7 research outputs found
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Inhibition of PIP4Kγ ameliorates the pathological effects of mutant huntingtin protein.
The discovery of the causative gene for Huntington's disease (HD) has promoted numerous efforts to uncover cellular pathways that lower levels of mutant huntingtin protein (mHtt) and potentially forestall the appearance of HD-related neurological defects. Using a cell-based model of pathogenic huntingtin expression, we identified a class of compounds that protect cells through selective inhibition of a lipid kinase, PIP4Kγ. Pharmacological inhibition or knock-down of PIP4Kγ modulates the equilibrium between phosphatidylinositide (PI) species within the cell and increases basal autophagy, reducing the total amount of mHtt protein in human patient fibroblasts and aggregates in neurons. In two Drosophila models of Huntington's disease, genetic knockdown of PIP4K ameliorated neuronal dysfunction and degeneration as assessed using motor performance and retinal degeneration assays respectively. Together, these results suggest that PIP4Kγ is a druggable target whose inhibition enhances productive autophagy and mHtt proteolysis, revealing a useful pharmacological point of intervention for the treatment of Huntington's disease, and potentially for other neurodegenerative disorders
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Expression Atlas update: insights from sequencing data at both bulk and single cell level
Acknowledgements: We would like to thank Olamidipupo Ajigboye and Helen Parkinson for their contributions in enriching EFO in terms needed to describe samples studied in Atlas; Awais Athar, Ahmed Ali, Ugis Sarkans for their help with the BioStudies interface and assistance in submissions of new functional genomics studies to BioStudies. We would like to thank the Bioconda community, the Galaxy community for assistance with Bioconda and Galaxy. We would like to thank the data wranglers, past and present of the Human Cell Atlas Data Coordination Platform for their assistance collating HCA data for the Single Cell Expression Atlas. Finally, we thank the Expression Atlas SAB members, Jurg Bahler (University College London), Angela Brookes (University of California Santa Cruz), Roderic Guigó (Center for Genomic Regulation, chair), Kathryn Lilley (Cambridge University) and Zemin Zhang (Peking University).Funder: European Molecular Biology Laboratory; DOI: https://doi.org/10.13039/100013060Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps
Recommended from our members
Expression Atlas update: insights from sequencing data at both bulk and single cell level
Acknowledgements: We would like to thank Olamidipupo Ajigboye and Helen Parkinson for their contributions in enriching EFO in terms needed to describe samples studied in Atlas; Awais Athar, Ahmed Ali, Ugis Sarkans for their help with the BioStudies interface and assistance in submissions of new functional genomics studies to BioStudies. We would like to thank the Bioconda community, the Galaxy community for assistance with Bioconda and Galaxy. We would like to thank the data wranglers, past and present of the Human Cell Atlas Data Coordination Platform for their assistance collating HCA data for the Single Cell Expression Atlas. Finally, we thank the Expression Atlas SAB members, Jurg Bahler (University College London), Angela Brookes (University of California Santa Cruz), Roderic Guigó (Center for Genomic Regulation, chair), Kathryn Lilley (Cambridge University) and Zemin Zhang (Peking University).Funder: European Molecular Biology Laboratory; DOI: https://doi.org/10.13039/100013060Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps
Recommended from our members
Expression Atlas update: insights from sequencing data at both bulk and single cell level
Acknowledgements: We would like to thank Olamidipupo Ajigboye and Helen Parkinson for their contributions in enriching EFO in terms needed to describe samples studied in Atlas; Awais Athar, Ahmed Ali, Ugis Sarkans for their help with the BioStudies interface and assistance in submissions of new functional genomics studies to BioStudies. We would like to thank the Bioconda community, the Galaxy community for assistance with Bioconda and Galaxy. We would like to thank the data wranglers, past and present of the Human Cell Atlas Data Coordination Platform for their assistance collating HCA data for the Single Cell Expression Atlas. Finally, we thank the Expression Atlas SAB members, Jurg Bahler (University College London), Angela Brookes (University of California Santa Cruz), Roderic Guigó (Center for Genomic Regulation, chair), Kathryn Lilley (Cambridge University) and Zemin Zhang (Peking University).Funder: European Molecular Biology Laboratory; DOI: https://doi.org/10.13039/100013060Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps
Recommended from our members
Expression Atlas update: insights from sequencing data at both bulk and single cell level
Acknowledgements: We would like to thank Olamidipupo Ajigboye and Helen Parkinson for their contributions in enriching EFO in terms needed to describe samples studied in Atlas; Awais Athar, Ahmed Ali, Ugis Sarkans for their help with the BioStudies interface and assistance in submissions of new functional genomics studies to BioStudies. We would like to thank the Bioconda community, the Galaxy community for assistance with Bioconda and Galaxy. We would like to thank the data wranglers, past and present of the Human Cell Atlas Data Coordination Platform for their assistance collating HCA data for the Single Cell Expression Atlas. Finally, we thank the Expression Atlas SAB members, Jurg Bahler (University College London), Angela Brookes (University of California Santa Cruz), Roderic Guigó (Center for Genomic Regulation, chair), Kathryn Lilley (Cambridge University) and Zemin Zhang (Peking University).Funder: European Molecular Biology Laboratory; DOI: https://doi.org/10.13039/100013060Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps
Recommended from our members
Expression Atlas update: insights from sequencing data at both bulk and single cell level
Acknowledgements: We would like to thank Olamidipupo Ajigboye and Helen Parkinson for their contributions in enriching EFO in terms needed to describe samples studied in Atlas; Awais Athar, Ahmed Ali, Ugis Sarkans for their help with the BioStudies interface and assistance in submissions of new functional genomics studies to BioStudies. We would like to thank the Bioconda community, the Galaxy community for assistance with Bioconda and Galaxy. We would like to thank the data wranglers, past and present of the Human Cell Atlas Data Coordination Platform for their assistance collating HCA data for the Single Cell Expression Atlas. Finally, we thank the Expression Atlas SAB members, Jurg Bahler (University College London), Angela Brookes (University of California Santa Cruz), Roderic Guigó (Center for Genomic Regulation, chair), Kathryn Lilley (Cambridge University) and Zemin Zhang (Peking University).Funder: European Molecular Biology Laboratory; DOI: https://doi.org/10.13039/100013060Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI’s knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users’ understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps