PROJECT SUMMARY
This mentored research scientist development award will allow the candidate, Dr. Ryan Phillips, to establish an
independent research career focused on understanding the complex circuit interactions that link pain and
respiration. The training plan outlined in this award, combined with the candidate's background in brainstem pain
circuits and computational modelling, make him ideally suited to successfully follow this career path. Opioid
receptors are expressed in multiple brainstem regions that regulate pain and respiration, and the link between
opioid-induced analgesia and respiratory depression poses a major clinical challenge. Indeed, the often difficult
to predict side-effect of opioid-induced respiratory depression (OIRD) is the leading cause of opioid-related
deaths driving the current opioid epidemic. Despite this public health significance, the mechanisms underlying
OIRD are not fully understood and therapeutic strategies to prevent or reverse OIRD while maintaining analgesia
are limited. A natural idea is that OIRD results from direct effects of opioids on respiratory rhythm-generating
neurons of the pre-Bötzinger Complex (preBötC) in the ventral medulla. Although some preBötC neurons do
express µ-opioid receptors (µORs), encoded by the Oprm1 gene, systemic opioids continue to produce
respiratory depression and apneas even after genetic deletion of µORs in this population. Importantly, the
contributions other µORs/Oprm1 expressing respiratory- and pain-related neurocircuitries to OIRD have been
described. These brainstem regions, including the parabrachial (PB), Kolliker Fuse (KF), and rostral ventromedial
medulla (RVM), all have important roles in regulating the respiratory rhythm generated by the preBötC. However,
how opioids alter the network dynamics of these interacting regions remains unknown. We hypothesize that
interruption of opioid sensitive network interactions is a major mechanism underlying OIRD caused by systemic
opioids. This hypothesis will be tested using computational modeling and experimental approaches performed
in tandem. Computational modeling has played a significant role in advancing the field of respiratory
neurobiology and is ideally suited for examining the implications of disparate datasets and for low-cost, high
throughput testing of hypothesized network features. In addition to existing data, our model will be guided and
constrained by new cell-type- and projection- specific experimental manipulations in vivo that will constitute a
significant training aspect of this award. By combining these strategies, our data-driven computational model will
yield novel insights into the network dynamics that underlie opioid-induced respiratory depression as well as
potential therapeutic targets to generate pain relief without respiratory side effects.