Biology subjects

Tavosanis, G.

Publications and source records attributed to Tavosanis, G..

3 recordsLinked to original sources

bioRxiv2020

The branching code: a model of actin-driven dendrite arborisation

Dendrites display a striking variety of neuronal type-specific morphologies, but the mechanisms and principles underlying such diversity remain elusive. A major player in defining the morphology of dendrites is the neuronal cytoskeleton, including evolutionarily conserved actin-modulatory proteins (AMPs). Still, we lack a clear understanding of how AMPs might support developmental phenomena such as neuron-type specific dendrite dynamics. To address precisely this level of in vivo specificity, we concentrated on a defined neuronal type, the class III dendritic arborisation (c3da) neuron of Drosophila larvae, displaying actin-enriched short terminal branchlets (STBs). Computational modelling reveals that the main branches of c3da neurons follow a general growth model based on optimal wiring, but the STBs do not. Instead, model STBs are defined by a short reach and a high affinity to grow towards the main branches. We thus concentrated on c3da STBs and developed new methods to quantitatively describe dendrite morphology and dynamics based on in vivo time-lapse imaging of mutants lacking individual AMPs. In this way, we extrapolated the role of these AMPs in defining STB properties. We propose that dendrite diversity is supported by the combination of a common step, refined by a neuron type-specific second level. For c3da neurons, we present a molecular model of how the combined action of multiple AMPs in vivo define the properties of these second level specialisations, the STBs. In briefA quantitative morphological dissection of the concerted actin-modulatory protein actions provides a model of dendrite branchlet outgrowth. HighlightsO_LIActin organisation in small terminal branchlets of Drosophila class III dendritic arborisation neurons C_LIO_LISix actin-modulatory proteins individually control the characteristic morphology and dynamics of branchlets C_LIO_LIQuantitative tools for dendrite morphology and branch dynamics enable a comparative analysis C_LIO_LIA two-step computational growth model reproduces c3da dendrite morphology C_LI

neuroscience↗

bioRxiv2020

Circuit reorganization in the Drosophila mushroom body calyx accompanies memory consolidation

The capacity of utilizing past experience to guide future action is a fundamental and conserved function of the nervous system. Associative memory formation initiated by the coincident detection of a conditioned stimulus (CS, e.g. odour) and an unconditioned stimulus (US, e.g. sugar reward) can lead to a short-lived memory trace (STM) within distinct circuits [1-5]. Memories can be consolidated into long-term memories (LTM) through processes that are not fully understood, but depend on de-novo protein synthesis [6, 7], require structural modifications within the involved neuronal circuits and might lead to the recruitment of additional ones [8-17]. Compared to modulation of existing connections, the reorganization of circuits affords the unique possibility of sampling for potential new partners [18-20]. Nonetheless, only few examples of rewiring associated with learning have been established thus far [14, 21-24]. Here, we report that memory consolidation is associated with the structural and functional reorganization of an identified circuit in the adult fly brain. The formation and retrieval of olfactory associative memories in Drosophila requires the mushroom body (MB) [25]. We identified the individual synapses of olfactory projection neurons (PNs) that deliver a conditioned odour to the MB and reconstructed the complexity of the microcircuit they form. Combining behavioural experiments with high-resolution microscopy and functional imaging, we demonstrated that the consolidation of appetitive olfactory memories closely correlates with an increase in the number of synaptic complexes formed by the PNs that deliver the conditioned stimulus and their postsynaptic partners. These structural changes result in additional functional synaptic connections.

neuroscience↗

bioRxiv2020

A developmental stretch-and-fill process that optimises dendritic wiring

Circuit connectivity and computation depend on how dendrites branch and occupy space within neural tissue. While optimal wiring principles have long been known to constrain dendritic morphology and their scaling behaviour, the growth dynamics that produce such optimised structures remain unclear. Leveraging structural imaging across development, we identify two complementary growth strategies - inside-out versus outside-in - that together generate mature dendritic arbours. We formalise these dynamics in a mathematical model that captures the two growth modes and show that their interplay yields wiring-efficient, space-filling morphologies and class-specific developmental trajectories across species. This framework provides an algorithmic account of how local branching dynamics give rise to globally optimised architectures. By linking dendritic growth rules to functional design constraints, our theory offers a unifying description of dendritic differentiation and a basis for understanding how coverage and connectivity emerge during neural circuit formation. In briefWe derive a detailed mathematical model that describes long-term time-lapse data of growing dendrites; it optimises total wiring and space-filling. HighlightsO_LIFly neurons stretch and fill a given target area with precise scaling relations. C_LIO_LIWe observe a sequence of two growth strategies. C_LIO_LIEach growth type implements optimal wiring which leads to optimal space filling. C_LIO_LIA model combining these programs captures the development of dendritic structures. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/191064v2_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@1f2aed5org.highwire.dtl.DTLVardef@1b449edorg.highwire.dtl.DTLVardef@161a400org.highwire.dtl.DTLVardef@1562be5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