12 research outputs found

    Effect of IL-10 in macrophages during <i>B</i>. <i>pseudomallei</i> infection.

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    <p>(A) Quantitative real-time PCR analysis of IL-10 expression in macrophages from C57BL/6 WT and <i>caspase6</i><sup><i>-/-</i></sup> mice 6 h after infection with <i>B</i>. <i>pseudomallei</i> strain E8 (MOI ~ 50). The data were logarithmized to achieve normal distribution and compared using Student’s <i>t</i>-test. Values are means ± standard deviations from three independent experiments. (B) Intracellular bacterial burden of IL-10-treated and non-treated C57BL/6 macrophages after infection with <i>B</i>. <i>pseudomallei</i> strain E8 (MOI ~ 25). Data were analysed using Student’s <i>t</i>-test. Values are means ± standard deviations from triplicate determinations. The experiment was repeated three times.</p

    Cytokine expression in caspase-6 deficient mice 24 hours after infection with <i>B</i>. <i>pseudomallei</i>.

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    <p>Quantitative real-time PCR analysis of inflammatory parameters in the spleens of C57BL/6 WT and <i>caspase6</i><sup><i>-/-</i></sup> mice. Mice were infected with 5 × 10<sup>4</sup> CFU of <i>B</i>. <i>pseudomallei</i> strain E8 i.v. for 24 h. Uninfected control animals received PBS. Each of the 4 groups contained 9 mice from 3 replicates (n = 36). Data were analysed using Student’s <i>t</i> test. Values are means ± standard deviations from three independent experiments.</p

    Cytokine expression in caspase-6 deficient mice 6 hours after infection with <i>B</i>. <i>pseudomallei</i>.

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    <p>Quantitative real-time PCR analysis of inflammatory parameters in the spleens of C57BL/6 WT (n = 8) and <i>caspase6</i><sup><i>-/-</i></sup> mice (n = 8) 6 hours after infection with 5 × 10<sup>4</sup> CFU <i>B</i>. <i>pseudomallei</i> strain E8. Data were analysed using Student’s <i>t</i> test. Values are means ± standard deviations from two independent experiments.</p

    Invasion and replication of <i>B</i>. <i>pseudomallei</i> in caspase-6 deficient macrophages.

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    <p>Intracellular bacterial burden of C57BL/6-WT and C57BL/6-<i>caspase6</i><sup><i>-/-</i></sup> macrophages after infection with <i>B</i>. <i>pseudomallei</i> strain E8 at an MOI of ~ 25. The CFU data were logarithmized to achieve normal distribution and compared using Student’s t-test. Values are means ± standard deviations from triplicate determinations. The experiment was repeated three times.</p

    Cell death induction in caspase-6 deficient macrophages after infection with <i>B</i>. <i>pseudomallei</i>.

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    <p>Course of cell damage in C57BL/6 WT and <i>caspase6</i><sup><i>-/-</i></sup> macrophages after infection with <i>B</i>. <i>pseudomallei</i> strain E8. (A) For the LDH release assay cells were infected with an MOI of ~10. Data were analysed using Student’s <i>t</i> test. Values are means ± standard deviations from triplicate determinations. (B) Real-time cell status analysis of C57BL/6 WT (blue, triangles) and <i>caspase6</i><sup><i>-/-</i></sup> macrophages (red, circles). Infected cells are represented by brighter lines (top-down triangle, small circle) and received an MOI of ~5. Uninfected controls are represented by darker lines (upright triangle, big circle). The y-axis is a relative scale for the cell status measured by the XCelligence system. The experiments were repeated twice.</p

    Caspase-6 mediates resistance against <i>Burkholderia pseudomallei</i> infection and influences the expression of detrimental cytokines

