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Results for “Plant Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2017

The unrealized potential of herbaria in global change biology

Plant and fungal specimens in herbaria are becoming primary resources for investigating how plant phenology and geographic distributions shift with climate change, greatly expanding inferences across spatial, temporal, and phylogenetic dimensions. However, these specimens contain a wealth of additional data--including nutrients, defensive compounds, herbivore damage, disease lesions, and signatures of physiological processes--that capture ecological and evolutionary responses to the Anthropocene but which are less frequently utilized. Here, we outline the diversity of herbarium data, global change topics to which they have been applied, and new hypotheses they could inform. We find that herbarium data have been used extensively to study impacts of climate change and invasive species, but that such data are less commonly used to address other drivers of biodiversity loss, including habitat conversion, pollution, and overexploitation. In addition, we note that fungal specimens are under-explored relative to vascular plants. To facilitate broader application of plant and fungal specimens in global change research, we outline the limitations of these data and modern sampling and statistical tools that may be applied to surmount challenges they present. Using a case study of insect herbivory, we illustrate how novel herbarium data may be employed to test hypotheses for which few data exist, despite potentially large biases. With the goal of positioning herbaria as hubs for global change research, we suggest future research directions and curation priorities.

plant biology↗

bioRxiv2018

Evaluating Named-Entity Recognition approaches in plant molecular biology

Text mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. However, few thorough studies on text mining and applications are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to train models able to detect entities such as genes, proteins and phenotypic traits. Since there is rare benchmarks for rice, we have to face various difficulties in exploiting advanced machine learning methods for accurate analysis of rice bibliography. In this article, we developed a new training datasets (Oryzabase) as the benchmark. Then, we evaluated the performance of several current approaches to find a methodology with the best results and assigned it as the state of the art method for our own technique in the future. We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 86% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.

bioinformatics↗

bioRxiv2016

Araport11: a complete reannotation of the Arabidopsis thaliana reference genome

The flowering plant Arabidopsis thaliana is a dicot model organism for research in many aspects of plant biology. A comprehensive annotation of its genome paves the way for understanding the functions and activities of all types of transcripts, including mRNA, noncoding RNA, and small RNA. The most recent annotation update (TAIR10) released more than five years ago had a profound impact on Arabidopsis research. Maintaining the accuracy of the annotation continues to be a prerequisite for future progress. Using an integrative annotation pipeline, we assembled tissue-specific RNA-seq libraries from 113 datasets and constructed 48,359 transcript models of protein-coding genes in eleven tissues. In addition, we annotated various classes of noncoding RNA including small RNA, long intergenic RNA, small nucleolar RNA, natural antisense transcript, small nuclear RNA, and microRNA using published datasets and in-house analytic results. Altogether, we identified 738 novel protein-coding genes, 508 novel transcribed regions, 5051 non-coding genes, and 35846 small-RNA loci that formerly eluded annotation. Analysis on the splicing events and RNA-seq based expression profile revealed the landscapes of gene structures, untranslated regions, and splicing activities to be more intricate than previously appreciated. Furthermore, we present 692 uniformly expressed housekeeping genes, 43% of whose human orthologs are also housekeeping genes. This updated Arabidopsis genome annotation with a substantially increased resolution of gene models will not only further our understanding of the biological processes of this plant model but also of other species.

Plant Biology↗

bioRxiv2018

Overexpression of Arabidopsis ubiquitin ligase AtPUB46 enhances tolerance to drought and oxidative stress

