Walck-Shannon E et al. (2016) Worm "Another morphogenetic movement on the map: Charting dorsal intercalation in C. elegans."

Hardin J, Walck-Shannon E

[Worm, 2016]

Dorsal intercalation is a coordinated cell migration event that rearranges hypodermal cells during C. elegans embryogenesis, and that resembles cell intercalation in many systems from flies to mice. Despite its conservation, the molecular mechanisms that govern dorsal intercalation in worms have remained elusive. Here, we comment on our recent publication, Walck-Shannon etal.,(1) which begins to spatially map the molecular requirements for intercalation. First, we provide a historical perspective on the factors that have previously hampered the study of dorsal intercalation. Next, we provide a summary of the molecular pathways identified in Walck-Shannon etal.,(1) pointing out surprises along the way. Finally, we consider the potential conservation of the molecular pathway we described and discuss future questions surrounding dorsal intercalation. Despite the challenges, dorsal intercalation is a process poised to advance our understanding of cell intercalation during morphogenesis throughout the animal kingdom.

Dorlean, Shad-Lee et al. (2021) International Worm Meeting "Investigating the connection between the DAF-7/TGF-beta signaling pathway and the dense core vesicle protein IDA-1"

McLaughlin, Shannon, Rojas Rodriguez, Paola A, McGehee, Annette, Dorlean, Shad-Lee

[International Worm Meeting, 2021]

The DAF-7/TGF-beta signaling pathway in C. elegans is affected by the environment. This includes factors such as inadequate food supply, high temperature, or overpopulation. We previously found that the DAF-7/TGF-beta signaling pathway regulates the abundance of the glutamate receptor GLR-1. Mutants in the DAF-7/TGF-beta signaling pathway have increased levels of GLR-1 and an increased rate of spontaneous reversals, a behavior which is regulated by GLR-1. However, the exact mechanism of how DAF-7/TGF-beta signaling regulates GLR-1 is unknown. We tested to see whether ida-1, which is expressed in cells that express glr-1, might be involved in this regulation. IDA-1 is normally found on the surface of dense core vesicles (DCV's) and is involved in normal neuropeptide and DCV signaling. To test the involvement of ida-1 in the DAF-7/TGF-beta-dependent regulation of GLR-1 we assessed spontaneous reversal frequency and GLR-1::GFP levels in single and double mutants. The increased rate of spontaneous reversals and the increased GLR-1::GFP levels that are detected in daf-7 mutants are blocked in daf-7 ida-1 double mutants. These results support the idea that IDA-1 does indeed play a role in the DAF-7/TGF-beta-dependent regulation of GLR-1. We then focused on testing whether IDA-1 expression and localization are affected in daf-7 mutants. We investigated this question using strains expressing IDA-1::GFP. We imaged neurons in the head of C. elegans worms of the appropriate genotypes at both the cell body and neurite in order to measure the intensity of the IDA-1::GFP. We found increased levels of IDA-1::GFP in both cell bodies and neuronal puncta in the daf-7 mutant as compared to wildtype. These results suggest that the DAF-7 pathway regulates levels of IDA-1. The exact mechanism however, is yet to be discovered.

Rao S et al. (2005) Genome Inform "Evaluating distance functions for clustering tandem repeats."

Rodriguez A, Benson G, Rao S

[Genome Inform, 2005]

Tandem repeats are an important class of DNA repeats and much research has focused on their efficient identification, their use in DNA typing and fingerprinting, and their causative role in trinucleotide repeat diseases such as Huntington Disease, myotonic dystrophy, and Fragile-X mental retardation. We are interested in clustering tandem repeats into groups or families based on sequence similarity so that their biological importance may be further explored. To cluster tandem repeats we need a notion of pairwise distance which we obtain by alignment. In this paper we evaluate five distance functions used to produce those alignments - Consensus, Euclidean, Jensen-Shannon Divergence, Entropy-Surface, and Entropy-weighted. It is important to analyze and compare these functions because the choice of distance metric forms the core of any clustering algorithm. We employ a novel method to compare alignments and thereby compare the distance functions themselves. We rank the distance functions based on the cluster validation techniques - Average Cluster Density and Average Silhouette Width. Finally, we propose a multi-phase clustering method which produces good-quality clusters. In this study, we analyze clusters of tandem repeats from five sequences: Human Chromosomes 3, 5, 10 and X and C. elegans Chromosome III.

Awal MR et al. (2018) Anesthesiology "Breakdown of Neural Function under Isoflurane Anesthesia: In Vivo, Multineuronal Imaging in Caenorhabditis elegans."

Gabel CV, Florman J, Austin D, Awal MR, Connor CW, Alkema M

[Anesthesiology, 2018]

