The endocannabinoid system is still poorly understood. Recently, the basic elements that constitute it, i.e., membrane receptors, endogenous ligands, and mechanisms for termination of the signaling process, have been partially characterized. There is a considerable lack of information, however, concerning the distribution, concentration, and function of those components in the human body, particularly during pathological events. We have studied the status of some of the components of the endocannabinoid system, fatty acid amide hydrolase and cannabinoid CB1 and CB2 receptors, in postmortem brains from patients with Alzheimer's disease. Using specific polyclonal antibodies, we have performed immunohistochemical analysis in hippocampus and entorhinal cortex sections from brains of Alzheimer's disease patients. Our results show that both fatty acid amide hydrolase and cannabinoid CB2 receptors are abundantly and selectively expressed in neuritic plaque-associated astrocytes and microglia, respectively, whereas the expression of CB1 receptors remains unchanged. In addition, the hydrolase activity seems to be elevated in the plaques and surrounding areas. Thus, some elements of the endocannabinoid system may be postulated as possible modulators of the inflammatory response associated with this neurodegenerative process and as possible targets for new therapeutic approaches.

Electrons feed into the mitochondrial electron transport chain (mETC) from NAD- or FAD-dependent enzymes. A shift from glucose to fatty acids increases electron flux through FAD, which can saturate the oxidation capacity of the dedicated coenzyme Q (CoQ) pool and result in the generation of reactive oxygen species. To prevent this, the mETC superstructure can be reconfigured through the degradation of respiratory complex I, liberating associated complex III to increase electron flux via FAD at the expense of NAD. Here, we demonstrate that this adaptation is driven by the ratio of reduced to oxidized CoQ. Saturation of CoQ oxidation capacity induces reverse electron transport from reduced CoQ to complex I, and the resulting local generation of superoxide oxidizes specific complex I proteins, triggering their degradation and the disintegration of the complex. Thus, CoQ redox status acts as a metabolic sensor that fine-tunes mETC configuration in order to match the prevailing substrate profile.

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.