Speaker Abstracts

General Session: Phenomic Insights into Quantitative Traits February 11, 2017 | 8:00 AM – 12:15 PM

Increasing Yield Prediction Accuracy for Wheat Breeding through Combined Genomic Selection and Field-based High-throughput Phenotyping Jesse Poland, Kansas State University

We are combining genomic information and plant physiology measurements from high-throughput

phenotyping of canopy temperature and spectral reflectance to predict grain yield in small plot trials in

wheat breeding programs. Using combined multivariate models we can show a marked increase in the

prediction accuracy by combining genomic and phenomic information from correlated secondary traits.

When implemented across the breeding program, this becomes a powerful selection tool for breeders.

Phenotypes Phirst: What Genes Control Plant Responses to Environments? Tom Mitchell-Olds, Duke University

It is now feasible to dissect the molecular basis of genotype x environment interactions and to identify

the genes and pathways that control plant responses to environments. We report analyses of large,

multi-environment experiments from rice, maize, and soybean, and discuss genetic control of yield

tradeoffs across environments.

Role of High-throughput Phenotyping Tools to Increase Breeding Efficiency in Maize

Natalia de Leon, University of Wisconsin-Madison

Yield is the single most relevant measurement of a crop plant but, by its integration, this metrics does

not provide important details about the mechanism by which the specific genetic constitution of that

plant is able to achieve that end of the season response. Recent advances in plant high-throughput

phenotyping are expected to enhance current capabilities to dissect the genetic architecture of

economically relevant traits and integrate information from relevant related traits into prediction

models to improve breeding efficiency. Using maize as a model species, this presentation will focus on

describing examples of field based phenotyping tools able to assess plant morphological characteristics

that otherwise cannot be quantified by physical measurements and discuss their potential relevance for

productivity.

Uncovering the Maize Pan-genome to Identify Loci Associated with Relevant Agronomic Phenotypic

Traits

C Robin Buell, Michigan State University

Extensive genome variation exists between individual maize accessions. Using a 959 accession panel of

maize inbreds, we are cataloguing presence/absence variation to permit associations with phenotypes

related to biofuel feedstock traits.

Engineering Biomass for the Billion-ton Bioeconomy

Maureen McCann, Purdue University

By exploiting natural genetic variation, genetic engineering and synthetic biology approaches, the

biofuels research community is poised to enable use of a national resource of over 1B tons of

lignocellulosic biomass for fuels, chemicals and materials. In our Center for Direct Catalytic Conversion

of Biomass to Biofuels, we are re-designing cell wall architecture and biomass structure to optimize

recovery of liquid hydrocarbon fuels and high-value co-products.

Metabolome-wide Association Studies in Arabidopsis, Wheat and Hop Using Central, Specialized and

Lipid Metabolomes

David Riewe, IPK-Gatersleben

Janine Wiebach, IPK-Gatersleben; Alexander Feiner, Hopsteiner; Manuela Nagel, IPK-Gatersleben; Paul

Matthews, Hopsteiner; Andreas Boerner, IPK-Gatersleben; Thomas Altmann, IPK-Gatersleben

The metabolome is typically regarded as the penultimate phenotypic expression leading to variation in

such complex traits like germination, growth, or pathogen resistance. Studying the relationship between

metabolite levels and trait expression allows the identification of the underlying molecular processes.

Using large untargeted metabolomics datasets generated by gas and liquid chromatography hyphenated

to (high resolution) mass spectrometry, we identified known and unknown metabolites correlated to

agronomic important traits like seed viability, plant growth and downy mildew resistance in different

plant species. By combining genomic, transcriptomic, enzymatic, metabolomic and phenomic data from

different Arabidopsis thaliana populations, we could link a promotor polymorphism in the FUMARASE2

gene (FUM2) with control over malate-to fumarate conversion and biomass production at every step

from genome to phenome (Riewe et al., 2016). 90 wheat (Triticum aestivum) seed stocks stored for five

to fifteen years at different temperatures at the German Federal in-situ genebank were subjected to

seed germination assays and untargeted profiling of central and (oxidized) lipid metabolites. Using an

accurate mass based annotation strategy and high resolution LC-MSMS spectra at high coverage, we

identified hundreds of unmodified an (multiply) oxidized lipids. Correlation analysis revealed that lipid

oxidation and hydrolysis was found to be the most closely related phenomenon to coincide with losses

in germination frequency. In an F1 hop (Humulus lupulus) population with 192 individuals with variation

in downy mildew resistance, inoculation with Pseudoperonospora humuli led to the production of a

large number of specialized metabolites within 48 hours after inoculation. Phenotypic expression of

resistance of the F1 population was scored five days after inoculation. Using linear regression, a small

number of metabolites with potential protective function against downy mildew were identified and

mapped to the phenylpropanoid pathway.

High Throughput Phenotyping of Photosynthesis to Identify Relevant Quantitative Trait Loci and

Underlying Genes in Arabidopsis thaliana

Mark Aarts, Lab of Genetics, Wageningen University

Roxanne van Rooijen, Heinrich Heine University; Aina Prinzenberg, Wageningen University; Jeremy

Harbinson, Wageningen University

Natural genetic variation in plant photosynthesis efficiency is hardly studied even though breeding for

photosynthesis would be interesting to maintain increases in crop yields. One of the reasons this is not

studied is the notorious difficulty in adequately phenotyping photosynthesis parameters for genetic

research. We designed a versatile plant phenotyper, the Phenovator, capable of multispectral imaging of

1440 plants per experiment. Imaging includes chlorophyll fluorescence to determine the light use

efficiency of photosystem II electron transport (ΦPSII or Fq’/Fm’), and near infrared reflection to

measure projected leaf area. This system has been used to phenotype 350 genetically diverse

Arabidopsis thaliana accessions for genome wide association analysis. Plants were phenotyped at non-

stressful conditions and in response to cold treatment (5 °C) or a switch from 100 to 550 µmol m-2 s-1

irradiance. The observed genotypic variation was used to identify quantitative trait loci (QTL). Our latest

progress in identification and confirmation of QTL for ΦPSII, plant growth and epinastic leaf

movement will be presented, including detailed analysis of the allelic variation at the nucleotide level of

some of the genes underlying these QTL. Our work has shown that there is sufficient genetic variation

for PSII efficiency in Arabidopsis amenable for gene identification, which suggests the same will be the

case for crop species. Such would offer interesting opportunities for future crop photosynthesis

improvements.

Time Dependent Genetic Analysis Links Field and Controlled Environment Phenotypes in the Model C4

Grass Setaria

Max Feldman, Donald Danforth Plant Science Center

Rachel Paul, University of Illinois; Darshi Banan, University of Illinois; Jennifer Barrett, Donald Danforth

Plant Science Center; Jose Sebastian, Carnegie Institute for Science; Muh-Ching Yee, Carnegie Institute

for Science; Hui Jiang, Donald Danforth Plant Science Center; Alexander Lipka, University of Illinois; Tom

Brutnell, Donald Danforth Plant Science Center; Jose Dinneny, Carnegie Institute for Science; Andrew

Leakey, University of Illinois; Ivan Baxter, USDA – ARS

Vertical growth of plants is a dynamic process, which is influenced by both genetic and environmental

factors. Variation in plant height presents an intriguing ecological trade-off between access to solar

radiation, energetic demand for construction of lignified support tissue and the fecundity risk associated

with lodging. As such, plant height has a profound effect on overall plant architecture and biomass

composition. The primary goal of our studies have been to identify the major genetic loci that contribute

to the variation of plant height within two independent genetically structured populations of Setaria sp.

and how such phenotypes change temporally in response to differences in water availability and

planting density. To achieve this objective we have performed twelve growth trials of an interspecific S.

italica x S. viridis recombinant inbred line population in field and controlled environment settings in

addition to two experimental growth trials of a S. viridis natural diversity panel in the Bellweather

Phenotyping Facility at Donald Danforth Plant Science Center. Results from these studies identify

numerous genetic loci associated with variation in plant height shared across all experimental

treatments and locations but that the contribution of individual loci differs temporally. The

methodology presented in this study is applicable to any number of single or composite traits and is

used to deconvolute the contribution of plant height to other complex plant traits of agronomic

importance (biomass accumulation/composition and water use efficiency).

Emerging Technologies in Imaging

February 11, 2017 | 2:00 PM – 5:30 PM

Integrated Whole-Plant Fluxomics Aided by Imaging Technologies

Richard Ferrieri, University of Missouri

This talk will highlight some of the recent developments using short-lived radioactive isotopes coupled

with dynamic whole-plant Positron Emission Tomography (PET), Root Radiography, and Radiometabolite

Flux Analysis, as well as using stable isotopes coupled with MS-based imaging technologies to explore

the dynamic relationships that exist belowground between living plants and the surrounding

rhizosphere.

Advances in the Deployment and Integration of Imaging Sensors for HTP Plant Phenotyping

Xavier Sirault, CSIRO

From autonomous ground platforms combining multi-modality sensors to deploying sensors in nano-

satellites, the development of robotics and advanced non-invasive imaging technologies has opened up

new avenues for high-throughput screening of plants across scales.

This presentation aims to review the potential contribution of proximal and remote imaging to

phenotyping research but also to highlight that as with any new phenotyping technology, turning raw

data into biological information for real-world decision support outcomes remains key to realising the

adoption of these tools by the agricultural sector and the research community.

There’s a World Going on Underground: Imaging Technologies to Understand Root Growth Dynamics

and Rhizosphere Interactions

Chris Topp, Donald Danforth Plant Science Center

Root systems are the foundation of plant health and productivity, but are often ignored in plant

research. I’ll be presenting state-of-the-art technologies and approaches for understanding the “hidden-

half”.

Computed Tomography at the Fraunhofer EZRT Applied for Belowground Phenotyping

Stefan Gerth, Fraunhofer Institute for Integrated Circuits IIS – Development Center for X-ray Technology

EZRT

The Fraunhofer Development Center X-ray technology (EZRT) drives the technological development and

the application of Computed Tomography in a wide range of research areas. Within the presentation we

will demonstrate how the non-destructive nature of 3D data can be used for belowground phenotyping

for tubers, storage roots and maize root systems.

Synchrotron X-ray Fluorescence Imaging as a High-Precision Phenotyping Tool for Studies of the

Relationship between Mineral Nutrient Homeostasis, Fertility and Crop Yield

Olena Vatamaniuk, Cornell University

Ju-Chen Chia, Cornell University; Huajin Sheng, Cornell University; Jiapei Yan, Cornell University; Rong

Huang, Cornell High Energy Synchrotron Source; Louisa Smieska, Cornell High Energy Synchrotron

Source; Arthur Woll, Cornell High Energy Synchrotron Source

Transition metals such as iron (Fe), zinc (Zn) and copper (Cu) are required for the growth and

development of plants. These elements are also essential components of the human diet. In fact, Fe-

deficiency anemia affects over 30% of the world’s population, especially in poor populations across the

globe. While Cu is not as limiting in human diets as Fe, Cu interacts with Fe and is required for plant

fertility and seed/grain set. While the global demand for high-yield crops with a high density of Fe, Zn,

and Cu in edible tissues is increasing, limited Fe, Zn and Cu bioavailability in many agricultural soils

worldwide significantly impedes crop production, their mineral quality and thus, the future of food

security. Sustainable approaches for improving crop yield and mineral nutrient density relies on the

knowledge of Fe, Cu and Zn transport systems and their regulators that ensure adequate mineral uptake

into roots and root-to-shoot partitioning. In this regard, high-precision synchrotron x-ray fluorescent

microscopy (XRM)- based mapping of the spatial distribution of mineral elements at the tissue, cellular

and subcellular resolution in different plant genotypes emerges as a powerful tool connecting the

genotype-to-phenotype gap as pertains to improving mineral nutrient uptake, delivery to reproductive

organs and loading into grains. We will present our recent data showing the power of the integration of

XRM imaging with functional genomics tools for uncovering the function of transporters and

transcription factors in shoot-to-root signaling of iron Fe status and the role of Cu in male fertility in a

model plant Arabidopsis thaliana. We will also discuss the advantages of including this phenotypic

strategy as a gene mining and QTL-identification tool in molecular breeding efforts for crops with

improved growth and yield/spike characteristics in marginal soils and soils now in cultivation.

Graft Junction Formation: A Dynamic Investigation Using PET and Confocal Imaging

Margaret Frank, Donald Danforth Plant Science Center

Sergey Komarov, Mallinckrodt Institute of Radiology at Washington University School of Medicine; Qiang

Wang, Nevis Laboratories, Columbia University; Ke Li, Department of Electrical and Systems Engineering,

Washington University in Saint Louis; Halley Fowler, University of Missouri - Saint Louis; Viktoriya

Coneva, Donald Danforth Plant Science Center; Daniel Chitwood, Donald Danforth Plant Science Center;

Yuan-Chuan Tai, Mallinckrodt Institute of Radiology, Washington University School of Medicine

Grafting, a technique that involves the physical joining of roots and shoots from independent genotypes,

allows for desirable traits from disparate species to be harnessed within a single organism. This simple

yet effective strategy is used around the globe to combat a wide sweep of biotic and abiotic threats that

would otherwise have crippling effects on crop production. Despite the tremendous advantages that

grafting provides for crop protection and enhanced crop yield, successful graft combinations are still

identified on an inefficient, case-by-case basis. This is largely due to our present lack of understanding

regarding the fundamental mechanisms underlying the success of grafting as a technique. Here, we

focus on formation of the graft junction – a unique anatomical region that unites newly joined root and

shoot systems into a single vascular conduit, and thus plays a pivotal role in determining the functional

success of root-shoot combinations during grafting. We have developed an integrated imaging approach

using positron emission tomography (PET), and laser scanning confocal microscopy, which allows for in

vivo tracking of vascular conductance to be combined with high resolution anatomical reconstructions

that follow the cellular processes guiding graft junction formation. The synthesis of these two datasets

into a unified model for graft junction formation is being used to build a fundamental understanding of

the physiological and anatomical coordination that underlies this complex biological process. As a

preliminary result, we have established that 11CO2 is a dynamic and reliable marker for shoot-to-root

phloem conductance. Using this radionuclide, we demonstrate that phloem function is restored within

5-6 days post-grafting, and that this functional restoration coincides with the first anatomical

appearance of elongated phloem sieve cells within the junction region. The extension of this functional

framework to problematic grafting combinations may help elucidate the rules that govern successful

graft combinations.

Emerging Technologies in Quantitative Field Data

February 11, 2017 | 2:00 PM – 5:30 PM

Automated, High-Throughput, Time-Lapse Photography for Field-based Maize Phenomics

Patrick Schnable, Iowa State University

Networked systems of hundreds of computer-controlled cameras that enable high-throughput, high-

resolution, field-based time-lapse photography for studying maize growth/development and responses

to environmental stresses have been deployed at four locations over two years.

From Plots to Plants to Organs

James Schnable, University of Nebraska-Lincoln

Research at the University of Nebraska-Lincoln seeks to address three specific challenges in plant

phenotyping. Firstly, disambiguating data from the plot level to individual plants, and individual organs

of individual plants. Here computer-vision algorithms designed with an awareness of how corn plants

grow and develop can track individual leaves from one day to the next. Secondly, most new phenotyping

approaches require calibration using large validation datasets. As a result, in the short term, adopting

high throughput phenotyping methods can actually result in drastic increases in demand for manual

phenotyping measurements. We are developing a robotic platform which can identify individual leaves

and clamp a sensor package to the leave, enabling the automated collection of “ground truth” data. The

final challenge concerns the selection of appropriate numerical representations of phenotypes extracted

from image data. Early efforts in this area have focused on replicating transitional phenotypes which

have been manually measured under field conditions in the past. However, image-based phenotyping

also creates the possibility to identify and study the genetic basis of variation novel phenotypes which

would not be practical to measure manually, but which may be linked to agronomic performance under

field conditions. In order to stimulate algorithm development and statistical analysis in this area, we are

releasing high throughput RGB and hyperspectral image datasets collected from many of the same

genotypes used by the Genomes 2 Fields project (available at http://plantvision.unl.edu/dataset).

Quantifying Basic and Complex Plant Traits at the Tissue-, Plant-, and Canopy-Level Scales of

Organization: Theory, Results, and Applications

April Agee Carroll, Purdue University

Plant phenomics seeks to predict crop performance in context of plant genetic potential and environmental conditions. Grain yield at harvest across multiple locations and years will continue to be a gold standard measurement, but there is clear benefit and high demand for surrogate assays that can predict yield as quickly and accurately as possible, with less resources invested. This continuum forces compromise between accuracy of yield estimation, control over environmental conditions, and speed to actionable information. Assays that isolate different genetics under highly consistent environmental conditions are of interest, but by design controlled environment experiments do not replicate all conditions in the field, and to be predictive must be modeled against validated measurements. With improved sensing systems and computational methods, our group aims to increase the resolution of field measurements from the canopy scale to the individual plant level, and to inform and be informed by models created through controlled environments studies. This multi-scale translational approach reduces the need for compromise by generating reliable methods for early prediction of yield in the field and under controlled conditions. Results will be presented quantifying plant-to-plant variability with Unmanned Aerial Vehicles (UAVs), the PhenoRover, a mobile ground-based phenotyping platform, RGB, hyperspectral, and LiDAR sensors, ground reference data, novel georeferencing techniques, and various image and data analysis methods such as feature extraction and image segmentation. Such information provides insights into research plot and field quality, farm equipment performance, environmental stress tolerance, physiological plasticity, and fine-scale spatial variability. Challenges from phenotyping at the individual plant level using traditional and emerging techniques will also be discussed.

Development of a Ground Vehicle and a Mobile Observation Tower to Integrate Plant Phenotyping

from Plant Organ to the Canopy Level

Felix Fritschi, University of Missouri

A semi-autonomous ground vehicle and a mobile observation tower were developed and deployed in

the field in 2016. The mobile tower was equipped with a 360-degree robotic vision system to monitor a

large area of a field, primarily to obtain canopy-level information in at regular intervals over the course

of the growing season. The ground vehicle was deployed in semiautonomous fashion and includes a

robotic arm that can handle a broad range of sensors for more detailed, plant and organ level

characterization. This presentation will focus on describing the two platforms and will include the

presentation of preliminary results.

A High Throughput Leaf Gas Exchange Method Identified QTLs Responsible for Photosynthesis and

Stomatal Conductance Sensitivity to Ozone in Maize

Timothy Wertin, University of Illinois

Nicole Choquette, University of Illinois; Lauren McIntyre, University of Florida; Patrick Brown, University

of Illinois; Elizabeth Ainsworth, University of Illinois; Andrew Leakey, University of Illinois

An important challenge to current and future maize production is tropospheric ozone, a damaging air

pollutant which could further increase by 10-30 ppb by the end of this century. Currently, ozone is

estimated to cause losses of $14-26 billion to global agricultural production, including reducing U.S.

maize yields by ~10%. Ozone enters leaves through the stomata where it rapidly reacts to form other

reactive oxygen species that can overwhelm the detoxification potential of the leaf to impact physiology

and development. Signs of ozone stress include reduced photosynthesis and stomatal conductance. In

this study, we used high throughput leaf gas exchange to study the effects of elevated ozone

concentration (100 ppb) on photosynthesis and stomatal conductance in populations of hybrid and

inbred maize. A half diallel population (45 hybrids) made from 10 diverse maize inbreds previously

shown to have variable responses to elevated ozone was used to test trait heritability. A population of

100 near isogenic lines introgressing B73 and Mo17 was used to map loci associated with trait variation

and ozone sensitivity. Both populations were grown at ambient (~40 ppb) and elevated ozone (100 ppb)

in the field at a Free Air Concentration Enrichment (FACE) facility at the University of Illinois, Urbana-

Champaign (n=4). Leaves were cut early in the morning, transported to the lab and kept in a controlled

environment before light-saturated photosynthesis and stomatal conductance were measured. This

protocol was used to measure 2560 leaves over a 10-day period. Photosynthesis and stomatal

conductance were both highly heritable (H2 approximately 0.65). Distinct quantitative trait loci were

detected for response of both photosynthesis and stomatal conductance to O3 (α=0.05).Our

study describes a new method to rapidly measure leaf gas exchange on a large number of samples,

while the findings suggest that leaf gas exchange is under strong genetic control.

High-Throughput Phenotyping of Chenopodium Quinoa from Seed to Shoot

Malia Gehan, Donald Danforth Plant Science Center

Steven Callen, Donald Danforth Plant Science Center; Elizabeth Castillo, Donald Danforth Plant Science

Center; Jose Tovar, Donald Danforth Plant Science Center; Michael Miller, University of Nebraska,

Lincoln; Monica Tessman, Milwaukee School of Engineering; Willam Kezele, Donald Danforth Plant

Science Center

Chenopodium quinoa (quinoa) is a highly nutritious grain and vegetable crop reported to be highly

tolerant of salinity, with certain varieties able to grow with salt concentrations comparable to sea water.

The crop is also highly drought tolerant, cold tolerant, and can grow on rocky/sandy soil media making it

ideal for growth in arid and cold regions where crops aren’t typically produced. Despite quinoa’s many

advantages as an alternative crop, there are several challenges to its viability as a global crop. The

lifecycle of quinoa is long, ranging from 120 to 210 days, making the crop uneconomical to grow in some

contexts. Therefore, it is key to identify and develop fast maturing strains that can still withstand and

thrive on marginal land. However, a major bottleneck in plant science is the ability to efficiently and

non-destructively quantify plant traits (phenotypes) through time. This research develops high-

throughput phenotyping technologies and open-source platform-independent analysis tools (PlantCV;

http://plantcv.danforthcenter.org/) to quantify quinoa seed, and shoot growth through time. In a pilot

study, we quantify seed characteristics for 400 quinoa accessions, and examine the fresh weight, height,

color, growth rate, and yield for a subset of accessions in replicate over time.

Emerging Technologies in Robotics and Remote Sensing

February 11, 2017 | 2:00 PM – 5:30 PM

Robot-Assisted Phenotyping in the Wild

Joshua Peschel, Iowa State University

This talk will focus on the field deployment of a small, low-cost ground robot, TERRA-MEPP (Mobile

Energy-Crop Phenotyping Platform), to support the high-throughput characterization of agricultural row

crops. Current measurement approaches, such as unmanned aerial vehicles and automated LiDAR, are

restrictive and costly, leading to potentially suboptimal efficincies for quantifying the sub-daily growth of

crops over large areas. Experimental results will be presented to elucidate lessons learned and provide

recommendations for designing and deploying robotic technologies for agricultural field-based domains.

Mapping Dynamic Seedling Phenotypes to the Maize and Arabidopsis Genomes by Automated Image

Analysis

Edgar Spalding, University of Wisconsin-Madison

Automated image capture and analysis enables phenotypes to be treated as developmental time

courses rather than static traits. When scaled up to population-level studies, quantitative trait loci maps

with a developmental time axis are produced.

Semantic Mapping of Farms with Heterogenous Robotic Systems

Volkan Isler, University of Minnesota

In this talk, Volkan will present an overview of our work on building aerial, ground and surface robots

which can collect multi-modal data over multiple spatial scales. I will also present an overview of our

efforts to extract semantic information from this data.

Innovations toward Robotic Plant Phenotyping

Lie Tang, Iowa State University

In this presentation, Lie will emphasize their critical needs in enabling technologies and tools for high-

quality and high-throughput plant phenotyping, and showcase how innovations in automation and

robotics can open up new avenues toward that goal. He will convey to the audience that, to realize

robotic phenotyping, they are facing numerous challenges in the development of platforms, sensors,

and data processing algorithms. He will share his experiences with the development of the high-

throughput and high-accuracy Plant Stand Analyzer for large scale plant breeding programs, and his

ongoing efforts with two NSF MRI instrument development projects for indoor (Enviratron) and infield

(PhenoNet) robotic phenotyping.

Dimensionality and Throughput are Now Compatible in Automated Plant Phenotyping Systems

Nathalie Wuyts, VIB Department of Plant Systems Biology

Stien Mertens, VIB Department of Plant Systems Biology; Lennart Verbraeken, VIB Department of Plant

Systems Biology; Stijn Dhondt, VIB Department of Plant Systems Biology; Rafael Abbeloos, VIB

Department of Plant Systems Biology; Dirk Inze, VIB Department of Plant Systems Biology

The first generation of plant phenotyping systems typically focuses on plant growth-related traits in the

evaluation of genotype x environment interactions. High dimensionality, or the integration of a larger

diversity of plant traits in the analysis of the phenotype, is achieved by considering the shoot, organ and

cellular level for the retrieval of the structural basis of differences in plant growth and development.

This type of high-resolution or in-depth phenotyping does not allow for high-throughput, except for the

shoot and root system level where automated RGB-imaging and image analysis have revolutionized

plant phenotyping, often at the expense of dimensionality. Nowadays, both translational research and

the awareness about the major influence of the plant’s environment and growth conditions on its

functioning, and consequently its growth, have stimulated the development of second-generation

phenotyping systems for crops under greenhouse and field conditions, and the measurement of plant

physiological traits on a larger scale and in high-throughput by means of imaging. We are running and

developing the PHENOVISION was specifically designed to foster high-dimensionality in plant trait

measurement in an automated, high-throughput system for crops throughout development and under

greenhouse conditions. An imaging setup suited to the three-dimensional reconstruction of plants to

measure both shoot- and leaf-level traits with higher precision delivers an extended range of plant

growth-and architecture-related traits. To determine plant physiology-related traits in high-throughput,

thermal infrared and hyperspectral imaging systems were included. These are to date the most relevant

cameras for close-range and remote sensing under both controlled and field phenotyping conditions.

Recent progress in image and data analysis in the PHENOVISION phenotyping system and the prospects

for technology development will be discussed.

Virtual Reality: A New Tool for the Modern Crop Breeding Toolbox

Marco van Schriek, Keygene

Modern breeding process has an ever-expanding set of tools at its disposal, all of which capture valuable

information from the crop. While the technological advances to-date incorporate systems to address the

complexity of plant breeding as a science, most of the valuable, artisanal nuances that influence critical

decision-making in plant-breeding are largely ignored. A system that can efficiently capture and convert

crop plant data into quantitative metrics while enabling the plant breeder, to a certain degree, the

added-value of spatio-temporal qualitative analyses presents a valuable tool for the modern plant

breeder. This presentation describes how KeyGene integrated its expertise in data sciences, digital

phenotyping and plant breeding with expertise in Virtual Reality (VR) to address this challenge. This

proprietary VR based breeding tool allows users, for the first time, to become fully immersed in a

customized crop growth environment where VR breeders can access, visualize and analyze large

amounts of genotypic and phenotypic data, to make better data-driven decisions. Such an integrated

VR-based interface expands professional insight and enables better decision-making in crop breeding.

An Automated Phenotyping Platform for Trait Discovery and Characterization in Corn

Gerie van der Heijden, DuPont Pioneer

Transgenic or genome editing approaches for agronomic traits of corn, for example to improve drought

tolerance or nitrogen use efficiency, require an accurate and precise phenotyping pipeline. The small

genetic differences may lead to relatively small phenotypic differences, and hence we need to reduce

the random variation and increase the precision of the measurements.

The agronomic traits discovery pipeline of DuPont Pioneer consists of precision phenotyping possibilities

in the greenhouse as well as extensive field testing trials in managed stress environments. The managed

stress environment in the field offers accurate measurements of the end goal: yield under different

realistic stress conditions. Precision phenotyping in the greenhouse offers the possibility to test leads

year-round, to apply well-defined stresses at an individual plant basis and to study in detail plant growth

and development of each plant with advanced equipment, like fluorescence imaging and hyperspectral

imaging.

In this presentation we will show the automated greenhouse phenotyping platform for screening

drought tolerant phenotypes in corn.

General Session: Environmental/Stress Biology

February 12, 2017 | 8:30 AM – 12:15 PM

Adaptive Plasticity at Multiple Scales of Gene Regulation in Three Crops

Julia Bailey-Serres, University of California, Riverside

Water extremes can promote developmental reprogramming that involves a subset of root cells. We

have resolved responses using tools that access multiple layers of gene regulation and individual cell

types in rice, tomato and medicago.

Modifications of Source-Sink Relationships and Abiotic Stress Tolerance in Crop Plants

Eduardo Blumwald, University of California, Davis

Changes in plant hormone homeostasis promoted alterations in source/sink relationships that resulted

in enhanced plant tolerance to water-deficit stress. A number of genes associated with source

strengthening have been identified and their expression has been modified in a number of crop species

resulting in improved yields under stress.

The Root Phenome and 21st Century Agriculture

Jonathan Lynch, Penn State University

Crops with improved water and nutrient capture are urgently needed to sustain a growing population in

a changing climate. The root phenome is a challenge and an opportunity to achieve this goal.

Getting to the Root of the Matter: The Growing Importance of Root Biology to Global Food Security

Leon Kochian, University of Saskatchewan

There is a growing realization in the plant research community of the importance of root system

development and function to crop adaptation to soil-based abiotic stress. In this talk, Leon will discuss

our research integrating root phenotyping with molecular genetics and genomics to identify genes

underlying agronomically important crop traits, and how this information is being used to facilitate crop

improvement.

Metabolic Phenotyping of Germination and Respiration in Lettuce Seeds

Oliver Fiehn, University of California, Davis

Lettuce seeds exhibit thermoinhibition, an inability to germinate above a threshold upper temperature

limit. Following respiration measurements of different types of seeds, primary metabolites and lipids

were measured along germination time points at different temperatures.

Measuring Gene Expression Dynamics with High Temporal and Spatial Resolution using Hacked

Illumina Sequencers and Open Source Software

Nick Kaplinsky, Swarthmore College

High temperature stress result in protein denaturation and increased membrane fluidity, changes that

are deleterious to normal cellular function. The heat shock response (HSR) is a conserved transcriptional

response that results in the expression of heat shock proteins, protein chaperones which function to

ameliorate the effects of high temperatures. While a fair amount is understood about cellular responses

to heat shock, less is known about the cellular thermostats which regulate these responses. Surprisingly,

none of the genes shown to function as thermostats in Arabidopsis have been identified using forward

genetics, probably due to a combination of high levels of functional overlap within thermostat gene

families and a reliance on end-point phenotyping approaches.

To overcome this problem we have built a high content screening system using parts scavenged from

obsolete Illumina sequencers called the Rootscope. The Rootscope can quantitate gene expression with

high spatial and temporal resolution in 180 plants at a time. We are using this system to identify new

genes which regulate the HSR. The first gene identified in our genetic screen encodes a spliceosome

subunit that, when mutated, results in plants that respond to heat stress as if they have miscalibrated

thermostat mechanisms – they respond as if they are several degrees hotter than the actual heat shock

they are exposed to. This gene regulates the kinetics of the HSR and modulates the temperature

dependent splicing of HSFA2, a transcription factor known to regulate the HSR, as well as several other

spliceosomal proteins. These results suggest that the gene we have identified functions to regulate heat

stress dependent splicing and may provide mechanistic insights into how plants sense and respond to

high temperature stress.

Role of Mineral Nanoparticles in Improving Attachment and Colonization by Plant Growth Promoting

Rhizobacteria

Salme Timmusk, Uppsala BioCenter

The soil surrounding plant roots is one of the main sources of bacteria expressing plant-beneficial

activities (PGPR). Recently we have shown that the PGPR have great potential in protecting plants

against abiotic and biotic stress situations and restoring marginal lands (1-3). Multiple factors influence

isolate stability. The effectiveness of the inoculum is related to the formulation technology protecting

the cells from the surrounding environmental conditions and elimination of secondary effects. Titania

nanoparticle (TN) formulation form stable, large and thick bacterial clumps as a biofilm which influence

plant growth via facilitating root hair length and density and improving mulch biofilm formation (1-3).

This effect is especially pronounced in complex environments there several PGPR strains are used in

combination with TNs (1). The biofilm substantially improves root soil contact and enhances significantly

plant nutrient or biologically active compound acquisition from soil or bacterial origin. In addition,

improved organic matter content and porosity restores marginal lands (1)

The key to further advancing our treatment with microbial products is (i) surface grafting of sands and

intercalation of clays and employing hybrid biopolymer-mineral nanocomposites (ii) distinguishing

between genes and pathways that drive passenger events both in PGPR and crop plant.

1) S. Timmusk, G. A. Seisenbaeva, A. Muraya, J. Muthony, L. Behers, Nano titania aid multiple plant

growth promoting bacterial interactions under complex environments. Frontiers in Plant

Sci, (2016).

2) S. Timmusk, et al, Perspectives and challenges for microbial application for crop

improvement. Frontiers in Plant Sci, (2016).

3) S. Timmusk et al., PloS ONE DOI 10.1371/journal.pone.0096086, (2014).

Overlap between the Ad/Abaxial Developmental and ABA Signaling Networks

Tie Liu, Stanford University

Adam Longhurst, Carnegie Institution for Science; Franklin Talavera-Rauh, Carnegie Institution for

Science; Samuel Hokin, Carnegie Institution for Science; Kathryn Barton, Carnegie Institution for Science

The actions of the plant hormone abscisic acid (ABA) in the repression of germination and the inhibition

of water loss through stomata are well studied. Less well understood are the functions of ABA on

vegetative development including the mechanisms through which it influences growth of roots and

shoots. In our study of gene networks that control development of the adaxial (towards meristem/inner)

and abaxial (away from meristem/outer) domains of the leaf, we identified a set of genes oppositely

regulated by REVOLUTA and KANADI. Observing that this set of genes is enriched for genes encoding

ABA signaling components, we reasoned that genes of unknown function within this set would also be

involved in ABA signaling. Indeed, we find that a mutation in one of these, ABA INSENSITIVE GROWTH 1

(ABIG1), results in ABA resistant vegetative growth of shoot. Conversely, conditional overexpression of

ABIG1 mimics the application of ABA to wildtype plants: it causes leaf yellowing, reduces the rate of leaf

production and causes reduced root growth. Surprisingly, the ABA resistant abig1 mutants are also

drought resistant. We hypothesize that this reflects the ablation of the branch of the pathway for ABA

inhibition of growth and promotion of senescence while leaving intact the pathway for stomate closure.

To characterize ABIG1 induced senescence and meristem arrest, we carried out phenotyping and

quantifying analysis on the growth and development of meristem and young leaf primordia under at

various times post induction and drought treatments for both Arabidopsis and the orphaned crop

quinoa.

Analysis of Water Limitation Effects on the Phenome and Ionome of Arabidopsis at the Plant Imaging

Consortium

Argelia Lorence, Arkansas State University

Lucia Acosta, Arkansas State University; Suxing Liu, Arkansas State University; Erin Langley, Arkansas

State University; Zachary Campbell, Arkansas State University; Norma Castro-Guerrero, University of

Missouri; David Mendoza-Cozatl, University of Missouri; Argelia Lorence, Arkansas State University

Food security is currently one of the major challenges that we are facing as a species.

Understanding plant responses and adaptations to limited water availability is key to maintain or

improve crop yield, and this is even more critical considering the different projections of climate change.

This is an NSF-funded collaborative effort between the Mendoza and Lorence groups as part of the

activities of the Plant Imaging Consortium (http://plantimaging.cast.uark.edu/). In this work, we

combined two high-throughput -omic platforms (phenomics and ionomics) to begin dissecting time-

dependent effects of water limitation in Arabidopsis leaves and, ultimately, seed yield. As proof of

concept, we acquired high-resolution images with visible, fluorescence and near-infrared cameras and

used commercial and open source algorithms to extract the information contained in those images. At a

defined point, samples were also taken for elemental profiling. Our results show that growth, biomass

and photosynthetic efficiency were most affected under severe water limitation regimes, and these

differences were exacerbated at later developmental stages. The elemental composition and seed yield,

however, changed across the different water regimes tested and these changes included under- and

over-accumulation of elements compared to well-watered plants. Our results demonstrate that this

combination of phenotyping techniques can be successfully used to identify specific bottlenecks during

plant development that could compromise biomass, yield and the nutritional quality of plants.

Climate Metadata Analysis and Challenges

February 12, 2017 | 2:00 PM – 5:30 PM

Microbiome and Data Science: Harnessing the Unfulfilled Dream

Melissa Dsouza, University of Chicago

The advent of high-throughput sequencing technology has generated an unprecedented volume of

microbiome data. I will discuss and outline issues caused by the lack of an open-source, centralized

resource that processes and integrates all microbiome data and propose a vision to advance

microbiome research as a data-driven science.

Bringing Data Layers to Life in Precision Agriculture

Keith Koutsky, The Climate Corporation

In this presentation, Keith will discuss how at The Climate Corporation, they bring together many data

layers, millions of data points and a variety of disciplines to quantify what’s occurring on the farm and

how farmers can get to the best productivity going forward.

Translating Metadata in Microbiome Discoveries

Fan Yang, Iowa State University

Physicochemical measurements and microbiome analyses can inform ecosystem health. However,

either of them alone is not sufficient to predict the changes in ecological functions. To better

understand the microbial interactions and their ecological impacts, we integrated physicochemical and

microbial sequencing data to decode the complex microbial network in their associated environments.

Genetic Metadata Analysis and Challenges

February 12, 2017 | 2:00 PM – 5:30 PM

Finding Common Language to Describe Phenotypes in Plant Databases

Lisa Harper, US Department of Agriculture

Biocurators from 27 agriculturally related databases, both plant and animal, have come together and

formed the AgBioData Group. We are working together on many projects, including establishing and

implementing common metadata standards, data and code sharing strategies, and implementing a

common ontological language to describe plant phenotypes in a human and machine readable way. This

talk will outline our strategies, difficulties and progress.

Improved Methods for High-Confidence GO Annotation

Carolyn Lawrence-Dill, Iowa State University

Making a genome sequence accessible and useful involves three basic steps: genome assembly,

structural annotation, and functional annotation. Current datasets of GO functional annotations for

genes are high-coverage and low-confidence in most plant species. We describe our efforts to deploy

methods that produce high-confidence GO datasets for plants and outline the results of applying these

methods to maize genes.

Plant Comparative Phenomics

George Gkoutos, University of Birmingham

The presentation will reflect on experience from the biomedical domain to propose and describe

approaches for enabling plant comparative phenomics.

Open-Source Tools for High-throughput Plant Phenotyping

Noah Fahlgren, Donald Danforth Plant Science Center

The recent development of high-throughput plant phenotyping platforms has created challenges in data

analysis and management. In this talk I will discuss the software tools we have developed to address

these challenges and their application to different biological questions.

Transcriptional Rewiring: Synthetic and Evolutionary Processes Governing Plant Environmental Stress

Responses

Oliver Windram, Imperial College London

Plant defence responses are modulated by substantial transcriptional reprogramming, up to 40% of the

genome can be differentially expressed following pathogen challenge. High temporal resolution

differential expression analysis and subsequent network inference has revealed a large transcriptional

networks with a complex hierarchies. Overall this indicates that transcription factors play an important

role in the defence response. The complexity, diversity and redundancy in these defence response

networks further suggests interesting evolutionary principles may govern and give rise to these highly

structured and plastic networks. Previously we have shown that a synthetic technique known as genetic

rewiring, where the open reading frames of transcription factors are fused to different promoters

altering the natural expression of the regulators, can be used to synthetically evolve novel phenotypic

diversity. As a transcription factor can potentially regulate thousands of target genes, alteration of its

natural expression has the potential to generate radically new phenotypes through cascading regulatory

events. We have shown, in yeast, that heterologous protein expression can be significantly enhanced

using this rewiring approach. Furthermore, network analysis reveals that rewired open reading frames

and promoters possess characteristic topological network features that can serve as predictive

measures for future rewiring endeavours. In conjunction with a presentation of our work on rewiring

the yeast transcriptome I will discuss how we are using a combined systems and synthetic biology

approach to first construct large scale transcriptional networks responding to pathogen challenge in

plants. Secondly, I will show how through a process of topological network analysis we are now selecting

promoters and open reading frames which we will use to rewire the plant defence transcriptome. This

novel transciptome diversity will be used to help reveal key regulatory points and potential weaknesses

in these networks as well as highlighting potential solutions to help improve tolerance to plant

pathogens.

A Multispecies, Multi-trait GWAS Approach to Uncover Fundamental Genetic and Molecular

Mechanisms Underlying Responses to Phosphate Deficiency

Marco Giovannetti, Gregor Mendel Institute

Christian Goeschl, Gregor Mendel Institute; Santosh Satbhai, Gregor Mendel Institute; Stanislav Kopriva,

University of Cologne; Wolfgang Busch, Gregor Mendel Institute

Phosphate is an essential plant macronutrient and its low soil availability leads farmers to massive

application of phosphate-based fertilizer. Given the negative environmental impact of fertilizers, and

phosphate being a non-renewable resource, a better understanding of plant genetic and molecular

bases of phosphate absorption and utilization is needed. Much progress in that area has been made

over the past years, but most approaches focused on one species. However, it is unclear which of the

discovered mechanisms are conserved and which ones are specific to the particular species that is

studied. Here, we approach this problem by using a combination of high throughput root phenotyping

and IC-based phosphate quantification of natural populations of two different plant species, the

brassicaceae Arabidopsis thaliana and the legume Lotus japonicus. This allowed us to identify multiple

candidate genes regulating phosphate metabolism and root growth responses to varying phosphate

levels. We could determine multiple orthologs whose variants are significantly associated with these

phosphate related traits in both plant species. We are currently testing the involvement of high

confidence candidate genes in phosphate homeostasis and root growth responses upon phosphate

levels in both species.

Imaging Metadata Analysis and Challenges

February 12, 2017 | 2:00 PM – 5:30 PM

Workflows for Trait Extraction from Digital Data: Integrating Machine Learning, Crowdsourcing and

High throughput Computing

Baskar Ganapathysubramanian, Iowa State University

Baskar will present results from a few phenotyping projects where a combination of domain knowledge,

machine learning, image processing and high performance computing are used to create automated

workflows for trait extraction. This is a collaborative work by an interdisciplinary group that includes

engineers, data scientists, and plant scientists.

Early-Stage Maize Phenome

Nathan Miller, University of Wisconsin-Madison

Three image-based, high-throughput seed and seedling pipelines will be presented. Machine vision

coupled with readily available imaging technologies make for flexible, robust platforms useful for

quantifying maize seed imbibition, root gravitropism, and coleoptile emergence.

Data on High; Successes and Lessons from the Texas A&M AgriLife Unmanned Aerial Systems (UAS)

Project

Seth Murray, Texas A&M University

Multidisciplinary teams of 40 faculty, using five different UAS platforms, collected field phenotyping and

agronomic data across different crops and over time. Plant height of maize and sorghum, along with

southern rust disease in maize were successfully estimated by UAS but many challenges remain to make

successful measurements routine, especially UAS reliability, data management and data analysis.

Next Generation Data Management Applied to Image Data for Plant Phenotyping

Ramona Walls, University of Arizona

Next generation data management is a set of scalable practices that aims to support big data

throughout their life cycle to ensure discoverability, reusability, and persistence. Using imaging for plant

phenotyping as a case study, this presentation will describe the challenges involved in managing big

data, highlight some contemporary tools and techniques that can be used to address those challenges,

and point out gaps that still need to be filled.

Detecting Variation in Photosynthetic Rates with Hyperspectral Imaging

Katherine Meacham, University of Illinois

Christopher Montes, University of Illinois at Urbana Champaign; Liujun Li, University of Illinois at Urbana

Champaign; Stephen Long, University of Illinois at Urbana Champaign; Carl Bernacchi, University of

Illinois at Urbana Champaign

Improved photosynthetic rates have been shown to increase crop biomass, making improved

photosynthesis a focus for driving future grain yield increases. The RIPE project (Realizing Increased

Photosynthetic Efficiency) aims to increase crop biomass to improve food security in key food crops such

as rice and cassava by making improvements to the photosynthetic pathway, currently using nicotiana

(tobacco) as a model species. Gas exchange measurements are the most widely used method of

measuring photosynthesis in field trials but this process is laborious and slow, and requires further

modeling to estimate meaningful parameters and to upscale to the plot or canopy level. To determine

field scale efficiency of genetic modifications made, high throughput phenotyping technologies to detect

variation in photosynthetic rates are required. With hyperspectral imaging, we infer the carboxylation

rate of Rubisco (Vcmax) and electron transport rate (J) and detect photosynthetic variation between

cultivars with a partial least squares regression technique. Ground-truth measurements from

photosynthetic gas exchange, a full spectrum (400-2500nm) hyperspectral leaf clip, and extractions of

leaf pigments support the model.

Optimization of Cooking Time Prediction in Dry Beans (Phaseolus vulgaris L) using Vis/SNIR

Spectroscopy and Hyperspectral Imaging Techniques

Fernando Mendoza, Michigan State University

Karen Cichy, USDA – ARS; Jason Wiesinger, Cornell University; Renfu Lu, USDA – ARS; James Kelly,

Michigan State University

Automatized nondestructive sensing techniques are needed by dry bean breeders and processors to

predict cooking time rapidly, accurately and economically. The successful application of these

breakthrough technologies for cooking time estimations would substantially help in the objective

characterization and screening of bean germplasm. The main goal of this study was to implement and

optimize an experimental procedure and data analysis for evaluating cooking time in dry beans with

large genetic diversity using two optical sensing techniques: visible and short near-infrared spectroscopy

(Vis/SNIR) over the wavelength range of 400–2,480 nm, and an hyperspectral imaging system (HYPERS)

over the range of 450–1,000 nm. A diversity panel of Andean beans (Phaseolus vulgaris) grown in

Michigan (USA) in different years (2013, 2014 and 2015) that exhibited wide variability in cooking time

(soaked beans ranging from 17.1–120.3 min) was tested in this study. Spectral preprocessing methods,

image preprocessing and analysis, feature selection and multivariate statistical methods were critically

evaluated and optimized for improving cooking time predictions on soaked and unsoaked dry seeds.

Overall results showed that applying the two-band ratios preprocessing method and a single feature

selection method, Vis/SNIR and HYPERS models have great potential for predicting cooking time of dry

beans over a wide range of measurements. Based on partial least squares regression models, correlation

coefficients for prediction ( and standard error of prediction for soaked beans were significantly better

for HYPERS (ranging from = 0.856–0.889, = 8.1–7.7 min, respectively) than those for Vis/SNIR (ranging

from = 0.681–0.761, = 11.5–9.0 min, respectively). Also, as expected cooking times were longer for

unsoaked beans (from 80.1–147.0 min), and their predictions using HYPERS models were less robust and

accurate (ranging from = 0.667–0.708, = 11.1–10.6 min, respectively) than for soaked beans. It should

be noted, however, that the robustness of calibration models is impacted by genotypic diversity,

planting year and phenotypic used for model building, and hence, chemometric models should be

maintained and updated with new data.

Design of a Biotech Trait Discovery Phenotyping Program

Cory Christensen, Dow AgroSciences

With rapid advancements in “omics” technologies, it has become axiomatic that phenotyping is the new

frontier in need of technical innovation. Recent years have witnessed increased competition among

developers of phenotyping systems and new solutions are coming to market. In this environment,

researchers and leaders tasked with developing a phenotyping program have many options. This

presentation will focus on important design considerations, key experimental questions and critical

lessons learned in the context of an industrial-scale agronomic trait discovery program.

General Session: Plasticity in Plant Traits

February 13, 2017 | 8:30 AM – 12:15 PM

Transgene Mediated Epimutagenesis Leads to Heritable Phenotypic Variation

Bob Schmitz, University of Georgia

There exists extensive natural epigenetic and cryptic variation in plant genomes some of which is linked

to heritable phenotypic variation. Accessing this source of variation to improve plant traits is

cumbersome as it often relies on the identification and incorporation of rare events into breeding

programs. We describe a novel methodology termed “epimutagenesis” to rapidly generate phenotypic

variation by randomly demethylating the genome of Arabidopsis thaliana via transgenic expression of a

DNA demethylase. Not only does this system lead to widespread loss of DNA methylation, but it also

leads to the production of heritable phenotypic variation. Application of epimutagenesis to other plant

species will lead to expression of alleles typically silenced by DNA methylation, uncovering previously

hidden genetic variation.

Methylome Evolution in Plants

Frank Johannes, Techinical University of Munich

DNA methylation is a heritable epigenetic modification, with important roles in regulating the

expression of transposable elements, repeats and genes. Here I review new insights into the

evolutionary forces that shape DNA methylation patterns in plant genomes over short and long time

scales.

Searching for the Mechanisms that Drive Novel Gene Expression Responses to Abiotic Stress in Maize

Nathan Springer, University of Minnesota

Changes in gene expression are frequently used to tolerate abiotic stress in plants. We are studying the

processes that lead to natural variation in gene expression responses to abiotic stress with a focus on

the potential role for transposons in this process.

Blinded by the Light; An Alternative Type of Seed Dormancy

Zsuzsanna Merai, Gregor Mendel Institute

As well-described in textbooks, Arabidopsis seeds require light for germination. However, these

principles are not universal, some plant species germinate independently, others are actually inhibited

by light. We found another Brassicaceae species showing natural variation in light-regulated seed

germination, including light induced secondary seed dormancy. These traits might represent ecologically

relevant adaptations to specific environments and provide a great opportunity to investigate their

molecular regulation.

