Biology subjects

Schandry, N.

Publications and source records attributed to Schandry, N..

3 recordsLinked to original sources

bioRxiv2022

Transcriptional response of a target plant to benzoxazinoid and diterpene allelochemicals highlights commonalities in detoxification

Plants growing in proximity to other plants are exposed to a variety of metabolites that these neighbors release into the environment. Some species produce allelochemicals to inhibit growth of neighboring plants, which in turn have evolved ways to detoxify these compounds. In order to understand how the allelochemical-receiving target plants respond to chemically diverse compounds, we performed whole-genome transcriptome analysis of Arabidopsis thaliana exposed to either the benzoxazinoid derivative 2-amino-3H-phenoxazin-3-one (APO) or momilactone B. These two allelochemicals belong to two very different compound classes, benzoxazinoids and diterpenes, respectively, produced by different cereal crop species. Despite their distinct chemical nature, we observed similar molecular responses of A. thaliana to these allelochemicals. In particular, many of the same or closely related genes belonging to the three-phase detoxification pathway were upregulated in both treatments. Further, we observed an overlap between genes upregulated by allelochemicals and those involved in herbicide detoxification. Our findings highlight the overlap in the transcriptional response of a target plant to natural and synthetic phytotoxic compounds and illustrate how herbicide resistance could arise via pathways involved in plant-plant interaction.

plant biology↗

bioRxiv2021

Plant-derived benzoxazinoids act as antibiotics and shape bacterial communities

Plants synthesize and release specialized metabolites into their environment that can serve as chemical cues for other organisms. Metabolites that are released from the roots are important factors in determining which microorganisms will colonize the root and become part of the plant rhizosphere. Root exudates can be converted by soil microorganisms, which can result in the formation of toxic compounds. How individual members of the plant rhizosphere respond to individual compounds and how the differential response of individual microorganisms contributes to the response of a microbial community remains an open question. Here, we investigated the impact of derivatives of benzoxazinoids, a class of plant root exudates released by important crops such as wheat and maize, on a collection of 180 root-associated bacteria. Phenoxazine, derived in soil from benzoxazinoids, inhibited the growth of root-associated bacteria in vitro in an isolate-specific manner, with sensitive and resistant isolates present in most of the studied clades. Using synthetic communities, we show that community stability is a consequence of the resilience of its individual members, with communities assembled from tolerant isolates being overall more tolerant to benzoxazinoids. However, the performance of an isolate in a community context was not correlated with its individual performance but appeared to be shaped by interactions between isolates. These interactions were independent of the overall community composition and were strain-specific, with interactions between different representatives of the same bacterial genera accounting for differential community composition.

plant biology↗

bioRxiv2020

araDEEPopsis: From images to phenotypic traits using deep transfer learning

Linking plant phenotype to genotype, i.e., identifying genetic determinants of phenotypic traits, is a common goal of both plant breeders and geneticists. While the ever-growing genomic resources and rapid decrease of sequencing costs have led to enormous amounts of genomic data, collecting phenotypic data for large numbers of plants remains a bottleneck. Many phenotyping strategies rely on imaging plants, which makes it necessary to extract phenotypic measurements from these images rapidly and robustly. Common image segmentation tools for plant phenotyping mostly rely on color information, which is error-prone when either background or plant color deviate from the underlying expectations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. O_SCPLOWARAC_SCPLOWO_SCPLOWDEEPC_SCPLOWO_SCPLOWOPSISC_SCPLOW was built around the deep-learning model DeepLabV3+ that was re-trained for segmentation of Arabidopsis thaliana rosettes. It uses semantic segmentation to classify leaf tissue into up to three categories: healthy, anthocyanin-rich, and senescent. This makes O_SCPLOWARAC_SCPLOWO_SCPLOWDEEPC_SCPLOWO_SCPLOWOPSISC_SCPLOW particularly powerful at quantitative phenotyping from early to late developmental stages, of mutants with aberrant leaf color and/or phenotype, and of plants growing in stressful conditions where leaf color may deviate from green. Using our tool on a panel of 210 natural Arabidopsis accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to map known loci related to anthocyanin production and early necrosis using the O_SCPLOWARAC_SCPLOWO_SCPLOWDEEPC_SCPLOWO_SCPLOWOPSISC_SCPLOW output in genome-wide association analyses. Our pipeline is able to handle images of diverse origins, image quality, and background composition, and could even accurately segment images of a distantly related Brassicaceae. Because it can be deployed on virtually any common operating system and is compatible with several high-performance computing environments, O_SCPLOWARAC_SCPLOWO_SCPLOWDEEPC_SCPLOWO_SCPLOWOPSISC_SCPLOW can be used independently of bioinformatics expertise and computing resources. O_SCPLOWARAC_SCPLOWO_SCPLOWDEEPC_SCPLOWO_SCPLOWOPSISC_SCPLOW is available at https://github.com/Gregor-Mendel-Institute/aradeepopsis.

plant biology↗