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de Bivort, B.

Publications and source records attributed to de Bivort, B..

4 recordsLinked to original sources

Evidence that Entomophthora muscae controls the timing of host death via its own circadian clock

Timing in the natural world is a matter of life or death; consequently, nearly all life on Earth has evolved internal circadian clocks. Many behavior-manipulating parasites exhibit striking daily timing, but whether this is clock-driven has remained unclear. Here, we leveraged the laboratory-tractable zombie fruit fly model, Drosophila melanogaster infected by the behavior manipulating fungus Entomophthora muscae, to tackle this long standing mystery. Using an automated behavioral paradigm, we found that the timing of death of wild-type flies continues to occur with [~]21 hour periodicity in the absence of environmental cues, indicating that death is controlled by a system-intrinsic clock. Experiments with circadian and photoreception mutants revealed that death is independent of host genotype, suggesting that the host does not drive this rhythm and raising the possibility that timing is dictated by E. muscae. Transcriptomic analysis of in vitro grown fungus revealed that E. muscae maintains rhythmic gene expression independent of the fly host that peaks at sunset and has a free-running period of [~]22 hours. Among cycling genes, we identified a transcript encoding a protein with high homology to white collar-1, the blue light sensor and core component of the molecular oscillator in the model ascomycete fungus Neurospora crassa. Altogether, our findings suggest that E. muscae has an endogenous circadian clock. Given that the host genetic background does not appear to dictate the timing of death, our results suggest that the E. muscae clock may be the primary driver of this rhythm. Our work supports a model in which a fungal clock influences fly outcomes, suggesting that endogenous parasitic clocks may play a broader role in the temporal coordination of host manipulation.

microbiology↗

Wild flies hedge their thermal preference bets in response to seasonal fluctuations

Fluctuating environmental pressures can challenge organisms by repeatedly shifting the optimum phenotype. Two contrasting evolutionary strategies to cope with these fluctuations are 1) evolution of the mean phenotype to follow the optimum (adaptive tracking) or 2) diversifying phenotypes so that at least some individuals have high fitness in the current fluctuation (bet-hedging). Bet-hedging could underlie stable differences in the behavior of individuals that are present even when genotype and environment are held constant. Instead of being simply noise, behavioral variation across individuals may reflect an evolutionary strategy of phenotype diversification. Using geographically diverse wild-derived fly strains and high-throughput assays of individual preference, we tested whether thermal preference variation in Drosophila melanogaster could reflect a bet-hedging strategy. We also looked for evidence that populations from different regions differentially adopt bet-hedging or adaptive-tracking strategies. Computational modeling predicted regional differences in the relative advantage of bet-hedging, and we found patterns consistent with that in regional variation in thermal preference heritability. In addition, we found that temporal patterns in mean preference support bet-hedging predictions and that there is a genetic basis for thermal preference variability. Our empirical results point to bet-hedging in thermal preference as a potentially important evolutionary strategy in wild populations.

evolutionary biology↗

The study of animal behaviour as a physical science

Behaviour is the ultimate output of an animals nervous system and choosing the right action at the right time can be critical for survival. The study of the organisation of behaviour in its natural context, ethology, has historically been a primarily qualitative science. A quantitative theory of behaviour would advance research in neuroscience as well as ecology and evolution. However, animal posture typically has many degrees of freedom and behavioural dynamics vary on timescales ranging from milliseconds to years, presenting both technical and conceptual challenges. Here we review 1) advances in imaging and computer vision that are making it possible to capture increasingly complete records of animal motion and 2) new approaches to understanding the resulting behavioural data sets. With the right analytical approaches, these data are allowing researchers to revisit longstanding questions about the structure and organisation of animal behaviour and to put unifying principles on a quantitative footing. Contributions from both experimentalists and theorists are leading to the emergence of a physics of behaviour and the prospect of discovering laws and developing theories with broad applicability. We believe that there now exists an opportunity to develop theories of behaviour which can be tested using these data sets leading to a deeper understanding of how and why animals behave.

animal behavior and cognition↗

A lineage-related reciprocal inhibition circuitry for sensory-motor action selection

The insect central complex and vertebrate basal ganglia are forebrain centres involved in selection and maintenance of behavioural actions. However, little is known about the formation of the underlying circuits, or how they integrate sensory information for motor actions. Here, we show that paired embryonic neuroblasts generate central complex ring neurons that mediate sensory-motor transformation and action selection in Drosophila. Lineage analysis resolves four ring neuron subtypes, R1-R4, that form GABAergic inhibition circuitry among inhibitory sister cells. Genetic manipulations, together with functional imaging, demonstrate subtype-specific R neurons mediate the selection and maintenance of behavioural activity. A computational model substantiates genetic and behavioural observations suggesting that R neuron circuitry functions as salience detector using competitive inhibition to amplify, maintain or switch between activity states. The resultant gating mechanism translates facilitation, inhibition and disinhibition of behavioural activity as R neuron functions into selection of motor actions and their organisation into action sequences.

neuroscience↗