An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

Circadian Structural Plasticity Drives Remodeling of E Cell Output

Behavioral outputs arise as a result of highly regulated yet flexible communication among neurons. The Drosophila circadian network includes 150 neurons that dictate the temporal organization of locomotor activity; under light-dark (LD) conditions, flies display a robust bimodal pattern. The pigment-dispersing factor (PDF)-positive small ventral lateral neurons (sLNv) have been linked to the generation of the morning activity peak (the "M cells"), whereas the Cryptochrome (CRY)-positive dorsal lateral neurons (LNds) and the PDF-negative sLNv are necessary for the evening activity peak (the "E cells") [1, 2]. While each group directly controls locomotor output pathways [3], an interplay between them along with a third dorsal cluster (the DN1ps) is necessary for the correct timing of each peak and for adjusting behavior to changes in the environment [4-7]. M cells set the phase of roughly half of the circadian neurons (including the E cells) through PDF [5, 8-10]. Here, we show the existence of synaptic input provided by the evening oscillator onto the M cells. Both structural and functional approaches revealed that E-to-M cell connectivity changes across the day, with higher excitatory input taking place before the day-to-night transition. We identified two different neurotransmitters, acetylcholine and glutamate, released by E cells that are relevant for robust circadian output. Indeed, we show that acetylcholine is responsible for the excitatory input from E cells to M cells, which show preferential responsiveness to acetylcholine during the evening. Our findings provide evidence of an excitatory feedback between circadian clusters and unveil an important plastic remodeling of the E cells' synaptic connections.

The circadian system: plasticity at many levels

Over the years it has become crystal clear that a variety of processes encode time-of-day information, ranging from gene expression, protein stability, or subcellular localization of key proteins, to the fine tuning of network properties and modulation of input signals, ultimately ensuring that physiology and behavior are properly synchronized to a changing environment. The purpose of this review is to put forward examples (as opposed to generate a comprehensive revision of all the available literature) in which the circadian system displays a remarkable degree of plasticity, from cell autonomous to circuit-based levels. In the literature, the term circadian plasticity has been used to refer to different concepts. The obvious one, more literally, refers to any change that follows a circadian (circa=around, diem=day) pattern, i.e. a daily change of a given parameter. The discovery of daily remodeling of neuronal structures will be referred herein as structural circadian plasticity, and represents an additional and novel phenomenon modified daily. Finally, any plasticity that has to do with a circadian parameter would represent a type of circadian plasticity; as an example, adjustments that allow organisms to adapt their daily behavior to the annual changes in photoperiod is a form of circadian plasticity at a higher organizational level, which is an emergent property of the whole circadian system. Throughout this work we will revisit these types of changes by reviewing recent literature delving around circadian control of clock outputs, from the most immediate ones within pacemaker neurons to the circadian modulation of rest-activity cycles.