Bonnet, P. | bioRxiv Science

bioRxiv2026

A new mechanism of regulation of LIM kinases, LIMK1 and LIMK2, modulates their activity on cofilin and actin filament remodelling

LIM kinases, LIMK1 and LIMK2, play a crucial role in cytoskeleton dynamics. They are involved in many physiological processes but also in several pathologies such as cancer, neuronal diseases and neurofibromatosis. Although LIM kinases appear as promising therapeutic targets, they remain undruggable. A better understanding of their activity and regulation is thus required to better design efficient targeted therapies. Here, we have shown the impact of a single amino acid on LIMK activity on cofilin, their main substrate in actin filament remodelling. We demonstrated that Y632 and Y630, for LIMK1 and LIMK2 respectively, mediate LIMK dimerization, resulting in their transphosphorylation. This process seems to be a prerequisite for their canonical phosphorylation on their respective T508 and T505 residues within the activation loop. These Tyrosine are not phosphorylated, their aromatic nature is rather critical to ensure proper LIMK activity on cofilin. These results bring new insights into LIMK molecular features.

Molecular Biology↗

bioRxiv2020

A new method for counting reproductive structures in digitized herbarium specimens using Mask R-CNN

Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from > 3000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with > 87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with [≥] 90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant response to ongoing climatic change.

ecology↗