New tools for detecting latent tuberculosis infection: evaluation of RD1-specific long-term response

<p>Abstract</p> <p>Background</p> <p>Interferon-gamma (IFN-γ) release assays (IGRAs) were designed to detect latent tuberculosis infection (LTBI). However, discrepancies were found between the tuberculin skin test (TST) and IGRAs results that cannot be attributed to prior Bacille Calmètte Guerin vaccinations. The aim of this study was to evaluate tools for improving LTBI diagnosis by analyzing the IFN-γ response to RD1 proteins in prolonged (long-term response) whole blood tests in those subjects resulting negative to assays such as QuantiFERON-TB Gold In tube (QFT-IT).</p> <p>Methods</p> <p>The study population included 106 healthy TST⁺individuals with suspected LTBI (recent contact of smear-positive TB and homeless) consecutively enrolled. As controls, 13 healthy subjects unexposed to <it>M. tuberculosis </it>(TST^-, QFT-IT^-) and 29 subjects with cured pulmonary TB were enrolled. IFN-γ whole blood response to RD1 proteins and QFT-IT were evaluated at day 1 post-culture. A prolonged test evaluating long-term IFN-γ response (7-day) to RD1 proteins in diluted whole blood was performed.</p> <p>Results</p> <p>Among the enrolled TST⁺subjects with suspected LTBI, 70/106 (66.0%) responded to QFT-IT and 64/106 (60.3%) to RD1 proteins at day 1. To evaluate whether a prolonged test could improve the detection of LTBI, we set up the test using cured TB patients (with a microbiologically diagnosed past pulmonary disease) who resulted QFT-IT-negative and healthy controls as comparator groups. Using this assay, a statistically significant difference was found between IFN-γ levels in cured TB patients compared to healthy controls (p < 0.006). Based on these data, we constructed a receiver operating characteristic (ROC) curve and we calculated a cut-off. Based on the cut-off value, we found that among the 36 enrolled TST+ subjects with suspected LTBI not responding to QFT-IT, a long term response to RD1 proteins was detected in 11 subjects (30.6%).</p> <p>Conclusion</p> <p>These results indicate that IFN-γ long-term response to <it>M. tuberculosis </it>RD1 antigens may be used to detect past infection with <it>M. tuberculosis </it>and may help to identify additional individuals with LTBI who resulted negative in the short-term tests. These data may provide useful information for improving immunodiagnostic tests for tuberculosis infection, especially in individuals at high risk for active TB.</p

Conf-VLKA: A structure-based revisitation of the Virtual Lock-and-key Approach

In a previous work, we developed the in house Virtual Lock-and-Key Approach (VLKA) in order to evaluate target assignment starting from molecular descriptors calculated on known inhibitors used as an information source. This protocol was able to predict the correct biological target for the whole dataset with a good degree of reliability (80%), and proved experimentally, which was useful for the target fishing of unknown compounds. In this paper, we tried to remodel the previous in house developed VLKA in a more sophisticated one in order to evaluate the influence of 3D conformation of ligands on the accuracy of the prediction. We applied the same previous algorithm of scoring and ranking but, this time, combining it with a structure-based approach as docking. For this reason, we retrieved from the RCSB Protein Data Bank (PDB), the available 3D structures of the biological targets included into the previous work, and we used them to calculate poses of the 7352 dataset compounds in the VLKA biological targets. First, docking protocol has been used to retrieve docking scores, then, from the docked poses of each molecule, 3D-descriptors were calculated (Conf-VLKA), While the use of the simple docking scores proved to be inadequate to improve compounds classification, the Conf-VLKA showed some interesting variations compared to the original VLKA, especially for targets whose ligands present a high number of rotamers. This work represent a first preliminary study to be completed using other techniques such as induced fit docking or molecular dynamics structure clustering to take into account the protein side chains adaptation to ligands structures

Zinc complexes as fluorescent chemosensors for nucleic acids: New perspectives for a "boring" element

Zinc(ii) complexes are effective and selective nucleic acid-binders and strongly fluorescent molecules in the low energy range, from the visible to the near infrared. These two properties have often been exploited to quantitatively detect nucleic acids in biological samples, in both in vitro and in vivo models. In particular, the fluorescent emission of several zinc(ii) complexes is drastically enhanced or quenched by the binding to nucleic acids and/or upon visible light exposure, in a different fashion in bulk solution and when bound to DNA. The twofold objective of this perspective is (1) to review recent utilisations of zinc(ii) complexes as selective fluorescent probes for nucleic acids and (2) to highlight their novel potential applications as diagnostic tools based on their photophysical properties. This journal i

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening