The root of plant-plant interactions: Belowground special cocktails.

Plants interact with each other via a multitude of processes among which belowground communication facilitated by specialized metabolites plays an important but overlooked role. Until now, the exact targets, modes of action, and resulting phenotypes that these metabolites induce in neighboring plants have remained largely unknown. Moreover, positive interactions driven by the release of root exudates are prevalent in both natural field conditions and controlled laboratory environments. In particular, intraspecific positive interactions suggest a genotypic recognition mechanism in addition to non-self perception in plant roots. This review concentrates on recent discoveries regarding how plants interact with one another through belowground signals in intra- and interspecific mixtures. Furthermore, we elaborate on how an enhanced understanding of these interactions can propel the field of agroecology forward.

Replication-independent change in the frequencies of distinct genome segments of a multipartite virus during its transit within aphid vectors.

Multipartite viruses exhibit a fragmented genome composed of several nucleic acid segments individually packaged in distinct viral particles. The genome of all species of the genus Nanovirus holds eight segments, which accumulate at a very specific and reproducible relative frequency in the host plant tissues. In a given host species, the steady state pattern of the segments' relative frequencies is designated the genome formula and is thought to have an adaptive function through the modulation of gene expression. Nanoviruses are aphid-transmitted circulative non-propagative viruses, meaning that the virus particles are internalized into the midgut cells, transferred to the hemolymph, and then to the saliva, with no replication during this transit. Unexpectedly, a previous study on the faba bean necrotic stunt virus revealed that the genome formula changes after ingestion by aphids. We investigate here the possible mechanism inducing this change by first comparing the relative segment frequencies in different compartments of the aphid. We show that changes occur both in the midgut lumen and in the secreted saliva but not in the gut, salivary gland, or hemolymph. We further establish that the viral particles differentially resist physicochemical variations, in particular pH, ionic strength, and/or type of salt, depending on the encapsidated segment. We thus propose that the replication-independent genome formula changes within aphids are not adaptive, contrary to changes occurring in plants, and most likely reflect a fortuitous differential degradation of virus particles containing distinct segments when passing into extra-cellular media such as gastric fluid or saliva. IMPORTANCE: The genome of multipartite viruses is composed of several segments individually packaged into distinct viral particles. Each segment accumulates at a specific frequency that depends on the host plant species and regulates gene expression. Intriguingly, the relative frequencies of the genome segments also change when the octopartite faba bean necrotic stunt virus (FBNSV) is ingested by aphid vectors, despite the present view that this virus travels through the aphid gut and salivary glands without replicating. By monitoring the genomic composition of FBNSV populations during the transit in aphids, we demonstrate here that the changes take place extracellularly in the gut lumen and in the saliva. We further show that physicochemical factors induce differential degradation of viral particles depending on the encapsidated segment. We propose that the replication-independent changes within the insect vector are not adaptive and result from the differential stability of virus particles containing distinct segments according to environmental parameters.

SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.

BACKGROUND: Investigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici-a frequently occurring and damaging disease on wheat crops. RESULTS: We developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions. CONCLUSIONS: SeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.

"Rhizoponics": a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants.

BACKGROUND: Well-developed and functional roots are critical to support plant life and reach high crop yields. Their study however, is hampered by their underground growth and characterizing complex root system architecture (RSA) therefore remains a challenge. In the last few years, several phenotyping methods, including rhizotrons and x-ray computed tomography, have been developed for relatively thick roots. But in the model plant Arabidopsis thaliana, in vitro culture remains the easiest and preferred method to study root development, which technically limits the analyses to young seedlings. RESULTS: We present here an innovative design of hydroponic rhizotrons (rhizoponics) adapted to Arabidopsis thaliana. The setup allows to simultaneously characterize the RSA and shoot development from seedling to adult stages, i.e. from seed to seed. This system offers the advantages of hydroponics such as control of root environment and easy access to the roots for measurements or sampling. Being completely movable and low cost, it can be used in controlled cabinets. We chose the case of cadmium treatment to illustrate potential applications, from cell to organ levels. CONCLUSIONS: Rhizoponics makes possible, on the same plants of Arabidopsis, RSA measurements, root sampling and characterization of aerial development up to adult size. It therefore provides a valuable tool for addressing fundamental questions in whole plant physiology.