Biology subjects

Busch, W.

Publications and source records attributed to Busch, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Nitrate regulates anchor root development

Nitrogen is a critical nutrient necessary for plant growth and survival. Plasticity in root architecture helps adapt to soil nitrogen levels for optimal nitrogen uptake; the nitrate form of soil nitrogen is a major modulator of root architecture. Although details of nitrate-regulated primary and lateral root growth are known, nitrate-regulated formation of anchor roots, which arise from the collet, is not understood. In this work, we uncover a role for nitrate in the regulation of anchor root formation. We find that cytokinin inhibits anchor root formation with rising nitrate. These cytokinin effects on anchor root formation rely on regulated indole-3-butyric acid (IBA) to indole-3-acetic acid (IAA) conversion. These data point toward a mechanism by which nitrate controls a previously underappreciated aspect of nitrate-dependent root architecture driven by anchor roots.

plant biology↗

bioRxiv2026

From Lab Notes to Linked Data: MeSyTo for Ontology-Driven Metadata in Toxicological Omics

Toxicological omics studies require comprehensive metadata to support reproducibility, interoperability, and regulatory reuse. However, metadata requirements differ across public repositories, reporting frameworks, and laboratory workflows, resulting in inconsistent annotation and limited data integration. To address this challenge, we developed MeSyTo (Metadata for Systems Toxicology), an ontology-driven framework for harmonizing metadata across toxicological omics. Metadata concepts from public repositories, the OECD Omics Reporting Framework (OORF), community standards, and institutional workflows were semantically aligned and implemented as the MeSyTo Metadata Model (MMM). The MMM serves as the basis for the automatic generation of SHACL validation shapes and framework-specific metadata profiles, while curated value sets are represented as SKOS controlled vocabularies to support metadata collection and validation. The current implementation comprises 105 ontology classes and 527 data properties and supports transcriptomics, proteomics, and metabolomics. A prototype web application demonstrates ontology-driven metadata collection with integrated semantic validation and ontology-based term resolution. The ontology, validation shapes, controlled vocabularies, generation scripts, and software are publicly available as open-source resources. MeSyTo provides a reusable semantic foundation for harmonized, machine-actionable metadata and facilitates repository submission, regulatory reporting, and interoperable data exchange across toxicological omics studies.

bioinformatics↗

bioRxiv2026

Inherited proliferation states organize plant transcriptomes

A central question in plant biology is whether natural transcriptome variation primarily reflects environmental adaptation or intrinsic genetic programs. Using transcriptomes from 665 natural Arabidopsis thaliana accessions, we show that inherited population structure, not environmental gradients, is the primary organizer of global transcriptome architecture. Genomic population structure uniquely explains 30.0% of transcriptome-wide variation, while environmental variables account for only 2.5%. This architecture is dominated by a single co-expression program (proliferation Module Eigengene: prolifME), enriched for cell cycle regulation and ribosome biogenesis, regulated by a bipartite transcription factor architecture, and genetically encoded at discrete loci with broad trans-regulatory effects. This proliferation program is associated with reduced plant size, biomass, growth rate and water-use efficiency, and these relationships persist under common-garden conditions. Gene contributions to this proliferation program are non-randomly conserved in rice and maize across 150-200 million years of divergence. These findings identify an intrinsic transcriptional proliferation program as a primary state of plant population transcriptomes, conserved across plant species.

plant biology↗

bioRxiv2025

Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana, we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

plant biology↗

bioRxiv2025

Hierarchical Approaches for Integrating Sparse, Multivariate Toxicological Effect Data in Whole Organism Molecular Dynamics Models

