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The screwworm fly, Cochliomyia hominivorax (Coquerel), was successfully eradicated from the United States by the sterile insect technique (SIT). However, recent detection of these flies in the Florida Keys, and increased risk of introductions to the other areas warrant novel tools for management of the flies. Surveillance, a key component of screwworm control programs, utilizes traps baited with rotting liver or a blend of synthetic chemicals such as swormlure-4. In this work, we evaluated the olfactory physiology of the screwworm fly and compared it with the non-obligate ectoparasitic secondary screwworm flies, C. macellaria, that invade necrotic wound and feed on dead tissue. These two species occur in geographically overlapping regions. C. macellaria, along with other blowflies such as the exotic C. megacephala, greatly outnumber C. hominivorax in the existing monitoring traps. Olfactory responses to swormlure-4 constituents between sex and mating status (mated vs unmated) in both species were recorded and compared. Overall, responses measured by the antennograms offered insights into the comparative olfactory physiology of the two fly species. We also present detailed analyses of the antennal transcriptome by RNA-Sequencing that reveal significant differences between male and female screwworm flies. The differential expression patterns were confirmed by quantitative PCR. Taken together, this integrated study provides insights into the physiological and molecular correlates of the screwworm’s attraction to wounds, and identifies molecular targets that will aid in the development of odorant-based fly management strategies.

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Introduction High amplitude, slow oscillations in the electroencephalogram (EEG) often characterize the central nervous system’s homeostatic drive for sleep. Slow oscillations dominate the first part of the night and often dissipate as sleep need is satiated. These physiological changes are also reflected in next day subjective measures of sleepiness. Fluctuations in the autonomic nervous system, particularly parasympathetic activity, also coincide with slow wave sleep and are understood to represent bodily homeostasis. However, it is unclear if autonomic indicators effectively predict next-day sleepiness. Here, we investigated whether slow oscillatory (SO) power and cardiac autonomic activity during a night of sleep can predict next day subjective sleepiness. Methods 88 young (aged 18-35), healthy participants spent the night in a University sleep lab. Before and after sleep, participants completed the Karolinska Sleepiness Scale (KSS) to measure subjective sleepiness. Each participant slept with polysomnography, including electroencephalography and electrocardiography. From these measures, we assessed slow oscillation power (0.5-1Hz) and high frequency heart rate variability (HRV; 0.15-0.45 Hz) --an indicator of parasympathetic, vagally-mediated cardiac tone-- during the first quartile of slow wave sleep. Paired T-tests compared the differences in KSS scores pre- and post-sleep. Pearson’s correlations assessed bivariate associations between slow oscillatory power, high frequency HRV, and KSS scores. Mixed linear models assessed the ability of SO power and high frequency HRV to predict next day subjective sleepiness. Results No significant differences were found in KSS ratings pre- (M±SD = 4.23±1.92) and post-sleep (4.38±1.80). No bivariate correlations were present between pre-sleep KSS, SO, or high frequency HRV. A significant correlation between KSS-post sleep and SO power emerged (r=0.286, p=0.001). In Model 1, we found that SO power was a predictor of subjective sleepiness (p<0.001; AIC: 777.469). In Model 2, we included high frequency HRV and reduced the AIC to 515.588. Both slow oscillatory power (p<0.001) and high frequency HRV (p<0.001) were significant predictors of KSS after sleep in Model 2. Conclusion We found evidence that both central and autonomic indicators of sleep predict psychological measures of sleepiness. Using autonomic indicators to characterize physiological sleepiness, compared to in-lab polysomnography, may be a more generalizable and cost-effective approach. Support (If Any)