Biology subjects

Gross, T.

Publications and source records attributed to Gross, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic De-Risking of TCR-Mimic Therapeutics Through Proteome-Wide Off-Target Landscaping and a Generalizable Design Rule Framework

TCR-mimic (TCRm) antibodies targeting peptide-human leukocyte antigen (pHLA) complexes enable precision immunotherapy against intracellular antigens, including cancer-testis antigens (CTAs). Achieving high specificity, however, remains challenging because of the vast diversity of the human immunopeptidome and the associated risk of off-target recognition. Here, we introduce ValidaTe, a unified framework for the proteome-scale prediction, validation, and mitigation of off-target liabilities in pHLA-directed therapeutics. ValidaTe integrates rational target prioritization, peptide-centric binder selection, proteome-wide off-target prediction, and therapeutic engineering into a hierarchical de-risking workflow. Using the CTA MAGE-A4 as a proof-of-concept, we identify the TCRm antibodies VR-4 and VR-6 with superior specificity and demonstrate how this workflow enables the discovery of safer pHLA-targeted binders. Furthermore, ValidaTe establishes the basis for the WiFi (Widened Fingerprint) engineering principle, which rationally combines TCRms with complementary off-target fingerprints in trivalent T-cell engagers to minimize unintended interactions while preserving potent target-specific activity. Together, these findings establish a generalizable framework for the rational development of safer and more selective pHLA-targeted therapeutics. We further discuss how orthogonal proteomic characterization may complement this workflow as a final layer of translational safety assessment prior to clinical development. TeaserValidaTe accelerates safe pHLA-targeted immunotherapy through proteome-wide off-target mapping and WiFi design

immunology↗

bioRxiv2026

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide association studies are the largest source of genetic evidence for disease risk but linking non-coding variants to effector genes remains a significant barrier to identifying causal targets. Current gene-mapping approaches suffer from proximity bias, largely ignoring distal genes. Here we introduce Rosalind, a DNA foundation model fine-tuned on human genetic variation from GTEx, that directly predicts variant-gene regulatory relationships from sequence without relying on nearest-gene heuristics. We demonstrate Rosalinds accuracy through extensive benchmarking, apply it to multiple complex traits to establish broad utility, and provide experimental validation in osteoporosis using a translational osteoblast assay. We demonstrate that genes distal to osteoporosis risk variants were significantly more likely to alter a bone formation phenotype than nearest genes. Together, these results highlight deep learning-based regulatory models as a general and scalable framework for translating novel genetic insights to drug discovery.

genetics↗

bioRxiv2026

Reference-free single-vesicle profiling of small extracellular vesicles from liquid biopsies with the PICO assay

Small extracellular vesicles (sEV) are membrane-enclosed nanoparticles found in body fluids that carry molecular cargo from their cells of origin. Their stability and disease-associated molecular signatures make them promising targets for the development of non-, or minimally invasive liquid biopsies, yet scalable approaches enabling single-vesicle quantification of sEV while resolving their heterogeneity remain limited. Here, we present PICO (Protein Interaction Coupling), a reference-free quantitative assay adapted for sensitive multiplex profiling of individual intact vesicles. PICO detects vesicle markers by requiring colocalization of two or more copies of the same protein or of distinct proteins (e.g., CD9 or CD9/CD63) on individual vesicles, using DNA-barcoded antibodies and digital PCR (dPCR) for quantitative readout. We demonstrate that this unique architecture of the assay provides high specificity by distinguishing EV-bound proteins from soluble counterparts, and can be adapted to target either surface-exposed or intravesicular biomarkers. PICO requires minimal sample input (1 {micro}l) and no specialized instrumentation beyond standard digital PCR. In a head-to-head comparison with nano-flow cytometry, PICO achieved a comparable limit of detection for sEV subpopulations. Profiling sEV isolates from blood for canonical markers (CD9, CD63 and CD81) and HER2 demonstrates precise, high-resolution quantification of sEV subpopulations in complex clinical samples and supports integration of scalable single-EV analysis into research and diagnostic workflows.

molecular biology↗

bioRxiv2025

UC Irvine Brain Initiative Cell Atlas Network (BICAN) Brain Procurement Program for the Center for Multiomic Human Brain Cell Atlas Project

