Background/Aims: Mycobacterium tuberculosis is an extremely successful intracellular pathogen armed with multiple tactics to subvert host immunity. PPE (Pro-Pro-Glu) family exclusively distributed in mycobacteria might be responsible for the virulence and pathogenicity of M.tuberculosis. The up-regulation of Rv1808 (PPE32) in many conditions prompted us to define its role in host innate immune response. Methods: The Rv1808 encoding gene was expressed in nonpathogenic fast growing Mycobacterium smegmatis, mycobacteria- Escherichia coli shuttle plasmid pNITmyc served as control. RT-PCR and ELISA were used to detect the transcription and translation of host cytokines in culture supernatant from macrophage incubated with purified Rv1808 protein. Pharmacological inhibitors were applied to confirm the specificity of the effector interfering of host signaling. Results: Recombinant Ms_Rv1808 survived better than Ms_pNITmyc within macrophage, accompanied by slightly higher host cell death. Rv1808 protein is associated with the cell wall and exposed on the cell surface. Physical binding of Rv1808 to TLR2 resulted in increase in the secretion of anti-inflammatory cytokine interleukin-10 (IL-10) and pro-inflammatory cytokines tumor necrosis factor (TNF-a) and interleukin-6 (IL-6) possibly via co-activation of NF-κB and MAPK (p38MAPK, JNK and ERK) signalling. Conclusion: Cell wall associated Rv1808 protein manipulated the host cytokines via MAPK and NF-κB signaling pathways.

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Background: The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.

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