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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]

Botulinum neurotoxins are the most potent protein toxins known to human. To date, seven subtypes of the BoNT/F serotype (BoNT/F1 to BoNT/F7) have been identified, among which BoNT/F5 and BoNT/F7 are the most divergent. However, little structural and functional information is available for these two subtypes due to a lack of suitable recombinant proteins for biochemical characterization, except that they appear to possess unique substrate recognition mechanisms, thereby impeding development of vaccine or inhibitors against these proteins. In the present study, we utilized a combinatorial approach which involved examining the effects of different affinity tags, mapping C-terminal truncation mutants and optimization of expression and purification conditions, that allowed us to successfully express and purify soluble and highly active recombinant LC/F5 and LC/F7 proteins. GST-LC/F5(1–450) and 6× His-LC/F5(1–405) were the formats which exhibit the highest level of solubility and activity, whereas GST-LC/F7(1–405) was the most active form of LC/F7. In comparison, GST-LC/F5(1–450) was more active than GST-LC/F7(1–405), which was in turn more active than the LC/F1 control. Our data suggest that solubility of these proteins strongly correlated with their catalytic activity. Successful expression and purification of LC/F5 and LC/F7 in this work will, for the first time, provide materials for further characterization of these two subtypes of BoNT/F, which is essential for future development of protective vaccine or other therapeutic strategies, as well as BoNT/F protein engineering. [ABSTRACT FROM AUTHOR]

The Rv0045c protein is predicted to be an esterase that is involved in lipid metabolism in Mycobacterium tuberculosis. The protein was overproduced in Escherichia coli, purified and crystallized using the hanging-drop vapour-diffusion method. The Rv0045c protein crystals diffracted to a resolution of 2.7 Å using a synchrotron-radiation source and belonged to space group P31 or P32, with unit-cell parameters a = b = 73.465, c = 48.064 Å, α = β = 90, γ = 120°. Purified SeMet-labelled Rv0045c protein was also crystallized and formed crystals that diffracted to a resolution of 3.0 Å using an in-house X-ray radiation source. [ABSTRACT FROM AUTHOR]

Cecropin B is a 37-residue cationic antimicrobial peptide derived from the haemolymph of Bombyx mori. The precise mechanism by which cecropins exert their antimicrobial and cytolytic activities is not well understood. Crystals of cecropin B were obtained by the hanging-drop vapour-diffusion method using polyethylene glycol as a precipitant at 289 K. The crystal diffracted to 1.43 Å resolution using X-ray radiation and belonged to the orthorhombic space group P1, with unit-cell parameters a = 15.08, b = 22.75, c = 30.20 Å, α = 96.9, β = 103.1, γ = 96.5°. The asymmetric unit contained only one molecule of cecropin B, with a calculated Matthews coefficient of 2.48 Å3 Da−1 and a solvent content of 50.4%. [ABSTRACT FROM AUTHOR]

MaoC-like dehydratase (MaoC) plays an important role in supplying ( R)-3-hydroxyacyl-CoA from the fatty-acid oxidation pathway to polyhydroxyalkanoate (PHA) biosynthetic pathways. PHAs have been attracting much attention as they can be used in the biosynthesis of synthetic plastics. Crystals of MaoC from Phytophora capsici were grown by the hanging-drop vapour-diffusion method at 289 K in a number of screening conditions. An MaoC crystal diffracted to 1.93 Å resolution using X-ray radiation and belonged to the orthorhombic space group P212121, with unit-cell parameters a = 81.458, b = 82.614, c = 124.228 Å, α = β = γ = 90°. [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.)