High-quality neurotypical postmortem human brain tissue is essential but difficult to obtain for constructing comprehensive human brain cell atlases. Here we describe the establishment of UC Irvines Brain Procurement Program, a coordinated initiative to collect and process neurotypical donor brains for multiomic mapping studies within the NIH BRAIN Initiative Cell Atlas Network (BICAN) consortium. Through partnerships with the Orange County Coroners Office, the UC Irvine Willed Body Program, UCI Medical Center, and the Childrens Hospital of Orange County, we have developed standardized workflows encompassing donor identification, postmortem brain recovery and processing, region-of-interest dissection, neurotypical donor selection, and data management. Our experience demonstrates the feasibility of a community-based, multi-institutional procurement framework while highlighting challenges in recruiting neurotypical donors and ensuring demographic representation reflective of Southern California. We further identify opportunities to strengthen outreach and donation pathways. This program provides a scalable model for advancing population-reflective, high-quality human brain cell atlas efforts. HighlightsO_LIEstablish a multi-site pipeline for procuring high-quality neurotypical human brains. C_LIO_LIDemonstrate feasibility of donor collection across childhood to adulthood. C_LIO_LIIdentify barriers and propose strategies to improve broad donor recruitment. C_LI

neuroscience↗

bioRxiv2025

Generalized dynamics of cooperating bacteria

Bacterial cooperation involves the exchange of metabolites, which can range from costless byproducts of metabolism to intentionally produced and costly molecules. These interactions occur across spatial scales, from direct cell contact to the diffusion of metabolites in the environment. Due in part to this variety of interaction modes, the impacts of mutualism on bacterial community dynamics remain unclear. Using generalized models, we derive conditions for the onset of different dynamical behaviors in communities of bacterial cooperators across these scenarios. These include exchanges of low-cost metabolites, costly cross-feeding, and cases where bacteria produce either most or only a small fraction of available metabolites. Stability depends strongly on metabolic production costs and the balance between metabolite uptake and production. We further show that perturbations to bacteria have larger impacts than to metabolites. Finally, we demonstrate that spatial metabolite diffusion drives pattern formation, emphasizing the links between local stability and spatial structures in bacterial cooperation.

ecology↗

bioRxiv2025

Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens

Cardiac fibrosis is mediated by the persistent activity of myofibroblasts, which differentiate from resident cardiac fibroblasts in response to tissue damage and stress signals. The signaling pathways and transcription factors regulating fibrotic transformation have been thoroughly studied. In contrast, the roles of chromatin factors in myofibroblast differentiation and their contribution to pathogenic cardiac fibrosis remain poorly understood. Here, we combined bulk and single-cell CRISPR screens to characterize the roles of chromatin factors in the fibrotic transformation of primary cardiac fibroblasts. We uncover strong regulators of fibrotic states including Srcap and Kat5 chromatin remodelers. We confirm that these factors are required for functional processes underlying fibrosis including collagen synthesis and cell contractility. Using chromatin profiling in perturbed cardiac fibroblasts, we demonstrate that pro-fibrotic chromatin complexes facilitate the activity of well-characterized pro-fibrotic transcription factors. Finally, we show that KAT5 inhibition alleviates fibrotic responses in patient-derived human fibroblasts.

cell biology↗

bioRxiv2024

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

bioinformatics↗

bioRxiv2024

Lossless Single-Molecule Counting To Absolute QuantifyProteoforms

A novel immunoassay, termed Protein Interaction Coupling (PICO), is introduced to deliver unequivocal, reference-free quantification of proteoforms - absolute quantification. PICO employs a compartmentalized, homogeneous single-molecule assay with a lossless and highly sensitive signal generation, capable of detecting down to a few molecules per reaction. Additionally, it utilises a background-free, digital enumeration principle, known as the decouplexing. PICO is presented as exact mathematical theories, providing a theoretical comprehension of its chemistry. Consequently, PICO demonstrates absolute quantification, as exemplified with recombinant and non-recombinant ErbB2 and multi-tagged peptide rTRX targets, validating absolute quantification against internal and external references in both analytical and cellular matrices. Furthermore, PICO enables combinatorial multiplexing (cplex), a readout between any two antibodies, demonstrated by an 8-plex antibody, 12-cplex PICO, measuring functional changes of ErbB pathway upon mock and dactolisib treatment delivering absolute quantitative cellular stoichiometry. PICO possesses immense potential for versatile, standardized, and accurate protein measurements, offering insights into physiological and perturbed cellular processes.

molecular biology↗

bioRxiv2022

scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

systems biology↗

bioRxiv2022

Systematic functional screening of chromatin factors identifies strong lineage and disease dependencies in normal and malignant haematopoiesis

