Biology subjects

Raphael, M.

Publications and source records attributed to Raphael, M..

3 recordsLinked to original sources

bioRxiv2025

A conserved stripe rust effector elicits variable resistance responses in wheat

Plant resistance (R) and pathogen avirulence (Avr) gene interactions are central to pathogen recognition and disease resistance in crops. Functional characterisation of recognised Avr effectors of Puccinia striiformis f. sp. tritici (Pst) lags other key fungal pathogens of wheat. Here, we used a wheat protoplast-based screen to identify Avr/R interactions via the proxy of effector-induced defence responses in a set of diverse wheat cultivars. We identified an Avr candidate, termed AvrPstB48, that triggers defence responses in 16 out of 24 cultivars tested. AvrPstB48 is hemizygous, and the Pst genome carries four divergent paralogs within a gene cluster. Analysis of these paralogs revealed partial redundancy in their ability to activate wheat defences and enabled us to identify a single amino acid in AvrPstB48 that is necessary but not sufficient for defence activation. Notably, the activation of defence signalling by AvrPstB48 in protoplasts did not directly correlate with disease outcomes. Whole-plant infection assays revealed that some cultivars which exhibited strong defence activation in the protoplast assay are susceptible to the Pst isolate Pst104E137A- from which AvrPstB48 is derived. Comparison of infection dynamics of two wheat cultivars that differ in their AvrPstB48 recognition capacity revealed a delay in disease progression in the recognising cultivar Avocet S compared to the non-recognising cultivar Morocco. While correlative only, our observations, combined with other recent reports, support a recognize-then-suppress model of plant-pathogen interaction where disease outcomes are driven not only by simple Avr/R interactions but also by pathogen effectors that suppress defence signalling downstream of effector recognition.

plant biology↗

bioRxiv2024

A Self-Supervised Learning Approach for High Throughput and High Content Cell Segmentation

In principle, AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large datasets, human input, and computational expertise, along with issues of limited generalizability and the necessity for specialized training are notable drawbacks of nominally "automated" segmentation tools. To overcome this roadblock, we introduce an innovative, user-friendly self-supervised learning method (SSL) for pixel classification that requires no dataset-specific modifications or curated labelled data sets, thus providing a more streamlined cell segmentation approach for high-throughput and high-content research. We demonstrate that our algorithm meets the criteria of being fully automated with versatility across various magnifications, optical modalities and cell types. Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. Our SSL technique displayed consistent F1 scores across segmented images, with scores ranging from 0.831 to 0.876, outperforming the popular Cellpose algorithm, which showed greater variance in F1 scores from 0.645 to 0.8815, mainly due to errors in segmentation. On average, our SSL method achieved an F1 score of 0.852 {+/-}0.017, exceeding Cellposes average of 0.804 {+/-}0.08. This novel SSL method not only advances segmentation accuracy but also minimizes the need for extensive computational expertise and data security concerns, making it easier for biological researchers to incorporate automated segmentation into their studies.

bioengineering↗

bioRxiv2021

Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements

Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs) yet how PRC2 is targeted to TEs remains unclear. To address this question, we performed tandem affinity purification combined with mass spectrometry and identified proteins that physically interact with the Paramecium Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the Paramecium PRC2 core complex comprises four subunits, each required in vivo for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.

molecular biology↗