Genetics of Adaptive Plasticity: From Model Systems to Crops

John McKay, Colorado State University

The long-term goal of my research is to provide detailed functional knowledge of mechanisms and

mutations regulating genotype by environment interactions underlying adaptation. I have largely

focused on drought responses, including water use physiology and the sensitivity of growth, fitness and

yield to variation in available soil moisture. My research combines field trials, high-throughput and

detailed physiological screens in segregating families with statistical and evolutionary genomics to

identify causal polymorphisms underlying differences in drought adaptation.

I will report on results of using natural variation in drought adaptation among diverse lines of

Arabidopsis. I will also discuss ongoing collaborations with IRRI to apply field based high throughput

phenotyping towards discovering mutations underlying GxE to drought in rice.

Novel Plastid Behavior Associated with the MSH1 Effect

Jesus Beltran, University of Nebraska-Lincoln

Evan LaBrant, University of Nebraska-Lincoln; Mon-Ray Shao, University of Nebraska-Lincoln; Sally

Mackenzie, University of Nebraska-Lincoln

In plants, plastids serve as sensors of developmental and environmental stimuli, allowing cells to

modulate gene expression and likely to reprogram the epigenome in a heritable fashion. MSH1 is an

organellar DNA binding and thylakoid protein with profound roles in development, stress responses and

redox balance. Current evidence supports MSH1 as important for influencing transgenerational

epigenetic inheritance. MSH1, together with its interacting partner PPD3, localize to small, distinctive

plastids that display low autofluorescence and reside within the epidermis and vascular parenchyma.

We have termed these organelles ‘sensory plastids’. Our observations imply proteome heterogeneity in

different plastid types across discrete cell populations. To gain insight into the role of sensory plastids,

we combined Fluorescence-Activated Cell Sorting (FACS) and proteomics to characterize their

proteomes in Arabidopsis floral stems. We used Translating Ribosome Affinity Purification (TRAP)

analysis followed by RNA sequencing technology (TRAP-SEQ) to assess both MSH1-containing and

MSH1-depleted, cell-specific translating RNAs. Early results suggest that protein counts for responders

to abiotic stimulus are more prominent in rank in sensory plastids than in chloroplasts, which suggests

plastid specialized function. TRAP-SEQ experiments revealed that cells depleted of MSH1 actively

translate proteins for oxidation-reduction processes, cell redox homeostasis and auxin response, which

suggest a role of MSH1 in triggering retrograde signaling in specific cell types. Finding correlations

between cell-specific translatomes and the plastid proteome profiles within these cells would help to

understand the underpinnings of organellar functional specialization.

Assessment of Plant Performance Traits in Controlled Environments and Translation to the Field

Astrid Junker, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)

Marc Heuermann, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Rongli Shi, Leibniz

Institute of Plant Genetics and Crop Plant Research (IPK); Henning Tschiersch, Leibniz Institute of Plant

Genetics and Crop Plant Research (IPK); Matthias Lange, Leibniz Institute of Plant Genetics and Crop

Plant Research (IPK); Daniel Arend, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK);

Jean-Michel Pape, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Rhonda Meyer,

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Kathleen Weigelt-Fischer, Leibniz

Institute of Plant Genetics and Crop Plant Research (IPK); Michael Grau, Leibniz Institute of Plant

Genetics and Crop Plant Research (IPK); Andreas Boerner, Leibniz Institute of Plant Genetics and Crop

Plant Research (IPK); Uwe Scholz, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK);

Thomas Altmann, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)

To meet the challenges in global food security requires the development of strategies towards the

optimization of yield formation and resource efficiency of crop plants under future climate scenarios. To

enable this, a deeper understanding of processes underlying plant acclimation to changing

environments is necessary. Using high throughput automated plant phenotyping systems at IPK, the

dynamics of plant growth and photosynthetic efficiency have been studied in accession panels of

Arabidopsis and maize under controlled conditions. This led to the identification of Arabidopsis and

maize candidate accessions with different acclimation strategies to high light and variations in biomass

yield and photosynthetic efficiency, respectively. To evaluate and enhance the prospects of lab-to field

translation, candidate maize accessions have furthermore been phenotyped for root- and shoot traits

under different cultivation conditions in the glasshouse and in the field. Modification of the standard

cultivation procedures improved the lab-to-field translation of phenotypic trait expression which will be

further optimized in the Plant Cultivation Hall currently being erected at IPK. This building will enable to

run specifically designed and reproducible cultivation scenarios that mimic field conditions. In this way,

trait expression similar to that in the field will be triggered and monitored using automated phenotyping

installations. Investigations involving precise variation of environmental parameters will yield a deeper

understanding of acclimation processes and underlying molecular mechanisms and genetic

determinants and will support the prediction of idiotypes with improved performance under future

climate scenarios. Furthermore recent achievements in phenotype data management, standardized

metadata representation (e.g. MIAPPE), and data publication will be presented.

Ectopic Expression of psNTP9, An Apyrase Gene from Pisum Sativum, Confers Drought Tolerance in

Transgenic Soybean

Roopadarshini Veerappa, University of Texas at Austin

Soybean (Glycine max (L.) Merr.) is one of the major resources of protein for human and animal

nutrition as well as a key source of vegetable oil. It is also considered a potential crop for the production

of biodiesel. Drought is a critical environmental factor that imposes water stress on crops, which is a

major constraint on plant growth and productivity, contributing to yield losses in crops, including

soybeans. Apyrases (Nucleoside Triphosphate-Diphosphohydrolases) are calcium- and magnesium-

activated enzymes that remove the terminal phosphate from nucleoside triphosphates (NTPs) and

nucleoside diphosphates (NDPs). By controlling the [NTP] and/or [NDP] in the Golgi, the ECM or other

subcellular sites, apyrases are known to play an essential role in plant growth, development and stress

responses. In the present study the pea apyrase gene (psNTP9), driven by CaMV 35S promoter, was

transferred into the soybean genome by Agrobacterium-mediated plant transformation. Two

independently transformed soybean lines expressing psNTP9 were produced. Segregation analyses

indicated single-locus insertion for both lines. Preliminary tests performed in the greenhouse evaluated

the phenotype of transgenic soybean plants under drought conditions. Transgenic plants outperformed

the wild-type plants when subjected to water deficit conditions, exhibiting notable drought tolerance

with a higher survival rate compared to the wild type. They also had a greater shoot and root biomass

and showed significantly higher seed production compared to non-transgenic plants under water deficit.

This is the first report showing that the ectopic expression of psNTP9 in transgenic soybeans confers

drought tolerance, highlighting the potential of this gene for molecular breeding.

Funded by grants from the National Science Foundation (IOS-1027514) and a private company to Drs.

Greg Clark and Stanley J. Roux at the University of Texas at Austin.

On the Statistical Mechanics of Cytosine DNA Methylation and the Dection of the Methylation

Regulatory Signal

Robersy Sanchez, University of Nebraska-Lincoln

DNA methylation patterning represents one feature of the epigenome that is highly responsive to

environmental stress and associates with transgenerational adaptation in plants. An information

thermodynamics theory on cytosine DNA methylation (CDM) was recently published. Results indicated

that most methylation changes occurring within cells are likely induced by thermal fluctuations to

ensure thermal stability of the DNA molecule, seemingly explainable by statistical mechanics laws.

Therefore, one does not observe a genome-wide relationship between methylation and gene

expression. Ignoring the statistical biophysics subjacent to CDM is a source of bias for the most common

statistical tests applied in methylome analysis. A key limitation to current genome-wide methylation

analysis is the inability to discriminate signal, induced by experimental treatment, from background

“noise” that emanates from this natural, dynamic methylation activity. Here, we provide novel insights

for application of statistical mechanics of CDM to detect methylation regulatory signal. Integration of

information thermodynamics of CDM and signal detection theory permits robust discrimination of

biological signal from physical noise-induced thermal fluctuations. The analytical steps summary follows:

i) Information thermodynamic theory computes the probabilities of signal plus noise, with use of

experimental controls to estimate the receiver’s threshold (cutoff Hellinger divergence value, HD) at

which the rate of false positive is minimal or at least acceptable for the experimental conditions. ii)

Potential differentially methylated positions (DMPs) are proposed based on the probabilities of their

corresponding HDs. iii) A logistic regression analysis is performed with the prior binary classification of

DMPs (from treatment versus control), and iv) a receiver operating curve (ROC) is built to estimate the

cutoff point for HD at which an observed methylation level represents a true DIMP. Our approach is

illustrated with examples from analysis of genome-wide CDM reprogramming induced by msh1 mutant

effect in Arabidopsis thaliana.

Climate and Crop Models to Predict Plant Productivity Based on

Environment

February 13, 2017 | 2:00 PM – 5:30 PM

Crops Models as Linking Technology Between Phenotyping and Genotype

Scott Chapman, CSIRO

This presentation will cover the role of dynamic models based on biological understanding as an avenue

to link molecular and whole plant levels of biological organization and how this offers advantages

beyond pure statistical methods in linking genotype to phenotype for plant breeding, especially when

confronted with genotype-by-environment interaction.

Improving Phenotypic Prediction through Crop Model-whole Genome Prediction Integration

Charlie Messina, DuPont Pioneer

Crop growth models integrate decades of knowledge in crop biology and provide frameworks to

integrate complex data to enable prediction. Crop model-whole genome prediction integration is

demonstrated in maize breeding for the US corn belt.

Global Trends in Crop Productivity

William Kolby Smith, University of Arizona

Feeding the world without destroying the environment is the biggest challenge of our time, and the

extent to which crop production can be increased will determine if we can feed a growing population

without land use change. Global-scale, empirical approaches can explore trends in crop productivity

and historical relationships between weather and yield. Here, we analyze high-resolution maps of yield

and harvested area for major crops, based on fusion of satellite and census data, to determine trends in

productivity, and impacts of climate on yields at the global scale. Examining yield gaps, defined as the

difference between current yields and realistically attainable yields, we find remarkable consistency at

the global scale, with substantial regional variation in closure of yield gaps. The absolute increase in

yield gaps suggests that global crop production can continue to increase for the foreseeable future.

Regional variations call for targeted intervention to improve management and seed quality.

Assessing Nitrogen Use Efficiency and Water Stress Interactions in Wheat Using High Resolution

Growth Analysis

Trevor Garnett, The University of Adelaide

Bettina Berger, The University of Adelaide; Nicholas Sitlington-Hansen, The University of Adelaide; Plett

Darren, The Australian Centre for Plant Functional Genomics; Mamoru Okamoto, The Australian Centre

for Plant Functional Genomics

Improved nitrogen use efficiency (NUE) in crop plants has the potential to significantly reduce fertilizer

application costs and increase crop yield. It is therefore important to develop efficient screening

methods for improved NUE. NUE is a complex, multi-component trait and screening for NUE in field

trials poses the challenge of low heritability and high environmental interaction. One of the major

restrictions to N uptake early in the season in southern Australia is lack of water, which restricts N

mobility in soil and hence N uptake. Phenotypic analysis in controlled environments offer the advantage

of focusing on individual components of NUE while controlling aspects of nitrogen (N)-availability, water

application and climate conditions. In results to be presented, the nitrogen response of wheat varieties

was investigated using a high throughput phenotyping platform. Treatments incorporated water

stresses early in the season and at flowering with the aim of better understanding the interaction

between these two important factors restricting grain crop yields. Plants were also grown on to maturity

to assess grain yield response. In terms of biomass there were large differences in N response between

varieties and major interactions with water stress. The shoot growth response of the varieties varied in

total biomass and also in the timing of growth. There were major differences in N response and

efficiency between varieties. There were good correlations between non-destructive measurements of

biomass and grain yields but this differed between water stress treatments. There were also good

correlations in performance between repeated experiments. In terms of developing a reliable,

controlled environment method for screening NUE in cereals the results are promising.

Phenotyping at Multiple Biological Scales Reveals Complex Interplay Between Traits and Environment

Elena Rice, Monsanto

Sarah Tucker, Monsanto; Frank Dohleman, Monsanto; Sean Yang, Monsanto; Shilpa Swarup, Monsanto;

Lex Flagel, Monsanto; Dmitry Grapov, Monsanto; Kimberly Wegener, Monsanto; Kevin Kosola,

Monsanto; Mike Hall, Monsanto; Ed Allen, Monsanto

There are increasing opportunities to improve crop yield using diverse approaches from traditional plant

breeding to recent technological developments such as genome editing, microbial enhancement, as well

as traditional biotechnology. Utilizing these technologies requires understanding of traits that can

contribute to an integrative phenotype like yield under diverse environmental conditions. The key

challenge here is the ability to measure large number of physiological and molecular traits using

sufficiently diverse and commercially-relevant populations, and the ability to integrate genotype,

phenotype, and environmental variables. Here, we systematically collected, 31 phenotypic traits

including yield, 83 annotated metabolites, over 21,000 transcripts on a set of 57 diverse, commercially

relevant maize hybrids across 3 environments, in the central corn belt of the USA, to understand two

central questions:

1) How do these traits vary over germplasm and environments?

2) How do traits relate to yield and to each other?

As expected, analyses showed a significant variability in measured phenotypic and molecular traits

across both pedigrees and environments and presented a complex picture of how groups of traits

interact and how they combine to produce yield. We also evaluated the opportunity to utilize

automated phenotyping system that combines throughput and resolution to characterize trait variability

for 52 commercially-relevant maize hybrids. Controlled environment maize phenotyping using high-

resolution and high-throughput can differentiate some phenotypic traits that contribute to increased

grain yield and provide and opportunity to answer specific questions under highly consistent

environmental conditions.

Integrating Multiscale Data into Models of Plant Growth and

Development

February 13, 2017 | 2:00 PM – 5:30 PM

Multi-scale Leaf Growth Phenotyping: Modelling the Relationships between Traits Over Time and in

Response to Environmental Stresses Gives Insight into Leaf Growth Control

Christine Granier, Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE)

Phenotyping platforms for measuring plant traits at different scales are developed. Challenges and

opportunities for directions in multi-trait analyses will be presented with leaf development as a proof of

concept.

What Do We Learn from Phenotyping Tools that Bridge the Gaps between the Lab and the World?

David Kramer, Michigan State University

Some of the most pressing open questions about photosynthesis concern how it is adapted to function

in living organisms to balance the needs for efficient energy capture and the avoidance of toxic side

reactions. The importance of this balancing is especially critical to cope with the dynamic environmental

conditions experienced in the field. To address these questions, we need to observe the

biophysical/biochemical machinery of photosynthesis in action under the conditions of the field. David

will describe a series of enabling technologies designed to bridge the gaps between the lab and the field

that can be and how these tools can be used to network communities of researchers to address this

type of question. The technologies include: PhotosynQ.org - Community-driven plant phenotyping

platform for data-driven innovations in science and agriculture; MultispeQ – A PhotosynQ-enabled tool

for large-scale field phenotyping; CoralspeQ - A field-deployable phenotyping platform for corals, algae

and cyanobacteria; Dynamic Environmental Phenotype Imager (DEPI)- A platform for high throughput

dynamic plant phenotyping. David will describe new results using these tools to explore the

relationships between photochemistry and photoprotection in a wide range of wild type and mutant

plants, both in the lab and the field. The major outcomes of these experiments are the identification of a

biophysical “Achilles Heel” of photosynthesis; the identification of a large number of “ancillary

regulatory components” of photosynthesis to tune its responses to the environment; and practical

applications of new phenotyping tools to improve the breeding and management of crops, especially in

the developing world.

Full Stack Biology: Moving Freely Between Biological Layers with Databases, Statistics and Network

Modeling

Cody Markelz, University of California, Davis

We will discuss Cody’s research, which focuses on bridging the gap between computational and

experimental plant biology. This talk will focus on a framework for storing, collecting, modeling and

developing predictive models by integrating phenomics data.

Genome to Phenome in Plant Model Organisms and Foundation Species

Justin Borevitz, Australian National University

In this presentation, Justin will discuss working on genome variation and trait identification in

Arabidopsis and Brachypodium and translation to wheat and eucalyptus. New growth facilities and

hyperspectral image mapping will be shown.

The ’Genotype to Phenotypes’ Map

Ümit Seren, Gregor Mendel Institute of Molecular Plant Biology

Dominik Grimm, Machine Learning and Computational Biology Lab, Department of Biosystems Science

and Engineering; Joffrey Fitz, Max Planck Institute for Developmental Biology; Detlef Weigel, Max Planck

Institute for Developmental Biology; Karsten Borgwardt, Machine Learning and Computational Biology

Lab, Department of Biosystems Science and Engineering; Magnus Nordborg, Gregor Mendel Institute of

Molecular Plant Biology; Arthur Korte, Center for Computational and Theoretical Biology

In nature, organisms and specifically plants exhibit a diverse range of phenotypic variability.

Understanding how genetic variation translates into phenotypic variation has been a prime focus of

population geneticists, and we have made substantial progress in the Arabidopsis community.

Since the first Genome Wide Association Studies (GWAS; Atwell et. al 2010) many research groups have

generated and mapped high quality phenotypic data ranging from disease resistance phenotypes to

high-dimension molecular phenotypes — e.g., RNA expression, metabolites. Recently, a detailed map of

genetic variation was released by The 1001 Genomes Consortium for the Arabidopsis community. With

this high quality genotype and phenotype data on hand, we can construct a comprehensive “genotype-

to-phenotypes” map that allow us to perform meta-analyses of pleiotropy.

To that end, we must develop databases and tools to store and examine phenotypes, respectively, and

make them available to the community. In this talk, I will present a variety of these tools ranging from a

web-portal that allows researchers to easily carry out GWAS by simply providing phenotype data to a

central database, to an intuitive interface for storing and navigating through published phenotypes.

The Planteome Project: Annotating Plant Phenotypes using Ontologies for Data Integration

Laurel Cooper, The Planteome Project

Austin Meier, Oregon State University; Justin Elser, Oregon State University; Marie-Angélique Laporte,

Bioversity International; Justin Preece, Oregon State University; Chris Mungall, Lawrence Berkeley Lab;

Elizabeth Arnaud, Bioversity International; Pankaj Jaiswal, Oregon State University

The Planteome project (www.planteome.org) features a suite of reference ontologies for plants,

associated with a growing corpus of genomics data in a centralized online plant informatics portal. The

species-neutral references, the Plant Ontology, Plant Trait Ontology, and Plant Environment Ontology,

(developed by the project), along with those developed by collaborating groups, such as the Gene

Ontology and the Phenotype and Trait Ontology, and others, are mapped to species-specific controlled

vocabularies for crop plant traits and phenotypes. Traditional plant breeding methods for crop

improvement may be combined with next-generation analysis methods and automated scoring of traits

and phenotypes to develop improved varieties. Linking these analyses to the growing corpus of

genomics data generated by high-throughput sequencing, transcriptomics, proteomics, phenomics and

genome annotation projects requires common, interoperable, reference vocabularies (ontologies) for

the description of the data. Data annotations to ontology terms link phenotypes and germplasm to

genomics resources, and provide semantic integration of widely diverse datasets with the goal of plant

improvement. Analysis and annotation tools are being developed to facilitate studies of plant traits,

phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of

plant species. The project database and the online resources provide researchers tools to search and

browse and access remotely via APIs for semantic integration in annotation tools and data repositories,

providing resources for plant biology, breeding, genomics and genetics. The project is supported by the

National Science Foundation award IOS #1340112

Statistical Analysis of Multivariate Data inc Machine/Deep Learning

February 13, 2017 | 2:00 PM – 5:30 PM

The Competing Environmental Influences on Plant Mating and Plant Floral Traits in a Common

Agricultural Weed

Gina Baucom, University of Michigan

The plant mating system is a labile trait that is strongly influenced by the environment. In this talk, I

discuss the relative role of both human-mediated and natural selection on plant mating patterns as well

as associated floral traits in the agricultural weed, Ipomoea purpurea.

Learning from Imprecise Labels for Remote Sensing and Phenotyping

Alina Zare, University of Florida

During this talk, Alina will introduce novel supervised and semi-supervised machine learning methods

that leverage imprecise, uncertain and incomplete labels. Application of these methods to remote

sensing and phenotyping will be presented and discussed.

Melding STEM and Plant Sciences towards Advanced Plant Phenotyping and Discovery

Jennifer Clarke, University of Nebraska-Lincoln

We will discuss the development of a cross-disciplinary research group to address computational and

analytical problems in plant phenomics and genomics. This development comes with associated

challenges, from cross-disciplinary communication to mentorship to balancing the interests and

expectations of various group members. We will highlight several successful projects associated with

plant phenotyping activities at the University of Nebraska-Lincoln. This presentation will conclude with

an introduction to the Midwest Big Data Hub focus area in Digital Agriculture and opportunities for

cross-institution and cross-sector collaborations.

Persistent Homology: A Powerful Tool for Mapping Variation in Complex Phenotypes

Washington Mio, University of Florida

I will discuss the concept of persistent homology and describe ways in which the method can be used to

construct compact, informative summaries of complex morphological and functional data. I also will

present an application of persistent homology to QTL analysis of tomato leaf shape.

A Meteorological Data Format that Makes Research Easier and will Work for Imaging Sensors

David LeBauer, University of Illinois

A common weather data format enabled a pipeline that matches many types of meterological data to

many crop and ecosystem simulation models. Extending this approach to imaging sensor data will

benefit the phenomics community.

Midwest Big Data Hub: Accelerating the Big Data Innovation Ecosystem

Melissa Cragin, Midwest Big Data Hub

The Midwest Big Data Hub (BDH) is one of four Big Data (BD) Innovation Hubs that were launched in

2015, with initial support from the National Science Foundation. The BD Innovation Hubs are intended

to accelerate and strengthen the data ecosystem, to develop effective, data-enabled cross-sector

networks to address scientific and societal problems of regional and national interest. The MBDH is a

growing organization of public and private partners investing in data, tools, infrastructure, and training

to improve access and use of shared and public data for scientific discovery and improved data-to-

decision systems. We will introduce the Big Data Hubs initiative, and report on some of the significant

activities underway in in the Midwest in Digital Agriculture, Food-Energy-Water, and Data Sharing, as

well as our collaborative demonstration projects. Finally, we will discuss opportunities to engage with

the Midwest Big Data Hub, and our growing networks of Public/Private partnerships.

General Session: Metabolomics and Large-scale Biochemical

Phenotyping

February 14, 2017 | 8:30 AM – 12:00 PM

Ionomics: Using Molecular Phenotypes to Dissect the Complex Interactions between Plant Genetics

and the Environment

Ivan Baxter, Donald Danforth Plant Science Center

Plants take up elements from their soil environment using processes controlled by the genome. The

complex nature of these interactions suggests that the elemental profile will be highly plastic with the

genetic factors influencing accumulation depending on which environment the plant is grown in. My lab

uses high throughput elemental profiling (‘ionomics’) as a tool for understanding plant adaptation. High-

throughput workflows allow a single inductively coupled plasma mass spectrometer (ICP-MS) to

precisely analyze hundreds of samples for more than 20 elements per day. We have used ionomic

profiling of 250,000+ maize kernels, soybeans and cotton seeds to detect the genetic and environmental

determinants of the ionome. I will focus on our attempts to understand the genetic by environmental

interactions that underlie elemental accumulation.

Unidentified Metabolomics Peaks and Metabolism’s Dark Underside

Andrew Hanson, University of Florida

Enzymes make mistakes and metabolites undergo spontaneous reactions; these facts are undeniable

but underappreciated. The damaged metabolites produced by enzyme errors and chemical side-

reactions are metabolism’s dark underside and can explain many unidentified metabolomics peaks.

Viewing Plants from the Inside-Out: Variation in the Sorghum Metabolome and Association to

Morpho-physiological Traits and Drought Tolerance

Courtney Jahn, Colorado State University

Changes in plant metabolism are instrumental to plant developmental processes, and thus underpin

many of the ways in which plants respond to the environment. Therefore, comprehensive study of plant

metabolism is valuable in evaluating phenotypic effects of abiotic stresses on plants and may uncover

potential breeding targets to improve sorghum for multiple uses in both benign and stressful

environments.

How Plants Became the Greatest Chemists - Capturing the Power of Diversity

Mark Lange, Washington State University

Plants synthesize and accumulate a breathtaking array of small molecules, with functions in all aspects

of plant life but also a myriad of uses in modern industries. This presentation will describe experimental

and computational approaches to gain insights into the unique metabolism of specialized tissues and

cell types in plants.

The PepSAVI-MS Pipeline for Natural Product Bioactive Peptide Discovery

Leslie Hicks, University of North Carolina at Chapel Hill

The PepSAVI-MS pipeline employs bioactivity assays, statistics, and mass spectrometry to identify

bioactive peptide targets from complex samples. While our initial focus demonstrates applicability for

antimicrobial and anticancer activities, the pipeline is broad-spectrum, high-throughput, and has the

potential to expedite the search for novel bioactive peptides.

Metabolomics and Large-scale Biochemical Phenotyping for Gene Discovery in Plant Specialized

Metabolism

Lloyd Sumner, University of Missouri

This presentation will describe our metabolomics approach and technologies utilized to generate high-

resolution biochemical phenotyping data. It will also illustrate how we are integrating this with other

omics technologies for the discovery of genes involved in plant specialized metabolite biosynthesis.

