Steinbrecher, T. | bioRxiv Science

bioRxiv2023

The dimorphic diaspore model Aethionema arabicum (Brassicaceae): Distinct molecular and morphological control of responses to parental and germination temperatures

Plants in habitats with unpredictable conditions are often characterized by diversifying their bet-hedging strategies that ensure fitness over a wider range of variable environmental factors. A striking example is the diaspore (seed and fruit) heteromorphism that evolved to maximize species survival in Aethionema arabicum (Brassicaceae) in which external and endogenous triggers allow the production of two distinct diaspores on the same plant. Using this dimorphic diaspore model, we identified contrasting molecular, biophysical, and ecophysiological mechanisms in the germination responses to different temperatures of the mucilaginous seeds (M+ seed morphs), the dispersed indehiscent fruits (IND fruit morphs), and the bare non-mucilaginous M- seeds obtained by pericarp (fruit coat) removal from IND fruits. Large-scale comparative transcriptome and hormone analyses of M+ seeds, IND fruits, and M- seeds provided comprehensive datasets for their distinct thermal responses. Morph-specific differences in co-expressed gene modules in seeds, as well as seed and pericarp hormone contents identified a role of the IND pericarp in imposing coat dormancy by generating hypoxia affecting ABA sensitivity. This involved expression of morph-specific transcription factors, hypoxia response and cell wall-remodeling genes, as well as altered abscisic acid (ABA) metabolism, transport, and signaling. Parental temperature affected ABA contents and ABA-related gene expression and altered IND pericarp biomechanical properties. Elucidating the molecular framework underlying the diaspore heteromorphism can provide insight into developmental responses to globally changing temperatures. IN A NUTSHELL BackgroundHeteromorphic diaspores (fruits and seeds) are an adaptive bet-hedging strategy to ensure survival in spatiotemporally variable environments. The stone cress Aethionema arabicum, an annual plant native to semi-arid habitats in Anatolia (Turkey), one of the worlds hotspots of biodiversity. It is a close relative of Arabidopsis, rapeseed, cabbage and other Brassica crops, but in contrast to these Ae. arabicum disperses two distinct diaspores from the same plant. These dimorphic diaspores are the mucilaginous seeds (dispersed by pod shatter) and indehiscent fruits (dispersed by abscission). The wing-like pericarp (fruit coat) of the single-seeded indehiscent fruit allows wind dispersal over large distances. The amounts and ratios of the dimorphic diaspores are variable and depend on the environmental conditions. The dimorphic diaspores differ in morphology, dormancy and germination properties and thereby make Ae. arabicum an excellent model for the comparative investigation of the underpinning molecular mechanisms. QuestionWe asked how temperature during fruit and seed formation and during seed germination affect dormancy release and germination speed, and how the morphology, hormonal regulation, and the expression of genes differ between the dimorphic diaspores. FindingsLarge-scale comparative transcriptome and hormone analyses of the mucilaginous seeds and the indehiscent fruits, as well as the seeds artificially extracted from indehiscent fruits by pericarp (fruit coat) removal, provided comprehensive datasets for their distinct thermal responses. Material obtained from plants grown at different temperatures during reproduction was imbibed at different temperatures for germination. This altered the abscisic acid (ABA) metabolism and the pericarp biomechanical properties. Diaspore-specific differences in response to distinct imbibition temperatures identified distinct gene expression patterns in seeds, distinct seed and pericarp hormone contents, and a role of the pericarp in generating hypoxia inside the fruit and imposing coat dormancy. This revealed distinct combinations of specific transcription factors, hypoxia responses and cell wall-remodeling genes, as well as altered signaling pathway genes. Next stepsOur large-scale comparative transcriptome datasets are easily and publicly accessible via the Aethionema arabicum web portal (https://plantcode.cup.uni-freiburg.de/aetar_db/index.php). We plan to expand this by future work on seedlings derived from the dimorphic diaspores, by comparing different Ae. arabicum genotypes, and by studying responses to specific stresses. Understanding the molecular basis of this fascinating example of developmental diversity and plasticity and its regulation by temperature is expected to add insight how plants respond to changing environmental conditions.

plant biology↗

bioRxiv2018

Prediction of Accurate Binding Modes using Combination of classical and accelerated Molecular dynamics and Free Energy Perturbation Calculations: An Application to Toxicity Studies

Estimating the correct binding modes of ligands in protein-ligand complexes is not only crucial in the drug discovery process, but also for elucidating potential toxicity mechanisms. In the current paper, we discuss and demonstrate a computational modelling protocol using the combination of docking, classical (cMD) and accelerated (aMD) molecular dynamics and free energy perturbation (FEP+ protocol) for identification of the binding modes of selected perfluorocarboxyl acids (PFCAs) in the PPAR{gamma} nuclear receptor.\n\nInitially, we employed both the regular and induced fit docking which failed to correctly predict the ligand binding modes and rank the compounds with respect to experimental free energies of binding, when they were docked into non-native X-ray structure. The cMD and aMD simulations identified the presence of multiple binding modes for these compounds, and the shorter chain PFCAs (C6-C8) continuously moved between a few energetically favourable binding conformations. These results demonstrate that the docking scoring function cannot rank compounds precisely in such cases, not due to its insufficiency, but because of the use of incorrect or only one unique bindings pose, neglecting the protein dynamics. Finally, based on MD predictions of binding conformations, the FEP+ sampling protocol was extended and then accurately reproduced experimental differences in the free energies. Thus, the preliminary MD simulations can also provide helpful information about correct set-up of the FEP+ calculations. These results show that the PFCAs binding modes were accurately predicted and the FEP+ protocol can be used to estimate free energies of binding of flexible molecules outside of typical drug-like compounds.\n\nOur in silico workflow revealed the main characteristics of the PFCAs, which are week PPAR{gamma} partial agonists and illustrated the importance of specific ligand-residue interactions within the LBD. This work also suggests a common workflow for identification of ligand binding modes, ligand-protein dynamics description and relative free energy calculations.

bioinformatics↗

bioRxiv2017

Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations

The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a root mean square error in binding free energy changes of [Formula] kcal/mol (95% confidence interval) and correctly classifying mutations as resistant or susceptible with [Formula] accuracy. Since these calculations are fast on modern GPUs, this benchmark establishes the potential for physical modeling to collaboratively support the rapid assessment and anticipation of the potential for patient mutations to affect drug potency in clinical applications.

cancer biology↗