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Mutwil, M.

Publications and source records attributed to Mutwil, M..

At least 19 recordsLinked to original sources

A biological spike-in enables cost-effective multi-species RNA-seq and reveals global transcriptional collapse under dark stress

RNA sequencing (RNA-seq) is the workhorse of plant functional genomics, but its per-sample cost limits the number of species, conditions and replicates that can be assayed, and its standard normalization assumes that most genes do not change and that total cellular RNA is roughly constant. Both assumptions fail when a treatment globally reprograms transcription. Here we address both problems at once. We pooled carefully weighed frozen tissue from three phylogenetically distant species, Arabidopsis thaliana (A), Brachypodium distachyon (B) and Oldenlandia corymbosa (O), into single RNA-seq libraries, applying a six-day dark stress to A and B while including unstressed O in every pool as an internal biological spike-in. Reads were mapped to a concatenated three-species coding-sequence index. Read assignment was essentially clean: every pure library was [≥]99.8 % correctly assigned and cross-species mis-mapping was [≤]0.2 %, establishing that pooling does not compromise species-level quantification. Because equal mass, not equal RNA, was pooled, the read share captured by the unchanging O reported the global RNA content of the stressed species directly: dark stress reduced total mRNA to [~]50-60 % of control in Arabidopsis and to only [~]25-37 % in Brachypodium, a magnitude difference invisible to conventional analysis. At the gene level, standard per-species analysis returned a balanced set of up- and down-regulated genes, whereas spike-in normalization revealed a response dominated by repression. Conventional pathway enrichment, measured against the bulk transcriptome, likewise failed to register the global shift. Multi-species multiplexing with a biological spike-in is therefore a cheap, quantitatively faithful strategy for stress transcriptomics.

Plant Biology↗

Research Process Graph: LLM-Driven Extraction and Hierarchical Organization of Research Logic

Plant biology now publishes thousands of experimental research articles each year, but their core research logic, namely what questions are being asked, with what methods, and what is being found, remains locked inside free text and invisible to systematic analysis. Here we present a structured, 20-year atlas of The Plant Cell in which every paper is converted into a typed, directed Research Process Graph (RPG) of Question (Q), Method (M) and Finding (F) nodes connected by Q[->]M and M[->]F edges. A benchmarked large language model pipeline applied to 2,633 Plant Cell research articles published 2005-2026 recovered >110,000 Q/M/F nodes and >126,000 directed Q[->]M[->]F chains with>98% precision. A second LLM pass generalises each node into a paper-independent canonical form and assigns it to one of 10 top-level (L1) and [~]90 sub-level (L2) categories for each node type, producing the first comprehensive map of plant-biology research logic at the resolution of individual research questions. The atlas reveals that Plant Cell papers fall into seven canonical paper recipes with characteristic Q[->]M[->]F sub-structures, that peripheral experimental techniques have largely turned over while a stable methodological core persisted, and that the strongest correlate of per-PI citation impact is methodological breadth, not productivity or topical breadth. We release the atlas as a public, browsable database with five complementary interfaces: paper views, an LLM-powered research assistant, expert profiles, a taxonomy browser, and a method explorer. The database, available at https://rpg.connectome.tools/, turns the literature into a queryable community resource.

plant biology↗

ORBIT: Orthogonal Rotation for Biological Inter-species Transfer

MotivationCross-species gene embeddings are central to transferring functional annotations between species. A recent method demonstrated that species-specific STRING (PPI) network embeddings can be aligned across 1322 eukaryotes with autoencoders (FedCoder), but this approach is computationally expensive, depends on careful hyperparameter selection, leaves substantial room for improvement in cross-species retrieval quality, and has not been demonstrated on coexpression networks. ResultsWe introduce an alignment pipeline for cross-species coexpression network embeddings based on orthogonal Procrustes rotation. Species-specific Node2Vec embeddings of coexpression networks are aligned to a shared space using ortholog anchors from OrthoFinder, solved in closed form via Singular Value Decomposition (SVD). Applied to 153 plant species and 5.7 million genes, Procrustes alignment achieves four-fold higher cross-species Spearman correlation and consistently higher retrieval metrics than the SPACE autoencoder, while leaving within-species coexpression structure invariant (preservation ratio 1.000 against the unaligned baseline). The full alignment completes in under three minutes on a single CPU, and on downstream tasks, Procrustes embeddings improve within-species GO term prediction and outperform SPACE for cross-species GO transfer. Procrustes and sequence embeddings remain complementary for biological-process prediction, consistent with observations from SPACE. AvailabilityCode for producing the embeddings is made available at https://github.com/pwissenberg/orbit.

