Mouse Genome Database (MGD) 2019

Bult, Carol J.; Blake, Judith A.; Smith, Cynthia L.; Kadin, James A.; Richardson, Joel E.

doi:10.1093/nar/gky1056

Cited by 664 publications

(636 citation statements)

References 16 publications

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“…This was likely the case because human cells express CHMP2A, a paralogue of CHMP2B which can mostly compensate for CHMP2B in our cell-based modelcombined knockdown of CHMP2A and CHMP2B phenocopied CHMP6 knockdown. CHMP6 does not have a paralogue in mammalian cells and is an essential gene, whereas CHMP2B is non-essential, based on the Cancer dependency map, depmap.org (47) , and knockout mouse phenotypes (48). This provides a rationale for CHMP6 deficiency not being associated with neurodegenerative diseases -it may not be compatible with life.…”

Section: Discussionmentioning

confidence: 99%

Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation

Chen

Nathaniel

Raghavan

et al. 2019

Preprint

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Intercellular propagation of protein aggregation is emerging as a key mechanism in the progression of several neurodegenerative diseases, including Alzheimer's Disease and frontotemporal dementia. However, we lack a systematic understanding of the cellular pathways controlling prion-like propagation. To uncover such pathways, we performed CRISPR interference (CRISPRi) screens in a human cellbased model of propagation of tau aggregation. Our screens uncovered that knockdown of several components of the ESCRT machinery, including CHMP6, or CHMP2A in combination with CHMP2B (a gene linked to familial frontotemporal dementia), promote propagation of tau aggregation. We found that knockdown of these genes caused damage to endolysosomal membranes, consistent with a role for the ESCRT

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Section: Discussionmentioning

confidence: 99%

Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation

Chen

Nathaniel

Raghavan

et al. 2019

Preprint

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“…When using Pathway Commons (PC) as a source for the gene reactions, we downloaded a simple interaction format (NodeA <relation_type> NodeB) PC network (PathwayCommons11.All.hgnc.sif.gz) from pathwaycommons.org , loaded the PC network as a networkx multigraph (with edge label the relation type), and maintained only the subnetwork of nodes (and existing edges between them) that corresponded to human DE gene symbols. When using a mouse DE gene list as an input, the MGD identifiers are first mapped to their human ortholog HGNC identifiers and gene symbols with INDRA's integrated HGNC and MGD mappings 28,29 before proceeding with the network assembly steps described above.…”

Section: Assembly Of Genewalk Network With Gene Regulation Go Ontolomentioning

confidence: 99%

“…We initiated GeneWalk with 1861 unique Mouse Gene Database (MGD) identifiers 28 corresponding to the DE gene set ( Figure 2B), of which 94% (1750) mapped to different human orthologs using INDRA's integrated mouse-to-human gene mappings 28,29 . INDRA statements were retrieved for 83% of the genes, of which the vast majority (82% of the initial 1861) had at least one connected GO term ( Figure 2C).…”

Section: Application Of Genewalk To Mouse Brain Rna-seq Datamentioning

confidence: 99%

GeneWalk identifies relevant gene functions for a biological context using network representation learning

Ietswaart

Gyori

Bachman

et al. 2019

Preprint

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The primary bottleneck in high-throughput genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Existing methods such as Gene Ontology (GO) enrichment analysis provide insight at the gene set level. For individual genes, GO annotations are static and biological context can only be added by manual literature searches . Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ), a method that identifies individual genes and their relevant functions under a particular experimental condition. After automatic assembly of an experiment-specific gene regulatory network, GeneWalk quantifies the similarity between vector representations of each gene and its GO annotations through representation learning, yielding annotation significance scores that reflect their functional relevance for the experimental context. We demonstrate the use of GeneWalk analysis of RNA-seq and nascent transcriptome (NET-seq) data from human cells and mouse brains, validating the methodology. By performing gene-and condition-specific functional analysis that converts a list of genes into data-driven hypotheses, GeneWalk accelerates the interpretation of high-throughput genetics experiments.

