The principles for data acquisition represent the code presented by Jordi et al. (15) but were further optimized. The following adaptations were made, enabling the measurement of two larvae per well: Larval intestines were identified using intensity and size thresholds. This thresholding led to a number of particles per well, which were sorted by signal strength (area × mean intensity), and the top two were stored as intestinal traces. Kinematic traces were quantified as described by Kokel et al. (41), where the number of times an animal crosses any of three parallel imaginary lines is counted. We counted larval line crosses for each well at a frame rate of 10 Hz. Next, the data were preprocessed as follows on a well-by-well basis (Fig. 2): The fluorescence measurements were binned down to two time periods reflecting the accumulated fluorescence in the first 40 min (F1, feeding period 1) and the last 80 min of the experiment (F2, feeding period 2), respectively. Concurrent larval locomotion activity was condensed into one bin for the entire 2 hours (S, spontaneous locomotion). Next, we aligned the repeats of the initial eight dark flashes, the eight taps, and the four post-dark flashes to construct triggered averages, each 28 s in length, using an interpolation-based alignment method to generate a standard time base. These time traces were normalized to the period preceding a given stimulus (2 s) to account for differences in spontaneous locomotion. These traces were then condensed into two metrics for the visual response (V, visual) and the acoustic response (A, acoustic), the first comprising the initial 2 s of the response (period 1) and the second comprising the remaining 26 s (period 2). The ratio between the values for the initial and final dark flashes quantified animal’s lethargy (L1 and L2, lethargy periods 1 and 2). The 30-tap habituation stimulus was condensed into a single number by taking the ratio of the maximum response values of the last 15 taps to the first 15 taps (H, habituation). Next, the entire plate was normalized to the median of on-plate vehicle controls to yield a fold change with respect to the vehicle. After this final calculation on a per-plate basis, we calculated the SSMD for the values from each compound by matching its replicate wells across plates and their corresponding vehicle control wells. The statistical advantages and limitations of SSMD are extensively discussed elsewhere (42). This calculation was performed on the individual metrics coming from the feeding behavior (F1 and F2), spontaneous activity (S), visual response (V1 and V2), acoustic response (A1 and A2), habituation (H), and lethargy (L1 and L2). These 10 SSMD values per compound were termed a given compound’s barcode and were used for the clustering detailed below (43). For the calculation of confidence boundaries, we performed the same SSMD calculations for all vehicle-treated animals split into measurements of six animals analogously to the per-compound measurement outlined before. This entire process was performed in a blinded fashion; i.e., there was no a priori knowledge of compounds before hit determination. For the analysis of the candidates’ validation (Fig. 5 and figs. S6 and S7), preliminary dose response (fig. S2A), and anti-obesity drug (Fig. 2) experiments, we slightly modified the aforementioned process to enable measurement of a single larva per well: Fluorescent traces were not binned, and the activity traces were condensed to 1-min bins using the fluorescent reads as a common time base. For dark flash, tap, and post-dark flash, we performed trigger-average alignment as above. Habituation reflects the activity ratio of the mean of the last three taps and the initial three taps, if not indicated differently. Validation barcodes were constructed accordingly.

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