Interactions between transcription factors (TF) and chromatin factors (CF) regulate gene expression programmes to determine cellular fate. However, unlike for TF, the exact role of CF in this process is poorly understood. Using haematopoiesis as a model system and utilising novel functional CRISPR screens ex vivo and in vivo, coupled with Perturb-Seq, CF binding and genome-wide chromatin accessibility in primary murine cells, we assess the role of 550 chromatin factors in lineage choice in normal haematopoiesis and the maintenance of acute myeloid leukaemia (AML). These studies demonstrate marked specificity for a large number of CFs in lineage determination, highlighting functional diversity within specific families of chromatin regulators, including MLL-H3K4-methyltransferases and different BAF-complexes, that regulate disparate lineage decisions across haematopoiesis. Conversely, we demonstrate that unrelated Repressive complexes function similarly to restrain excessive myeloid differentiation and protect lineage diversity. We identify interactions between CF and TF that, at least in part, explain the regulatory function of CF and link Brd9-loss to a premalignant state. Utilising similar experiments in a relevant murine AML model, we demonstrate opposing effects for CF in normal haematopoiesis and AML, where MLL-H3K4-methyltransferases, c-BAF-remodelers and Repressive complexes prevent differentiation and maintain leukaemic fitness. We show that this alteration relates to differential utilisation of TF by CF complexes between normal and malignant haematopoiesis, highlighting corrupted TF-CF interactions as potential novel avenues for therapeutic intervention in AML. Together, this study provides novel insights on the functional diversity of chromatin factors in governing cell-fate.

genomics↗

bioRxiv2022

Quantification of metabolic niche occupancy dynamics in a Baltic Sea bacterial community

Progress in molecular methods has enabled the monitoring of bacterial populations in time. Nevertheless, understanding community dynamics and its links with ecosystem functioning remains challenging due to the tremendous diversity of microorganisms. Conceptual frameworks that make sense of time-series of taxonomically-rich bacterial communities, regarding their potential ecological function, are needed. A key concept for organizing ecological functions is the niche, the set of strategies that enable a population to persist and define its impacts on the surroundings. Here we present a framework based on manifold learning, to organize genomic information into potentially occupied bacterial metabolic niches over time. We apply the method to re-construct the dynamics of putatively occupied metabolic niches using a long-term bacterial time-series from the Baltic Sea, the Linnaeus Microbial Observatory (LMO). The results reveal a relatively low-dimensional space of occupied metabolic niches comprising groups of taxa with similar functional capabilities. Time patterns of occupied niches were strongly driven by seasonality. Some metabolic niches were dominated by one bacterial taxon whereas others were occupied by multiple taxa, and this depended on season. These results illustrate the power of manifold learning approaches to advance our understanding of the links between community composition and functioning in microbial systems. ImportanceThe increase in data availability of bacterial communities highlights the need for conceptual frameworks to advance our understanding of these complex and diverse communities alongside the production of such data. To understand the dynamics of these tremendously diverse communities, we need tools to identify overarching strategies and describe their role and function in the ecosystem in a comprehensive way. Here, we show that a manifold learning approach can coarse grain bacterial communities in terms of their metabolic strategies and that we can thereby quantitatively organize genomic information in terms of potentially occupied niches over time. This approach therefore advances our understanding of how fluctuations in bacterial abundances and species composition can relate to ecosystem functions and it can facilitate the analysis, monitoring and future predictions of the development of microbial communities.

microbiology↗

bioRxiv2022

Integrated workflow of down-scaled Smart-seq2 and absolute quantitative, single-cell digital PCR validates lack of representability of low-abundant transcripts

Single-cell RNA sequencing (scRNA-seq) can unmask transcriptional heterogeneity facilitating the detection of rare subpopulations at unprecedented resolution. In response to challenges related to coverage and quantity of transcriptome analysis, the lack of unbiased and absolutely quantitative validation methods hampers further improvements. Digital PCR (dPCR) represents such a method as we could show that the inherent partitioning enhances molecular detections by increasing effective mRNA concentrations. We developed a scRT-ddPCR method and validated it using two breast cancer cell lines, MCF7 and BT-474, and bulk methods. ErbB2, a low-abundant transcript in MCF7 cells, suffers from dropouts in scRNA-seq and thus calculated fold changes are biased. Using our scRT-ddPCR, we could improve the detection of ErbB2 and based on the absolute counts obtained we could validate the scRNA-seq fold change. We think this workflow is a valuable addition to the single-cell transcriptomic research toolbox and could even become a new standard in fold change validation because of its reliability, ease of use and increased sensitivity.

