Aoki, S. | Search bioRxiv

bioRxiv2026

Arabidopsis thaliana ACTIN DEPOLYMERIZING FACTORs are novel susceptibility factors for Colletotrichum higginsianum

Colletotrichum higginsianum (Ch) is a hemibiotrophic fungal pathogen that infects Brassicaceae plants, including Arabidopsis thaliana. The molecular mechanisms underlying the Ch-A. thaliana interaction are not fully understood. Particularly, the susceptibility factor against Ch infection remains to be determined. Here, we report that A. thaliana ACTIN DEPOLYMERIZING FACTORs (ADFs), ancient proteins that regulate the organization and dynamics of actin filaments (AFs), function as susceptibility factors during Ch infection. Among 11 ADFs encoded in A. thaliana genome, subclass I ADFs that include ADF1, -2, -3, and -4, express throughout the plant. We found that knockout mutant of ADF4 and transgenic plants in which the expression of all of subclass I members is suppressed (ADF1-4Ri) exhibited increased resistance to Ch. Cytological analyses revealed that both Ch penetration and secondary hyphae formation were suppressed in adf4 and ADF1-4Ri. This enhanced resistance was associated with suppression of Ch-induced AF fragmentation. In addition, we found that PENETRATION 2 (PEN2) plays a critical role in the Ch resistance in adf4 and ADF1-4Ri. Our findings suggest that subclass I ADFs promote AF fragmentation during Ch infection, thereby suppressing PEN2-associated mitochondria accumulation at Ch entry sites. Together, these results raise the possibility that Ch exploits host ADF-dependent actin regulation to facilitate successful infection.

plant biology↗

bioRxiv2017

Sparse ordinal logistic regression and its application to brain decoding

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.

neuroscience↗