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Previous work on the action of volatile anesthetics has focused at either the molecular level or bulk neuronal measurement such as electroencephalography or functional magnetic resonance imaging. There is a distinct gulf in resolution at the level of cellular signaling within neuronal systems. We hypothesize that anesthesia is caused by induced dyssynchrony in cellular signaling rather than suppression of individual neuron activity. METHODS: Employing confocal microscopy and Caenorhabditis elegans expressing the calcium-sensitive fluorophore GCaMP6s in specific command neurons, we measure neuronal activity noninvasively and in parallel within the behavioral circuit controlling forward and reverse crawling. We compare neuronal dynamics and coordination in a total of 31 animals under atmospheres of 0, 4, and 8% isoflurane. RESULTS: When not anesthetized, the interneurons controlling forward or reverse crawling occupy two possible states, with the activity of the "reversal" neurons AVA, AVD, AVE, and RIM strongly intercorrelated, and the "forward" neuron AVB anticorrelated. With exposure to 4% isoflurane and onset of physical quiescence, neuron activity wanders rapidly and erratically through indeterminate states. Neuron dynamics shift toward higher frequencies, and neuron pair correlations within the system are reduced. At 8% isoflurane, physical quiescence continues as neuronal signals show diminished amplitude with little correlation between neurons. Neuronal activity was further studied using statistical tools from information theory to quantify the type of disruption caused by isoflurane. Neuronal signals become noisier and more disordered, as measured by an increase in the randomness of their activity (Shannon entropy). The coordination of the system, measured by whether information exhibited in one neuron is also exhibited in other neurons (multiinformation), decreases significantly at 4% isoflurane (P = 0.00015) and 8% isoflurane (P = 0.0028). CONCLUSIONS: The onset of anesthesia corresponds with high-frequency randomization of individual neuron activity coupled with induced dyssynchrony and loss of coordination between neurons that disrupts functional signaling.

Cho, Jaehyoung et al. MicroPubl Biol

Cho, Jaehyoung, Sternberg, Paul W., Chan, Juancarlos, Gao, Sibyl, Grove, Christian, Van Auken, Kimberly

[MicroPubl Biol, 2018]

Protein interaction is an important data type to understand the biological function of proteins involved in the interaction, and helps researchers to deduce the biological nature of unknown proteins from the well-characterized functions of their interaction partners. High-throughput studies, coupled with the aggregation of individual experiments, provides a global 'snapshot' of the protein interactions occurring at all levels of biological processes or circumstances. This snapshot of the interaction network, the interactome, is important to understand the overall events up to the level of comparison between species or pathway simulation, or to find new factors yet undefined in the processes, or to add details to the biological processes and pathways. As of September 2018, WormBase (www.wormbase.org) (Lee et al. 2018) contains 28,279 physical protein-protein interactions for the roundworm Caenorhabditis elegans. Among these, 1500 protein-protein interactions have been curated by BioGRID as a collaboration with WormBase. Within the data set, 17,990 protein-protein interactions are unique, and 6,079 unique genes are involved in these interactions. In order to visualize the overall interaction map, a network diagram for all the unique interactions was generated by using the Cytoscape program, version 3.6.1 (Shannon et al. 2003) (Figure 1A). These numbers represent a 108% increase in the number of interaction annotations since last year, 2017. These interaction data were curated from 1,251 peer-reviewed papers, which were selected from the literature by Textpresso Central using automatic SVM (Support Vector Machine)-based text mining approaches (Fang et al. 2012; Mller et al. 2018) and manual verification. Compared to other databases providing C. elegans protein-protein interaction, WormBase now presents the largest data set, which has 1.72-fold more interaction annotations than IMEx (Orchard et al. 2012) and 4.51-fold more than BioGRID (Chatr-Aryamontri et al. 2017) (Figure 1B). Most significantly, WormBase now houses the complete protein interaction data from almost all of the C. elegans literature published from 1993 to 2018. The data sets presented at IMEx and BioGRID are annotated from 253 and 174 papers, respectively. All the physical interaction data in WormBase are supported by experimental evidence from original research papers. The statistics of the detection methods used as experimental evidence are shown in Figure 1C. The majority of the interaction data came from high throughput analysis such as large-scale yeast two-hybrid assays or mass-spectrometry, however, a significant portion of the data (13.1%) are supported by more direct detection methods using small-scale, low throughput methods such as co-immunoprecipitation or co-crystallography (Figure 1C). In WormBase, protein-protein interaction data can be found as a subclass of physical interaction data in the Interactions widget on the gene report page. The Interactions widget provides all types of interaction data related to the gene of interest, such as physical, genetic, regulatory, and predicted interactions. All the interaction data are represented together in a graph created with Cytoscape.js and a table. In the table, the gene names of interaction partners (bait-target) in the interaction are displayed along with the publication. The interaction details including the detection method are also captured in the summary and the remark field in the Interactions page. Users can query the data by using the search bar on the WormBase front page or download all the available data files from the WormBase FTP site (ftp://ftp.wormbase.org/pub/wormbase/releases/current-production-release/species/c_elegans/PRJNA13758 /annotation/c_elegans.PRJNA13758.WSXXX.interactions.txt.gz, where WSXXX is the database version release, like WS267). All the interaction data in WormBase will be available soon at the new information resource for multiple model organisms, the Alliance of Genome Resources (https://www.alliancegenome.org/). This site will integrate all the interaction data from human and from model organisms C. elegans, budding yeast (Saccharomyces cerevisiae), fruit fly (Drosophila melanogaster), zebrafish (Danio rerio), mouse (Mus musculus) and rat (Rattus norvegicus). Integrated views of interaction data from diverse model organisms will be extremely helpful to build interaction databases for species-to-species comparison, and to establish a disease model quickly based on the database. For the most efficient analysis of the interaction data in WormBase, we are now working on developing a new Venn diagram tool and integrating the Gene Set Enrichment Analysis tool (https://wormbase.org/tools/enrichment/tea/tea.cgi) into the Interactions widget. We will continue to curate other types of macro-molecular interactions including protein-DNA, protein-RNA and RNA-RNA interactions, as well as newly reported protein-protein interaction data to serve our research community.