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Biology subjects

Kraus, J. M.

Publications and source records attributed to Kraus, J. M..

2 recordsLinked to original sources

Non-aging despite high mutation rate - genomic insights into the evolution of Hydra

Hydra is a genus of freshwater polyps with remarkable regeneration abilities and a non-senescent phenotype under laboratory conditions. Thus, this animal is particularly interesting for aging research. Here, we gained insights into Hydras recent genetic evolution by genome sequencing of single cells and whole individuals. Despite its extreme longevity, Hydra does not show a lower somatic mutation rate than humans or mice. While we identify biological processes that have evolved under positive selection in animals kept in optimal laboratory conditions for decades, we found no signs of strong negative selection during this tiny evolutionary window. Interestingly, we observe the opposite pattern for the preceding evolution in the wild over a longer time period. Moreover, we found evidence that Hydra evolution in captivity was accompanied and potentially accelerated by loss of heterozygosity. Processes under positive selection in captive animals include pathways associated with Hydras simple nervous system, its nucleic acid metabolic process, cell migration, and hydrolase activity. Genes associated with organ regeneration, regulation of mRNA splicing, histone ubiquitination, and mitochondrial fusion were identified as highly conserved in the wild. Remarkably, several of the processes under strongest selection are closely related to those considered essential for the exapted, i. e. not brought about by natural selection, feature: Hydras non-aging.

evolutionary biology↗

Dynamic characteristics rather than static hubs are important in biological networks

Biological processes are rarely a consequence of single protein interactions but rather of complex regulatory networks. However, interaction graphs cannot adequately capture temporal changes. Among models that investigate dynamics, Boolean network models can approximate simple features of interaction graphs integrating also dynamics. Nevertheless, dynamic analyses are time-consuming and with growing number of nodes may become infeasible. Therefore, we set up a method to identify minimal sets of nodes able to determine network dynamics. This approach is able to depict dynamics without calculating exhaustively the complete network dynamics. Applying it to a variety of biological networks, we identified small sets of nodes sufficient to determine the dynamic behavior of the whole system. Further characterization of these sets showed that the majority of dynamic decision-makers were not static hubs. Our work suggests a paradigm shift unraveling a new class of nodes different from static hubs and able to determine network dynamics.

bioinformatics↗