bioinformatics↗

Kingdom-wide comparative transcriptomics reveals deeply conserved and predictable stress response programs across Viridiplantae

How conserved stress responses are across the plant kingdom remains poorly understood. Here, we present a kingdom-wide stress transcriptome atlas of 36 Viridiplantae species, from chlorophytes to angiosperms, across nine abiotic and biotic stresses. The atlas integrates reanalyzed public RNA-seq data with new in-house stress experiments on three species representing basal lineages, yielding 13.6 million differential expression calls from over 3,200 manually curated control-treatment comparisons. We find that ancient gene families respond broadly but moderately, while lineage-specific families respond narrowly but intensely, revealing a division of labor in stress gene deployment. Stress response conservation decays with phylogenetic distance yet remains detectable across more than 700 million years of divergence, with upregulated genes diverging faster than downregulated genes. Functional co-occurrence analysis uncovers a deeply conserved growth-defence tradeoff alongside stress-specific transcriptional rewiring. Conserved stress co-expression modules undergo regulatory subfunctionalization through duplication, with whole-genome duplicate pairs preferentially retained within modules. Finally, DNA and RNA foundation models predict stress responsiveness from sequence alone (auROC 0.755), suggesting a partially conserved cis-regulatory code underlying stress responses across the kingdom.

plant biology↗

From Parametric Guessing to Graph-Grounded Answers: Building Reliable ChatGPT-like tools for Plant Science

Large language models (LLMs) are increasingly used by plant biologists to summarize literature, generate hypotheses, and interpret experimental results. However, LLMs are unreliable sources of exhaustive, source-attributed facts, a critical limitation for the list-style queries that pervade plant biology (e.g., "list all transcription factors regulating secondary cell wall (SCW) biosynthesis in Arabidopsis"). Here, we query ChatGPT, Claude, and Gemini with such queries and demonstrate that none return complete gene lists with reliable citations. We trace these failures to how LLMs store knowledge: as statistical patterns distributed across billions of internal parameters, with no mechanism to guarantee completeness, provenance, or reproducibility. We also review fine-tuning mitigation strategies, including multi-task instruction tuning, parameter-efficient methods, and context engineering, that alleviate but do not resolve these limitations. We then discuss retrieval-augmented generation (RAG), which feeds relevant documents to the LLM at query time, and argue that while it improves source attribution, it remains impractical when answers require synthesizing information scattered across hundreds of papers. As an alternative, we advocate graph retrieval-augmented generation (GraphRAG), in which the LLM serves as a reasoning and language interface over a structured, provenance-linked knowledge graph (KG) that returns complete result sets reproducibly. We outline a practical GraphRAG architecture and survey existing plant KG resources. Finally, we discuss open challenges, including entity disambiguation, relation normalization and evidence grading, and propose a roadmap for building open, continuously updated plant KGs that can turn "read 1,000 papers" into a single reproducible query.

bioinformatics↗

Unravelling the processes controlling pollen formation and functions with cross-species comparative analysis

Pollen and anthers are essential for plant reproduction, yet the genetic mechanisms governing their formation and function remain incompletely understood. In this study, we performed a comprehensive cross-species comparative analysis leveraging 16,904 RNA-seq samples from 90 plant species, encompassing diverse lineages from bryophytes to angiosperms. By calculating tissue specificity metrics and applying clustering and functional enrichment analyses, we identified both conserved and lineage-specific gene families associated with pollen and anther development. Our computational pipeline allowed for the discovery of previously unstudied genes and predictions of their critical biological roles, which were experimentally validated through mutant characterization, genotyping, and detailed morphological analysis. Notably, we observed evolutionary patterns in pollen morphology, cell wall structure, cytoplasmic content, and aperture characteristics, highlighting differences and gradual transitions among major plant clades. These findings provide significant insights into the evolutionary processes shaping pollen and anther traits, and establish a valuable resource for future studies in plant reproductive biology and biotechnology.

plant biology↗

A First-in-Class Therapy Targeting Transcription-Replication Conflicts Drives Synthetic Lethality in Homologous Recombination Deficient Cancers

