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4079 Publications

Showing 1761-1770 of 4079 results
04/27/24 | hkb is required for DIP-α expression and target recognition in the Drosophila neuromuscular circuit
Robert A Carrillo , Yupu Wang , Rio Salazar , Luciano Simonetta , Violet Sorrentino , Terrence J Gatton , Bill Wu , Christopher G Vecsey
Communications Biology. 2024 Apr 27;7(507):. doi: 10.1038/s42003-024-06184-8

Our nervous system contains billions of neurons that form precise connections with each other through interactions between cell surface proteins (CSPs). In Drosophila, the Dpr and DIP immunoglobulin protein subfamilies form homophilic or heterophilic interactions to instruct synaptic connectivity, synaptic growth and cell survival. However, the upstream regulation and downstream signaling mechanisms of Dprs and DIPs are not clear. In the Drosophila larval neuromuscular system, DIP-α is expressed in the dorsal and ventral type-Is motor neurons (MNs). We conducted an F1 dominant modifier genetic screen to identify regulators of Dprs and DIPs. We found that the transcription factor, huckebein (hkb), genetically interacts with DIP-α and is important for target recognition specifically in the dorsal Is MN, but not the ventral Is MN. Loss of hkb led to complete removal of DIP-α expression. We then confirmed that this specificity is through the dorsal Is MN specific transcription factor, even-skipped (eve), which acts downstream of hkb. Genetic interaction between hkb and eve revealed that they act in the same pathway to regulate dorsal Is MN connectivity. Our study provides insight into the transcriptional regulation of DIP-α and suggests that distinct regulatory mechanisms exist for the same CSP in different neurons.

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05/05/15 | HMMER web server: 2015 update.
Finn RD, Clements J, Arndt W, Miller BL, Wheeler TJ, Schreiber F, Bateman A, Eddy SR
Nucleic Acids Research. 2015 May 5:. doi: 10.1093/nar/gkv397

The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.

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Eddy/Rivas Lab
07/01/11 | HMMER web server: interactive sequence similarity searching.
Finn RD, Clements J, Eddy SR
Nucleic Acids Research. 2011 Jul;39:W29-37. doi: 10.1093/nar/gkr367

HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them.

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01/05/24 | Homeodomain proteins hierarchically specify neuronal diversity and synaptic connectivity
Chundi Xu , Tyler B. Ramos , Ed M. Rogers , Michael B. Reiser , Chris Q. Doe
eLife. 2024 Jan 05:. doi: 10.7554/eLife.90133

The brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.

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10/17/13 | Homeostatic plasticity shapes the visual system’s first synapse
Johnson RE, Tien N, Shen N, Pearson JT, Soto F, Kerschensteiner D
Nature Communications. 10/2017;8(1):. doi: 10.1038/s41467-017-01332-7

Vision in dim light depends on synapses between rods and rod bipolar cells (RBCs). Here, we find that these synapses exist in multiple configurations, in which single release sites of rods are apposed by one to three postsynaptic densities (PSDs). Single RBCs often form multiple PSDs with one rod; and neighboring RBCs share ~13% of their inputs. Rod-RBC synapses develop while ~7% of RBCs undergo programmed cell death (PCD). Although PCD is common throughout the nervous system, its influences on circuit development and function are not well understood. We generate mice in which ~53 and ~93% of RBCs, respectively, are removed during development. In these mice, dendrites of the remaining RBCs expand in graded fashion independent of light-evoked input. As RBC dendrites expand, they form fewer multi-PSD contacts with rods. Electrophysiological recordings indicate that this homeostatic co-regulation of neurite and synapse development preserves retinal function in dim light.

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Eddy/Rivas Lab
04/01/15 | Homology searches for structural RNAs: from proof of principle to practical use.
Eddy SR
RNA. 2015 Apr;21(4):605-7. doi: 10.1261/rna.050484.115
06/26/18 | Honeybee detection and pose estimation using convolutional neural networks.
Rodriguez IF, Branson KM, Acuna E, Agosto-Rivera J, Giray T, Megret R
RFIAP 2018. 2018 Jun 26:

The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony.

In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose. 

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Riddiford Lab
01/15/76 | Hormonal control of insect epidermal cell commitment in vitro.
Riddiford LM
Nature. 1976 Jan 15;259(5539):115-7
Riddiford Lab
03/01/01 | Hormonal regulation and patterning of the broad-complex in the epidermis and wing discs of the tobacco hornworm, Manduca sexta.
Zhou B, Riddiford LM
Developmental Biology. 2001 Mar 1;231(1):125-37. doi: 10.1006/dbio.2000.0143

Expression of Manduca Broad-Complex (BR-C) mRNA in the larval epidermis is under the dual control of ecdysone and juvenile hormone (JH). Immunocytochemistry with antibodies that recognize the core, Z2, and Z4 domains of Manduca BR-C proteins showed that BR-C appearance not only temporally correlates with pupal commitment of the epidermis on day 3 of the fifth (final) larval instar, but also occurs in a strict spatial pattern within the abdominal segment similar to that seen for the loss of sensitivity to JH. Levels of Z2 and Z4 BR-C proteins shift with Z2 predominating at pupal commitment and Z4 dominant during early pupal cuticle synthesis. Both induction of BR-C mRNA in the epidermis by 20-hydroxyecdysone (20E) and its suppression by JH were shown to be independent of new protein synthesis. For suppression JH must be present during the initial exposure to 20E. When JH was given 6 h after 20E, suppression was only seen in those regions that had not yet expressed BR-C. In the wing discs BR-C was first detected earlier 1.5 days after ecdysis, coincident with the pupal commitment of the wing. Our findings suggest that BR-C expression is one of the first molecular events underlying pupal commitment of both epidermis and wing discs.

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05/23/22 | Hormone-controlled changes in the differentiation state of post-mitotic neurons.
Lai Y, Miyares RL, Liu L, Chu S, Lee T, Yu H
Current Biology. 2022 May 23;32(10):2341-2348. doi: 10.1016/j.cub.2022.04.027

While we think of neurons as having a fixed identity, many show spectacular plasticity. Metamorphosis drives massive changes in the fly brain; neurons that persist into adulthood often change in response to the steroid hormone ecdysone. Besides driving remodeling, ecdysone signaling can also alter the differentiation status of neurons. The three sequentially born subtypes of mushroom body (MB) Kenyon cells (γ, followed by α'/β', and finally α/β) serve as a model of temporal fating. γ neurons are also used as a model of remodeling during metamorphosis. As γ neurons are the only functional Kenyon cells in the larval brain, they serve the function of all three adult subtypes. Correspondingly, larval γ neurons have a similar morphology to α'/β' and α/β neurons-their axons project dorsally and medially. During metamorphosis, γ neurons remodel to form a single medial projection. Both temporal fate changes and defects in remodeling therefore alter γ-neuron morphology in similar ways. Mamo, a broad-complex, tramtrack, and bric-à-brac/poxvirus and zinc finger (BTB/POZ) transcription factor critical for temporal specification of α'/β' neurons, was recently described as essential for γ remodeling. In a previous study, we noticed a change in the number of adult Kenyon cells expressing γ-specific markers when mamo was manipulated. These data implied a role for Mamo in γ-neuron fate specification, yet mamo is not expressed in γ neurons until pupariation, well past γ specification. This indicates that mamo has a later role in ensuring that γ neurons express the correct Kenyon cell subtype-specific genes in the adult brain.

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