MotivationIntegrating time-resolved toxicity data observed along multiple steps along the whole-organism exposure-to-effect pathway into mechanistic ordinary differential equation (ODE) models promises improved chemical risk assessment and predictive toxicology. However, effective incorporation of such multivariate observations is mainly hindered by the inherent sparsity and variability of observation matrices characterized by low overlap in observation types between different experiments, due to tissue consumption and cost of whole-organism experiments. ResultsWe analyzed the potential of bayesian hierarchical ODE models to estimate model parameters from sparse, non-overlapping, multivariate, time resolved datasets subject to experimental variability. The dataset in this study consisted of time series of chemical residue, gene-expression and survival obtained from 5 years of chemical exposure experiments with zebrafish embryos. Using the hierarchical approach, it was possible to increase the number of observations used in the model because experimental deviations in the exposure concentration could be accounted for. We identified inadequate dynamics of the uptake and metabolization kinetics in the ODE system, which led to strong overfitting when coupled to the hierarchical approach when wide prior distributions for the experimental error were used. In contrast, narrow distribution for the experimental error successfully avoided overfitting, despite model inadequacy. An additional simulation study of an adequate model confirmed that hierarchical models estimate parameters with low (< 10%) bias even under complete sparsity (i.e. no experimental overlap between measured variables); filling even small amounts (2.5%) of the missing information further reduced the parameter bias to (< 5%). Availability and ImplementationThe ODE model used in this study was implemented in the pymob model-building framework (https://github.com/flo-schu/pymob/), which provides the infrastructure for specifying high-dimensional ODE models and provides tools for bayesian inference. The case-study is available under https://github.com/flo-schu/hierarchical_molecular_tktd. The snakemake workflow for fitting pymob models is available under https://github.com/flo-schu/pymob-workflow. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/652942v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@a68678org.highwire.dtl.DTLVardef@c66e29org.highwire.dtl.DTLVardef@16a85beorg.highwire.dtl.DTLVardef@72a193_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

From Toxicogenomics Data to Cumulative Assessment Groups: A Mechanistic Framework for Chemical Grouping

The grouping of chemicals based on shared properties or molecular mechanisms of action is pivotal for advancing regulatory toxicology, reducing data gaps, and enabling cumulative risk assessments. This study introduces a novel framework usingChemical-Gene-Phenotype-Disease (CGPD) tetramers derived from the Comparative Toxicogenomics Database (CTDbase). Our approach integrates toxicogenomics data to identify and cluster chemicals with similar molecular and phenotypic effects across diverse categories, including pesticides, pharmaceuticals, and industrial chemicals such as bisphenols and per- and poly-fluoroalkyl substances (PFAS). We validated our method by comparing CGPD Tetramer-based clusters with cumulative assessment groups (CAGs) for pesticides, demonstrating strong overlap with established groupings while identifying additional compounds relevant for risk assessment. Key examples include clusters associated with endocrine disruption and metabolic disorders.By bridging omics-derived molecular data with phenotypic and disease endpoints, this framework provides a comprehensive tool for chemical grouping and supports evidence-based regulatory decision-making, facilitating the transition to next-generation risk assessment methodologies.

pharmacology and toxicology↗

bioRxiv2024

Leveraging zebrafish embryo phenotypic observations to advance data-driven analyses in toxicology

Zebrafish have emerged as a central model organism in toxicological research. Zebrafish embryos are exempt from certain animal testing regulations which facilitates their use in toxicological testing. Next to the zebrafish embryo acute toxicity test (ZFET) according to the OECD TG 236, fish embryos are used in mechanistic investigations, chemical screenings, in ecotoxicology, and drug development. However, inconsistencies in the applied test protocols and the monitored endpoints in addition to a lack of standardized data formats, impede comprehensive meta-analyses and cross-study comparisons. To address these challenges, we developed the Integrated Effect Database for Toxicological Observations (INTOB), a comprehensive data management tool that standardizes collection of metadata and phenotypic observations using a controlled vocabulary. By incorporating data from more than 600 experiments into the database and subsequent comprehensive data analyses, we demonstrate its utility in improving the comparability and interoperability of toxicity data. Our results show that the ZFET can detect toxicity spanning seven orders of magnitude at the scale of effect concentrations. We also highlight the potential of read-across analyses based on morphological fingerprints and their connection to chemical modes of action, provide information on control variability of the ZFET, and highlight the importance of time for mechanistic understanding in chemical exposure-effect assessments. We provide the full FAIR dataset as well as the analysis workflow and demonstrate how professional data management, as enabled with INTOB, marks a significant advancement by offering a comprehensive framework for the systematic use of zebrafish embryo toxicity data, thus paving the way for more reliable, data-driven chemical risk assessment. SynopsisThis article shows how a novel data management tool for zebrafish embryo toxicity data advances comparative chemical risk assessment by enhancing data and metadata standardization, comparability, and cross-study analyses.

