Dendritic structural plasticity

Tavosanis, Gaia

doi:10.1002/dneu.20951

Cited by 70 publications

(50 citation statements)

References 131 publications

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“…These neurons have long axonal projections, which were reported to undergo daily changes in morphology (Fernandez et al, 2008). These rhythmic changes are also activity-dependent (Depetris-Chauvin et al, 2011) and may be related to activity-dependent neuronal changes extensively investigated in vertebrate as well as invertebrate model systems (Bushey and Cirelli, 2011; Greer and Greenberg, 2008; Tavosanis, 2011; West and Greenberg, 2011). …”

Section: Introductionmentioning

confidence: 99%

The Transcription Factor Mef2 Links the Drosophila Core Clock to Fas2, Neuronal Morphology, and Circadian Behavior

Sivachenko

Abruzzi

et al. 2013

Neuron

131

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Summary The transcription factor Mef2 regulates activity-dependent neuronal plasticity and morphology in mammals, and clock neurons are reported to experience activity-dependent circadian remodeling in Drosophila. We show here that Mef2 is required for this daily fasciculation-defasciculation cycle. Moreover, the master circadian transcription complex CLK/CYC directly regulates Mef2 transcription. ChIP-Chip analysis identified numerous Mef2 target genes implicated in neuronal plasticity, including the cell-adhesion gene Fas2. Genetic epistasis experiments support this transcriptional regulatory hierarchy, CLK/CYC->Mef2-> Fas2, indicate that it influences the circadian fasciculation cycle within pacemaker neurons and suggest that this cycle also contributes to circadian behavior. Mef2 therefore transmits clock information to machinery involved in neuronal remodeling, which contributes to locomotor activity rhythms.

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Section: Introductionmentioning

confidence: 99%

The Transcription Factor Mef2 Links the Drosophila Core Clock to Fas2, Neuronal Morphology, and Circadian Behavior

Sivachenko

Abruzzi

et al. 2013

Neuron

131

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“…However, only a handful of examples of such plasticity in the CNS in either adult vertebrates or invertebrates have been described, mainly in a few different species or in particular injury scenarios (Murphey et al, 1975; Bulloch and Ridgway, 1989; Büschges et al, 1992; Wolf and Büschges, 1997; Krüger et al, 2011b; Tavosanis, 2012). One of the more striking, positive, compensatory responses to injury has been described in the auditory systems of several species of field crickets (Hoy et al, 1985; Schildberger et al, 1986; Brodfuehrer and Hoy, 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the cricket, many denervation experiments in embryonic and developing animals typically result in post-synaptic neuronal decline and death (Parks, 1979; Trune, 1982a,b; Nordeen et al, 1983; Born and Rubel, 1985) while denervation in mature animals minimally affects post-synaptic neurons (Born and Rubel, 1985; Hashisaki and Rubel, 1989; Moore, 1990). The general conclusions derived from these studies are that pre-synaptic input is necessary for growth and stabilization of their post-synaptic partners during development, but once neuronal systems mature in the adult, many classes of neurons reach a stable state and do not need active input to maintain their structure (Tavosanis, 2012). …”

Section: Introductionmentioning

confidence: 99%

Quantification of dendritic and axonal growth after injury to the auditory system of the adult cricket Gryllus bimaculatus

Pfister¹,

Johnson²,

Ellers³

et al. 2013

Front. Physiol.

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Dendrite and axon growth and branching during development are regulated by a complex set of intracellular and external signals. However, the cues that maintain or influence adult neuronal morphology are less well understood. Injury and deafferentation tend to have negative effects on adult nervous systems. An interesting example of injury-induced compensatory growth is seen in the cricket, Gryllus bimaculatus. After unilateral loss of an ear in the adult cricket, auditory neurons within the central nervous system (CNS) sprout to compensate for the injury. Specifically, after being deafferented, ascending neurons (AN-1 and AN-2) send dendrites across the midline of the prothoracic ganglion where they receive input from auditory afferents that project through the contralateral auditory nerve (N5). Deafferentation also triggers contralateral N5 axonal growth. In this study, we quantified AN dendritic and N5 axonal growth at 30 h, as well as at 3, 5, 7, 14, and 20 days after deafferentation in adult crickets. Significant differences in the rates of dendritic growth between males and females were noted. In females, dendritic growth rates were non-linear; a rapid burst of dendritic extension in the first few days was followed by a plateau reached at 3 days after deafferentation. In males, however, dendritic growth rates were linear, with dendrites growing steadily over time and reaching lengths, on average, twice as long as in females. On the other hand, rates of N5 axonal growth showed no significant sexual dimorphism and were linear. Within each animal, the growth rates of dendrites and axons were not correlated, indicating that independent factors likely influence dendritic and axonal growth in response to injury in this system. Our findings provide a basis for future study of the cellular features that allow differing dendrite and axon growth patterns as well as sexually dimorphic dendritic growth in response to deafferentation.

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“…The diversity is said to express the difference between neuron classes while variation represents the intra-class differences (Soltesz, 2005). Diversity originates from the genetic make-up of neurons (Jan and Jan, 2010; Tavosanis, 2012). By contrast, the variance can be assumed to originate from interactions between the developing neuron and the brain substrate, its context (McAllister, 2000; Scott and Luo, 2001; Landgraf and Evers, 2005; Jan and Jan, 2010; Tavosanis, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Diversity originates from the genetic make-up of neurons (Jan and Jan, 2010; Tavosanis, 2012). By contrast, the variance can be assumed to originate from interactions between the developing neuron and the brain substrate, its context (McAllister, 2000; Scott and Luo, 2001; Landgraf and Evers, 2005; Jan and Jan, 2010; Tavosanis, 2012). Indeed, in both axonal (Mortimer et al, 2008) and dendritic (Gao, 2007; Cove et al, 2009) development a plethora of microscopic interactions have been revealed to influence branching patterns and “guide” the direction of growth.…”

Section: Introductionmentioning

confidence: 99%

Context-aware modeling of neuronal morphologies

Torben‐Nielsen

Schutter

2014

Front. Neuroanat.

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Neuronal morphologies are pivotal for brain functioning: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.

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Dendritic structural plasticity

Cited by 70 publications

References 131 publications

The Transcription Factor Mef2 Links the Drosophila Core Clock to Fas2, Neuronal Morphology, and Circadian Behavior

The Transcription Factor Mef2 Links the Drosophila Core Clock to Fas2, Neuronal Morphology, and Circadian Behavior

Quantification of dendritic and axonal growth after injury to the auditory system of the adult cricket Gryllus bimaculatus

Context-aware modeling of neuronal morphologies

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