Deep-sequencing reveals broad subtype-specific HCV resistance mutations associated with treatment failure

A percentage of hepatitis C virus (HCV)-infected patients fail direct acting antiviral (DAA)-based treatment regimens, often because of drug resistance-associated substitutions (RAS). The aim of this study was to characterize the resistance profile of a large cohort of patients failing DAA-based treatments, and investigate the relationship between HCV subtype and failure, as an aid to optimizing management of these patients. A new, standardized HCV-RAS testing protocol based on deep sequencing was designed and applied to 220 previously subtyped samples from patients failing DAA treatment, collected in 39 Spanish hospitals. The majority had received DAA-based interferon (IFN) a-free regimens; 79% had failed sofosbuvir-containing therapy. Genomic regions encoding the nonstructural protein (NS) 3, NS5A, and NS5B (DAA target regions) were analyzed using subtype-specific primers. Viral subtype distribution was as follows: genotype (G) 1, 62.7%; G3a, 21.4%; G4d, 12.3%; G2, 1.8%; and mixed infections 1.8%. Overall, 88.6% of patients carried at least 1 RAS, and 19% carried RAS at frequencies below 20% in the mutant spectrum. There were no differences in RAS selection between treatments with and without ribavirin. Regardless of the treatment received, each HCV subtype showed specific types of RAS. Of note, no RAS were detected in the target proteins of 18.6% of patients failing treatment, and 30.4% of patients had RAS in proteins that were not targets of the inhibitors they received. HCV patients failing DAA therapy showed a high diversity of RAS. Ribavirin use did not influence the type or number of RAS at failure. The subtype-specific pattern of RAS emergence underscores the importance of accurate HCV subtyping. The frequency of “extra-target” RAS suggests the need for RAS screening in all three DAA target regions

8-Aminomethyl-7-hydroxy-4-methylcoumarins as Multitarget Leads for Alzheimer's Disease

This work is part of our ongoing research in the discovery of multitarget therapeutic agents for Alzheimer's disease (AD). A literature screening, based on our recently proposed pharmacophore, led to the identification of 8-aminomethyl-7-hydroxy-4-methyl coumarins as potential multitarget leads for AD. The results of a computer-assisted protocol developed by us to validate multitarget hits for AD indicated that our coumarin candidates were viable leads only for AChE inhibition as later validated by biological assays. The results of BChE binding and propidium displacement assays indicate that our first generation compounds bind to the PAS site in AChE. We designed new generations of coumarin derivatives with a longer substituent at position 8 aimed at leads with more efficient interaction at the catalytic anionic site (CAS). Inhibition data and docking simulations indicated that an anilino-capping group reached the CAS region of AChE and determined also a higher inhibitory potency towards BChE. The best compound obtained, with a N-benzylpiperidine fragment, displayed sub-micromolar affinity for AChE, affinity for BChE, and precluded Abamyloid aggregation with a potency similar to that of 9,10-anthraquinone, making it a multitarget lead viable for further improvement

Qualitative modeling, analysis, and control of synthetic regulatory circuits

International audienceQualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives