Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.07.24.604550

The m6A Reader ECT1 Mediates Seed Germination via the DAG2-ECT1-PHYB Regulatory Cascade in Arabidopsis

Abstract

The N6-methyladenosine (m6A) RNA modification, a significant epitranscriptomic mark, is integral to plant growth and development. m6A reader proteins are capable of recognizing m6A modifications to impact RNA metabolic and biological processes. Here, we discovered ECT1 as an m6A reader protein that directly binds to the m6A site and plays a role in positively regulating seed germination in the context of gibberellins (GAs). ECT1 undergoes m6A modification to ensure its own stability and engages in self-interaction to enhance its protein stability. Moreover, ECT1 establishes a regulatory circuit with DAG2, which has been reported to play a positive regulatory role in GA-mediated seed germination. We found that DAG2 directly binds to the ECT1 promoter to control its transcription. ECT1 modulates the mRNA stability of DAG2. Furthermore, we identified PHYB, which encodes a key photoreceptor controlling GA-regulated seed germination, as a common downstream target of DAG2 and ECT1. DAG2 and ECT1 regulate the expression of PHYB at the transcriptional and post-transcriptional levels, respectively. ECT1 binds directly to PHYB, thereby influencing its stability. While DAG2 binds to the PHYB promoter to regulate its transcription. Together, these results unveil that ECT1 participates in the m6A signaling pathway through complex and multifaceted molecular mechanisms, broadening the spectrum of m6A reader functions and deepening our insight into the m6A signaling network in Arabidopsis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, Z., Ma, Y., Sun, W., Ding, P., Bu, Y., Qi, Y., Jia, C., Lei, B., Ma, C.. 2024-07-24. The m6A Reader ECT1 Mediates Seed Germination via the DAG2-ECT1-PHYB Regulatory Cascade in Arabidopsis. https://doi.org/10.1101/2024.07.24.604550

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

MpILR1 Hydrolyzes Jasmonate-Amino Acid Conjugates to Activate dn-iso-OPDA Signaling in Marchantia polymorpha.

Jasmonates are essential phytohormones that coordinate defense responses and developmental programs across land plants. In angiosperms, the active jasmonate ligand jasmonoyl-L-isoleucine (JA-Ile), is produced through GH3-mediated conjugation of jasmonic acid to isoleucine and JA-Ile homeostasis is further shaped by ILR1/ILL-family amidohydrolases. In contrast, the primary bioactive jasmonate ligand in bryophytes, dinor-12-oxo-phytodienoic acid (dn-iso-OPDA), is inactivated through conjugation with amino acids, raising the question of whether these conjugates constitute a reversible hormone reservoir or an irreversible catabolic end point. Although the ILR1-like family has been characterized extensively for its role in auxin and jasmonate homeostasis in angiosperms, its function in bryophytes remains basically unexplored. Here we show that MpILR1, the sole Marchantia ortholog of the ILR1/ILL family, hydrolyzes a specific subset of dn-iso-OPDA-amino acid conjugates in vivo. Loss-of-function Mpilr1 mutants exhibit enhanced accumulation of dn-iso-OPDA conjugated to hydrophobic amino acids (Val, Leu and Ile) but not to hydrophilic residues (His, Glu and Gln), demonstrating substrate-selective hydrolysis. MpILR1 hydrolytic activity is required for full dn-iso-OPDA-mediated responses, including transcriptional activation and defense against gastropod herbivory. These findings establish MpILR1 as a key positive regulator of jasmonate signaling in Marchantia polymorpha and suggest that hormone conjugation/deconjugation is an ancient regulatory mechanism evolved during plant terrestrialization.

plant biology↗

Drought-Spec-Net: Early Tomato Drought Detection and Potential Yield-Impact Assessment Using Vis NIR Data

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

plant biology↗

BSA101: Unlocking Historical Mutant Collections with BSA-Seq

Forward genetics is a powerful approach for gene discovery, but identifying causal mutations becomes difficult when mutants are maintained in heterogeneous populations with uncertain pedigrees. This is exemplified by classical tasselseed (ts) mutants, which have long served as a genetic model for studying sex determination and carpel suppression. Decades of repeated outcrossing to diverse inbred lines have created substantial genetic heterogeneity, limiting the effectiveness of conventional bulked-segregant analysis sequencing (BSA-Seq). To address this, we developed a BSA-Seq framework that integrates flexible experimental designs, multiple reference genomes, and complementary statistical methods tailored for genetically heterogeneous populations. Applying this framework revealed that reference genome selection is critical for mapping success and that Euclidean distance raised to the fourth power (ED4) outperformed homozygosity mapping (HM). Furthermore, the framework enables simultaneous mapping of multiple mutations within a single population, eliminating the need for additional mapping populations. Applying this framework to 26 ts mutant stocks from the Maize Genetics Cooperation Stock Center, we successfully mapped 24 mutants to genomic intervals containing known ts genes, while the remaining mutants mapped to distinct genomic intervals, defining novel candidate regions underlying carpel suppression. Together, these results demonstrate that historical mutant collections represent an underutilized resource for gene discovery and establish a generalizable mapping strategy for unlocking their genetic potential across diverse species.

plant biology↗