Glycerol Monolaurate and Dodecylglycerol Effects on Staphylococcus aureus and Toxic Shock Syndrome Toxin-1 In Vitro and In Vivo

BACKGROUND:Glycerol monolaurate (GML), a 12 carbon fatty acid monoester, inhibits Staphylococcus aureus growth and exotoxin production, but is degraded by S. aureus lipase. Therefore, dodecylglycerol (DDG), a 12 carbon fatty acid monoether, was compared in vitro and in vivo to GML for its effects on S. aureus growth, exotoxin production, and stability. METHODOLOGY/PRINCIPAL FINDINGS:Antimicrobial effects of GML and DDG (0 to 500 microg/ml) on 54 clinical isolates of S. aureus, including pulsed-field gel electrophoresis (PFGE) types USA200, USA300, and USA400, were determined in vitro. A rabbit Wiffle ball infection model assessed GML and DDG (1 mg/ml instilled into the Wiffle ball every other day) effects on S. aureus (MN8) growth (inoculum 3x10(8) CFU/ml), toxic shock syndrome toxin-1 (TSST-1) production, tumor necrosis factor-alpha (TNF-alpha) concentrations and mortality over 7 days. DDG (50 and 100 microg/ml) inhibited S. aureus growth in vitro more effectively than GML (p<0.01) and was stable to lipase degradation. Unlike GML, DDG inhibition of TSST-1 was dependent on S. aureus growth. GML-treated (4 of 5; 80%) and DDG-treated rabbits (2 of 5; 40%) survived after 7 days. Control rabbits (5 of 5; 100%) succumbed by day 4. GML suppressed TNF-alpha at the infection site on day 7; however, DDG did not (<10 ng/ml versus 80 ng/ml, respectively). CONCLUSIONS/SIGNIFICANCE:These data suggest that DDG was stable to S. aureus lipase and inhibited S. aureus growth at lower concentrations than GML in vitro. However, in vivo GML was more effective than DDG by reducing mortality, and suppressing TNF-alpha, S. aureus growth and exotoxin production, which may reduce toxic shock syndrome. GML is proposed as a more effective anti-staphylococcal topical anti-infective candidate than DDG, despite its potential degradation by S. aureus lipase

Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Background: Diagnostic errors can occur, in infectious diseases, when anti-microbial immune responses involve several temporal scales. When responses span from nanosecond to week and larger temporal scales, any pre-selected temporal scale is likely to miss some (faster or slower) responses. Hoping to prevent diagnostic errors, a pilot study was conducted to evaluate a four-dimensional (4D) method that captures the complexity and dynamics of infectious diseases. Methods: Leukocyte-microbial-temporal data were explored in canine and human (bacterial and/or viral) infections, with: (i) a non-structured approach, which measures leukocytes or microbes in isolation; and (ii) a structured method that assesses numerous combinations of interacting variables. Four alternatives of the structured method were tested: (i) a noise-reduction oriented version, which generates a single (one data point-wide) line of observations; (ii) a version that measures complex, three-dimensional (3D) data interactions; (iii) a non-numerical version that displays temporal data directionality (arrows that connect pairs of consecutive observations); and (iv) a full 4D (single line-, complexity-, directionality-based) version. Results: In all studies, the non-structured approach revealed non-interpretable (ambiguous) data: observations numerically similar expressed different biological conditions, such as recovery and lack of recovery from infections. Ambiguity was also found when the data were structured as single lines. In contrast, two or more data subsets were distinguished and ambiguity was avoided when the data were structured as complex, 3D, single lines and, in addition, temporal data directionality was determined. The 4D method detected, even within one day, changes in immune profiles that occurred after antibiotics were prescribed. Conclusions: Infectious disease data may be ambiguous. Four-dimensional methods may prevent ambiguity, providing earlier, in vivo, dynamic, complex, and personalized information that facilitates both diagnostics and selection or evaluation of anti-microbial therapies

Glycerol monolaurate (GML) inhibition of <i>Staphylococcus aureus</i>.

