Ontology type: schema:ScholarlyArticle Open Access: True
2021-12-03
AUTHORSHazal Haytural, Rui Benfeitas, Sophia Schedin-Weiss, Erika Bereczki, Melinda Rezeli, Richard D. Unwin, Xusheng Wang, Eric B. Dammer, Erik C. B. Johnson, Nicholas T. Seyfried, Bengt Winblad, Betty M. Tijms, Pieter Jelle Visser, Susanne Frykman, Lars O. Tjernberg
ABSTRACTMass spectrometry (MS)-based proteomics is a powerful tool to explore pathogenic changes of a disease in an unbiased manner and has been used extensively in Alzheimer disease (AD) research. Here, by performing a meta-analysis of high-quality proteomic studies, we address which pathological changes are observed consistently and therefore most likely are of great importance for AD pathogenesis. We retrieved datasets, comprising a total of 21,588 distinct proteins identified across 857 postmortem human samples, from ten studies using labeled or label-free MS approaches. Our meta-analysis findings showed significant alterations of 757 and 1,195 proteins in AD in the labeled and label-free datasets, respectively. Only 33 proteins, some of which were associated with synaptic signaling, had the same directional change across the individual studies. However, despite alterations in individual proteins being different between the labeled and the label-free datasets, several pathways related to synaptic signaling, oxidative phosphorylation, immune response and extracellular matrix were commonly dysregulated in AD. These pathways represent robust changes in the human AD brain and warrant further investigation. More... »
PAGES312
http://scigraph.springernature.com/pub.10.1038/s41597-021-01090-8
DOIhttp://dx.doi.org/10.1038/s41597-021-01090-8
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PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/34862388
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"value": [
"pub.1143568409"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41597-021-01090-8"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"34862388"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41597-021-01090-8",
"https://app.dimensions.ai/details/publication/pub.1143568409"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:39",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_908.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1038/s41597-021-01090-8"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41597-021-01090-8'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41597-021-01090-8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41597-021-01090-8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41597-021-01090-8'
This table displays all metadata directly associated to this object as RDF triples.
393 TRIPLES
22 PREDICATES
114 URIs
82 LITERALS
15 BLANK NODES