21 research outputs found
Influence of ethanol on TNF-alpha and IFN-gamma producing CD4+ and CD8+ T cells in surgical infectious mice model
Alkoholkranke haben ein erhöhtes Risiko, postoperativ an einer häufig durch
Klebsiella pneumoniae (K. pneumoniae) verursachten Pneumonie zu erkranken. Die
beiden Zytokine Tumornekrosefaktor-alpha (TNF-α) und Interferon-gamma (IFN-γ)
sind bei der Abwehr von K. pneumoniae wichtig. Um die Auswirkung einer
präoperativen Alkoholbehandlung auf die Immunantwort bei K. pneumoniae
Pneumonie zu untersuchen, wurde ein operatives, infektiöses Mausmodell
etabliert. 50 weibliche Balb/c Mäuse wurden acht Tage intraperitoneal mit
Alkohol (3 mg/g Körpergewicht) oder Kochsalz behandelt, bevor am achten Tag
eine mediane Laparotomie erfolgte. Am zweiten postoperativen Tag wurde die
Hälfte der Tiere jeder Gruppe intranasal mit K. pneumoniae infiziert. 24
Stunden später wurden die Tiere getötet. Lunge und Leber wurden für die
mikrobiologische und histologische Untersuchung sowie die Milz für die
Zellisolation entnommen. Die Anzahl TNF-α und IFN-γ produzierender CD4+ und
CD8+ T-Zellen der Milz wurde durchflusszytometrisch untersucht. Ergänzend
erfolgten Gewichtskontrollen und die Erhebung eines klinischen Scores. Nach
Infektion mit K. pneumoniae und Ethanolbehandlung zeigte sich eine
signifikante Reduktion von TNF-α produzierenden CD4+ und CD8+ T-Zellen und
IFN-γ produzierenden CD4+ Zellen. Die Anzahl IFN-γ produzierender CD8+
T-Zellen blieb unverändert. Histologisch war ein schwererer Verlauf der
Klebsiellenpneumonie zu verzeichnen. Damit einher ging eine signifikante
Gewichtsreduktion und ein schlechterer klinischer Score. Eine
Ethanolbehandlung in diesem operativen, infektiösen Tiermodell führte nach
Infektion mit K. pneumoniae zu einer Immunsuppression, die vermutlich
ausgeprägtere histologische Lungenschäden und ein schlechteres Befinden der
Tiere zur Folge hatte. Somit tragen T-Lymphozyten scheinbar durch ihr
inadäquat sezerniertes Zytokinmuster zur Entstehung und Unterhaltung einer
Klebsiellenpneumonie bei.The risk of bacterial pneumonia caused by Klebsiella pneumoniae (K.
pneumoniae) is increased in alcoholic patients. Tumor necrosis factor alpha
(TNF-α) and Interferon gamma (IFN-γ) are critical mediators of antibacterial
host defence in Klebsiella pneumonia. A surgical infectious mice model was
established in order to assess the impact of preoperative ethanol treatment on
immune response during K. pneumoniae pneumonia. Fifty female Balb/c mice were
treated with ethanol (3 mg/g body weight) or saline solution intraperitoneally
for eight days. On the eighth day all mice underwent a median laparotomy. In
half of each group K. pneumoniae was administered intranasally two days post
surgery. Mice were killed twenty-four hours after infection. Lung and liver
were extracted for microbiological and histological assessment as well as
spleen for cell isolation. The number of TNF-α und IFN-γ producing splenic T
cells were determined by FACS analysis. Additionally weights were registered
and clinical appearance was assessed. The combination of infection with K.
pneumoniae and ethanol treatment caused a significant decrease of TNF-α
producing CD4+ and CD8+ T cells and IFN-γ producing CD4+ T cells. IFN-γ
producing CD8+ T cells were not affected. Furthermore, the histological
assessment showed a distinct deterioration of the pulmonary structure. Mice
exhibited significant weight loss and a degraded clinical appearance. Ethanol
treatment in this surgical infectious murine model led to immunosuppression
after infection with K. pneumoniae. This immunosupression probably aggravated
lung tissue damage as well as worsened the state of health. The inadequate
cytokine pattern of T cells apparently contributes to the development of
severe pneumonia
Differential T cell response against BK virus regulatory and structural antigens: A viral dynamics modelling approach.
BK virus (BKV) associated nephropathy affects 1-10% of kidney transplant recipients, leading to graft failure in about 50% of cases. Immune responses against different BKV antigens have been shown to have a prognostic value for disease development. Data currently suggest that the structural antigens and regulatory antigens of BKV might each trigger a different mode of action of the immune response. To study the influence of different modes of action of the cellular immune response on BKV clearance dynamics, we have analysed the kinetics of BKV plasma load and anti-BKV T cell response (Elispot) in six patients with BKV associated nephropathy using ODE modelling. The results show that only a small number of hypotheses on the mode of action are compatible with the empirical data. The hypothesis with the highest empirical support is that structural antigens trigger blocking of virus production from infected cells, whereas regulatory antigens trigger an acceleration of death of infected cells. These differential modes of action could be important for our understanding of BKV resolution, as according to the hypothesis, only regulatory antigens would trigger a fast and continuous clearance of the viral load. Other hypotheses showed a lower degree of empirical support, but could potentially explain the clearing mechanisms of individual patients. Our results highlight the heterogeneity of the dynamics, including the delay between immune response against structural versus regulatory antigens, and its relevance for BKV clearance. Our modelling approach is the first that studies the process of BKV clearance by bringing together viral and immune kinetics and can provide a framework for personalised hypotheses generation on the interrelations between cellular immunity and viral dynamics
Results of the model fitting for the hypotheses on dominant immune modes of action.
<p>Results of the model fitting for the hypotheses on dominant immune modes of action.</p
Parameter for the viral load clearance model under hypothesis VPε-sLTμ.
<p>Parameter for the viral load clearance model under hypothesis VPε-sLTμ.</p
Schematic representation of the ODE model.
<p>Healthy cells produce other healthy cells (rate proportional to <i>g</i>) and die at rate <i>d</i>. The virus triggers the conversion of healthy cells into infected cells (rate <i>β</i>). Infected cells die at rate <i>d·k</i> and produce the virus at rate <i>p</i>, which is cleared at rate <i>c</i>. The immune system can intervene through three different mechanisms: blocking virus production (<i>ε(t)</i>), enhancing infected cell death (<i>μ(t)</i>) and blocking infection (<i>ν(t)</i>).</p
Modelled time course of BKV viral load clearance for hypothesis VPε-sLTμ.
<p>The results of the model (Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.e005" target="_blank">3</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.e014" target="_blank">5</a>) under hypothesis VPε-sLTμ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.s002" target="_blank">S2 Table</a>) using the parameters in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005998#pcbi.1005998.t004" target="_blank">Table 4</a> are plotted: viral load (<i>V</i>(t)) is shown as a black line, the immune responses virus production blockage (<i>ε</i>(t)) and accelerated killing of infected cells (<i>μ</i>(t)) are shown in green and red, respectively. Observed viral load values are shown as black plus signs. Please note the difference of time scales between the rows.</p
Viral load and immune response data of the patients.
<p>For each patient, the time course of viral load (black) and the Elispot read-out for each immunogenic BKV antigen (coloured) are plotted. The change of immunosuppressant therapy is marked as a dashed blue line. This change in immunosuppressant therapy is known to foster the development of an immune response against BKV. On the upper row the patients that had not cleared within 700 days after transplantation are shown, while those that achieved clearance in a shorter time appear in the lower row. Please note the difference of time scales between the rows.</p