Dataset Information

A Statistical Growth Property of Plant Root Architectures.

ABSTRACT: Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three (x, y, z) growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies.

SUBMITTER: Sultan S

PROVIDER: S-EPMC7706341 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Statistical Growth Property of Plant Root Architectures.

Sultan Sam S Snider Joseph J Conn Adam A Li Mao M Topp Christopher N CN Navlakha Saket S

Plant phenomics (Washington, D.C.) 20201108

Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density funct ...[more]

PMID: 33313546

Similar Datasets

Project description:BACKGROUND:In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS:We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS:We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever.

Project description:BACKGROUND AND AIMS:Auxin response factors (ARFs) as transcription activators or repressors have important roles in plant growth and development, but knowledge about the functions of wheat ARF members is limited. A novel ARF member in wheat (Triticum aestivum), TaARF4, was identified, and its protein function, haplotype geographic distribution and allelic frequencies were investigated. METHODS:Tissue expression of TaARF4 was analysed by real-time PCR. Sub-cellular localization was performed using green fluorescent protein (GFP)-tagged TaARF4. Ectopic expression of TaARF4-A in arabidopsis was used to study its functions. Electrophoretic mobility shift assays (EMSAs), chromatin immunoprecipitation (ChIP) analyses and gene expression were performed to detect TaARF4 target genes. A dCAPS (derived cleaved amplified polymorphic sequence) marker developed from TaARF4-B was used to identify haplotypes and association analysis between haplotypes and agronomic traits. KEY RESULTS:TaARF4-A was constitutively expressed and its protein was localized in the nucleus. Ectopic expression of TaARF4-A in arabidopsis caused abscisic acid (ABA) insensitivity, shorter primary root length and reduced plant height (PH). Through expression studies and ChIP assays, TaARF4-A was shown to regulate HB33 expression which negatively responded to ABA, and reduced root length and plant height by repressing expression of Gretchen Hagen 3 (GH3) genes that in turn upregulated indole-3-acetic acid content in arabidopsis. Association analysis showed that TaARF4-B was strongly associated with PH and root depth at the tillering, jointing and grain fill stages. Geographic distribution and allelic frequencies suggested that TaARF4-B haplotypes were selected in Chinese wheat breeding programmes. An amino acid change (threonine to alanine) at position 158 might be the cause of phenotype variation in accessions possessing different haplotypes. CONCLUSIONS:Ectopic expression and association analysis indicate that TaARF4 may be involved in root length and plant height determination in wheat. This work is helpful for selection of wheat genotypes with optimal root and plant architecture.

Project description:Volatile compounds produced by plant-associated microorganisms represent a diverse resource to promote plant growth and health. Here, we investigated the effect of volatiles from root-associated Microbacterium species on plant growth and development. Volatiles of eight strains induced significant increases in shoot and root biomass of Arabidopsis but differed in their effects on root architecture. Microbacterium strain EC8 also enhanced root and shoot biomass of lettuce and tomato. Biomass increases were also observed for plants exposed only briefly to volatiles from EC8 prior to transplantation of the seedlings to soil. These results indicate that volatiles from EC8 can prime plants for growth promotion without direct and prolonged contact. We further showed that the induction of plant growth promotion is tissue specific; that is, exposure of roots to volatiles from EC8 led to an increase in plant biomass, whereas shoot exposure resulted in no or less growth promotion. Gas chromatography-quadrupole time of flight mass spectometry (GC-QTOF-MS) analysis revealed that EC8 produces a wide array of sulfur-containing compounds, as well as ketones. Bioassays with synthetic sulfur volatile compounds revealed that the plant growth response to dimethyl trisulfide was concentration-dependent, with a significant increase in shoot weight at 1 ?M and negative effects on plant biomass at concentrations higher than 1 mM. Genome-wide transcriptome analysis of volatile-exposed Arabidopsis seedlings showed upregulation of genes involved in assimilation and transport of sulfate and nitrate. Collectively, these results show that root-associated Microbacterium primes plants, via the roots, for growth promotion, most likely via modulation of sulfur and nitrogen metabolism.IMPORTANCE In the past decade, various studies have described the effects of microbial volatiles on other (micro)organisms in vitro, but their broad-spectrum activity in vivo and the mechanisms underlying volatile-mediated plant growth promotion have not been addressed in detail. Here, we revealed that volatiles from root-associated bacteria of the genus Microbacterium can enhance the growth of different plant species and can prime plants for growth promotion without direct and prolonged contact between the bacterium and the plant. Collectively, these results provide new opportunities for sustainable agriculture and horticulture by exposing roots of plants only briefly to a specific blend of microbial volatile compounds prior to transplantation of the seedlings to the greenhouse or field. This strategy has no need for large-scale introduction or root colonization and survival of the microbial inoculant.

Dataset Information

A Statistical Growth Property of Plant Root Architectures.

Publications

A Statistical Growth Property of Plant Root Architectures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets