μL of octacosane (internal standard solution at 1000 ng µL −1 ) (Sigma-Aldrich, St Louis, MO, USA). The derivatised samples were initially analysed by gas chromatography-flame ionization detection (GC-FID, Shimadzu GC-2010, Kyoto, Japan) using an HP-1 capillary column (Agilent Scientific, Santa Clara, CA, USA; 30 m × 0.25 mm × 0.25 µm). A 1 µL aliquot of each sample was injected in the splitless mode with an injector temperature of 240 °C using helium as the carrier gas. The column temperature was held at 60 °C for 1 min and then increased to 320 °C (15 °C min −1 ) and held for 10 min. The extract with the best resolution was reanalysed with a gas chromatograph coupled to a mass spectrometer (GC-MS, Varian 4000, Palo Alto, CA, USA) using an HP5-MS column (JeW Scientific, Folsom, CA, USA; 30 m × 0.25 mm × 0.25 µm) and helium as the carrier gas. Injection (1 µL aliquot) was conducted in the splitless mode, and the column temperature programme was the same as that described for the GC-FID procedure above. The two major peaks were identified by comparing their mass spectra with those of the NIST 98 library and confirmed by co-injecting the authentic standards (Sigma-Aldrich, St Louis, MO, USA) with the sample. Amounts were estimated based on the peak area relative to the amount of internal standard (octacosane) and corrected according to the volume of foam used for the derivatisation.
Carbohydrate analysis. Total carbohydrate was estimated using the phenol-sulfuric acid method 27 . Briefly, five samples of foam produced by M. fimbriolata nymphs were collected, and an aliquot of 1 ml of each sample was vortexed with 25 µL of phenol (80% m/v) and 2.5 ml of sulfuric acid. The resulting mixture was allowed to stand for 20 min and then vortexed again. For the control, we used 1 ml of distilled water and followed the same steps as above. The absorbance of the mixed samples was measured spectrophotometrically using a UV mini 1240 Shimadzu (Shimadzu, Tokyo, Japan) at a wavelength of 490 nm. The total concentration was calculated based on a standard curve using glucose (0.01 mg L −1 ) in the range of 100-1000 µg.
Protein analysis. Total protein present in the foam was quantified according to the Bradford technique 28 .
Five samples of foam were collected in the same sugarcane field and 50 µL of each sample was dissolved in 3.95 ml of Milli-Q water, and 1 ml of Bradford reagent (Coomassie Brilliant Blue G with phosphoric acid and methanol) was added. The absorbance of the samples was measured using a spectrophotometer UV mini 1240 Shimadzu (Shimadzu, Tokyo, Japan) at a wavelength of 595 nm. As a control, we used 4 ml of Milli-Q water and 1 ml of protein reagent. The total protein concentration in the foam was calculated based on a standard curve for bovine serum albumin (BSA) at intervals of 0.02 to 0.3 mg.
Statistical analysis. The normality and homogeneity of the temperatures recorded in the field observations and in the laboratory assay were analysed using Kolmogorov-Smirnov and Bartlett tests. To limit the experiment-wise error rates to acceptable level in multiple comparisons with a low numbers of related groups, means the temperatures of the foam and those of other recorded sites were compared using one-way ANOVA followed by a Bonferroni post hoc test (P < 0.05) 44 . All analyses were performed using the SAS statistical software 45 .


s2fieldsofstudy: Biology Medicine

text:
Synergistic Anti-MRSA Activity of Cationic Nanostructured Lipid Carriers in Combination With Oxacillin for Cutaneous Application

Nanoparticles have become a focus of interest due to their ability as antibacterial agents. The aim of this study was to evaluate the anti-methicillin-resistant Staphylococcus aureus (MRSA) activity of cationic nanostructured lipid carriers (NLC) combined with oxacillin against ATCC 33591 and clinical isolate. The cationic resource on the NLC surface was soyaethyl morpholinium ethosulfate (SME). NLC loaded with oxacillin was produced to assess the antibacterial activity and the effectiveness of topical application for treating cutaneous infection. The hydrodynamic diameter and zeta potential of oxacillin-loaded NLC were 177 nm and 19 mV, respectively. When combined with NLC, oxacillin exhibited synergistic MRSA eradication. After NLC encapsulation, the minimum bactericidal concentration (MBC) of oxacillin decreased from 250 to 62.5 μg/ml. The combined NLC and oxacillin reduced the MRSA biofilm thickness from 31.2 to 13.0 μm, which was lower than the effect of NLC (18.2 μm) and antibiotic (25.2 μm) alone. The oxacillin-loaded NLC showed significant reduction in the burden of intracellular MRSA in differentiated THP-1 cells. This reduction was greater than that achieved with individual treatment. The mechanistic study demonstrated the ability of cationic NLC to disrupt the bacterial membrane, leading to protein leakage. The cell surface disintegration also increased oxacillin delivery into the cytoplasm, activating the bactericidal process. Topical NLC treatment of MRSA abscess in the skin decreased the bacterial load by log 4 and improved the skin’s architecture and barrier function. Our results demonstrated that a combination of nanocarriers and an antibiotic could synergistically inhibit MRSA growth.

INTRODUCTION
The growing amount of drug-resistant strains has become a serious health threat, especially the methicillin-resistant Staphylococcus aureus (MRSA) (Tong et al., 2015). Some MRSA strains are even resistant to second-line treatment such as vancomycin and doxycycline (Cihalova et al., 2015). The skin is the major organ infected by MRSA (Dréno et al., 2016). Topical application can be an efficient route to administer antibiotics for direct MRSA eradication. Only 6 antibacterial agents have been approved by the USFDA for MRSA management. Since none of these drugs is used for topical treatment (Rodvold and McConeghy, 2014), the development of new anti-MRSA agents for cutaneous use is urgently needed.
In the past 10 years, nanomedicine has become an innovative approach for combating drug-resistant pathogens. The large surface-to-volume ratio, the possibility of surface functionalization, and the capacity for drug entrapment contribute to the efficient antibacterial activity of nanoparticles (Zazo et al., 2016). Among these nanosystems, lipid-based nanoparticles such as liposomes, nanoemulsions, and nanostructured lipid carriers (NLC) are usually employed for carrying antibacterial drugs. In addition to their role as carriers for antibiotics, some cationic surfactants exhibiting antimicrobial impact can be intercalated in the surface of lipid-based nanoparticles; these include amino acid-based surfactants and quaternary ammonium salts (Colomer et al., 2013;Hwang et al., 2013;Tavano et al., 2014). The combination therapy of more than one antibacterial agent can reveal the synergistic activity against MRSA; thus the dose can be reduced to minimize the adverse effects (Henson et al., 2017). Some investigations involve combining nanoparticles and antibacterial agents for the synergistic inhibition of MRSA infection. For instance, Banche et al. (2015) developed chitosan nanodroplets loaded with oxygen for efficiently eradicating MRSA and Candida albicans without resultant cytotoxicity on keratinocytes. Argenziano et al. (2017) demonstrated that ultrasound-mediated vancomycin-loaded nanobubbles were more effective than free vancomycin for killing MRSA. In this study, we aimed to investigate the applicability of synergistic MRSA inhibition by cationic nanocarriers in combination with antibiotic for topical delivery. NLC consisting of mixed liquid and crystalline lipids in the nanoparticulate cores were utilized to load cationic surfactant for enhanced anti-MRSA potency. Soyaethyl morpholinium ethosulfate (SME) was chosen as the cationic surfactant because of the low cytotoxicity against mammalian cells such as neutrophils and keratinocytes (Hwang et al., 2015;Yang et al., 2016).
Oxacillin is a β-lactam commonly used to treat complicated skin infections, but it is ineffective against MRSA invasion (Thomsen et al., 2006). We used oxacillin entrapped in NLC for increased effectiveness against MRSA. A panel comprising S. aureus, MRSA, and drug-resistant clinical isolate was used as the pathogens to assess the antibacterial activity of the nanosystems. MRSA in the planktonic, biofilm, and intracellular forms was tested in the present study. The encapsulation of oxacillin in lipid nanoparticles may enhance the delivery ability into the biofilm and host cells. To estimate the in vivo efficiency of combined NLC and oxacillin, the transepidermal water loss (TEWL), MRSA burden, and histology were evaluated using a BALB/c mouse model with MRSA skin infection.

Preparation of NLC
The lipid and water phases of NLC were prepared separately. The lipid phase consisted of 2% squalene, 2% hexadecyl palmitate, 1.5% soy phosphatidylcholine (Phospholipon 80H R ), 1% deoxycholic acid, and 0.4% SME. The water phase consisted of 1.5% Pluronic F68 and double-distilled water. Both phases were heated to 85 • C for 15 min. The water phase was added to the lipid phase in the presence of high-shear homogenization (Pro250, Pro Scientific) at 12,000 rpm for 20 min. The mixture was subsequently treated using a probetype sonicator (VCX600, Sonics and Materials) for 15 min at 35 W. Oxacillin (0.1%) was included in the lipid phase as needed.

Size and Surface Charge of NLC
The average diameter and zeta potential of NLC with and without oxacillin were determined using dynamic light scattering (Nano ZS90, Malvern). The nanocarriers were diluted by doubledistilled water 100-fold before measurement.

Oxacillin Encapsulation in NLC
The entrapment percentage of oxacillin was determined by utilizing the ultracentrifugation method to separate the incorporated compound from the free form. The NLC was centrifuged at 48,000 × g and 4 • C for 40 min. The free antibiotic in the supernatant and the encapsulated antibiotic in the precipitate were analyzed by high-performance liquid chromatography to measure the entrapment efficiency.

Bacterial Strains and the Culture Conditions
Staphylococcus aureus (ATCC 6538) and MRSA (ATCC 33591) were obtained from American Type Culture Collection. KM1 was a clinical isolate of MRSA purchased from Kaohsiung Medical University Hospital. The strains were grown in tryptic soy broth (TSB) medium at 37 • C and 150 rpm.

Minimum Bactericidal Concentration (MBC)
A broth with twofold serial dilution method was utilized to measure the MBC. An overnight culture of bacteria was diluted in TSB to achieve OD 600 of 0.01 (about 5 × 10 6 CFU/ml). The bacteria population was exposed to several dilutions of oxacillin and/or NLC with TSB and incubated at 37 • C for 18 h. Subsequently, the CFU was counted. The MBC was defined as the lowest concentration that killed ≥ 99.9% of the bacteria.

