Biology subjects

Veley, K. M.

Publications and source records attributed to Veley, K. M..

5 recordsLinked to original sources

bioRxiv2026

Multiple approaches for CRISPR-based targeting of DNA methylation to promoters of bacterial and viral susceptibility genes in cassava

Targeted epigenetic modifications of specific gene regulatory regions have the potential to confer beneficial traits for crop improvement. Two recently developed CRISPR/Cas9-based epigenome editing tools were tested in transgenic cassava to target cytosine methylation to the promoter region of MeSWEET10a, a necessary gene for infection by the Cassava bacterial blight pathogen, Xanthomonas phaseoli pv. manihotis. The two systems leverage unique methyltransferases, and each induced distinct DNA methylation profiles at the targeted site, decreased effector-triggered MeSWEET10a expression, and attenuated water-soaking symptoms in inoculated leaves. Further, DNA methylation was simultaneously targeted, in addition to MeSWEET10a, to two susceptibility genes for Cassava brown streak virus. Relative levels of de novo DNA methylation at the three loci were inversely correlated with DNA methylation-antagonizing H3K4me3 marks. Finally, an initial assessment of DNA methylation after one generation indicated specific inheritance of CpG methylation that was unstable in the absence of the methyltransferase systems.

plant biology↗

bioRxiv2023

CRISPR/Cas9 generated MeSWEET10a mutants show reduced susceptibility to cassava bacterial blight and produce viable seed

Bacteria from the genus Xanthomonas are prolific phytopathogens that elicit disease in over 400 plant species. Xanthomonads carry a repertoire of specialized proteins called transcription activator-like (TAL) effectors that promote disease and pathogen virulence by inducing expression of host susceptibility (S) genes. Xanthomonas phaseoli pv. manihotis (Xpm) causes bacterial blight on the staple food crop, cassava. The Xpm effector, TAL20, induces ectopic expression of the S gene, MeSWEET10a, a sugar transporter that contributes to cassava bacterial blight susceptibility. We used CRISPR/Cas9 to generate multiple cassava lines with edits to the MeSWEET10a TAL20 effector binding site and/or coding sequence. In several of the regenerated lines, MeSWEET10a expression was no longer induced by Xpm and in these cases, we observed reduced cassava bacterial blight disease symptoms post Xpm infection. MeSWEET10a is expressed in cassava flowers. Therefore, we investigated flower development and reproductive function in an MeSWEET10a mutant line. We found that the MeSWEET10a mutant produced phenotypically wildtype cassava flowers and viable F1 seed. Thus, blocking MeSWEET10a induction is a viable strategy for decreasing cassava susceptibility to CBB.

plant biology↗

bioRxiv2022

Improving disease resistance in plants 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.

plant biology↗

bioRxiv2018

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.

plant biology↗

bioRxiv2017

High-Throughput Profiling Identifies Resource Use Efficient And Abiotic Stress Tolerant Sorghum Varieties

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. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. 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, both in terms of 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.

plant biology↗