Plant Phenotyping Research Articles

ZmPGIP1 regulates stem strength by enhancing lignin and cellulose biosynthesis in Arabidopsis thaliana

Polygalacturonase inhibiting proteins (PGIPs) are typical leucine-rich repeat (LRR) proteins that inhibit the activity of polygalacturonase (PG) in pathogens, but their function in promoting to plant stem development has been less studied. In this study, the PGIP gene from Zea mays was cloned and heterologously expressed in Arabidopsis thaliana, and then the plant phenotypes were observed and a set of physiological and biochemical indicators were measured. The results indicate that the ZmPGIP1 protein is expressed both in the cell membrane and cell wall. Our research observed that the heterologous expression of ZmPGIP1 significantly increased the production of lignin, cellulose and hemicellulose in transgenic lines. The stems of transgenic plants also showed a thicker cortical tissue. The increase in lignin deposition observed during histochemical staining suggests that the ZmPGIP1 gene may improve plant mechanical strength. These were tightly associated with the upregulated expression of lignin/cellulose-synthesis genes (C4H, C3H, F5H, PAL1/3, CCR1, CAD1-3/9, CCOMT, MYB58, CESA1/10) to enhance plant cell wall strength in transgenic lines. Our findings provide new perspectives on the growth regulation mechanisms of PGIP proteins in plant cell walls and provide a potential candidate gene for breeding increased disease-resistance and higher cellulose production in plants.

Zero‐shot insect detection via weak language supervision

AbstractCheap and ubiquitous sensing has made collecting large agricultural datasets relatively straightforward. These large datasets (for instance, citizen science data curation platforms like iNaturalist) can pave the way for developing powerful artificial intelligence (AI) models for detection and counting. However, traditional supervised learning methods require labeled data, and manual annotation of these raw datasets with useful labels (such as bounding boxes or segmentation masks) can be extremely laborious, expensive, and error‐prone. In this paper, we demonstrate the power of zero‐shot computer vision methods—a new family of approaches that require (almost) no manual supervision—for plant phenomics applications. Focusing on insect detection as the primary use case, we show that our models enable highly accurate detection of insects in a variety of challenging imaging environments. Our technical contributions are two‐fold: (a) We curate the Insecta rank class of iNaturalist to form a new benchmark dataset of approximately 6 million images consisting of 2526 agriculturally and ecologically important species, including pests and beneficial insects. (b) Using a vision‐language object detection method coupled with weak language supervision, we are able to automatically annotate images in this dataset with bounding box information localizing the insect within each image. Our method succeeds in detecting diverse insect species present in a wide variety of backgrounds, producing high‐quality bounding boxes in a zero‐shot manner with no additional training cost. This open dataset can serve as a use‐inspired benchmark for the AI community. We demonstrate that our method can also be used for other applications in plant phenomics, such as fruit detection in images of strawberry and apple trees. Overall, our framework highlights the promise of zero‐shot approaches to make high‐throughput plant phenotyping more affordable.

Patch-based probabilistic identification of plant roots using convolutional neural networks

Recently, computer vision and artificial intelligence are being used as enabling technologies for plant phenotyping studies, since they allow the analysis of large amounts of data gathered by the sensors. Plant phenotyping studies can be devoted to the evaluation of complex plant traits either on the aerial part of the plant as well as on the underground part, to extract meaningful information about the growth, development, tolerance, or resistance of the plant itself. All plant traits should be evaluated automatically and quantitatively measured in a non-destructive way. This paper describes a novel approach for identifying plant roots from images of the root system architecture using a convolutional neural network (CNN) that operates on small image patches calculating the probability that the center point of the patch is a root pixel. The underlying idea is that the CNN model should embed as much information as possible about the variability of the patches that can show chaotic and heterogeneous backgrounds. Results on a real dataset demonstrate the feasibility of the proposed approach, as it overcomes the current state of the art.

Novel rapid screening assay to incorporate complexity and increase throughput in early-stage plant biological testing

The search for new biological products with a positive impact on crop performance is typically initiated by laboratory based in vitro assays. However, live plants and their associated microbes are often removed from in vitro testing assays as a way to reduce biological complexity (variation) and facilitate molecular techniques in the pursuit of uncovering mode-of-action (MoA) mechanisms. Nevertheless, when studying biological candidates intended for use in agriculture, it is essential to incorporate this complexity and validate mechanisms under conditions as close to in situ as possible in order to understand the capacities and MoA of the biologicals in the intended application environments. To address this paradox, we have developed a high-capacity early-stage plant assay that incorporates a live non-sterile plant while also enabling molecular MoA investigations, and that can be conducted in laboratories without greenhouse facilities. The high-capacity design features plants grown in 8-chamber transparent boxes to allow for multiplex imaging and increased biological replicates for greater statistical power. The transparent box design allows the visualization of shoots, roots, tagged-microbes, or visible substrates, and further non-destructive access to shoots or roots for sampling. The boxes are held in racks that hold eight plant boxes during growth in a 19 by 17 cm space, further increasing the throughput to >670 plants per m2 and easing the logistical challenges of plant assays. Furthermore, the box can support various levels of microbial complexity with the option to select the plant growth medium that meets experimental objectives, as well as using sterile or non-sterile seeds. A script-based post-imaging quantification was developed to automate image processing and allow for individual plant readings, further enabling increased statistical confidence. As proof of concept, we use the high-capacity plant system to evaluate the biocontrol potential of Pseudomonas protegens and the biostimulation potential of Pseudomonas koreensis, and are in both cases able to show statistically significant differing plant biomass between treatments under these closer-to-nature conditions. We further demonstrate that the high-capacity plant system is suitable for paired molecular investigations by performing metabolomics and qPCR DNA quantification directly from the plant box to explore in situ chemical MoA, as well as confirm the survival of the P. protegens strains to validate their role in the improved plant phenotype. In conclusion, the study presents a modular high-capacity plant assay system that enables increased throughput functional testing of microbial biocontrol and biostimulant candidates in planta. This novel assaying system saves time, reduces human error, provides quantitative and non-destructive in planta data, and can be used in laboratories without greenhouse facilities. We therefore believe it provides a potent early-stage testing option that bridges in vitro and greenhouse testing, and will expedite the discovery of superior next-generation biological products in agriculture.

Cloning and functional validation of DsWRKY6 gene from Desmodium styracifolium

ABSTRACT The purpose of this study was to analyze the role of transcription factor in Desmodium styracifolium, proving that the DsWRKY6 transcription factor was related to the plant phenotypes of Desmodium styracifolium - cv. ‘GuangYaoDa1’ and it could be used in molecular-assisted breeding. ‘GuangYaoDa1’ was used as the material and its DNA was the template to clone DsWRKY6, the transgenic Arabidopsis thaliana line was constructed by agrobacterium tumefaciens‑mediated transformation. Transgenic Arabidopsis thaliana was cultivated to study phenotype and physiological and biochemical indexes. Phenotypic observation showed that DsWRKY6 transgenic Arabidopsis thaliana had a faster growth rate while compared with the control group, they had longer lengths of main stem, lateral branches of cauline leaves, and root, but a lower number of cauline leaves and lateral branches of cauline leaves. And it also showed that their flowering and fruiting periods were advanced. The results of physiological and biochemical indexes showed that the relative expressions of DsWRKY6 increased and the abscisic acid content significantly increased in DsWRKY6 transgenic Arabidopsis thaliana compared with the control group. According to the above results, DsWRKY6 could regulate the advancing of flowering and fruiting periods caused by the improvement of abscisic acid content, and expression of the DsWRKY6 transcription factor might be the cause of the upright growth of ‘GuangYaoDa1’.

Why aphid virus retention needs more attention: modelling aphid behaviour and virus manipulation in non-persistent plant virus transmission.

Abstract Plant viruses threaten food security and are often transmitted by insect vectors. Non-persistently transmitted (NPT) plant viruses are transmitted almost exclusively by aphids. Because virions attach to the aphid's stylet (mouthparts) and are acquired and inoculated via brief epidermal probes, the aphid-virus interaction is highly transient, with a very short aphid virus retention time. Many NPT viruses manipulate their host plant's phenotype to change aphid behaviour to optimise virus transmission. Epidemiological models of this have overlooked a key feature of aphid NPT virus retention: probing or feeding on a plant causes aphids to lose the virus. Additionally, experimental studies suggest aphids could possibly inoculate multiple healthy plants within one infective period. However, consequences of this for virus manipulation of host plant phenotype have not been explored. Our new compartmental epidemiological model includes both behaviour-based aphid dispersal and infectivity loss rates, and the ability of infective aphids to probe more than one plant before virus loss. We use our model to explore how NPT virus-induced host phenotypes affect epidemic outcomes, comparing these results to representative previous models. We find that previous models behave fundamentally differently and underestimate the benefit of an 'attract-and-deter' phenotype, where the virus induces increased aphid attraction to infected plants but deters them from prolonged feeding. Our results also highlight the importance of characterising NPT virus retention upon the aphid during probing. Allowing for multiple infective probes increases disease incidence and the effectiveness of virus manipulation, with implications for epidemic prediction and control.

Transgenerational plasticity in salinity tolerance of rice: unraveling non-genetic phenotypic modifications and environmental influences.

Transgenerational plasticity in plants enables rapid adaptation to environmental changes, allowing organisms and their offspring to adapt to the environment without altering their underlying DNA. In this study, we investigated the transgenerational plasticity in salinity tolerance of rice plants using a reciprocal transplant experimental strategy. Our aim was to assess whether non-genetic environment-induced phenotypic modifications and transgenerational salinity affect the salinity tolerance of progeny while excluding nuclear genomic factors for two generations. Using salt-tolerant and salt-sensitive rice genotypes, we observed that the parentally salt-stressed salt-sensitive genotype displayed greater growth performance, photosynthetic activity, yield performance, and transcriptional responses than the parentally non-stressed salt-sensitive plants under salt stress conditions. Surprisingly, salt stress-exposed salt-tolerant progeny did not exhibit as much salinity tolerance as salt stress-exposed salt-sensitive progeny under salt stress. Our findings indicate that the phenotypes of offspring plants differed based on the environment experienced by their ancestors, resulting in heritable transgenerational phenotypic modifications in salt-sensitive genotypes via maternal effects. These results elucidated the mechanisms underlying transgenerational plasticity in salinity tolerance, providing valuable insights into how plants respond to changing environmental conditions.

Transcriptomic Analysis of Green Leaf Plants and White-Green Leaf Mutants in Haworthia cooperi var. pilifera.

Haworthia cooperi var. pilifera is a succulent plant with ornamental value. The white-green leaf mutant (wl) showed a significant difference in leaf color from the wild-type plant (WT). In this study, we integrated the transcriptomes of wl and WT plants to screen differentially expressed genes related to leaf color variation. The results of transcriptome analysis showed that 84,163 unigenes were obtained after de novo assembly and the NR database annotated the largest number of unigenes, which accounted for 57.13%, followed by NT (43.02%), GO (39.84%), Swiss-Prot (39.25%), KEGG (36.06%), and COG (24.88%). Our finding showed that 2586 genes were differentially expressed in the two samples, including 1996 down-regulated genes and 590 up-regulated genes. GO analysis predicted that these differentially expressed genes (DEGs) participate in 12 cellular components, 20 biological processes, and 13 molecular function terms and KEGG analysis showed that metabolic pathways, plant-pathogen interaction, glycerophospholipid metabolism, endocytosis, plant hormone signal transduction, and ether lipid metabolism were enriched among all identified pathways. Through functional enrichment analysis of DEGs, we found that they were involved in chloroplast division and the biosynthesis of plant pigments, including chlorophyll, carotenoids, anthocyanin, and transcription factor families, which might be related to the formation mechanism of leaf color. Taken together, these results present insights into the difference in gene expression characteristics in leaves between WT and wl mutants and provide a new insight for breeding colorful leaf phenotypes in succulent plants.

