Myeloid Sirtuin 2 expression does not impact long-term Mycobacterium tuberculosis control

Sirtuins (Sirts) regulate several cellular mechanisms through deacetylation of several transcription factors and enzymes. Recently, Sirt2 was shown to prevent the development of inflammatory processes and its expression favors acute Listeria monocytogenes infection. The impact of this molecule in the context of chronic infections remains unknown. We found that specific Sirt2 deletion in the myeloid lineage transiently increased Mycobacterium tuberculosis load in the lungs and liver of conditional mice. Sirt2 did not affect long-term infection since no significant differences were observed in the bacterial burden at days 60 and 120 post-infection. The initial increase in M. tuberculosis growth was not due to differences in inflammatory cell infiltrates in the lung, myeloid or CD4+ T cells. The transcription levels of IFN-?, IL-17, TNF, IL-6 and NOS2 were also not affected in the lungs by Sirt2-myeloid specific deletion. Overall, our results demonstrate that Sirt2 expression has a transitory effect in M. tuberculosis infection. Thus, modulation of Sirt2 activity in vivo is not expected to affect chronic infection with M. tuberculosis.

IL-17A promotes intracellular growth of Mycobacterium by inhibiting apoptosis of infected macrophages

The fate of infected macrophages is a critical aspect of immunity to mycobacteria. By depriving the pathogen of its intracellular niche, apoptotic death of the infected macrophage has been shown to be an important mechanism to control bacterial growth. Here, we show that IL-17 inhibits apoptosis of Mycobacterium bovis BCG- or Mycobacterium tuberculosis-infected macrophages thus hampering their ability to control bacterial growth. Mechanistically, we show that IL-17 inhibits p53, and impacts on the intrinsic apoptotic pathway, by increasing the Bcl2 and decreasing Bax expression, decreasing cytochrome c release from the mitochondria, and inhibiting caspase-3 activation. The same effect of IL-17 was observed in infected macrophages upon blockade of p53 nuclear translocation. These results reveal a previously unappreciated role for the IL-17/p53 axis in the regulation of mycobacteria-induced apoptosis and can have important implications in a broad spectrum of diseases where apoptosis of the infected cell is an important host defense mechanism.

Poly-N-acetyl glucosamine expression in Staphylococcus epidermidis biofilm cells affects the immune response and promotes tissue pathology in infected hosts

Staphylococcus epidermidis is a biofilm - forming bacterium and a leading etiological agent of nosocomial infections. The ability to establish biofilms on indwelling medical devices is a key virulence factor for this bacterium. Still, the influence of poly - N - acetyl glucosamine (PNAG), the major component of the extracellular biofilm matrix, in the host immune response has been scarcely studied. Here, t h is influence was assessed in mice challenged i.p. with PNAG - p roducing (WT) and isogenic - mutant lacking PNAG (M10) bacteria grown in biofilm - inducing conditions. Faster bacterial clearance was observed in the mice infected with WT bacteria than in M10 - infected counterparts , which w as accompanied by earlier neutrophil recruitment and higher IL - 6 production. Interestingly, in the WT - infected mice, but not in those infected with M10 , elevated serum IL - 10 was detected . To further study the effe ct of PNAG in the immune response, mice were primed with WT or M10 biofilm bacteria and subsequently infected with WT biofilm - released cells. WT - primed mice presented a higher frequency of splenic IFN - γ + and IL - 17 + CD4 + T cells, and more severe liver patho logy than M10 - primed counterparts. Nevertheless, T reg cells obtained from the WT - primed mice presented a higher suppressive function than those obtained from M10 - primed mice. This effect was abrogated when IL - 10 - deficient mice were similarly primed and infected indicating that PNAG promotes the differentiati on of highly suppressive T reg cells by a mechanism dependent on IL - 10. Altogether, these results provide evidence help ing explain ing the coexistence of inflammation and bacterial persistence often observed in biofilm - originated S. epidermidis infections

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.