MRSA Viability Detection by Fluorescence Microscopy
The viability and death of MRSA after oxacillin and/or NLC treatment were monitored using a Live/Dead BacLight R kit (Molecular Probes). The bacterial pellet was obtained by centrifugation at 12,000 rpm for 3 min. The pellet was resuspended in culture medium (1 ml) with oxacillin (125 µg/ml) and/or NLC (equivalent to 500 µg/ml SME). After incubation at 37 • C for 2 h, the resulting suspension was stained with the kit for 15 min. The stained sample was analyzed two-dimensionally by fluorescence microscopy (IX81, Olympus).

Biofilm Detection
The MRSA biofilm was grown in a Cellview R dish by incubating the bacteria (OD 600 = 0.1) in TSB containing 1% glucose at 37 • C for 24 h. The biofilm was treated with 125 µg/ml cetylpyridium chloride (CPC, the positive control), 125 µg/ml oxacillin, NLC (equivalent to 500 µg/ml SME), or NLC + oxacillin for 24 h. The biofilm was then stained using a Live/Dead BacLight R kit for 15 min. The biofilm was gently rinsed with PBS. The three-dimensional structure was visualized by Leica TSC SP2 confocal microscopy. The SYTO9 green color intensity and biofilm thickness in the confocal images were estimated.

MRSA Morphology Visualization by Transmission Electron Microscopy (TEM)
MRSA was incubated overnight at 37 • C in TSB broth. The bacterial suspension was diluted to achieve an OD 600 of 0.3. The microbes were then fixed in 3% glutaraldehyde in 0.1 M cacodylate buffer for 2 h. After fixation in 1% osmium teroxide for 2 h, followed by dehydration in an ascending series of ethanol concentrations, the samples were embedded in Spurr's resin. Sections of 70 nm were stained with 4% uranyl acetate and 0.4% lead citrate prior to observation under Hitachi H-7500 TEM.

Intracellular MRSA Eradication
Differentiated THP-1 were employed as the host cells to examine the activity of oxacillin and NLC in relation to intracellular MRSA. The differentiation of THP-1 into macrophages was carried out at a phorbol 12-myristate 13-acetate concentration of 100 µg/ml. The cell line was infected by MRSA at an MOI of 50 for 20 min. After being washed with PBS, the cells were incubated in the fresh medium supplemented with 125 µg/ml oxacillin and/or NLC (equivalent to 500 µg/ml SME). Triton X-100 (1%) was pipetted into the medium for cell lysis. The lysate of the cell medium was cultured on the agar dish for 18 h to count the CFU. For the confocal imaging of MRSA killing in THP-1, 4 -6-diamidino-2-phenylindole and anti-S. aureus antibody/Alexa Fluor R 488 goat anti-mouse IgG were used to stain the THP-1 nucleus and MRSA, respectively. We also stained the THP-1 actin using an anti-α tubulin antibody/microtubule marker (Alexa Fluor R 594) for visualizing the cytoskeleton under confocal microscopy.

Proteomic Identification
The MRSA was treated using oxacillin and/or NLC for 3 h. After centrifugation, the bacterial pellet was suspended with 0.5 ml double-distilled water. The MRSA was then centrifuged at 10,000 rpm and 4 • C for 15 min after 20-min sonication. The total protein content of MRSA was measured using a Bio-Rad protein assay kit with ELISA at 595 nm. The SDS-PAGE analysis was conducted with a 5% stacking gel and a 10% separating gel followed by silver staining. The bands in the protein gel staining were digested by trypsin at 37 • C for 24 h. The digested proteins were acidified with 0.5% trichloroacetic acid and then loaded into an AnchorChip R 600/384. A Bruker Ultraflex R spectrometer was employed for MALDI/TOF/TOF identification. The procedure for this analysis was shown in a previous report (Pan et al., 2010).

Genomic DNA Analysis
MRSA genomic DNA was extracted using a Presto R Mini bacteria kit according to the manufacturer's instruction. The aliquot of purified genomic DNA (100 ng) was analyzed by electrophoresis on a 0.8% agarose gel.

Animal
An 8-week-old female BALB/c mouse was purchased from the National Laboratory Animal Center (Taipei, Taiwan). The animal experiment was done in strict accordance with the recommendations in the Guidelines for the Care and Use of Laboratory Animals of Chang Gung University. The protocol was approved by the Committee of Care and Use of Laboratory Animals.

In Vivo MRSA Infection
The mouse's back hair was shaved. The back was subcutaneously injected with 1 × 10 6 CFU MRSA in PBS (150 µl). Subsequently, oxacillin and/or NLC with a volume of 0.2 ml was topically administered on the injection area every 24 h for 3 days. The gross and microscopic appearance of the skin surface was monitored each day. A handheld digital magnifier (Mini Scope-V, M&T Optics) was used to visualize the microscopic skin appearance. TEWL was estimated by Tewameter R TM300 (Courage and Khazaka) from 0 to 3 days post-injection of MRSA. At the end of the experiment, the skin was excised for homogenization by NagNA Lyser (Roche) to count the CFU of MRSA in the skin. The treated skin sample was fixed in 10% formalin, buffered in the phosphate saline, and processed for hematoxylin and eosin (H&E) staining. The unstained slices of formalin-fixed paraffinembedded skin samples were prepared for immunohistochemical (IHC) staining of lymphocyte antigen 6 complex locus G6D (Ly6G), which is the indicator of neutrophil infiltration. The skin section was incubated with anti-mouse Ly6G antibody for 1 h at room temperature and observed under optical microscopy (DMi8, Leica).

