ASHS 2024 Conference

New insights into the Yellow2 locus and its role in beta-carotene accumulation in carrots (Daucus carota) - Michael Paulsmeyer
Overexpression of the Coding Sequence of Ma1 Leads to Enhanced Anthocyanin Biosynthesis in Apple via MYB73. - Mengxia Zhang
Rapid Detection and Coinfection Analysis of Aspergillus flavus in Peanut SeedsRapid Detection and Coinfection Analysis of Aspergillus flavus in Peanut Seeds - Emran Ali
RNA-seq Analysis Reveals Differentially Expressed Genes in Sweetpotato cv. Beauregard under Lead Stress - Mary Ann Munda
RNASeq Analysis Reveals Key Pathways Involved in Low Nitrogen Tolerance at the Onset of Storage Root Formation in Sweetpotato cv Bayou Belle - Lisa Arce
Introducing CHiDO – a No Code Genomic Prediction Software implementation for the Characterization & Integration of Driven Omics - Diego Jarquin
Tissue Culture-Free Genome Editing in Plants Using RNA Viruses - Degao Liu

Carrots (Daucus carota) are a unique model for the accumulation of carotenoids. Beta-carotene accumulates in large amounts in the taproot if the proper alleles of the following three loci are present: OR, Y, and Y2. These three loci are not carotenoid biosynthetic genes but rather post-transcriptional regulation of carotenoid accumulation. The genes underlying the OR and Y loci have been characterized, but the gene underlying the Y2 locus is unknown. Through genomic and transcriptomic analyses, a single candidate that may interact with light signaling was found. To determine the function of this gene, the functional transcript from wild carrot was overexpressed in orange carrots and used in a transient infiltration assay with a GFP fusion tag in tobacco. The orange allele of this gene has a large transposon insertion that theoretically inactivates the gene. However, full length transcript can still be detected in orange carrots. This begs the question of whether the transposon is still active in certain accessions. In this study, the proportion properly assembled Y2 transcript was analyzed via qRT-PCR. A KASP marker was also developed to assist plant breeders in selection for the Y2 locus.

Anthocyanins, a group of secondary metabolites synthesized in the phenylpropanoid pathway, largely determine fruit peel color of fleshy fruits, but it is not known if its synthesis is linked to vacuolar malate accumulation that determines fruit acidity. Here, we show that when the coding sequence of Ma1, the major gene controlling apple fruit acidity, is overexpressed in ‘Royal Gala’ (RG), anthocyanin biosynthesis in the fruit peel is enhanced, corresponding to the downregulation of the expression of MYB73, an R2R3-MYB transcription factor. RNAi suppression of MYB73 expression via virus-induced gene silencing increases anthocyanin biosynthesis whereas its transient overexpression decreases anthocyanin biosynthesis in apple fruit peel. MYB73 binds to the promoter of the gene encoding UDP-glycose: flavonoid-3-O-glycosyltransferase (UFGT), the enzyme that catalyzes the last step in anthocyanin synthesis, to repress its expression. When MYB73 expression is suppressed by RNAi, UFGT expression is enhanced, leading to more anthocyanin synthesis, but this effect is blocked by RNAi suppression of UFGT expression. RNAi suppression of MYB73 enhances anthocyanin synthesis in wild-type RG apples whereas its overexpression decreases anthocyanin synthesis in Ma1-OE fruit. In the meantime, MYB73 competes with MYB1, one of the key activators of anthocyanin biosynthesis, binding to the promoter of UFGT and regulating its expression. These results indicate that MYB73 negatively regulates anthocyanin biosynthesis via repressing UFGT expression in apple peel. In Ma1-OE fruit, down-regulation of MYB73 releases UFGT from MYB73 repression and allows more MYB1 binding to UFGT promoter, leading to enhanced anthocyanin biosynthesis.

Aspergillus flavus is a widespread pathogen affecting crops like peanuts, contributing significantly to mycotoxin contamination and subsequent crop losses. Discriminating between toxigenic and non-toxigenic strains is crucial, yet conventional methods are often cumbersome and time-consuming. In this study, we developed rapid molecular tools to differentiate between these strains. Using morphological characteristics and species-specific PCR-sequencing, we identified isolates collected from peanut seeds in southern Georgia. Through primer optimization and qPCR targeting aflatoxin regulatory genes, we successfully distinguished aflatoxin-producing and non-producing isolates. Additional genes involved in aflatoxin biosynthesis were also analyzed, showing clear expression distinctions. Our findings demonstrate the specificity and efficiency of these primer sets, providing a valuable tool for managing A. flavus contamination in peanut seed lots. Additionally, research on the seed microbiome's impact on mycotoxin production remains limited. In this study, we assessed microbial communities in peanut seeds collected over various years using ITS gene sequencing. Our results revealed a diverse microbial population, including A. flavus and other fungal pathogens, highlighting the complexity of seed microbiota. This approach offers novel insights into peanut seed-associated microbiomes and aflatoxin contamination, shedding light on the correlation between microbial communities and aflatoxin pollution.

Lead (Pb) is a widespread toxic element in agricultural soils and Pb accumulation in plant roots represents a potential health risk for human beings. The sweetpotato (Ipomoea batatas L.) is a globally important root crop and one of the leading raw products for baby food processing. Limited information is available about the mechanism by which sweetpotato responds to Pb stress at the molecular level. Understanding the genetic mechanism of Pb uptake is essential for developing management approaches to mitigate Pb uptake in this crop. To address this knowledge gap, RNA-seq was used to characterize the transcriptome and identify differentially expressed genes from Pb-treated and untreated sweetpotato cv. Beauregard. Samples were taken from adventitious root tips at 5, 10, and 15 days after planting (DAP). Transcriptomic analysis revealed 4,077, 5,159, and 3,206 differentially expressed genes at 5, 10, and 15 DAP respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that ABC transporters and sulfur metabolism pathways are upregulated at 5 DAP but are downregulated at 15 DAP, indicating that there may be a threshold in sweetpotato Pb tolerance. The results provide a deeper insight into the species-specific response of sweetpotato to Pb stress which can lead to the development of screening methods and evaluation of management strategies that reduce Pb uptake in this crop.

Nitrogen (N) is a key limiting macronutrient for crop growth and development and affects sweetpotato storage root formation and yield potential. In high-input production areas, excessive N application can suppress storage root formation and results in environmental pollution. The crop is also grown in low-input production systems with little or no N applications. In this study, sweetpotato cv Bayou Belle response to N deprivation during the establishment and storage root formation stages was investigated through a transcriptomic approach. RNA-seq data revealed a number of differentially expressed genes (DEGs) between N sufficient ( N) and N deficient (–N) conditions at 5, 10, and 15 days after planting (DAP). The number of significantly upregulated genes varied between timepoints. DEGs were further classified into functional categories and pathways to reveal putative functions. Gene Ontology annotation together with KEGG analysis revealed that majority of the DEGs are involved in sulfur compound metabolic process at 5 DAP and in ammonium transport for both 10 DAP and 15 DAP. These results provide valuable insights about the molecular mechanism of N regulation in sweetpotato adventitious roots undergoing storage root formation. These findings can lead to the development of tools and processes for improving N use efficiency and consistent storage root yields while reducing environmental impact in this globally important crop.

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties. Leveraging large and diverse data sets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This presentation introduces CHiDO, an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process data sets, CHiDO's intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platform's mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available.