The U-Box E3 ubiquitin ligase, AtPUB46, functions in the drought response: T-DNA insertion mutants of this single paralogous gene are hypersensitive to water- and oxidative stress (Adler et al. BMC Plant Biology 17:8, 2017). Here we analyze the phenotype of AtPUB46 overexpressing (OE) plants. AtPUB46-OE show increased tolerance to water stress and have smaller leaf blades and reduced stomatal pore area and stomatal index compared with wild type (WT). Despite this, the rate of water loss from detached rosettes is similar in AtPUB46-OE and WT plants. Germination of AtPUB46-OE seeds was less sensitive to salt than WT whereas seedling greening was more sensitive. We observed a complex response to oxidative stress applied by different agents: AtPUB46-OE plants were hypersensitive to H2O2 but hyposensitive to methyl viologen. AtPUB46-GFP fusion protein is cytoplasmic, however, in response to H2O2 a considerable proportion translocates to the nucleus. We conclude that the differential stress phenotype of the AtPUB46-OE does not result from its smaller leaf size but from a change in the activity of a stress pathway(s) regulated by a degradation substrate of the AtPUB46 E3 and also from a reduction in stomatal pore size and index.\n\nAccession NumbersSequence data for this article can be found in: The Arabidopsis Information Resource database (http://www.arabidopsis.org) under accession numbers At5G18320 (PUB46).

plant biology↗

bioRxiv2018

Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics

HighlightFunctional phenomics is an emerging field in plant biology that relies on high-throughput phenotyping, big data analytics, controlled manipulative experiments, and simulation modelling to increase understanding of plant physiology.\n\nAbstractThe emergence of functional phenomics signifies the rebirth of physiology as a 21st century science through the use of advanced sensing technologies and big data analytics. Functional phenomics highlights the importance of phenotyping beyond only identifying genetic regions because a significant knowledge gap remains in understanding which plant properties will influence ecosystem services beneficial to human welfare. Here, a general approach for the theory and practice of functional phenomics is outlined including exploring the phene concept as a unit of phenotype. The functional phenomics pipeline is proposed as a general method for conceptualizing, measuring, and validating utility of plant phenes. The functional phenomics pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize the throughput of phene measurements. A mapping population is screened measuring target phenes and indicators of plant performance such as yield and nutrient composition. Traditional forward genetics allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive experiments to understand phene-environment interactions in depth. Simulation modeling can be used to understand the phenotypes and all stages of the pipeline feed back to ideotype and phenotyping platform development.

plant biology↗

bioRxiv2026

City life: airborne DNA metagenomic biodiversity monitoring reveals dynamic changes across time and space

Airborne environmental DNA can capture biodiversity across the tree of life, but low sample biomass makes rapid, untargeted detection technically challenging. We combined 45-min air collection, nanopore sequencing and real-time taxonomic analysis in a shotgun metagenomic workflow capable of producing results within 3 hours. Across 77 samples from 13 London sites, including a year of weekly sampling at the Natural History Museum Wildlife Garden, we detected 1,916 species spanning bacteria, fungi, plants and animals. Communities varied spatially and seasonally, shifting from plant dominance in spring to ascomycete dominance in summer and basidiomycete dominance in late autumn and winter. Plant read abundance increased with upwind vegetation, linking airborne signals to surrounding habitat. Detection of catalogued garden plants depended on reference availability, dispersal biology, plant size and proximity to the collector. Together, these findings establish airborne shotgun metagenomics as a platform for rapid, repeated and scalable biodiversity assessment across space and time.

ecology↗

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

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↗

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↗

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↗

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↗

bioRxiv2018

Tracking transient fluorescent events in structured point clouds

BackgroundParticle-tracking in 3D is an indispensable computational tool to extract critical information on dynamical processes from raw time-lapse imaging. This is particularly true with in vivo time-lapse fluorescence imaging in cell and developmental biology, where complex dynamics are observed at high temporal resolution. Common tracking algorithms used with time-lapse data in fluorescence microscopy typically assume a continuous signal where background, recognisable keypoints and independently moving objects of interest are permanently visible. Under these conditions, simple registration and identity management algorithms can track the objects of interest over time. In contrast, here we consider the case of transient signals and objects whose movements are constrained within a tissue, where standard algorithms fail to provide robust tracking. ResultsTo optimize 3D tracking in these conditions, we propose the merging of registration and tracking tasks into a fuzzy registration algorithm to solve the identity management problem. We describe the design and application of such an algorithm, illustrated in the domain of plant biology, and make it available as an open-source software implementation. The algorithm is tested on mitotic events in 4D data-sets obtained with light-sheet fluorescence microscopy on growing Arabidopsis thaliana roots expressing CYCB::GFP. We validate the method by comparing the algorithm performance against both surrogate data and manual tracking. ConclusionThis method fills a gap in existing tracking techniques, following mitotic events in challenging data-sets using transient fluorescent markers in unregistered images.

bioinformatics↗

bioRxiv2018

Dynamic regulation of HYL1 provides new insights into its multifaceted role in Arabidopsis

MicroRNAs (miRNAs) are 21 to 24 nucleotide non-coding RNAs that regulate gene expression. Biogenesis of miRNAs is fine-tuned by specialized microprocessor complex, the regulation of which is being continuously understood. Recruitment of HYL1 to the microprocessor complex is crucial for accurate primary-miRNA (pri-miRNA) processing and accumulation of mature miRNA in Arabidopsis thaliana. HYL1 is a double-stranded RNA binding protein also termed as DRB1, has two double-stranded RNA binding domain at N-terminal and a highly disordered C-terminal region. Also, the biological activity of HYL1 is dynamically regulated through transition from hyperphosphorylation to hypophosphorylation state. HYL1 is known to be phosphorylated by a MAP kinase MPK3 and SnRK2. However, the precise role of its phosphorylation are still unknown. Recently, the stability of HYL1 protein has been shown to be regulated by an unknown protease X. However, the identity of the protease and its molecular mechanisms are poorly understood. Here, we describe, three functionally important facets of HYL1, which provide a better picture of its association with molecular processes. First, we identified a conserved MPK3 phosphorylation site on HYL1 and its possible role in the miRNA biogenesis. Secondly, the C-terminal region of HYL1 displays tendencies to bind dsDNA. Lastly, the role of C-terminal region of HYL1 in the regulation of its protein stability and the regulation of miRNA biogenesis is documented. We show the unexplored role of C-terminal and hypothesize the novel functions of HYL1 in addition to miRNA biogenesis. We anticipate that the data presented in this study, will open a new dimension of understanding the role of double stranded RNA binding proteins in diverse biological processes of plants and animal.

plant biology↗

bioRxiv2017

Heritability of the structures and 13C fractionation in tomato leaf wax alkanes: a genetic model system to inform paleoenvironmental reconstructions

Leaf wax n-alkanes are broadly used to reconstruct paleoenvironmental information. However, the utility of the n-alkane paleoclimate proxy is modulated by the extent to which genetic as well as environmental factors influence the structural and isotopic variability of leaf waxes. In paleoclimate applications, there is an implicit assumption that most variation of leaf wax traits through a time series can be attributed to environmental change and that biological sources of variability within plant communities are small. For example, changes in hydrology affect the {delta}2 H of waxes though rainwater and the {delta}13C of leaf waxes by changing plant communities (i.e., C3 versus C4 input). Here we test the assumption of little genetic control over 5 C variation of leaf wax by presenting the results of an experimental greenhouse growth study in which we estimate the role of genetic variability on structural and isotopic leaf wax traits in a set of 76 introgression lines (ILs) between two interfertile Solanum (tomato) species: S. lycopersicum cv M82 (hereafter cv M82) and S. pennellii. We found that the leaves of S. pennellii, a wild desert tomato relative, produces significantly more iso-alkanes than cv M82, a domesticated tomato cultivar adapted to water-replete conditions; we introduce a methylation index to summarize the ratio of branched (iso- and anteiso-) to total alkanes. Between S. pennellii and cv M82, the iso-alkanes were found to be enriched in 13C by 1.2-1.4%o over n-alkanes. By modeling our results from the ILs, we report the broad-sense heritability values (H2) of leaf wax traits to describe the degree to which genetic variation contributes to variation of these traits. Individual carbon isotope values of alkanes are of low heritability (H2 = 0.13-0.19), suggesting that {delta}13C of leaf waxes from this study are strongly influenced by environmental variance, which supports the interpretation that variation in the 5 C of wax compounds recorded in sediments reflects paleohydrological changes. Average chain length (ACL) values of n-alkanes are of intermediate heritability (H2 = 0.30), suggesting that ACL values are strongly influenced by genetic cues.