In Vivo Cutaneous Irritation
Oxacillin (625 µg/ml) and/or NLC at a volume of 150 µl were immersed in a non-woven cloth (1.5 × 1.5 cm 2 ). This cloth was applied to the dorsal skin of the mouse. Tegaderm R film was used to fix the cloth onto the mouse's back. The bacterial agent was applied daily for 5 consecutive days. After the treatment, the skin area was monitored by a handheld digital magnifier and TEWL.

Statistical Measurement
The statistical measurement was conducted using GraphPad Prism 5 software. Dual comparisons were made with unpaired Student's t-test. Groups of three or more were analyzed by ANOVA with Tukey or Dunnett posttests. The significance was indicated as * for p < 0.05, * * for p < 0.01, and * * * for p < 0.001 in the figures.

Size and Surface Charge of NLC
The molecular structure of oxacillin-loaded NLC is illustrated in Figure 1A. We proposed that oxacillin was entrapped in the inner core of NLC, whereas SME was intercalated in the emulsifier layer (oil-water interface). The two antibacterial agents were resided in the different phases of nanoparticulate system. Table 1 summarizes the diameter, polydispersity index (PDI), and surface charge of the lipid nanocarriers. The average particle size of NLC without the antibiotic was 117 nm, and that of NLC containing oxacillin was 177 nm. Both nanosystems revealed stable unimodal size distribution with PDI of ≤0.3, demonstrating a narrow  Each value represents the mean ± SD (n = 3). PDI, polydispersity index.
distribution. Positively charged nanoparticles were achieved (13 mV) for NLC without oxacillin because of the existence of SME on the particulate surface. The oxacillin addition generated greater zeta potential than did the nanoparticles without the drug. The result revealed that the entrapment percentage of oxacillin in NLC was 76.8 ± 7.0%. The encapsulation could be reduced to 59.1 ± 5.5% after 24 h of fresh preparation, indicating a drug release during the experiment. Table 2 shows the MBC value of oxacillin alone, NLC alone, and the NLC-oxacillin combination. The MBC of oxacillin alone against non-resistant S. aureus was 0.488∼0.976 µg/ml, whereas the MBC for SME in NLC was 62.5 µg/ml. The oxacillin MBC was not reduced after NLC incorporation. The combined NLC and oxacillin reduced SME MBC by 16-fold. MRSA was found to be more resistant to NLC and oxacillin as compared to drug-sensitive bacteria. Oxacillin synergized with NLC to inhibit MRSA growth. The oxacillin MBC of treatment alone and in combination with NLC was 250 and 62.5 µg/ml, respectively. The SME MBC of NLC for MRSA decreased twofold after oxacillin entrapment. The clinical MRSA strain (KM1) was more strongly inhibited by NLC and oxacillin than ATCC 33591. The anti-KM1 activity of oxacillin increased in the presence of cationic NLC. The MBC profile clearly indicates an enhancement in antibacterial potency of NLC and oxacillin upon the combination of both agents. Figure 1B represents the concentration-dependent microbicidal action of NLC and oxacillin on MRSA ATCC 33591. The counting of CFU was log-transformed in this figure. No significant decrease of CFU was detected in the oxacillin concentrations of ≤31.25 µg/ml. The oxacillin concentrations of >31.25 µg/ml showed a dose-dependent decrease in viability. With respect to NLC, the inhibitory effect increased with the increased concentration against MRSA. The combination of NLC with oxacillin reduced CFU/ml counts by at least log 5, leading to a killing of >99.9% MRSA at the oxacillin concentration of 62.5 µg/ml. The viability of MRSA was observed with fluorescence microscopy with the staining of dead and live bacteria by propidium iodide (PI) and SYTO9, respectively ( Figure 1C). The untreated control MRSA was mainly composed of live cells, which were green-stained. Fluorescence analysis revealed that some bacteria co-localized with PI after incubation with NLC and/or oxacillin. Combining nanocarriers and antibiotic proved superior for killing microorganisms as compared to individual treatment because of the increased PI staining. Figure 1D shows the anti-biofilm activity of NLC and/or oxacillin against MRSA under confocal microscopy. The bacteria were able to create a dense biofilm with a large amount of live MRSA after 24 h, which is shown as the negative control. CPC is a cationic surfactant affecting the antibacterial effect via cell wall destruction, with a strong potency. As a positive control, CPC markedly reduced the green signal and enhanced the red signal in the biofilm. Oxacillin exhibited a limited activity against biofilm due to the significant green signal after treatment. The image of biofilm from MRSA treated with NLC alone showed that the biofilm had disintegrated, with an obvious reduction in the number of live bacteria. A similar result was observed in the case of combined NLC and oxacillin. The quantification of green fluorescence in biofilm showed a negligible signal of live cells after CPC treatment ( Figure 1E). Incubation of oxacillin and NLC alone decreased the green color intensity by about 2-and 6-fold, respectively. The combination permitted a synergistic effect on intensity reduction with statistical significance. Oxacillin-loaded NLC exhibited greater biofilm thickness reduction (13.0 µm) compared to that of drug or NLC alone ( Figure 1F). Figure 2A presents the TEM images of the MRSA morphology. The intact bacteria (control) reveal an integrated cell surface with homogenous cytosol distribution. Oxacillin caused cell membrane deformation with a rough surface (arrow in Figure 2A). The MRSA membrane was disrupted after NLC treatment. Some cytoplasmic materials were released from the cytosol (arrow in Figure 2A). The observation of fewer dark zones in the cells treated by NLC than in the control can be attributed to cytoplasm dissolution. The cell wall tended to separate from the cytoplasmic membrane since vacuoles formed between them. More cell wall damage and cytoplasmic leakage are seen after combined treatment (arrow in Figure 2A). The antibacterial efficacy of oxacillin and NLC was examined using macrophages as the host cells for MRSA infection. Timedependent intracellular MRSA killing was detected, and is depicted in Figure 2B. The MRSA burden in the mammalian cells gradually increased following the increase of time in the untreated control group. Although treatment with NLC and/or Each value represents the mean ± SD (n = 3). * * p < 0.01; * p < 0.05. oxacillin reduced MRSA production at 1 h, no significant difference was shown after a comparison with the control group. At 2 h, NLC with and without antibiotic resulted in a marked reduction in intracellular MRSA survival. On the other hand, oxacillin alone had no effect on the intracellular viability of bacteria for all the time points tested. Synergy was demonstrated by the combination of NLC and the drug after 2-h incubation. There was a threefold decrease of MRSA survival for the combined treatment as compared with the untreated control. A complete inhibition of MRSA growth occurred consistently after 4 h of contact with NLC alone or NLC + oxacillin. Figure 2C shows the confocal microscopic images of MRSAinfected THP-1 cells. In the images of THP-1 without any treatment, the cytosol is full of red signals, indicating the presence of cytoskeleton stained by actin. Some punctuated green signals in the MRSA-infected THP-1 cytoplasm indicate the invasion of bacteria inside the macrophages. After 4-h incubation of oxacillin or cationic NLC alone, less green fluorescence was visualized in the cytosol. The oxacillin-entrapped NLC resulted in negligible MRSA residence in the cytosol, demonstrating a synergistic effect. We hypothesize that NLC can be utilized as Trojan horses to promote the antibiotic delivery into host cells for killing bacterial.

