Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P05109 (S100A8)
 1,212 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

F344 rats chronically infected with Ureaplasma parvum develop two distinct profiles: asymptomatic urinary tract infection (UTI) and UTI complicated by struvite urolithiasis. To identify factors that affect disease outcome, we characterized the temporal host immune response during infection by histopathologic analysis and in situ localization of U. parvum. We also used differential quantitative proteomics to identify distinguishing host cellular responses associated with complicated UTI. In animals in which microbial colonization was limited to the mucosal surface, inflammation was indistinguishable from that which occurred in sham-inoculated controls, and the inflammation resolved by 72 h postinoculation (p.i.) in both groups. However, inflammation persisted in animals with microbial colonization that extended into the deeper layers of the submucosa. Proteome profiling showed that bladder tissues from animals with complicated UTIs had significant increases (P < 0.01) in proteins involved in apoptosis, oxidative stress, and inflammation. Animals with complicated UTIs (2 weeks p.i.) had the highest concentrations of the proinflammatory protein S100A8 (P <or= 0.005) in bladder tissues, and the levels of S100A8 positively correlated with those of proinflammatory cytokines GRO/KC (P <or= 0.003) and interleukin-1 alpha (P <or= 0.03) in urine. The bladder uroepithelium was a prominent cell source of S100A8-S100A9 in animals with complicated UTIs (2 weeks p.i.), which was not detected in animals with asymptomatic UTIs (2 weeks p.i.) or in any bladder tissues harvested at earlier p.i. time points. Based on these results, we surmise that invasive colonization of the bladder triggers chronic inflammation and immune dysregulation, which may be critical to struvite formation.
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PMID:Complicated urinary tract infection is associated with uroepithelial expression of proinflammatory protein S100A8. 1966 50

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.
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PMID:Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle. 2947 Apr 89