Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

MicroRNAs are amplifiers of monocyte inflammatory networks and repressors of metabolism

ABSTRACT: MicroRNAs are amplifiers of monocyte inflammatory networks and repressors of metabolism Milk and blood isolated CD14+ monocyte cells taken from 5 infected Holstein friesians and 5 control Holstein friesians. Five animal infected with live S. uberis, cells extracted at 0, 12, 24, 36, and 48 hours post infection.

ORGANISM(S): Bos taurus

SUBMITTER: Nathan Lawless

PROVIDER: E-GEOD-51848 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

Lawless Nathan N Reinhardt Timothy A TA Bryan Kenneth K Baker Mike M Pesch Bruce B Zimmerman Duane D Zuelke Kurt K Sonstegard Tad T O'Farrelly Cliona C Lippolis John D JD Lynn David J DJ

G3 (Bethesda, Md.) 20140127 6

Bovine mastitis is an inflammation-driven disease of the bovine mammary gland that costs the global dairy industry several billion dollars per year. Because disease susceptibility is a multifactorial complex phenotype, an integrative biology approach is required to dissect the molecular networks involved. Here, we report such an approach using next-generation sequencing combined with advanced network and pathway biology methods to simultaneously profile mRNA and miRNA expression at multiple time ...[more]

PMID: 24470219

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Project description:Background: Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, most of which are caused by atherosclerosis. Discerning processes that participate in macrophage-to-foam cell formation are critical for understanding the basic mechanisms underlying atherosclerosis. To explore the molecular mechanisms of foam cell formation, the differentially expressed proteins were identified. Methods: In this paper, human monocytes, macrophage colony-stimulating factor induced macrophages, and oxidized low-density lipoprotein induced foam cells were cultured, and tandem mass tag (TMT) labeling combined with mass spectrometry (MS) were performed to find associations between foam cell transformation and proteome profiles. Results: Totally, 5146 quantifiable proteins were identified, among which 1515 and 182 differentially expressed proteins (DEPs) were found in macrophage/monocyte and foam cell/macrophage, respectively, using a cutoff of 1.5-fold change. Subcellular localization analysis revealed that downregulated DEPs of macrophages/monocytes were mostly located in the nucleus and upregulated DEPs of foam cells/macrophages mostly located in the plasma membrane and extracellular. Functional analysis of DEPs demonstrated that cholesterol metabolism related proteins were upregulated in foam cells, whereas the immune response-related proteins were downregulated in foam cells. The protein-interaction network showed that the DEPs with the highest interaction intensity between macrophages and foam cells were mainly concentrated in lysosomes and the endoplasmic reticulum. Conclusions: This study for the first time to perform quantitative proteomic investigation by TMT labeling and LC-MS/MS to identify differentially expressed proteins in human monocyte, macrophage, and foam cell. The results confirmed cholesterol metabolism was upregulated in foam cells, while immune response was suppressed, which suggested that foam cells were not the population that promote inflammation. In addition, KEGG enrichment analysis and protein-protein interaction indicated that the differentially expressed proteins locating in the endoplasmic reticulum and lysosomes may be key targets to regulate foam cell formation. These data provide a basis for identifying the potential proteins associated with the molecular mechanism involved in the transformation of macrophages to foam cells.

Dataset Information

MicroRNAs are amplifiers of monocyte inflammatory networks and repressors of metabolism

Publications

MicroRNA regulation of bovine monocyte inflammatory and metabolic networks in an in vivo infection model.

Similar Datasets

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