This review emphasizes the important role of cross-talk between P53 and microRNAs in physiological stress signaling. P53 responds to stress in a variety of ways ranging from activating survival-promotion pathways to triggering programmed cell death to eliminate damaged cells. In physiological stress generated by any external or internal condition that challenges cell homeostasis, P53 exerts its function as a transcription factor for target genes or by regulating the expression and maturation of a class of small non-coding RNA molecules (miRNAs). The miRNAs control the level of P53 through direct control of P53 or through indirect control of P53 by targeting its regulators (such as MDMs). In turn, P53 controls the expression level of miRNAs targeted by P53 through the regulation of their transcription or biogenesis. This elaborate regulatory scheme emphasizes the relevance of miRNAs in the P53 network and vice versa.

Resilience is conceived as a dynamic developmental process involving the achievement of positive adaptation within the context of significant adversity. Resilience is not a unique ability but rather a set of capacities of a system put in place to absorb a disturbance and to reorganize while trying to retain the same function, structure, and identity. This review describes the characteristics and the molecular mechanisms of resilience to understand the core elements of resilience and its indicators. The objectives of this review are: (1) to define some of the leading environmental stressors and clarify the mechanism of vulnerability or resilience outcomes; (2) to clarify some of the prominent epigenetic modulations mediating resilience or vulnerability as a stress response; (3) to highlight the neural mechanisms related to stress resilience since the central nervous system is a highly dynamic structure characterized by an everlasting plasticity feature, which therefore has the opportunity to modify resilience. The review aims to introduce the reader to the concept of resilience seen as an ability acquired in life and not only inherited from birth.

Our research explores serum extracellular circulating miRNAs (ecmiRNAs) involved in dog stress response immediately after the search and rescue (SAR) of missing people. The experimental plan considers four arduous SAR simulations. The SAR dogs are trained by the Alpine School of the Military Force of Guardia di Finanza (Passo Rolle, Italy). The First SAR Trial analyzed dog serum samples at rest time (T0), and immediately after SAR performance (T1) using the miRNome-wide screening next-generation sequencing (NGS). T1 versus T0 NGS results revealed a different expression level of let-7a and let-7f. Subsequently, in a large sample size including: 1st (n = 6), 2nd (n = 6), 3rd (n = 6), and 4th (n = 4) trials, let-7a and let-7f were validated by qPCR. Bioinformatics analysis with TarBase (v.8) and the Diana-mirPath (v.3) revealed a functional role of let-7a and let-7f in the p53 pathway to restore cellular homeostasis. Let-7a and let-7f, highly expressed at T1, could stop MDMs-p53 inhibition inducing the p53 increase in level. In addition, let-7a and let-7f, via p53 post-transcriptional regulation, buffers p53 transcription spikes. During SAR stress, the possibility of p53 preconditioning could explain the phenomenon of “stress hardening” where the tolerance of particular stress increases after preconditioning.

Cell-free miRNAs, called circulating miRNAs (cmiRNAs), can act in a paracrine manner by facilitating a diversity of signaling mechanisms between cells. Real-time qPCR is the most accepted method for quantifying miRNA expression levels. The use of stable miRNA endogenous control (EC) for qPCR data normalization allows an accurate cross-sample gene expression comparison. The appropriate selection of EC is a crucial step because qPCR data can change drastically when normalization is performed using an unstable versus a stable EC. To find EC cmiRNA with stable expression in search and rescue (SAR) working dogs, we explored the serum miRNome by Next-Generation Sequencing (NGS) at T0 (resting state) and T1 immediately after SAR performance (state of physiologically recovered stress). The cmiRNAs selected in the NGS circulating miRNome as probable ECs were validated by qPCR, and miRNA stability was evaluated using the Delta Ct, BestKeeper, NormFinder, and GeNorm algorithms. Finally, RefFinder was used to rank the stability orders at both T0 and T1 by establishing miR-320 and miR-191 as the best-circulating ECs. We are confident that this study not only provides a helpful result in itself but also an experimental design for selecting the best endogenous controls to normalize gene expression for genes beyond circulating miRNAs.