Poster Abstracts

Phenomic Insights into Quantitative Traits

100 Aiming for Perennial Wheat: Can Studying Root Systems Identify Key Traits for Perenniality?

Ian Clark, Washington State University

Kevin Murphy, Washington State University; Karen Sanguinet, Washington State University

What makes a successful perennial plant? Researchers have been attempting to convert common

annual grains, such as wheat, to a perennial life cycle for many decades. The development of perennial

wheat germplasm often involves the wide hybridization of annual wheat with its nearest perennial

relatives. It has previously been shown that perennial grasses have longer roots and larger root systems

than their annual relatives, but little is known about the spatiotemporal differences in root growth

across related annual and perennial genera. This study compares root system architecture throughout

development in annual fall seeded wheat (Triticum aestivum), tall wheatgrass (Thinopyrum ponticum)

and a hybrid of the two species. The hybrid was selected for wheat-like traits that expressed the post-

sexual cycle regrowth trait critical to the perennial growth habit. Roots are periodically monitored in situ

using a minirhizotron (to a depth of ~150 cm) in the Palouse region of Eastern Washington. Once scans

are taken, roots are mapped using the RootSnap! (CID Bio-Science, Inc.) software. Scans were taken

starting at germination (Autumn, 2015) at an interval of every 2-3 weeks. The annual wheat roots grew

deeper, initiated growth earlier in the season, and showed a steeper rooting angle. The roots exceeded

the measurable depth on April 15th compared to May 4th for the hybrid and May 25th for the

wheatgrass. All plots exceeded our measurable depth by May 25th, when the plants varied from Feekes

5 (T. ponticum) to 10 (T. aestivum). When this spatiotemporal aspect of root growth is added to the

general life history strategy of a perennial, we hope to better understand how our hybrids differ from

both parents and how this may affect survival throughout multiple years.

101 A Multispecies, Multi-trait GWAS Approach to Uncover Fundamental Genetic and Molecular

Mechanisms Underlying Responses to Phosphate Deficiency

Marco Giovannetti, Gregor Mendel Institute

Christian Goeschl, Gregor Mendel Institute; Santosh Satbhai, Gregor Mendel Institute; Stanislav Kopriva,

University of Cologne; Wolfgang Busch, Gregor Mendel Institute

Phosphate is an essential plant macronutrient and its low soil availability leads farmers to massive

application of phosphate-based fertilizer. Given the negative environmental impact of fertilizers, and

phosphate being a non-renewable resource, a better understanding of plant genetic and molecular

bases of phosphate absorption and utilization is needed. Much progress in that area has been made

over the past years, but most approaches focused on one species. However, it is unclear which of the

discovered mechanisms are conserved and which ones are specific to the particular species that is

studied. Here, we approach this problem by using a combination of high throughput root phenotyping

and IC-based phosphate quantification of natural populations of two different plant species, the

brassicaceae Arabidopsis thaliana and the legume Lotus japonicus. This allowed us to identify multiple

candidate genes regulating phosphate metabolism and root growth responses to varying phosphate

levels. We could determine multiple orthologs whose variants are significantly associated with these

phosphate related traits in both plant species. We are currently testing the involvement of high

confidence candidate genes in phosphate homeostasis and root growth responses upon phosphate

levels in both species.

102 From Phenome to Genome: Lessons from the Planteome

Dennis Stevenson, New York Botanical Garden

Dario Cavaliere, New York Botanical Garden; Brandon Sinn, New York Botanical Garden

A main goal of the Planteome, besides providing vocabularies for land plants, is to develop an ontologies

that can connect the phenome with the genes responsible for phenetic expression. Traditionally, our

vocabulary was developed as descriptors of the world around us, which is to describe what we see.

Thus, the same term is often allied strictly to appearance regardless of development and evolutionary

history, in a large part because the terminology was developed before the concept of evolution. For

example, the term ligule is applied to many tongue shaped plant structures, but many of these are

composed of entirely different organs and tissues. Lenticel is another example-- those on stems look

just like those on roots, but each are developmentally and structurally distinct. Today, morphological

terms need to be used for two very different purposes, one to provide universal descriptors for plant

identification and also to be used in the sense of homology to identify the genes responsible for the

development of these descriptors. The issue at hand is how to reconcile these without discarding

existing knowledge. The use of phylogenetic information from across the tree of life will be discussed as

applied to salient examples of conflicts between utility and homology when constructing ontologies.

These have been reconciled in Planteome ontologies, but many other such examples remain to be

addressed for the evo/devo world.

103 Identification and Characterization of Root System Architecture Mutants in Medicago truncatula

Chenglin Chai, The Samuel Roberts Noble Foundation

Parna Ghosh, The Samuel Roberts Noble Foundation; Sarah Oliver, Oklahoma State University; Yuhui

Chen, The Samuel Roberts Noble Foundation; Rujin Chen, The Samuel Roberts Noble Foundation

Root system architecture (RSA) refers to the spatial configuration of a root system in the soil. RSA plays

an essential role in foraging soil resources to maintain sustainable plant productivity. To maintain and

improve sustainable plant productivity particularly under suboptimal water and nutrient levels in the

soil, it is essential to understand how root system architecture is regulated at the molecular genetic level

and how this level of regulation is responsive to the environment (plasticity). To achieve this goal, we

employed three phenotyping methods and screened approximately 200 fast neutron bombardment

(FNB)-induced mutants in Medicago truncatula cv. Jemalong A17. Out of these mutants, we are focusing

on nine mutants with altered RSA traits including long roots (lr1~2), short roots (sr1~3), and fewer

lateral roots (flr1) and long lateral roots (llr1~3). Quantitative analysis revealed that short root

phenotype was due to either a low primary growth rate (in case of sr1 and sr3) or early termination of

primary root growth (in case of sr2); while a high growth rate of the primary root led to the long root

phenotype of the lr2 mutant. The total root length of all three sr mutants was significantly less than that

of the wildtype (A17). The sr1 also exhibited less total root length and higher number of lateral root than

the wildtype. By comparative genomic hybridization analysis, we identified three deletion mutations in

the sr1 mutant, which were confirmed by PCR. An F2 population derived from the cross between sr1 and

A17 will be used to establish genetic linkage between the responsible deletion and the short root

phenotype. The possible functions of deleted genes in the sr1 mutant are currently being investigated.

Detailed phenotyping and cloning of the responsible gene are also being conducted in these RSA

mutants.

104 High Throughput Phenotyping of Photosynthesis to Identify Relevant Quantitative Trait loci and

Underlying Genes in Arabidopsis thaliana

Mark Aarts, Wageningen University

Roxanne van Rooijen, Heinrich Heine University; Aina Prinzenberg, Wageningen University; Jeremy

Harbinson, Wageningen University

Natural genetic variation in plant photosynthesis efficiency is hardly studied even though breeding for

photosynthesis would be interesting to maintain increases in crop yields. One of the reasons this is not

studied is the notorious difficulty in adequately phenotyping photosynthesis parameters for genetic

research. We designed a versatile plant phenotyper, the Phenovator, capable of multispectral imaging of

1440 plants per experiment. Imaging includes chlorophyll fluorescence to determine the light use

efficiency of photosystem II electron transport (ΦPSII or Fq’/Fm’), and near infrared reflection to

measure projected leaf area. This system has been used to phenotype 350 genetically diverse

Arabidopsis thaliana accessions for genome wide association analysis. Plants were phenotyped at non-

stressful conditions and in response to cold treatment (5 °C) or a switch from 100 to 550 µmol m-2 s-1

irradiance. The observed genotypic variation was used to identify quantitative trait loci (QTL). Our latest

progress in identification and confirmation of QTL for ΦPSII, plant growth and epinastic leaf

movement will be presented, including detailed analysis of the allelic variation at the nucleotide level of

some of the genes underlying these QTL. Our work has shown that there is sufficient genetic variation

for PSII efficiency in Arabidopsis amenable for gene identification, which suggests the same will be the

case for crop species. Such would offer interesting opportunities for future crop photosynthesis

improvements.

105 Time Dependent Genetic Analysis Links Field and Controlled Environment Phenotypes in the Model

C4 Grass Setaria

Max Feldman, Donald Danforth Plant Science Center

Rachel Paul, University of Illinois; Darshi Banan, University of Illinois; Jennifer Barrett, Donald Danforth

Plant Science Center; Jose Sebastian, Carnegie Institute for Science; Muh-Ching Yee, Carnegie Institute

for Science; Hui Jiang, Donald Danforth Plant Science Center; Alexander Lipka, University of Illinois; Tom

Brutnell, Donald Danforth Plant Science Center; Jose Dinneny, Carnegie Institute for Science; Andrew

Leakey, University of Illinois; Ivan Baxter, USDA – ARS

Vertical growth of plants is a dynamic process, which is influenced by both genetic and environmental

factors. Variation in plant height presents an intriguing ecological trade-off between access to solar

radiation, energetic demand for construction of lignified support tissue and the fecundity risk associated

with lodging. As such, plant height has a profound effect on overall plant architecture and biomass

composition. The primary goal of our studies have been to identify the major genetic loci that contribute

to the variation of plant height within two independent genetically structured populations of Setaria sp.

and how such phenotypes change temporally in response to differences in water availability and

planting density. To achieve this objective we have performed twelve growth trials of an interspecific S.

italica x S. viridis recombinant inbred line population in field and controlled environment settings in

addition to two experimental growth trials of a S. viridis natural diversity panel in the Bellweather

Phenotyping Facility at Donald Danforth Plant Science Center. Results from these studies identify

numerous genetic loci associated with variation in plant height shared across all experimental

treatments and locations but that the contribution of individual loci differs temporally. The

methodology presented in this study is applicable to any number of single or composite traits and is

used to deconvolute the contribution of plant height to other complex plant traits of agronomic

importance (biomass accumulation/composition and water use efficiency).

106 Using Association Genetics to Identify Common and Species-specific Mechanisms of Nitrate

Dependent Root Growth Responses

Anna Malolepszy, Gregor Mendel Institute of Molecular Plant Biology

Wolfgang Busch, Gregor Mendel Institute of Molecular Plant Biology

Nitrogen availability is one of the most limiting factors for most organisms. While nitrogen gas is the

most abundant constituent of the Earth’s atmosphere, it isn’t available as nitrogen source for most

organisms, including plants. Legumes evolved the mechanisms to cope with a low level of nitrate by

establishing the beneficial interaction with rhizobia bacteria, which ensure the fixation of atmospheric

nitrogen to a plant-usable form. This unique adaptation strategy enables them to grow in the nitrate-

limiting condition. The formation of nodulation pathway clearly impacted the nitrate homeostasis and

signaling networks in legumes. We would like to understand how nitrate signaling and homeostasis were

modified to accommodate the evolutionary distinct way of obtaining nitrate via nodules. To get insights

into this adaptation we investigate the root plasticity of natural accessions of the model legumes Lotus

japonicus and Medicago truncatula under low and high nitrate conditions with and without rhizobia

inoculation. The root traits are quantified via an automated pipeline for high-throughput phenotyping

(BRAT) and are then used to conduct genome wide association studies (GWAS). Using a comparative

approach and analyzing GWAS data from Lotus japonicus, Medicago truncatula as well as Arabidopsis

thaliana, we can determine whether genetic variation underlying root growth responses to nitrate

abundance is located in the same or different sets of genes. Overall, using this multi-species comparison

we aim to shed light on the molecular basis of the nitrate economy in different species.

107 Phenotyping Solanaceous Crops for Abiotic Stress Tolerance Traits

Sridhar Gutam, Indian Council of Agricultural Research

The solanaceous crops like tomato, brinjal and potato are the most popular and delicious vegetables

grown and consumed in India and south of Asia as well. However these crops are being subjected to

various abiotic stresses due to the changing climatic conditions. And the most problematic abiotic stress,

drought stress is creating difficulties in cultivation of these crops in larger areas and producing in large

quantities to meet the demand. Therefore an attempt was made to study the root traits of these crops.

And in an experiment with tomato genotypes to study the root characteristics, there found a significant

differences in the genotypes with respect to the root phenotypic characters which can be suitably used

for the development of suitable genotypes of desired root characters or can be simply used as

rootstocks in grafting for the production of drought tolerant genotypes.

108 Improving the Stomatal Resistance to Water-loss and Water-use Efficiency in Sorghum bicolar for

Bioenergy Production

John Ferguson, University of Illinois at Urbana-Champaign

Victoria Scaven, University of Illinois at Urbana-Champaign; Patrick Brown, University of Illinois at

Urbana-Champaign; Tom Clemente, University of Nebraska-Lincoln; Edgar Spalding, University of

Wisconsin; Nathan Miller, University of Wisconsin; Andrew Leakey, University of Illinois at Urbana-

Champaign

Improving the water-use efficiency (WUE; ratio of biomass production to transpiration) of bioenergy

Sorghum by 40% is predicted to reduce its seasonal water requirement by 250 mm and improve

production by 27%. Such an improvement in WUE would make >9 million acres of marginal Midwestern

land economically viable for Sorghum bioenergy production. Mechanistic modelling has suggested that

reducing the number of stomata on leaves, thus reducing resistance to water loss, is a potentially

efficacious strategy through which WUE could be improved in Sorghum. Additionally, empirical evidence

from C4 crop species suggests that photosynthesis and biomass accumulation are not penalised when

concurrently reducing stomatal conductance, due to rising atmospheric [CO2].

As a precursor to the development of novel Sorghum varieties with improved WUE, we are assessing the

natural variation that exists for multiple physiological characteristics that underpin WUE, including

stomatal density, leaf-level gas exchange, and δ13C (integrated measure of WUE), in >800

Sorghum accessions. Initial findings reveal that substantial variation exists for stomatal number and

WUE proxies. Importantly, this variation demonstrates significant heritability, thereby providing

incentive that future association mapping should be successful in terms of identifying genetic loci linked

to the observed variation. In addition, we are genetically manipulating stomatal number via the

downregulation and overexpression of key stomatal development genes, such as Sb-EPF1 and Sb-

ERECTA. Imaging of the T1 generation of multiples transgenic events of the RNA interference of Sb-EPF1

demonstrates that disrupting stomatal development appears to reduce stomatal number. Future work

aims to further characterise the WUE and response to drought of the genetic manipulation stomatal

development and to assess leaf-level gas exchange and stomatal number from a larger number of

accessions during the 2017 growing season.

109 An Automated Phenotyping Platform for Trait Discovery and Characterization in Corn

Gerie van der Heijden, DuPont Pioneer

Transgenic or genome editing approaches for agronomic traits of corn, for example to improve drought

tolerance or nitrogen use efficiency, require an accurate and precise phenotyping pipeline. The small

genetic differences may lead to relatively small phenotypic differences, and hence we need to reduce

the random variation and increase the precision of the measurements.The agronomic traits discovery

pipeline of DuPont Pioneer consists of precision phenotyping possibilities in the greenhouse as well as

extensive field testing trials in managed stress environments. The managed stress environment in the

field offers accurate measurements of the end goal: yield under different realistic stress conditions.

Precision phenotyping in the greenhouse offers the possibility to test leads year-round, to apply well-

defined stresses at an individual plant basis and to study in detail plant growth and development of each

plant with advanced equipment, like fluorescence imaging and hyperspectral imaging. In this

presentation we will show the automated greenhouse phenotyping platform for screening drought

tolerant phenotypes in corn.

110 Quantitative Trait loci Analysis of Fiber Auality Traits using an Tntrogressed Random-mated

Recombinant Inbred Population

Khezir Bhatti, Kahramanmaras Sutcu Imam University

Adem Bardak, Kahramanmaras Sutcu Imam University; Halil Tekrek, Kahramanmaras Sutcu Imam

University; Ismail Akyol, Kahramanmaras Sutcu Imam University

Cotton is one of the most important natural fiber crops in the world. Cotton is of immense importance

for global economy by producing natural fiber for textile industry and edible oil for human consumption.

In recent decades, improvements in cotton fiber quality and yield have been stagnant and unable to

meet the demands of the modern textile industry. However, yield is often negatively correlated with

fiber quality in upland cotton. Therefore, yield must at least be maintained when improving fibre quality

for a cultivar to remain competitive. Molecular linkage maps provide essential tools for plant genetic

research, facilitating quantitative trait locus (QTL) mapping, marker-assisted selection and map based

cloning. Random-mating provides a potential methodology to break this correlation. The suite of fiber

quality traits that affect the yarn quality includes the length, strength, maturity, fineness, elongation,

uniformity and color. Identification of stable fiber quantitative trait loci (QTL) in Upland cotton is

essential in order to improve cotton cultivars with superior quality using marker-assisted selection

(MAS) strategy By crossing two Gossypium hirsutum parents, cv. Nazilli 84S and IS-4, and through

subsequent selfings, we obtained recombinant inbred line (RIL) population of 165 individuals. In the

present study, we used SSR and SRAP markers to construct a linkage map using 165 RIL individuals

derived from a cross between G. hirsutum x G. barbadense. The purpose of the present research to

detect quantitative trait loci (QTL) for fiber quality and provide information applicable to cotton

breeding.

111 Gene-based Crop Models: A Modular Approach to Model Early Vegetative and Reproductive

Development of the Common Bean (Phaseolus vulgaris L.)

Christopher Hwang, University of Florida

Melanie Correll, University of Florida; Salvador Gezan, University of Florida; Li Zhang, Texas University;

Mehul Bhakta, Monsanto; Kenith Boote, University of Florida; Jose Clavijo Michelangeli, Thomson

Reuters; James Jones, University of Florida

To assist plant breeders in designing new cultivars for increased yield and meet global food demands,

the next generation of mechanistic gene-based crop models offers the potential of predicting crop

vegetative and reproductive development based on genetic, environment, and management

information. Here, we illustrate an approach for developing a dynamic modular gene-based model to

simulate time to first flower, main stem node number, and leaf area by node position of common bean

(Phaseolus vulgaris L.). Crop characteristics in the model are functions of relevant genes (quantitative

trait loci (QTL)), the environment (E), and QTL x E interactions. The model was based on data from 187

recombinant inbred (RI) genotypes and the two parents grown at five sites (Citra, FL; Palmira, Colombia;

Popayan, Colombia; Isabela Puerto Rico; and Prosper, North Dakota). The model consists of four

dynamic QTL effect models for node addition rate (NAR, No. d-1), daily rate of progress from emergence

toward flowering (RF), daily maximum main stem node number (MSNODmax), and potential leaf size for

a node on the main stem (MSLAmax), that were integrated to simulate main stem leaf area for each

node vs. time, and date of first flower using daily time steps. Funding from the Borlaug Fellowship in

2016 allowed for an additional trial with seven RI lines to be conducted at the international center for

tropical agriculture (CIAT) for model evaluation. The use of mixed-effects models to analyze multi-

environment data from a wide range of genotypes holds considerable promise for assisting

development of dynamic QTL effect models capable of simulating vegetative and reproductive

development.

112 Phenomics of Reproductive Stage Drought Tolerance in Rice

Sudhir Kumar, IARI

Dhandapani Raju, IARI; Viswanathan V. – IARI

Drought stress adversely affect yield in about 45% of the rice grown area in India. In these area rice crop

experiences intermittent cycles of drought stress and recovery and/or terminal drought stress. Extensive

efforts are being made to improve drought tolerance and WUE of rice. Large-scale phenotyping under

field conditions often led to the identification and use of the drought avoidance genotypes which

mature early or genotypes with dehydration avoidance by water mining. An experiment with 60 diverse

rice genotypes was conducted, in automated Plant Phenomics Facility of ICAR-IARI, New Delhi, to

identify genotypic variation for reproductive stage water-deficit stress tolerance. Pot grown plants were

subjected to drought stress by withholding irrigation at booting stage. The evapotranspiration was

measured for both well watered and drought set plants by using automated weighing and watering

station. The drought set of plants were rewatered when the available soil moisture content decreased

to about 40%, and again the plants were subjected to the second cycles of stress. Plants were imaged by

using visual, IR, NIR and chlorophyll fluorescence imaging platforms at regular intervals. Temporal

responses in several image-based features measured by using visual, IR, NIR and chlorophyll

fluorescence imaging platforms showed considerable variations among rice genotypes. The potential

association of these image-based features with the component mechanisms of drought tolerance

viz.,cellular dehydration avoidance and cellular dehydration tolerance will be discussed.

113 Phenotyping Thousands of T-DNA Insertion Arabidopsis thaliana Mutants with the unPAK Project:

The Power of 200,000 Phenotypes

Courtney Murren, College of Charleston

Matthew Rutter, College of Charleston; Hillary Callahan, Barnard College/Columbia University; Jim

Leebens-Mack, University of Georgia; Michael Wolyniak, Hampden Sydney College; April Bisner, College

of Charleston; Allan Strand, College of Charleston

Determining how genes are associated with complex plant phenotypes is a major challenge in modern

biology. The unPAK project — undergraduates Phenotyping Arabidopsis Knockouts — has phenotyped

thousands of non-lethal insertion mutation lines from a single genomic background in the model

organism Arabidopsis thaliana. unPAK phenotypes are complex macroscopic vigor and fitness-related

traits of ecological, evolutionary, and agricultural importance. We contextualize the mutant line

variation with natural accessions (phytometers) for which whole genome information is available. With

our database, we evaluate phenotypic consequences of insertion mutations, to identify individual

mutant lines with distinct phenotypes that are candidates for further study and to describe broad

patterns in the effects of mutation on phenotype. We screen mutant lines across environments to

uncover context dependent mutational effects. We have detected mutational effects that change fruit

production (via increases or decreases), influence trait relationships, and are outside of the variation of

natural accessions (novel phenotypes). Undergraduate researchers across more than 15 institutions are

central to unPAK activities, both as research apprentices and as students participating in courses

through CURE (course based undergraduate research experiences). unPAK is providing students an

opportunity to obtain research experience while simultaneously answering central questions in plant

phenomics.

114 Nitrogen Utilization Efficiency of Arabidopsis Natural Variants

Shanhua Lin, Institute of Molecular Biology, Academia Sinica

Yi-Fang Tsay – Institute of Molecular Biology, Academia Sinica

Hundred million tons of nitrogen fertilizer was consumed each year. High demand of nitrogen fertilizer

not only makes the cost of farming and energy consumption increase, but also leads to eutrophication in

river and ocean and makes marine dead zone expanded. Improving nitrogen utilization efficiency (NUE)

for crops is an essential and urgent issue to increase crop production and at the same time reduce N

fertilizer demand. We would like to identify new strategy to improve NUE by genome-wise association

(GWAS) approach. This approach may help us to isolate NUE associated genes and to understand how

NUE is regulated. 95 Arabidopsis natural variations were grown with NH4NO3 at five different

concentrations, ranging from 0.2 mM (nitrogen limited) to 10 mM (nitrogen sufficient) for 21 days, and

then the biomass (dry weight) of rosette leaf were measured. Some ecotypes showed linear growth

increase when N was increased from 0.2 up to 10 mM. But, some ecotypes showed growth inhibition by

high N. The GWAS analyses of several traits, including the biomass at different nitrogen concentrations

and the ratio of biomass at different concentration of nitrogen were performed using the GWA-Portal

(https://gwas.gmi.oeaw.ac.at) platform to identify the potential genes associated with NUE at

vegetative stage. Twenty-seven SNPs with -log(p-value) >5 were observed. Further analysis of mutants

of these potential candidate genes nearby those SNPs will help us to find out novel regulatory

mechanism of NUE.

115 The National Crop Phenomics Center in Korea

Kyung-Hwan Kim, National Institute of Agricultural Sciences

Inchan Choi, National Institute of Agricultural Sciences; Hyeonso Ji, National Institute of Agricultural

Sciences; YongSeok Jung, Chungnam National University; TaekRyoun Kwon, National Institute of

Agricultural Sciences

It is well known that crop phenomics is an emerging area in the era of ‘omics’. Crop phenomics needs a

convergence of biotechnology, information technology, automated machine technology and so on, in

order to measure the morphological and physiological traits of crop plants. The National Institutes of

Agricultural Sciences (NIAS), Korea, has invested on agricultural biotechnology to create a new variation

of crop plants. So far there are many genes to be tested for their phenotypes. So NIAS sets up a high-

throughput crop phenotyping (HTCP) facility with size of 1,000 pots in this year. The HTCP facility

consists of an environmental control greenhouse, image acquisition devices, convey belt and data

management programs. At first, this center tried to analyze height, leaf area, and leaf color of rice plants

using RGB images. This center shall continue to analyze various important traits of rice plants such as

abiotic stress tolerances and yield components in the future.

