Rapamycin reverses age-related increases in mitochondrial ROS production at complex I, oxidative stress, accumulation of mtDNA fragments inside nuclear DNA, and lipofuscin level, and increases autophagy, in the liver of middle-aged mice

Rapamycin consistently increases longevity in mice although the mechanism of action of this drug is unknown. In the present investigation we studied the effect of rapamycin on mitochondrial oxidative stress at the same dose that is known to increase longevity in mice (14 mg of rapamycin/kg of diet). Middle aged mice (16 months old) showed significant age-related increases in mitochondrial ROS production at complex I, accumulation of mtDNA fragments inside nuclear DNA, mitochondrial protein lipoxidation, and lipofuscin accumulation compared to young animals (4 months old) in the liver. After 7 weeks of dietary treatment all those increases were totally or partially (lipofuscin) abolished by rapamycin, middle aged rapamycin-treated animals showing similar levels in those parameters to young animals. The decrease in mitochondrial ROS production was due to qualitative instead of quantitative changes in complex I. The decrease in mitochondrial protein lipoxidation was not due to decreases in the amount of highly oxidizable unsaturated fatty acids. Rapamycin also decreased the amount of RAPTOR (of mTOR complex) and increased the amounts of the PGC1-α and ATG13 proteins. The results are consistent with the possibility that rapamycin increases longevity in mice at least in part by lowering mitochondrial ROS production and increasing autophagy, decreasing the derived final forms of damage accumulated with age which are responsible for increased longevity. The decrease in lipofuscin accumulation induced by rapamycin adds to previous information suggesting that the increase in longevity induced by this drug can be due to a decrease in the rate of aging. © 2016 Elsevier Inc.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.