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Liberman, A. C.

Publications and source records attributed to Liberman, A. C..

2 recordsLinked to original sources

DrosoTracker: a web application with a self-calibrating thermal model for husbandry scheduling and lifespan analysis in Drosophila melanogaster

Planning husbandry tasks and experiments with Drosophila melanogaster requires converting a target date into development times that depend on the rearing temperature. This calculation needs to be done for each cross, genotype, and temperature, and the risk of error grows quickly. Available laboratory management tools let users register stocks, crosses, and track them, but they do not create schedules based on a clear, adjustable thermal model. To fill that gap, we developed DrosoTracker, a self-contained web application that works offline and predicts Drosophila development with a thermal summation model recalibrated through regression on data from Powsner (1935) (T0 = 11.78 {degrees}C, DD = 116.38 {degrees}C{middle dot}days, R{superscript 2} = 0.997). The model offers an optional two-level calibration driven by user observations. A wild-type strain first adjusts the model to the laboratorys own conditions. Then each genotype is calibrated against that reference using a random-effects shrinkage estimator that accounts for measurement error and between-batch variability. The model creates schedules for husbandry tasks, evaluates adult cohort survival with the Kaplan-Meier estimator and the log-rank test, and calculates sample size for lifespan studies using Schoenfelds formula. The quantitative components were checked against independent references, including Rs survival package and manual calculations. Ongoing work is focused on validating the calibrated model using cohorts specifically bred for this purpose. DrosoTracker runs entirely in the browser, stores data locally, and is available in English and Spanish.

developmental biology↗

Network Pharmacology-Guided Discovery of Fungal Autophagy Modulators for Tauopathies: Structural and Proteomic Evidence

Autophagic clearance of hyperphosphorylated tau is impaired in tauopathies, leading to the progressive accumulation of toxic tau species. Fungal metabolites provide a rich yet largely untapped source of neuroactive molecules with therapeutic potential. Here, we investigated metabolites from Lions Mane (Hericium erinaceus), Magic Mushrooms (Psilocybe spp.), and Ergot fungi (Claviceps spp.) using a computational drug-discovery workflow. We characterised their structural diversity, predicted blood-brain barrier permeability and toxicological properties, and integrated network pharmacology with protein-protein interaction and functional enrichment analyses to identify autophagy-related targets. Peroxisome proliferator-activated receptor gamma (PPARG), glycogen synthase kinase-3 beta (GSK3B), and casein kinase 2 alpha 1 (CSNK2A1) emerged as the three most promising candidates, given their complementary roles linking autophagy and tau pathology and their attractiveness as targets for multi-target drug discovery. Their interactions with fungal metabolites were evaluated by molecular docking, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring, molecular dynamics simulations, complemented by machine-learning quantitative structure-activity relationship (QSAR) modelling as an additional, ligand-based line of evidence. Molecular dynamics and MM/GBSA analyses confirmed stable, target-specific binding for Corallocin A and Erinacerin M (PPARG), Chaetopyranin and Ergocryptine (GSK3B), and Hericioic Acid D and Isohericerin (CSNK2A1), alongside Emodin, a reference compound with previously reported activity against all three targets. QSAR predictions were informative primarily for the PPARG candidates, which fell within the models applicability domain; predictions for the remaining candidates fell outside their respective models applicability domains and were therefore not interpretable as evidence for or against their prioritisation. Reanalysis of an independent hippocampal proteomic dataset from Alzheimers disease patients showed CSNK2A1 protein levels to be significantly altered in the CA3 subfield, providing an additional, correlative line of support for this target; PPARG and GSK3B showed no significant changes in protein abundance, which does not preclude their functional involvement given their extensive post-translational regulation. Overall, these findings identify fungal metabolites as promising multi-target autophagy modulator candidates and provide a systematic computational strategy for prioritising them for experimental validation in tauopathies.

pharmacology and toxicology↗