116 Rapid Ground-based Phenotyping Methods for Canopy Cover and Canopy Reflectance Estimates in

a Durum Wheat Diversity Panel

Maria Newcomb, University of Arizona, School of Plant Sciences

Jeffrey White, USDA-ARS Arid Land Agricultural Research Center; Richard Ward, University of Arizona;

Michael Pumphrey, Washington State University; Roberto Tuberosa, University of Bologna; John Heun,

University of Arizona; Andrew French, USDA-ARS Arid Land Agricultural Research Center; William

Luckett, USDA-ARS Arid Land Agricultural Research Center; Pedro Andrade-Sanchez, University of

Arizona

There is increasing demand for simple, rapid methods to evaluate crop plants growing under field

conditions to separate and identify the interacting effects of genetics, environment and management on

crop growth and development. We assessed canopy cover using digital images and canopy reflectance

using spectral data collected from a tractor-mounted phenotyping system for a diversity panel of 260

accessions of durum wheat (Triticum turgidum Desf.), which was grown in 2016 at the Maricopa

Agricultural Center in Arizona. The diversity panel lines originated from different countries across the

wheat-growing areas, and was previously genotyped using a high-density single-nucleotide

polymorphism (SNP) assay. We manually photographed (Lumix DMC digital camera) each plot at two

positions within the plot, using a custom-built metal A-frame stand designed to ensure a nadir view from

a constant height and providing a field of view matched to the 0.76 m wide rows. Using a tractor-

mounted sensor array system, we measured canopy reflectance with a multi-spectral crop sensor

deployed on the front boom. Canopy cover was estimated from each digital camera image using a batch

script implemented in ImageJ (1.49V), with analysis of 600 images requiring approximately one hour.

Spectral reflectance measurements were recorded with a data logger and the data corresponding to the

red (670nm) and NIR (780nm) wavelengths were used to calculate the normalized difference vegetation

index (NDVI). Plot-level estimates of canopy cover and NDVI were assessed at different times during

crop growth. Comparisons of the two phenotypes showed good agreement but also high-lighted

potential confounding effects of plant population. Both methods show value as simple, low-cost options

for field-based phenotyping to facilitate genotype-phenotype association studies and better understand

the impacts of abiotic and biotic stresses on crops.

117 Functional Phenomics: Relating Phenes to Function using High-throughput Phenotyping and Data

Analytics

Larry York, Samuel Roberts Noble Foundation

The emergence of functional phenomics signifies the rebirth of physiology as a 21st century science

through the use of advanced sensing technologies and big data analytics. Functional phenomics

highlights the importance of phenotyping beyond only identifying QTL and gene candidates. Linking

form to function is imperative for human welfare, for while great advances have been made in genetics,

we have relatively less knowledge of what plants do. Which plant properties are important for

ecosystem services like forage production, grain yield, and soil carbon sequestration? Using vast

datasets of predictors (plant properties) and responses (yield) we can infer causal relationships using

multivariate and big data analytics. These data are generated by high-throughput phenotyping of

genetic mapping populations, so function and genetics are simultaneously and synergistically studied.

This presentation will outline a general approach for the theory and practice of functional phenomics.

First, the concept of phene will be explored as the fundamental unit of phenotype as a replacement for

more ambiguous terms such as ‘trait.’ The lack of semantics and grammar for phenomics hinders both

thought and experimental work. Next, the functional phenomics pipeline is proposed as a general

method for conceptualizing, measuring, and validating utility of plant phenes. The functional phenomics

pipeline begins with ideotype development. Second, a phenotyping platform is developed to maximize

the throughput of phene measurements. A mapping population is screened measuring target phenes

and indicators of plant performance such as yield and nutrient composition. Traditional forward genetics

allows genetic mapping, while functional phenomics links phenes to plant performance. Based on these

data, genotypes with contrasting phenotypes can be selected for smaller yet more intensive

experiments to understand phene-environment interactions in depth. Simulation modeling is also used

to understand the phenotypes and all stages of the pipeline feed back to ideotype and phenotyping

platform development.

118 Initial Assessment of Utilizing High-throughput Phenotyping in a Cotton Breeding Program

Alison Thompson, USDA-ARS

Kelly Thorp, USDA-ARS; Matthew Conley, USDA-ARS; Andy French, USDA-ARS

High-Throughput Phenotyping (HTP) is quickly becoming a highly sought after technique for rapid trait

measurements of plants in field and greenhouse settings. With changing technologies, the capabilities

of HTP increase each year, opening many opportunities for incorporation into different research

programs. One area of research that could greatly improve with the application of HTP is plant breeding,

but how best to incorporate this technology is still unclear. In 2009, a HTP tractor was developed in

Maricopa, AZ, through a joint effort between the US Arid Land Agricultural Research Center and The

University of Arizona, Maricopa Agricultural Center. Since 2012, the tractor has been used to evaluate

upland cotton for heat and water stress tolerance as part of a national breeders testing network. The

experimental design incorporated 35 cotton breeder lines as well as checks, which were planted in 2-

row, 12-meter long plots, spaced 1-meter apart. Well-watered and water-limited treatments were

applied when 50% of lines exhibited first flower, and continued until harvest. The objectives of this

research were to evaluate tractor based HTP in a breeding program and validate tractor measurements.

Plant height, canopy temperature, and spectral reflectance values were captured using proximal

sensors on the tractor once per week at solar noon. The proximal sensor data was analyzed using a

custom developed processing pipeline. Three cotton lines were identified as potentially drought tolerant

using plant canopy temperature and will undergo further evaluation. Challenges to incorporate tractor

based HTP in a breeding program include tractor logistics and engineering, field design, data processing

and management, data validation, and determining the best statistical approaches to analyze large

quantities of data.

119 Testing Character Displacement in the Belowground Root Traits of Two Closely Related Morning

Glory Species

Sara Colom, University of Michigan

Regina Baucom, University of Michigan

Character displacement, the processes whereby competing species evolve in their resource associated

traits as a response to competition, may be a crucial process underlying coexistence between closely

related plants, and further, may explain why such species are phenotypically divergent in regions where

they co-occur. Despite its potential to explain patterns of diversity and improve our understanding of

the adaptive value of diverse resource associated traits, research on character displacement in plant

systems is limited primarily to above-ground traits. As a consequence, consideration of character

displacement in belowground root traits is currently lacking. My dissertation research is aimed at

characterizing the belowground root structures in two species of morning glories, Ipomoea hederacea

and I. purpurea, and examines how variation in root traits between these two species may contribute to

their competitiveness. As a first step I conducted a preliminary common garden experiment where I

planted replicate seeds of multiple maternal lines of I.hederacea and I.purpurea individually in custom

built rhizotrons and phenotypically characterized their root structures during early growth. I measured

various root traits, examined how the species differ in their respective trait space and identified whether

maternal line variation existed in these traits. I found evidence for maternal variation in some individual

root traits, suggesting that under natural conditions selection from competition may influence root trait

evolution and subsequently lead to character displacement between the species.

120 Genetic Control of Maize Biochemical Defense Revealed by An Integrated Metabolomics,

Transcriptomics and Quantitative Genetics Approach

Shaoqun Zhou, Cornell University

Georg Jander, Boyce Thompson Institute

Appropriate selection and accurate measurement of quantitative phenotypes are two major challenges

in identifying the actual genetic basis of quantitative trait loci (QTL) related to plant disease resistance.

Classical QTL mapping with direct measurement of disease severity is often affected by highly variable

disease outcome and subjective severity scoring. In this study, we found that Fusarium graminearum-

resistant maize (Zea mays) inbred line Mo17 constitutively accumulates many metabolites that are

inducible by the same pathogen strain in the susceptible inbred line B73. We hypothesized that the

contrasting F. graminearum susceptibility levels of these two inbred lines could be attributed to

differences in their constitutive biochemical defense, and further, to the genes regulating the

accumulation of these defense-related metabolites. Hence, we conducted non-targeted metabolite

profiling by high resolution HPLC-MS and RNAseq transcript profiling of 80 B73 x Mo17 recombinant

inbred lines, thereby identifying significant metabolite QTL (mQTL) for over 700 mass features and

expression QTL (eQTL) for 1,200 genes that show constitutive differences between B73 and Mo17

seedling roots. Through this analysis, we identified an mQTL hotspot affecting over 70 mass features,

including ones known to be defense-related. To pinpoint the causative gene(s) underlying this mQTL

hotspot, we performed a correlative network analysis across the metabolomic and transcriptomic data,

and thereby identifying a gene that likely encodes a vesicular transport protein as the most probable

candidate. Transposon insertions in this gene result in significant depletion of DIMBOA-glucoside, a

major defense-related metabolite in maize seedling roots, which could cause compromised defense

against F. graminearum. In summary, we dissected the complex and highly variable disease severity

phenotype into an array of biochemical components that can be quantified more accurately.

Furthermore, we identified a gene affecting this complex disease resistance phenotype by combining

high-throughput transcriptomics, metabolomics, and quantitative genetics approaches.

121 Using field-based High-throughput Phenotyping for Genetic Discovery in Rice

Brook Moyers, Colorado State University

Paul Tanger, Colorado State University; Stephen Klassen, International Rice Research Institute; Julius

Mojica, Duke University; John Lovell, University of Texas, Austin; Brook Moyers, Colorado State

University; Marietta Baraoidan, International Rice Research Institute; Maria Elizabeth Naredo,

International Rice Research Institute; Kenneth McNally, International Rice Research Institute; Daniel

Bush, Colorado State University; Hei Leung, International Rice Research Institute; Jan Leach, Colorado

State University; John McKay, Colorado State University

To ensure food security in the face of population growth, decreasing water and land for agriculture, and

increasing climate variability, crop yields must increase faster than the current rates. Increased yields

will require implementing novel approaches in genetic discovery and breeding. We demonstrate the

potential of field-based high throughput phenotyping (HTP) on a large recombinant population of rice

(Oryza sativa) to identify genetic variation underlying important traits. We find that detecting

quantitative trait loci (QTL) with HTP phenotyping is as accurate and effective as traditional labor-

intensive measures of flowering time, height, biomass, grain yield, and harvest index. Genetic mapping

in this population, derived from a cross of an modern cultivar (IR64) with a landrace (Aswina), reveals

four alleles with negative effect on grain yield that are fixed in IR64, demonstrating the potential for HTP

of large populations as a strategy for the second green revolution.

122 Comparison of Genome-wide Association Study for First Flowring Time with Different Size of

Soybean Panel

Jung-Kyung Moon, National Acedemy Agriculture Science

Sookwon Park, National Institite of Crop Science; Sungtaeg Kang, Dankook university; Soon-Chun Jeong,

Korea Research of Bioscience & Biotechnology; Namshin Kim, Korea Research Institute of Bioscience &

Biotechnology; Yong Seok Jung, Chungnam National University

Recent achievements in soybean [Glycine max (Merr.) L.] genomics by the development of high

throughput genotyping technology have produced large amount of genomic polymorphism data and

therefore, genome-wide association studies (GWAS) become feasible tool to identify associations

between genomic variation and quantitative traits. Here, we provide the information about current

progress in soybean GWAS study using different size of core collection and original populations in

Korean soybean. The Korean soybean core collection was constructed using 391 and 814 soybean

varieties and recently developed 180K Axiom® Soybean Genotyping Array was employed to obtain the

genome-wide variation information. The trait of first flowering time was used to evaluate the GWAS

procedure using GAPIT, and its application to identify the novel genes. The result showed that precise

location of previously reported genes (e.g., GIGANTEA homologos, GIa, for flowering time) were

detected in small size of core collection. However, two other flowering genes, previously reported for

E2, E3, were well detected in bigger size of core collection set and original populations, Our results

suggested that GWAS in soybean with the size of 400 accessions could provide opportunity to discover

the novel genes/QTLs for various traits for sustainable crop breeding.

123 High Throughput Phenotyping and Genetic Dissection Reveal Plant Vigour and Water Saving

Traits are Co-mapped with Root “QTL-hotspot region” in Chickpea (Cicer arietinum L.)

Sivasakthi Kaliamoorthy, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)

Mahendar Thudi, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Murugesan

Tharanya, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Sandeep Kale,

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Jana Kholová, International

Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Mahamat Halime, International Crops

Research Institute for the Semi-Arid Tropics (ICRISAT); Deepa Jaganathan, International Crops Research

Institute for the Semi-Arid Tropics (ICRISAT); Rekha Baddam, International Crops Research Institute for

the Semi-Arid Tropics (ICRISAT); Thiyagarajan Thirunalasundari, Bharathidasan University; Pooran Gaur,

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Rajeev Varshney,

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT); Vincent Vadez, International

Crops Research Institute for the Semi-Arid Tropics (ICRISAT)

Chickpea is the 2nd most important legume and terminal drought stress leads to substantial yield losses.

Water conservation at vegetative growth (due to canopy conductivity, size, and development) allows

plants to increase soil water extraction during grain filling and is hypothesised to help chickpea

adaptation to water limitation. With an aim of identifying genomic regions for these canopy traits, 232

RILs, derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under

well water conditions using a high throughput phenotyping platform (LeasyScan). These data competed

other phenotype data for root traits and field-based assessment of agronomic traits collected in earlier

mapping studies. Different density genetic maps (241-SSR-Low density, 1007-SSR+SNPs-High density and

1557-SNPs-Ultra high density) were used for QTLs identification. Several major QTLs (M-QTLs) for plant

vigour related traits (3D-leaf area, shoot biomass, plant height and growth related traits) were identified

on CaLG04. These co-mapped with a previously identified major drought tolerance QTL-hotspot region

on CaLG04 (~300Kb). Furthermore, canopy conductance traits QTLs were mapped on CaLG03.

Comparative analysis of plant vigour and canopy conductance traits with different density genetic maps

resulted in decreased QTL size and increased LOD score and % of phenotypic variation considerably.

Majority of the QTL interactions identified by GMM at single locus were consistent with those detected

by CIM analysis of QTL cartographer. Most of plant vigour traits had alleles from ICC4958 whereas in

case of canopy conductance traits, favourable alleles were contributed by ICC1882. Plant vigour traits on

CaLG04 and canopy conductance related traits on CaLG03 provide opportunity to manipulate these loci

to tailor recombinants having lower transpiration rate and high plant vigour. This ideotype might be

useful in enhancing the water stress adaptation in chickpea. This work also demonstrated the value of

combining phenotyping at different levels of plant organization.

124 Determination of Cold Tolerance QTLs via High Throughput Photosynthetic Phenotyping

Donghee Hoh, Michigan State University

Isaac Osei-Bonsu, Michigan State University; Jeffrey Cruz, Michigan State University; Linda Savage,

Michigan State University; David Hall, Michigan State University; David Kramer, Michigan State

University

Global warming affects the crop yields, so there is increasing the need for improvement of the

efficiency, robustness, and sustainability of food crops. Cowpea (Vigna unguiculata (L.) Walp.) is a warm

season grain legume crop commonly cultivated in developing countries (Wellington, 2009). It is an

important source of protein and symbiotic N fixation. In addition, many varieties have resistance to

drought, low soil fertility (Ehlers ad Hall, 1997). However, heat stress during the flowering stage, insects

attacks in hot summer cause severe negative impacts on cowpea production. Early planting of cowpea

(when seasonal temperatures tend to be low) can be considered an opportunity to avoid the insect’s

attacks and heat stress. However, a major concern is how the chilling stress impacts performance/yield.

The goal of this research is to figure out chilling stress related genes and mechanisms. Dynamic

Environmental Photosynthesis Imager (DEPI) and photosynQ are powerful phenotype tools for

photosynthesis developed in Kramer Lab were used to collect photosynthetic data for QTL mapping.

Two approaches for this research are the mapping of quantitative trait loci (QTLs) to identify genes

related to chilling stress tolerance and identify biophysical/biochemical mechanisms of cold-tolerance in

photosynthesis. Low day/night temperature decreases ΦII and increases photoinhibition, and this

effect was observed significant genetic variance, so this condition was used for mapping QTLs related to

cold tolerance.

125 Studies on microRNAs in Selected Species of Zingiberales, Liliales, and Arecales: Monocot Orders

that Develop Fleshy Fruits.

Divya Nair, Texas Tech University

Chris Rock, Texas Tech University

Small RNAs such as microRNAs (miRNAs) are fundamental to the integrity and cohesion of the genome,

and a powerful influence on genome evolution. Economically important gingers, banana, and palms

develop fleshy fruits. Our study hypothesizes that fleshy fruit development is plesiomorphic (an ancient

basal trait) in monocots and mediated in part by MICRORNA828 (MIR828) which is a negative regulator

of anthocyanin and polyphenolic nutraceuticals biosynthesis. The implication is that species in those

monocot taxa with fleshy fruits will be proved in due course to have functional MIR828 genes, whereas

it is predicted that the most closely-related species that have recently evolved wind-dispersed seeds

such as Xerophyllum, vis-à-vis Trillium of the basal order Liliales, will not have a functional MIR828 locus.

The fact that gymnosperms and the single extant species (Amborella) of the sister lineage to all

flowering plants have a functional MIR828 gene is consistent with the hypothesis. We are integrating

genomic data across scales and species to explore the role of MIR828 and its target MYB transcription

factors' function in fruit development and nutraceutical metabolism. Results may impact our

understanding of homoplasy/convergent evolution: seemingly independent apomorphic appearances

based on cladistics of developmental similarity to aid endozoic seed dispersal by some monocots.

Emerging Technologies

200 Identifying Molecular Networks Underlying Root-root Interactions in High Plant Density Adapted

Maize

Nathanael Ellis, Donald Danforth Plant Science Center

Kari Miller, Donald Danforth Plant Science Center; Keith Duncan, Donald Danforth Plant Science Center;

Jody Edwards, USDA-ARS; Yuan-Chuan Tai, Donald Danforth Plant Science Center; Christopher Topp,

Donald Danforth Plant Science Center

Since the 1930’s, annual gains in U.S. maize production have largely been driven by the adaptation of

maize plants to increasing plant density. Two historically important maize populations, Iowa Stiff Stalk

Synthetic and Corn Borer, have been recurrently selected for increased hybrid grain yield, resulting in

density adapted plants. Shoot architectures, specifically increased leaf angles, are known outcomes of

selection during maize density adaptation, but little empirical data exists about the contribution from

roots. Preliminary work suggests that not only have root architectures changed in these populations, but

their sensing and growth responses to neighboring root systems have changed as well, pointing to a new

paradigm for understanding the key process of crop density adaptation. Selected cycles from

progressive density adapted germplasm will be assayed in several ways to quantify root system

architecture of plants grown individually or paired, to identify changes in root growth as a function of

density adaption. In this project, the effects of density adaptation on 3D root architecture and growth

will be assayed by Optical Projection Tomography, X-ray Computed Tomography, and Positron Emission

Tomography to study dynamic carbon allocation. These efforts will identify the most relevant time

points from which to sample leaf and root tissue for transcriptional profiling, as well as profiling of root

exudates via gas chromatography-mass spectrometry. This project will contribute to modeling the

relationship of carbon allocation and root growth in respect to root-root communication and to identify

the underlying genetic mechanisms controlling these processes. This research aims to identify the

molecular mechanisms of adaptation to high plant density in maize roots. Thus the research will

contribute to our basic understanding of how roots, the "hidden-half" of plants, communicate with one

another and exploit this knowledge for crop improvement. This material is based upon work supported

by the National Science Foundation under Award IOS number 1612033.

201 CHESS-U: A New Resource for Synchrotron-based X-ray Microscopy for High-precision Plant

Phenotyping

Arthur Woll, Cornell High Energy Synchrotron Source

Olena Vatamaniuk, Cornell University; Ju-Chen Chia, Cornell University; Haujin Sheng, Cornell University;

Jaipei Yan, Cornell University; Joel Brock, Cornell University

In the last decade, synchrotron-based x-ray microscopy has become increasingly recognized as a critical

tool for investigating metal distributions and speciation in plants and other biological systems. In

particular, such information has been utilized to determine or clarify genetic influences on metal

transport, signaling and storage. 2D and 3D implementations of x-ray fluorescence (XRF) mapping at the

micron scale can quickly and efficiently reveal quantitative variations in the total concentration and

distributions in a wide variety of samples with minimal sample preparation. Furthermore, x-ray

absorption fine structure (XAFS) permits obtaining metal speciation in vivo. Finally, high-energy phase

contrast imaging can image plant structure at the micron scale in sub-second time-scales. Yet, the utility

of these methods to the plant research community as a whole is limited both by available beamtime at

existing facilities, and, arguably, by a lack of specialized instrumentation to support particular needs of

plant research.

The Cornell High Energy Synchrotron Source (CHESS) has recently identified plant genotype / phenotype

interactions as one of seven science drivers motivating a two-year upgrade project, “CHESS-U,” to be

completed in late 2018. Recent applications of x-ray microscopy at CHESS will be described, both to

illustrate its utility and to emphasize future capabilities and opportunities enabled by CHESS-U.

202 Applying Hyperspectral Imaging to Studying Temperature Stress Responses in Maize

Tara Enders, University of Minnesota

Nathan Springer, University of Minnesota

Average yields of maize could drop substantially within the next century due to global climate change.

Understanding how maize varieties respond to temperature extremes will be instrumental in developing

varieties that can withstand future temperature stresses while still producing high yield. Hyperspectral

imaging of maize in response to abiotic stress is an unexplored concept, and is likely to provide useful

data to researchers interested in both abiotic stress and hyperspectral imaging. Hyperspectral imaging

tools are being developed to study phenotypes of temperature-stressed maize seedlings. Current efforts

include minimizing environmental and plant morphological effects on obtained data, as well as

developing methods for data analysis in MATLAB and R. We will document the variation of

hyperspectral traits in maize seedlings in response to both high and low temperature stresses in multiple

maize genotypes.Documenting spectra across genotypes and growth conditions will uncover the

dynamics of maize spectra in response to changing temperatures and allow for the discovery of genomic

loci that could provide improved tolerance.

203 Detection and Identification of Bitter Compounds in Alfalfa Protein Isolates using a Combination

of Sensory and Analytical Approaches

Pavel Aronov, Impossible Foods

David Ruiz, Impossible Foods; Harshada Natekar, Impossible Foods; Michelle Mai, Impossible Foods;

Ranjani Vardan, Impossible Foods; Pat Brown, Impossible Foods

Alfalfa is highly valued as a feed crop for cattle, but its consumption by humans is limited to alfalfa

sprouts because phytochemicals present in mature plants make it unpalatable (bitter and astringent) or

unsafe for human consumption (canavanine and other antimetabolites). Alfalfa protein isolate could be

suitable for human consumption if undesirable co-purifying metabolites were efficiently removed. To

achieve such a goal we need to identify and characterize the compounds responsible for off-flavors in

alfalfa protein extract. We prepared protein-enriched extracts from alfalfa leaves and fractionated them

by solid phase extraction (Oasis HLB, Waters) or semi preparative reversed phase Ultimate 3000 HPLC

with Hypersyl GOLD C18 column (Thermo Scientific). The fractions were evaporated, tested for trace

solvents, heavy metals and known toxic metabolites to ensure safety for human consumption and

subjected to a human sensory panel to identify bitter fractions. Bitter fractions were further analyzed by

Q Exactive Plus LC/MS to identify their small-molecule constituents. We also explored an alternative

metabolomics approach in which we subjected diverse alfalfa protein isolates to untargeted LC/MS

analysis and searched for correlations between variation among the isolates in the abundance of

individual small molecules and variation in the intensity of bitter taste. As a result, several candidate

bitter small molecules were identified in alfalfa and confirmed by sensory panel assays.

204 A Motorized High-Throughput & High-Accuracy Plant Stand Analyzer for Large Scale Maize

Breeding Programs

Lie Tang, FieldRobo LLC

This poster presentation reports a newly developed motorized high-throughput & high-accuracy plant

stand analyzer (PSA) for large scale maize breeding programs. At the core of the PSA technology is its

high-speed laser scanning and data processing unit which generates multiple plant stand phenotypic

data metrics including stand count, stand spacing, stalk size, tillers, and skips and doubles all in real-

time. Currently PSA can be towed by an ATV to measure 4 rows simultaneously or be hitched to a

tractor to form an 8-row system. PSA has a capacity of moving at 4 mph and sense maize plants from V4

to V7 when using ATV or tractor as its carrier. The productivity of a 4-row PSA will be equivalent to a

human crew of 23 tireless workers. If high ground clearance vehicles such as sprayers are used, PSA can

measure maize plants of up to R1 growth stage. PSA is equipped with a compressed air system to keep

the sensors clean from dust. Extensive field experiments were conducted to evaluate the performance

of PSA. On average PSA has achieved 98% stand counting accuracy with 1% repeatability error. A post-

pressing software program will fuse the RTK-GPS and wheel odometry data to accurately geo-tag each

plant stand. Subsequently the generated plant stand map can be imported into GIS software and

overlaid with planting polygons to aggregate stand data into rows and plots for statistical analysis.

