Biology subjects

Iglesias, M. J.

Publications and source records attributed to Iglesias, M. J..

8 recordsLinked to original sources

bioRxiv2026

Nitric Oxide Modulates Auxin Signaling through TIR1 S-Nitrosylation During Thermomorphogenesis in Arabidopsis

Auxin, a central hormone coordinating plant growth, integrates both environmental and developmental signals to regulate cell expansion, division, and organ patterning. Among these environmental cues, elevated ambient temperatures trigger a suite of developmental adaptations collectively known as thermomorphogenesis. In this study, we identify nitric oxide (NO) as a key mediator in the temperature-dependent regulation of auxin signaling. Our results show that warm temperatures (28-29 {degrees}C) enhance auxin-induced NO accumulation in Arabidopsis thaliana seedlings. Using pharmacological and genetic approaches, we demonstrate that NO is required for proper thermomorphogenic responses in aerial tissues. This redox signal promotes the stabilization and nuclear localization of the F-box auxin receptor TIR1, a crucial step for the activation of downstream auxin responses. Specifically, tir1-1 seedlings expressing a non-nitrosylatable TIR1 variant mutated at the Cys140 residue exhibit impaired hypocotyl elongation and hyponasty under warm conditions compared to seedlings complemented with wild-type TIR1. These results highlight the functional relevance of the TIR1 Cys140 residue, a known target for S-nitrosylation, in coordinating thermomorphogenic responses. In contrast, the absence of TIR1 S-nitrosylation restricts primary root elongation at 22 {degrees}C but does not affect the growth-promoting effects of warm temperatures. Our findings uncover a novel redox-dependent regulatory layer in auxin signaling, where S-nitrosylation of TIR1 may modulate its stability and subcellular localization in a temperature- and organ-specific manner. This mechanism allows differential growth responses between shoot and root organs and highlights the complexity of hormonal and redox interplay during plant adaptation to elevated temperatures.

plant biology↗

bioRxiv2025

In vivo binding by Arabidopsis SPLICING FACTOR 1 shifts 3' splice site choice, regulating circadian rhythms and immunity in plants

Alternative splicing expands proteome diversity and enables phenotypic plasticity across eukaryotes. In plants, mutations in spliceosomal components impair development and stress responses, but the molecular mechanisms remain unclear. Here, we define the molecular function of SPLICING FACTOR1 (AtSF1) in Arabidopsis thaliana using individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) combined with RNA sequencing of sf1 mutants. We identify the in vivo branch point sequences bound by AtSF1 and delineate its RNA-binding landscape, revealing pervasive splicing defects dominated by aberrant 3' splice site selection. Structural comparison with human SF1 indicates that AtSF1 retains branch point recognition capacity but features a distinct domain organization, including a restructured C-terminal region absent in metazoans, suggesting a divergent RNA-binding mode that evolved to meet plant-specific splicing demands. AtSF1 targets are enriched for core circadian clock and defense genes, consistent with the long-period phenotype and immune-compromised phenotypes of sf1 mutants. Together, these findings establish that AtSF1 orchestrates alternative 3' splice site choice through intron binding and branch point recognition, coupling RNA processing with circadian and immune regulation in plants. SignificancePre-mRNA splicing is a fundamental process that shapes gene expression and proteome diversity, yet how it integrates with physiological pathways in plants remains poorly understood. Our study identifies the spliceosomal component SPLICING FACTOR1 (AtSF1) as a central modulator of alternative 3' splice site choice in Arabidopsis thaliana. By defining branch point sequences and direct RNA targets of AtSF1 in vivo, we reveal its dual regulatory role in circadian timing and immune responses. Comparative analysis with human SF1 uncovers a distinct domain architecture in the plant homolog, suggesting an alternative RNA-binding mode that evolved to meet plant-specific demands. These findings illuminate how conserved splicing machinery was molecularly adapted in the plant lineage to coordinate RNA processing with environmental and developmental cues.