plant biology↗

bioRxiv2018

Cytokinin activity of N6-benzyladenine derivatives assayed by interaction with the receptors in planta, in vitro, and in silico

Biological effects of hormones in both plants and animals are based on high-affrnity interaction with cognate receptors resulting in their activation. The signal of cytokinins, classical plant hormones, is perceived in Arabidopsis by three homologous membrane receptors: AHK2, AHK3, and CRE1/AHK4. To study the cytokinin-receptor interaction, we used 25 derivatives of potent cytokinin N6-benzyladenine (BA) with substituents in the purine heterocycle and/or in the side chain. The study was focused primarily on individual cytokinin receptors from Arabidopsis. The main in planta assay system was based on Arabidopsis double mutants retaining only one isoform of cytokinin receptors and harboring cytokinin-sensitive reporter gene. Classical cytokinin biotest with Amaranthus seedlings was used as an additional biotest. In parallel, the binding of ligands to individual cytokinin receptors was assessed in the in vitro test system. Quantitative comparison of results of different assays confirmed the partial similarity of ligand-binding properties of receptor isoforms. Substituents at positions 8 and 9 of adenine moiety, elongated linker up to 4 methylene units, replacement of N6 by sulfur or oxygen, resulted in suppression of cytokinin activity of the derivative towards all receptors. Introduction of a halogen into position 2 of adenine moiety, on the contrary, often increased the ligand activity, especially toward AHK3. Features both common and distinctive of cytokinin receptors in Arabidopsis and Amaranthus were revealed, highlighting species specificity of the cytokinin perception apparatus. Correlations between extent of compound binding to a receptor in vitro and its ability to activate the same receptor in planta were evaluated for each AHK protein. Interaction patterns between individual receptors and ligands were rationalized by structure analysis and molecular docking in sensory modules of AHK receptors. The best correlation between docking scores and specific binding was observed for AHK3. In addition, receptor-specific ligands have been discovered with unique properties to predominantly activate or block distinct cytokinin receptors. These ligands are promising for practical application and as molecular tools in the study of the cytokinin perception by plant cells.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC=\"FIGDIR/small/241281_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (85K):\norg.highwire.dtl.DTLVardef@1fef3f9org.highwire.dtl.DTLVardef@143a10eorg.highwire.dtl.DTLVardef@102d62corg.highwire.dtl.DTLVardef@13b1db1_HPS_FORMAT_FIGEXP M_FIG C_FIG Individual cytokinin receptors from Arabidopsis were assayed in planta, in vitro and in silico with 25 different 6-benzyladenine derivatives, new receptor-specific cytokinins were revealed.

plant biology↗

bioRxiv2016

Compromised External Validity: Federally Produced Cannabis Does Not Reflect Legal Markets

As the most widely used illicit drug, the basis of the fastest growing major industry in the US, and as a source of numerous under-studied psychoactive compounds, understanding the psychological and physiological effects of Cannabis is essential. The National Institute on Drug Abuse (NIDA) is designated as the sole legal producer of Cannabis for use in US research studies. We sought to compare the chemical profiles of Cannabis varieties that are available to consumers in states that have state-legalized use versus what is available to researchers interested in studying the plant and its effects. Our results demonstrate that the federally produced Cannabis has significantly less variety and lower concentrations of cannabinoids. Current research, which has focused on material that is far less diverse and less potent than that used by the public, limits our understanding of the plants chemical, biological, psychological, medical, and pharmacological properties. Investigation is urgently needed on the diverse forms of Cannabis used by the public in state-legal markets.