<p>GML concentrations 50, 100, and 500 µg/ml were tested versus <i>S. aureus</i> isolates from different PFGE types, USA400 MRSA (A), USA400 MSSA (B), USA300 MRSA (C), USA200 MRSA (D), USA200 MSSA (E), atopic dermatitis strains (F), vaginal strains from healthy women (G), for 18 h at 37°C with shaking. The dashed line indicates the starting inocula. Each square (▪) indicates one isolate. The bars represent the mean±SEM of bacterial density in the group.</p

Antistaphylococcal effects of GML and DDG in a rabbit Wiffle ball infection model.

<p>Rabbits (n = 5 in each group) were infected with 3×10⁸ CFU/ml <i>S. aureus</i> MN8, and compounds (final concentration 1 mg/ml) were instilled into the Wiffle balls every-other-day and rabbits monitored up to 7 days. Survival of the rabbits (A), bacterial counts (B), TSST-1 production (C), and TNF-α levels (D) in the Wiffle balls. TSST-1 presented as percent of day 2 TSST-1 concentrations of the control rabbits (GML, close bars; DDG, open bars). Error bars are SEM. Symbols: ○, control; ▪, GML; ▴, DDG; *, p<0.05.</p

Stability of the compounds to <i>Staphylococcus aureus</i> (MN8) lipase.

<p>(A) Glycerol monolaurate (GML). (B) Dodecylglycerol (DDG). Clear zone indicates that the compound was degraded. Arrow denotes of the radius of the clear zone on the slide.</p

Effects of GML and DDG on <i>Staphylococcus aureus</i> Toxic Shock Syndrome Toxin-1 (TSST-1) production.

<p>(A) <i>S. aureus</i> MN8 was exposed to GML 0, 25 and 50 µg/ml for 6 and 24 h, and bacterial densities at 6 and 24 h were determined by plate counts. (B) The corresponding concentrations of TSST-1 of the above GML experiment. (C) <i>S. aureus</i> MN8 was exposed to DDG 0, 5, 15, and 25 µg/ml for 6 and 24 h. (D) The corresponding concentrations of TSST-1 from above DDG experiments. TSST-1 concentrations are presented as percent of the TSST-1 concentrations in 24 h control samples. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007499#s2" target="_blank">Results</a> are mean±SEM. The dashed line indicates the starting inocula. *, p<0.05.</p

Cytotoxicity of GML and DDG to Human Vaginal Epithelial Cells (HVECs).

<p>HVECs were exposed to GML (▪) and DDG (▴) for 6 h. Cytotoxicity was accessed by measuring the release of LDH. Error bars are SEM. The dashed line indicates median cell survival (LD₅₀). Symbols:▪, GML; ▴, DDG.</p

Dodecylglycerol (DDG) inhibition of <i>Staphylococcus aureus</i>.

<p>DDG concentrations 25, 50, and 100 µg/ml were tested versus <i>S. aureus</i> isolates from different PFGE types, USA400 MRSA (A), USA400 MSSA (B), USA300 MRSA (C), USA200 MRSA (D), USA200 MSSA (E), atopic dermatitis strains (F), vaginal strains from healthy women (G), for 18 h at 37°C with shaking. The dashed line indicates the starting inocula. Each triangle (▴) indicates one isolate. The bars represent the mean±SEM of bacterial density in the group.</p

Longitudinal relationships in septic humans.

<p>Spatial patterns differentiated three data subsets among 7 septic patients analyzed with dimensionless indicators: (i) a vertical subset, (ii) a right subset, and (iii) the remaining observation, or ‘left’ subset (<b>a</b>). Higher M% and M/N ratio values distinguished the ‘right’ subset from the remaining data points, while higher L% and lower N/L ratio values differentiated the ‘left’ data point from the remaining observations (horizontal lines, <b>b</b>). Discrimination further improved when temporal and multidirectional data flows were assessed: several numerically similar observations displayed different directionalities (<b>c</b>). While not all observations could be analyzed statistically because some patterns included only one or two data point(s), the spatial-temporal analysis detected non-overlapping M% and M/N ratio distributions that differentiated by the ‘right’ subset with a left-to-right directional flow from the ‘right’ subset with a right-to-left flow (boxes, <b>d</b>). Non-numerical information (arrows) also distinguished ‘bottom/right-to-left’ from ‘bottom/left-to-right’ observations (boxes, <b>d</b>).</p