CD36 and Fyn kinase mediate malaria-induced lung endothelial barrier dysfunction in mice infected with Plasmodium berghei.

PMC3744507Severe malaria can trigger acute lung injury characterized by pulmonary edema resulting from increased endothelial permeability. However, the mechanism through which lung fluid conductance is altered during malaria remains unclear. To define the role that the scavenger receptor CD36 may play in mediating this response, C57BL/6J (WT) and CD36-/- mice were infected with P. berghei ANKA and monitored for changes in pulmonary endothelial barrier function employing an isolated perfused lung system. WT lungs demonstrated a >10-fold increase in two measures of paracellular fluid conductance and a decrease in the albumin reflection coefficient (σalb) compared to control lungs indicating a loss of barrier function. In contrast, malaria-infected CD36-/- mice had near normal fluid conductance but a similar reduction in σalb. In WT mice, lung sequestered iRBCs demonstrated production of reactive oxygen species (ROS). To determine whether knockout of CD36 could protect against ROS-induced endothelial barrier dysfunction, mouse lung microvascular endothelial monolayers (MLMVEC) from WT and CD36-/- mice were exposed to H2O2. Unlike WT monolayers, which showed dose-dependent decreases in transendothelial electrical resistance (TER) from H2O2 indicating loss of barrier function, CD36-/- MLMVEC demonstrated dose-dependent increases in TER. The differences between responses in WT and CD36-/- endothelial cells correlated with important differences in the intracellular compartmentalization of the CD36-associated Fyn kinase. Malaria infection increased total lung Fyn levels in CD36-/- lungs compared to WT, but this increase was due to elevated production of the inactive form of Fyn further suggesting a dysregulation of Fyn-mediated signaling. The importance of Fyn in CD36-dependent endothelial signaling was confirmed using in vitro Fyn knockdown as well as Fyn-/- mice, which were also protected from H2O2- and malaria-induced lung endothelial leak, respectively. Our results demonstrate that CD36 and Fyn kinase are critical mediators of the increased lung endothelial fluid conductance caused by malaria infection.JH Libraries Open Access Fun

Comparison of polynomial fitting versus single time point analysis of ECIS data for barrier assessment

Electrical cell‐substrate impedance sensing (ECIS) is an in vitro methodology for measuring the barrier integrity of a variety of cell types, including pulmonary endothelial cells. These experiments are frequently used for in vitro assessment of lung injury. The data derived from ECIS experiments consists of repeated measures of resistance across an endothelial monolayer. As such, these data reflect the dynamic changes in electrical resistance that occur over time. Currently methodologies for assessing ECIS data rely on single point assessments of barrier function, such as the maximal drop in trans‐endothelial electrical resistance (TERMax). However, this approach ignores the myriad of changes in resistance that occur before and after the TERMax data point. Herein, we utilize polynomial curve fitting on experimentally generated ECIS data, thus allowing for comparing ECIS experiments by examining the mean polynomial coefficients between groups. We show that polynomial curves accurately fit a variety of ECIS data, and that concordance between TERMax and coefficient analysis varies by type of stimulus, suggesting that TERMax differences may not always correlate with a significant difference in the overall shape of the ECIS profile. Lastly, we identify factors that impact coefficient values obtained in our analyses, including the length of time devoted to baseline measurements before addition of stimuli. Polynomial coefficient analysis is another tool that can be used for more comprehensive interrogation of ECIS data to better understand the biological underpinnings that lead to changes in barrier dysfunction in vitro. Electrical cell‐substrate impedance sensing (ECIS) is a common method for assessing cell permeability. Herein, we use polynomial fitting to improve fitting and quantification of resistance measurements generated by ECIS experiments