Overall, PSA has for the first time made field-based plant stand analysis not only highly practical and

economical but also highly accurate for commercial maize breeding programs.

205 Indiana Corn and Soybean Innovation Center: Purdue’s Automated Field Phenomics Facility

Jason Adams, Purdue University

April Carroll, Purdue University

Purdue’s Indiana Corn and Soybean Innovation Center (ICSC) is a 25,500 square-foot research facility at

the Agronomy Center for Research and Education, a 1,400 acre research farm in West Lafayette,

Indiana. ICSC was designed to facilitate research on improved plant traits and varieties and to develop

profitable farming practices. These goals are achieved by facilitating collaborations between

agronomists, basic plant biologists, engineers, aviation scientists, data scientists, and disciplines beyond.

In addition to high-tech workspaces for diverse collaborators, the facility features an automated

threshing and shelling line for plant and seed processing as well as ample seed and plant processing labs.

Researchers also use phenotyping equipment such as root and leaf scanners, root washing stations, seed

counters and color sorters, as well as 3-D printers and scanners, ovens and grinders. The Center also

includes a 5,000 ft2 high bay phenomics tool development lab. This large flexible workspace is designed

for research and development of remote sensing platforms such as UAVs and Purdue’s PhenoRover, a

ground-based mobile platform, and for experimentation with novel measurement tools, cameras and

sensors. With plans to collect up to ten terabytes of data per week, a high speed fiber-optic cable

transfers data back to Purdue’s campus data storage and supercomputing clusters. ICSC is a core

component of the Institute for Plant Sciences, part of Purdue Moves, with investments over $20 million

for world-changing research. The Center is also supported with a combined $4 million investment from

the Indiana Soybean Alliance and Indiana Corn Marketing Council, and donors including AgReliant

Genetics, DuPont Pioneer, Ag Alumni Seed and ALMACO. The unique data collection capabilities in

addition to the multi-disciplinary collaborations at the Indiana Corn and Soybean Innovation Center

greatly enhance the ability of Purdue University researchers to address the grand challenge of food

security in the 21stcentury.

206 Introducing Moving Circular Spatial Adjustment for Large Scale High-Throughput Phenotypic Data

Extracted from Unmanned Aerial System (UAS) Imagery

Atena Haghighattalab, Kansas State University

Suchismita Mondal, CIMMYT; Ravi Prakash Singh, CIMMYT; Jesse Poland, Kansas State University

Unmanned aerial systems (UASs) have the potential to provide breeders with phenological data such as

plant height and vegetation indices (VIs) to help with more accurate selections on larger populations.

The phenotypes are determined by the genetics, but also influenced by the spatial variation across the

field. To improve the accuracy of selections, breeders need to account for non-heritable environmental

variation in the form of spatial field effects. There has been extensive investigation into field design and

statistical models to account for field effects, however this previous work was generally limited in the

overall treatment of the breeding field experiments as geospatial entities.

The objective of this study is to evaluate the use of coordinate based geostatistical spatial analysis for

controlling the heterogeneity in yield data as well as high throughput phenotypic data derived from UAS

imagery in a large wheat breeding nursery. We analyzed the temporal phenotypic data extracted from

UAS imagery for two environments during the growing season and detected the spatially non-random

patterns in the data. To correct the trends observed in the field, we used the coordinates of wheat plots

extracted from geo-referenced aerial imagery in a latitude-longitude grid to obtain an adjusted

phenotypic value of the plot in the center of the moving circular window. After spatial adjustment, we

found an increase in broad-sense heritability for VIs obtained from UAS platforms and grain yield in both

environments. The analysis implemented in this study enables breeders with collection and post-

processing of HTP measurements on larger populations and can provide increased accuracy for selection

in plant breeding. Overall the use of UAS imaging to improve selection in plant breeding has great

promise when combined with analysis and statistical methods that fully leverage the spatial nature of

the breeding trial data.

207 High-throughput Infrared Imaging: the New Crop Breeding Frontier

Smitha Kurup, Maharashtra Hybrid Seeds Company (Pvt) Ltd

Abhijeet Shillak, Maharashtra Hybrid Seeds Company Limited; Viajy Dalvi, Maharashtra Hybrid Seeds

Company Limited; Govindaraj K, Maharashtra Hybrid Seeds Company(Pvt) Limited; Bharat Char,

Maharashtra Hybrid Seeds Company(Pvt) Limited; Usha Zehr, Maharashtra Hybrid Seeds Company (Pvt)

Limited

The efficiency and adaptability of crops to various biotic and abiotic stresses must be improved

significantly to achieve global food demand by 2050. Towards this goal, phenomics has recently

emerged as a promising tool for accurate phenotyping of large set of genotypes. Phenomics includes

non-invasive techniques using sensors along with advanced computational methodologies for predictive

breeding. In the present study, thermal cameras were used for screening drought and salinity effects in

wheat and rice at field and greenhouse conditions. The experimental set up for detecting salinity effects

included rice (Oryza sativa) cultivars classified as two different categories ie., tolerant and susceptible

groups, subjected to varying levels of salt concentrations. Infrared image processing cope with

appropriate statistical analysis explicitly demonstrated that for salinity studies there is significant

difference between tolerant and susceptible cultivars at both 15 mM and 30 mM. This non-destructive

screening method will be helpful to identify salt tolerant as well as susceptible cultivars before the onset

of symptoms. Similarly, drought studies were performed on wheat (Triticum aestivum) accessions along

with checks at critical stages of drought. Field-based thermal profiling of wheat indicated a negative

correlation of canopy temperature with yield under stress condition. Infrared imaging enabled to screen

promising superior performing lines from large set of wheat lines. The relevant data of this study have

been integrated into an in-house developed phenomics database. With advancements in phenome data

capturing and analysing capabilities, phenomics-assisted breeding can be a reality in the near future.

Complementary use of a variety of sensors for screening a particular trait will increase the efficiency of

crop genetic improvement to meet future needs.

208 Stable Isotopic Labeling of Intact Plants for Molecular Turnover Measurement by Mass

Spectrometry: New Labeling Apparatus and Data Processing Approaches

Dana Freund, University of Minnesota

Calvin Peters, University of MInnesota; Aaron Rendahl, University of Minnesota; Erin Evans, University of

Minnesota; Jerry Cohen, University of Minnesota; Adrian Hegeman, University of Minnesota

There has been a recent rise in the use of high-throughput bioanalytical, ‘omics-scale’ techniques such

as metabolomics (metabolites) and proteomics (proteins). Stable (non-radioactive) isotopes as labels

can be applied for proteome or metabolome-scale labeling for the improvement of metabolite

annotation, metabolic pathway elucidation, quantification, and the measurement of molecular

dynamics. Plants can be grown under conditions where then metabolically incorporate either 15N from

mineral nutrients or 13C from labeled carbon dioxide gas. By switching between labeled and unlabeled

metabolic inputs and monitoring using mass spectrometry-based proteomics and metabolomics

approaches over time, it is possible to derive biomolecular turnover information for proteins and

metabolites. Here we discuss high-throughput computational tools for deriving turnover information

from high-resolution mass spectrometry data as well as some recent innovations in plant transient 13C-

labeling chambers. Automated modular 13C-carbon dioxide labeling chambers were designed to allow

plant materials to be harvested at various times following the initiation of labeling without perturbing

plants harvested at other time points. Proteins or metabolites are extracted from plant material and

analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) using a high-resolution

accurate mass instrument. Here we present the results of our first plant growth experiments using the

modular chamber design. MS analysis of plant materials is used to demonstrate the extent of isotope

enrichment as a function of time during labeling experiments. In addition to labeling apparatus, we also

provide an update on the current status of our software tools (all open source and written in R) for

calculating turnover of proteins and metabolites in an automated high-throughput manner. The tools in

some cases use maximum likelihood estimation to derive fractional isotope enrichment and then fit

those enrichment values over time to obtain first order kinetic constants.

209 Detecting Northern Leaf Blight from Field Images using Machine Learning

Tyr Wiesner-Hanks, Cornell University

Chad DeChant, Columbia University; Siyuan Chen, Columbia University; Ethan Stewart, Cornell

University; Jason Yosinski, Geometric Intelligence; Michael Gore, Cornell University; Rebecca Nelson,

Cornell University; Hod Lipson, Columbia University

Northern leaf blight (NLB) is a fungal foliar disease of corn that has grown more severe in recent years,

causing roughly $2 billion in losses in the US in 2015. Geneticists, breeders, and growers all need to

accurately detect and quantify NLB infection in its early stages, but scouting for symptoms is very time-

consuming. Automatic detection of NLB lesions in images taken from the air or ground would be

preferable, but these images are often very noisy. We used human annotations to train a convolutional

neural network (CNN), a class of machine learning model, to detect NLB lesions in field images taken

from the ground with 97% accuracy. Testing of canopy images is ongoing, and early results are

promising.

210 High-throughput Phenotyping Platform for Compact Plants at WSU

Kiwamu Tanaka, Washington State University

Karen Sanguinet, Washington State University; Hans-Henning Kunz, Washington State University

Considering recent adverse environmental changes, increasing food demand, and predicted population

growth, we are in urgent need of designing more abiotic and biotic stress resistant crop species. A

complete understanding of the genomic mechanisms responsible for plant stress responses is required

to ensure sustainable food resources for society. High-throughput phenotyping will accelerate plant

research by overcoming the current technical limitations in phenomics. Washington State University

(WSU) envisions continued investment in phenomics. As we accumulate infrastructure for phenomics

research, we will start operating a high-throughput phenotyping service for compact plants other model

organisms as a core facility to enhance the capabilities of multiple researchers. The automated

phenomics platform, named Octopus (LemnaTec), will be built into a light/temperature/humidity/CO2-

controlled growth chamber. The platform carries a suite of cameras for visible RGB, photon counting,

pulsed chlorophyll fluorescence, and near-infrared imaging systems, as well as a Laser 3D Scanner. The

platform can accomplish phenomics for ~6000 Arabidopsis or ~500 crop seedlings in a single

autonomous experiment. High-content screening enabled by the broad detection power will result in an

in-depth understanding of phenotype to genotype relationships. Understanding genome-phenome

relationships accurately and in detail will help scientists unveil molecular mechanisms involved in plant-

environment interactions. For example, genomic-based prediction of plant performance under multiple

stress conditions and a complex genetic architecture of heritable phenotypic traits can be addressed

with this system. The goal is to establish this phenomics platform as a paid-use resource for the WSU

community and beyond.

211 A Novel Method for the Continuous and Direct Measurement of Water-stress in Tissues and Whole

Plants

Timothy Aston, University of Wyoming

Brent Ewers, University of Wyoming; Earl Wood, University of Wyoming; Carmela Guadagno, University

of Wyoming

Technological developments in our ability to phenotype plants has not kept pace with our ability to

sequence and edit genomes, resulting in whole-plant screening methods becoming the bottleneck to

applying plant biotechnology. The plant vascular system contains water with charged solutes, which

forms a network highly conductive to electricity. Previous work has tested the impact of electrical

signals through plants by assuming that the electrical resistance across plant tissue remains constant. In

this study we test a new approach that measures the electrical resistance across different parts of a

plant as a means of continually measuring the integrity of the plant transport system through tissues

and whole plants.

We used this method to monitor the impact of mild to lethal drought on plant physiology. In the well-

watered plants we observed a decrease in electrical resistance of the leaf blade, petiole and stem

through the morning the onset of which was well-correlated with light, likely as a result of increasing

tissue sugar concentrations as a consequence of photosynthesis and water loss. The electrical resistance

of the tissues of mildly droughted plants increased through the day, potentially as a result of hydraulic

failure. Previously, we identified chlorophyll fluorescence and membrane leakage as physiological

changes that co-occur in Brassica rapa when tissues and whole plants die from drought. Our

measurements of electrical resistance agreed well with the timing of these physiological changes,

suggesting that measuring plant electrical resistance may be an accurate method to measure when

tissues and whole plants are dead from drought. Because we used open source, inexpensive data

acquisition tools and the method is non-destructive and continuous it could be easily scaled up to

continually screen thousands of genotypes in high-throughput plant phenotyping studies.

212 Using Phenotypic Databases and Biotechnology to Unleash the Drug Discovery Potential of

Ethnobotany

John de la Parra, Northeastern University

A large database, covering thousands of years of human-plant interactions and containing culturally and

geographically diverse ethnobotanical information on the selection and use of medicinal plants for

neglected protozoan diseases has been created and mined for provable drug discovery leads. Candidate

species were prioritized and subsequent phytochemical characterization and bioassays were completed.

This study’s large-scale accounting for indigenous plant knowledge indicates that medicinal plants are

regularly chosen because of specific environmental conditions. Additionally, plants subjected to attack

by insect herbivores are often observed to have higher levels of more diverse specialized metabolites,

many with unique bioactivities. By querying the database, an initial test candidate species was chosen.

Pentalinon luteum, a traditional Seminole medicinal plant chosen with careful attention to

environmental conditions, regularly undergoes heavy seasonal herbivory by the larvae of the Oleander

moth, Syntomeida epilais. These larval predators consume and store unique cardiac glycosides that are

produced by their plant host. While these compounds are highly toxic to their predators, as the

ethnobotanical database in this study shows, plants with high cardiac glycoside content have had a long

history of medicinal value to humans. In addition, a very closely related traditional medicinal species

(Pentalinon andreuxii) mined from the database, was shown to be effective against the neglected

protozoan disease Leishmaniasis. To control for this variability of indigenous selection and herbivory-

induced stress responses, in vitro tissue cultures have been created to investigate the effects of the

addition of stress-signaling hormones as correlated to indigenous plant selection. By scanning a large

dataset of available ethnobotanical information, a robust study of the relatively unexplored aspects of

how humans choose medicinal plants is presented.

213 Planteye F500: Combine 3D and Multispectral Information in One Sensor

Gregoire Hummel, Phenospex

PlantEye is a high-resolution 3D laser scanner that computes a robust and validated set of morphological

plant parameters fully automatically. A core feature of PlantEye is that it can be operated in full sunlight

without any restrictions - crucial for plant phenotyping under field conditions or if you follow a “sensor-

to-plant-concept”. Phenospex has now developed a new sensor generation, that combines the actual

features of PlantEye on the fly with a 7-channel multispectral camera in the range between 400 –

900nm. This unique hardware-based sensor fusion concept allows us to deliver spectral information for

each data point of the plant in X, Y, Z-direction and we compute parameters like NDVI, color index and

many other vegetation indices. This new sensor generation opens a wide range of new possibilities in

plant phenotyping and increases it´s efficiency.

214 Measuring Plant Canopy Traits from UAV Imagery

Ethan Stewart, Cornell University

Nicholas Kaczmar, Cornell University; James Clohessy, Cornell University; Michael Gore, Cornell

University

The advent of affordable unmanned aerial vehicle (UAV) technology and recent relaxing of regulation make UAVs attractive tools for gathering plant data in the field. Common measures such as plant height are important for research and breeding but are time and labor intensive to collect. Vegetation indices such as the normalized difference vegetation index (NDVI) are typically gathered by plane or satellite platforms and do not offer the resolution needed to asses individual trial plots. UAVs offer the potential to gather large field data sets at high spatial and temporal resolution. Integration of GPS allows data to be accurately geolocated. UAVs fitted with RGB and multispectral cameras were flown at low elevation over corn fields consisting of small research plots. Images were stitched together and geo-referenced using ground control points of known positions to give geo-referenced orthomosaic images of the field. Plot boundaries were established from the orthoimages. 3D point clouds were produced from the RGB stiched images and used to calculate mean plant height for each plot. Plot level NDVI was calculated from the multispectral images.

Gathering field phenotypic data at high spatial and temporal resolution is beneficial to both plant research and breeding. Higher resolution datasets will allow the underlying genetic components to be investigated at finer detail than is currently feasible. The expansion of the platform to include measurements of other agronomically important traits is currently ongoing.

215 A high-throughput Plant Phenotyping System

Madan Bhattacharyya, Iowa State University

Dipak Sahoo, Iowa State University; Chimney Hegde, Iowa State University

High-throughput objective phenotyping with the aid of digital photography facilitates identification of

natural allelic variants as well as quantitative phenotyping of mutants for functional analyses of plant

genes. Our long-term goal is to identify novel genetic mechanisms for developing climate resilient crop

plants to secure food in the 21st century. The objective of this study is to develop a system for

phenotyping over 1,000 Arabidopsis ecotypes that have been sequenced. To accomplish this objective,

Arabidopsis Percival growth chambers (AR22LC9) were equipped with digital cameras (CropScore,

Tübingen, Germany) that are connected to a central server through Ethernets. We have developed and

validated a method to batch process images of hundreds of Arabidopsis seedlings to numerical data in a

spreadsheet-ready CSV file. We have developed a method for sowing Arabidopsis. We have phenotyped

157 Arabidopsis ecotypes for their responses to heat. By conducting GWAS of the phenotypic data, we

have identified nine genes. Among these genes, one encodes a putative RING/U-box-type E3 ubiquitin-

protein ligase, presumably involved in specific degradation of certain proteins through 26S proteasome

pathway under heat stress. This system will also be applicable in phenotyping seedlings of other crop

species including wheat, rice, maize, and alfalfa.

Environmental Stress Response in Plants

300 Tuber Size Distribution on One Stem of a Potato Plant (Morphological Approach to Study the

Ability of Individual Potato Tuber to Compete for Photosynthates)

Khaoula Mokrani, Higher Institut of Ariculture Chott Mariam

Wassim Saadaoui, Higher Agronomic Institute of Chott Mariam; Neji Tarchoun, Higher Agronomic

Institute of Chott Mariam

The main objective of this experiment was to study variation in tubers size on one potato stem and to

compare capacities of daughter tubers to import photoassimilates. The experiment was laid done in

factorial desing (3 x 3) during the main season of 2015 in Tunisia. Morphological parameters (Length of

stolons, median diameter of stolons, caliber of daughter tubers) were measured in eleven potato

cultivars (Solanum tuberosum L.), considering the five first stolons for each stem of each plant.

Parameters was measured four times during the development cycle of potato plant (49, 57, 71 and 82

day after plantation) and showed a high level of variation. Results showed Paramera and Dounia had the

highest length of stolons with 6.39 cm and 6.05 cm respectively. For caliber of daughter tubers, Evora

produced the biggest tubers with 31.78 mm, followed by Liseta (29.63 mm). The effect of measurment

dates on various characters, was very pronouced, specially for caliber of daughter tubers. High caliber of

daughter tubers was regestred during the fourth date of measure, with 37.66 mm, followed by the third

date (30.75 mm), then the second date (22.35 mm) and finally, the first date with value of 15.01 mm per

tuber. Results showed also that position of stolon on stem has a huge impact on caliber of daughter

tubers, first stolon produced the important tuber size with 30.09 mm, followed by the second stolon

(28.04 mm), then the third (24.24 mm), then the fourth (20.94 mm), while small tubers were produced

by the fifth stolon (15.49 mm).

301 Analysis of Water Limitation Effects on the Phenome and Ionome of Arabidopsis at the Plant

Imaging Consortium

Argelia Lorence, Arkansas State University

Lucia Acosta, Arkansas State University; Suxing Liu, Arkansas State University; Erin Langley, Arkansas

State University; Zachary Campbell, Arkansas State University; Norma Castro-Guerrero, University of

Missouri; David Mendoza-Cozatl, University of Missouri

Food security is currently one of the major challenges that we are facing as a species. Understanding

plant responses and adaptations to limited water availability is key to maintain or improve crop yield,

and this is even more critical considering the different projections of climate change. This is an NSF-

funded collaborative effort between the Mendoza and Lorence groups as part of the activities of the

Plant Imaging Consortium (http://plantimaging.cast.uark.edu/). In this work, we combined two high-

throughput -omic platforms (phenomics and ionomics) to begin dissecting time-dependent effects of

water limitation in Arabidopsis leaves and, ultimately, seed yield. As proof of concept, we acquired high-

resolution images with visible, fluorescence and near-infrared cameras and used commercial and open

source algorithms to extract the information contained in those images. At a defined point, samples

were also taken for elemental profiling. Our results show that growth, biomass and photosynthetic

efficiency were most affected under severe water limitation regimes, and these differences were

exacerbated at later developmental stages. The elemental composition and seed yield, however,

changed across the different water regimes tested and these changes included under- and over-

accumulation of elements compared to well-watered plants. Our results demonstrate that this

combination of phenotyping techniques can be successfully used to identify specific bottlenecks during

plant development that could compromise biomass, yield and the nutritional quality of plants.

302 Molecular Characterization of Ca2+/cation Antiporters (CaCA) Revealed Their Role in Stress

Response in Bread Wheat (Triticum aestivum L.)

Santosh Upadhyay, Panjab University

Mehak Taneja, Panjab University

Modulation of Ca2+ ion concentration is an important event during various stress responses. The

Ca2+/cation antiporters (CaCA) play the vital function in Ca2+ ion homeostasis, however, their

characterization is not performed in bread wheat (Triticum aestivum), which is an important crop plant.

Here, we identified thirty-four TaCaCA superfamily proteins in T. aestivum genome from four different

families (CAX, CCX, NCL, and MHX) based on their structural homology with known proteins from model

plants. Each A, B, and D-subgenome of allohexaploid T. aestivum genome contributed in the total

composition of TaCaCA superfamily in the form of homeologous genes, which consisted of comparable

gene and protein structure. About ten transmembranes and two Na_Ca_ex domains and α-repeat

regions were detected in the majority of TaCaCA proteins except for TaNCL, which consisted of single

α-repeat. The majority of genes showed differential expression during tissue developmental

stages, which indicated their role in development. Further, the modulated expression of a few genes

especially from CCX and NCL family during the fungal infestation, and heat, drought and salt stress

suggested their role during the stress response. The present study enlightened various characteristic

features of CaCA superfamily proteins in T. aestivum, however, the function of the individual gene needs

to be investigated in future studies.

303 Heterologous Expression of Serine hydroxymethyltransferase-3 from Rice Confer Tolerance to

Salinity Stress in Arabidopsis

Vandna Rai, National Research Centre on Plant Biotechnology

Pragya Mishra, National Research Centre on Plant Biotechnology; Ajay Jain, National Research Centre on

Plant Biotechnology; Teruhiro Takabe, Meijo University; Yoshito Tanaka, Meijo University; Nisha Singh,

National Research Centre on Plant Biotechnology; Neha Jain, National Research Centre on Plant

Biotechnology; Nagendra K Singh, National Research Centre on Plant Biotechnology

Among abiotic stresses, salinity stress adversely affects growth and development of rice. Contrasting

salt-tolerant and salt-sensitive rice varieties provided repository of genes that may be amenable for

manipulation through biotechnological interventions. In our earlier studies, omic (trasncriptomics and

proteomics) approach was employed in identifying salinity stress-induced serine hydroxyl

methyltransferase-3 (OsSHMT3). Overexpression of OsSHMT3 in E. coli conferred tolerance to

differentsalinity regime (0 mM to 700 mM NaCl). Augmented levels of osmolytes (glycine betane and

choline), and several amino acids (glycine and serine) in transformed E. coli could have possibly

contributed towards the observed phenotype. Transgenic Arabidopsis overexpressing OsSHMT3 (OEs)

also exhibited distinct tolerance towards salinity stress, and many of the vegetative and reproductive

morphological traits were comparable with the wild-type. A comprehensive omic approach (ionomic,

transcriptomic, metabolomic, and proteomic profiling) was employed for identifying the molecular

mechanism underlying OsSHMT3-mediated tolerance to salinity stress in Arabidopsis. The study

provided empirical evidence towards the potential role of SHMT3 in alleviating salinity stress in a

heterologous system. Efforts are now underway to explore the feasibility of alleviating salinity stress in

salt-sensitive rice varieties by overexpressing this gene.

304 Metabolome Analysis of Some Effective PGPR Strains Isolated from Rhizosphere of Arnebia

benthamii in North Western Himalaya

Javid Parray, University of Kashmir

Azra Kamii, University of Kashmir; Sumira Jan, University of Kashmir; Nowsheen Shameem, University of

Kashmir; Suhaib Bandh, University of Kashmir; Bashir Lone, University of Kashmir

Arnebia benthamii L. (Boraginaceae) commonly called as ‘Kahzaban’, critically endangered perennial

medicinal herb growing in the sub alpine and alpine zones of North West Himalaya at an altitude of 3500

–4000 m with limited density. Current study evaluates the diversity of culturable root-associated

bacteria from this plant which resulted in a total of 80 morphologically different isolates. The

composition of nutritive growth medium had a significant effect on the diversity of the isolated bacterial

isolates. The isolates were characterized for various metabolic, plant growth promoting (PGP) and other

useful activities, based on which they were clustered into groups. Majority of the isolates belonged to

Bacillus, Pseudomonas and ptoteabacteria and were the most dominant species of the total isolates.

