Biology subjects

Gaggion, N.

Publications and source records attributed to Gaggion, N..

4 recordsLinked to original sources

bioRxiv2026

ALTERNATIVE SPLICING OF A CODING GENE PRODUCES A NUCLEAR REGULATORY LONG NON-CODING RNA

Alternative splicing (AS) is traditionally understood to increase transcript and protein diversity by generating multiple coding isoforms from a single gene. In addition, many alternative isoforms are degraded through non-sense mediated decay (NMD). Furthermore, we show here that AS also produces nuclear long non-coding RNAs (lncRNAs) from protein-coding genes, which can serve regulatory functions rather than expanding proteomic complexity. Focusing on the Arabidopsis thaliana SR protein gene At-RS31, we found that a non-coding isoform (mRNA3) accumulates in the nucleus under dark conditions. This transcript binds the protein product of its own transcriptional unit, modulating splicing decisions and balancing gene activity in response to environmental cues. Overexpression of mRNA3 down-regulates the action of At-RS31 on its target genes in trans and restores the phenotype induced by intron-less At-RS31 accumulation. Further sub-cellular fractionation and RT-PCR analyses show that many SR genes generate nuclear-retained non-coding isoforms, especially under dark conditions, suggesting a widespread mechanism. These findings redefine the role of AS in plants, highlighting its capacity to generate regulatory lncRNAs to fine-tune gene expression beyond protein diversification. Highlights- Alternative splicing generates regulatory lncRNAs from protein-coding genes - A nuclear-retained isoform forms an autoregulatory RNA-protein feedback loop - Non-coding isoforms of SR genes accumulate in the nucleus under dark conditions - Co-expression of the non-coding isoform rescues splicing factor overexpression phenotypes

molecular biology↗

bioRxiv2025

What Large Language Models Know About Plant Molecular Biology

Large language models (LLMs) are rapidly permeating scientific research, yet their capabilities in plant molecular biology remain largely uncharacterized. Here, we present MOBIPLANT, the first comprehensive benchmark for evaluating LLMs in this domain, developed by a consortium of 112 plant scientists across 19 countries. MOBIPLANT comprises 565 expert-curated multiple-choice questions and 1,075 synthetically generated questions, spanning core topics from gene regulation to plant-environment interactions. We benchmarked seven leading chat-based LLMs using both automated scoring and human evaluation of open-ended answers. Models performed well on multiple-choice tasks (exceeding 75% accuracy), although most of them exhibited a consistent bias towards option A. In contrast, expert reviews exposed persistent limitations, including factual misalignment, hallucinations, and low self-awareness. Critically, we found that model performance strongly correlated with the citation frequency of source literature, suggesting that LLM knowledge inherits the visibility distribution of the underlying scientific corpus. Consequently, models tend to be more reliable on consolidated topics and less reliable on under-cited or recently emerging ones. We also benchmarked agents equipped with web-search and additional tools in more complex tasks involving DNA sequence analysis. These agents were outperformed by domain specific models in sequence classification and regression tasks, indicating an opportunity for joint agentic systems that combine both the reasoning power of LLMs and the dedicated processing of DNA models. This understanding is key to guiding both the development of next-generation models and the informed use of current tools in the everyday work of plant researchers. MOBIPLANT is publicly available online in this link.

plant biology↗

bioRxiv2023

The transcription factor NF-YA10 determines the area explored by Arabidopsis thaliana roots through direct regulation of LAZY and TAC genes

Root developmental plasticity relies on transcriptional reprogramming, which largely depends on the activity of transcription factors (TFs). NF-YA2 and NF-YA10 (Nuclear Factor A2 and A10) are down-regulated by the specific miRNA isoform miR169defg, in contrast to miR169a. Here, we analyzed the role of the Arabidopsis thaliana TF NF-YA10 in the regulation of lateral root development. Plants expressing a version of NF-YA10 resistant to miR169 cleavage showed a perturbation in the lateral root gravitropic response. By extracting novel features of root architecture using the ChronoRoot deep-learning-based phenotyping system, we uncovered a differential emergence angle of lateral roots over time when compared to Col-0. Detailed phenotyping of root growth dynamics revealed that NF-YA10 activity modulates the area explored by Arabidopsis roots. Furthermore, we found that NF-YA10 directly regulates TAC1 and LAZY genes by targeting their promoter regions, genes previously linked to gravitropism. Hence, the TF NF-YA10 is a new element in the control of LR gravitropism and root system architecture.

plant biology↗

bioRxiv2020

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

BackgroundDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. ResultsWe developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. ConclusionsAltogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.

plant biology↗