Hecht, C. | Search bioRxiv

bioRxiv2021

Loss of function of bHLH transcription factor Nrd1 in tomato induces an arabinogalactan protein-encoding gene and enhances resistance to Pseudomonas syringae pv. tomato

Basic helix-loop-helix (bHLH) transcription factors constitute a superfamily in eukaryotes but their roles in plant immunity remain largely uncharacterized. We found that the transcript abundance in tomato leaves of one bHLH transcription factor-encoding gene, Nrd1 (negative regulator of resistance to DC3000 1), was significantly increased after treatment with the immunity-inducing flgII-28 peptide. Plants carrying a loss-of-function mutation in Nrd1 ({Lambda}nrd1) showed enhanced resistance to Pseudomonas syringae pv. tomato (Pst) DC3000 although early pattern-triggered immunity responses such as generation of reactive oxygen species and activation of mitogen-activated protein kinases after treatment with flagellin-derived flg22 and flgII-28 peptides were unaltered compared to wild-type plants. An RNA-Seq analysis identified a gene, Agp1, whose expression is strongly suppressed in an Nrd1-dependent manner. Agp1 encodes an arabinogalactan protein and overexpression of the Agp1 gene in Nicotiana benthamiana led to [~]10-fold less Pst growth compared to the control. These results suggest that the Nrd1 protein promotes tomato susceptibility to Pst by suppressing the defense gene Agp1. RNA-Seq also revealed that loss of Nrd1 function has no effect on the transcript abundance of immunity-associated genes including Bti9, Core, Fls2, Fls3 and Wak1 upon Pst inoculation, suggesting that the enhanced immunity observed in the {Delta}nrd1 mutants is due to the activation of key PRR signaling components as well as loss of Nrd1-regulated suppression of Agp1.

plant biology↗

bioRxiv2021

Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology. Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

pharmacology and toxicology↗