molecular biology↗

bioRxiv2022

De-tensioning of collagen fibers optimizes endometrial receptivity and improves the rate of embryo implantation

Successful embryo implantation within the uterine wall requires intricate endometrial remodeling. Impaired endometrial receptivity, a common cause of infertility, often results from ineffective remodeling processes. Here, we demonstrate that a single dose of human collagenase-1 administered into the mouse uterus enhances embryo implantation rates. Mechanistically, collagenase-1 induces remodeling of the endometrial extracellular matrix (ECM), leading to the degradation of collagen fibers and proteoglycans. This process releases matrix-bound bioactive factors, such as VEGF, which facilitates local vascular permeability and angiogenesis. Furthermore, collagenase-1 treatment increases NK cell infiltration and elevates levels of the cytokine LIF, a key factor in embryo implantation. Remarkably, the overall structural integrity of the uterine tissue remains uncompromised, even in the presence of reduced tension in endometrial collagen fibers. To assess pre-clinical potential, in-uteri application of collagenase-1 successfully rescued implantation in mouse models subjected to heat stress and embryo transfer, conditions known for their adverse impact on implantation rates. Importantly, ex-vivo exposure of human uterine tissue to collagenase-1 induced collagen de-tensioning and the release of VEGF, demonstrating similar processes observed in the mouse settings, and the potential relevance of this treatment to human conditions. Our findings underscore the immense clinical potential and feasibility of collagenase treatment to enhance uterine receptivity for embryo implantation, offering a controlled and minimally invasive intervention. This innovative approach not only demonstrates the potential to enhance efficiency in livestock breeding but, more importantly, signifies a substantial promise in supporting medical interventions for human reproduction in clinical settings.

biochemistry↗

bioRxiv2021

Transcription factor action orchestrates the complex expression pattern of CRABS CLAW, a gynoecium developmental regulator in Arabidopsis

The flower of angiosperms is the most complex organ that plants generate and many transcription factors (TFs) are involved to regulate its morphogenesis in a coordinated way. In its center, the gynoecium develops consisting of specialized tissues such as secondary meristems, sites of postgenital fusion, ovules, pollen transmitting tract, all to assure successful sexual reproduction. Gynoecium development requires tight regulation of developmental regulators across time and tissues. However, while we know of several examples how simple on/off regulation of gene expression is achieved in plants, it remains unclear which regulatory processes generate complex expression patterns. Here, we use the gynoecium developmental regulator CRABS CLAW (CRC) from Arabidopsis to study regulatory mechanisms contributing to its sophisticated expression pattern. Using a combination of in silico promoter analyses, global TF-DNA interaction screens, co-expression and mutant analysis we find that miRNA action, DNA methylation, and chromatin remodeling do not contribute substantially to CRC regulation. We show that a plethora of TFs bind to the CRC promoter to fine-tune transcript abundance by activation of transcription, linking CRC to specific developmental processes but not biotic or abiotic stress. Interestingly, the temporal-spatial aspects of regulation of expression may be under the control of redundantly acting genes and may require higher order complex formation at TF binding sites. We conclude that endogenous regulation of complex expression pattern of Arabidopsis genes requires orchestrated transcription factor action on several conserved promotor sites over almost 4 kb in length. Significance statementDifferent to genes that are simply switched on or off, depending on an environmental cue we find that genes directing development in plants often show complex expression pattern dependent on internal factors only. Here, we addressed the question how an complex expression pattern is achieved and use the CRABS CLAW (CRC) gene required for gynoecium development as an example. Combining wet lab experiments and in silico analysis we find that epigenetic regulation plays only a minor role and that a large number of transcription factors activates the transcription of CRC. Single regulators may have a profound effect on CRC transcript abundance but less so on the pattern of expression. Complex patterns most likely require the interplay of several transcription factors.

genetics↗

bioRxiv2020

Characterization of CRISPR/Cas9 RANKL knockout mesenchymal stem cell clones based on single-cell printing technology and emulsion coupling assay as a low-cellularity workflow for single-cell cloning

