Mouse Anti-Human IgG Hybridoma [D4-235]

Modifications of gravity levels induce generalized adaptation of mammalian physiology, including vascular, brain, muscle, bone and immunity functions. As a crucial interface between the vascular system and the brain, the blood-brain barrier (BBB) acts as a filter to protect neurons from pathogens and inflammation. Here we compare the effects of several protocols of hypergravity induced by centrifugation and whole-body vibrations (WBV) on BBB integrity. The immunohistochemistry revealed immunoglobulin G (IgG) extravasation from blood to hippocampal parenchyma of mice centrifuged at 2 x g during 1 or 50 days, whereas short exposures to higher hypergravity mimicking the profiles of spaceflight landing and take-off (short exposures to 5 x g) had no effects. These results suggest prolonged centrifugation (>1 days) at 2 x g induced a BBB leakage. Moreover, WBV were similarly tested. The short exposure to +2 x g vibrations (900 s/day at 90 Hz) repeated for 63 days induced IgG extravasation in hippocampal parenchyma, whereas the progressive increase of vibrations from +0.5 to +2 x g for 63 days was not able to affect the IgG crossing through the BBB. Overall, these results suggest that the BBB permeability is sensitive to prolonged external accelerations. In conclusion, we advise that the protocols of WBV and centrifugation, proposed as countermeasure to spaceflight, should be designed with progressively increasing exposure to reduce potential side effects on the BBB.

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.