4 Papers found (0.008 seconds)

Riazati, Niknaz et al. (2017) International Worm Meeting "The orcokinin neuropeptide NLP-14 regulates sleep."

Nelson, Matthew, Boran, Jaqueline, Riazati, Niknaz, Barrett, Natalie, Orlando, Gerald

[

International Worm Meeting,

2017]

Caenorhabditis elegans sleeps during a life stage called lethargus (Raizen et al., 2008). Lethargus is timed by a transcription factor called LIN-42, homologous to the protein PERIOD, a circadian clock protein which regulates sleep timing in both Drosophila melanogaster and mammals (Jeon et al., 1999; Monsalve et al., 2011). However, the mechanisms by which LIN-42 controls sleep are largely unknown. However, rhythmic secretion of neuropeptides may play a central role (Nelson et al., 2013; Turek et al., 2016). The neuropeptide encoding genes, nlp-14 and nlp-15, show higher expression levels during lethargus (George-Raizen et al., 2014). NLP-14 and -15 share sequence similarities (~69%) with a class of neuropeptides in arthropods called orcokinins (Nathoo et al., 2001). Orcokinins appear to be highly conserved within the Ecdysozoa group. Interestingly, in cockroaches, injection of exogenous orcokinin into the accessory medulla, a circadian pacemaker in the brain, results in a circadian phase shift (Hofer and Homberg, 2006). Taken together, this led us to hypothesize that NLP-14 and -15 regulate sleep in C. elegans. Our data show that nlp-14 but not nlp-15 over-expression induces behavioral quiescence in active adult animals. Specifically, when nlp-14 over-expression is induced under the control of a heat shock promoter, animals become quiescent for locomotion and show prolonged defecation cycles. To understand how NLP-14 induces quiescence, we have conducted a forward genetic screen in which nlp-14 over-expressing worms were randomly mutagenized with EMS, an approach we have taken in the past (Iannacone et al., 2017). The mutated F2 progeny were screened and rare suppressor mutants, that did not become quiescent upon heat-induced over-expression of nlp-14, were isolated. We are currently isolating genomic DNA, which will be followed by whole genome sequencing to identify the causative alleles.

Hallam SJ et al. (1997) International C. elegans Meeting "syd-1 May Regulate Type D Neuron Synaptic Specificity"

Jin YS, Hallam SJ

[

International C. elegans Meeting,

1997]

Previously we reported the isolation of syd-1, a gene that appears to control the synaptic specificity of type D GABAergic neurons1. In syd-1(ju2) mutants DDs innervate dorsal body wall muscles in early L1 stage. In older larval and adult stages VDs make fewer synapses to ventral body wall muscles. The overall cell morphology of D neurons in syd-1 mutants appears normal. syd-1 mutants coil ventrally when backing and also exhibit mild defects in egg laying. We cloned syd-1 by germline transformation rescue. The predicted gene product for syd-1 bears sequence homology to GTPase activating proteins (GAPs), such as p50rho, N-Chimaerin and BCR. However, the GAP domain of syd-1 appears to be divergent3. Compared with recent structural studies of the p50rhoGAP domain, a conserved Arg85 residue, implicated in binding G proteins and enhancing their intrinsic GTPase activity, has been replaced with Val in SYD-1, suggesting that syd-1 may define a novel family of GAPs. The GAP domains of N-Chimaerin and BCR regulate the small GTPases Rac and Cdc42Hs, both of which have been implicated in the clustering of integrins and membrane ruffling2. The sequence similarity between syd-1, BCR and N-Chimaerin hints at the possibility that SYD-1 may regulate the the subcellular organization of actin in the D neurons. We are in the process of identifying the molecular lesions in syd-1 alleles and determining the cells in which it is expressed . 1 S. Hallam and Y. Jin (1996) WBG vol 14, no.5. 2 C.D. Nobes and A. Hall (1995) Cell 81, 53-63. 3 T. Barrett et, al., (1996) Nature 385, 458-61

Barrett, Alec et al. (2021) International Worm Meeting "Integrating bulk and single cell transcriptomics for accurate detection of tissue-specific gene expression"

Barrett, Alec, Taylor, Seth R., Weinreb, Alexis, Miller III, David M., Varol, Erdem, Sestan, Nenad, Hobert, Oliver, McWhirter, Rebecca, Li, Mingfeng, Hammarlund, Marc

