Biology subjects

Hessler, N.

Publications and source records attributed to Hessler, N..

3 recordsLinked to original sources

bioRxiv2024

Jasmonic acid signalling is targeted by a smut fungal Tin2-fold effector

In plants, jasmonate signaling is a hub integrating environmental cues with growth and development. Due to its role in balancing defense responses against pathogens, it is a target of effector proteins from various pathogens. Here, we characterized the fungal effector protein Tue1 from the Brassicaceae smut fungus Thecaphora thlaspeos. T. thlaspeos naturally infects Arabis hirsuta but can also colonize the non-host Arabidopsis thaliana. In planta, the fungal protein Tue1 hijacks the plant importin- dependent nuclear transporter to reach the plant nucleus. It interacts with jasmonate ZIM domain 10 (JAZ10) proteins of both A. thaliana and Ar. hirsuta. Structure-guided analysis of Tue1 suggests that it binds the Jas motif of JAZ10 indicating a role in stabilization or binding competition with proteins like MYC3 and COI1. A subset of jasmonate-responsive genes is differentially regulated during T. thlaspeos infection, proposing a link of the Tue1 function to infection. Tue1 share structural similarity to the Tin2-fold family recently described in the corn smut Ustilago maydis. Our study therefore suggests that this structural effector family is expanded across fungal pathogens, although future studies have to reveal whether targeting JAZ-repressors is a conserved mechanism or specifically acquired as an adaptation to its perennial host.

biochemistry↗

bioRxiv2023

Engineering and implementation of synthetic molecular tools in the basidiomycete fungus Ustilago maydis

The basidiomycete Ustilago maydis is a well-characterized model organism for studying pathogen-host interactions and of great interest for a broad spectrum of biotechnological applications. To facilitate research and enable applications, in this study, three luminescence-based and one enzymatic quantitative reporter were implemented and characterized. We generated dual-reporter constructs for ratiometric normalization that can be used as a fast-screening platform for reporter gene expression, applicable to in vitro and in vivo detection. Furthermore, we constructed and implemented synthetic bi-directional promoters, that enable bicisitronic expression for gene expression studies and engineering strategies. These non-invasive, quantitative reporters and expression tools will widen significantly the application range of biotechnology in U. maydis and enable in planta detection of fungal infection.

microbiology↗

bioRxiv2018

Evaluating predictive biomarkers for a binary outcome with linear versus logistic regression - Practical recommendations for the choice of the model

A predictive biomarker can forecast whether a patient benefits from a specific treatment under study. To establish predictiveness of a biomarker, a statistical interaction between the biomarker status and the treatment group concerning the clinical outcome needs to be shown. In clinical trials looking at a binary outcome, linear or logistic regression models may be used to evaluate the interaction, but the effects in the two models are different and differently interpreted. Specifically, the effects are estimated as absolute risk reductions (ARRs) and odds ratios (ORs) in the linear and logistic model, thus measuring the effect on an additive and multiplicative scale, respectively.\n\nWe derived the relationship between the effects of the linear and the logistic regression model allowing for translations between the effect estimates between both models. In addition, we performed a comprehensive simulation study to compare the power of the two models under a variety of scenarios in different study designs. In general, the differences in power to detect interaction were minor, and visible differences were detected in rather unrealistic scenarios of effect size combinations and were usually in favor of the logistic model.\n\nBased on our results and theoretical considerations, we recommend to 1) estimate logistic regression models because of their statistical properties, 2) test for interaction effects and 3) calculate and report both ARRs and ORs from these using the formulae provided.

epidemiology↗