pharmacology and toxicology↗

bioRxiv2024

Balancing Growth: GROOT Genes Link Plant Biomass and Temperature Adaptation in Arabidopsis

Root systems take up water and nutrients from the soil and thereby underpin all essential plant functions. The size of the root system affects the ability of roots to capture these resources and determine the amount of carbon that roots transfer into the soil. Understanding the genetic basis of root biomass is therefore very important for enhancing crop resilience and productivity, especially in the face of climate change, as for soil carbon sequestration efforts. In our study, we catalogued root biomass of a diverse set of Arabidopsis accessions mainly derived from Sweden and Spain and utilized GWAS to identify loci associated with root biomass in Arabidopsis thaliana. We found that genetic variants associated with high biomass are enriched in accessions originating from distinct biogeographic regions in Spain. Among the most significant SNPs, one SNP on chromosome 3, was closely linked to three genes for which loss of function mutations caused significant increases in root, shoot and seed biomass and that we named GROOT genes. These genes act as general growth limiters. Additionally, our analysis revealed that the GROOT genes are in strong linkage disequilibrium, implying a potential coordinated function in regulating growth. Furthermore, accessions carrying the non-reference allele at this SNP showed markedly higher biomass under elevated temperatures, suggesting that these genes may also play a role in temperature adaptation. The discovery that the GROOT genes can enhance biomass without negative trade-offs across multiple traits opens new avenues for further research aimed at understanding genetically determined growth limitations, and for improving crop resilience and adaptability, as well as carbon sequestration.

plant biology↗

bioRxiv2024

A genome-wide association screen for genes affecting leaf trichome development and epidermal metal accumulation in Arabidopsis

To identify novel genes engaged in plant epidermal development, we characterized the phenotypic variability of rosette leaf epidermis of 310 sequenced Arabidopsis thaliana accessions, focusing on trichome shape and distribution, compositional characteristics of the trichome cell wall, and histologically detectable metal ion distribution. Some of these traits correlated with climate parameters of ourq accessions locations of origin, suggesting environmental selection. A novel metal deposition pattern in stomatal guard cells was observed in some accessions. Subsequent GWAS analysis identified 1546 loci with protein sequence-altering SNPs associated with one or more traits, including 5 genes with previously reported relevant mutant phenotypes and 80 additional genes with known or predicted roles in relevant developmental and cellular processes. Some candidates, including GFS9/TT9, exhibited environmentally correlated allele distribution. Several large gene families, namely DUF674, DUF784, DUF1262, DUF1985, DUF3741, cytochrome P450, receptor-like kinases, Cys/His-rich C1 domain proteins and formins were overrepresented among the candidates for various traits, suggesting epidermal development-related functions. A possible participation of formins in guard cell metal deposition was supported by observations in available loss of function mutants. Screening of candidate gene lists against the STRING interactome database uncovered several predominantly nuclear protein interaction networks with possible novel roles in epidermal development.

plant biology↗

bioRxiv2024

Genome-Wide Association Study Reveals Influence of Cell-specific Gene Networks on Soybean Root System Architecture

Root system architecture (RSA) describes the shape and arrangement of a plants roots in the soil including the angle, rate of growth, and type of individual roots, which facilitates the uptake of nutrients and water. In crop improvement efforts, RSA has been less well studied due to the technical challenges associated with phenotyping roots as well as a focus on above-ground traits such as yield. We developed a gel-based root phenotyping system called RADICYL (Root Architecture 3D Cylinder), which is a non-invasive, high-throughput approach that enabled us to measure 15 RSA traits. We leveraged RADICYL to perform a comprehensive genome-wide association study (GWAS) with a panel of 371 diverse soybean elite lines, cultivars, landraces, and closely related species to identify gene networks underlying RSA. We identified 54 significant single nucleotide polymorphisms (SNPs) in our GWAS, some of which were shared across multiple RSA traits while others were specific to a given trait. We generated a single cell atlas of the soybean root using single nuclei RNA sequencing (snRNAseq) to explore the associated genes in the context of root tissues. Using gene co-expression network (GCN) analyses applied to RNA-seq of soybean root tissues, we identified network-level associations of genes predominantly expressed in endodermis with root width, and of those expressed in metaphloem with lateral root length. Our results suggest that pathways active in the endodermis and metaphloem cell-types influence soybean root system architecture.

