Systematic analysis of proximal midgut- and anorectal-originating contractions in larval zebrafish using event feature detection and supervised machine learning algorithms.

BACKGROUND: Zebrafish larvae are translucent, allowing in vivo analysis of gut development and physiology, including gut motility. While recent progress has been made in measuring gut motility in larvae, challenges remain which can influence results, such as how data are interpreted, opportunities for technical user error, and inconsistencies in methods. METHODS: To overcome these challenges, we noninvasively introduced Nile Red fluorescent dye to fill the intraluminal gut space in zebrafish larvae and collected serial confocal microscopic images of gut fluorescence. We automated the detection of fluorescent-contrasted contraction events against the median-subtracted signal and compared it to manually annotated gut contraction events across anatomically defined gut regions. Supervised machine learning (multiple logistic regression) was then used to discriminate between true contraction events and noise. To demonstrate, we analyzed motility in larvae under control and reserpine-treated conditions. We also used automated event detection analysis to compare unfed and fed larvae. KEY RESULTS: Automated analysis retained event features for proximal midgut-originating retrograde and anterograde contractions and anorectal-originating retrograde contractions. While manual annotation showed reserpine disrupted gut motility, machine learning only achieved equivalent contraction discrimination in controls and failed to accurately identify contractions after reserpine due to insufficient intraluminal fluorescence. Automated analysis also showed feeding had no effect on the frequency of anorectal-originating contractions. CONCLUSIONS & INFERENCES: Automated event detection analysis rapidly and accurately annotated contraction events, including the previously neglected phenomenon of anorectal contractions. However, challenges remain to discriminate contraction events based on intraluminal fluorescence under treatment conditions that disrupt functional motility.

EPAC1 and EPAC2 promote nociceptor hyperactivity associated with chronic pain after spinal cord injury.

Chronic pain following spinal cord injury (SCI) is associated with electrical hyperactivity (spontaneous and evoked) in primary nociceptors. Cyclic adenosine monophosphate (cAMP) signaling is an important contributor to nociceptor excitability, and knockdown of the cAMP effector, exchange protein activated by cAMP (EPAC), has been shown to relieve pain-like responses in several chronic pain models. To examine potentially distinct roles of each EPAC isoform (EPAC1 and 2) in maintaining chronic pain, we used rat and mouse models of contusive spinal cord injury (SCI). Pharmacological inhibition of EPAC1 or 2 in a rat SCI model was sufficient to reverse SCI-induced nociceptor hyperactivity, indicating that EPAC1 and 2 signaling activity are complementary, with both required to maintain hyperactivity. However, EPAC activation was not sufficient to induce similar hyperactivity in nociceptors from naïve rats, and we observed no change in EPAC protein expression after SCI. In the mouse SCI model, inhibition of both EPAC isoforms through a combination of pharmacological inhibition and genetic deletion was required to reverse SCI-induced nociceptor hyperactivity. This was consistent with our finding that neither EPAC1-/- nor EPAC2-/- mice were protected against SCI-induced chronic pain as assessed with an operant mechanical conflict test. Thus, EPAC1 and 2 activity may play a redundant role in mouse nociceptors, although no corresponding change in EPAC protein expression levels was detected after SCI. Despite some differences between these species, our data demonstrate a fundamental role for both EPAC1 and EPAC2 in mechanisms maintaining nociceptor hyperactivity and chronic pain after SCI.