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At least 109 records · Page 6Linked to original sources

bioRxiv2024

Predictable modulation of plant root development using engineered cytokinin regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

plant biology↗

bioRxiv2020

Orchard layout and plant traits influence fruit yield more strongly than pollinator behaviour and density in a dioecious crop

Mutualistic plant-pollinator interactions are critical for the functioning of both non-managed and agricultural systems. Mathematical models of plant-pollinator interactions can help understand key determinants in pollination success. However, most previous models have not addressed pollinator behavior and plant biology combined. Information generated from such a model can inform optimal design of crop orchards and effective utilization of managed pollinators like honey bees, and help generate hypotheses about the effects of management practices and cultivar selection. We expect that honey bee density per flower and male to female flower ratio will influence fruit yield. To test the relative importance of these effects, both singly and simultaneously, we utilized a delay differential equation model combined with Latin hypercube sampling for sensitivity analysis. Empirical data obtained from historical records and collected in kiwifruit orchards in New Zealand were used to parameterize the model. We found that, at realistic bee densities, the optimal orchard had 65-75% female flowers, and the most benefit was gained from the first 6-8 bees/1000 flowers, with diminishing returns thereafter. While bee density significantly impacted fruit production, plant-based parameters-flower density and male:female flower ratio-were the most influential. The predictive model provides strategies for improving crop management.

plant biology↗

bioRxiv2025

High-efficiency, transgene-free plant genome editing by viral delivery of an engineered TnpB

Genome editing has revolutionized plant biology research. However, efficient and straightforward delivery of editing reagents remains a major challenge. Viral delivery systems can address these issues, but CRISPR-Cas nucleases are often too large for viral vectors. Recently, smaller editors like TnpBs have been identified, but wild-type TnpBs are significantly less active than commonly used Cas9 nucleases. Here, we optimized a tobacco rattle virus (TRV)-based system to deliver newly discovered, highly active engineered ISDra2 TnpB variants. Our results demonstrate that the eTnpBc variant delivered via TRV enables effective somatic editing in systemic leaves and achieves up to 90% editing efficiency at target loci, significantly higher than that of wild-type ISDra2 TnpB. Additionally, up to 89% of offspring exhibit a mutant phenotype, with editing efficiencies reaching 100%. The design principles outlined here are expected to accelerate broader adoption of eTnpBc for transformation- and transgene-free genome editing in plants.

plant biology↗

bioRxiv2022

PEP7 is a ligand for receptor kinase SIRK1 to regulate aquaporins and root growth

Plant receptor kinases constitute a large protein family that regulate various aspects of development and responses to external biotic and abiotic cues. Functional characterization of this protein family and particularly the identification of their ligands remains a major challenge in plant biology. Previously, we identified plasma membrane-intrinsic SUCROSE INDUCED RECEPTOR KINASE 1 (SIRK1) and QIAN SHOU KINASE 1 (QSK1) as a receptor / co-receptor pair involved in regulation of aquaporins in response to osmotic conditions induced by sucrose. Here, we identified a member of the Elicitor Peptide (PEP) family, namely PEP7, as the specific ligand of receptor kinase SIRK1. PEP7 binds to the extracellular domain of SIRK1 with a binding constant of 1.44{+/-}0.79 {micro}M and is secreted to the apoplasm specifically in response to sucrose treatment. Stabilization of a signaling complex involving SIRK1, QSK1 and aquaporins as substrates is mediated by alterations in the external sucrose concentration or by PEP7 application. Moreover, the presence of PEP7 induces the phosphorylation of aquaporins in vivo and enhance water influx into protoplasts. The loss-of-function mutant of SIRK1 is not responsive to external PEP7 treatment regarding kinase activity, aquaporin phosphorylation and water influx activity. Our data indicate that the PEP7/SIRK1/QSK1 complex represents a crucial perception and response module mediating sucrose-controlled water flux in plants.

