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Results for “plant biology”

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At least 127 records · Page 7Linked to original sources

bioRxiv2022

Chemical inhibition of PIN auxin transporters by the anti-inflammatory drug Naproxen

The phytohormone auxin plays central roles in many growth and developmental processes in plants. Development of chemical tools targeting the auxin pathway is useful for both plant biology and agriculture. Here we uncover that Naproxen, a synthetic compound with anti-inflammatory activity in humans, acts as an auxin transport inhibitor targeting PIN transporters in plants. Physiological experiments indicate that exogenous Naproxen treatment affects pleiotropic auxin-regulated developmental processes. Further cellular and biochemical evidence supports that Naproxen suppresses auxin transport, specifically PIN-mediated auxin efflux. Moreover, biochemical and structural analysis confirms that Naproxen binds directly to PIN1 protein, via the same binding cavity as the IAA substrate. Thus, by combining cellular, biochemical, and structural approaches, this study well establishes that Naproxen is a PIN inhibitor and elucidates the underlying mechanisms. Further use of the compound may advance our understanding on the molecular mechanisms of PIN-mediated auxin transport, and expand our toolkit in auxin biology and agriculture.

plant biology↗

bioRxiv2024

TIMAHAC: Streamlined Tandem IMAC-HILIC Workflow for Simultaneous and High-Throughput Plant Phosphoproteomics and N-glycoproteomics

Protein post-translational modifications (PTMs) are crucial in plant cellular processes, particularly in protein folding and signal transduction. N-glycosylation and phosphorylation are notably significant PTMs, playing essential roles in regulating plant responses to environmental stimuli. However, current sequential enrichment methods for simultaneous analysis of phosphoproteome and N-glycoproteome are labor-intensive and time-consuming, limiting their throughput. Addressing this challenge, this study introduces a novel tandem S-Trap-IMAC-HILIC (S-Trap: suspension trapping; IMAC: immobilized metal ion affinity chromatography; HILIC: hydrophilic interaction chromatography) strategy, termed TIMAHAC, for simultaneous analysis of plant phosphoproteomics and N-glycoproteomics. This approach integrates IMAC and HILIC into a tandem tip format, streamlining the enrichment process of phosphopeptides and N-glycopeptides. The key innovation lies in the use of a unified buffer system and an optimized enrichment sequence to enhance efficiency and reproducibility. The applicability of TIMAHAC was demonstrated by analyzing the Arabidopsis phosphoproteome and N-glycoproteome in response to abscisic acid (ABA) treatment. Up to 1,954 N-glycopeptides and 11,255 phosphopeptides were identified from Arabidopsis, indicating its scalability for plant tissues. Notably, distinct perturbation patterns were observed in the phosphoproteome and N-glycoproteome, suggesting their unique contributions to ABA response. Our results reveal that TIMAHAC offers a comprehensive approach to studying complex regulatory mechanisms and PTM interplay in plant biology, paving the way for in-depth investigations into plant signaling networks.

biochemistry↗

bioRxiv2024

pLM-DBPs: Enhanced DNA-Binding Protein Prediction in Plants Using Embeddings From Protein Language Models

DNA-binding proteins (DBPs) play critical roles in gene regulation, development, and environmental response across various species, including plants, animals, and microorganisms. While various machine learning and deep learning models have been developed to distinguish DNA-binding proteins (DBPs) from non-DNA-binding proteins (NDBPs), most available tools have focused on human and mouse datasets. As a result, there are limited studies specifically addressing plant-based DNA-binding proteins, which restricts our understanding of their unique roles and functions in plant biology. Developing an efficient framework for improving DBP prediction in plants would enhance our knowledge and enable precise gene expression control, accelerate crop improvement, enhance stress resilience, and optimize metabolic engineering for agricultural advancement. In this work, we developed a tool that uses a protein language model (pLM) pre-trained on millions of sequences. We evaluated several leading models, including ProtT5, Ankh, and ESM-2, and leveraged their high-dimensional, information-rich representations to improve the accuracy of DNA-binding protein prediction in plants significantly. Our final model, pLM-DBPs, a feed-forward neural network classifier utilizing ProtT5-based representations, outperformed existing approaches with a Matthews Correlation Coefficient (MCC) of 83.8% on the independent test set. This represents a 10% improvement over the previous state-of-the-art model for plant-based DBP prediction, highlighting its superior performance compared to the existing approaches.

bioinformatics↗

bioRxiv2024

Prediction of plant resistance proteins using alignment-based and alignment-free approaches

Plant Disease Resistance (PDR) proteins are critical in identifying and killing plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as BLAST for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning-based methods using compositional features of proteins. Our machine learning-based model, developed using compositional features of proteins, achieved a maximum performance AUROC of 0.92. The performance of our model improved significantly from AUROC of 0.92 to 0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best machine learning model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, "PlantDRPpred" (https://webs.iiitd.edu.in/raghava/plantdrppred). HighlightsO_LIDevelopment of a Machine-learning model for resistance protein prediction. C_LIO_LIUsed alignment-based and alignment-free ensemble methods. C_LIO_LIWeb server development and standalone package. C_LIO_LIPrediction and design of PDR proteins. C_LI

bioinformatics↗

bioRxiv2020

Systematic tools for reprogramming plant gene expression in a simple model, Marchantia polymorpha.

We present the OpenPlant toolkit, a set of interlinked resources and techniques to develop Marchantia as testbed for bioengineering in plants. Marchantia is a liverwort, a simple plant with an open form of development that allows direct visualization of gene expression and dynamics of cellular growth in living tissues. We describe new techniques for simple and efficient axenic propagation and maintenance of Marchantia lines with no requirement for glasshouse facilities. Marchantia plants spontaneously produce clonal propagules within a few weeks of regeneration, and lines can be amplified million-fold in a single generation by induction of the sexual phase of growth, crossing and harvesting of progeny spores. The plant has a simple morphology and genome with reduced gene redundancy, and the dominant phase of its life cycle is haploid, making genetic analysis easier. We have built robust Loop assembly vector systems for nuclear and chloroplast transformation and genome editing. These have provided the basis for building and testing a modular library of standardized DNA elements with highly desirable properties. We have screened transcriptomic data to identify a range of candidate genes, extracted putative promoter sequences, and tested them in vivo to identify new constitutive promoter elements. The resources have been combined into a toolkit for plant bioengineering that is accessible for laboratories without access to traditional facilities for plant biology research. The toolkit is being made available under the terms of the OpenMTA and will facilitate the establishment of common standards and the use of this simple plant as testbed for synthetic biology.

synthetic biology↗

bioRxiv2024

Diverse transcriptome reprogramming trajectories underlie quantitative disease resistance at the species level

Quantitative disease resistance (QDR) is an immune response limiting pathogen damage in plants. It involves transcriptomic reprogramming of numerous genes, each having a small contribution to plant immunity. Despite QDR broad-spectrum nature, the evolution of its underlying transcriptome reprogramming remains largely uncharacterized. Here, we analyzed global gene expression in response to the necrotrophic fungus Sclerotinia sclerotiorum in 23 Arabidopsis thaliana accessions of diverse origin and contrasted QDR phenotype. Over half of the species pan-transcriptome displayed local responses to S. sclerotiorum, with global reprogramming patterns incongruent with accessions phylogeny. Due to frequent small-amplitude variations, only [~]11% of responsive genes were common across all accessions, defining a core transcriptome enriched in highly-responsive genes. Co-expression and correlation analyses showed that QDR phenotypes result from the integration of numerous genes expression. Promoter sequence comparisons revealed that variation in DNA-binding sites within cis-regulatory regions contributing to gene expression rewiring. Finally, transcriptome-phenotype maps revealed abundant neutral networks connecting diverse QDR transcriptomes with no loss of resistance, hallmarks of robust and evolvable traits. This navigability associated with regulatory variation in core genes highlights their role in QDR evolvability. This work provides insights into the evolution of complex immune responses, informing models for plant disease dynamics. Classification: Biological Sciences, Plant Biology

plant biology↗

bioRxiv2017

Lessons in effector and NLR biology of plant-microbe systems

A diversity of plant-associated organisms secrete effectors--proteins and metabolites that modulate plant physiology to favor host infection and colonization. However, effectors can also activate plant immune receptors, notably nucleotide-binding domain and leucine-rich repeat-containing (NLR) proteins, enabling plants to fight off invading organisms. This interplay between effectors, their host targets, and the matching immune receptors is shaped by intricate molecular mechanisms and exceptionally dynamic coevolution. In this article, we focus on three effectors, AVR-Pik, AVR-Pia, and AVR-Pii, from the rice blast fungus Magnaporthe oryzae (syn. Pyricularia oryzae), and their corresponding rice NLR immune receptors, Pik, Pia, and Pii, to highlight general concepts of plant-microbe interactions. We draw 12 lessons in effector and NLR biology that have emerged from studying these three little effectors and are broadly applicable to other plant-microbe systems.

plant biology↗

bioRxiv2026

Form I and II Rubiscos Exhibit Temperature Dependent Carbon Kinetic Isotope Effects

Nearly all carbon on Earth today is fixed by the enzyme ribulose-1,5-bisphopshate carboxylase/oxygenase ( rubisco), which converts carbon dioxide (CO2) to sugar phosphates. All rubiscos measured thus far display a kinetic isotope effect (KIE) where 12CO2 is fixed at a faster rate than 13CO2. The relationship between rubiscos KIE and the carbon isotope composition of plants, algae, and organic matter is central to many fields in the Earth sciences, plant biology, and biochemistry. Currently, all applications assume that the KIE does not vary with temperature. Here, we examine this assumption experimentally with in vitro KIE measurements of two rubiscos from phylogenetically distinct host organisms and rubisco protein clades - a Form I rubisco from the plant, Spinacia oleracea (spinach) and a Form II rubisco from the bacterium Rhodosprillium rubrum. We that find that both KIEs decrease linearly by [~]4.5{per thousand} from 10-35{degrees}C with statistically indistinguishable slopes. We place these results into biological and geologic contexts by comparing them to observed variations in the carbon isotope composition of modern terrestrial plants and marine organic carbon, the geologic carbon isotope record, and rubiscos biochemistry. We show that the measured temperature dependencies are sufficiently large to impact our interpretations of the enzymatic processes that drive variations in rubisco KIEs, as well as applications of stable carbon isotopes in the Earth and biological sciences. Significance StatementThe carbon isotope composition of plants, algae, and organic matter are interpreted with models that assume the kinetic isotope effect of the carbon-fixing enzyme rubisco is temperature-independent, even though temperature varies by tens of degrees across the Earth today and in the past. Here, we demonstrate that the kinetic isotope effect of rubisco is temperature-dependent, suggesting that some of this isotopic variation may be due to intrinsic enzyme properties alone. In addition, though the rubiscos we measured are from diverse organisms (plant vs. bacteria), their KIEs show statistically indistinguishable temperature dependencies. This data forms the basis for future thermodynamic models on rubisco biochemistry.

biochemistry↗

bioRxiv2017

A critical comparison of technologies for a plant genome sequencing project

A high quality genome sequence of your model organism is an essential starting point for many studies. Old clone based methods are slow and expensive, whereas faster, cheaper short read only assemblies can be incomplete and highly fragmented, which minimises their usefulness. The last few years have seen the introduction of many new technologies for genome assembly. These new technologies and new algorithms are typically benchmarked on microbial genomes or, if they scale appropriately, human. However, plant genomes can be much more repetitive and larger than human, and plant biology makes obtaining high quality DNA free from contaminants difficult. Reflecting their challenging nature we observe that plant genome assembly statistics are typically poorer than for vertebrates. Here we compare Illumina short read, PacBio long read, 10x Genomics linked reads, Dovetail Hi-C and BioNano Genomics optical maps, singly and combined, in producing high quality long range genome assemblies of the potato species S. verrucosum. We benchmark the assemblies for completeness and accuracy, as well as DNA, compute requirements and sequencing costs. We expect our results will be helpful to other genome projects, and that these datasets will be used in benchmarking by assembly algorithm developers.

genomics↗

bioRxiv2022

Soil microbiota and herbivory influence the structure of plant-associated microbial communities in tomato plants

Plant-associated microbial communities are key to shaping many aspects of plant biology. In this study, we tested whether soil microbial communities and herbivory influence the bacterial community of tomato plants and whether their influence in different plant compartments is driven by microbial spillover between compartments or whether plants are involved in mediating this effect. We grew our plants in soils hosting three different microbial communities and covered (or not) the soil surface to prevent (or allow) passive microbial spillover between compartments, and we exposed them (or not) to herbivory by Manduca sexta. The results showed that the soil-driven effect on aboveground compartments was consistently detected regardless of soil coverage, whereas soil cover influenced the herbivore-driven effect on belowground microbiota. Together, our results suggest that the soil microbiota influences aboveground plant and insect microbial communities via changes in plant metabolism and physiology or by sharing microorganisms via xylem sap. In contrast, herbivores influence the belowground plant microbiota via a combination of microbial spillover and changes in plant metabolism. These results demonstrate the important role of plants in linking aboveground and belowground microbiota, and can foster further research on soil microbiota manipulation for sustainable pest management.

ecology↗

bioRxiv2025

The Characteristic Timescales of the Arabidopsis thaliana Metabolism and the Phenotypic Effects of Forcing Them

Response mechanisms preserve steady states in complex systems (including living systems). These mechanisms respond with characteristic timescales. Therefore, they can be identified and probed with oscillatory forcing. This approach is rarely used in plant biology. Sub-circadian intermittent illumination (i.e., square waves of light/dark periods of equal duration) of Arabidopsis thaliana plants cause remarkable phenotypical effects. We show these effects are highly dependent on the duration of the light-dark cycle, identifying characteristic timescales in three distinct windows (10.5-42 s, 168-336 s, and 1350-5400 s). Among these effects is an extension of the life cycle by 60%, a 2-fold increase in flower setting, and a near complete elimination of lateral root development. This oscillatory light forcing is compatible with vertical farming where it could reduce energy and chemical inputs and highlights the methodological value of temporal-forcing of environmental parameters on sub-circadian timescales in plant systems.

biophysics↗

bioRxiv2026

Nonlinear Impacts of Herbivory on Plants Explain the Herbivory Paradox

Herbivores strongly shape plant ecology and evolution. Yet plants typically sustain low, tolerable levels of damage, raising the question of how herbivory influences plant biology despite often weak fitness effects. Analyzing 1,145 datasets of the effects of damage on fitness for 103 plant species worldwide, we show that nonlinear tolerance to damage is one explanation for this paradoxical contradiction. Plants are tolerant of low levels of damage, but infrequent, severe damage levels disproportionately reduce plant fitness. Models incorporating nonlinear tolerance suggest that disproportionate tolerance to low levels of damage stabilizes population dynamics and promotes coevolutionary feedback with herbivores. Pervasive nonlinear tolerance shows consistent patterns across environmental gradients, may alter the stability of food webs, and may explain why herbivory matters despite the world being green.

ecology↗

bioRxiv2021

Multiple nucleotide polymorphism DNA markers for the accurate evaluation of genetic variations

DNA markers are an essential tool for the detection and evaluation of genetic variations, a central theme in genetics and biology. Effective markers must be highly reproducible, polymorphic, accurate and efficient to profile. We developed multiple dispersed nucleotide polymorphism (MNP) DNA marker and an efficient MNP genotyping method called MNP-Seq. The MNP marker was 17.48% more polymorphic than the highly polymorphic marker of microsatellites on a collection of hybrid rice plants. When applied to genotype more than 80,000 individual MNP markers of diploid rice and polyploidy hybrid cotton varieties which were notoriously difficult to genotype accurately, MNP-Seq finished in two days and achieved accuracies of 99.999% and 99.988%, respectively. We adopted MNP-Seq to reveal the ubiquitous, albeit subtle and neglected, genetic heterogeneities in homonyms of Nipponbare rice, a popular model organism for plant biology. This result raised a question on the consistency of the published results using the model plant. We also used MNP-Seq to accurately and efficiently determine the identities of plant varieties, a key but difficult problem for the protection of plant intellectual property rights. While being applied to plants in the current study, the MNP marker and MNP-Seq are general and readily applicable to similar problems in animals and micro-organisms.

molecular biology↗

bioRxiv2025

A Large Language Model-Powered Map of Metabolomics Research

We present a comprehensive map of the metabolomics research landscape, synthesizing insights from over 80,000 publications. Using PubMedBERT, we transformed abstracts into 768-dimensional embeddings that capture the nuanced thematic structure of the field. Dimensionality reduction with t-SNE revealed distinct clusters corresponding to key domains such as analytical chemistry, plant biology, pharmacology, and clinical diagnostics. In addition, a neural topic modeling pipeline refined with GPT-4o mini reclassified the corpus into 20 distinct topics--ranging from "Plant Stress Response Mechanisms" and "NMR Spectroscopy Innovations" to "COVID-19 Metabolomic and Immune Responses." Temporal analyses further highlight trends including the rise of deep learning methods post-2015 and a continued focus on biomarker discovery. Integration of metadata such as publication statistics and sample sizes provide additional context to these evolving research dynamics. An interactive web application (https://metascape.streamlit.app/) enables dynamic exploration of these insights. Overall, this study offers a robust framework for literature synthesis that empowers researchers, clinicians, and policymakers to identify emerging research trajectories and address critical challenges in metabolomics, while also sharing our perspectives on key trends shaping the field.

scientific communication and education↗

bioRxiv2024

The Genetic Puzzle of Multicopy Genes: Challenges and Troubleshooting

BackgroundStudies with multicopy genes impose challenges related to gene redundancy and sequence similarity among copies. Recent advances in molecular biology and genomics tools associated with dedicated databases facilitate their study. Thus, the present work emphasizes the need for rigorous methodologies and standardized approaches to interpret RT-qPCR results accurately. ResultsThe present work in Physcomitrium patens provides a comprehensive five-step protocol, using thiamine thiazole synthase (THI1) and sucrose 6-phosphate phosphohydrolase (S6PP) genes as proof of concept, to showcase a systematic workflow for studying multicopy genes. Beyond examining genes of interest, we highlight the critical role of choosing appropriate internal controls in the analytical process for accurately interpreting gene expression patterns. We emphasized the importance of identifying the most relevant orthologous gene, recognizing the inherent challenges in determining the most functional copy for subsequent studies. Our objective is to enhance comprehension of gene redundancy by dissecting multicopy genes genomic landscape and its characteristics. Furthermore, we address the decision-making process surrounding the quantification of expression levels of multicopy genes. ConclusionsThe study of multicopy genes discloses early events of functional adaptation. We emphasize the significance of multicopy genes in plant biology and provide a practical protocol for their study. Plant systems are strongly influenced by light/dark cycles, and the challenges inherent to this process are acknowledged. In conclusion, our comprehensive approach aims to advance the understanding of multicopy gene dynamics, offering practical methodologies and contributing valuable insights to the scientific community.

molecular biology↗

bioRxiv2019

A Synthetic Peptide Encoded by a Random DNA Sequence Inhibits Discrete Red Light Responses

We have identified a synthetic peptide that interrupts discrete aspects of seedling development under red light. Previous reports have demonstrated that plants transformed with random DNA sequences produce synthetic peptides that affect plant biology. In this report one specific peptide is characterized that inhibits discrete aspects of red-light-mediated Arabidopsis thaliana development during photomorphogenesis. Seedlings expressing the PEP6-32 peptide presented longer hypocotyls and diminished cotyledon expansion when grown under red light. Other red-light-mediated seedling processes such as induction of Lhcb (cab) transcripts or loss of vertical growth remained unaffected. Long-term responses to red light in PEP6-32 expressing plants, such as repression of flowering time, did not show defects in red light signaling or integration. A synthesized peptide applied exogenously induced the long-hypocotyl phenotype under red light in non-transformed seedlings. The results indicate that the PEP6-32 peptide causes discrete cell expansion defects during early seedling development in red light, mimicking weak phyB alleles in some aspects of seedling photomorphogenesis. The findings demonstrate that new chemistries derived from random peptide expression can modulate specific facets of plant growth and development.\n\nOne Sentence SummaryA plant line expressing random DNA sequence expresses a synthetic peptide that affects specific red-light responses in a developing seedling.

synthetic biology↗

bioRxiv2026

GeneReL: A Large Language Model-Powered Platform for Gene Regulatory Relationship Extraction with Community Curation

MotivationGene regulatory networks provide fundamental insights into plant biology, yet extracting structured interaction data from scientific literature remains a significant bottleneck. Traditional manual curation cannot scale to meet the demands of modern research, while automated text mining approaches struggle with the complexity of gene nomenclature and relationship classification. Large language models offer promising capabilities for information extraction, but integrated platforms combining LLM extraction with community validation for plant regulatory databases remain scarce. ResultsWe developed GeneReL, an integrated platform combining LLM-based extraction with community-driven curation for gene regulatory networks in Arabidopsis thaliana. The system employs a tiered pipeline using Claude Haiku 4.5 for screening, Claude Sonnet 4 for extraction, and Claude Opus 4 for verification, along with a novel five-step gene normalization pipeline incorporating paper-text search and LLM-based disambiguation with UniProt annotations. The database contains 13,710 curated interactions across 51 relationship types, with 90.2% classified as high confidence based on linguistic certainty markers in source text. Comparison with IntAct reveals 86.8% of interactions are unique to our literature-derived database, demonstrating complementary coverage to existing resources. The web platform provides card-based browsing with voting capabilities, interactive network visualization using Cytoscape.js with locus-ID-based node consolidation, and administrative interfaces for curator review of ambiguous gene mappings. Availability and ImplementationGeneReL is freely accessible at https://generel.newgenes.me. Contactkangyangjae@gnu.ac.kr

bioinformatics↗

bioRxiv2022

PdeMIXTA04 triggers epidermal cells of placenta to differentiate into poplar catkins through forming MBW complexes with PdeMYC and PdeWD40

Differentiation of plant epidermal cells is a keen research topic in plant biology. Our study on Populus deltoides revealed that epidermal cells of the female flower placenta protruded to form catkin fibers immediately after pollination. We discovered that PdeMIXTA04 was explicitly expressed in placenta of female poplar flowers. Heterologous expression of PdeMIXTA04 in Arabidopsis demonstrated that it significantly promoted the leaf epidermal cells to differentiate into trichomes. Compared with the wild type, significant increases in trichome density and trichome branches were observed on leaves of all 35S:PdeMIXTA04 transgenic lines. Furthermore, transformation of PdeMIXTA04 in the trichomeless Arabidopsis mutant (gl1) restored trichome development to that of the wild type. GUS expression in poplar, driven by the promoter of PdeMIXTA04, also confirmed trichome-specific expression. We then screened a yeast library with PdeMIXTA04 and captured two interacting genes, PdeMYC and PdeWD40. Interactions between these two proteins were verified by yeast two-hybrid (Y2H), biomolecular fluorescence complementation (BiFC), dual-luciferase (dual-LUC), and pull-down assays, indicating that PdeMIXTA04 functions through the MYB-BHLH-WD40 (MBW) ternary complex. Our work presents evidence of PdeMIXTA04 as a candidate gene for editing to resolve catkins associated pollution and provides distinctive understanding of the molecular mechanism triggering differentiation of plant epidermal cells.

molecular biology↗