Some isolates exhibited very high antioxidant activity and were able to protect the DNA damage as well

and few were able to inhibit the pathogenic fungal strains significantly.

305 Signal Transduction in Plant Stress – Perception and Response to Pathogens and Environmental

Stress at Molecular and System Levels

Sorina Popescu, Mississippi State University

Gizem Dimlioglu, Mississippi State University; Norbert Bokros, Mississippi State University; Priyardashini

Bhorali, Mississippi State University

Understanding response and adaptation to stress is a fundamental problem in plant biology. Our

research focuses on the molecular processes triggered by pathogens or environmental stress factors,

and how they shape the phenotypical response of the whole organism. In particular, we aim to

understand the organization of signal processing pathways and of the principles that govern their

activation and function in experimental models and crops. The prospect of generating synthetic

signaling pathways for broad stress tolerance in plants makes our work intriguing and exciting. Two

current projects in our lab exemplify my lab’s most recent work. One study is centered on the

calmodulin-binding protein INTEGRIN-LINKED KINASE1 (ILK1). ILK1 coordinates signaling during the

plant’s basic immune response and ionic stress triggered by high salt or osmolite concentration. We

demonstrate the importance of ILK1 at the confluence of biotic and abiotic stress response and highlight

the importance of the nutritional status of the plant and ion-mediated processes at the plasma

membrane. In a second study, an in vivo plant-pathogen interactomics screen identified tomato kinases

with critical functions in cellular responses associated with the immune response to bacterial pathogens.

In planta functional assays and bioinformatics analyses revealed general rules for the organization and

regulation of signaling networks in immunity and cell death, alongside strategies by which pathogen

virulence factors may impact the immune network.

306 Integrated ‘OMICs’ Approach to Tag Blister Blight Leaf Disease Resistance in Tea (Camellia

sinensis L.)

Kooragodage Mewan, Tea Research Institute of Sri Lanka

KH Tissa Karunarathne, University of Colombo; Jagath Weerasena, University of Colombo, Cumaratunga

Munidasa Mawatha, Colombo 03; E Nishantha Edirisinghe, Tea Research Institute of Sri Lanka; Pradeepa

Liyanage, Tea Research Institute of Sri Lanka; ISB Abeysinghe, Tea Research Institute of Sri Lanka

With the global concern on ‘high quality, clean and safe food’, development of resistant crop varieties to

biotic/ abiotic stresses is pivotal where accurate selection plays a fundamental role thus becoming

marker-assisted-selection as an indispensable tool in enhancing efficiency of conventional breeding

programs, especially for perennial crops like tea (Camellia sinensis L.). For tea, as the economically most

important plantation crop in Sri Lanka, genetic improvement for Blister Blight Leaf Disease (BB)

resistance, which causes 20-30% annual crop loss, is one of the foremost objectives in tea breeding

where breeders have to evaluate the ‘trait’ by laborious, environment dependent and time consuming

multi-location field assessments. To overcome above constraints, studies were extended to identify a

potential marker to BB resistance by integrating ‘Genomic’, ‘Metabolomic’ and ‘Phenomic’ tools.

307 RERJ1 – A Wound Responsive JA Dependent Early Inducible bHLH Transcription Factor – Is Involved

in the Rice JA-signaling System Together with OsMYC2 and OsJAZ

Ioana Valea, The University of Tokyo

Koji Miyamoto, Teikyo University; Okada Kazunori, The University of Tokyo

Pathogen and herbivore-induced damage in plants is the main cause for yearly yield losses and

subsequent economic ones. (Oerke, 2006) The jasmonic acid (JA) pathway is one of the significant

pathways shaping the fitness straining plant defense, as well as the balance to be established to growth

and of course plant growth itself among other influences on plant life stages. (Staswick et al., 1992)

RERJ1 (rice early responsive to jasmonates 1) has been previously shown to be a basic helix-loop-helix

(bHLH) transcription factor involved in the early JA signaling in rice. In response to wounding it reaches

its peak within 30 minutes after treatment. (Kiribuchi et al., 2004)

The main focus of our study is to characterize the function of RERJ1 within the JA signaling system in rice

with regard to OsMYC2, acting as an activator, and OsJAZ, the main known inhibitor group (Uji et al.,

2016). Our approach is on transcriptional level, tracking the transcriptional activity of RERJ1 in mutants

such as rerj1-TOS17 knock-down and OsMYC2 RNAi, and on protein level identifying direct interactions

between RERJ1 with OsMYC2 and OsJAZ.

A differential temporal pattern of expression until 4h after JA treatment and wounding in the rerj1-

TOS17 mutant shows that RERJ1 influences a wide range of the 15 known OsJAZ, several of which are

known to be involved in defense mechanisms. OsMYC2 transcript as well seems to be slightly affected

by RERJ1. In support the protein-protein interaction within a yeast two-hybrid assay shows RERJ1 being

able to physically interact with a wide range of OsJAZ, leading to the hypothesis that RERJ1 is directly

involved and shapes the known feedback loop between OsMYC2 and OsJAZ (Kazan and Manners, 2013).

Further transcriptional analysis in RERJ1/OsMYC2 overexpressing rice plants is currently on going.

308 Linking Duplication of the CALCINEURIN B-LIKE10 (CBL10) Calcium Sensor to Plant Salt Tolerance

Shea Monihan, University of Arizona

Choong-Hwan Ryu, University of Arizona; Courtney Magness, University of Arizona; Karen Schumaker,

University of Arizona

The Salt-Overly-Sensitive (SOS) pathway in Arabidopsis thaliana (Arabidopsis) functions to prevent the

toxic accumulation of sodium in the cytosol when plants are growing in saline conditions. In this

pathway, the SOS3 (roots) and CALCINEURIN B LIKE10 (CBL10, leaves) calcium sensors interact with the

SOS2 protein kinase to activate the SOS1 sodium/proton exchanger in the plasma membrane. CBL10

has been duplicated in Eutrema salsugineum (Eutrema), a salt-tolerant relative of Arabidopsis. Because

Eutrema maintains growth in salt-affected soils that kill most crop plants, the duplication of CBL10 in

Eutrema provides a unique opportunity to directly test the outcome of gene duplication and to link this

duplication to plant salt tolerance.

In Eutrema, down-regulation of the duplicated CBL10 genes (EsCBL10a and EsCBL10b) singly and in

combination reduces growth in saline conditions suggesting that both genes function in plant responses

to salt. Expression analyses and cross-species complementation assays in Arabidopsis and

Saccharomyces cerevisiae demonstrate that EsCBL10b is similar to AtCBL10 but with an enhanced

function; EsCBL10b is expressed in leaves, complements the Atcbl10 mutant, and has an enhanced

ability to activate the SOS pathway. EsCBL10a has a unique role in response to salt; it is expressed in

both leaves and roots, complements the Atcbl10 mutant, but only weakly activates the SOS pathway.

Based on its expanded expression into roots, EsCBL10a was expressed in the Atsos3 mutant in which

root growth is significantly reduced in saline conditions. In contrast to AtCBL10 or EsCBL10b, EsCBL10a

complemented the Atsos3 salt-sensitive phenotype, suggesting that EsCBL10a has a unique function.

These results suggest that both EsCBL10a and EsCBL10b function in plant responses to salt, but their

functions have diverged. EsCBL10a has a unique role in salt tolerance compared to AtCBL10 and

EsCBL10b, while EsCBL10b has an enhanced function in the SOS pathway relative to AtCBL10 and

EsCBL10a.

309 Ectopic Expression of psNTP9, An Apyrase Gene from Pisum Sativum, Confers Drought Tolorance in

Transgenic Soybean

Roopadarshini Veerappa, University of Texas at Austin

Soybean (Glycine max (L.) Merr.) is one of the major resources of protein for human and animal

nutrition as well as a key source of vegetable oil. It is also considered a potential crop for the production

of biodiesel. Drought is a critical environmental factor that imposes water stress on crops, which is a

major constraint on plant growth and productivity, contributing to yield losses in crops, including

soybeans. Apyrases (Nucleoside Triphosphate-Diphosphohydrolases) are calcium- and magnesium-

activated enzymes that remove the terminal phosphate from nucleoside triphosphates (NTPs) and

nucleoside diphosphates (NDPs). By controlling the [NTP] and/or [NDP] in the Golgi, the ECM or other

subcellular sites, apyrases are known to play an essential role in plant growth, development and stress

responses. In the present study the pea apyrase gene (psNTP9), driven by CaMV 35S promoter, was

transferred into the soybean genome by Agrobacterium-mediated plant transformation. Two

independently transformed soybean lines expressing psNTP9 were produced. Segregation analyses

indicated single-locus insertion for both lines. Preliminary tests performed in the greenhouse evaluated

the phenotype of transgenic soybean plants under drought conditions. Transgenic plants outperformed

the wild-type plants when subjected to water deficit conditions, exhibiting notable drought tolerance

with a higher survival rate compared to the wild type. They also had a greater shoot and root biomass

and showed significantly higher seed production compared to non-transgenic plants under water deficit.

This is the first report showing that the ectopic expression of psNTP9 in transgenic soybeans confers

drought tolerance, highlighting the potential of this gene for molecular breeding.

Funded by grants from the National Science Foundation (IOS-1027514) and a private company to Drs.

Greg Clark and Stanley J. Roux at the University of Texas at Austin.

310 Overlap Between the Ad/Abaxial Developmental and ABA Signaling Networks

Tie Liu, Stanford University

Adam Longhurst, Carnegie Institution for Science; Franklin Talavera-Rauh, Carnegie Institution for

Science; Samuel Hokin, Carnegie Institution for Science; Kathryn Barton, Carnegie Institution for Science

The actions of the plant hormone abscisic acid (ABA) in the repression of germination and the inhibition

of water loss through stomata are well studied. Less well understood are the functions of ABA on

vegetative development including the mechanisms through which it influences growth of roots and

shoots. In our study of gene networks that control development of the adaxial (towards meristem/inner)

and abaxial (away from meristem/outer) domains of the leaf, we identified a set of genes oppositely

regulated by REVOLUTA and KANADI. Observing that this set of genes is enriched for genes encoding

ABA signaling components, we reasoned that genes of unknown function within this set would also be

involved in ABA signaling. Indeed, we find that a mutation in one of these, ABA INSENSITIVE GROWTH 1

(ABIG1), results in ABA resistant vegetative growth of shoot. Conversely, conditional overexpression of

ABIG1 mimics the application of ABA to wildtype plants: it causes leaf yellowing, reduces the rate of leaf

production and causes reduced root growth. Surprisingly, the ABA resistant abig1 mutants are also

drought resistant. We hypothesize that this reflects the ablation of the branch of the pathway for ABA

inhibition of growth and promotion of senescence while leaving intact the pathway for stomate closure.

To characterize ABIG1 induced senescence and meristem arrest, we carried out phenotyping and

quantifying analysis on the growth and development of meristem and young leaf primordia under at

various times post induction and drought treatments for both Arabidopsis and the orphaned crop

quinoa.

311 Role of Mineral Nanoparticles in Improving Attachment and Colonization by Plant Growth

Promoting Rhizobacteria

Salme Timmusk, Uppsala BioCenter

Gulaim Seisenbaeva, Uppsala BioCenter

The soil surrounding plant roots is one of the main sources of bacteria expressing plant-beneficial

activities (PGPR). Recently we have shown that the PGPR have great potential in protecting plants

against abiotic and biotic stress situations and restoring marginal lands (1-3). Multiple factors influence

isolate stability. The effectiveness of the inoculum is related to the formulation technology protecting

the cells from the surrounding environmental conditions and elimination of secondary effects. Titania

nanoparticle (TN) formulation form stable, large and thick bacterial clumps as a biofilm which influence

plant growth via facilitating root hair length and density and improving mulch biofilm formation (1-3).

This effect is especially pronounced in complex environments there several PGPR strains are used in

combination with TNs (1). The biofilm substantially improves root soil contact and enhances significantly

plant nutrient or biologically active compound acquisition from soil or bacterial origin. In addition,

improved organic matter content and porosity restores marginal lands (1)

The key to further advancing our treatment with microbial products is (i) surface grafting of sands and

intercalation of clays and employing hybrid biopolymer-mineral nanocomposites (ii) distinguishing

between genes and pathways that drive passenger events both in PGPR and crop plant.

1) S. Timmusk, G. A. Seisenbaeva, A. Muraya, J. Muthony, L. Behers, Nano titania aid multiple plant

growth promoting bacterial interactions under complex environments. Frontiers in Plant

Sci, (2016).

2) S. Timmusk, et al, Perspectives and challenges for microbial application for crop

improvement. Frontiers in Plant Sci, (2016).

3) S. Timmusk et al., PloS ONE DOI 10.1371/journal.pone.0096086, (2014).

312 Novel Plastid Behavior Associated with the MSH1 Effect

Jesus Beltran, University of Nebraska-Lincoln

Evan LaBrant, University of Nebraska-Lincoln; Mon-Ray Shao, University of Nebraska-Lincoln; Sally

Mackenzie, University of Nebraska-Lincoln

In plants, plastids serve as sensors of developmental and environmental stimuli, allowing cells to

modulate gene expression and likely to reprogram the epigenome in a heritable fashion. MSH1 is an

organellar DNA binding and thylakoid protein with profound roles in development, stress responses and

redox balance. Current evidence supports MSH1 as important for influencing transgenerational

epigenetic inheritance. MSH1, together with its interacting partner PPD3, localize to small, distinctive

plastids that display low autofluorescence and reside within the epidermis and vascular parenchyma.

We have termed these organelles ‘sensory plastids’. Our observations imply proteome heterogeneity in

different plastid types across discrete cell populations. To gain insight into the role of sensory plastids,

we combined Fluorescence-Activated Cell Sorting (FACS) and proteomics to characterize their

proteomes in Arabidopsis floral stems. We used Translating Ribosome Affinity Purification (TRAP)

analysis followed by RNA sequencing technology (TRAP-SEQ) to assess both MSH1-containing and

MSH1-depleted, cell-specific translating RNAs. Early results suggest that protein counts for responders

to abiotic stimulus are more prominent in rank in sensory plastids than in chloroplasts, which suggests

plastid specialized function. TRAP-SEQ experiments revealed that cells depleted of MSH1 actively

translate proteins for oxidation-reduction processes, cell redox homeostasis and auxin response, which

suggest a role of MSH1 in triggering retrograde signaling in specific cell types. Finding correlations

between cell-specific translatomes and the plastid proteome profiles within these cells would help to

understand the underpinnings of organellar functional specialization.

313 Efficacies of Seventeen Organically made Northern Fertilisers on Sustainable Crops Production in

Acidic Soil of Food Security Under Climate Change Bangladesh Context

Durlave Roy, Northern Agro Services Ltd

Different field study was conducted to evaluate the efficacies of Northern organically made fertilizers on

yield and quality of crops in a study. BF (Rice) M-54, Yield increased by 17-20%, Urea saved by 50%, BF

(Wheat) M-51, Yield increased by 17-21%, BF (Potato) M-48, Yield increased by 25%, BF (Mustard) M-50,

Yield increased by 28.5 %, BF (Maize) M-53,Yield increased by 28%, BF (Onion) M-47, Yield increased by

5kg per decimal average, BF (Banana) M-49, Saving in Urea fertiliser per plant 50-100g, BF (Cauliflower)

M-66, Chemical fertiliser saves by 50%, BF (Sugarcane) M-63,Yield increased by 36%, BF (Garlic) M-201,

Yield increased by 5kg per decimal average, BF (Tomato) M-199, Chemical fertiliser saves by 50%, BF

(Cabbage) M-198, Chemical fertilisers saves by 50%,BF (Chili) M-200, Chemical fertiliser saves by 50%, BF

(Brinjal) M-403, Chemical fertiliser saves by 50%, BF (Watermelon) M-402, Saving in Urea per decimal by

15-200g, Shakti Fertiliser (Mango, Tea, Vegetables, Cotton), M-52, Yield increased by 45.8% of Mango,

15% of Tea, Chemical fertiliser saves by 50% of Rice and Vegetables, Higher yield growth of 2550kg per

hectare of Cotton, Organic Fertilizer (Potato, Sugarcane, Betel Leaf, Mango, Tea, Vegetables) M-65,Yield

increased by 20% of Mango, 9% of Tea, chemical fertilizer saves by 50% of Rice and Vegetables, Higher

yield growth of 68.41 tons per hectare of Sugarcane, 8850 betel Leafs per decimal, 23.68 tons per

hectare of Potato.

314 Heat and Drought Stress Effects on Seed Germination and Seedling Development of Sorghum

(Sorghum bicolor, L. Moench) in the Semi-Arid Sahel of Mali

Siaka Dembele, Institut d’Economie Rurale (IER)

Abiotic stresses such as extremes temperatures and drought, have detrimental effects on germination,

growth and yield of sorghum (Sorghum bicolor L. Moench) The objective of this pot trial was to evaluate

nine sorghum varieties on their potential of germination and seedling development when subjected to

different levels of early season drought and heat stresses. Treatments included 5 levels of heat stress

imposed by incorporating different layers (0, 0.5, 1, 2 and 3 cm) of charcoal powder on top of pots and

different levels of water stress (No water stress, one, two, three and four week water stress), or

combinations thereof. The experiment was set up as a complete randomized design with 3 replications.

Increasing drought and heat stress, in general, seed germination and seedling development decreased

as evidenced by various germination traits such as germination percentage at 8 days after sowing (DAS),

mean germination time (MGT), germination rate index (GRI), seedling vigour index (SVI), root vigour

index (RVI), and seedling dry weight (SDW). But despite of these abiotic stresses imposed. Nevertheless,

irrespective of the heat levels imposed, some varieties such as Banidoka, CSM63E, and Saba-tienda

showed good germination and seedling development as evidenced by a high GRI and SVI as compare to

the control. Differential varieties responses to heat and drought stress can prepare a suitable metabolic

reaction in seeds and can improve seed germination performance and seedling under drought and heat

prone area.

315 Reduced Germination of the siz1 Mutants is Caused by Low SLY1 Activity and Hyperdormancy

Hak Soo Seo, Seoul National University

Woo Seob Kim, Seoul National University; Beom Seok Choi, Seoul National University

Germination is a critical step for vegetative growth and seed production. Here, we examined the effect

of the E3 SUMO ligase AtSIZ1 on seed dormancy and germination. Result showed that the germination

rates of the siz1 mutants were less than 50%. But, their germination rates increased to wild-type levels

after cold stratification or long periods of ripening, and also exogenous gibberellin (GA) application. In

addition, suppression of AtSIZ1 caused rapid post-translational degradation of SLEEPY1 (SLY1) during

germination, and inducible AtSIZ1 overexpression led to increased SLY1 levels. The germination ratios

of siz1 mutant seeds in immature developing siliques were much lower than those of the wild type.

Moreover, SLY1 and DELAY OF GERMINATION 1 (DOG1) transcript levels were reduced in

the siz1 mutants, while the transcript levels of DELLA and ABSCISIC ACID INSENSITIVE 3 (ABI3) were

higher than those of the wild type. Taken together, these results indicate that the reduced germination

of the siz1 mutants results from impaired GA signaling due to low SLY1 levels and activity, as well as

hyperdormancy due to high levels of expression of dormancy-related genes including DOG1.

This work was supported by a grant from the Next-Generation BioGreen 21 Program (Plant Molecular

Breeding Center no. PJ01108701), Rural Development Administration, Republic of Korea.

316 Flowering Repression Activity of FLOWERING LOCUS C is Negatively Regulated by HIGH PLOIDY2

Hak Soo Seo, Seoul National University

Woosub Kim – Seoul National University

Sumoylation is an important post-translational modification for growth and development I plant and vertibrate. We thus examined the effect of sumoylation on Flowering Locus C (FLC)-mediated flowering. Here, we identified Arabidopsis HIGH PLOIDY2 (HPY2) as an E3 SUMO ligase for FLC. In vitro and vivo pull-down assays showed that FLC physically interacts with HPY2. In vitro assays showed that the stimulation of FLC sumoylation by HPY2 was dependent on SUMO-activating enzyme E1 and -conjugating enzyme E2, indicating that HPY2 was an E3 SUMO ligase for FLC. In transgenic plants, inducible HPY2 overexpression increased the concentration of FLC, indicating that HPY2 stabilized FLC through direct sumoylation. Flowering time in hpy2-2 mutants was shorter than in wild-type plants under long- and short-day conditions, with a greater effect under short-day conditions, and FLC was downregulated in hpy2-2 mutants. Taken together, these data indicate that HPY2 regulates FLC function and stability at both the transcriptional and post-translational levels through its E3 SUMO ligase activity. This work was supported by a grant from the Next-Generation BioGreen 21 Program (Plant Molecular Breeding Center no. PJ01108701), Rural Development Administration, Republic of Korea.

317 Phenotyping of Plants for Drought and Salt Tolerance using Infra-red Thermography

Taek-ryoun Kwon, The National Institute of Agricultural Sciences

Kyung-hwan Kim, The National Institute of Agricultural Sciences; Zamin Shaheed Siddiqui, Karachi

University

Drought and salinity are the major environmental constrains in global agricultural production. Plant

breeding for the drought and salt tolerance needs a proper assessment procedure to overcome stress

constrain. Fundamental understanding on the physiological nature of the plant tolerance provides

valuable information for the genetically modified crop’s development. Drought or salt stress induces

several common physiological responses in plants such as water relation and photosynthetic capacity. It

is because both stresses lead cellular dehydration in the plants, particularly, during the early phase of

stress imposition. Drought and salinity decrease CO2 availability for photosynthesis via stomatal

limitation as well as elevate leaf temperature due to partially closed stomata. In this scenario, stomatal

regulation and plant water status are important aspects in abiotic stress environment. These

physiological responses have a function to stabilize the temperature inside plant/leaf. Therefore

phenotyping through an infra-red thermography (heat sensitive sensor), could be a useful tool in the

selection of a tolerant genotypes. Infra-red thermography is a part of the electromagnetic spectrum

which emits a certain amount of radiation as a function of their temperatures. In general, the plants

which have less water, would have higher temperature and display more infra-red radiations. In abiotic

stresses such as drought and salinity, plant water status is affected and varied from the sensitive to

tolerant level. Infra-red images of plants are often linked with some of the physiological attributes to the

tolerance. This review covers the limits, advantages, linkages, comparison and other prospective of

using thermal images in modern phenotyping techniques.

318 Rice Bacterial Leaf Blight is Controlled by Rapid Systemic Movement of Niclosamide and its Effect

is Maintained for a Long Time

Beom Seok Choi, Seoul National University

Hak Soo Seo, Seoul National University

Bacterial leaf blight is one of the major diseases in rice and affects yields. Thus, various methods have been applied to protect rice from this disease. Here, we show systemic translocation of the human drug niclosamide (5-chloro-N-(2-chloro-4-nitrophenyl)-2-hydroxybenzamide) in rice and its long-term effect on prevention of rice leaf blight. The development of Xanthomonas oryzae pv. oryzae-induced rice leaf blight was effectively inhibited in untreated systemic leaves as in niclosamide-treated leaves, although its effect gradually decreased in a time-dependent manner. Time-course examination after niclosamide treatment showed that the niclosamide level was highest after 3 h in non-treated distal leaves, suggesting fast systemic movement of niclosamide from the treated local site to untreated distal regions. Our data indicate that niclosamide controls rice leaf blight by its rapid systemic movement and that its effect is maintained for a long time. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: 2015057573).

319 Using RNAseq to Identify Key Player Genes Driving Soybean Drought Response in High and Low

Nitrogen Fixing Genotypes

Marianne Emery, University of Missouri

Arun Dhanapal, University of Missouri; Andy King, University of Arkansas; Larry Purcell, University of

Arkansas; Jeffrey Ray, USDA-ARS; James Smith, USDA-ARS; Felix Fritschi, University of Missouri; Christine

Elsik, University of Missouri

Patterns of gene expression provide insight into plant responses to stress conditions such as drought. In

legumes, the process of nitrogen (N) fixation has been shown to be sensitive to water deficit. A better

understanding of the gene(s) that drive major pathway(s) involved in soybean (Glycine max) response to

drought, and their effect on N fixation, will assist in the development of soybean cultivars better able to

adapt in drought conditions. Here we report a comparison of two soybean cultivars, identified

previously as high and low N fixing, exposed to varying levels of water availability. RNA extracted from

leaf, nodule, and root tissues were sequenced using Illumina Hi-Seq (100 bp, single-end) and quantified

at the gene level across environments and tissues. Gene expression values were used to build a mutual

information network resulting in nine gene clusters. The genes contained in each cluster were

characterized, and gene hubs, here defined as key player genes, were identified. We believe these key

player genes in each modeled cluster may be main drivers in soybean drought response and could be

putative targets for improvement in future breeding efforts.