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    <div><p>Caspase-6 is a member of the executioner caspases and known to play a role in innate and adaptive immune processes. However, its role in infectious diseases has rarely been addressed yet. We here examined the impact of caspase-6 in an <i>in vivo</i> infection model using the Gram-negative rod <i>Burkholderia pseudomallei</i>, causing the infectious disease melioidosis that is endemic in tropical and subtropical areas around the world. <i>Caspase-6</i><sup>-/-</sup> and C57BL/6 wild type mice were challenged with <i>B</i>. <i>pseudomallei</i> for comparing mortality, bacterial burden and inflammatory cytokine expression. Bone-marrow derived macrophages were used to analyse the bactericidal activity in absence of caspase-6. Caspase-6 deficiency was associated with higher mortality and bacterial burden <i>in vivo</i> after <i>B</i>. <i>pseudomallei</i> infection. The bactericidal activity of <i>caspase-6</i><sup>-/-</sup> macrophages was impaired compared to wild type cells. <i>Caspase-6</i><sup>-/-</sup> mice showed higher expression of the IL-1β gene, known to be detrimental in murine melioidosis. Expression of the IL-10 gene was also increased in <i>caspase-6</i><sup>-/-</sup> mice as early as 6 hours after infection. Treatment with exogenous IL-10 rendered mice more susceptible against <i>B</i>. <i>pseudomallei</i> challenge. Thus, caspase-6 seems to play a crucial role for determining resistance against the causative agent of melioidosis. To our knowledge this is the first report showing that caspase-6 is crucial for mediating resistance in an <i>in vivo</i> infection model. Caspase-6 influences the expression of detrimental cytokines and therefore seems to be important for achieving a well-balanced immune response that contributes for an efficient elimination of the pathogen.</p></div

    Quantitative Molecular Detection of Putative Periodontal Pathogens in Clinically Healthy and Periodontally Diseased Subjects

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    <div><p>Periodontitis is a multi-microbial oral infection with high prevalence among adults. Putative oral pathogens are commonly found in periodontally diseased individuals. However, these organisms can be also detected in the oral cavity of healthy subjects. This leads to the hypothesis, that alterations in the proportion of these organisms relative to the total amount of oral microorganisms, namely their abundance, rather than their simple presence might be important in the transition from health to disease. Therefore, we developed a quantitative molecular method to determine the abundance of various oral microorganisms and the portion of bacterial and archaeal nucleic acid relative to the total nucleic acid extracted from individual samples. We applied quantitative real-time PCRs targeting single-copy genes of periodontal bacteria and 16S-rRNA genes of <i>Bacteria</i> and <i>Archaea</i>. Testing tongue scrapings of 88 matched pairs of periodontally diseased and healthy subjects revealed a significantly higher abundance of <i>P. gingivalis</i> and a higher total bacterial abundance in diseased subjects. In fully adjusted models the risk of being periodontally diseased was significantly higher in subjects with high <i>P. gingivalis</i> and total bacterial abundance. Interestingly, we found that moderate abundances of <i>A. actinomycetemcomitans</i> were associated with reduced risk for periodontal disease compared to subjects with low abundances, whereas for high abundances, this protective effect leveled off. Moderate archaeal abundances were health associated compared to subjects with low abundances. In conclusion, our methodological approach unraveled associations of the oral flora with periodontal disease, which would have gone undetected if only qualitative data had been determined.</p></div

    Subject characteristics.

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    <p>Data are presented as numbers (percentages) or mean ± SD.</p>1<p>paired t-test or McNemar test.</p><p>CAL, clinical attachment loss; PD, probing depth.</p

    Adjusted Odds Ratios quantifying chance of being periodontally diseased depending on detection (yes/no) or abundance of different pathogens in tongue scrapings in the overall study population (N = 88 pairs).

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    <p>Conditional logistic regression modeling periodontal status (cases versus controls, dependent variable) on detection (yes/no) or abundances adjusting for age (cont.), school education, smoking status and BMI.</p>◊<p>Abundances were categorized as tertiles (T1–T3). Numbers within tertiles were: <i>P. gingivalis</i>: 75-43-58, <i>A. actinomycetemcomitans</i>: 92-26-58, <i>F. nucleatum</i>: 93-25-58, <i>S. sanguinis</i>: 75-43-58, Archaea 85-33-58, %Archaea: 85-33-58, Bacteria: 59-59-58.</p>1<p>proportion of 16S rRNA gene copies per ng extracted DNA; N, number of matched pairs.</p>2<p>percent of archaeal 16S rRNA gene copies per prokaryotic 16S rRNA gene copies (Archaea+Bacteria)*100.</p><p>*p<0.05, ** p<0.01, *** p<0.001 versus reference group.</p
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