Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.24.35 (matrix metalloproteinase 9)
 2,207 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Streptococcus suis type 2 (SS2) is an important swine pathogen and zoonosis agent. A/J mice are significantly more susceptible than C57BL/6 (B6) mice to SS2 infection, but the genetic basis is largely unknown. Here, alterations in gene expression in SS2 (strain HA9801)-infected mice were identified using Illumina mouse BeadChips. Microarray analysis revealed 3,692 genes differentially expressed in peritoneal macrophages between A/J and B6 mice due to SS2 infection. Between SS2-infected A/J and control A/J mice, 2646 genes were differentially expressed (1469 upregulated; 1177 downregulated). Between SS2-infected B6 and control B6 mice, 1449 genes were differentially expressed (778 upregulated; 671 downregulated). These genes were analyzed for significant Gene Ontology (GO) categories and signaling pathways using the Kyoto Encylopedia of Genes and Genomes (KEGG) database to generate a signaling network. Upregulated genes in A/J and B6 mice were related to response to bacteria, immune response, positive regulation of B cell receptor signaling pathway, type I interferon biosynthesis, defense and inflammatory responses. Additionally, upregulated genes in SS2-infected B6 mice were involved in antigen processing and presentation of exogenous peptides, peptide antigen stabilization, lymphocyte differentiation regulation, positive regulation of monocyte differentiation, antigen receptor-mediated signaling pathway and positive regulation of phagocytosis. Downregulated genes in SS2-infected B6 mice played roles in glycolysis, carbohydrate metabolic process, amino acid metabolism, behavior and muscle regulation. Microarray results were verified by quantitative real-time PCR (qRT-PCR) of 14 representative deregulated genes. Four genes differentially expressed between SS2-infected A/J and B6 mice, toll-like receptor 2 (Tlr2), tumor necrosis factor (Tnf), matrix metalloproteinase 9 (Mmp9) and pentraxin 3 (Ptx3), were previously implicated in the response to S. suis infection. This study identified candidate genes that may influence susceptibility or resistance to SS2 infection in A/J and B6 mice, providing further validation of these models and contributing to understanding of S. suis pathogenic mechanisms.
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PMID:Identification of candidate susceptibility and resistance genes of mice infected with Streptococcus suis type 2. 2238 61

In the event of a radiological or nuclear attack, advanced clinical countermeasures are needed for screening and medical management of the exposed population. In such a scenario, minimally invasive biomarkers that can accurately quantify radiation exposure would be useful for triage management by first responders. In this murine study, we evaluated the efficacy of a novel combination of radiation responsive proteins, Flt3 ligand (FL), serum amyloid A (SAA), matrix metalloproteinase 9 (MMP9), fibrinogen beta (FGB) and pentraxin 3 (PTX3) to predict the received dose after whole- or partial-body irradiation. Ten-week-old female C57BL6 mice received a single whole-body or partial-body dose of 18 Gy from a Pantak X-ray source at a dose rate of 2.28 Gy/min. Plasma was collected by cardiac puncture at 24, 48, 72 h and 1 week postirradiation. Plasma protein levels were determined via commercially available ELISA assay. A multivariate discriminant analysis was utilized to generate best-fit dose prediction models for whole-body exposures using the selected biomarker panel and its potential application to partial-body exposures was examined. The combination of values from FL, SAA, MMP9, FGB and PTX3 between 24 h and 1 week postirradiation yielded novel dose-response relationships. For day 1 postirradiation, the best-fit model yielded a predictive accuracy of 81% utilizing FL alone. The use of additional proteins did not enhance the model accuracy whereas, at day 2 postirradiation, the addition of PTX3 and FGB to FL increased the accuracy to 100%. At day 3 the use of FL and PTX3 yielded a predictive accuracy of 93% and at day 7 use of FL and SAA had an accuracy of 90%. Dose prediction of partial-body exposures based on the TBI model had a higher predictive accuracy when the percentage of the body exposed to radiation increased. Our findings indicate that this novel combination of radiation responsive biomarker proteins are an efficient method for predicting radiation exposure and are more accurate when used in concert compared to using any single biomarker protein alone.
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PMID:Multivariate Analysis of Radiation Responsive Proteins to Predict Radiation Exposure in Total-Body Irradiation and Partial-Body Irradiation Models. 2811 15

In the event of a radiological or nuclear attack, advanced clinical countermeasures are needed for screening and medical management of the exposed population. Such a population will represent diverse heterogeneity in physiological response to radiation exposure. The current study seeks to compare the expression levels of five previously established proteomic biodosimetry biomarkers of radiation exposure, i.e., Flt3 ligand (FL), matrix metalloproteinase 9 (MMP9), serum amyloid A (SAA), pentraxin 3 (PTX3) and fibrinogen (FGB), across multiple murine strains and to test a multivariate dose prediction model based on a single C57BL6 strain against other murine strains. Female mice from five different murine strains (C57BL6, BALB/c, C3H/HeJ, CD2F1 and outbred CD-1 mice) received a single whole-body dose of 1-8 Gy from a Pantak X-ray source at a dose rate of 3.59 Gy/min. Plasma was collected by cardiac puncture at days 1, 2, 3 and 7 postirradiation. Plasma protein levels were determined via commercially available ELISA assay. Significant differences were found between radiation-induced expression levels of FL, MMP9, SAA, PTX3 and FGB among the C57BL6, BALB/c, C3H/HeJ, CD2F1 and CD-1 strains (P < 0.05). The overall trends of dose-dependent biomarker elevation, however, were similar between strains, with FL and PTX3 showing the highest degree of correlation. Application of a previous C57BL6 multivariate dose prediction model using additional murine strains showed the limitations of a model based on a single strain and the need for data normalization for variance generated by technical assay variables. Our findings indicate that strain specific differences do exist between expression levels of FL, MMP9, SAA, PTX3 and FGB in C57BL6, BALB/c, C3H/HeJ, CD2F1 and CD-1 murine strains and that use of multiple biomarkers for dose prediction strengthens the predictive accuracy of a model when challenged with a heterogeneous population.
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PMID:Comparison of Proteomic Biodosimetry Biomarkers Across Five Different Murine Strains. 3161 22