Anti-MRSA Mechanisms of NLC and/or Oxacillin
We next explored the anti-MRSA mechanisms of NLC in the presence or absence of oxacillin intervention. Figure 3A illustrates the profiles of SDS-PAGE. The bands of NLC-and/or oxacillin-treated MRSA are quite different from those of the untreated microbes. There were 12 protein bands differentially expressed after treatment. The quantification of the protein level was conducted in mass as shown in Table 3. All 12 proteins exerted comparable or slightly higher expression by treatment with oxacillin alone, in comparison to the untreated group. No protein exhibited upregulation greater than 2-fold after oxacillin application. The 3 proteins with the highest molecular weights: DNA-directed RNA polymerase subunit β, chaperone ClpB, and elongation factor G, were downregulated by the NLC treatment. Increased expression was detected for the other 9 proteins. However, the expression increment of these 9 proteins was insignificant (<1.25-fold). The NLC and oxacillin combination produced a notable decrease in protein expression, as shown in the SDS-PAGE profiles, especially in the case of ornithine carbamoyltransferase and 30S ribosomal protein S4. Both proteins were decreased by >5-fold in the MRSA with NLC+oxacillin. The total MRSA protein amount was measured after the application of NLC and/or oxacillin as shown in Figure 3B. Oxacillin and NLC alone caused a 60 and 66% loss of total protein content compared to the control, respectively. A further reduction was observed with the use of antibiotic-loaded NLC, resulting in a 13-fold decrease.
We studied the anti-MRSA mechanisms of oxacillin and NLC by genomic DNA detection, as shown in Figure 3C. We differentiated 6 plasmid bands in the agarose gel image. DNA obtained from MRSA treated with NLC and/or the drug exhibited a pattern similar to that found in the control. No significant elimination of DNA was observed by oxacillin or NLC treatment. This result suggests that the extensive damage to the integrity of DNA might not occur when MRSA is treated with both agents; however, small region deletions/insertions or inactivating point mutations of DNA might be occurred. We also examined the possible genomic mutation of the MRSA. The mutation rate assay was performed by spotting a 10-fold dilution of overnight culture onto the agar supplemented with 62.5 µg/ml SME in NLC or NLC + oxacillin. The plate was incubated at 37 • C overnight and imaged. As shown in the upper panel of Figure 3D, the treatment of NLC alone or the combined strategy was able to modify the color of some colonies from yellow to orange or white, indicating of bacterial mutation. Oxacillin alone did not a Ratios to control indicated the fold changes in protein volume among Oxacillin, NLC, NLC + oxacillin treated samples versus MRSA samples, respectively. The higher ratios (>1.0) mean the proteins whose expression levels were increased upon treatments of compounds, while lower ratios (<−1.0) indicate the proteins were downregulated under the exposure to compounds. b Analyzing the gel images using GeneTools software.
Frontiers in Microbiology | www.frontiersin.org change the colony color. The mutation rate was calculated based on the MRSA numbers of orange or white colonies normalized to the numbers of total colonies. As shown in the bottom panel of Figure 3D, NLC and NLC + oxacillin caused a mutation rate of about 6 × 10 −4 . No colony color phenotype mutation was detected for oxacillin alone.

In Vivo MRSA Infection
Nanostructured lipid carriers and/or oxacillin were topically applied onto the region of subcutaneous abscess generated in a mouse model by local MRSA infection. Figure 4A shows the demonstrative gross appearance of the mouse back after 3-day treatment. The abscess caused by MRSA is indicated by the arrow in this figure. A significant lesion is seen in the group of MRSA infection without treatment. The improvement in lesion healing was limited in the mouse receiving oxacillin alone. The lesion was reduced with no open wound by topical application of NLC and antibiotic-containing NLC. A nearly complete abscess resolution was detected for the combined NLC and oxacillin. The handheld digital magnifier offered visualization of the changes caused by the MRSA on the demonstrative mouse skin surface, as demonstrated in Figure 4B. The end face view of the skin exhibited that the wound was worse following the increase of time in the MRSA and MRSA + oxacillin groups. The treatment with NLC and drug-loaded NLC significantly cleared the abscess with skin texture comparable to healthy skin. The open wound formed by MRSA infection disturbed the cutaneous barrier function. The TEWL-time curves are plotted in Figure 4C. The baseline of TEWL (non-treatment) was maintained at 4∼6 g/m 2 /h during 3 days. MRSA inoculation resulted in an immediate and large increase of TEWL, indicating the barrier function deficiency. Oxacillin alone was ineffective in reducing TEWL, while NLC and NLC + oxacillin demonstrated a significant inhibition of water loss in the infected area. The bacterial count in the skin was estimated 3 days postinjection, as shown in Figure 4D. MRSA injection produced a 4-log enhancement in CFU/ml as compared to normal skin. No significant reduction in the MRSA count of oxacillin treatment alone was observed when compared to bacterial infection without intervention. In the mouse infected with MRSA, both NLC and drug-loaded NLC resulted in a 10 4 reduction of CFU/ml compared with the placebo control. Figure 4E shows the qualitative evaluation of skin histopathology of the infection of MRSA with and without intervention. As compared to healthy skin, MRSA injection created a significant disorganization in the epidermis, degenerated dermis, and immune cell infiltration. A large MRSA burden was seen under the subcutis. The generation of the abscess led to thickened tissue. The epidermal damage confirmed the deficiency of barrier integrity as measured by the enhanced TEWL. The wound treated with either NLC or oxacillin-loaded NLC showed a minor inflammation. The distribution of the infiltrated neutrophils in the skin was visualized using Ly6G IHC, as shown in the bottom panel of Figure 4E. The large neutrophil infiltration overlapped the MRSA distribution in the subcutaneous region, suggesting deep inflammation. We could also observe the neutrophil diffusion to viable skin. Topical oxacillin suppressed the neutrophil migration in viable skin but not in the subcutaneous area. We found attenuation of neutrophil accumulation after nanoparticle treatment, with the oxacillin-loaded NLC displaying greater amelioration.

In Vivo Cutaneous Irritation
The formulations tested in this study were topically applied on healthy mouse skin each day for 5 days. Figure 5A illustrates the representative skin surface images visualized by a handheld magnifier. Slight erythema and scaling occurred when the mouse skin was administered with the vehicle (double-distilled water). In contrast, no visible erythema or edema was observed in the oxacillin-treated skin. The severity of erythema and excoriation was worsened by NLC alone. It was surprising that the addition of oxacillin in NLC could lessen the cutaneous abnormalities caused by NLC. The results of TEWL after 5-day treatment reflected the condition of the skin surface. As shown in Figure 5B, the increased TEWL induced by vehicle control was reversed to the non-treatment baseline by oxacillin administration. Application of NLC alone revealed an approximately 4-fold higher TEWL compared to healthy skin. Oxacillin incorporation significantly decreased TEWL from 26 to 18 g/m 2 /h, demonstrating that oxacillin as a protector possesses the capability to reduce possible cutaneous irritation raised by the vehicle or NLC.

DISCUSSION
Oxacillin is effective in inhibiting non-resistant S. aureus, whereas MRSA is known to resist antibiotics such as methicillin and oxacillin (Cihalova et al., 2015). Our results confirm that oxacillin has limited anti-MRSA activity. Oxacillin incorporation in cationic NLC increased the killing efficacy against MRSA. The assembling ability of SME on the nanoparticulate shell was responsible for the antibacterial activity of cationic NLC. The synergistic effect on the antibacterial effect can be described as the combination of two different approaches producing greater activity than either approach alone (Basri and Sandra, 2016). Qin et al. (2013) also define the synergism of two drugs combined causing bacterial killing by a 4-fold lower dose than that of either agent used separately. We achieved the synergism of anti-MRSA activity by combining NLC and antibiotic according to the MBC profile of oxacillin.
Bacterial wall integrity is vital for their survival because it is the outermost and most accessible layer encountering the surrounding environment. It is an important area of action for many antibiotics (Radovic-Moreno et al., 2012). Oxacillin presents cell wall biosynthesis inhibition. The cationic quaternary ammonium surfactants demonstrate antimicrobial activity by targeting and disrupting the cell wall via electrostatic and lipophilic interactions (Zhou et al., 2017). The cell wall of MRSA carries negative charges due to the presence of lipopolysaccharides and teichoic acid (Hemeg, 2017). SME on the NLC surface can bind to the negatively charged cell membrane, permeabilizing it to induce lysis and cellular content leakage. The long alkyl chains in the SME structure might assist the interaction in the cell wall because of the facile intercalation into the lipid bilayers in the membrane. The destabilization of the FIGURE 5 | Skin tolerance examination of mouse skin by a 5-day treatment of topically applied oxacillin and/or NLC. (A) The representative skin surface of mice viewed under handheld digital magnifier. (B) TEWL of mice skin at day 5. Each value represents the mean ± SD (n = 6). * p < 0.05, * * p < 0.01, and * * * p < 0.001. bacterial wall involves the creation of pores on the cell surface to release ions and molecules as confirmed by our TEM results, after which bacterial death occurs. The demonstration of synergistic antibacterial activity by the combined treatment herein suggests the different mechanisms of action for both antibacterial agents (Bassolé and Juliani, 2012). The oxacillin-loaded NLC interacted strongly with the MRSA surface to damage the membrane. A considerable amount of oxacillin was released from the nanocarriers to generate high local drug concentration near the bacterial surface or inside the bacteria. The sustained bactericidal concentration of combined SME and oxacillin exhibit an anti-MRSA effect superior to that of separate treatment. Oxacillin entrapment increased the positive zeta potential of cationic NLC. The more-positive charges of antibiotic-loaded nanocarriers improved the electrostatic targeting to exert greater MRSA eradication.
Different from the case with planktonic bacteria, conventional antibiotics are less effective in treating biofilm bacteria due to the resistance to antibiotic delivery and avoidance of innate immune intervention (Abee et al., 2011). Using biofilm, we demonstrated that oxacillin-loaded nanoparticles penetrated into the extracellular polymer substance (EPS) and eradicated biofilm MRSA more effectively than individual treatment did. Extracellular DNA plays a key role in biofilm production, acting as a chelator of cationic molecules (Baelo et al., 2015). The interaction between EPS and the nanoparticles featuring lipids can cause a strong affinity and biofilm disintegration (Cheow et al., 2011). The cationic NLC designed in this study fit these criteria. The extremely non-wetting property of biofilm led to the restricted diffusion of antimicrobial liquid formulations (Lin et al., 2017). The low surface tension of cationic NLC caused by the presence of emulsifier systems might assist the penetration into non-wetting biofilm. NLC can hide the physicochemical characteristics of oxacillin to diminish the unfavorable interaction with biofilm.
The killing of intracellular MRSA is a complicated procedure. The pathogens in the host cells favor intracellular replication and the extensive spread of infection. Most of the antibiotics poorly penetrate into the host cells and therefore do not display satisfactory intracellular infection inhibition (Xie et al., 2014). Our results showed that the nanocarriers were preferentially taken up by macrophages, revealing greater activity against intracellular MRSA compared to free oxacillin. The change in the cytoskeleton morphology by the nanoparticles is evidence of phagocytosis (May and Machesky, 2001). The cationic NLC can modify the actin distribution in THP-1 cells; however, it must be noted that NLC might produce some toxicity on macrophages. It is generally recognized that the lipophilic nanoparticles are more facilely phagocytosed into macrophages than are hydrophilic nanoparticles, by the hydrophobic interaction with the cellular membrane (Hsu et al., 2017). The cationic nanoparticles ensure better uptake to macrophages with negatively charged membrane as compared to neutral and anionic ones (Zazo et al., 2016).
Soyaethyl morpholinium ethosulfate on the NLC shell can directly interfere with the bacterial cell wall and damage the membrane. The cationic nanocarriers altered the membrane to release cytoplasmic materials, as shown in the TEM images.
The significant loss of proteins in the MRSA co-treated with NLC and oxacillin verified the cell wall leakage; however, the leakage can be acknowledged as mild according to the TEM. The anti-MRSA activity of oxacillin-loaded NLC may also rely on other mechanisms of action. The nanoparticles possibly interact with DNA and the proteins of microbes to disturb the replication, translation, and transcription of the cellular pathways (Hemeg, 2017;Richter et al., 2017). Our previous study (Yang et al., 2016) suggested the antibacterial mechanisms of SME to evoke the Fenton reaction and reactive oxygen species (ROS). The preliminary genome analysis showed no significant alteration of the DNA level after NLC and/or oxacillin management. The greater molecular size of bacterial DNA compared to proteins retarded the leakage to the extracellular space. It is hypothesized that, besides membrane leakage by direct targeting, the nanosystems predominantly acted on proteins, thereby constraining MRSA growth.
