bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.12.21.572927

Integrated data models on Receptor-Like Kinases for novel domain discovery and functional inference in the plant kingdom

Abstract

Receptor-like kinases (RLKs) are the largest signal transduction component in plants, determining how different plants adapt to their ecological environment, resulting in plant-specific ecological niches. Current research on RLKs has focused mainly on a small number of typical RLK members of a few model plants. There is an urgent need to study the composition, distribution, and evolution of RLKs at the holistic level to accelerate the understanding of how RLK assists in the ecological adaptation of different plants. In this study, we have collected 528 plant genomes and established an RLK data model, resulting in the discovery and characterization of 524,948 RLK members. Each member is subject to systematic topology classification and coherent gene ID assignment. Using this data model, we discovered two novel families (Xiao and Xiang) of RLKs. Evolutionary analysis of the RLK families indicates that RLCK-XVII and RLCK-XII-2 exist exclusively in dicots, suggesting that the diversification in RLKs between monocots and dicots could cause differences in downstream cytoplasmic responses. We also use interaction proteome to help empower the data mining of inferring new functions of RLK from a global perspective, with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environment/ecology. The RLK data model compiled herein, together with the annotations and analytic tools, form an integrated data foundation involving multi-omics data and is publicly accessible via the web portal (http://metaRLK.biocloud.top).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Liu, Q., Fu, Q., Yan, Y., Jiang, Q., Mao, L., Wang, L., Yu, F., Zheng, H.. 2023-12-23. Integrated data models on Receptor-Like Kinases for novel domain discovery and functional inference in the plant kingdom. https://doi.org/10.1101/2023.12.21.572927

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