bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.09.22.677750

Insights into the patterns of molecular evolution and functional diversification of NAC gene family in land plants

Yuxin, L.·Zelong, L.·Yilu, Z.·Yu, W.·Liyuan, G.·Xiaohong, C.·Zhaoxue, H.
View original source ↗Export metadata ↓

Abstract

NAC proteins are involved in various aspects of plant development and stress responses. However, the evolutionary history and diversification patterns of NAC genes have not been examined systematically in land plants. By identifying 5052 NAC genes in 46 species of land plants and constructing their phylogenies, we found that NAC genes fell into three categories of evolutionary clades. An integrative analysis of evolutionary clades and synteny network revealed that NAC syntelogs exist widely across angiosperms, and ancient tandem duplications and multiple lineage-specific transposition events contributed to expansion and divergence of NAC family. Based on the reconstructed phylogenies, a potential evolutionary framework of NAC genes in land plants was presented, indicating that 13 ancestral genes which showed distinct expansion patterns evolved all current NAC repertoires of Arabidopsis and maize. ZmNAC genes exhibit divergent expression diversification patterns, which are generally in agreement with the expected clade- and ancestral gene specific expression characterization. The subcellular localization analysis of 14 ZmNAC proteins demonstrated that they are predominantly localized in the nucleus. Transcriptional activity analysis revealed their functional divergence in transcriptional activity. Through ZmNACs overexpression Arabidopsis, we identified the roles of ZmNACs under drought and salt stresses, thereby substantiating functional differentiation of NAC transcription factors in abiotic stress responses. Overall, the synteny network analysis and the reconstructed evolutionary framework of NAC genes in this study increased our understanding for the evolutionary history and functional consequences of NAC genes, and in return, ZmNAC expression profiles are also crucial for understanding the evolutionary sequence divergence from a functional perspective. Our results deepen the knowledge of the evolutionary mechanisms of NAC genes and the function of maize NAC genes in abiotic stress responses. HighlightsIdentified 5,052 NAC genes across 46 land plant species, revealing a maize comprehensive evolutionary framework with 13 ancestral genes. Ancient tandem duplications and lineage-specific transposition events drove NAC family expansion, as uncovered by synteny network analysis. Maize NAC genes exhibit clade-specific expression divergence, nuclear localization, and functional variability in transcriptional regulation under abiotic stress. Transgenic Arabidopsis overexpressing ZmNACs confirmed their functions in drought and salt stress tolerance, showing functional differentiation among NAC clades.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuxin, L., Zelong, L., Yilu, Z., Yu, W., Liyuan, G., Xiaohong, C., Zhaoxue, H.. 2025-09-24. Insights into the patterns of molecular evolution and functional diversification of NAC gene family in land plants. https://doi.org/10.1101/2025.09.22.677750

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

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