Hidden beneath the layer of tumor malignancy lies unchecked proliferation driven by hyperactive transcription, which fosters the formation of RNA:DNA hybrids. These structural intermediates precipitate collisions between the transcription and replication machineries. Central to mitigating this genomic threat is interleukin enhancer-binding factor 2 (ILF2), which orchestrates the recruitment of RNA:DNA helicases to resolve R-loops and, in doing so, reveals cancer cells dependency on stress-mitigating mechanisms to avert genome catastrophe. Our discovery of Molephantin and its lead derivative, NYH0002 is the outcome of delineating ILF2s function. NYH002 exerts direct binding to the ILF2 complex and disrupts RNA:DNA helicase activity to elicit genome-wide DNA breakages. Tumors with elevated cyclin E and E2F1 expression are sensitive to NYH002, while those deficient in homologous recombination repair exhibit lethality. Collectively, these findings position ILF2 as a key regulator of R-loop homeostasis and reveal a therapeutic vulnerability that NYH002 exploits to suppress tumor malignancy.

cancer biology↗

Yeast Knowledge Graphs Database for Exploring Saccharomyces cerevisiae and Schizosaccharomyces pombe

Biomedical literature contains an extensive wealth of information on gene and protein function across various biological processes and diseases. However, navigating this vast and often restricted-access data can be challenging, making it difficult to extract specific insights efficiently. In this study, we introduce a high-throughput pipeline that leverages OpenAIs Generative Pre-Trained Transformer Model (GPT) to automate the extraction and analysis of gene function information. We applied this approach to 84,427 publications on Saccharomyces cerevisiae and 6,452 publications on Schizosaccharomyces pombe, identifying 3,432,749 relationships for budding yeast and 421,198 relationships for S. pombe. This resulted in a comprehensive, searchable online Knowledge Graph database, available at yeast.connectome.tools and spombe.connectome.tools, which offers users extensive access to various interactions and pathways. Our analysis underscores the power of integrating artificial intelligence with bioinformatics, as demonstrated through key insights into important nodes like Hsp104 and Atg8 proteins. This work not only facilitates efficient data extraction in yeast research but also presents a scalable model for similar studies in other biological systems. HIGHLIGHTSO_LIGenerated Yeast Knowledge Graphs from full-text research articles. C_LIO_LIAnalyzed over 90,000 publications for Saccharomyces and Schizosaccharomyces species. C_LIO_LIExtracted millions of relationships using GPT-based natural language processing. C_LIO_LIYeast Knowledge Graphs accessible through interactive web platforms and APIs. C_LIO_LIAdvanced tool enabling insights into gene networks and functional interactions. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/626523v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@15afd5corg.highwire.dtl.DTLVardef@afa4bcorg.highwire.dtl.DTLVardef@16858f2org.highwire.dtl.DTLVardef@1a7a19f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Comparative transcriptomics in ferns reveals key innovations and divergent evolution of secondary cell wall

Despite ferns being crucial to understanding plant evolution, their large and complex genomes has kept their genetic landscape largely uncharted, with only a handful of genomes sequenced and sparse transcriptomic data. Addressing this gap, we generated extensive RNA-sequencing data for multiple organs across 22 representative species over the fern phylogeny, assembling high-quality transcriptomes. These data facilitated the construction of a time-calibrated fern phylogeny covering all major clades, revealing numerous whole-genome duplications and highlighting the uniqueness of fern genetics, with half of the uncovered gene families being fern-specific. Our investigation into fern cell walls through biochemical and immunological analyses identified occurrences of the lignin syringyl unit and its independent evolution in ferns. Moreover, the discovery of an unusual sugar in fern cell walls hints at a divergent evolutionary path in cell wall biochemistry, potentially driven by gene duplication and sub-functionalization. We provide an online database preloaded with genomic and transcriptomic data for ferns and other land plants, which we used to identify an independent evolution of lignocellulosic gene modules in ferns. Our data provide a framework for the unique evolutionary path that ferns have navigated since they split from the last common ancestor of euphyllophytes more than 360 million years ago.

plant biology↗

Constructing Ensemble Gene Functional Networks Capturing Tissue/condition-specific Co-expression from Unlabled Transcriptomic Data with TEA-GCN

Gene co-expression networks (GCNs) can reveal useful gene co-functional and co-regulatory relationships. However, current GCN construction methodologies are sensitive to batch effects and sample composition, limiting their performance in generating GCNs from public RNA-seq samples abundant for many species. Here, we detail the development of TEA-GCN (Two-Tier Ensemble Aggregation-GCN), a GCN construction method that leverages unsupervised transcriptomic dataset partitioning and multi-metric co-expression scoring to derive ensemble gene co-expression. Benchmarking over 450,000 public RNA-seq samples across 12 species, TEA-GCN outperforms the state-of-the-art in predicting gene functions and inferring gene regulatory networks. Through the use of natural language processing, we also show that the biologically-relevant dataset partitions with high co-expression can identify tissue-/condition-specific co-expression in TEA-GCN, providing an unprecedented level of explainability. Furthermore, we show that TEA-GCNs exhibit enhanced conservation across species, making them suitable for multi-species comparative studies. TEA-GCN is available at https://github.com/pengkenlim/TEA-GCN.

