The Trissin/TrissinR signaling pathway in the circadian network regulates evening activity in Drosophila melanogaster under constant dark conditions.

The circadian clock in Drosophila is governed by a neural network comprising approximately 150 neurons, known as clock neurons, which are intricately interconnected by various neurotransmitters. The neuropeptides that play functional roles in these clock neurons have been identified; however, the roles of some neuropeptides, such as Trissin, remain unclear. Trissin is expressed in lateral dorsal clock neurons (LNds), while its receptor, TrissinR, is expressed in dorsal neuron 1 (DN1) and LNds. In this study, we investigated the role of the Trissin/TrissinR signaling pathway within the circadian network in Drosophila melanogaster. Analysis involving our newly generated antibody against the Trissin precursor revealed that Trissin expression in the LNds cycles in a circadian manner. Behavioral analysis further demonstrated that flies with Trissin or TrissinR knockout or knockdown showed delayed evening activity offset under constant darkness conditions. Notably, this observed delay in evening activity offset in TrissinRNAi flies was restored via the additional knockdown of Ion transport peptide (ITP), indicating that the Trissin/TrissinR signaling pathway transmits information via ITP. Therefore, this pathway may be a key regulator of the timing of evening activity offset termination, orchestrating its effects in collaboration with the neuropeptide, ITP.

UAV-based individual Chinese cabbage weight prediction using multi-temporal data.

The use of unmanned aerial vehicles (UAVs) has facilitated crop canopy monitoring, enabling yield prediction by integrating regression models. However, the application of UAV-based data to individual-level harvest weight prediction is limited by the effectiveness of obtaining individual features. In this study, we propose a method that automatically detects and extracts multitemporal individual plant features derived from UAV-based data to predict harvest weight. We acquired data from an experimental field sown with 1196 Chinese cabbage plants, using two cameras (RGB and multi-spectral) mounted on UAVs. First, we used three RGB orthomosaic images and an object detection algorithm to detect more than 95% of the individual plants. Next, we used feature selection methods and five different multi-temporal resolutions to predict individual plant weights, achieving a coefficient of determination (R2) of 0.86 and a root mean square error (RMSE) of 436 g/plant. Furthermore, we achieved predictions with an R2 greater than 0.72 and an RMSE less than 560 g/plant up to 53 days prior to harvest. These results demonstrate the feasibility of accurately predicting individual Chinese cabbage harvest weight using UAV-based data and the efficacy of utilizing multi-temporal features to predict plant weight more than one month prior to harvest.

A four-oscillator model of seasonally adapted morning and evening activities in Drosophila melanogaster.

The fruit fly Drosophila melanogaster exhibits two activity peaks, one in the morning and another in the evening. Because the two peaks change phase depending on the photoperiod they are exposed to, they are convenient for studying responses of the circadian clock to seasonal changes. To explain the phase determination of the two peaks, Drosophila researchers have employed the two-oscillator model, in which two oscillators control the two peaks. The two oscillators reside in different subsets of neurons in the brain, which express clock genes, the so-called clock neurons. However, the mechanism underlying the activity of the two peaks is complex and requires a new model for mechanistic exploration. Here, we hypothesize a four-oscillator model that controls the bimodal rhythms. The four oscillators that reside in different clock neurons regulate activity in the morning and evening and sleep during the midday and at night. In this way, bimodal rhythms are formed by interactions among the four oscillators (two activity and two sleep oscillators), which may judiciously explain the flexible waveform of activity rhythms under different photoperiod conditions. Although still hypothetical, this model would provide a new perspective on the seasonal adaptation of the two activity peaks.

The tyrosine capsid mutations on retrograde adeno-associated virus accelerates gene transduction efficiency.

Adeno-associated virus (AAV) vector is a critical tool for gene delivery through its durable transgene expression and safety profile. Among many serotypes, AAV2-retro is typically utilized for dissecting neural circuits with its retrograde functionality. However, this vector requires a relatively long-term incubation period (over 2 weeks) to obtain enough gene expression levels presumably due to low efficiency in gene transduction. Here, we aimed to enhance transgene expression efficiency of AAV2-retro vectors by substituting multiple tyrosine residues with phenylalanines (YF mutations) in the virus capsid, which is previously reported to improve the transduction efficiency of AAV2-infected cells by evading host cell responses. We found that AAV2-retro with YF mutations (AAV2-retroYF)-mediated transgene expression was significantly enhanced in the primary culture of murine cortical neurons at 1 week after application, comparable to that of the conventional AAV2-retro at 2 week after application. Moreover, transgene expressions in the retrogradely labeled neurons mediated by AAV2-retroYF were significantly increased both in the cortico-cortical circuits and in the subcortical circuits in vivo, while the retrograde functionality of AAV2-retroYF was equally effective as that of AAV2-retro. Our data indicate that YF mutations boost AAV2-retro-mediated retrograde gene transduction in vivo and suggest that the AAV2-retroYF should be useful for efficient targeting of the projection-defined neurons, which is suited to applications for dissecting neural circuits during development as well as future clinical applications.

The lateral posterior clock neurons of Drosophila melanogaster express three neuropeptides and have multiple connections within the circadian clock network and beyond.

Drosophila's lateral posterior neurons (LPNs) belong to a small group of circadian clock neurons that is so far not characterized in detail. Thanks to a new highly specific split-Gal4 line, here we describe LPNs' morphology in fine detail, their synaptic connections, daily bimodal expression of neuropeptides, and propose a putative role of this cluster in controlling daily activity and sleep patterns. We found that the three LPNs are heterogeneous. Two of the neurons with similar morphology arborize in the superior medial and lateral protocerebrum and most likely promote sleep. One unique, possibly wakefulness-promoting, neuron with wider arborizations extends from the superior lateral protocerebrum toward the anterior optic tubercle. Both LPN types exhibit manifold connections with the other circadian clock neurons, especially with those that control the flies' morning and evening activity (M- and E-neurons, respectively). In addition, they form synaptic connections with neurons of the mushroom bodies, the fan-shaped body, and with many additional still unidentified neurons. We found that both LPN types rhythmically express three neuropeptides, Allostatin A, Allostatin C, and Diuretic Hormone 31 with maxima in the morning and the evening. The three LPN neuropeptides may, furthermore, signal to the insect hormonal center in the pars intercerebralis and contribute to rhythmic modulation of metabolism, feeding, and reproduction. We discuss our findings in the light of anatomical details gained by the recently published hemibrain of a single female fly on the electron microscopic level and of previous functional studies concerning the LPN.