High-density genetic map construction and QTL mapping of a zigzag-shaped stem trait in tea plant (Camellia sinensis)

The highly unique zigzag-shaped stem phenotype in tea plants boasts significant ornamental value and is exceptionally rare. To investigate the genetic mechanism behind this trait, we developed BC1 artificial hybrid populations. Our genetic analysis revealed the zigzag-shaped trait as a qualitative trait. Utilizing whole-genome resequencing, we constructed a high-density genetic map from the BC1 population, incorporating 5,250 SNP markers across 15 linkage groups, covering 3,328.51 cM with an average marker interval distance of 0.68 cM. A quantitative trait locus (QTL) for the zigzag-shaped trait was identified on chromosome 4, within a 61.2 to 97.2 Mb range, accounting for a phenotypic variation explained (PVE) value of 13.62%. Within this QTL, six candidate genes were pinpointed. To better understand their roles, we analyzed gene expression in various tissues and individuals with erect and zigzag-shaped stems. The results implicated CsXTH (CSS0035625) and CsCIPK14 (CSS0044366) as potential key contributors to the zigzag-shaped stem formation. These discoveries lay a robust foundation for future functional genetic mapping and tea plant genetic enhancement.

Application of deep learning for the analysis of stomata: A review of current methods and future directions.

Plant physiology and metabolism relies on the function of stomata, structures on the surface of above ground organs, which facilitate the exchange of gases with the atmosphere. The morphology of the guard cells and corresponding pore which make up the stomata, as well as the density (number per unit area) are critical in determining overall gas exchange capacity. These characteristics can be quantified visually from images captured using microscopes, traditionally relying on time-consuming manual analysis. However, deep learning (DL) models provide a promising route to increase the throughput and accuracy of plant phenotyping tasks, including stomatal analysis. Here we review the published literature on the application of DL for stomatal analysis. We discuss the variation in pipelines used; from data acquisition, pre-processing, DL architecture and output evaluation to post processing. We introduce the most common network structures, the plant species that have been studied, and the measurements that have been performed. Through this review, we hope to promote the use of DL methods for plant phenotyping tasks and highlight future requirements to optimise uptake; predominantly focusing on the sharing of datasets and generalisation of models as well as the caveats associated with utilising image data to infer physiological function.

Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system.

Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.

GROWTH REGULATING FACTOR 7-mediated arbutin metabolism enhances rice salt tolerance.

Salt stress is an environmental factor that limits plant growth and crop production. With the rapid expansion of salinized arable land worldwide, investigating the molecular mechanisms underlying the salt stress response in plants is urgently needed. Here, we report that GROWTH REGULATING FACTOR 7 (OsGRF7) promotes salt tolerance by regulating arbutin (hydroquinone-β-D-glucopyranoside) metabolism in rice (Oryza sativa). Overexpression of OsGRF7 increased arbutin content, and exogenous arbutin application rescued the salt-sensitive phenotype of OsGRF7 knockdown and knockout plants. OsGRF7 directly promoted the expression of the arbutin biosynthesis genes URIDINE DIPHOSPHATE GLYCOSYLTRANSFERASE 1 (OsUGT1) and OsUGT5, and knockout of OsUGT1 or OsUGT5 reduced rice arbutin content, salt tolerance, and grain size. Furthermore, OsGRF7 degradation through its interaction with F-BOX AND OTHER DOMAINS CONTAINING PROTEIN 13 reduced rice salinity tolerance and grain size. These findings highlight an underexplored role of OsGRF7 in modulating rice arbutin metabolism, salt stress response, and grain size, as well as its broad potential use in rice breeding.

The fungus Acremonium alternatum enhances salt stress tolerance by regulating host redox homeostasis and phytohormone signaling.

While endophytic fungi offer promising avenues for bolstering plant resilience against abiotic stressors, the molecular mechanisms behind this biofortification remain largely unknown. This study employed a multifaceted approach, combining plant physiology, proteomic, metabolomic, and targeted hormonal analyses to illuminate the early response of Brassica napus to Acremonium alternatum during the nascent stages of their interaction. Notably, under optimal growth conditions, the initial reaction to fungus was relatively subtle, with no visible alterations in plant phenotype and only minor impacts on the proteome and metabolome. Interestingly, the identified proteins associated with the Acremonium response included TUDOR 1, Annexin D4, and a plastidic K+ efflux antiporter, hinting at potential processes that could counter abiotic stressors, particularly salt stress. Subsequent experiments validated this hypothesis, showcasing significantly enhanced growth in Acremonium-inoculated plants under salt stress. Molecular analyses revealed a profound impact on the plant's proteome, with over 50% of salt stress response proteins remaining unaffected in inoculated plants. Acremonium modulated ribosomal proteins, increased abundance of photosynthetic proteins, enhanced ROS metabolism, accumulation of V-ATPase, altered abundances of various metabolic enzymes, and possibly promoted abscisic acid signaling. Subsequent analyses validated the accumulation of this hormone and its enhanced signaling. Collectively, these findings indicate that Acremonium promotes salt tolerance by orchestrating abscisic acid signaling, priming the plant's antioxidant system, as evidenced by the accumulation of ROS-scavenging metabolites and alterations in ROS metabolism, leading to lowered ROS levels and enhanced photosynthesis. Additionally, it modulates ion sequestration through V-ATPase accumulation, potentially contributing to the observed decrease in chloride content.

A non-invasive method for phenotyping scab-tolerant apple plants using volatile organic compounds.

One of the most devastating diseases of apples is scab, caused by the fungus Venturia inaequalis. Most commercial apple varieties are susceptible to this disease; only a few are resistant. Breeding approaches are being used to develop better apple varieties that are resistant to scab. Volatile organic compounds (VOCs) contribute greatly to a plant's phenotype, and their emission profile largely depends on the genotype. In the non-destructive phenotyping of plants, VOCs can be used as biomarkers. In this study, we assessed non-destructively the scab tolerance potential of resistant (cv. 'Prima') and susceptible (cv. 'Oregon Spur') apple cultivars by comparing their major leaf VOC compositions and relative proportions. A comparison of the leaf VOC profiles of the two cultivars revealed 16 different VOCs, with cis-3-hexenyl acetate (3HA) emerging as a biomarker of cultivar differences. V. inaequalis growth was significantly inhibited in vitro by 3HA treatment. 3HA was significantly effective in reducing scab symptoms on V. inaequalis-inoculated leaves of 'Oregon Spur.' The resistant cultivar 'Prima' also exhibited higher lipoxygenase (LOX) activity and α-linolenic acid (ALA) levels, suggesting that V. inaequalis resistance is linked to LOX activity and 3HA biosynthesis. This study proposes 3HA as a potential biomarker for rapid non-destructive screening of scab-resistant apple germplasm of 'Prima' based on leaf VOCs.

Transcription factor ABF3 modulates salinity stress-enhanced jasmonate signaling in Arabidopsis