systems biology↗

Diurnal.plant.tools in 2024: expanding to Marchantia polymorpha and four angiosperms

Diurnal gene expression is a pervasive phenomenon occurring across all kingdoms of life, orchestrating adaptive responses to daily environmental fluctuations and thus enhancing organismal fitness. Our understanding of the plant circadian clock is primarily derived from studies in Arabidopsis and direct comparisons are difficult due to differences in gene family sizes. To this end, the identification of functional orthologs based on diurnal and tissue expression is necessary. The diurnal.plant.tools database constitutes a repository of gene expression profiles from 17 members of the Archaeplastida lineage, with built-in tools facilitating cross-species comparisons. In this database update, we expand the dataset with diurnal gene expression from 4 agriculturally significant crop species and Marchantia, a plant of evolutionary significance. Notably, the inclusion of diurnal gene expression data for Marchantia enables researchers to glean insights into the evolutionary trajectories of the circadian clock and other biological processes spanning from algae to angiosperms. Moreover, by integrating data from tissue, and perturbation gene expression datasets for Marchantia from other databases, the diurnal dataset complements it to offer a comprehensive overview of gene functions across varied biological contexts. This expanded database serves as a valuable resource for elucidating the intricacies of diurnal gene regulation and its evolutionary underpinnings in plant biology.

plant biology↗

Parallel comparison reveals conservation and re-wiring of abiotic stress response networks across three plant species

Abiotic stresses trigger significant alterations in plant morphology, yield, and nutrient composition. To counter these stresses, plants have developed strategies involving gene expression adjustments, impacting their growth and development to ensure survival and reproduction. Although transcriptomic studies have extensively examined stress responses, our understanding of their conservation remains limited. We conducted a comprehensive study on the effects of 24 different environmental and nutrient conditions on the growth yield of three hydroponically grown leafy crops (cai xin, lettuce, and spinach). This research identified optimal conditions for maximizing yield and vitamin content and pinpointed conditions that induce abiotic stress. Our findings highlight the substantial impact of stress on various plant functions, with photosynthesis playing a pivotal role in vitamin content regulation. A detailed comparative analysis of gene expression across these crops revealed conserved gene sets potentially crucial for abiotic stress response. Furthermore, we introduced a novel method that merges regression and genomics to construct reliable gene regulatory networks for stress response. Despite significant conservation of these networks among the studied crops, their key transcription factors showed limited similarity to those in Arabidopsis thaliana, suggesting notable functional divergence. To give easy access to the data and comparative transcriptomic tools, we established StressPlantTools, an public online database (https://stress.sbs.ntu.edu.sg/), facilitating further research into stress-responsive genes.

plant biology↗

Comparative transcriptome database for Camellia sinensis reveals genes important for flavonoid synthesis in tea plants

Tea, as one of the most popular beverages in the world, possesses a plethora of secondary metabolites that are beneficial to human health. Tea plants (Camellia sinensis) exhibit rich genetic diversity, where different cultivars can vary significantly in terms of yield, adaptability, morphology, and composition of secondary metabolites. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify valuable functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 cultivars of Camellia sinensis were assembled and combined with the coding sequences of 13 cultivars of Camellia sinensis to study the differences and similarities of gene expression and biological functions among cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that provides user-friendly tools and interactive visualizations that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform cross-cultivar comparison of co-expressed gene neighborhoods, clusters, and tissue-specific gene expression. We show that the members of the chalcone synthase (CHS) gene family, important for flavonoid synthesis, exhibit the highest expression variability, specific expression in leaves and buds, and significant modulation by methyl jasmonate (MeJA) treatment. By using comparative co-expression tools of TeaNekT, we identified multiple conserved genes involved in flavonoid biosynthesis among cultivars that have not been previously studied, warranting further research.

bioinformatics↗

A method for mining condition-specific co-expressed genes in Camellia sinensis based on K-means clustering: A case study of "Anji Baicha" tea cultivar