plant 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↗

bioRxiv2024

Assessing the role of AtGRP7 arginine 141, a target of dimethylation by PRMT5, in flowering time control

Arginine (R) methylation, catalyzed by PROTEIN ARGININE METHYLTRANSFERASES (PRMTs), is critical for regulation of gene expression at the transcriptional and post-transcriptional level. Among nine PRMT genes in Arabidopsis, PRMT5 catalyzing symmetric R dimethylation of its targets is best characterized. PRMT5 mutants are late flowering and show altered responses to environmental stress. Among PRMT5 targets are Arabidopsis thaliana GLYCINE RICH RNA BINDING PROTEIN 7 (AtGRP7) and AtGRP8 that promote the transition to flowering. AtGRP7 R141 has been shown to be modified by PRMT5. Here, we tested whether this symmetric dimethylation of R141 is important for AtGRP7s physiological role in flowering time control. We constructed AtGRP7 mutant variants with non-methylable R141 (R141A, R141K). Genomic clones containing these variants complemented the late flowering phenotype of the grp7-1 mutant to the same extend as wild-type AtGRP7. Furthermore, overexpression of AtGRP7 R141A or R141K promoted flowering similar to overexpression of the wild-type protein. Thus, flowering time does not depend on R141 and its modification. However, AtGRP7 R141 contributes to the activity of GRP7 in response to abscisic. Immunoprecipitation of AtGRP7-GFP in the prmt5 background revealed that antibodies against dimethylated arginine still recognized AtGRP7, suggesting that additional methyltransferases may be responsible for modification of AtGRP7.

plant biology↗

bioRxiv2023

Antagonistic effects of arginine methylation of LSM4 on alternative splicing during plant stress responses

Arabidopsis PROTEIN ARGININE METHYLTRANSFERASE 5 (PRMT5) post-translationally modifies RNA-binding proteins by arginine (R) methylation. The impact of this modification on the regulation of RNA processing is largely unknown. Here we use LSM4, a component of the spliceosome, as a paradigm to study the impact of R-methylation on its function in RNA processing. We identify in vivo targets of LSM4 and show that LSM4 regulates alternative splicing of a suite of them. Furthermore, LSM4 affects mRNA levels of some of the targets, showing for the first time its role in both AS and steady-state abundance. The lsm4 and prmt5 mutants show a considerable overlap of genes with altered splicing patterns, suggesting that these might be regulated by PRMT5-dependent LSM4 methylation. Wild-type LSM4 and an unmethylable version complement the lsm4-1 growth and circadian rhythms defects, suggesting that methylation is not critical for growth in normal environments. However, LSM4 methylation increases with ABA and is necessary for plants to respond properly to salt stress. In contrast, LSM4 methylation is reduced by bacterial infection, and plants expressing unmethylable LSM4 are more resistant than plants expressing wild-type LSM4. This tolerance correlates with decreased intron retention of immune-response genes upon infection, augmenting the functional isoform. Taken together, this provides the first direct evidence that R methylation adjusts LSM4 function on pre-mRNA splicing in an antagonistic manner in response to biotic and abiotic stress. HighlightPlease provide a statement that, in fewer than 30 words, highlights the novelty of the paper for the non-expert. Arginine methylation of the LSM4 spliceosome component by PROTEIN ARGININE METHYLTRANSFERASE 5 fine-tunes alternative splicing of a set of stress-related genes to antagonistically control biotic and abiotic responses in Arabidopsis.

