Search

Ferritin, a key regulator of iron homeostasis in macrophages, has been reported to confer host defenses against Mycobacterium tuberculosis (Mtb) infection. Nuclear receptor coactivator 4 (NCOA4) was recently identified as a cargo receptor in ferritin degradation. Here, we show that Mtb infection enhanced NCOA4-mediated ferritin degradation in macrophages, which in turn increased the bioavailability of iron to intracellular Mtb and therefore promoted bacterial growth. Of clinical relevance, the upregulation of FTH1 in macrophages was associated with tuberculosis (TB) disease progression in humans. Mechanistically, Mtb infection enhanced NCOA4-mediated ferritin degradation through p38/AKT1- and TRIM21-mediated proteasomal degradation of HERC2, an E3 ligase of NCOA4. Finally, we confirmed that NCOA4 deficiency in myeloid cells expedites the clearance of Mtb infection in a murine model. Together, our findings revealed a strategy by which Mtb hijacks host ferritin metabolism for its own intracellular survival. Therefore, this represents a potential target for host-directed therapy against tuberculosis., Introduction A variety of mechanisms have evolved in mammalian cells to control intracellular pathogens. For example, preventing intracellular pathogens from accessing iron, an essential nutrient for both host and pathogen, [...]

Background: Perturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker.Methods: We recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model.Results: We observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity.Conclusions: The established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics. [ABSTRACT FROM AUTHOR]

Background: Perturbed iron homeostasis is a risk factor for tuberculosis (TB) progression and an indicator of TB treatment failure and mortality. Few studies have evaluated iron homeostasis as a TB diagnostic biomarker., Methods: We recruited participants with TB, latent TB infection (LTBI), cured TB (RxTB), pneumonia (PN) and healthy controls (HCs). We measured serum levels of three iron biomarkers including serum iron, ferritin and transferrin, then established and validated our prediction model., Results: We observed and verified that the three iron biomarker levels correlated with patient status (TB, HC, LTBI, RxTB or PN) and with the degree of lung damage and bacillary load in patients with TB. We then built a TB prediction model, neural network (NNET), incorporating the data of the three iron biomarkers. The model showed good performance for diagnosis of TB, with 83% (95% CI 77 to 87) sensitivity and 86% (95% CI 83 to 89) specificity in the training data set (n=663) and 70% (95% CI 58 to 79) sensitivity and 92% (95% CI 86 to 96) specificity in the test data set (n=220). The area under the curves (AUCs) of the NNET model to discriminate TB from HC, LTBI, RxTB and PN were all >0.83. Independent validation of the NNET model in a separate cohort (n=967) produced an AUC of 0.88 (95% CI 0.85 to 0.91) with 74% (95% CI 71 to 77) sensitivity and 92% (95% CI 87 to 96) specificity., Conclusions: The established NNET TB prediction model discriminated TB from HC, LTBI, RxTB and PN in a large cohort of patients. This diagnostic assay may augment current TB diagnostics., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To prepare 5-fluorouracil (5-FU) immunogen and develop polyclonal antibodies against 5-FU., Methods: The derivant of 5-FU (5-fluorouracil-1-yl-aceto amino acid, 5-FUAA) was synthesized, and then conjugated with bovine serum albumin (BSA) or ovalbumin (OVA) by carbodiimide (CDI) method. 5-FUAA conjugating BSA (5-FUAA-BSA) was used to immunize BALB/c mice to produce antiserum, and 5-FUAA conjugating OVA (5-FUAA-OVA) was used as coating antigen to detect the titer of the antiserum by indirect ELISA. Furthermore, specificity of the polyclonal antiserum was identified by ELISA and Western blotting., Results: 5-FU derivant 5-FUAA was successfully synthesized and conjugated with BSA or OVA. Indirect ELISA showed that the titer of the antiserum from BALB/c mice immunized with 5-FUAA-BAS reached 1:1 280 000. Moreover, ELISA and Western blotting proved that the anti-serum could combine 5-FU specifically., Conclusion: The experiment has prepared high-specific and high-titer polyclonal antibody against 5-FU.