bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.06.27.661899

The time of the quantitative biology in cell level: Increasing stomatal density to an intermediate level through over-expressing FSTOMAGEN improved the biomass of plants

Zhao, Y.
View original source ↗Export metadata ↓

Abstract

Stomata are the pores on plant surface, and these tiny pores are responsible for the flow of gas between plants and atmosphere. Currently, whether quantifying stomatal density to a moderately high level via genetical manipulation could improve Arabidopsis biomass remains unknown. The Arabidopsis transgenic lines with increased stomatal density were acquired through overexpressing a gene (the homologs of STOMAGEN, FSTOMAGEN, the homologs in Flaveria). The biomass of Arabidopsis transgenic lines with intermediate stomatal density increased. Compared with the other lines, the biomass of the transgenic lines with excessive stomatal density exhibited no difference or decrease. There was a positive and significant correlation between biomass and relative water content. During the earlier phase of growth, the leaf growth rate and leaf area of the lines with higher stomatal density was faster and larger respectively, and there was a significant linear relationship between stomatal density and leaf growth rate and a strong linear relationship between stomatal density and leaf area, which was in agreement with the notion that more stomata indicated faster CO2 assimilation and then faster accumulation of biomass, although it occurs in earlier phase. This suggested that the increased biomass was attributed to the earlier phase. Results indicated that finely tuning stomatal density could improve Arabidopsis biomass by genetically manipulating FSTOMAGEN. The functions of STOMAGEN homologs are extremely conserved in the evolution, thus it is promising that this genetically modified strategy could be applied in other species, which benefits the reduction of the concentration of atmosphere CO2 and the production.

Explore related subjects

cell biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhao, Y.. 2025-07-02. The time of the quantitative biology in cell level: Increasing stomatal density to an intermediate level through over-expressing FSTOMAGEN improved the biomass of plants. https://doi.org/10.1101/2025.06.27.661899

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Not all TOP RNAs are created equal: 3'UTR length and TSS selection predict the translational regulation of LARP1-bound mRNAs in CD4+ T cells

Naive T cells are poised for activation and contain a pool of translationally repressed ribosomal protein (RP) mRNA prepared to induce ribosome biogenesis to support protein synthesis, cell growth and proliferation. RP mRNA are the prototypical members of a class of transcripts initiating at cytosine followed by a CU rich element called terminal oligo pyrimidine (TOP) RNAs. TOP RNAs are regulated by an RNA binding protein LARP1, which promotes transcript stabilisation and translational repression. We investigated LARP1 function in T cell activation by generating cross-linking immunoprecipitation (CLIP) datasets detailing the LARP1-RNA interactions in naive and activated CD4+ T cells and identifying novel TOP RNAs. TOP RNAs identified by this analysis were functionally diverse. RP mRNAs were typified by high stability, and translational repression in naive T cells followed by MTORC1-dependent translation increases following T cell activation. However, other TOP RNAs varied in these aspects of their regulation. Notably, TOP RNAs with longer 3'UTRs had a relaxed dependency on LARP1 for stability and a reduced dependency on MTORC1 for their translation. Transcription start site heterogeneity also impacted TOP RNA regulation by generating a mixture of transcript isoforms with different TOP motif lengths. Longer terminal oligo pyrimidine stretches were associated with a greater dependency on MTORC1 for translation. Differential regulation of TOP RNAs may allow tuneable translational responses to MTORC1 and indicates potential roles for LARP1 beyond translation regulation and stability.

cell biology↗
bioRxiv2026

Sex-specific metabolic regulation by the Drosophila RNA-binding protein Nab2

Conserved RNA binding proteins (RBPs) regulate key steps of gene expression including mRNA processing, export, localization, stability and translation. Human ZC3H14 is a conserved RBP that regulates pre-mRNA processing in neurons and loss of ZC3H14 leads to neurological defects. Studies of Nab2, the Drosophila orthologue of ZC3H14, have identified potential target RNAs involved in metabolism, suggesting Nab2 may influence neurometabolic circuitry. Here, we show a female-specific increase in dilp2 and dilp5 mRNA levels. The dilps encode insulin-like peptides that signal from the brain insulin producing cells (IPCs) to peripheral tissues. Nab2null females have enlarged lipid droplets in the fat body, a tissue analogous to human adipose tissue and liver. Notably, neuronal depletion of Nab2 increases lipid droplet size while neuronal expression of Nab2 in Nab2null female rescues this phenotype supporting a role for Nab2 in a neuronal circuit that regulates dilp levels. Furthermore, depletion of dilp2 or dilp5 from IPCs rescues the enlarged lipid droplet phenotype in Nab2null females indicating that elevated dilp2/dilp5 contributes to enlarged lipid droplets. Together, these data support a female-specific role for Nab2 in brain neurons to support insulin signaling and fat storage, expanding the known functions of RBPs linking neuronal function and metabolic homeostasis.

cell biology↗
bioRxiv2026

Deep generative embeddings of gene expression and splicing reposition the interpretation of single-cell transcriptomic signatures

Single-cell transcriptomic analysis predominantly derives cell identity from gene expression analysis, while alternative splicing is processed separately despite its fundamental role for cell homeostasis. To overcome the limits of separate investigations, we developed a probabilistic deep learning framework, Crecerelle, enabling resolution of the contributions of gene expression and alternative splicing in each cell. Crecerelle learns cell embeddings from gene expressions and alternative splicing isoforms, to decipher their mutually dependent impact on the functional characterisation of cells in a data-driven manner, exemplified for the Tabula Muris dataset. This is enabled through a zero-and-N-inflated Dirichlet-Multinomial for a variational autoencoder that learns cell embeddings solely from splicing profiles, as well as a bi-modal variational autoencoder with a relevance-weighted mixture-of-experts variational posterior to consolidate the modality-specific contribution at single-cell level. Crecerelle reveals cell-type-specific isoform markers as well as subpopulations with unique isoforms and uncovers regulatory and disease-associated pathways not detected by gene expression analyses alone. This scalable and interpretable framework thus allows a more holistic study of transcriptomic regulation and will open a route to modality-relevance-weighted investigations across single-cell multiomics datasets and their influence on cellular homeostasis, tissue development and disease phenotypes.

cell biology↗