plant biology↗

bioRxiv2023

A tissue centric atlas of cell type transcriptome enrichment signatures

Genes with cell type specific expression typically encode for proteins that have cell type specific functions. Single cell RNAseq (scRNAseq) has facilitated the identification of such genes, but various challenges limit the analysis of certain cell types and lowly expressed genes. Here, we performed an integrative network analysis of over 6000 bulk RNAseq datasets from 15 human organs, to generate a tissue-by-tissue cell type enrichment prediction atlas for all protein coding genes. We profile all the major constituent cell types, including several that are fragile or difficult to process and thus absent from existing scRNAseq-based atlases. The stability and read depth of bulk RNAseq data, and the high number of biological replicates analysed, allowed us to identify lowly expressed cell type enriched genes that are difficult to classify using existing methods. We identify co-enriched gene panels shared by pancreatic alpha and beta cells, chart temporal changes in cell enrichment signatures during spermatogenesis, and reveal that cells in the hair root are a major source of skin enriched genes. In a cross-tissue analysis, we identify shared gene enrichment signatures between highly metabolic and motile cell types, and core identity profiles of cell types found in across tissue types. Our study provides the only cell type gene enrichment atlas generated independently of scRNAseq, representing a new addition to our existing toolbox of resources for the understanding of gene expression across human tissues.

systems biology↗

bioRxiv2019

Adaptive resetting of tuberoinfundibular dopamine (TIDA) network activity during lactation in mice

AO_SCPCAPBSTRACTC_SCPCAPGiving birth triggers a wide repertoire of physiological and behavioural changes in the mother to enable her to feed and care for her offspring. These changes require coordination and are often orchestrated from the central nervous system, through as of yet poorly understood mechanisms. A neuronal population with a central role in puerperal changes is the tuberoinfundibular dopamine (TIDA) neurons that control release of the pituitary hormone, prolactin, which triggers key maternal adaptations, including lactation and maternal care. Here, we used Ca2+ imaging on mice from both sexes and whole-cell recordings on female mouse TIDA neurons in vitro to examine if they adapt their cellular and network activity according to reproductive state. In the high-prolactin state of lactation, TIDA neurons shift to faster membrane potential oscillations, a reconfiguration that reverses upon weaning. During the estrous cycle, however, which includes a brief, but pronounced, prolactin peak, oscillation frequency remains stable. An increase in the hyperpolarization-activated mixed cation current, Ih, possibly through unmasking as dopamine release drops during nursing, may explain the reconfiguration of TIDA rhythms. These findings identify a reversible plasticity in hypothalamic network activity that can serve to adapt the dam for motherhood.\n\nSO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWMotherhood requires profound behavioural and physiological adaptations to enable caring for offspring, but the underlying CNS changes are poorly understood. Here, we show that during lactation, neuroendocrine dopamine neurons, the \"TIDA\" cells that control prolactin secretion, reorganize their trademark oscillations to discharge in faster frequencies. Unlike previous studies, which typically have focused on structural and transcriptional changes during pregnancy and lactation, we demonstrate a functional switch in activity and one that, distinct from previously described puerperal modifications, reverses fully upon weaning. We further provide evidence that a specific conductance - Ih - may underlie the altered network rhythm. These findings identify a new facet of maternal brain plasticity at the level of membrane properties and consequent ensemble activity.

neuroscience↗

bioRxiv2017

A data-driven model for the assessment of age-dependent patterns of Tuberculosis burden and impact evaluation of novel vaccines.

In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age-strata that are common to all air-borne diseases, but still typically neglected in the TB case. Furthermore, whilst demographic structures of many countries are rapidly aging, demographic dynamics is pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels prospected for the next decades in the areas of the world that are most hit by the disease nowadays. Similarly, we show that considering realistic patterns of contacts among individuals in different age-strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.\n\nSignificance StatementEven though tuberculosis (TB) is acknowledged as a strongly age-dependent disease, it remains unclear how TB epidemics would react, in the following decades, to the generalized aging that human populations are experiencing worldwide. This situation is partly caused by the limitations of current transmission models at describing the relationship between demography and TB transmission. Here, we present a data-driven epidemiological model that, unlike previous approaches, explicitly contemplates relevant aspects of the coupling between agestructure and TB dynamics, such as demographic evolution and contact heterogeneities. Using our model, we identify substantial biases in epidemiological forecasts rooted in an inadequate description of these aspects, both at the level of aggregated incidence and mortality rates and their distribution across age-strata.

epidemiology↗