plant biology↗

bioRxiv2023

Plasmodesmata act as unconventional membrane contact sites regulating inter-cellular molecular exchange in plants

Membrane contact sites (MCS) are fundamental for intracellular communication, but their role in intercellular communication remains unexplored. We show that in plants, plasmodesmata communication bridges function as atypical endoplasmic reticulum (ER)-plasma membrane (PM) tubular MCS, operating at cell-cell interfaces. Similar to other MCS, ER-PM apposition is controlled by a protein-lipid tethering complex, but uniquely, this serves intercellular communication. Combining high-resolution microscopy, molecular dynamics, pharmacological and genetic approaches, we show that cell-cell trafficking is modulated through the combined action of Multiple C2 domains and transmembrane domain proteins (MCTP) 3, 4, and 6 ER-PM tethers, and phosphatidylinositol-4-phosphate (PI4P) lipid. Graded PI4P amounts regulate MCTP docking to the PM, their plasmodesmata localization and cell-cell permeability. SAC7, an ER-localized PI4P-phosphatase, regulates MCTP4 accumulation at plasmodesmata and modulates cell-cell trafficking capacity in a cell-type specific manner. Our findings expand MCSs functions in information transmission, from intracellular to intercellular cellular activities. In briefPlant intercellular communication is regulated via tubular membrane contact through PI4P binding-ER-PM tether MCTP proteins HighlightsO_LIPlasmodesmata are unconventional ER/PM tubular contact sites located at cell-cell interface C_LIO_LIPlasmodesmata operate as control valves, modulating ER-PM contacts to regulate transport C_LIO_LIMCTP3, MCTP4, MCTP6 and PI4P tethering elements act as valve regulators C_LIO_LISAC7 PI4P phosphatase controls plasmodesmata MCS in a cell-type-specific manner C_LI

plant biology↗

bioRxiv2023

Fast and efficient root phenotyping via pose estimation

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plants phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

plant biology↗

bioRxiv2023

Nutrient levels control root growth responses to high ambient temperature in plants

Global warming will lead to significantly increased temperatures on earth. Plants respond to high ambient temperature with altered developmental and growth programs, termed thermomorphogenesis. Here we show that thermomorphogenesis is conserved in Arabidopsis, soybean, and rice and that it is linked to a decrease in the levels of the two macronutrients nitrogen and phosphorus. We also find that low external levels of these nutrients abolish root growth responses to high ambient temperature. We show that in Arabidopsis, this is due to the function of the transcription factor ELONGATED HYPOCOTYL 5 (HY5) and its transcriptional regulation of the transceptor NITRATE TRANSPORTER 1.1 (NRT1.1). Soybean and Rice homologs of these genes are expressed consistently with a conserved role in regulating temperature responses in a nitrogen and phosphorus level dependent manner. Overall, our data show that root thermomorphogenesis is a conserved feature in species of the two major groups of angiosperms, monocots and dicots, that it leads to a reduction of nutrient levels in the plant, and that it is dependent on sufficient environmental nutrient supply, a regulatory process mediated by the HY5-NRT1.1 module.

plant biology↗

bioRxiv2023

The AFB1 auxin receptor controls the cytoplasmic auxin response pathway in Arabidopsis thaliana

The phytohormone auxin triggers root growth inhibition within seconds via a non-transcriptional pathway. Among members of the TIR1/AFBs auxin receptor family, AFB1 has a primary role in this rapid response. However, the unique features that confer this specific function have not been identified. Here we show that the N-terminal region of AFB1, including the F-box domain and residues that contribute to auxin binding, are essential and sufficient for its specific role in the rapid response. Substitution of the N-terminal region of AFB1 with that of TIR1 disrupts its distinct cytoplasm-enriched localization and activity in rapid root growth inhibition. Importantly, the N-terminal region of AFB1 is indispensable for auxin-triggered calcium influx which is a prerequisite for rapid root growth inhibition. Furthermore, AFB1 negatively regulates lateral root formation and transcription of auxin-induced genes, suggesting that it plays an inhibitory role in canonical auxin signaling. These results suggest that AFB1 may buffer the transcriptional auxin response while it regulates rapid changes in cell growth that contribute to root gravitropism.

plant biology↗

bioRxiv2022

Root Walker: an automated pipeline for large scale quantification of early root growth responses at high spatial and temporal resolution