This year’s PB session will combine a keynote speaker session, business meeting and PB 2024 award ceremony together.
1. Keynote speaker: Hui Duan, USDA-ARS-REE (45 minutes) Biotechnology of Woody Ornamental Plants
2. PB business meeting (30 minutes) Chair annual report, elect new chair, chair elected and secretory. Plan next year’s work.
3. 2024 Plant Biotechnology Interest Group annual award ceremony (45 minutes)

Introduction and Overview

Speaker: Kathryn Orvis
Professor
Department of Horticulture and Landscape Architecture
Purdue University
625 Ag Mall Drive
West Lafayette, IN 47907-2010

Title: Digital Agriculture and AI on Specialty Crops Production

Description: Digital agriculture is the 4th agricultural revolution and Artificial Intelligence (AI) is part of it. Currently, in the "connected agriculture"; era, many technologies have been released on the marked regarding the use of multispectral
sensors for many purposes in agriculture. This talk is going to cover information on how to use Digital Agriculture online platforms to process multispectral imagery, and how AI can be used to collect individual in-field plant data.

Speaker: Luan Pereira de Oliveira
Assistant Professor and Precision Agriculture Extension Specialist
Department of Horticulture
University of Georgia
139 Engineering Building
2329 Rainwater Road
Tifton, GA 31793

Title: Bringing the Future of AI to the Farm.
Description: In this talk, we will cover the multitude of use cases where AI can be applied in farming – from weed detection and robotics to Generative AI-based farm assistants and Virtual Reality. We go through the industry trends of applied Artificial Intelligence and think big about farm automation for the future.

Speaker: Justin Hoffman
Chief Technology Officer of AgTechLogic


Title: From Concept to Impact: The Evolution of Moss Robotics through Industry-
University Collaboration

Description: Moss Robotics' journey began with a project focused on autonomous driving technology for tree nurseries, born out of a collaboration between Carnegie Mellon University, Robotics Institute and Hale; Hine Nursery in Tennessee. In this talk, we share the story of how we discovered the real value our solution could offer to growers, and how we refined our ideas through continuous iteration. This process transformed moss robotics from a simple concept into the company it is today. We will cover the steps of our evolution, emphasizing the practical benefits of combining academic research with industry needs to innovate effectively. Additionally, we look ahead to how emerging technologies might further influence our growth and the agricultural industry as a whole, aiming for advancements in farming practices that are both technologically sophisticated and grounded in real-world applications.

Speaker: Di Hu
Founder and CEO
Moss Robotics

Title: AI-Enhanced Computer Vision for Crop Monitoring in Controlled Environment
Agriculture

Description: Controlled environment agriculture (CEA) production remains expensive due to high operation costs. Growers can reduce production costs by nurturing crops with data, however, the data is highly diverse, and growers lack the expertise to analyze this data to derive actionable insights for informed decision-making. In addition, traditional crop monitoring is carried out manually, which makes it unfeasible to collect data daily to get actionable insights for high yields. Recent advancements in sensing and computing technologies, such as AI, computer vision, edge computing, and edge-
cloud integration, have opened opportunities to develop data-driven technologies to enhance decision-making capabilities. Integrating AI and computer vision technologies has emerged as a transformative toolset that can collect real-time plant data at high spatial and temporal resolutions, pivotal in optimizing resource management and maximizing production. The CE Engineering lab delves into cutting-edge computer vision applications within CEA, focusing on various applications, including phenotyping leafy greens, yield estimation, disease monitoring, and plant spacing optimization. This presentation will explore the details of lettuce phenotyping, disease classification, strawberry fruit classification, and yield estimation. We will delve into the technical aspects of these algorithms, including image processing techniques, machine learning models, and data integration strategies. This presentation will showcase state-of-the-art deep learning approaches, including segmentation algorithms, model training, and deep classifiers. Overall, this presentation aims to provide insights into the transformative potential of computer vision in CEA, offering a glimpse into the future of data-driven and sustainable CE production.

Speaker: Azlan Zahid
Assistant Professor,
Department of Biological and Agricultural Engineering
Texas A&M AgriLife Research
Texas A&M University System
Dallas, TX 75252, USA


Panel: 30-minute panel with the above speakers, to allow time for Q&A and discussion.