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Conference Paper: hnRNP A1-associated co-expression networks by Alzhemier's disease

TitlehnRNP A1-associated co-expression networks by Alzhemier's disease
Authors
Issue Date2022
Citation
International Virtual Symposium on Healthy Aging How to Cite?
Abstractbackground HNRNPA1 is widely associated with neurodegenerative diseases (Bekenstein U, 2013), which in turn, commonly accompany senescence. Specifically, investigating gene expression networks, which link to Alzheimer disease, and networks that alter by various genetic manipulations, in this case – the HNRNPA1 knockout – promise to shed light on the nature and flow of the pathology. aims/goals Initially, Differentially Expressed Gene (DEG) analysis, alternative splicing (AS) events, and sPLS-DA machine learning algorithm demonstrated a significant difference in expression profile by HNRNPA1 knockout mice. Therefore, at next step, extracted co-expression networks and enrichment pathways showed the mechanisms of change and the place of gene in the pathway. methods Sequenced, quality-controlled, indexed, and differential-expression analyzed genes entered the weighted gene co-expression network analysis (WGCNA) pipeline. The software yielded modules-association of the genes, and these modules then went through pathway analysis on Gene Ontology and in Kyoto Encyclopedia of Genes and Genomes (KEGG) with clusterProfiler software. The pathways thus obtained underwent analysis on the subject of biological sense, and also the overlap with the DEGs by comparison in different states from the previous research (young vs. old mice, AD vs. wildtype, HNRNPA1 knockout vs. AD, etc.). Cytoscape visualized the networks thus aggregated. results Juxtaposing the DEGs, AS events, and modules of WGCNA uncovered significant genes and allowed to speculate concerning the potential mechanisms that lead to the typical Alzheimer’s disease profile, and the role of HNRNPA1 in it. impact This work seeks to further advance on diagnosis of devastating age-associated degenerative state at early stage and also holds potential for uncovering molecular mechanisms to remedy and reverse the deleterious effects of Alzheimer’s disease. References Bekenstein U, Soreq H. Heterogeneous nuclear ribonucleoprotein A1 in health and neurodegenerative disease: from structural insights to post-transcriptional regulatory roles. Mol Cell Neurosci. 2013 Sep;56:436-46. doi: 10.1016/j.mcn.2012.12.002. Epub 2012 Dec 14. PMID: 23247072.
Persistent Identifierhttp://hdl.handle.net/10722/319527

 

DC FieldValueLanguage
dc.contributor.authorSong, Y-
dc.contributor.authorSAPOZHNIKOV, G-
dc.contributor.authorYUE, M-
dc.contributor.authorCHEN, Z-
dc.date.accessioned2022-10-14T05:14:57Z-
dc.date.available2022-10-14T05:14:57Z-
dc.date.issued2022-
dc.identifier.citationInternational Virtual Symposium on Healthy Aging-
dc.identifier.urihttp://hdl.handle.net/10722/319527-
dc.description.abstractbackground HNRNPA1 is widely associated with neurodegenerative diseases (Bekenstein U, 2013), which in turn, commonly accompany senescence. Specifically, investigating gene expression networks, which link to Alzheimer disease, and networks that alter by various genetic manipulations, in this case – the HNRNPA1 knockout – promise to shed light on the nature and flow of the pathology. aims/goals Initially, Differentially Expressed Gene (DEG) analysis, alternative splicing (AS) events, and sPLS-DA machine learning algorithm demonstrated a significant difference in expression profile by HNRNPA1 knockout mice. Therefore, at next step, extracted co-expression networks and enrichment pathways showed the mechanisms of change and the place of gene in the pathway. methods Sequenced, quality-controlled, indexed, and differential-expression analyzed genes entered the weighted gene co-expression network analysis (WGCNA) pipeline. The software yielded modules-association of the genes, and these modules then went through pathway analysis on Gene Ontology and in Kyoto Encyclopedia of Genes and Genomes (KEGG) with clusterProfiler software. The pathways thus obtained underwent analysis on the subject of biological sense, and also the overlap with the DEGs by comparison in different states from the previous research (young vs. old mice, AD vs. wildtype, HNRNPA1 knockout vs. AD, etc.). Cytoscape visualized the networks thus aggregated. results Juxtaposing the DEGs, AS events, and modules of WGCNA uncovered significant genes and allowed to speculate concerning the potential mechanisms that lead to the typical Alzheimer’s disease profile, and the role of HNRNPA1 in it. impact This work seeks to further advance on diagnosis of devastating age-associated degenerative state at early stage and also holds potential for uncovering molecular mechanisms to remedy and reverse the deleterious effects of Alzheimer’s disease. References Bekenstein U, Soreq H. Heterogeneous nuclear ribonucleoprotein A1 in health and neurodegenerative disease: from structural insights to post-transcriptional regulatory roles. Mol Cell Neurosci. 2013 Sep;56:436-46. doi: 10.1016/j.mcn.2012.12.002. Epub 2012 Dec 14. PMID: 23247072.-
dc.languageeng-
dc.relation.ispartofInternational Virtual Symposium on Healthy Aging-
dc.titlehnRNP A1-associated co-expression networks by Alzhemier's disease-
dc.typeConference_Paper-
dc.identifier.emailSong, Y: songy@hku.hk-
dc.identifier.authoritySong, Y=rp00488-
dc.identifier.hkuros338354-

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