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1. Members of the DIP and Dpr adhesion protein families use cis inhibition to shape neural development in Drosophila

   https://doi.org/10.1371/journal.pbio.3003030  

   Morano, Nicholas C; Lopez, Davys H; Meltzer, Hagar; Sergeeva, Alina P; Katsamba, Phinikoula S; Rostam, Kevin D; Gupta, Himanshu Pawankumar; Becker, Jordan E; Bornstein, Bavat; Cosmanescu, Filip; et al (March 2025, PLOS Biology)

   Ye, Bing (Ed.)

   InDrosophila, two interacting adhesion protein families, Defective proboscis responses (Dprs) and Dpr interacting proteins (DIPs), coordinate the assembly of neural networks. While intercellular DIP::Dpr interactions have been well characterized, DIPs and Dprs are often co-expressed within the same cells, raising the question as to whether they also interact incis. We show, in cultured cells andin vivo, that DIP-α and DIP-δ can interact inciswith their ligands, Dpr6/10 and Dpr12, respectively. When co-expressed inciswith their cognate partners, these Dprs regulate the extent oftransbinding, presumably through competitivecisinteractions. We demonstrate the neurodevelopmental effects ofcisinhibition in fly motor neurons and in the mushroom body. We further show that a long disordered region of DIP-α at the C-terminus is required forcisbut nottransinteractions, likely because it alleviates geometric constraints oncisbinding. Thus, the balance betweencisandtransinteractions plays a role in controlling neural development. 

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2. Deciphering the combinatorial expression pattern and genetic regulatory mechanisms of Beats and Sides in the olfactory circuits of Drosophila

   https://doi.org/10.1101/2025.05.31.657193  

   Duan, Ǫichen; Okuwa, Sumie; Estrella, Rachel; Yeung, Chun; Chen, Yu-Chieh David; Rio, Laura Ǫuintana; Vien, Khanh M; Volkan, Pelin C (June 2025, bioRxiv)

   Abstract Over the past decades, many critical molecular players have been uncovered to control distinct steps in olfactory circuit assembly in Drosophila. Among these, multi-member gene families of cell surface proteins are of interest because they can act as neuron-specific identification/recognition tags in combinations and contribute to circuit assembly in complex brains through their heterophilic or homophilic interactions. Recently, a multi-protein interactome has been described between the Beat and Side families of IgSF proteins. Here, we use the publicly available single-cell RNA-seq datasets and newly generated gene trap transgenic driver lines to probe thein vivospatial expression pattern of thebeat/sidegene families in odorant receptor neurons (ORNs) and their synaptic target projection neurons (PNs).Our results revealed that each ORN and its synaptic target PN class expresses a class-specific combination ofbeat/sidegenes, hierarchically regulated by lineage-specific genetic programs. Though ORNs or PNs from closer lineages tend to possess more similarbeat/sideprofiles, we also found many examples of divergence from this pattern among closely related ORNs and closely related PNs. To explore whether the class-specific combination ofbeats/sidesdefines ORN-PN matching specificity, we perturbed presynapticbeat-IIaand postsynapticside-IVin two ORN-PN partners. However, disruption of Beat-IIa-Side-IV interaction did not produce any significant mistargeting in these two examined glomeruli. Though without affecting general glomerular targeting, knockdown ofsidein ORNs leads to the reduction of synaptic development. Interestingly, we found conserved expression patterns ofbeat/sideorthologs across ORNs in ants and mosquitoes, indicating the shared regulatory strategies specifying the expression of these duplicated paralogs in insect evolution. Overall, this comprehensive analysis of expression patterns lays a foundation for in-depth functional investigations into how Beat/Side combinatorial expression contributes to olfactory circuit assembly. 

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3. SNS-Toolbox: A Suite of Tools for the Design, Optimization, and Implementation of Synthetic Nervous Systems

   Nourse, W (August 2024, Ohiolink)

   In this dissertation I present SNS-Toolbox, an open-source software package for the design and simulation of networks of biologically inspired neurons and synapses, also known as synthetic nervous systems (SNS). SNS-Toolbox allows SNS networks to be designed using a lightweight Python API, simulated in real-time on consumer computer hardware, and executed onboard physical robotic systems. I also present a companion package to SNS-Toolbox which allows simulation and training of large SNS networks using gradient backpropagation. This software is released under an open-source license with online documentation for ease of use, and has been disseminated to other researchers for their use. As a demonstration, I use SNS-Toolbox to implement a stereo visual motion detector, based on circuitry present within the Drosophila melanogaster (fruit fly) optic lobe. This network analyzes local motion at each point within a visual field, and returns an estimate of global motion when subjected to grating stimuli. Finally I showcase the design of FlyWheel, a robotic benchmark for studying models of insect vision and applying SNS networks to physical hardware. This body of work marks the first tool which is capable of simulating SNS networks with hundreds to thousands of neurons and synaptic connections in real-time or faster, optimize networks with chemical reversal potentials using gradient backpropagation, and interface these networks for control of external systems. 

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4. Coordinated control of neuronal differentiation and wiring by sustained transcription factors

   https://doi.org/10.1126/science.add1884  

   Özel, Mehmet Neset; Gibbs, Claudia Skok; Holguera, Isabel; Soliman, Mennah; Bonneau, Richard; Desplan, Claude (December 2022, Science)

   INTRODUCTION Neurons are by far the most diverse of all cell types in animals, to the extent that “cell types” in mammalian brains are still mostly heterogeneous groups, and there is no consensus definition of the term. The Drosophila optic lobes, with approximately 200 well-defined cell types, provides a tractable system with which to address the genetic basis of neuronal type diversity. We previously characterized the distinct developmental gene expression program of each of these types using single-cell RNA sequencing (scRNA-seq), with one-to-one correspondence to the known morphological types. RATIONALE The identity of fly neurons is determined by temporal and spatial patterning mechanisms in stem cell progenitors, but it remained unclear how these cell fate decisions are implemented and maintained in postmitotic neurons. It was proposed in Caenorhabditis elegans that unique combinations of terminal selector transcription factors (TFs) that are continuously expressed in each neuron control nearly all of its type-specific gene expression. This model implies that it should be possible to engineer predictable and complete switches of identity between different neurons just by modifying these sustained TFs. We aimed to test this prediction in the Drosophila visual system. RESULTS Here, we used our developmental scRNA-seq atlases to identify the potential terminal selector genes in all optic lobe neurons. We found unique combinations of, on average, 10 differentially expressed and stably maintained (across all stages of development) TFs in each neuron. Through genetic gain- and loss-of-function experiments in postmitotic neurons, we showed that modifications of these selector codes are sufficient to induce predictable switches of identity between various cell types. Combinations of terminal selectors jointly control both developmental (e.g., morphology) and functional (e.g., neurotransmitters and their receptors) features of neurons. The closely related Transmedullary 1 (Tm1), Tm2, Tm4, and Tm6 neurons (see the figure) share a similar code of terminal selectors, but can be distinguished from each other by three TFs that are continuously and specifically expressed in one of these cell types: Drgx in Tm1, Pdm3 in Tm2, and SoxN in Tm6. We showed that the removal of each of these selectors in these cell types reprograms them to the default Tm4 fate. We validated these conversions using both morphological features and molecular markers. In addition, we performed scRNA-seq to show that ectopic expression of pdm3 in Tm4 and Tm6 neurons converts them to neurons with transcriptomes that are nearly indistinguishable from that of wild-type Tm2 neurons. We also show that Drgx expression in Tm1 neurons is regulated by Klumpfuss, a TF expressed in stem cells that instructs this fate in progenitors, establishing a link between the regulatory programs that specify neuronal fates and those that implement them. We identified an intronic enhancer in the Drgx locus whose chromatin is specifically accessible in Tm1 neurons and in which Klu motifs are enriched. Genomic deletion of this region knocked down Drgx expression specifically in Tm1 neurons, leaving it intact in the other cell types that normally express it. We further validated this concept by demonstrating that ectopic expression of Vsx (visual system homeobox) genes in Mi15 neurons not only converts them morphologically to Dm2 neurons, but also leads to the loss of their aminergic identity. Our results suggest that selector combinations can be further sculpted by receptor tyrosine kinase signaling after neurogenesis, providing a potential mechanism for postmitotic plasticity of neuronal fates. Finally, we combined our transcriptomic datasets with previously generated chromatin accessibility datasets to understand the mechanisms that control brain wiring downstream of terminal selectors. We built predictive computational models of gene regulatory networks using the Inferelator framework. Experimental validations of these networks revealed how selectors interact with ecdysone-responsive TFs to activate a large and specific repertoire of cell surface proteins and other effectors in each neuron at the onset of synapse formation. We showed that these network models can be used to identify downstream effectors that mediate specific cellular decisions during circuit formation. For instance, reduced levels of cut expression in Tm2 neurons, because of its negative regulation by pdm3 , controls the synaptic layer targeting of their axons. Knockdown of cut in Tm1 neurons is sufficient to redirect their axons to the Tm2 layer in the lobula neuropil without affecting other morphological features. CONCLUSION Our results support a model in which neuronal type identity is primarily determined by a relatively simple code of continuously expressed terminal selector TFs in each cell type throughout development. Our results provide a unified framework of how specific fates are initiated and maintained in postmitotic neurons and open new avenues to understanding synaptic specificity through gene regulatory networks. The conservation of this regulatory logic in both C. elegans and Drosophila makes it likely that the terminal selector concept will also be useful in understanding and manipulating the neuronal diversity of mammalian brains. Terminal selectors enable predictive cell fate reprogramming. Tm1, Tm2, Tm4, and Tm6 neurons of the Drosophila visual system share a core set of TFs continuously expressed by each cell type (simplified). The default Tm4 fate is overridden by the expression of a single additional terminal selector to generate Tm1 ( Drgx ), Tm2 ( pdm3 ), or Tm6 ( SoxN ) fates. 

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5. SNS-Toolbox: A Tool for Efficient Simulation of Synthetic Nervous Systems

   Nourse W., Szczecinski N.S. (July 2022, Biomimetic and Biohybrid Systems. Living Machines 2022. Lecture Notes in Computer Science. Springer, Cham)

   We introduce SNS-Toolbox, a Python software package for the design and simulation of networks of conductance-based neurons and synapses, also called Synthetic Nervous Systems (SNS). SNS-Toolbox implements non-spiking and spiking neurons in multiple software backends, and is capable of simulating networks with thousands of neurons in real-time. We benchmark the toolbox simulation speed across multiple network sizes, characterize upper limits on network size in various scenarios, and showcase the design of a two-layer convolutional network inspired by circuits within the Drosophila melanogaster optic lobe. SNSToolbox, as well as the code to generate all of the figures in this work, is located at https://github.com/wnourse05/SNS-Toolbox. 

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