Ontology type: schema:ScholarlyArticle Open Access: True
2022-05-02
AUTHORSHongyan Sun, Yexin Yang, Yuxuan Cao, Huan Li, Lujiang Qu, Susan J. Lamont
ABSTRACTBackgroundReceptor interacting serine/threonine kinase 2 (RIP2), ubiquitous in many tissue/cell types, is the key regulator of immune and inflammatory responses for many diseases, including avian pathogenic E. coli (APEC), which causes a wide variety of localized or systemic infections. However, the molecular mechanisms by which RIP2 drives its transcriptional program to affect immune and inflammatory response upon APEC infection remains poorly understood.ResultsIn this study, RNA-seq and bioinformatics analyses were used to detect gene expression and new direct/indirect RIP2 targets in the treatments of wild type HD11 cells (WT), RIP2 knockdown cells (shRIP2), APEC stimulation cells (APEC), and RIP2 knockdown cells combined with APEC infection (shRIP2 + APEC). The results revealed that a total of 4691 and 2605 differentially expressed genes (DEGs) were screened in shRIP2 + APEC vs. APEC and shRIP2 vs. WT, respectively. Functional annotation analysis showed that apoptosis, MAPK, p53, Toll-like receptor, and Nod-like receptor signaling pathways were involved in APEC-induced RIP2 knockdown HD11 cells. By analyzing the enriched pathway and gene networks, we identified that several DEGs, including HSP90AB1, BID, and CASP9 were targeted by RIP2 upon APEC infection.ConclusionAs a whole, this study can not only provide data support for constructing gene networks of RIP2 knockdown with APEC challenge but also provide new ideas for improving the immune and inflammatory response. More... »
PAGES341
http://scigraph.springernature.com/pub.10.1186/s12864-022-08595-5
DOIhttp://dx.doi.org/10.1186/s12864-022-08595-5
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1147539742
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/35501708
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biological Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Medical and Health Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Biochemistry and Cell Biology",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Genetics",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1107",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Immunology",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Animals",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Chickens",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Escherichia coli",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Expression Profiling",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Gene Regulatory Networks",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Macrophages",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Joint International Research Laboratory of Agriculture & Agri-Product Safety, Ministry of Education, Yangzhou University, 225009, Yangzhou, China",
"id": "http://www.grid.ac/institutes/grid.268415.c",
"name": [
"College of Animal Science and Technology, Yangzhou University, 225009, Yangzhou, China",
"Joint International Research Laboratory of Agriculture & Agri-Product Safety, Ministry of Education, Yangzhou University, 225009, Yangzhou, China"
],
"type": "Organization"
},
"familyName": "Sun",
"givenName": "Hongyan",
"id": "sg:person.0774272326.99",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774272326.99"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "College of Animal Science and Technology, Yangzhou University, 225009, Yangzhou, China",
"id": "http://www.grid.ac/institutes/grid.268415.c",
"name": [
"College of Animal Science and Technology, Yangzhou University, 225009, Yangzhou, China"
],
"type": "Organization"
},
"familyName": "Yang",
"givenName": "Yexin",
"type": "Person"
},
{
"affiliation": {
"alternateName": "College of Animal Science and Technology, Yangzhou University, 225009, Yangzhou, China",
"id": "http://www.grid.ac/institutes/grid.268415.c",
"name": [
"College of Animal Science and Technology, Yangzhou University, 225009, Yangzhou, China"
],
"type": "Organization"
},
"familyName": "Cao",
"givenName": "Yuxuan",
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Biological and Chemical Engineering, Yangzhou Polytechnic College, 225009, Yangzhou, China",
"id": "http://www.grid.ac/institutes/grid.495274.9",
"name": [
"School of Biological and Chemical Engineering, Yangzhou Polytechnic College, 225009, Yangzhou, China"
],
"type": "Organization"
},
"familyName": "Li",
"givenName": "Huan",
"id": "sg:person.013305171757.92",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013305171757.92"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "College of Animal Science and Technology, China Agricultural University, 100091, Beijing, China",
"id": "http://www.grid.ac/institutes/grid.22935.3f",
"name": [
"College of Animal Science and Technology, China Agricultural University, 100091, Beijing, China"
],
"type": "Organization"
},
"familyName": "Qu",
"givenName": "Lujiang",
"id": "sg:person.01356475732.11",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01356475732.11"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Department of Animal Science, Iowa State University, 50011, Ames, Iowa, USA",
"id": "http://www.grid.ac/institutes/grid.34421.30",
"name": [
"Department of Animal Science, Iowa State University, 50011, Ames, Iowa, USA"
],
"type": "Organization"
},
"familyName": "Lamont",
"givenName": "Susan J.",
"id": "sg:person.0607026424.04",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607026424.04"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/onc.2008.307",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008538232",
"https://doi.org/10.1038/onc.2008.307"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/s41467-017-02088-w",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1100092794",
"https://doi.org/10.1038/s41467-017-02088-w"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nrd2658",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030857223",