plant biology↗

bioRxiv2026

Somatic-to-germline transmission of horizontally acquired extrachromosomal circular DNA in Brassica graft chimeras

Whether somatically acquired traits can contribute to heritable variation has remained an open question in plant biology for over a century. Here, using graft-induced periclinal chimeras between Brassica juncea and Brassica oleracea, we identified extrachromosomal circular DNA (eccDNA) as mobile genetic elements capable of crossing histological boundaries and entering the germline. We demonstrated that grafting could promote the horizontal transfer of eccDNA between somatic cell layers, enabling its stable maintenance in recipient tissues and permitting transmission through sexual reproduction across multiple generations. We found that transmitted eccDNA is non-randomly distributed across the genome, preferentially originating from gene-dense regions, and enriched for features associated with molecular persistence, such as inverted repeats, hairpin-forming sequences, and autonomously replicating sequence (ARS) consensus motifs. These genetic regions conferred replication competence, as evidenced by autonomous propagation in an ARS-less heterologous yeast system. Sexual progeny carrying graft-acquired eccDNA exhibited reproducible and lineage-dependent alterations in leaf morphology and drought tolerance, accompanied by coordinated transcriptional reprogramming. Notably, a subset of inherited eccDNA remained transcriptionally active in progeny, producing transcripts absent from self-grafted controls. Our findings establish eccDNA as heritable extrachromosomal elements that link graft-mediated somatic genetic transfer with stable germline transmission, thereby expanding the molecular scope of heritable variation in plants and providing a conceptual framework for graft-based trait transmission.

plant biology↗

bioRxiv2025

A tRNA-gRNA multiplexing system for CRISPR genome editing in Marchantia polymorpha.

The liverwort Marchantia polymorpha is a widely used model organism for studying land plant biology, which has also proven to be a promising testbed for bioengineering. CRISPR/Cas9 technology has emerged as a transformative tool for precise genome modifications in M. polymorpha. However, a robust method for the simultaneous expression of multiple gRNAs, which is crucial for enhancing the efficiency and versatility of CRISPR/Cas9-based genome editing, has yet to be fully developed. In this study, we introduce an adaptation from the OpenPlant kit CRISPR/Cas9 tools, that facilitates expression of multiple gRNAs from a single transcript through incorporation of tRNA sequences. This approach significantly improves the efficiency and scalability of genome editing in M. polymorpha. Additionally, by combining this vector system with a simplified and optimized protocol for thallus transformation, we further streamline the generation of CRISPR/Cas9 mutants in M. polymorpha. The resulting gene- editing system offers a versatile, time-saving and straightforward tool for advancing functional genomics in M. polymorpha, enabling more comprehensive genetic modifications and genome engineering.

plant biology↗

bioRxiv2021

ARGONAUTE10 is required for cell fate specification and the control of formative cell divisions in the Arabidopsis root meristem

A key question in plant biology is how oriented cell divisions are integrated with patterning mechanisms to generate organs with adequate cell type allocation. In the root vasculature, a miRNA gradient controls the abundance of HD-ZIP III transcription factors, which in turn control cell fate and spatially restrict vascular cell proliferation to specific cells. Here, we show that a functional miRNA gradient requires an opposing gradient of ARGONAUTE10, which sequesters miRNAs to protect HD-ZIP III transcripts from degradation. In the absence of ARGONAUTE10, xylem precursor cells undergo periclinal divisions that lead to continuous strands of differentiated xylem elements at ectopic positions. Notably, periclinal daughter cells maintain xylem identity even when they are located outside of the xylem axis, resulting in disrupted tissue boundaries. We further demonstrate that ARGONAUTE10 and HD-ZIP IIIs buffer cytokinin signalling to control formative cell divisions, providing a framework for integration of phytohormone and miRNA-mediated patterning.

plant biology↗

bioRxiv2020

Area from image analyses accurately estimates dry-weight biomass of juvenile moss tissue

O_LIMosses have long served as models for studying many areas of plant biology. Investigators have used two-dimensional measurements of juvenile growth from photographs as a surrogate for dry-weight biomass. The relationship between area and biomass, however, has not been critically evaluated. C_LIO_LIHere we grew axenic tissue cultures of ten Ceratodon purpureus isolates to study the relationship between these parameters. We measured area and biomass on replicate cultures with two distinct starting inoculum sizes each week for three weeks and examined the correlation between area and biomass as well as the influence of variation in inoculum size on both parameters. C_LIO_LIWe found a strong correlation between area and biomass after two weeks of growth. Furthermore, we found inoculum size affected biomass during the first week of growth but not subsequent weeks and inoculum size had no detectible effect on area. C_LIO_LIThese analyses provide experimental confirmation that area is a suitable proxy for biomass and provide clear guidelines for when inoculum size variation may affect downstream growth estimates. C_LI

plant biology↗

bioRxiv2025

Developmentally-specific physiological and metabolic responses support drought resilience in switchgrass and constrains biofuel yield

Switchgrass (Panicum virgatum) is a promising bioenergy crop due in part to its resilience to drought stress. However, the significance of drought timing remains poorly understood, both from a plant biology perspective and its impact on downstream biofuel production. This study determines the developmental stage-specific physiological and metabolic responses of switchgrass to drought stress and its implications for biofuel production using a custom-built programmable irrigation system. Vegetative, flowering, and senescence-stage drought significantly reduced CO2 assimilation, and stomatal conductance without affecting biomass yield. Metabolic profiling revealed significant accumulation of glucose, fructose, quinic acid, shikimate and GABA during vegetative-stage drought, while flowering and senescence stages exhibited limited metabolic changes. Similarly, specialized metabolites also displayed distinct developmental patterns, with vegetative-stage drought driving the most pronounced metabolic alterations. Thermochemically-treated and hydrolyzed switchgrass biomass from vegetative-stage drought showed elevated lignocellulose-derived compounds and saponins with the latter most positively correlating with fermentation lag times. Conversely, senescence-stage drought enhanced ethanol yields while lowering saponin levels in the hydrolysates. While vegetative-stage drought enhanced physiological resilience, it compromises downstream biofuel production by introducing fermentation inhibitors, particularly saponins.

plant biology↗

bioRxiv2019

The power of model-to-crop translation illustrated by reducing seed loss from pod shatter in oilseed rape

In the 1980s, plant scientists descended on a small weed Arabidopsis thaliana (thale cress) and developed it into a powerful model system to study plant biology. The massive advances in genetics and genomics since then has allowed us to obtain incredibly detailed knowledge on specific biological processes of Arabidopsis growth and development, its genome sequence and the function of many of the individual genes. This wealth of information provides immense potential for translation into crops to improve their performance and address issues of global importance such as food security. Here we describe how fundamental insight into the genetic mechanism by which seed dispersal occurs in members of the Brassicaceae family can be exploited to reduce seed loss in oilseed rape (Brassica napus). We demonstrate that by exploiting data on gene function in model species, it is possible to adjust the pod-opening process in oilseed rape thereby significantly increasing yield. Specifically, we identified mutations in multiple paralogues of the INDEHISCENT and GA4 genes in B. napus and have overcome genetic redundancy by combining mutant alleles. Finally, we present novel software for the analysis of pod shatter data that is applicable to any crop for which seed dispersal is a serious problem. These findings highlight the tremendous potential of fundamental research in guiding strategies for crop improvement.\n\nKeymessageElucidation of key regulators in Arabidopsis fruit patterning has facilitated knowledge-translation into crop species to address yield loss caused by premature seed dispersal (pod shatter).

plant biology↗

bioRxiv2018

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

plant biology↗

bioRxiv2026

SNPstar: A Web Server Linking Allelic Variation to Protein Structure and Function in Arabidopsis thaliana

Understanding how natural genetic variants affect protein structure and function is central to plant biology. The 1001 Genomes Project has catalogued millions of single nucleotide polymorphisms (SNPs) across more than a thousand Arabidopsis thaliana accessions, offering an unprecedented opportunity to relate sequence variation to three-dimensional protein structure and population context. Yet, realizing it requires tools that integrate these scales in one accessible framework. Here we present SNPstar, a web server that links allelic variation in A. thaliana to AlphaFold3-predicted structures through a gene-centric, interactive interface. SNPstar annotates each variant with descriptive features, thermodynamic stability estimates, protein domain context, and genome-wide association results, and computes haplotypes and proteotypes that group accessions by shared DNA or protein sequence. Researchers can characterize variants, visualize their structural context, map their geographic distribution, and prioritize accessions for experimental validation without local computational infrastructure. We demonstrate SNPstar with two case studies. The first recapitulates known loss-of-function variation in the cadmium transporter HMA3, validating that SNPstar prioritizes functionally consequential alleles. The second uses SNPstar-defined proteotypes to identify an N-terminal SNP combination in ARGONAUTE 2 that distinguishes accessions differing in in vitro siRNA-directed target cleavage, linking protein-coding variation to a measurable molecular phenotype. SNPstar thus helps translate natural variation into mechanistic insight.

plant biology↗

bioRxiv2019

Design of a comprehensive microfluidic and microscopic toolbox for the ultra-wide spatio-temporal study of plant protoplasts development and physiology

BackgroundOne of the main features of plant cells is their strong plasticity, and their propensity to regenerate an organism from a single cell. Plant protoplasts are basic plant cells units in which the pecto-cellulosic cell wall has been removed, but the plasma membrane is intact. One of the main features of plant cells is their strong plasticity, which in some species, can be very close from what is defined as cell totipotency. Methods and differentiation protocols used in plant physiology and plant biology usually involve macroscopic vessels and containers that make difficult, for example, to follow the fate of the same protoplast all along its full development cycle, but also to perform continuous studies of the influence of various gradients in this context. These limits have hampered the precise study of regeneration processes. ResultsHerein, we present the design of a comprehensive, physiologically relevant, easy-to-use and low-cost microfluidic and microscopic setup for the monitoring of Physcomitrella patens (P. patens) growth and development on a long-term basis. The experimental solution we developed is made of two parts (i) a microfluidic chip composed of a single layer of about a hundred flow-through microfluidic traps for the immobilization of protoplasts, and (ii) a low-cost, light-controlled, custom-made microscope allowing the continuous recording of the moss development in physiological conditions. We validated the experimental setup with three proofs of concepts: (i) the kinetic monitoring of first division steps and cell wall regeneration, (ii) the influence of the photoperiod on growth of the protonemata, and (iii) finally the induction of leafy buds using a phytohormone, cytokinin. ConclusionsWe developed the design of a comprehensive, physiologically relevant, easy-to-use and low-cost experimental setup for the study of P. patens development in a microfluidic environment. This setup allows imaging of P. patens development at high resolution and over long time periods.

plant biology↗

bioRxiv2025

Phytoene synthase modulates seed longevity via the action of β-carotene derived metabolites

Seed longevity, the seeds ability to stay viable over time, is an important trait in agriculture. Despite extensive research, seed longevity remains one of the fundamental topics in plant biology. Here, we discovered the novel role of carotenoid metabolites in prolonging seed lifespan. We found that phytoene synthase (PSY), the gene encoding a major rate-limiting enzyme in carotenoid biosynthesis, modulated seed storability in Arabidopsis under both natural and artificial aging conditions. Seeds from the PSY overexpression (OE) lines exhibited significantly enhanced lifespan with low levels of reactive oxygen species (ROS), a major factor affecting longevity, whereas those from the psy mutants had decreased viability with high ROS levels. While lutein and {beta}-carotene were detected in seeds, only {beta}-carotene and its derived apocarotenoids, i.e. {beta}-cyclocitral and {beta}-ionone, were found to improve seed lifespan. Notably, the carotenoid cleavage dioxygenase 1 and 4 (ccd1 ccd4) double mutant and PSY ccd1 ccd4 seeds showed significantly reduced seed longevity, indicating that {beta}-carotene cleavage is necessary for or it is apocarotenoids playing the role in preserving seed lifespan. Comparative proteomic analysis identified TIP2;2, an aquaporin protein, which showed differential abundances in seeds of PSY OE and psy mutant vs wild type. The mutant tip2;2 had reduced seed longevity, and its promoter was transactivated by apocarotenoids. Collectively, this study uncovers a novel role of apocarotenoids in protecting seed longevity and highlights the importance of seed carotenoid production in strengthening agriculture. One Sentence SummaryPhytoene synthase, the gene encoding a major rate-limiting enzyme in carotenoid biosynthesis, modulates seed longevity via {beta}-carotene derived apocarotenoids and an aquaporin protein TIP2;2 identified is a new player that responds to apocarotenoid signaling and influences seed longevity.

plant biology↗

bioRxiv2022

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

MotivationSpatially resolved transcriptomics (SRT) shows its impressive power in yielding biological insights into neuroscience, disease study, and even plant biology. However, current methods do not sufficiently explore the expressiveness of the multi-modal SRT data, leaving a large room for improvement of performance. Moreover, the current deep learning based methods lack interpretability due to the "black box" nature, impeding its further applications in the areas that require explanation. ResultsWe propose conST, a powerful and flexible SRT data analysis framework utilizing contrastive learning techniques. conST can learn low-dimensional embeddings by effectively integrating multi-modal SRT data, i.e. gene expression, spatial information, and morphology (if applicable). The learned embeddings can be then used for various downstream tasks, including clustering, trajectory and pseudotime inference, cell-to-cell interaction, etc. Extensive experiments in various datasets have been conducted to demonstrate the effectiveness and robustness of the proposed conST, achieving up to 10% improvement in clustering ARI in the commonly used benchmark dataset. We also show that the learned embedding can be used in complicated scenarios, such as predicting cancer progression by analyzing the tumour microenvironment and cell-to-cell interaction (CCI) of breast cancer. Our framework is interpretable in that it is able to find the correlated spots that support the clustering, which matches the CCI interaction pairs as well, providing more confidence to clinicians when making clinical decisions.

bioinformatics↗

bioRxiv2026

Port of Protein-Protein Interactomes: An experiment-based protein-protein interactome database for rice

Protein-protein interactions (PPIs) play a crucial role in enabling proteins to carry out their functions within various biological processes (Hui et al., 2003). Since the introduction of the yeast two-hybrid (Y2H) method for PPI detection in 1989 (Fields and Song, 1989), the identification of PPIs has become a significant focus in modern biological research. PPI goes beyond examining individual proteins, allowing researchers to establish a comprehensive network that regulates biological processes. Rice, as a key model organism in plant biological studies, has been at the forefront of PPI research. In 2008, prominent rice scientists in China called for concerted efforts to define a comprehensive protein-protein interaction network experimentally, which aimed to facilitate the prediction of the functional mechanisms operating throughout a plants lifecycle (Zhang et al., 2008). With efforts for 2 decades, the experimentally identified rice PPIs have reached over ten thousand. Several public databases have been established to systematically collate and store PPIs, including STRING (Szklarczyk et al., 2019), BioGRID (Oughtred et al., 2020), IntAct (del Toro et al., 2022), PRIN (Gu et al., 2011), RicePPINet (Liu et al., 2017) and RiceNet v2 (Lee et al., 2015). However, most PPI datasets in rice stem from computational predictions, while experiment-based rice PPI datasets are fragmented due to the lack of systematic profiling at the rice PPIome level, which largely hinders information sharing in the rice research community. To bridge this gap, we constructed the Port of Protein-Protein Interactomes (POPPIN; https://riceome.hzau.edu.cn/poppin/), an integrated database dedicated to sharing experimentally verified PPIs and functional clues in rice. Empowered by high-throughput PPIome profiling technologies and text mining assisted by a large language model (Huang et al., 2025; Liu et al., 2025), POPPIN currently has deposited over 150,451 pieces of rice PPI-related information. Additionally, POPPIN provides detailed protein information, including GO annotations, subcellular localizations, domains, trait ontology (TO) information, and hyperlinks to external biological databases. Through offering a user-friendly web interface for search and dynamic network visualization, POPPIN serves as the first large-scale, experiment-based database for searchable PPIs in rice, and has the potential to be extended to other species under this structural framework.

systems biology↗

bioRxiv2020

The Sequenced Genomes of Non-Seed Land Plants Reveal the (R)Evolutionary History of Peptide Signaling

An understanding of land plant evolution is a prerequisite for in-depth knowledge of plant biology. Here we extract and explore information hidden in the increasing number of sequenced plant genomes, from bryophytes to angiosperms, to elucidate a specific biological question - how peptide signaling evolved. To conquer land and cope with changing environmental conditions, plants have gone through transformations that must have required a revolution in cell-to-cell communication. We discuss peptides mediating endogenous and exogenous changes by interaction with receptors activating intracellular molecular signaling. Signaling peptides were discovered in angiosperms and operate in tissues and organs like flowers, seeds, vasculature, and 3D meristems that are not universally conserved across land plants. Nevertheless, orthologues of angiosperm peptides and receptors have been identified in non-flowering plants. These discoveries provoke questions regarding the co-evolution of ligands and their receptors, and whether de novo interactions in peptide signaling pathways may have contributed to generate novel traits in land plants. The answers to such questions will have profound implications for the understanding of evolution of cell-to-cell communication and the wealth of diversified terrestrial plants. Under this perspective we have generated, analyzed and reviewed phylogenetic, genomic, structural, and functional data to elucidate the evolution of peptide signaling.

evolutionary biology↗

bioRxiv2018

GeneHummus: A pipeline to define gene families and their expression in legumes and beyond

During the last decade, plant biotechnological laboratories have sparked a monumental revolution with the rapid development of next sequencing technologies at affordable prices. Soon, these sequencing technologies and assembling of whole genomes will extend beyond the plant computational biologists and become commonplace within the plant biology disciplines. The current availability of large-scale genomic resources for non-traditional plant model systems (the so-called orphan crops) is enabling the construction of high-density integrated physical and genetic linkage maps with potential applications in plant breeding. The newly available fully sequenced plant genomes represent an incredible opportunity for comparative analyses that may reveal new aspects of genome biology and evolution. Analysis of the expansion and evolution of gene families across species is a common approach to infer biological functions. To date, the extent and role of gene families in plants has only been partially addressed and many gene families remain to be investigated. Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family, typically combining numerous BLAST searches and manually cleaning data. Due to the increasing abundance of genome sequences and the agronomical interest in plant gene families, the field needs a clear, automated annotation tool. Here, we present the GeneHummus pipeline, a step-by-step R-based pipeline for the identification, characterization and expression analysis of plant gene families. The impact of this pipeline comes from a reduction in hands-on annotation time combined with high specificity and sensitivity in extracting only proteins from the RefSeq database and providing the conserved domain architectures based on SPARCLE. As a case study we focused on the auxin receptor factors gene (ARF) family in Cicer arietinum (chickpea) and other legumes. We anticipate that our pipeline should be suitable for any plant gene family, and likely other gene families, vastly improving the speed and ease of genomic data processing.

bioinformatics↗