Orexin contributes to eupnea within a critical period of postnatal development

Abstract

Orexin neurons are active in wakefulness and mostly silent in sleep. In adult rats and humans, orexin facilitates the hypercapnic ventilatory response, but has little effect on resting ventilation. The influence of orexin on breathing in the early postnatal period, and across states of vigilance, have not been investigated. This is relevant as the orexin system may be impaired in Sudden Infant Death Syndrome (SIDS) cases. We addressed three hypotheses: 1) orexin provides a drive to breathe in infancy; 2) the effect of orexin depends on stage of postnatal development; 3) orexin has a greater influence on breathing in wakefulness compared to sleep. Whole-body plethysmography was used to monitor breathing of infant rats at three ages: postnatal days (P) 7-8, 12-14, 17-19. Respiratory variables were analyzed in wakefulness (W), quiet sleep (QS), and active sleep (AS), following suvorexant (5 mg/kg, i.p), a dual-orexin receptor antagonist, or vehicle (DMSO). Effects of suvorexant on ventilatory responses to graded hypercapnia (FICO2=0.02, 0.04, 0.06), hypoxia (FIO2=0.10) and hyperoxia (FIO2=1.0) at P12-14 were also tested. At P12-14, but not at other ages, suvorexant significantly reduced respiratory frequency in all states, reduced the ventilatory equivalent in QW and QS, and increased PaCO2 ~5mmHg. Suvorexant had no effect on ventilatory responses to graded hypercapnia or hypoxia. Hyperoxia eliminated the effects of suvorexant on respiratory frequency at P12-14. Our data suggest that orexin preserves eupneic frequency and ventilation in rats, specifically at ~2 weeks of age, perhaps by facilitating tonic peripheral chemoreflex activity.

An automated respiratory data pipeline for waveform characteristic analysis

“…Labour-intensive, time-intensive and expertise-dependent manual selection of data remains the most common approach (Brown et al, 2019;Spinieli et al, 2021), which results in a significant bottleneck. Current annotation methods limit reproducibility within and between labs (Table 1); is subject to observer bias (i.e.…”

“…respiratory frequency and tidal volume, and (2) the statistical assessment and graphical display of those measures and their associative relationships with independent variables. Labour‐intensive, time‐intensive and expertise‐dependent manual selection of data remains the most common approach (Brown et al., 2019; Spinieli et al., 2021), which results in a significant bottleneck. Current annotation methods limit reproducibility within and between labs (Table 1); is subject to observer bias (i.e.…”

“…Current annotation methods limit reproducibility within and between labs (Table 1); is subject to observer bias (i.e. where humans tend to select for particularly slow, even breaths), especially when identifying and excluding movement artifacts; and typically includes only a tiny sample of all available data (usually only examining 30 s to 2 min of breathing per experimental condition; Brown et al., 2019; Ebel et al., 2017; Spinieli et al., 2021). Analysis of the selected data can be similarly encumbered by a heavy reliance on more manual approaches that involve copying and pasting manually selected data into inherited spreadsheets with default formulae (Brown et al., 2019; Ebel et al., 2017).…”

An automated respiratory data pipeline for waveform characteristic analysis

“…The analysis of plethysmography data consists of two components: 1) the conversion of collected waveforms into physiological measures, e.g., respiratory frequency and tidal volume, and 2) the statistical assessment and graphical display of those measures and their associative relationships with independent variables. Labor-intensive, time-intensive, and expertise- dependent manual selection of data remains the most common approach 1, 2 , which results in a significant bottleneck. Current annotation methods limit reproducibility within and between labs (Supplemental Table 1); is subject to observer bias (i.e., where humans tend to select for particularly slow, even breaths), especially when identifying and excluding movement artifacts; and typically includes only a tiny sample of all available data (usually only examining 30 seconds – 2 minutes of breathing per experimental condition 1–3 ).…”

“…Labor-intensive, time-intensive, and expertise- dependent manual selection of data remains the most common approach 1, 2 , which results in a significant bottleneck. Current annotation methods limit reproducibility within and between labs (Supplemental Table 1); is subject to observer bias (i.e., where humans tend to select for particularly slow, even breaths), especially when identifying and excluding movement artifacts; and typically includes only a tiny sample of all available data (usually only examining 30 seconds – 2 minutes of breathing per experimental condition 1–3 ). Analysis of the selected data can be similarly encumbered by a heavy reliance on more manual approaches that involve copying and pasting manually-selected data into inherited spreadsheets with default formulae 2, 3 .…”