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Objective:To investigate the proteins interacting with Mycobacterium tuberculosis Rv1705c in human body. Methods:Rv1705c was prokaryotically expressed and inclusion bodies were collected for further lysis and the purification of Rv1705c. ELISA assay was used to detect the secretion of IFN-γ after stimulating macrophages with Rv1705c protein. Purified and biotin-labeled Rv1705c sample was incubated on the HuProt? human proteome microarray to screen the interacting proteins. GenePix Pro 6.0 software was used to extract all features of the data obtained from the scanned images and further analysis was performed based on bioinformatics databases such as GO and KEGG. GST pull-down was performed to verify the interaction of Rv1705c with PSMA3 and RSAD2.Results:The purification results showed that Rv1705c was expressed in endosomes. The secretion of IFN-γ increased significantly after stimulating macrophages with Rv1705c. A total of 29 potential Rv1705c-interacting proteins were screened, and nine of them showed signal-to-noise ratio (SNR)>1.6, namely PSMA3, NLN, THOP1, UPF3A, RSAD2, OMG, PNKD, STEAP3 and MED8. Further bioinformatics analysis revealed that PSMA3, RSAD2 and C1QBP were involved in innate immune signaling pathway, and there were interactions of PSMA3 and RSAD2 with IFN. GST pull-down assay validated that PSMA3 and RSAD2 interacted with Rv1705c.Conclusions:This study showed that PSMA3 and RSAD2 interacted with Rv1705c, providing reference for further investigation on the mechanism of Mycobacterium tuberculosis infection.
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<p><b>OBJECTIVE</b>To identify potential serum biomarkers for distinguishing between latent tuberculosis infection (LTBI) and active tuberculosis (TB).</p><p><b>METHODS</b>A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability.</p><p><b>RESULTS</b>Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability.</p><p><b>CONCLUSION</b>Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.</p>
Subject(s)
Adolescent , Adult , Aged , Female , Humans , Male , Middle Aged , Young Adult , Antibodies, Bacterial , Antibody Specificity , Antigens, Bacterial , Biomarkers , Blood , Latent Tuberculosis , Blood , Diagnosis , Logistic Models , Mycobacterium tuberculosis , Protein Array Analysis , Methods , Proteome , Genetics , Proteomics , Methods , ROC CurveABSTRACT
Objective · To globally study the influence of arsenite to the various biological pathways of Escherichia coli as a model organism.Methods · The protein-arsenite interactions was globally studied based on a proteome microarray constructed by 4256 affinity-purified Escherichia coli proteins. The functions of interacting proteins and their network were then analyzed by bioinformatics. Results · 91 proteins that remarkably interact with arsenic were successfully identified. Bioinformatics analysis found that most of the proteins possess catalytic activityand are involved in various biosynthesis and cellular metabolism pathways. The interactions of arsenic with proteins encoded by malY, cfa and hypF genes were further validated by Western blotting, which proves the results of proteome microarray reliable. Conclusion · Arsenite interacts with a variety of enzymes ofEscherichia coli and can greatly affect its biological metabolism.