"https://doi.org/10.1038/nrd2658"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/416190a",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025227643",
"https://doi.org/10.1038/416190a"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nprot.2008.211",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039987283",
"https://doi.org/10.1038/nprot.2008.211"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/ncomms7442",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016434672",
"https://doi.org/10.1038/ncomms7442"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/gb-2004-6-1-r6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038886052",
"https://doi.org/10.1186/gb-2004-6-1-r6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-1-62703-239-1_3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033362956",
"https://doi.org/10.1007/978-1-62703-239-1_3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/onc.2015.76",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038375593",
"https://doi.org/10.1038/onc.2015.76"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/cdd.2014.126",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000198075",
"https://doi.org/10.1038/cdd.2014.126"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1186/1471-2164-16-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010608153",
"https://doi.org/10.1186/1471-2164-16-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/sj.cdd.4400600",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000574949",
"https://doi.org/10.1038/sj.cdd.4400600"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11033-013-2630-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018908528",
"https://doi.org/10.1007/s11033-013-2630-3"
],
"type": "CreativeWork"
}
],
"datePublished": "2022-05-02",
"datePublishedReg": "2022-05-02",
"description": "BackgroundReceptor interacting serine/threonine kinase 2 (RIP2), ubiquitous in many tissue/cell types, is the key regulator of immune and inflammatory responses for many diseases, including avian pathogenic E. coli (APEC), which causes a wide variety of localized or systemic infections. However, the molecular mechanisms by which RIP2 drives its transcriptional program to affect immune and inflammatory response upon APEC infection remains poorly understood.ResultsIn this study, RNA-seq and bioinformatics analyses were used to detect gene expression and new direct/indirect RIP2 targets in the treatments of wild type HD11 cells (WT), RIP2 knockdown cells (shRIP2), APEC stimulation cells (APEC), and RIP2 knockdown cells combined with APEC infection (shRIP2\u2009+\u2009APEC). The results revealed that a total of 4691 and 2605 differentially expressed genes (DEGs) were screened in shRIP2\u2009+\u2009APEC vs. APEC and shRIP2 vs. WT, respectively. Functional annotation analysis showed that apoptosis, MAPK, p53, Toll-like receptor, and Nod-like receptor signaling pathways were involved in APEC-induced RIP2 knockdown HD11 cells. By analyzing the enriched pathway and gene networks, we identified that several DEGs, including HSP90AB1, BID, and CASP9 were targeted by RIP2 upon APEC infection.ConclusionAs a whole, this study can not only provide data support for constructing gene networks of RIP2 knockdown with APEC challenge but also provide new ideas for improving the immune and inflammatory response.",
"genre": "article",
"id": "sg:pub.10.1186/s12864-022-08595-5",
"inLanguage": "en",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.8908865",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1023790",
"issn": [
"1471-2164"
],
"name": "BMC Genomics",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "23"
}
],
"keywords": [
"avian pathogenic E. coli",
"gene networks",
"APEC infection",
"knockdown cells",
"tissue/cell types",
"serine/threonine kinase 2",
"HD11 cells",
"functional annotation analysis",
"gene expression profiling",
"inflammatory response",
"transcriptional programs",
"annotation analysis",
"NOD-like receptors",
"RNA-seq",
"enriched pathways",
"bioinformatics analysis",
"expression profiling",
"key regulator",
"gene expression",
"RIP2 knockdown",
"kinase 2",
"molecular mechanisms",
"cell types",
"pathogenic E. coli",
"E. coli",
"Toll-like receptors",
"pathway",
"RIP2",
"APEC challenge",
"cells",
"systemic infection",
"infection",
"potential pathways",
"receptors",
"genes",
"knockdown",
"MAPK",
"regulator",
"HSP90AB1",
"CASP9",
"HD11",
"coli",
"apoptosis",
"stimulation cells",
"profiling",
"wide variety",
"p53",
"deg",
"expression",
"response",
"data support",
"BackgroundReceptor",
"disease",
"ResultsIn",
"target",
"ConclusionAs",
"treatment",
"total",
"mechanism",
"study",
"vs.",
"analysis",
"variety",
"types",
"program",
"network",
"support",
"bid",
"results",
"challenges",
"whole",
"new ideas",
"idea"
],
"name": "Gene expression profiling of RIP2-knockdown in HD11 macrophages \u2014 elucidation of potential pathways (gene network) when challenged with avian pathogenic E.coli (APEC)",
"pagination": "341",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1147539742"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1186/s12864-022-08595-5"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"35501708"
]
}
],
"sameAs": [
"https://doi.org/10.1186/s12864-022-08595-5",
"https://app.dimensions.ai/details/publication/pub.1147539742"
],
"sdDataset": "articles",
"sdDatePublished": "2022-06-01T22:25",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_926.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1186/s12864-022-08595-5"
}
]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.10.1186/s12864-022-08595-5'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s12864-022-08595-5'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12864-022-08595-5'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12864-022-08595-5'
This table displays all metadata directly associated to this object as RDF triples.
268 TRIPLES
22 PREDICATES
121 URIs
97 LITERALS
13 BLANK NODES