plant biology↗

bioRxiv2017

Comparative Qualitative Phosphoproteomics Analysis Identifies Shared Phosphorylation Motifs and Associated Biological Processes in Flowering Plants

Phosphorylation is regarded as one of the most prevalent post-translational modifications and plays a key role in regulating cellular processes. In this work we carried out a comparative bioinformatics analysis of phosphoproteomics data, to profile two model species representing the largest subclasses in flowering plants the dicot Arabidopsis thaliana and the monocot Oryza sativa, to understand the extent to which phosphorylation signaling and function is conserved across evolutionary divergent plants. Using pre-existing mass spectrometry phosphoproteomics datasets and bioinformatic tools and resources, we identified 6,537 phosphopeptides from 3,189 phosphoproteins in Arabidopsis and 2,307 phosphopeptides from 1,613 phosphoproteins in rice. The relative abundance ratio of serine, threonine, and tyrosine phosphorylation sites in rice and Arabidopsis were highly similar: 88.3: 11.4: 0.4 and 86.7: 12.8: 0.5, respectively. Tyrosine phosphorylation shows features different from serine and threonine phosphorylation and was found to be more frequent in doubly-phosphorylated peptides in Arabidopsis. We identified phosphorylation sequence motifs in the two species to explore the similarities, finding nineteen pS motifs and two pT motifs that are shared in rice and Arabidopsis; among them are five novel motifs that have not previously been described in both species. The majority of shared motif-containing proteins were mapped to the same biological processes with similar patterns of fold enrichment, indicating high functional conservation. We also identified shared patterns of crosstalk between phosphoserines with motifs pSXpS, pSXXpS and pSXXXpS, where X is any amino acid, in both species indicating this is an evolutionary conserved signaling mechanism in flowering plants. However, our results are suggestive that there is greater co-occurrence of crosstalk between phosphorylation sites in Arabidopsis, and we were able to identify several pairs of motifs that are statistically significantly enriched to co-occur in Arabidopsis proteins, but not in rice.

bioinformatics↗

bioRxiv2017

Peripheral infrastructure vectors and an extended set of plant parts for the modular cloning system

Standardized DNA assembly strategies facilitate the generation of multigene constructs from collections of building blocks in plant synthetic biology. A common syntax for hierarchical DNA assembly following the Golden Gate principle employing Type IIs restriction endonucleases was recently developed, and underlies the Modular Cloning and GoldenBraid systems. In these systems, transcriptional units and/or multigene constructs are assembled from libraries of standardized building blocks, also referred to as phytobricks, in several hierarchical levels and by iterative Golden Gate reactions. This combinatorial assembly strategy meets the increasingly complex demands in biotechnology and bioengineering, and also represents a cost-efficient and versatile alternative to previous molecular cloning techniques. For Modular Cloning, a collection of commonly used Plant Parts was previously released together with the Modular Cloning toolkit itself, which largely facilitated the adoption of this cloning system in the research community. Here, a collection of approximately 80 additional phytobricks is provided. These phytobricks comprise e.g. modules for inducible expression systems, different promoters or epitope tags, which will increase the versatility of Modular Cloning-based DNA assemblies. Furthermore, first instances of a \"peripheral infrastructure\" around Modular Cloning are presented: While available toolkits are designed for the assembly of plant transformation constructs, vectors were created to also use coding sequence-containing phytobricks directly in yeast two hybrid interaction or bacterial infection assays. Additionally, DNA modules and assembly strategies for connecting Modular Cloning with Gateway Cloning are presented, which may serve as an interface between available resources and newly adopted hierarchical assembly strategies. The presented material will be provided as a toolkit to the plant research community and will further enhance the usefulness and versatility of Modular Cloning.

plant biology↗