Molecular impact of launch related dynamic vibrations and static hypergravity in planarians

Abstract

Although many examples of simulated and real microgravity demonstrating their profound effect on biological systems are described in literature, few reports deal with hypergravity and vibration effects, the levels of which are severely increased during the launch preceding the desired microgravity period. Here, we used planarians, flatworms that can regenerate any body part in a few days. Planarians are an ideal model to study the impact of launch-related hypergravity and vibration during a regenerative process in a “whole animal” context. Therefore, planarians were subjected to 8.5 minutes of 4 g hypergravity (i.e. a human-rated launch level) in the Large Diameter Centrifuge (LDC) and/or to vibrations (20–2000 Hz, 11.3 Grms) simulating the conditions of a standard rocket launch. The transcriptional levels of genes (erg-1, runt-1, fos, jnk, and yki) related with the early stress response were quantified through qPCR. The results show that early response genes are severely deregulated after static and dynamic loads but more so after a combined exposure of dynamic (vibration) and static (hypergravity) loads, more closely simulating real launch exposure profiles. Importantly, at least four days after the exposure, the transcriptional levels of those genes are still deregulated. Our results highlight the deep impact that short exposures to hypergravity and vibration have in organisms, and thus the implications that space flight launch could have. These phenomena should be taken into account when planning for well-controlled microgravity studies.

Aquatic organisms as models in biomedical space research

“…This suggests further space experiments and investigations to clarify whether observed phenotype was due to space travel (48). Another recent study showed that rocket launch related vibrations and hypergravity can affect the expression of the early stress response genes in planarians during a regenerative process (49).…”

Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

“…In addition, researchers also conducted a series of experiments using engine vibration signal [46,47], adaptive correlation algorithm [48], frequency band peak ratio [49,50], characteristic frequency band root mean square value [51], and principal component analysisbased extraction of eigenvectors. Fault detection approaches using signal processing are studied, such as binary time series analysis of phase plane trajectory [52,53], sliding time window principal component analysis [54], fast Fourier transform [55], etc.…”