Identification of small-molecule inhibitors of the XendoU endoribonucleases family

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Regulation of Nucleotide Metabolism and Germline Proliferation in Response to Nucleotide Imbalance and Genotoxic Stresses by EndoU Nuclease

Nucleotide deprivation and imbalance present detrimental conditions for animals and are thus expected to trigger cellular responses that direct protective changes in metabolic, developmental, and behavioral programs, albeit such mechanisms are vastly underexplored. Following our previous finding that Caenorhabditis elegans shut down germ cell proliferation in response to pyrimidine deprivation, we find in this study that endonuclease ENDU-2 regulates nucleotide metabolism and germ cell proliferation in response to nucleotide imbalance and other genotoxic stress, and that it affects mitotic chromosomal segregation in the intestine and lifespan. ENDU-2 expression is induced by nucleotide imbalance and genotoxic stress, and ENDU-2 exerts its function in the intestine, mostly by inhibiting the phosphorylation of CTPS-1 through repressing the PKA pathway and histone deacetylase HDA-1. Human EndoU also affects the response to genotoxic drugs. Our work reveals an unknown role of ENDU-2 in regulating nucleotide metabolism and animals' response to genotoxic stress, which may link EndoU function to cancer treatment.

The anti-STAT1 polyphenol myricetin inhibits M1 microglia activation and counteracts neuronal death

Microglia activation toward M1 pro-inflammatory phenotype represents one of the earliest events of neurological disorders. Therefore, reducing microglia activation should inhibit neuroinflammation, thereby delaying the progression of neurodegeneration. Recently, we pointed out the role of STAT1 signaling in hypoxia-induced M1 activation and proposed STAT1 as a suitable molecular target for the prevention and treatment of neurodegeneration. Myricetin (MYR) is a natural flavonoid that exhibits a specific anti-STAT1 activity correlated with its direct interaction with STAT1 protein itself. Herein, we investigated the anti-inflammatory effect of MYR and its ability to protect neurons from death in an in vitro model of neurotoxicity using the neuroblast-like SH-SY5Y cells that were exposed to conditioned media from hypoxia-activated microglia BV2 cells. We demonstrate that MYR pretreatment is able to switch off hypoxia-induced M1 microglia polarization through the inhibition of STAT1 signaling. The analysis of the molecular mechanism suggests that the direct interaction of MYR with STAT1 impairs its S-glutathionylation and phosphorylation. Moreover, treatment of SH-SY5Y cells with conditioned medium from hypoxia-activated microglia pretreated with MYR produced a significant reduction in neuronal viability. Our data indicate that MYR may represent a promising candidate for prevention and treatment of neuroinflammation in neurodegenerative disorders.

Ligand-based virtual screening and inductive learning for identification of SIRT1 inhibitors in natural products

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide-dependent deacetylase, and its dysregulation can lead to ageing, diabetes, and cancer. From 346 experimentally confirmed SIRT1 inhibitors, an inhibitor structure pattern was generated by inductive logic programming (ILP) with DMax Chemistry Assistant software. The pattern contained amide, amine, and hetero-aromatic five-membered rings, each of which had a hetero-atom and an unsubstituted atom at a distance of 2. According to this pattern, a ligand-based virtual screening of 1 444 880 active compounds from Chinese herbs identified 12 compounds as inhibitors of SIRT1. Three compounds (ZINC08790006, ZINC08792229, and ZINC08792355) had high affinity (-7.3, -7.8, and -8.6 kcal/mol, respectively) for SIRT1 as estimated by molecular docking software AutoDock Vina. This study demonstrated a use of ILP and background knowledge in machine learning to facilitate virtual screening.

ER morphology: sculpting with XendoU

Endoplasmic reticulum (ER) sheet membranes are covered with ribosomes and RNAs that are involved in protein synthesis. A new study reveals that a calcium-activated endoribonuclease of the EndoU protein family promotes the formation of tubular ER networks, contributing to dynamic shaping of the ER in cells.

Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application

Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.