Engineering a detect and destroy skin probiotic to combat methicillin-resistant Staphylococcus aureus.

The prevalence and virulence of pathogens such as methicillin-resistant Staphylococcus (S.) aureus (MRSA), which can cause recurrent skin infections, are of significant clinical concern. Prolonged antibiotic exposure to treat or decolonize S. aureus contributes to development of antibiotic resistance, as well as depletion of the microbiome, and its numerous beneficial functions. We hypothesized an engineered skin probiotic with the ability to selectively deliver antimicrobials only in the presence of the target organism could provide local bioremediation of pathogen colonization. We constructed a biosensing S. epidermidis capable of detecting the presence of S. aureus quorum sensing autoinducer peptide and producing lysostaphin in response. Here, we demonstrate in vitro activity of this biosensor and present and discuss challenges to deployment of this and other engineered topical skin probiotics

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features

<i>In vitro</i> efficacy of <i>S</i>. <i>epidermidis</i> producing different antimicrobials.

A) Concept of design to express antimicrobial peptide (AMP) genes under the biosensor. AMPs are tagged for export with a signal peptide (SP) in S. epidermidis under control of either (1) tetracycline inducible promoter (Pxyl/tet) for testing under tight inducible control, or (2) agr P2P3 biosensor. B) Pull-down SDS-PAGE verifying AMPs elafin, LL-37, hiracin, and lysostaphin secretion from S. epidermidis. Several different signaling peptides (e.g., SP-14, SP-15, SP-17) were tested for LL-37. The endogenous signal peptide for lysostaphin was used. C) S. epidermidis expressing AMPs under the tet-inducible promoter demonstrate strong growth inhibition of S. aureus in an overlay assay. Concentration of anhydrotetracycline (ATc) is shown and is dose-dependent. D) In vitro activity and specificity of lysostaphin-expressing agr type I, II, and III biosensors integrated into Tü3298Δagr genome. Parent strain included as control. Two biosensor integrant clones (i.e., biological replicates “1” and “2”) were tested at initial doses of 104 and 106 CFUs of S. epidermidis spotted on for each overlay assay.</p

Primer sets used for amplifying specific DNA fragments by PCR.

Primer sets used for amplifying specific DNA fragments by PCR.</p

Biosensor design.

A) Illustration of the staphylococcal agr quorum-sensing circuit. AgrD pro-peptide is modified and exported by AgrB as an auto-inducer peptide (AIP). AIP stimulates two component sensor AgrCA, which upregulates transcription of the P2/P3 promoter, upregulating agr component and RNAIII expression. B) Concept illustration of a S. epidermidis “detect and destroy” probiotic biosensor. The sensing component from S. aureus’ quorum sensing circuit in A) (agrA and agrC) are introduced into an agr mutant S. epidermidis strain. Binding of AgrA to the P2/P3 promoter controlling expression of a S. aureus-targeting antimicrobial then results in density-dependent production of the antimicrobial of choice, here, lysostaphin. C) Staphylococcal biosensor circuit design for validation. agrCA sensor genes were cloned from S. aureus agr groups I-IV and placed downstream of promoter P2, with GFP under control of P3. D) Biosensor induction by different AIPs from different S. aureus agr types, testing the efficacy and the cross-reactivity of a plasmid-borne biosensor. Supernatants of overnight S. aureus cultures from each agr type were filter-sterilized, diluted 1:10 in TSB and co-incubated with S. epidermidis strains expressing the corresponding agr biosensor reporter constructs (sensor). Control was empty vector (pCN34). Bidirectional T-test compared to TSB control group, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, n = 2 replicates. E) Testing efficacy and sensitivity of biosensor induction by the matched agr type AIP, using a biosensor integrated into the S. epidermidis genome. Given low cross-reactivity from D), we tested each biosensor with its matching agr type in a dilution series to test sensitivity. Biosensors were co-incubated with dilutions of supernatant from S. aureus strains of the matching agr type. Media was used as control. Significance between groups determined within each panel by ANOVA with post-hoc Tukey HSD test, p < 0.05, n = 3 replicates. Groups that do not share the same letter (a, b, c, or d) are significantly different in post-hoc multiple comparisons test.</p

Trials to define biosensor control of MRSA proliferation <i>in vivo</i>.

