Monocyte-dependent oncostatin M and TNF-alpha synergize to stimulate unopposed matrix metalloproteinase-1/3 secretion from human lung fibroblasts in tuberculosis.
ABSTRACT Leukocyte-derived matrix metalloproteinases (MMP) are implicated in the tissue destruction characteristic of tuberculosis (TB). The contribution of lung stromal cells to MMP activity in TB is unknown. Oncostatin M (OSM) is an important stimulus to extrapulmonary stromal MMP induction, but its role in regulation of pulmonary MMP secretion or pathophysiology of TB is unknown. We investigated OSM secretion from Mycobacterium tuberculosis (Mtb)-infected human monocytes/macrophages and the networking effects of such OSM on lung fibroblast MMP secretion. Mtb increased monocyte OSM secretion dose dependently in vitro. In vivo tuberculous granulomas immunostained positively for OSM. Further, conditioned media from Mtb-infected monocytes (CoMTb) induced monocyte OSM secretion (670 +/- 55 versus 166 +/- 14 pg/mL in controls), implicating an autocrine loop. Mtb-induced OSM secretion was prostaglandin (PG) sensitive, and required activation of surface G-protein coupled receptors. OSM induction was ERK MAP kinase dependent, p38-requiring but JNK-independent. OSM synergized with TNF-alpha, a key cytokine in TB granuloma formation, to stimulate pulmonary fibroblast MMP-1/-3 secretion, while suppressing secretion of tissue inhibitors of metalloproteinases-1/-2. In summary, Mtb infection of monocytes results in PG-dependent OSM secretion, which synergizes with TNF-alpha to drive functionally unopposed fibroblast MMP-1/-3 secretion, demonstrating a previously unrecognized role for OSM in TB.
- SourceAvailable from: Zing Tsung-Yeh Tsai[Show abstract] [Hide abstract]
ABSTRACT: To unravel the cytotoxic effect of the recombinant CFP-10/ESAT-6 protein (rCFES) on WI-38 cells, an integrative analysis approach, combining time-course microarray data and annotated pathway databases, was proposed with the emphasis on identifying the potentially crucial pathways. The potentially crucial pathways were selected based on a composite criterion characterizing the average significance and topological properties of important genes. The analysis results suggested that the regulatory effect of rCFES was at least involved in cell proliferation, cell motility, cell survival, and metabolisms of WI-38 cells. The survivability of WI-38 cells, in particular, was significantly decreased to 62% with 12.5 microM rCFES. Furthermore, the focal adhesion pathway was identified as the potentially most-crucial pathway and 58 of 65 important genes in this pathway were downregulated by rCFES treatment. Using qRT-PCR, we have confirmed the changes in the expression levels of LAMA4, PIK3R3, BIRC3, and NFKBIA, suggesting that these proteins may play an essential role in the cytotoxic process in the rCFES-treated WI-38 cells.BioMed Research International 02/2009; 2009(1110-7243):917084. DOI:10.1155/2009/917084
Article: Update in tuberculosis 2008.American Journal of Respiratory and Critical Care Medicine 04/2009; 179(5):337-43. DOI:10.1164/rccm.200812-1852UP
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ABSTRACT: In order to identify genes involved in complex diseases, it is crucial to study the genetic interactions at the systems biology level. By utilizing modern high throughput microarray technology, it has become feasible to obtain gene expressions data and turn it into knowledge that explains the regulatory behavior of genes. In this study, an unsupervised nonlinear model was proposed to infer gene regulatory networks on a genome-wide scale. The proposed model consists of two components, a robust correlation estimator and a nonlinear recurrent model. The robust correlation estimator was used to initialize the parameters of the nonlinear recurrent curve-fitting model. Then the initialized model was used to fit the microarray data. The model was used to simulate the underlying nonlinear regulatory mechanisms in biological organisms. The proposed algorithm was applied to infer the regulatory mechanisms of the general network in Saccharomyces cerevisiae and the pulmonary disease pathways in Homo sapiens. The proposed algorithm requires no prior biological knowledge to predict linkages between genes. The prediction results were checked against true positive links obtained from the YEASTRACT database, the TRANSFAC database, and the KEGG database. By checking the results with known interactions, we showed that the proposed algorithm could determine some meaningful pathways, many of which are supported by the existing literature.Bio Systems 07/2009; 98(3):160-75. DOI:10.1016/j.biosystems.2009.05.013