Getting into rhythm: developmental emergence of circadian clocks and behaviors

Energetic Demands Regulate Sleep-Wake Rhythm Circuit Development

Sleep and feeding patterns lack strong daily rhythms during early life. As diurnal animals mature, feeding is consolidated to the day and sleep to the night. In Drosophila, circadian sleep patterns are initiated with formation of a circuit connecting the central clock to arousal output neurons; emergence of circadian sleep also enables long-term memory (LTM). However, the cues that trigger the development of this clock-arousal circuit are unknown. Here, we identify a role for nutritional status in driving sleep-wake rhythm development in Drosophila larvae. We find that in the 2nd instar larval period (L2), sleep and feeding are spread across the day; these behaviors become organized into daily patterns by the 3rd instar larval stage (L3). Forcing mature (L3) animals to adopt immature (L2) feeding strategies disrupts sleep-wake rhythms and the ability to exhibit LTM. In addition, the development of the clock (DN1a)-arousal (Dh44) circuit itself is influenced by the larval nutritional environment. Finally, we demonstrate that larval arousal Dh44 neurons act through glucose metabolic genes to drive onset of daily sleep-wake rhythms. Together, our data suggest that changes to energetic demands in developing organisms trigger the formation of sleep-circadian circuits and behaviors.

Developmental emergence of sleep rhythms enables long-term memory in Drosophila

In adulthood, sleep-wake rhythms are one of the most prominent behaviors under circadian control. However, during early life, sleep is spread across the 24-hour day. The mechanism through which sleep rhythms emerge, and consequent advantage conferred to a juvenile animal, is unknown. In the second-instar Drosophila larvae (L2), like in human infants, sleep is not under circadian control. We identify the precise developmental time point when the clock begins to regulate sleep in Drosophila, leading to emergence of sleep rhythms in early third-instars (L3). At this stage, a cellular connection forms between DN1a clock neurons and arousal-promoting Dh44 neurons, bringing arousal under clock control to drive emergence of circadian sleep. Last, we demonstrate that L3 but not L2 larvae exhibit long-term memory (LTM) of aversive cues and that this LTM depends upon deep sleep generated once sleep rhythms begin. We propose that the developmental emergence of circadian sleep enables more complex cognitive processes, including the onset of enduring memories.

Control systems theory revisited: new insights on the brain clocks of time-to-action

To outline the complex biological rhythms underlying the time-to-action of goal-oriented behavior in the adult brain, we employed a Boolean Algebra model based on Control Systems Theory. This suggested that "timers" of the brain reflect a metabolic excitation-inhibition balance and that healthy clocks underlying goal-oriented behavior (optimal range of signal variability) are maintained by XOR logic gates in parallel sequences between cerebral levels. Using truth tables, we found that XOR logic gates reflect healthy, regulated time-to-action events between levels. We argue that the brain clocks of time-to-action are active within multileveled, parallel-sequence complexes shaped by experience. We show the metabolic components of time-to-action in levels ranging from the atom level through molecular, cellular, network and inter-regional levels, operating as parallel sequences. We employ a thermodynamic perspective, suggest that clock genes calculate free energy versus entropy and derived time-to-action level-wise as a master controller, and show that they are receivers, as well as transmitters of information. We argue that regulated multileveled time-to-action processes correspond to Boltzmann's thermodynamic theorem of micro- and macro-states, and that the available metabolic free-energy-entropy matrix determines the brain's reversible states for its age-appropriate chrono-properties at given moments. Thus, healthy timescales are not a precise number of nano- or milliseconds of activity nor a simple phenotypic distinction between slow vs. quick time-to-action, but rather encompass a range of variability, which depends on the molecules' size and dynamics with the composition of receptors, protein and RNA isoforms.