Germ-free C57BL6/J mice were colonized on the ears on days 0, 2 and 4, and growth of S. epidermidis (agr type I lysostaphin producing biosensor or Tü3298Δagr parent strain) and S. aureus was quantified by swabbing the ear, directly spreading on mannitol salt agar, and then counting CFUs on days 2, 4, 6, 8, and 10 (select cases). In A) we show representative control samples for colonization alone by S. aureus USA300 (MRSA), S. epidermidis parent, or S. epidermidis biosensor strains. B) and C), D) and E), and F) and G) are replicate experiments. In B) and C), we examined biosensor efficacy in suppressing growth of MRSA when co-colonized. MRSA was mixed with the biosensor or parent in a suspension at a 1:100 ratio (107/109 CFUs, respectively) and then applied to the mouse ears. n = 5 mice for all groups. Spearman linear regression line is shown with 95% confidence intervals. P-value to assess statistical significance in CFU counts between groups, accounting for time as a covariate, was determined within each panel by ANCOVA. In D) and E), we examined biosensor efficacy in preventing colonization (and suppressing growth), colonizing mouse ears with biosensor or parent alone at day 0, then challenging with the suspension of S. aureus USA300 with S. epidermidis biosensor or parent on day 2. n = 5 for all groups. In F) and G), we examined growth in a wound model, in which mice underwent a dorsal punch biopsy which was then inoculated with the suspension of S. aureus USA300 plus biosensor or parent. After 48 h, dorsal skin surrounding the wound was harvested for CFU quantitation on mannitol salt agar. n = 5 mice/group (left), n = 12/group (right). P-value was determined by bidirectional t-test.</p

Raw data underlying figures.

(XLSX)</p

Parameter choices do not qualitatively change the biological interpretation of our information entropy measure.

<p>Cohen’s d statistic [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.ref029" target="_blank">29</a>] comparing distributions of entropy values for cellular calcium activity of (a) stage 14 and stage 18 <i>Xenopus laevis</i> embryos, (b) stage 18 and stage 22 <i>Xenopus laevis</i> embryos, (c) stage 14 and stage 22 <i>Xenopus laevis</i> embryos, and (d) mature retrotrapezoid nucleus neurons from embryonic mice in pH 7.4 solution vs. pH 7.2 solution (data in (d) obtained from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.ref015" target="_blank">15</a>]). At large values of <i>n</i> and <i>k</i>, a sign change in d value occurs which is a technical artifact arising from there being more entries in the transition matrix than can be filled by data from our time series. The numerical values of d which generated this figure can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.s007" target="_blank">S2 Table</a>.</p

Separation between calcium activity distributions from two biologically distinct populations as a function of sample size.

<p>The p-value obtained from a two-sample Kolmogorov-Smirnov test between distributions of calcium activity traces processed by a given analysis method from stage 14 Xenopus neural progenitors and stage 22 Xenopus neural progenitors is used as a measure of separation between the two biologically distinct populations. A smaller p-value indicates a more confident separation between the distributions. Each point represents mean + SD of 5,000 comparisons between samples of a given size taken with replacement from the two distributions. Markovian Entropy is calculated with <i>n</i> = 2 and <i>k =</i> 1. A randomized control is included that compares two samples which both come from the stage 14 Xenopus population. The Cohen’s d values associated with this data can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.s003" target="_blank">S3 Fig</a>.</p

Distributions of Markovian Entropy and other analysis measures of calcium activity from synaptic neurons.

<p>Univariate scatterplots represent the (a) Markovian Entropy, (b) Number of Spikes, (c) Average Power, and (d) Hurst Exponent of murine retrotrapezoid nucleus neurons’ calcium activity in solution with pH 7.2 or 7.4. Data received from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.ref015" target="_blank">15</a>]. Lines represent mean ± SD of 397 and 244 cells, respectively. All comparisons between distributions were statistically significant according to a two-sample Kolmogorov-Smirnov Test (p < 0.01). Hence stars are used to represent the effect size, rather than the significance of difference, between distributions via Cohen’s d statistic (*: |d| ≥ 0.20, **: |d| ≥ 0.50, ***: |d| ≥ 0.80, ****: |d| ≥ 1.00, *****: |d| ≥ 2.00) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168342#pone.0168342.ref029" target="_blank">29</a>]. Markovian Entropy is calculated with <i>n</i> = 2 and <i>k</i> = 2.</p