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Sökning: WFRF:(Jonsson Pernilla) > Medicin och hälsovetenskap > Naturvetenskap

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1.
  • Chen, Yao, et al. (författare)
  • Thyroid hormone enhances nitric oxide mediated bacterial clearance and promotes survival after meningococcal infection
  • 2012
  • Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:7
  • Tidskriftsartikel (refereegranskat)abstract
    • Euthyroid sick syndrome characterized by reduced levels of thyroid hormones (THs) is observed in patients with meningococcal shock. It has been found that the level of THs reflects disease severity and is predictive for mortality. The present study was conducted to investigate the impact of THs on host defense during meningococcal infection. We found that supplementation of thyroxine to mice infected with Neisseria meningitidis enhanced bacterial clearance, attenuated the inflammatory responses and promoted survival. In vitro studies with macrophages revealed that THs enhanced bacteria-cell interaction and intracellular killing of meningococci by stimulating inducible nitric oxide synthase (iNos)-mediated NO production. TH treatment did not activate expression of TH receptors in macrophages. Instead, the observed TH-directed actions were mediated through nongenomic pathways involving the protein kinases PI3K and ERK1/2 and initiated at the membrane receptor integrin alpha v beta 3. Inhibition of nongenomic TH signaling prevented iNos induction, NO production and subsequent intracellular bacterial killing by macrophages. These data demonstrate a beneficial role of THs in macrophage-mediated N. meningitidis clearance. TH replacement might be a novel option to control meningococcal septicemia.
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2.
  • Jonsson, Pär, et al. (författare)
  • Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples
  • 2015
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 11:6, s. 1667-1678
  • Tidskriftsartikel (refereegranskat)abstract
    • Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.
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