Heterogeneous Escape from X Chromosome Inactivation Results in Sex Differences in Type I IFN Responses at the Single Human pDC Level.

Immune responses differ between women and men, and type I interferon (IFN) responses following Toll-like receptor 7 (TLR7) stimulation are higher in women. The precise mechanisms driving these sex differences in immunity are unknown. To investigate possible genetic factors, we quantify escape from X chromosome inactivation (XCI) for TLR7 and four other genes (RPS6KA3, CYBB, BTK, and IL13RA1) at the single plasmacytoid dendritic cell (pDC) level. We observe escape from XCI for all investigated genes, leading to biallelic expression patterns. pDCs with biallelic gene expression have significantly higher mRNA levels of the respective genes. Unstimulated pDCs with biallelic TLR7 expression exhibit significantly higher IFNα/ß mRNA levels, and IFNα exposure results in significantly increased IFNα/ß protein production by pDCs. These results identify unanticipated heterogeneity in escape from XCI of several genes in pDCs and highlight the important contribution of X chromosome factors to sex differences in type I IFN responses, which might explain observed sex differences in human diseases.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches.

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores.

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separated.