Tsukada, Y. | Search bioRxiv

bioRxiv2017

Uncoordinated centrosome duplication cycle underlies the instability of non-diploid states in mammalian somatic cells

In animals, somatic cells are usually diploid and are unstable when haploid for unknown reasons. In this study, by comparing isogenic human cell lines with different ploidies, we found frequent centrosome loss specifically in the haploid state, which profoundly contributed to haploid instability through monopolar spindle formation and subsequent mitotic defects. We also found that efficiency of centriole licensing and duplication, but not that of DNA replication, changes proportionally to ploidy level, causing gradual loss or frequent overduplication of centrioles in haploid and tetraploid cells, respectively. Centriole licensing efficiency seemed to be modulated by astral microtubules, whose development scaled with ploidy level, and artificial enhancement of aster formation in haploid cells restored centriole licensing efficiency to diploid levels. Haploid-specific centrosome loss was also observed in parthenogenetic mouse embryos. We propose that incompatibility between the centrosome duplication and DNA replication cycles arising from different scaling properties of these bioprocesses upon ploidy changes, underlies the instability of non-diploid somatic cells in mammals.\n\nSummaryYaguchi et al. show that a delay or acceleration of centriole licensing compromises the control of centrosome number in haploid or tetraploid human cells, respectively, suggesting a cellular basis of the instability of non-diploid somatic cells in mammals.

cell biology↗

bioRxiv2017

Identification Of Animal Behavioral Strategy By Inverse Reinforcement Learning ~ Its Application To Thermotaxis In C. elegans ~

Animals are able to reach a desired state in an environment by controlling various behavioral patterns. Identification of the behavioral strategy used for this control is important for understanding animals decision-making and is fundamental to dissect information processing done by the nervous system. However, methods for quantifying such behavioral strategies have not been fully established. In this study, we developed an inverse reinforcement-learning (IRL) framework to identify an animals behavioral strategy from behavioral time-series data. As a particular target, we applied this framework to C. elegans thermotactic behavior; after cultivation at a constant temperature with or without food, the fed and starved worms prefer and avoid from the cultivation temperature on a thermal gradient, respectively. Our IRL approach revealed that the fed worms used both absolute and temporal derivative of temperature and that their strategy comprised mixture of two strategies: directed migration (DM) and isothermal migration (IM). The DM is a strategy that the worms efficiently reach to specific temperature, which explained thermotactic behaviors of the fed worms. The IM is a strategy that the worms track along a constant temperature, which reflects isothermal tracking well observed in previous studies. We also showed the neural basis underlying the strategies, by applying our method to thermosensory neuron-deficient worms. In contrast to fed animals, the strategy of starved animals indicated that they escaped the cultivation temperature using only absolute, but not temporal derivative of temperature. Thus, our IRL-based approach is capable of identifying animal strategies from behavioral time-series data and will be applicable to wide range of behavioral studies, including decision-making of other organisms.\n\nAuthor SummaryUnderstanding animal decision-making has been a fundamental problem in neuroscience and behavioral ecology. Many studies analyze actions that represent decision-making in behavioral tasks, in which rewards are artificially designed with specific objectives. However, it is impossible to extend this artificially designed experiment to a natural environment, because in a natural environment, the rewards for freely-behaving animals cannot be clearly defined. To this end, we must reverse the current paradigm so that rewards are identified from behavioral data. Here, we propose a new reverse-engineering approach (inverse reinforcement learning) that can estimate a behavioral strategy from time-series data of freely-behaving animals. By applying this technique with thermotaxis in C. elegans, we successfully identified the reward-based behavioral strategy.

neuroscience↗