Strickland, B. | Search bioRxiv

bioRxiv2024

Separate domains of the Arabidopsis ENHANCER OF PINOID drive its own polarization and recruit PIN1 to the plasma membrane

The Arabidopsis ENHANCER OF PINOID (ENP) protein and the AGC-kinase PINOID (PID) synergistically impact on polarization of the auxin transporter PIN-FORMED1 (PIN1) required for plant leaf and flower organ development. ENP offers a PID-independent input for PIN-polarity since enp pid double mutants lead to cotyledon- and flower-less plants in contrast to pid single mutants, which develop cotyledons and abnormal albeit fertile flowers. This indicated that ENP, which depicts a similar polar localization as PIN1, is a potential interactor of PINs especially PIN1. Here we show that the modular structure of ENP predicted by AlphaFold separates the capability for its own cellular polarization and its function linked to polar PIN1 activity. The anterior part of ENP is subdivided into three structured domains. They are supportive and/or essential for cellular polarity. In contrast, the C-terminus, which is an intrinsically disordered region (IDR), is completely dispensable for polarity but essential for ENP-mediated PIN-function. FLIM-FRET shows ENP to be closely associated with the plasma membrane and its IDR to significantly interact with PINs. Moreover, the modification status of two prominent phosphorylation sites in the IDR determines ENPs stability and its capability in supporting PIN1. Our results show ENP to be an element in the assumed PIN-multiprotein complex and explain its impact on PID-independent PIN1 activity.

plant biology↗

bioRxiv2021

Solving the Sample Size Problem for Resource Selection Analysis

O_LISample size sufficiency is a critical consideration for conducting Resource-Selection Analyses (RSAs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M [≥] 30 and as many relocations per animal N as possible. These thresholds render many RSA-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. C_LIO_LIWe provide the first comprehensive solution for RSA sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 2 biologically meaningful quantities: habitat selection strength and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores living in boreal, temperate, and tropical forests, montane woodlands, swamps, and arctic tundra). C_LIO_LIOur analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results contradict conventional wisdom by demonstrating that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with far fewer data than is commonly assumed. C_LIO_LIWe identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) sampling intensity for background (absence/pseudo-absence) data within a given definition of availability. We show that random sampling of background data violates the underlying mathematics of RSA, leading to incorrect values for necessary M and N and potentially incorrect RSA model outputs. We argue that these equations should be a mandatory component for all future RSA studies. C_LI

ecology↗