[

International Worm Meeting,

2021]

Advances in RNA-seq for bulk and single cell (sc) approaches have produced increasingly fine dissections of the C. elegans transcriptome. Although both techniques can yield transcriptomes for individual cell types, each comes with strengths and weaknesses. scRNA-Seq affords high resolution, but suffers from dropout, leading to false negatives. Bulk sequencing detects more genes, but suffers from contaminating cell types, resulting in false positives. In this work we integrated these orthogonal approaches to improve the accuracy of both methods. We used bulk samples collected for specific neuron types and sc datasets for all C. elegans neurons and additional non-neuronal cells (1). We used sc data to estimate contamination in each bulk sample, and developed novel methods for removing these gene counts. In one approach we used linear histogram matching to scale sc counts, and subtracted putative contamination using data from non-neuronal clusters. In another approach we used bootstrapping to estimate gene level contributions from target and contaminating tissues in sc data and apply them to bulk counts, providing a bootstrap sample distribution of corrected expression data. We assessed these approaches in two ways: 1) Measuring improvements in calling genes with known expression in all neurons; 2) Examining effects on eliminating genes expressed exclusively in contaminating tissues. We found that our analysis reduced false positives, while maintaining robust true positive detection, thus offering a unique strategy for utilizing complementary bulk and sc RNA-Seq data sets to enhance the accuracy of cell-specific expression profiling data. 1. Taylor SR, Santpere G, Weinreb A, Barrett A, Reilly MB, Xu C, et al. Molecular topography of an entire nervous system. bioRxiv. 2020:2020.12.15.422897.

Varol, Erdem et al. (2021) International Worm Meeting "A computational approach linking neuron-specific gene expression with connectivity"

[

International Worm Meeting,

2021]

There is strong prior evidence for genetic encoding of synaptogenesis, axon guidance, and synaptic pruning in neural circuits. Despite these foundational observations, the transcriptional codes that drive neural connectivity have not been elucidated. The C. elegans nervous system is a particularly useful model for studying the interplay between genetics and connectivity since its wiring diagram is highly stereotyped and uniquely well-defined by electron microscopy. Furthermore, recent evidence in C. elegans has suggested that a unique combination of transcription factors specifies each of the 118 neuron classes[1]. Motivated by evidence for the stereotypy of neural circuits and for the genetic encoding of neural identity, we introduce a novel statistical technique, termed Network Differential Gene expression analysis (nDGE), to test the hypotheses that neuron-specific gene expression dictates connectivity. Specifically, we test the hypothesis that pre-synaptic neural identity is defined by a "key" gene combination whose post-synaptic targets are determined by a "lock" gene combination. For our approach, we utilize neuron-specific gene expression profiles from the CeNGEN project[2] to investigate transcriptional codes for connectivity in the nerve ring[3]. We hypothesize that the expression of specific cell adhesion molecules (CAM) among synaptically-connected neurons drives synaptic maintenance in the mature nervous system. We posit that CAMs mediating synaptic stability would be more highly expressed in synaptically-connected neurons than in adjacent neurons with membrane contacts but no synapses. Thus, for each neuron, we compare the expression of all possible combinations of pairs of CAMs in the neuron and its synaptic partners relative to the neuron and its non-synaptic adjacent neurons. Two independent comparisons are generated, one for presynaptic neurons and a second result for postsynaptic neurons. Our nDGE analysis reveals that specific combinations of CAMs are correlated with connectivity in different subsets of neurons and thus provides a uniquely comprehensive road map for investigating the genetic blueprint for the nerve ring wiring diagram. Open source software of Network Differential Gene Expression (nDGE) is publicly available at https://github.com/cengenproject/connectivity_analysis along with a vignette showcasing the CAM results. 1. Reilly, M. B., Cros, C., Varol, E., Yemini, E., & Hobert, O. (2020). Unique homeobox codes delineate all the neuron classes of C. elegans. Nature, 584(7822), 595-601. 2. Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C. Varol, E., ... & Miller, D. M. (2020). Molecular topography of an entire nervous system. bioRxiv. 3. Cook, S. J.,... & Emmons, S. W. (2019). Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature, 571(7763), 63-71.