The three proteins with the highest molecular weights revealed no significant change under combined NLC and oxacillin. On the other hand, all proteins with the molecular weight of <75 kDa exhibited loss after co-treatment. The mild leakage in the bacterial membrane created by antibiotic-entrapped nanoparticles might allow the liberation of proteins of <75 kDa. Among the detected proteins of >75 kDa, chaperone is central to survival in stress and antibiotic resistance (Frees et al., 2014). It is also a protein predominating in the resistance of MRSA to oxacillin (Jousselin et al., 2012). Treatment of oxacillin and NLC alone moderately increased and decreased chaperone expression, respectively. The combined NLC and oxacillin exhibited an offset effect on chaperone expression. A similar trend was shown in the other two proteins with high molecular weights (DNA-directed RNA polymerase and elongation factor G).
Ribosomes, a primary target for some antibiotics, such as macrolides and tetracyclines, are a critical catalyst for substrate stabilization of S. aureus protein synthesis (Wall et al., 2015). Oxacillin-loaded nanocarriers might interact and deactivate ribosomal subunits in the same way that some metal nanoparticles do (Hemeg, 2017), which can lead to the obstruction of protein translation. Elongation factor Tu is responsible for the protein synthesis through translation in the ribosomes (Pereira et al., 2015). NLC in combination with the drug showed significant downregulation of both ribosomes and elongation factor Tu. The attachment of invasive phenotype S. aureus to the biological surface is a requirement for eliciting virulence infection. Enolase and ornithine carbamoyltransferase are the proteins on the bacterial surface for binding with extracellular matrix proteins such as fibronectin, elastin, and collagen (Hussain et al., 1999;Carneiro et al., 2004). Both surface proteins were largely decreased by NLC and oxacillin co-treatment, thus impeding the pathogenesis of MRSA in the biological system. A similar case was the significant reduction of arginine deiminase after combined treatment. Arginine deiminase is a virulence factor of bacteria in biofilm growth and intracellular survival (Lindgren et al., 2014). The presence of NLC can produce some bacterial mutation. The change of colony color on the agar plate could be due to a response to oxidative stress (Strand et al., 2003), and also indicates of the loss of infectious force. However, it should be noted with caution that the bacterial resistance against the antibiotics may increase after mutation. The oxacillin encapsulation was slightly reduced 24 h post-preparation. Since most of the in vitro and in vivo experiments were performed within 24 h, we believed that the structure of oxacillin-NLC complex generally remained intact during the experiments. Of course some oxacillin molecules were released from NLC nanoparticles in the nanosystem. It is our opinion that the unencapsulated oxacillin still could synergize with cationic NLC to eradicate MRSA because of the different antibacterial mechanisms of both agents. The possible mechanisms of oxacillin-loaded NLC for killing MRSA are illustrated in Figure 6.
The biofilm-like property of bacterial abscess in the skin weakens conventional antibiotic therapy (Han et al., 2009). MRSA contributes to cutaneous inflammation and barrier deterioration. NLC was able to diffuse into the nidus to decrease the MRSA burden and repair the barrier capacity, especially the drug-loaded NLC, which showed smaller abscess size and neutrophil infiltration compared to the NLC without the drug in the in vivo experiment. According to the previous studies (Hung et al., 2015), NLC would remain intact because of the soft and deformable characteristics for facile transport into the skin. The fusion of NLC in SC lipids is another possibility (Gelfuso et al., 2016). MRSA infection would damage the skin barrier function because of the formation of wound (Soong et al., 2012;Lin et al., 2017). It was possible that NLC could penetrate into the skin in the intact form. Although NLC can be a potential therapy for MRSA eradication, it is important to examine whether nanotoxicity is induced by the lipid nanocarriers. Our in vivo cutaneous tolerance study suggests the symptoms of erythema and excoriation on the skin surface treated by NLC. The TEWL increased 4-fold after 5 consecutive days of topical NLC administration, suggesting barrier disruption. The cutaneous irritation could be classified as mild. A previous study (Shimada et al., 2008) suggests about a 5-fold TEWL increase in dog skin after stratum corneum stripping. Another study (Yan et al., 2010) reports a 10∼25-fold increase of TEWL in rat skin receiving microneedle puncture, which is a permeationenhancing approach. Both tape stripping and microneedles demonstrate a greater barrier loss as compared to cationic NLC.
It is well-known that quaternary ammonium-based surfactants are typical skin irritants producing some toxicity, including CPC, cetyltrimethylammonium bromide, tri(dodecyldimethylammonioacetoxyl)diethyltriamine trichloride, and benzalkonium chloride (BKC) (Kano and Sugibayashi, 2006;Zhou et al., 2016Zhou et al., , 2017. Toxicity is usually a concern in developing antibacterial nanoparticles such as cationic surfactant-coated, zinc oxide, and silver nanoparticles (Pati et al., 2014;Pérez-Díaz et al., 2016). It is critical to improve the safety of cationic nanocarriers while maintaining the antibacterial effect. We had screened a series of cationic surfactants for the cytotoxicity and found that SME demonstrated a wider therapeutic window than the others such as CPC and BKC (Yang et al., 2016). Our previous result approved a safe use of SME. It is the reason why we employed SME in the study.

CONCLUSION
We assembled cationic NLC to load oxacillin as a drugdelivery nanosystem for MRSA infection therapy. Since oxacillin encapsulation in NLC achieved > 70%, the free oxacillin was also present in the nanosystems. However, the high loading efficiency of NLC for oxacillin led to the elucidation that the synergistic anti-MRSA effect was mainly attributed to the oxacillin-NLC complex but not free drug or NLC alone. The combined NLC and oxacillin showed lower MBC against MRSA compared to individual treatment. NLC and oxacillin co-treatment increased the efficacy against MRSA residing both extracellularly and intracellularly. The combination was superior in eradicating biofilm compared to mono treatment. Topical administration of oxacillin-loaded nanoparticles significantly reduced cutaneous infection and improved skin barrier function and architecture. NLC has potential for use in combination with antibiotic against MRSA, especially with the currently increasing drug resistance among microbial species. The dose and dosage interval can be reduced with this association. The reduced dose, easy scale-up fabrication, and inexpensiveness of the SME-coated NLC may lessen the expenditure needed for antibacterial therapy. Our nanocarriers can be potential candidates for topical treatment of MRSA infection.