Improving cassava bacterial blight resistance by editing the epigenome.

Pathogens rely on expression of host susceptibility (S) genes to promote infection and disease. As DNA methylation is an epigenetic modification that affects gene expression, blocking access to S genes through targeted methylation could increase disease resistance. Xanthomonas phaseoli pv. manihotis, the causal agent of cassava bacterial blight (CBB), uses transcription activator-like20 (TAL20) to induce expression of the S gene MeSWEET10a. In this work, we direct methylation to the TAL20 effector binding element within the MeSWEET10a promoter using a synthetic zinc-finger DNA binding domain fused to a component of the RNA-directed DNA methylation pathway. We demonstrate that this methylation prevents TAL20 binding, blocks transcriptional activation of MeSWEET10a in vivo and that these plants display decreased CBB symptoms while maintaining normal growth and development. This work therefore presents an epigenome editing approach useful for crop improvement.

Increased signal-to-noise ratios within experimental field trials by regressing spatially distributed soil properties as principal components.

Environmental variability poses a major challenge to any field study. Researchers attempt to mitigate this challenge through replication. Thus, the ability to detect experimental signals is determined by the degree of replication and the amount of environmental variation, noise, within the experimental system. A major source of noise in field studies comes from the natural heterogeneity of soil properties which create microtreatments throughout the field. In addition, the variation within different soil properties is often nonrandomly distributed across a field. We explore this challenge through a sorghum field trial dataset with accompanying plant, microbiome, and soil property data. Diverse sorghum genotypes and two watering regimes were applied in a split-plot design. We describe a process of identifying, estimating, and controlling for the effects of spatially distributed soil properties on plant traits and microbial communities using minimal degrees of freedom. Importantly, this process provides a method with which sources of environmental variation in field data can be identified and adjusted, improving our ability to resolve effects of interest and to quantify subtle phenotypes.

A comparison of ImageJ and machine learning based image analysis methods to measure cassava bacterial blight disease severity.

BACKGROUND: Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. RESULTS: In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava-Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas, a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. CONCLUSIONS: Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.

Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons.

Drought is a major abiotic stress limiting agricultural productivity. Previous field-level experiments have demonstrated that drought decreases microbiome diversity in the root and rhizosphere. How these changes ultimately affect plant health remains elusive. Toward this end, we combined reductionist, transitional and ecological approaches, applied to the staple cereal crop sorghum to identify key root-associated microbes that robustly affect drought-stressed plant phenotypes. Fifty-three Arabidopsis-associated bacteria were applied to sorghum seeds and their effect on root growth was monitored. Two Arthrobacter strains caused root growth inhibition (RGI) in Arabidopsis and sorghum. In the context of synthetic communities, Variovorax strains were able to protect plants from Arthrobacter-caused RGI. As a transitional system, high-throughput phenotyping was used to test the synthetic communities. During drought stress, plants colonized by Arthrobacter had reduced growth and leaf water content. Plants colonized by both Arthrobacter and Variovorax performed as well or better than control plants. In parallel, we performed a field trial wherein sorghum was evaluated across drought conditions. By incorporating data on soil properties into the microbiome analysis, we accounted for experimental noise with a novel method and were able to observe the negative correlation between the abundance of Arthrobacter and plant growth. Having validated this approach, we cross-referenced datasets from the high-throughput phenotyping and field experiments and report a list of bacteria with high confidence that positively associated with plant growth under drought stress. In conclusion, a three-tiered experimental system successfully spanned the lab-to-field gap and identified beneficial and deleterious bacterial strains for sorghum under drought.

Systems-wide analysis revealed shared and unique responses to moderate and acute high temperatures in the green alga Chlamydomonas reinhardtii.

Different intensities of high temperatures affect the growth of photosynthetic cells in nature. To elucidate the underlying mechanisms, we cultivated the unicellular green alga Chlamydomonas reinhardtii under highly controlled photobioreactor conditions and revealed systems-wide shared and unique responses to 24-hour moderate (35°C) and acute (40°C) high temperatures and subsequent recovery at 25°C. We identified previously overlooked unique elements in response to moderate high temperature. Heat at 35°C transiently arrested the cell cycle followed by partial synchronization, up-regulated transcripts/proteins involved in gluconeogenesis/glyoxylate-cycle for carbon uptake and promoted growth. But 40°C disrupted cell division and growth. Both high temperatures induced photoprotection, while 40°C distorted thylakoid/pyrenoid ultrastructure, affected the carbon concentrating mechanism, and decreased photosynthetic efficiency. We demonstrated increased transcript/protein correlation during both heat treatments and hypothesize reduced post-transcriptional regulation during heat may help efficiently coordinate thermotolerance mechanisms. During recovery after both heat treatments, especially 40°C, transcripts/proteins related to DNA synthesis increased while those involved in photosynthetic light reactions decreased. We propose down-regulating photosynthetic light reactions during DNA replication benefits cell cycle resumption by reducing ROS production. Our results provide potential targets to increase thermotolerance in algae and crops.

Heat stress changes mineral nutrient concentrations in Chenopodium quinoa seed.

Quinoa is a popular seed crop, often consumed for its high nutritional quality. We studied how heat stress in the roots or the shoots of quinoa plants affected the concentrations of 20 elements (aluminum, arsenic, boron, calcium, cadmium, cobalt, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorous, rubidium, sulfur, selenium, strontium, and zinc) in quinoa seed. Elemental concentrations in quinoa seed were significantly changed after an 11-day heat treatment during anthesis. The type of panicle (main, secondary, and tertiary) sampled and the type of heat treatment (root only, shoot only, or whole plants) significantly affected elemental profiles in quinoa seed. Plants were also divided into five sections from top to bottom to assess the effect of panicle position on seed elemental profiles. Plant section had an effect on the concentrations of arsenic, iron, and sodium under control conditions and on copper with heat treatment. Overall, the time of panicle development in relation to the time of heat exposure had the largest effect on seed elemental concentrations. Interestingly, the quinoa plants were exposed to heat only during anthesis of the main panicle, but the elemental concentrations of seeds produced after heat treatment ended were still significantly changed, indicating that heat stress has long-lasting effects on quinoa plants. These findings demonstrate how the nutritional quality of quinoa seeds can be changed significantly even by relatively short heat spells.

Large structural variations in the haplotype-resolved African cassava genome.

Cassava (Manihot esculenta Crantz, 2n = 36) is a global food security crop. It has a highly heterozygous genome, high genetic load, and genotype-dependent asynchronous flowering. It is typically propagated by stem cuttings and any genetic variation between haplotypes, including large structural variations, is preserved by such clonal propagation. Traditional genome assembly approaches generate a collapsed haplotype representation of the genome. In highly heterozygous plants, this results in artifacts and an oversimplification of heterozygous regions. We used a combination of Pacific Biosciences (PacBio), Illumina, and Hi-C to resolve each haplotype of the genome of a farmer-preferred cassava line, TME7 (Oko-iyawo). PacBio reads were assembled using the FALCON suite. Phase switch errors were corrected using FALCON-Phase and Hi-C read data. The ultralong-range information from Hi-C sequencing was also used for scaffolding. Comparison of the two phases revealed >5000 large haplotype-specific structural variants affecting over 8 Mb, including insertions and deletions spanning thousands of base pairs. The potential of these variants to affect allele-specific expression was further explored. RNA-sequencing data from 11 different tissue types were mapped against the scaffolded haploid assembly and gene expression data are incorporated into our existing easy-to-use web-based interface to facilitate use by the broader plant science community. These two assemblies provide an excellent means to study the effects of heterozygosity, haplotype-specific structural variation, gene hemizygosity, and allele-specific gene expression contributing to important agricultural traits and further our understanding of the genetics and domestication of cassava.

ER71/ETV2 Promotes Hair Regeneration from Chemotherapeutic Drug-Induced Hair Loss by Enhancing Angiogenesis.

Chemotherapy-induced alopecia and hair loss can be stressful in patients with cancer. The hair grows back, but sometimes the hair tends to stay thin. Therefore, understanding mechanisms regulating hair regeneration may improve the management of chemotherapy-induced alopecia. Previous studies have revealed that chemotherapeutic agents induce a hair follicle vascular injury. As hair growth is associated with micro-vessel regeneration, we postulated that the stimulation of angiogenesis might enhance hair regeneration. In particular, mice treated with 5-fluorouracil (5-FU) showed delayed anagen initiation and reduced capillary density when compared with untreated controls, suggesting that the retardation of anagen initiation by 5-FU treatment may be attributed to the loss of perifollicular micro-vessels. We investigated whether the ETS transcription factor ETV2 (aka ER71), critical for vascular development and regeneration, can promote angiogenesis and hair regrowth in a 5-FU-induced alopecia mouse model. Tie2-Cre; Etv2 conditional knockout (CKO) mice, which lack Etv2 in endothelial cells, presented similar hair regrowth rates as the control mice after depilation. Following 5-FU treatment, Tie2-Cre; Etv2 CKO mice revealed a significant reduction in capillary density, anagen induction, and hair restoration when compared with controls. Mice receiving lentiviral Etv2 injection after 5-FU treatment showed significantly improved anagen induction and hair regrowth. Two-photon laser scanning microscopy revealed that enforced Etv2 expression restored normal vessel morphology after 5-FU mediated vessel injury. Our data suggest that vessel regeneration strategies may improve hair regrowth after chemotherapeutic treatment.

Escalation in the host-pathogen arms race: A host resistance response corresponds to a heightened bacterial virulence response.

The zig-zag model of host-pathogen interaction describes the relative strength of defense response across a spectrum of pathogen-induced plant phenotypes. A stronger defense response results in increased resistance. Here, we investigate the strength of pathogen virulence during disease and place these findings in the context of the zig-zag model. Xanthomonas vasicola pv. holcicola (Xvh) causes sorghum bacterial leaf streak. Despite being widespread, this disease has not been described in detail at the molecular level. We divided diverse sorghum genotypes into three groups based on disease symptoms: water-soaked lesions, red lesions, and resistance. Bacterial growth assays confirmed that these three phenotypes represent a range of resistance and susceptibility. To simultaneously reveal defense and virulence responses across the spectrum of disease phenotypes, we performed dual RNA-seq on Xvh-infected sorghum. Consistent with the zig-zag model, the expression of plant defense-related genes was strongest in the resistance interaction. Surprisingly, bacterial virulence genes related to the type III secretion system (T3SS) and type III effectors (T3Es) were also most highly expressed in the resistance interaction. This expression pattern was observed at multiple time points within the sorghum-Xvh pathosystem. Further, a similar expression pattern was observed in Arabidopsis infected with Pseudomonas syringae for effector-triggered immunity via AvrRps4 but not AvrRpt2. Specific metabolites were able to repress the Xvh virulence response in vitro and in planta suggesting a possible signaling mechanism. Taken together, these findings reveal multiple permutations of the continually evolving host-pathogen arms race from the perspective of host defense and pathogen virulence responses.

CelltrackR: An R package for fast and flexible analysis of immune cell migration data.

Visualization of cell migration via time-lapse microscopy has greatly advanced our understanding of the immune system. However, subtle differences in migration dynamics are easily obscured by biases and imaging artifacts. While several analysis methods have been suggested to address these issues, an integrated tool implementing them is currently lacking. Here, we present celltrackR, an R package containing a diverse set of state-of-the-art analysis methods for (immune) cell tracks. CelltrackR supports the complete pipeline for track analysis by providing methods for data management, quality control, extracting and visualizing migration statistics, clustering tracks, and simulating cell migration. CelltrackR supports the analysis of both 2D and 3D cell tracks. CelltrackR is an open-source package released under the GPL-2 license, and is freely available on both GitHub and CRAN. Although the package was designed specifically for immune cell migration data, many of its methods will also be of use in other research areas dealing with moving objects.

Antiviral ARGONAUTEs Against Turnip Crinkle Virus Revealed by Image-Based Trait Analysis.

RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis (Arabidopsis thaliana) plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent nonadditive, anti-TCV effectors, whereas AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.

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.

Requisite endothelial reactivation and effective siRNA nanoparticle targeting of Etv2/Er71 in tumor angiogenesis.

Angiogenesis, new blood vessel formation from preexisting vessels, is critical for solid tumor growth. As such, there have been efforts to inhibit angiogenesis as a means to obstruct tumor growth. However, antiangiogenic therapy faces major challenges to the selective targeting of tumor-associated-vessels, as current antiangiogenic targets also disrupt steady-state vessels. Here, we demonstrate that the developmentally critical transcription factor Etv2 is selectively upregulated in both human and mouse tumor-associated endothelial cells (TAECs) and is required for tumor angiogenesis. Two-photon imaging revealed that Etv2-deficient tumor-associated vasculature remained similar to that of steady-state vessels. Etv2-deficient TAECs displayed decreased Flk1 (also known as Vegfr2) expression, FLK1 activation, and proliferation. Endothelial tube formation, proliferation, and sprouting response to VEGF, but not to FGF2, was reduced in Etv2-deficient ECs. ROS activated Etv2 expression in ECs, and ROS blockade inhibited Etv2 expression in TAECs in vivo. Systemic administration of Etv2 siRNA nanoparticles potently inhibited tumor growth and angiogenesis without cardiovascular side effects. These studies highlight a link among vascular oxidative stress, Etv2 expression, and VEGF response that is critical for tumor angiogenesis. Targeting the ETV2 pathway might offer a unique opportunity for more selective antiangiogenic therapies.

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

Neutrophil FcγRIIA promotes IgG-mediated glomerular neutrophil capture via Abl/Src kinases.

The kidney glomerular capillaries are frequent sites of immune complex deposition and subsequent neutrophil accumulation in post-infectious and rapidly progressive glomerulonephritis. However, the mechanisms of neutrophil recruitment remain enigmatic, and there is no targeted therapeutic to avert this proximal event in glomerular inflammation. The uniquely human activating Fc receptor FcγRIIA promotes glomerular neutrophil accumulation and damage in anti-glomerular basement membrane-induced (anti-GBM-induced) glomerulonephritis when expressed on murine neutrophils. Here, we found that neutrophils are directly captured by immobilized IgG antibodies under physiological flow conditions in vitro through FcγRIIA-dependent, Abl/Src tyrosine kinase-mediated F-actin polymerization. Biophysical measurements showed that the lifetime of FcγRIIA-IgG bonds increased under mechanical force in an F-actin-dependent manner, which could enable the capture of neutrophils under physiological flow. Kidney intravital microscopy revealed that circulating neutrophils, which were similar in diameter to glomerular capillaries, abruptly arrested following anti-GBM antibody deposition via neutrophil FcγRIIA and Abl/Src kinases. Accordingly, inhibition of Abl/Src with bosutinib reduced FcγRIIA-mediated glomerular neutrophil accumulation and renal injury in experimental, crescentic anti-GBM nephritis. These data identify a pathway of neutrophil recruitment within glomerular capillaries following IgG deposition that may be targeted by bosutinib to avert glomerular injury.

Genomics-enabled analysis of the emergent disease cotton bacterial blight.

Cotton bacterial blight (CBB), an important disease of (Gossypium hirsutum) in the early 20th century, had been controlled by resistant germplasm for over half a century. Recently, CBB re-emerged as an agronomic problem in the United States. Here, we report analysis of cotton variety planting statistics that indicate a steady increase in the percentage of susceptible cotton varieties grown each year since 2009. Phylogenetic analysis revealed that strains from the current outbreak cluster with race 18 Xanthomonas citri pv. malvacearum (Xcm) strains. Illumina based draft genomes were generated for thirteen Xcm isolates and analyzed along with 4 previously published Xcm genomes. These genomes encode 24 conserved and nine variable type three effectors. Strains in the race 18 clade contain 3 to 5 more effectors than other Xcm strains. SMRT sequencing of two geographically and temporally diverse strains of Xcm yielded circular chromosomes and accompanying plasmids. These genomes encode eight and thirteen distinct transcription activator-like effector genes. RNA-sequencing revealed 52 genes induced within two cotton cultivars by both tested Xcm strains. This gene list includes a homeologous pair of genes, with homology to the known susceptibility gene, MLO. In contrast, the two strains of Xcm induce different clade III SWEET sugar transporters. Subsequent genome wide analysis revealed patterns in the overall expression of homeologous gene pairs in cotton after inoculation by Xcm. These data reveal important insights into the Xcm-G. hirsutum disease complex and strategies for future development of resistant cultivars.

High-throughput profiling and analysis of plant responses over time to abiotic stress.

Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) interactions remains a progress bottleneck. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity), and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, in terms of both biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis, we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low-cost, and high-throughput option for characterizing G x E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open-access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.

Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease.

Plant disease symptoms exhibit complex spatial and temporal patterns that are challenging to quantify. Image-based phenotyping approaches enable multidimensional characterization of host-microbe interactions and are well suited to capture spatial and temporal data that are key to understanding disease progression. We applied image-based methods to investigate cassava bacterial blight, which is caused by the pathogen Xanthomonas axonopodis pv. manihotis (Xam). We generated Xam strains in which individual predicted type III effector (T3E) genes were mutated and applied multiple imaging approaches to investigate the role of these proteins in bacterial virulence. Specifically, we quantified bacterial populations, water-soaking disease symptoms, and pathogen spread from the site of inoculation over time for strains with mutations in avrBs2, xopX, and xopK as compared to wild-type Xam ∆avrBs2 and ∆xopX both showed reduced growth in planta and delayed spread through the vasculature system of cassava. ∆avrBs2 exhibited reduced water-soaking symptoms at the site of inoculation. In contrast, ∆xopK exhibited enhanced induction of disease symptoms at the site of inoculation but reduced spread through the vasculature. Our results highlight the importance of adopting a multipronged approach to plant disease phenotyping to more fully understand the roles of T3Es in virulence. Finally, we demonstrate that the approaches used in this study can be extended to many host-microbe systems and increase the dimensions of phenotype that can be explored.