Physiological controls of large‐scale patterning in planarian regeneration: a molecular and computational perspective on growth and form

Abstract

Planaria are complex metazoans that repair damage to their bodies and cease remodeling when a correct anatomy has been achieved. This model system offers a unique opportunity to understand how large‐scale anatomical homeostasis emerges from the activities of individual cells. Much progress has been made on the molecular genetics of stem cell activity in planaria. However, recent data also indicate that the global pattern is regulated by physiological circuits composed of ionic and neurotransmitter signaling. Here, we overview the multi‐scale problem of understanding pattern regulation in planaria, with specific focus on bioelectric signaling via ion channels and gap junctions (electrical synapses), and computational efforts to extract explanatory models from functional and molecular data on regeneration. We present a perspective that interprets results in this fascinating field using concepts from dynamical systems theory and computational neuroscience. Serving as a tractable nexus between genetic, physiological, and computational approaches to pattern regulation, planarian pattern homeostasis harbors many deep insights for regenerative medicine, evolutionary biology, and engineering.

Transcriptomic, proteomic, and metabolomic landscape of positional memory in the caudal fin of zebrafish

“…Patterning and size control during development and regeneration are also regulated by bioelectric signaling (52,(71)(72)(73)(74)(75)(76)(77)(78). Mutations in two strains of zebrafish with fin size phenotypes map to genes involved in bioelectric signaling (41,79).…”

Utilizing the planarian voltage-gated ion channel transcriptome to resolve a role for a Ca 2+ channel in neuromuscular function and regeneration

“…Ca v 1B mediated Ca 2+ influx could impact muscle function by altering neurotransmission, or by directly impacting muscle Ca 2+ signaling – mRNA localization data suggests expression in both excitable cell types (Figure 4C, (Wurtzel et al, 2015)). Numerous studies evidence that dysregulation of the excitable cell niche by perturbation of CNS or muscle biology can impact regenerative polarity signaling (Yazawa et al, 2009; Oviedo et al, 2010; Zhang et al, 2011; Cowles et al, 2013; Cowles et al, 2014; Durant et al, 2016; Lei et al, 2016), and it seems reasonable to speculate that a broad range of pharmacological agents that miscue regenerative polarity share the capacity to target the excitable cell niche and dysregulate muscle function (Chan et al, 2015). This would provide an unexpected commonality amongst a lengthy historical pharmacopeia of exogenous agents that impact regeneration (Rustia, 1925; Teshirogi, 1955; Kanatani, 1958; Flickinger, 1959; Rodriguez and Flickinger, 1971; Nogi and Levin, 2005; Nogi et al, 2009; Salvetti et al, 2009; Oviedo et al, 2010; Beane et al, 2011) and prioritize further investigation of excitable cell physiology, voltage-operated ion channels and Ca 2+ signals in the control of regenerative outcomes.…”

Accelerated cell divisions drive the outgrowth of the regenerating spinal cord in axolotls

“…This approach allows to establish causal relationship between the individually quantified cellular processes and it has been previously employed to unravel the stem cell dynamics during spinal cord development in chick and mouse (Kicheva et al, 2014). Although less frequent so far, modeling is more and more being used in the regeneration arena (Durant et al, 2016; for an overview see Chara et al, 2014). In this study, we model the number of proliferative, $true0N_{p}false(tfalse)$, and quiescent cells, $true0N_{q}false(tfalse)$, in the high-proliferation zone at time t  by the following ordinary differential equations (Figure 3A):$true0\begin{array}{c}right\; left\; right\; left\; right\; left\; right\; left\; right\; left\; right\; left0em\; 2em\; 0em\; 2em\; 0em\; 2em\; 0em\; 2em\; 0em\; 2em\; 0emtrue3pt\text{(3)}& \frac{d{N}_p(t)}{dt}& =\stackrel{\mathrm{normalc}\mathrm{normale}\mathrm{normall}\mathrm{normall}\mathrm{normald}\mathrm{normali}\mathrm{normalv}\mathrm{normali}\mathrm{normals}\mathrm{normali}\mathrm{normalo}\mathrm{normaln}\mathrm{normals}}{\overbrace{0\phantom{\frac{N_q}{N_{p}false(tfalse)}}r(t)N_p(t)}}+\stackrel{\text{activation}}{\overbrace{\phantom{\frac{N_p(t)}{N_p(t)}}k{N}_q(t)\phantom{((t))}}}+\stackrel{\text{influx}}{\overbrace{\frac{N_p(t)}{N_p(t)+N_q(t)}v\rho }},N_p(t=0)=N_p^0,& \text{(4)}& \frac{d{N}_q(t)}{dt}& =\phantom{\stackrel{}{\stackrel{}{0\phantom{\frac{N_q}{N_{p}false(t}}}}}\end{array}$…”