Core temperature circadian rhythm across aging in Spontaneously Hypertensive Rats

Effects of physical training on hypothalamic neuronal activation and expressions of vasopressin and oxytocin in SHR after running until fatigue

To assess the influence of physical training on neuronal activation and hypothalamic expression of vasopressin and oxytocin in spontaneously hypertensive rats (SHR), untrained and trained normotensive rats and SHR were submitted to running until fatigue while internal body and tail temperatures were recorded. Hypothalamic c-Fos expression was evaluated in thermoregulatory centers such as the median preoptic nucleus (MnPO), medial preoptic nucleus (mPOA), paraventricular nucleus of the hypothalamus (PVN), and supraoptic nucleus (SON). The PVN and the SON were also investigated for vasopressin and oxytocin expressions. Although exercise training improved the workload performed by the animals, it was reduced in SHR and followed by increased internal body temperature due to tail vasodilation deficit. Physical training enhanced c-Fos expression in the MnPO, mPOA, and PVN of both strains, and these responses were attenuated in SHR. Vasopressin immunoreactivity in the PVN was also increased by physical training to a lesser extent in SHR. The already-reduced oxytocin expression in the PVN of SHR was increased in response to physical training. Within the SON, neuronal activation and the expressions of vasopressin and oxytocin were reduced by hypertension and unaffected by physical training. The data indicate that physical training counterbalances in part the negative effect of hypertension on hypothalamic neuronal activation elicited by exercise, as well as on the expression of vasopressin and oxytocin. These hypertension features seem to negatively influence the workload performed by SHR due to the hyperthermia derived from the inability of physical training to improve heat dissipation through skin vasodilation.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.