Normal dendrite growth in Drosophila motor neurons requires the AP‐1 transcription factor

Abstract

During learning and memory formation, information flow through networks is regulated significantly through structural alterations in neurons. Dendrites, sites of signal integration, are key targets of activity-mediated modifications. Although local mechanisms of dendritic growth ensure synapse-specific changes, global mechanisms linking neural activity to nuclear gene expression may have profound influences on neural function. Fos, being an immediate-early gene, is ideally suited to be an initial transducer of neural activity, but a precise role for the AP-1 transcription factor in dendrite growth remains to be elucidated. Here we measure changes in the dendritic fields of identified Drosophila motor neurons in vivo and in primary culture to investigate the role of the immediate-early transcription factor AP-1 in regulating endogenous and activity-induced dendrite growth. Our data indicate that (a) increased neural excitability or depolarization stimulates dendrite growth, (b) AP-1 (a Fos, Jun heterodimer) is required for normal motor neuron dendritic growth during development and in response to activity induction, and (c) neuronal Fos protein levels are rapidly but transiently induced in motor neurons following neural activity. Taken together, these results show that AP-1 mediated transcription is important for dendrite growth, and that neural activity influences global dendritic growth through a gene-expression dependent mechanism gated by AP-1.

Temporal coherency between receptor expression, neural activity and AP-1-dependent transcription regulatesDrosophilamotoneuron dendrite development

“…The Drosophila genetic model has yielded useful insights into the molecular basis underlying neuron type-specific dendritic morphology (Moore et al, 2002;Jefferis et al, 2001;, dendritic spacing (Zhu and Luo, 2004;Grueber et al, 2002;Grueber et al, 2003;Corty et al, 2009) and dendritic guidance (Komiyama et al, 2002;Sweeney et al, 2002;Furrer et al, 2003;Mauss et al, 2009;Brierley et al, 2009). By contrast, few studies have addressed the role of neuronal activity during dendritic development in the Drosophila CNS (Tripodi et al, 2008;Hartwig et al, 2008;Duch et al, 2008).…”

“…However, in vertebrates, both local changes in dendritic calcium concentrations as well as global changes in calcium throughout the neuron can initiate long-term global changes in gene expression via activity-dependent regulators, such as CREB or Crest (West et al, 2002;Flavell and Greenberg, 2008;Redmond, 2008). Similarly, in Drosophila, increased firing activity promotes dendritic overgrowth in developing larval (Hartwig et al, 2008) and adult motoneurons (Duch et al, 2008). In larval motoneurons, activity-induced overgrowth requires the transcription factor AP-1 (Hartwig et al, 2008), a heterodimer of Fos and Jun (Curran and Franza, 1988), encoded in Drosophila by the genes kayak and Jra (Jun-related antigen), respectively.…”

Tiling among stereotyped dendritic branches in an identified Drosophila motoneuron

“…This is not surprising because three independent studies have demonstrated that Drosophila motoneuron dendritic branching is affected by neural activity (Hartwig et al, 2008;Duch et al, 2008;Tripodi et al, 2008). By contrast, the mean dendritic radius and the mean length of the individual segments show high constancy between animals (CV < 0.08).…”

“…Analyses of the dendritic branching patterns of sensory neurons paired with genetic screens and manipulations have already yielded fundamental insight into key regulatory mechanisms of dendrite development (Grueber and Jan, 2004;Corty et al, 2009). However, much less is known about the specific architecture principles and the constancy of the complex dendritic trees of identified central Drosophila neurons, although these exhibit stereotyped morphologies and have proven useful for analyzing mechanisms of dendritic morphogenesis in the central nervous system (CNS) (Williams and Truman, 2005;Landgraf and Thor, 2006;Tripodi et al, 2008;Hartwig et al, 2008).…”

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“…Analysis code was written in 136 Python (version 2.7.6) to extract burst durations (time from the start to the end of a 137 burst), cycle durations (time from the start of one burst to start of the next), duty 138 cycles (burst duration divided by cycle duration), and quiescence intervals (time from 139 the end of one burst to the start of the next) from the recordings. Although intraburst 140 firing frequency was of interest due to the effects of EKI on MN firing [19] …”