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“…Definitive Endoderm (DE) clusters (4,448 cells) were defined by the co-expression of Foxa1/2, Cdh1 and/or Epcam, whereas the splanchnic SM (10,097 cells) were defined by co-expression of Dimensionality reduction, clustering and marker prediction steps were performed as described above. DE and SM cell subtypes were annotated by manual curation comparing the cluster marker genes with overpublished expression profiles of over 160 different genes in the MGI database 10 and our own gene expression validations.…”

Section: In Silico Selection and Clustering For Definitive Endoderm Amentioning

confidence: 99%

“…To comprehensively characterize lineage diversification in the foregut we analyzed the DE and SM transcriptomes separately, defining 26 DE (E8.5, 6 clusters; E9.0, 8 clusters; E9.5, 12 clusters) and 36 SM clusters (E8.5, 7 clusters; E9.0, 12 clusters; E9.5, 17 clusters), which were annotated by comparing their distinguishing genes with published expression patterns of over 160 genes in the Mouse Genome Informatics (MGI) database 10 . This identified all the previously known DE lineages ( Fig.…”

mentioning

confidence: 99%

Single cell transcriptomics reveals a signaling roadmap coordinating endoderm and mesoderm diversification during foregut organogenesis

Han

Koike

Chaturvedi

et al. 2019

Preprint

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BDR), Kobe, 650-0047, Japan 5 CuSTOM-RIKEN BDR collaborative laboratory, Cincinnati OH, USA 45229. # equal contribution $ correspondence: Aaron.zorn@cchmc.org;2 Visceral organs, such as the lungs, stomach, liver and pancreas, derive from the fetal foregut through a series of inductive interactions between the definitive endoderm (DE) epithelium and the surrounding splanchnic mesoderm (SM) 1,2 . This foregut patterning, which occurs between embryonic day (E) 8.5 and E9.5 in the mouse embryo, equivalent to 17-23 days of human gestation, defines the landscape of the thoracic cavity and disruptions in this process can lead to severe congenital defects. While patterning of the endoderm lineages has been fairly well studies, the SM which is known to provide many paracrine factors required for organogeneis is virtually unstudied 1,2 . In particular we lack a comprehensive understanding of the molecular nature of SM regional identity, the mechanisms by which SM signaling boundaries are established, the role of the epithelium in SM patterning and how SM and DE lineages are dynamically coordinated during organogenesis. Here we used single cell transcriptomics to generate a high-resolution expression map of the embryonic mouse foregut. This uncovered an unexpected diversity in SM progenitors that developed in close register with the organ-specific epithelium.These data allowed us to infer a spatial and temporal signaling roadmap of the combinatorial endoderm-mesoderm interactions that orchestrate foregut organogenesis.We validated key predictions with mouse genetics, showing importance of epithelial signaling in mesoderm patterning. Finally, we leveraged the signaling road map to generate different SM subtypes from human pluripotent stem cells (hPSCs), which previously have been elusive.Single cell transcriptomics allows examination of organogenesis at an unprecedented resolution 3-6 , however, to date studies have either taken a broad overview of many organ systems or focused only on the epithelial component of the developing gut 7-9 . To comprehensively characterize the signaling networks orchestrating foregut organogenesis, we performed single cell RNA sequencing (scRNA-seq) of the mouse embryonic foregut at three time points that span early patterning through organ lineage induction: Embryonic day (E) E8.5 (5-10 somites, 's'), E9.0 (12-15s) and E9.5 (25-30s) ( Fig. 1a, b). We micro-dissected the foregut between the posterior pharynx and the midgut, pooling tissue from 15-20 embryos for each time point. At E9.5, we isolated anterior and posterior regions separately, containing lung/esophagus and liver/pancreas primordia, respectively. A total of 31,268 single-cell transcriptomes passed quality control measures with an average read depth of 3,178 transcripts/cell. Cells were clustered based on the expression of highly variable genes across the population and visualized using t-distributed stochastic neighbor embedding (t-SNE) ( Fig. 1c; Supplementary Fig. 1). This identified 9 major 3 cell lineages: DE, SM, cardiac, other...

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Mouse Genome Database (MGD) 2019

Cited by 664 publications

References 16 publications

Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation

Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation

GeneWalk identifies relevant gene functions for a biological context using network representation learning

Single cell transcriptomics reveals a signaling roadmap coordinating endoderm and mesoderm diversification during foregut organogenesis

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