Inhibitory control of active expiration by the Bötzinger complex in rats

Abstract

26The expiratory neurons of the Bötzinger complex (BötC) provide inhibitory inputs to 27 the respiratory network, which, during eupnea, are critically important for respiratory phase 28 transition and duration control. Herein, we investigated how the BötC neurons interact with the 29 expiratory oscillator located in the parafacial respiratory group (pFRG) and control the 30 abdominal activity during active expiration. Using the decerebrated, arterially perfused in situ 31 rat preparations, we recorded the neuronal activity and performed pharmacological 32 manipulations of the BötC and pFRG during hypercapnia or after the exposure to short-term 33 sustained hypoxia -conditions that generate active expiration. The experimental data were 34 integrated in a mathematical model to gain new insights in the inhibitory connectome within 35 the respiratory central pattern generator. Our results reveal a complex inhibitory circuitry within 36 the BötC that provides inhibitory inputs to the pFRG thus restraining abdominal activity under 37 resting conditions and contributing to abdominal expiratory pattern formation during active 38 expiration. 39 40 41 Keywords: abdominal activity, hypercapnia, hypoxia, parafacial respiratory group, ventral 42 respiratory column. 48 et al., 2015, Harris-Warrick, 2010, Ramirez and Baertsch, 2018. In mammals, rhythmical 49 contraction and relaxation of respiratory muscles emerges from interacting excitatory and 50 inhibitory neurons with specific cellular properties, distributed within the pons and the medulla 51 oblongata (Richter and Smith, 2014, Del Negro et al., 2018, Lindsey et al., 2012. Coupled 52 oscillators embedded in this brainstem respiratory network are essential to generate and 53 distribute synaptic inputs for the initiation of respiratory rhythmicity and the control of pattern 54 formation (Anderson and Ramirez, 2017, Del Negro et al., 2018). Defining the arrangement 55 and connections of the respiratory oscillators and circuitries are essential to understand how 56 breathing is generated and adjusted to attend metabolic and behavior demands.57

Bötzinger inhibitory neurons and the control of active expiration

“…In conclusion, the study by Flor and colleagues (2020) provides important evidence for additional inhibitory input from BötC to pFRG in the control of active expiration under high metabolic demand and their results, together with the recent literature, provide yet another piece of the role of the pFRG/RTN in breathing regulation. What is still needed is a more detailed understanding of this region of the brain.…”

“…One important contribution that investigates the role of inhibition to trigger active expiration was recently published by Flor and colleagues in The Journal of Physiology (Flor et al . 2020). One crucial issue that needs attention is why we need inhibitory input to avoid active expiration at rest.…”

“…Flor and colleagues (Flor et al . 2020), using standard physiological and pharmacological approaches and a mathematical model, tested the hypothesis that a possible source of inhibitory inputs to the pFRG could come from the BötC and control the emergence of active expiration in conditions of elevated respiratory drive (Fig. 1).…”

“…A schematic illustrating current understanding of the inhibitory input to pFRG to control active expiration is shown in Fig. 1, based on recent work from my laboratory and the work by Flor and colleagues (2020).…”

To model or not to model – the physiologist's dilemma

“…(For a comprehensive review of these models, see Lindsey and colleagues' work that highlights these approaches over the past 60 years; Lindsey et al 2012. ) In this issue of The Journal of Physiology, Flor et al (2020) present work focused on the role that Bötzinger complex neurons play in the generation of active expiration. Their paper is an excellent exemplar of the synthesis of experimental and computational work, showcasing the power of this hybrid approach for understanding physiological neural networks.…”