Salinity is a severe abiotic stress that affects plant growth and yield. Salinity stress activates jasmonate (JA) signaling in Arabidopsis thaliana, but the underlying molecular mechanism remains to be elucidated. In this study, we confirmed the activation of JA signaling under saline conditions and demonstrated the importance of the CORONATINE INSENSITIVE1 (COI1)-mediated signaling for this process. Phenotypic analyses reflected the negative regulation of JASMONATE ZIM-DOMAIN (JAZ) repressors during salinity stress-enhanced JA signaling. Mechanistic analyses revealed that JAZ proteins physically interact with ABSCISIC ACID-RESPONSIVE ELEMENT BINDING FACTOR1 (ABF1), AREB1/ABF2, ABF3, and AREB2/ABF4, which belong to the basic leucine zipper (bZIP) transcription factor family and respond to salinity stress. Analyses on the ABF3 overexpression plants and ABF mutants indicated the positive role of ABF3 in regulating JA signaling under saline condition. Furthermore, ABF3 overexpression partially recovered the JA-related phenotypes of JAZ1-Δ3A plants. Moreover, ABF3 was observed to indirectly activate ALLENE OXIDE SYNTHASE (AOS) transcription, but this activation was inhibited by JAZ1. In addition, ABF3 competitively bind to JAZ1, thereby decreasing the interaction between JAZ1 and MYC2, which is the master transcription factor controlling JA signaling. Collectively, our findings have clarified the regulatory effects of ABF3 on JA signaling and provide new insights into how JA signaling is enhanced following an exposure to salinity stress.

A maize enzyme from the 2-oxoglutarate-dependent oxygenase family with unique kinetic properties, mediates resistance against pathogens and regulates senescence.

In plants, salicylic acid (SA) hydroxylation regulates SA homoeostasis, playing an essential role during plant development and response to pathogens. This reaction is catalysed by SA hydroxylase enzymes, which hydroxylate SA producing 2,3-dihydroxybenzoic acid (2,3-DHBA) and/or 2,5-dihydroxybenzoic acid (2,5-DHBA). Several SA hydroxylases have recently been identified and characterised from different plant species, but no such activity has yet been reported in maize. In this work, we describe the identification and characterisation of a new SA hydroxylase in maize plants. This enzyme, with high sequence similarity to previously described SA hydroxylases from Arabidopsis and rice, converts SA into 2,5-DHBA; however, it has different kinetic properties to those of previously characterised enzymes, and it also catalysers the conversion of the flavonoid dihydroquercetin into quercetin in in vitro activity assays, suggesting that the maize enzyme may have different roles in vivo to those previously reported from other species. Despite this, ZmS5H can complement the pathogen resistance and the early senescence phenotypes of Arabidopsis s3h mutant plants. Finally, we characterised a maize mutant in the S5H gene (s5hMu) that has altered growth, senescence and increased resistance against Colletotrichum graminicola infection, showing not only alterations in SA and 2,5-DHBA but also in flavonol levels. Together, the results presented here provide evidence that SA hydroxylases in different plant species have evolved to show differences in catalytic properties that may be important to fine tune SA levels and other phenolic compounds such as flavonols, to regulate different aspects of plant development and pathogen defence.

Synergistic effects of plant genotype and soil microbiome on growth in Lotus japonicus.

The biological interactions between plants and their root microbiomes are essential for plant growth, and even though plant genotype [G], soil microbiome [M], and growth conditions (environment) [E] are the core factors shaping root microbiome, their relationships remain unclear. In this study we investigated the effects of G, M, and E and their interactions on the Lotus root microbiome and plant growth using an in vitro cross-inoculation approach which reconstructed the interactions between nine Lotus accessions and four soil microbiomes under two different environmental conditions. Results suggested that a large proportion of the root microbiome composition is determined by M and E, while G-related (G, G × M, and G × E) effects were significant but small. In contrast, the interaction between G and M had a more pronounced effect on plant shoot growth than M alone. Our findings also indicated that most microbiome variations controlled by M have little effect on plant phenotypes, whereas G × M interactions have more significant effects. Plant genotype-dependent interactions with soil microbes warrant more attention to optimize crop yield and resilience.

Cherry growth modeling based on Prior Distance Embedding contrastive learning: Pre-training, anomaly detection, semantic segmentation, and temporal modeling

In current plant phenotyping research, the study of plant time-series images based on deep learning has received widespread attention. While such image data is relatively easy to obtain, the cost of annotation is high. One efficient method for achieving cost-effective training is through contrastive learning. Plant growth is slow, and the changes in image sequences over a period of time are small, with simple semantic information. Previous contrastive pre-training models struggled to effectively distinguish positive samples from the same image with different augmented views and similar negative samples from different images. Therefore, this paper proposes a method called self-supervised contrastive learning method for plant time-series images with a Priori Distance Embedding (PDE). The semantic information in images corresponding to different phenological stages of plants varies. This method transforms this crucial domain knowledge into prior distances for image pairs and conducts contrastive learning pre-training. The learned weights can be transferred to downstream tasks. Building upon this method, experiments were conducted on cherry time-series images to assess the quality of pre-training through a plant phenotyping image semantic segmentation task. To provide a comprehensive example of plant time-series image phenotypic analysis, this paper establishes a cherry growth temporal model, specifically including PDE pre-training, anomaly detection, semantic segmentation, and recording the results from the temporal dimension. The experiments indicate that this self-supervised contrastive learning method can be effectively applied to the pre-training of plant time-series images, demonstrating broad applicability in various computer vision studies related to plant phenotyping.

A microbiome survey of contrasting potato terroirs using 16S rRNA long-read sequencing

Abstract Aims As a consequence of the increasing impact of climate change on crop production and food security worldwide, the need to explore agricultural systems in a sustainable manner is also intensified. The improvement of long-read metagenomics approaches might give valuable information not only on soil microbial communities, but also on their potential effects on plant phenotypes. Soil properties, climate conditions, and agricultural techniques are the main factors shaping microbial communities found in the soil and on the surface of the potatoes, influencing plant health and performance. The objective of this study was to decipher the bacterial communities in contrasting potato terroirs using long-read sequencing of the 16S rRNA gene. Methods To do so, 18 soil samples were taken from different potato fields in the island of Naxos (Island Terroir) and Northern Greece (Continental Terroir). Differences in soil properties and climatic conditions were also regarded to explore the possible motif of microbial structure and diversity in each region. Results Our results highlighted that contrasting potato terroirs strongly affected microbial community composition and diversity. A richer microbial composition in the island terroir was evident. A wide range of soil bacteria were identified, including Vicinamibacter, Neobacillus, Povalibacter, Microvirga, Thermoanaerobaculum, Arenimonas, and Rubrobacter, with different distribution patterns that resulted in characteristic microbial footprints. Conclusions In combination with soil analysis, microbial mapping might be a valuable tool, not only for gaining a deeper knowledge of their impact on potato production, but also for developing biomarkers that would uniquely define and characterize each potato habitat.

Appropriate Drought Training Induces Optimal Drought Tolerance by Inducing Stepwise H2O2 Homeostasis in Soybean.

Soybean is considered one of the most drought-sensitive crops, and ROS homeostasis can regulate drought tolerance in these plants. Understanding the mechanism of H2O2 homeostasis and its regulatory effect on drought stress is important for improving drought tolerance in soybean. We used different concentrations of polyethylene glycol (PEG) solutions to simulate the progression from weak drought stress (0.2%, 0.5%, and 1% PEG) to strong drought stress (5% PEG). We investigated the responses of the soybean plant phenotype, ROS level, injury severity, antioxidant system, etc., to different weak drought stresses and subsequent strong drought stresses. The results show that drought-treated plants accumulated H2O2 for signaling and exhibited drought tolerance under the following stronger drought stress, among which the 0.5% PEG treatment had the greatest effect. Under the optimal treatment, there was qualitatively describable H2O2 homeostasis, characterized by a consistent increasing amplitude in H2O2 content compared with CK. The H2O2 signal formed under the optimum treatment induced the capacity of the antioxidant system to remove excess H2O2 to form a primary H2O2 homeostasis. The primary H2O2 homeostasis further induced senior H2O2 homeostasis under the following strong drought and maximized the improvement of drought tolerance. These findings might suggest that gradual drought training could result in stepwise H2O2 homeostasis to continuously improve drought tolerance.