As one of the worlds most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a PPR-type RNA editing factor gene (CWM1) that specifically functions during the bud-prealbinism stage of the Camellia sinensis cultivar "Anji Baicha". We hypothesize that this gene may be upregulated and play a role in inhibiting chloroplast development, ultimately resulting in albino phenotypes in "Anji Baicha".

bioinformatics↗

Super-silencer perturbation by EZH2 and REST inhibition leads to large loss of chromatin interactions and reduction in cancer growth

Human silencers have been shown to exist and regulate developmental gene expression. However, the functional importance of human silencers needs to be elucidated, such as whether they can form "super-silencers" and whether they are linked to cancer progression. Here, through interrogating two putative silencer components of FGF18 gene, we found that two nearby silencers can cooperate via compensatory chromatin interactions to form a "super-silencer". Furthermore, double knockout of two silencers exhibited synergistic upregulation of FGF18 expression and changes of cell identity. To perturb the "super-silencers", we applied combinational treatment of an EZH2 inhibitor GSK343, and a REST inhibitor, X5050 ("GR"). We found that GR led to severe loss of TADs and loops, while the use of one inhibitor by itself only showed mild changes. Such changes in TADs and loops were associated with reduced CTCF and TOP2A mRNA levels. Moreover, GSK343 and X5050 synergistically upregulated super-silencer-controlled genes related to cell cycle, apoptosis and DNA damage, leading to anticancer effects both in vitro and in vivo. Overall, our data demonstrated the first example of a "super-silencer" and showed that combinational usage of GSK343 and X5050 to disrupt "super-silencers" could potentially lead to cancer ablation.

molecular biology↗

PlantConnectome: knowledge networks encompassing >100,000 plant article abstracts

One of the main quests of plant biology is understanding how genes and metabolites work together to form complex networks that drive plant growth, development, and responses to environmental stimuli. However, the ever-growing volume and diversity of scientific literature make it increasingly challenging to stay current with the latest advances in gene function studies. Here, we tackle the challenge by deploying the text-mining capacities of large language models to process over 71,000 plant biology abstracts. Our approach unveiled nearly 5 million functional relationships between a wide array of biological entities--genes, metabolites, tissues, and others--with a high accuracy of over 85%. We encapsulated these findings in PlantConnectome, a user-friendly database, and demonstrated its diverse utility by providing insights into gene regulatory networks, protein-protein interactions, and stress responses. We believe this innovative use of AI in the life sciences will allow plant scientists to keep up to date with the rapidly growing corpus of scientific literature. PlantConnectome is available at https://plant.connectome.tools/.

bioinformatics↗

Plant Expression Omnibus - a comparative transcriptomic database for 103 Archaeplastida

The Plant Expression Omnibus (PEO) is a web application that provides biologists with access to gene expression insights across over 100 plant species, [~]60,000 manually annotated RNA-seq samples, and more than four million genes. The tool allows users to explore the expression patterns of genes across different organs, identify organ-specific genes, and discover top co-expressed genes for any gene of interest. PEO also provides functional annotations for each gene, allowing for the identification of genetic modules and pathways. PEO is designed to facilitate comparative kingdom-wide gene expression analysis and provide a valuable resource for plant biology research. We provide two case studies to demonstrate the utility of PEO in identifying candidate genes in pollen coat biosynthesis and investigating the biosynthetic pathway components of capsaicin in Capsicum annuum. The database is freely available at https://expression.plant.tools/.

plant biology↗

Transcriptomic and metabolomic characterization of antibacterial activity of Melastoma dodecandrum

Antibacterial resistance poses a significant global threat, necessitating the discovery of new therapeutic agents. Plants are a valuable source of secondary metabolites with demonstrated anticancer and antibacterial properties. In this study, we reveal that Melastoma dodecandrum exhibits both bacteriostatic and bactericidal effects against Pseudomonas aeruginosa and Staphylococcus aureus. Treatment with plant extracts results in membrane damage and a reduction in Pseudomonas swimming and swarming motility. A comparative analysis of bacterial transcriptomes exposed to Melastoma extracts and four distinct antibiotics indicates that the extracts trigger similar transcriptomic responses as triclosan, a fatty acid inhibitor. Activity-guided fractionation suggests that the antibacterial activity is not attributable to hydrolyzable tannins, but to unidentified minor compounds. Additionally, we identified 104 specialized metabolic pathways and demonstrated a high level of transcriptional coordination between these biosynthetic pathways and phytohormones, highlighting potential regulatory mechanisms in plant metabolism.

plant biology↗