The homogeneity of the genetically modified single-cells is a necessity for many applications such as cell line development, gene therapy, and tissue engineering and in particular for regenerative medical applications. The lack of tools to effectively isolate and characterize CRISPR/Cas9 engineered cells is considered as a significant bottleneck in these applications. Especially the incompatibility of protein detection technologies to confirm protein expression changes without a preconditional large-scale clonal expansion, creates a gridlock in many applications. To ameliorate the characterization of engineered cells, we propose an improved workflow, including single-cell printing/isolation technology based on fluorescent properties with high yield, a genomic edit screen (surveyor assay), mRNA rtPCR assessing altered gene expression and a versatile protein detection tool called emulsion-coupling to deliver a high-content, unified single-cell workflow. The workflow was exemplified by engineering and functionally validating RANKL knockout immortalized mesenchymal stem cells showing altered bone formation capacity of these cells. The resulting workflow is economical, without the requirement of large-scale clonal expansions of the cells with overall cloning efficiency above 30% of CRISPR/Cas9 edited cells. Nevertheless, as the single-cell clones are comprehensively characterized at an early, highly parallel phase of the development of cells including DNA, RNA, and protein levels, the workflow delivers a higher number of successfully edited cells for further characterization, lowering the chance of late failures in the development process. Author summaryI completed my undergraduate degree in biochemistry at the University of Ulm and finished my master's degree in pharmaceutical biotechnology at the University of Ulm and University of applied science of Biberach with a focus on biotechnology, toxicology and molecular biology. For my master thesis, I went to the University of Freiburg to the department of microsystems engineering, where I developed a novel workflow for cell line development. I stayed at the institute for my doctorate, but changed my scientific focus to the development of the emulsion coupling technology, which is a powerful tool for the quantitative and highly parallel measurement of protein and protein interactions. I am generally interested in being involved in the development of innovative molecular biological methods that can be used to gain new insights about biological issues. I am particularly curious to unravel the complex and often poorly understood protein interaction pathways that are the cornerstone of understanding cellular functionality and are a fundamental necessity to describe life mechanistically.

cell biology↗

bioRxiv2020

Localization of hyphal growth associated with mycotoxin production during the malting of Fusarium head blight infected grains

Fusarium head blight (FHB) and the occurrence of mycotoxins is the largest food safety threat to malting and brewing grains. Objectives of the current study were to localize the growth of Fusarium within FHB infected kernels and to associate it with the production of DON that occurred during malting. FHB infected barley, wheat, rye, and triticale grains that exhibited large increases in Fusarium Tri5 DNA and trichothecene mycotoxins following malting, were screened for hyphal localization. The growth of hyphae, both on the surface of kernels and within tissues of grain and malt was, imagined by scanning electron microscopy and confocal laser scanning microscopy assisted with WGA-Alexa Fluor 488 pre-staining, respectively. In barley, hyphae were primarily present on or within husk, vascular bundle, and pericarp cavities. Following malting, large amounts of hyphal growth were observed in not only these regions, but also in the aleurone layer, endosperm, and embryo. Extensive fungal growth was also observed following malting of wheat, rye, and triticale. Interestingly, these grains already had an extensive internal presence of hyphae in unmalted grain, occurring in the pericarp, testa, vascular bundle, nucellar projection, aleurone layer, endosperm, pericarp and endosperm cavities, and embryo. Shotgun sequencing followed by metagenomics analysis verified that Fusarium spp. accounted for above 90% of the fungal hyphae growing in the interior of grains during malting, which coincided with the significant production of mycotoxins.

plant biology↗

bioRxiv2020

Identifiability and experimental design in perturbation studies

MotivationA common strategy to infer and quantify interactions between components of a biological system is to deduce them from the networks response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters. ResultsWe show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments. AvailabilityIdentiFlow is available at github.com/GrossTor/IdentiFlow. Contactnils.bluethgen@charite.de

systems biology↗

bioRxiv2019

Transcriptional changes suggest a major involvement of Gibberellins in Trifolium pratense regrowth after mowing

Red clover (Trifolium pratense) is used worldwide as a fodder plant due its high nutritional value. In response to mowing, red clover exhibits specific morphological traits to compensate the loss of biomass. The morphological reaction is well described, but knowledge of the underlying molecular mechanisms are still lacking. Here we characterize the molecular genetic response to mowing of red clover by using comparative transcriptomics in greenhouse conditions and agriculturally used field. The analysis of mown and control plants revealed candidate genes possibly regulating crucial steps of the genetic network governing the regrowth reaction. In addition, multiple identified gibberellic acid (GA) related genes suggest a major role for GA in establishing the regrowth morphology of red clover. Mown red clover plants showing this regrowth morphology were partially \"rescued\" by exogenous GA application, demonstrating the influence of GA during regrowth. Our findings provide insights into the physiological and genetic processes of mowing red clover, to serve as a base for red clover yield improvement.

plant biology↗