Plants are sessile organisms that constantly adapt to their changing environment. The root is exposed to numerous environmental signals ranging from nutrients and water to microbial molecular patterns. These signals can trigger distinct responses including the rapid increase or decrease of root growth. Consequently, using root growth as a readout for signal perception can help decipher which external cues are perceived by roots, and how these signals are integrated. To date, studies measuring root growth responses using large numbers of roots have been limited by a lack of high-throughput image acquisition, poor scalability of analytical methods, or low spatiotemporal resolution. Here, we developed the Root Walker pipeline, which uses automated microscopes to acquire time-series images of many roots exposed to controlled treatments with high-spatiotemporal-resolution, in conjunction with fast and automated image analysis software. We demonstrate the power of Root Walker by quantifying root growth rate responses at different time and throughput scales upon treatments with natural auxin, and upon treatment with two mitogen-associated protein kinase cascade inhibitors. We find a concentration-dependent root growth response to auxin and reveal the specificity of one MAPK inhibitor. We further demonstrate the ability of Root Walker for conducting genetic screens by performing a genome wide association study on 260 accessions under 2 weeks. This revealed known and unknown root growth regulators. Root Walker promises to be a useful toolkit for the plant science community, allowing large-scale screening of root growth dynamics for a variety of purposes, including genetic screens for root sensing and root growth response mechanisms.

plant biology↗

bioRxiv2022

Ubiquitination steers SRF3 plasma membrane nano-organization to specify signaling outputs

Organisms cope with myriads of competing and conflicting environmental signals. These signals are often perceived by cell surface receptor kinases to mount appropriate adaptive responses. However, it is not well understood by which mechanism single receptor kinases can transduce different signals. The plant receptor kinase SRF3 transduces low iron and bacteria-derived signals. We found that upon these signals, ubiquitinated SRF3 is recognized by clathrin-mediated endocytosis for vacuolar targeting. Live super resolution microscopy revealed that cell surface SRF3 is present in a fast diffusible fraction, which is sustained by ubiquitination, and that non-ubiquitinated SRF3 is present in immobile nanodomains. Ubiquitination-mediated degradation of SRF3 is required for signaling only under low iron but not upon flg22 perception. Flg22-triggered SRF3 phosphorylation leads to SRF3 accumulation in the immobile fraction in which degradation is restricted, thereby preventing low iron signaling. We therefore propose that ubiquitination-dependent plasma membrane nano-organization of SRF3 specifies its signal transduction pathways.

plant biology↗

bioRxiv2022

Identification of a small molecule, Mebendazole, as a novel ethylene signaling activator reveals a role of ethylene signaling on the regulation of lateral root angles

The lateral root angle or gravitropic set-point angle (GSA) is an important trait for root system architecture (RSA) that determines the radial expansion of the root system. The GSA therefore plays a crucial role for the ability of plants to access nutrients and water in the soil. Despite its importance, only few regulatory pathways and mechanisms that determine GSA are known, and these mostly relate to auxin and cytokinin pathways. Here, we report the identification of a small molecule, Mebendazole (MBZ) that modulates GSA in Arabidopsis thaliana roots and acts via the activation of ethylene signaling. We uncover that MBZ directly acts on the serine/threonine protein kinase CTR1, which is a negative regulator of ethylene signaling. Our study not only reveals that the ethylene signaling pathway is essential for GSA regulation, but it also identifies a small molecular modulator of RSA and the first small molecule that acts downstream of ethylene receptors and that directly activates ethylene signaling. In briefHe et al. identify a small molecule that regulates lateral root angle. They show that the compound increases lateral root angle by inhibiting CTR1 kinase activity, which in turn activates ethylene signaling. Therefore, they uncover that the ethylene pathway is involved in lateral root angle regulation. HighlightsO_LIMBZ increases lateral root angle C_LIO_LIMBZ regulates lateral root angle by activating ethylene signaling C_LIO_LIMBZ inhibits CTR1 kinase activity C_LIO_LIThe ethylene pathway is involved in lateral root angle regulation C_LI

plant biology↗

bioRxiv2021

AI for predicting chemical-effect associations at the universe level - deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods - even if high throughput - are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn. Supplementary informationSupplementary data are available at bioRxiv online. Contactjana.schor@ufz.de

bioinformatics↗