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  1. Free, publicly-accessible full text available March 1, 2025
  2. Abstract

    Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

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  3. Despite the enormous developments in asymmetric catalysis, the basis for asymmetric induction is largely limited to the spatial interaction between the substrate and catalyst. Consequently, asymmetric discrimination between two sterically similar groups remains a challenge. This is particularly formidable for enantiodifferentiation between two aryl groups without a directing group or electronic manipulation. Here we address this challenge by using a robust organocatalytic system leading to excellent enantioselection between aryl and heteroaryl groups. With versatile 2-indole imine methide as the platform, an excellent combination of a superb chiral phosphoric acid and the optimal hydride source provided efficient access to a range of highly enantioenriched indole-containing triarylmethanes. Control experiments and kinetic studies provided important insights into the mechanism. DFT calculations also indicated that while hydrogen bonding is important for activation, the key interaction for discrimination of the two aryl groups is mainly π–π stacking. Preliminary biological studies also demonstrated the great potential of these triarylmethanes for anticancer and antiviral drug development. 
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