320 Utilizing Cysteine Protease Inhibitor Gene to Enhance Stress Resistance in Soybean

Sophie LaRochelle, SUNY Cobleskill

Clara Richardson, SUNY Cobleskill; Angela Zhu, SUNY Cobleskill

Programmed cell death is a mechanism within plant cells in which the cell undergoes a series of changes

and dies due to a variety of factors. These factors include the cell cytoplasm decreasing, the nuclear

material condensing and the membrane beginning to disintegrate. Proteases within the plant play an

important role in the regulation of this process of programmed cell death. Within plant systems,

proteases are regulated at a variety of different levels including; transcription, translation, post

translation and specific protease inhibitor proteins. Soybean cells have inactivated proteases present in

order to oxidize stress through an indirect mechanism. Cysteine protease inhibitors may function in

modifying programmed cell death that is activated when a plant is under environmental stress or

pathogen attack. The ability to engineer soybean with the Cysteine Protease Inhibitor (CPI) gene can

provide resistance against stresses such as chilling, salt, drought and pathogen attacks.

321 PLANT U-BOX PROTEIN10 Regulates MYC2 Stability in Arabidopsis

Choonkyun Jung, Seoul National University

Pingzhi Zhao, Rockefeller University; Jun Sung Seo, Rockefeller University; Nobutaka Mitsuda, National

Institute of Advanced Industrial Science and Technology; Shulin Deng, Rockefeller University; Nam-Hai

Chua, Rockefeller University

MYC2 is an important regulator for jasmonic acid (JA) signaling, but little is known about its

posttranslational regulation. Here, we show that the MYC2 C-terminal region interacted with the PLANT

U-BOX PROTEIN10 (PUB10) armadillo repeats in vitro. MYC2 was efficiently polyubiquitinated by PUB10

with UBC8 as an E2 enzyme and the conserved C249 in PUB10 was required for activity. The inactive

PUB10(C249A) mutant protein retained its ability to heterodimerize with PUB10, thus blocking PUB10 E3

activity as a dominant-negative mutant. Both MYC2 and PUB10 were nucleus localized and

coimmunoprecipitation experiments confirmed their interaction in vivo. Although unstable in the wild

type, MYC2 stability was enhanced in pub10, suggesting destabilization by PUB10. Moreover, MYC2 half-

life was shortened or prolonged by induced expression of PUB10 or the dominant-negative

PUB10(C249A) mutant, respectively. Root growth of pub10 seedlings phenocopied 35S:MYC2 seedlings

and was hypersensitive to methyl jasmonate, whereas 35S:PUB10 and jin1-9 (myc2) seedlings were

hyposensitive. In addition, the root phenotype conferred by MYC2 overexpression in double transgenic

plants was reversed or enhanced by induced expression of PUB10 or PUB10(C249A), respectively.

Similar results were obtained with three other JA-regulated genes, TAT, JR2, and PDF1.2. Collectively,

our results show that MYC2 is targeted by PUB10 for degradation during JA responses.

322 Improving the Viability of Sorghum for Bioenergy Production

Sarah Pfaff, Rutgers University

Michael Pashos, Washington University; Christie Peebles, Rice University; Courtney Jahn, University of

Wisconsin

Sorghum is a drought tolerant crop that can be used as a feedstock for bioenergy production. Here we

report a three pronged approach to improve the viability of sorghum for conversion to bioenergy

through: (1) increased biomass production; (2) improved drought tolerance; (3) development of a

variety that is resistant to herbicides. Specific outcomes of this work include primers designed and

verified for Targeted Induced Local Lesions in Genomes analysis (TILLING) of a non-GMO mutant

sorghum population to identify mutations that enhance desired traits, such as increased biomass or

drought tolerance. Secondly, a plasmid was designed and assembled for use in sorghum plastid

transform to confer herbicide resistance. Lastly, a Recombinant Inbred Line (RIL) population between a

drought tolerant and drought susceptible sorghum line was planted and evaluated in the field to

determine Quantitative Trait Loci (QTL) for above and below ground traits that confer drought

tolerance. Preliminary results show success towards each of the three project goals.

323 Chlorophyll Fluorescence and Leaf Gas Exchange based Phenotyping of Heat Stress Tolerance in

Tepary, Common Bean and Cowpea at the Seedling and Juvenile Stages

Isaac Osei-Bonsu, Michigan State University

Donghee Hoh, Michigan State University; Wayne Loescher, Michigan State University; Dan TerAvest,

Michigan State University; David Kramer, Michigan State University

The recently prevalent consequences of climate change and the predicted rise in temperature

necessitates development of heat stress tolerant crops to be able to feed the growing population.

Photosynthesis is one of the processes negatively affected by high temperature (HT), the threshold of

which varies with crop species. Existing and new tools and platforms based on Chlorophyll fluorescence

(CF) and leaf gas exchange (LGE) techniques enable phenotyping of photosynthesis signatures for crops

of interest. In the current study we aimed to determine genotypic differences in CF and LGE-based

photosynthetic parameters under HT stress and explore the possibility of using tools based on these

techniques for phenotyping HS tolerance in genotypes of three legumes, viz tepary, common bean and

cowpea with differing HS tolerance levels. Seedlings grown at 30/25°C day/night temperatures were

subjected to a 5°C rise in both day and night temperatures every two to five days up to a maximum

temperature of 45/40°C at different growth stages starting from 3 d after germination. The common

bean line, Zorro, consistently emerged as the most heat susceptible of the three species irrespective of

the growth stage (seedling or juvenile/vegetative stage), whereas tepary beans and cowpea alternated

between high and medium HS tolerance depending on the line, growth stage and leaf type (unifoliate vs

trifoliate). Susceptibility of Zorro became more apparent after 2 d at 45/40°C with drastic decline in

ɸII (quantum yield of PSII), net assimilation (A), stomatal conductance (gs) and chlorophyll content,

whereas non photochemical quenching (NPQt) and yield of NPQ (ɸNPQ) increased. Results from

chlorophyll fluorescence parameters assessed with the multispeQ, connected to the photosynQ

platform (www.photosynq.org) corroborated that of LGE parameters measured with the Li-Cor,

indicating the utility of these two devices as tools for phenotyping HS tolerance in legumes.

324 Phenotyping Biochemical Bypasses to Photorespiration to Discover Improvements in

Photosynthesis and Crop Production

Amanda Cavanagh, University of Illinois

Paul South, USDA-ARS Photosynthesis Research Unit; Donald Ort, USDA-ARS Photosynthesis Research

Unit

As climate changes globally and human population increases, traditional methods of crop improvement

have become less effective in adapting and improving agricultural production. Improving photosynthetic

efficiency has been a long standing goal toward increasing crop yield, but has played a minor role in crop

improvements to date. In C3 crops such as wheat and rice approximately 25% of the fixed carbon

dioxide is lost to photorespiration, the energy expensive metabolic pathway that recycles toxic

compounds produced by Rubisco oxygenation reactions. Reducing photorespiratory yield losses by 5%

(i.e., to 31% for soybean and 15% for wheat) would be worth ~$500 million annually in the United

States. Synthetic biology has provided new opportunities to altering photorespiratory metabolism to

improve photosynthetic efficiency, but the phenotyping bottleneck hinders the speed at which benefits

can be realized. Using the Golden Gate synthetic biology approach, we have assembled and

transformed a series of multigene constructs that contain metabolic pathways to bypass

photorespiration. We developed a fluorescence based screen to phenotype single construct

transformed lines for rescue of photorespiratory deficient plants in the model plants Arabidopsis and

Tobacco. Our results indicate that large multigene constructs containing a metabolic bypass to

photorespiration can rescue changes in fluorescence caused by low CO2 stress in deficient plants. We

confirmed these results on plant phenotype through gas-exchange measurements of photosynthesis

under both greenhouse and field grown conditions. Determining robust photorespiratory bypass

constructs provide insight into next generation crops and our use of standard parts and fluorescent

screening to complement traditional photosynthetic phenotyping methods provide a tool kit for plant

synthetic biology to engineer improvements in photosynthetic efficiency.

325 Root Cortical Senescence Influences Metabolic Costs and Radial Water and Nutrient Transport in

Barley

Hannah Schneider, Penn State University

Tobias Wojciechowski, Forschungszentrum Juelich; Johannes Postma, Forschungszentrum Juelich;

Dagmar van Dusschoten, Forschungszentrum Juelich; Jonathan Lynch, Penn State University

Root cortical senescence (RCS) is a type of programmed cell death in cortical cells of many

Poaceaespecies. The functional implications of RCS formation are poorly understood, but studies

suggest that RCS formation confers both benefits and costs. The objectives of this research were to test

the hypotheses that: (1) genetic variation exists in RCS; (2) RCS reduces the metabolic cost of root tissue;

(3) RCS decreases radial water and nutrient transport. Using a Pitman chamber, radial water and

nutrient transport were measured from excised roots of barley using isotopes. We observed large

genetic variation for RCS between landraces and modern germplasm. RCS decreases the metabolic cost

of root tissue. Nitrogen and phosphorus deficient conditions increased the rate of RCS development.

RCS reduced root nitrogen content by 66%, phosphorus content by 63%, and respiration by 87%

compared to root segments with no RCS. RCS decreases radial water and nutrient transport. Barley roots

with complete RCS had 90% less radial water, 92% less radial nitrate, and 84% less radial phosphorus

transport compared to root segments with no RCS. RCS increased aliphatic suberin content by 30% in

the endodermis. RCS may be a useful adaptation to drought by reducing the metabolic costs of soil

exploration. As RCS progresses, less metabolic resources need to be invested in root maintenance of the

cortex, which could permit allocation of saved resources to the growth of shoots, other roots, and

reproduction. Reduced hydraulic conductivity induced by RCS may also be advantageous under drought

conditions by preventing desiccation of the root tip and surrounding soil. Under drought conditions,

plants must conserve the soil water throughout the growth season as a drought tolerance mechanism.

We propose that RCS merits investigation as a useful trait in improving the performance of barley and

other RCS forming species under edaphic stress.

326 Increasing Sugar Transport to Improve Soybean Response to Elevated [CO2]

Pauline Lemonnier, University of Illinois

Jennifer Quebedeaux, University of Illinois

Elevated atmospheric [CO2]causes a direct increase in instantaneousphotosynthesis and sugar

production in C3 plants, leading to a yield increase which is promising to meet future food demand.

However, previous studies have shown that soybean yield does not increase as much as predicted under

elevated [CO2]. This is possibly the result of feedback inhibition of photosynthetic capacity by

accumulated photoassimilates, suggesting insufficient sugar export capacity from the photosynthetic

source to sink tissues. Increasing sugar export capacity through over-expressing proton/sucrose

symporters involved in phloem loading has the potential to increase photosynthesis and yield under

elevated [CO2]. In this study, soybean was transformed to overexpress the sucrose

transporter AtSUC1H65K. Transgenic soybeans were grown at ambient (400 ppm) and elevated (600 ppm)

[CO2] at the Soybean Free Air Concentration Enrichment facility during 2016. Instantaneous light-

saturated photosynthesis and stomatal conductance were measured as well as the response of

photosynthesis to intercellular [CO2]. Leaf and pod tissues were collected to confirm the expression

of AtSUC1H65K, determine sucrose transport activity, and measure carbohydrate content. The effects of

altered sucrose transport and elevated [CO2] on seed yield were measured at the end of the growing

season.

327 Dissecting the Mechanism of Grain Yield Under Drought in Rice- Phenotypic and Genomic Insight

Supratim Basu, New Mexico Consortium

Miranti Rahmaningsih; Anuj Kumar; Venkategowda Ramegowda; Julie Thomas; Andy Pereira

Water deficit therefore strongly affects rice production. Among the major stages of rice growth and

development, the reproductive phase is the most affected by water deprivation. Water stress imposed

during this stage can reduce grain yield by up to 77% and longer periods of drought can reduce 90% of

grain yield. Plants are protected against drought by three different mechanisms: drought avoidance,

drought tolerance, and drought escape. To evaluate the genetic variation among rice accessions for

drought tolerance, fifteen rice genotypes from the USDA mini-core collection were randomly selected

for the study with N22 and Vandana and Nipponbare as the positive and negative control, respectively.

Water stress was applied 3-4 days before anthesis stage and the drought response was measured by

analysis of the reduction in yield parameters. Drought resistance levels were categorized by comparing

genotypes under drought and wellwatered control for measurements of panicle length, number of grain

per panicle, number of filled grain per panicle, 100-grain weight, and total grain weight per panicle.

Three genotypes, AMANE, WIR 3039, HKG98 identified from screening and resistant reference genotype

N22 were used further for gene expression analysis. Gene expression analysis revealed that

inflorescence tissue gives positive and higher correlation with phenotypic measurements than flag leaf

during reproductive stage drought. Expression levels of invertase genes and transcription factors exhibit

positive effects to drought resistance particularly in relation to number of grain per panicle and panicle

length. Therefore, our results will provide valuable information for dissecting the genetic basis of

drought resistance in rice as well as provide valuable resource for developing high yielding drought

resistant rice cultivars by breeding approaches.

328 Spatio-temporal Metabolome Profile in Tobacco and Soybean under Water Deficit Conditions

Roel Rabara, New Mexico Consortium

Prateek Tripathi, The Scripps Research Institute; Paul Rushton, 22nd Century Group Inc

Drought is a major environmental stress affecting crop production in a global scale. Understanding how

plants respond to water deficit is important in order to develop crops tolerant to drought. In this study,

we compare metabolome profile of tobacco and soybeans at different stages of water stress in both

leaves and roots. Comparisons between the two datasets reveal common responses between the two

species, responses specific to one of the species, responses that occur in both root and leaf tissues, and

responses that are specific to one tissue. Stomatal closure is the immediate response of the plant and

this did not coincide with accumulation of abscisic acid. A total of 116 and 140 metabolites were

observed in tobacco leaves and roots, respectively, while 241 and 207 were observed in soybean leaves

and roots, respectively. Accumulation of metabolites is significantly correlated with the extent of

dehydration in both species. Among the metabolites that show increases that are restricted to just one

plant, 4-hydroxy-2-oxoglutaric acid (KHG) in tobacco roots and coumestrol in soybean roots show the

highest tissue-specific accumulation. The comparisons of these two large nontargeted metabolomics

datasets provide novel information and suggest that KHG will be a useful marker for drought stress for

some members of Solanaceae and coumestrol for some legume species

329 The Development of a Predictive Model for Freezing Resistance in Zea mayusing In-situ

Measurements of Biochemical Modifications of Epicuticular Waxfollowing Chilling Treatment in

Controlled Environment and Field Conditions

Kaila Hamilton, University of Saskatchewan

Karen Tanino, University of Saskatchewan

The inability of Zea mays to resist yield-reducing frost damage has limited the opportunity for its

production in underutilized regions such as the Canadian prairies. Underutilized regions will be

especially important as climate change begins to dictate new landscapes. The in-situ surface lipid

composition of mature leaves in grain corn was studied under controlled environment and field

conditions using Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FTIR)

Spectromicroscopy to evaluate chemical changes induced by chilling treatment in the epicuticular layer.

Plants were exposed to chilling treatment conditions over a 10 day (18℃/6℃) period and lethal freezing

temperature was determined using thermal imaging. We are developing a multifactor regression model

to use as a predictor to estimate lethal freezing temperature. The lipid composition, specifically aliphatic

compounds moderate leaf surface hydrophobicity. Hydrophobicity has been linked to increased freezing

resistance. Experimental FTIR results showed changes, following chilling treatment, in the asymmetrical

stretching region of the CH2 group associated with aliphatics which comprise the plant cuticle (cutin,

waxes and cutan). The ongoing development of this model in Zea mays appears to be a useful system

with practical applications for evaluating the correlation between abiotic stress induced epicuticular wax

modification on freezing resistance in a whole plant system.

Bioinformatics Tools, Data Standards, and Data Processing

400 Bioinformatics Tools, Data Standards, and Data Processing

Laurel Cooper, The Planteome Project

Austin Meier, Oregon State University; Justin Elser, Oregon State University; Marie-Angélique Laporte,

Bioversity International; Justin Preece, Oregon State University; Chris Mungall, Lawrence Berkeley Lab;

Elizabeth Arnaud, Bioversity International; Pankaj Jaiswal, Oregon State University

The Planteome project (www.planteome.org) features a suite of reference ontologies for plants,

associated with a growing corpus of genomics data in a centralized online plant informatics portal. The

species-neutral references, the Plant Ontology, Plant Trait Ontology, and Plant Environment Ontology,

(developed by the project), along with those developed by collaborating groups, such as the Gene

Ontology and the Phenotype and Trait Ontology, and others, are mapped to species-specific controlled

vocabularies for crop plant traits and phenotypes. Traditional plant breeding methods for crop

improvement may be combined with next-generation analysis methods and automated scoring of traits

and phenotypes to develop improved varieties. Linking these analyses to the growing corpus of

genomics data generated by high-throughput sequencing, transcriptomics, proteomics, phenomics and

genome annotation projects requires common, interoperable, reference vocabularies (ontologies) for

the description of the data. Data annotations to ontology terms link phenotypes and germplasm to

genomics resources, and provide semantic integration of widely diverse datasets with the goal of plant

improvement. Analysis and annotation tools are being developed to facilitate studies of plant traits,

phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of

plant species. The project database and the online resources provide researchers tools to search and

browse and access remotely via APIs for semantic integration in annotation tools and data repositories,

providing resources for plant biology, breeding, genomics and genetics. The project is supported by the

National Science Foundation award IOS #1340112

401 On the Statistical Mechanics of Cytosine DNA Methylation and the Detection of the Methylation

Regulatory Signal

Robersy Sanchez, University of Nebraska-Lincoln

Sally Mackenzie, University of Nebraska-Lincoln

DNA methylation patterning represents one feature of the epigenome that is highly responsive to

environmental stress and associates with transgenerational adaptation in plants. An information

thermodynamics theory on cytosine DNA methylation (CDM) was recently published. Results indicated

that most methylation changes occurring within cells are likely induced by thermal fluctuations to

ensure thermal stability of the DNA molecule, seemingly explainable by statistical mechanics laws.

Therefore, one does not observe a genome-wide relationship between methylation and gene

expression. Ignoring the statistical biophysics subjacent to CDM is a source of bias for the most common

statistical tests applied in methylome analysis. A key limitation to current genome-wide methylation

analysis is the inability to discriminate signal, induced by experimental treatment, from background

“noise” that emanates from this natural, dynamic methylation activity. Here, we provide novel insights

for application of statistical mechanics of CDM to detect methylation regulatory signal. Integration of

information thermodynamics of CDM and signal detection theory permits robust discrimination of

biological signal from physical noise-induced thermal fluctuations. The analytical steps summary follows:

i) Information thermodynamic theory computes the probabilities of signal plus noise, with use of

experimental controls to estimate the receiver’s threshold (cutoff Hellinger divergence value, HD) at

which the rate of false positive is minimal or at least acceptable for the experimental conditions. ii)

Potential differentially methylated positions (DMPs) are proposed based on the probabilities of their

corresponding HDs. iii) A logistic regression analysis is performed with the prior binary classification of

DMPs (from treatment versus control), and iv) a receiver operating curve (ROC) is built to estimate the

cutoff point for HD at which an observed methylation level represents a true DIMP. Our approach is

illustrated with examples from analysis of genome-wide CDM reprogramming induced by msh1 mutant

effect in Arabidopsis thaliana.

402 Annotating Germplasm to Planteome Reference Ontologies

Austin Meier, Oregon State University

Justin Elser, Oregon State Univeristy; Laurel Cooper, Oregon State University; Pankaj Jaiswal, Oregon

State University; Marie-Angélique Laporte, Bioversity International

An expected use case of plant phenotype ontologies will be the identification of germplasm containing

particular traits of interest. If phenotype data from experiments is annotated using ontologies, it makes

sense to include annotations to that germplasm source. A lack of standardized data formatting reduces

the utility of these data. Standardizing germplasm data, including links to germplasm databases, or

distribution locations improves collaboration, and benefits both researchers and the scientific

community as a whole. All plant traits contained in the Planteome reference ontologies are searchable,

and interconnected through relationships in the ontology. All data annotated to these reference

ontologies will be displayed, shareable, and computable through the Planteome website

(www.planteome.org) and APIs. This manuscript will discuss the advantages of standardizing germplasm

trait annotation, and the semi-automated process developed to achieve such standardization.

403 Diversity Seek - A Community Driven Effort to Harness the Genetic Potential of Genebanks

Ruth Bastow, Global Plant Council

DivSeek is a community driven effort that aims to unlock the potential of crop diversity stored in

genebanks around the globe for food and nutritional security, and societal and economic benefits.

Assessing and identifying new sources of genetic variation is a critical part of any long-term strategy to

enhance the productivity, sustainability and resilience of crop varieties and agricultural systems.

Approximately seven million crop accessions are currently being conserved in genebanks collections

worldwide. This resource represents one of the greatest, largely untapped, opportunities for

accelerating yield gains and overcoming emerging crop productivity bottlenecks.

Many projects across the globe are characterizing crop diversity using genomic and phenomic

technologies, associated with cyberinfrastructure and high performance computing. As a result a great

many data sets are being generated. To ensure this data is findable, accessible, interoperable and

reusable (FAIR) the DivSeek initiative is working with partners across the globe to help bridge the

information requirements of gene bank curators, plant breeders and upstream biological researchers to

facilitate the use of plant genetic variation to accelerate the rate of crop improvement and furnish food

and agricultural products to the growing human population.

http://www.divseek.org/

404 Designing Intra-plot Variability Features from High-Throughput Sorghum Phenotypes

Karthikeyan Ramamurthy, IBM Research

Addie Thompson, Purdue University; Zhou Zhang, Purdue University; Fangning He, Purdue University;

Melba Crawford, Purdue University; Clifford Weil, Purdue University; Ayman Habib, Purdue University;

Mitchell Tuinstra, Purdue University

Traditional crop breeding approaches have relied on manually collecting ground measurements from

hundreds of phenotypes. Data collection protocols in automated, high throughput phenotyping have

resulted in more variety in the acquired data (RGB, hyperspectral, thermal images, and LiDAR point

clouds), more data points per plot, more genotypes and more time points.

Currently, machine learning methods are used to train trait prediction models using features extracted

from the image data as input and the desired traits as output. Good feature engineering using a

combination of domain knowledge as well as data considerations is crucial for accurate and robust high-

throughput phenotyping. The small sample size of manually collected output traits limits the use of fully

automated feature engineering methods.

Generally, summary features extracted from each plot are used for training such machine learning

models. For example, in our previous work we used primitive features such as height histograms of 3D

point clouds reconstructed from RGB images, and raw values, band ratios and morphological features

extracted from hyperspectral data, all summarized at the plot level. This was used to predict height,

stalk diameter, volume, and biomass of sorghum.

In this work, we develop principled feature engineering methods to represent the intra-plot variability of

such primitive features across different spatial dimensions. We model the spatial variability of different

heights in the 3D point cloud using eigen-spectral techniques. Since the hyperspectral data is registered

to the point cloud data at a pixel level, we use similar methods to capture its spatial variability across the

plot as well as along the height dimension. We assess the improvement obtained in phenotypic trait

prediction accuracy in sorghum by incorporating these intra-plot variability features. We also attempt to

intuitively comprehend the information provided by these features about plot-level variability.

405 A Sorghum Panicle Annotation and Counting Tool

Karthikeyan Ramamurthy, IBM Research

Peder Olsen, IBM Research; Upendra Chitnis, IBM Research; Addie Thompson, Purdue University; Naoki

Abe, IBM Research; Zhou Zhang, Purdue University; Melba Crawford, Purdue University; Fangning He,

Purdue University; Ayman Habib, Purdue University; Mitchell Tuintstra, Purdue University

Detecting and counting sorghum panicles from aerial images is of significant interest in automated high-

throughput phenotyping. The number of panicles in a plot correlates well with the number of fertile

tillers as well as the contribution of grain to biomass, both key phenotypes. However, panicle detection

is difficult due to the many variations in panicle appearance, camera viewing angle, occlusion, and

overlap. The appearance varies in size (20cm2 to 1000cm2 in area), shape (spindle, broom, cylinder or a

lax cone), color (chalky white, green, yellow, rusty brown or black), pose, and grain-size. Simple

classification methods based on color, size, or texture features do not work in panicle recognition. To

develop a robust machine learning model for recognizing and counting panicles, a well-annotated,

diverse data set is needed.

A data set collected by Purdue University for the ARPA-E TERRA program contains 400 RGB images over

a field with hundreds of sorghum varieties. Each image contains 8000x5000 pixels with 1cmx1cm

resolution. Rectangular or ellipsoidal bounding boxes are not appropriate for panicle annotation. Hence

we used super-pixels to annotate the extents of panicles. Super-pixels are natural and perceptually

meaningful segments of an image. We generate super-pixels automatically, and let a user click on a

super-pixel to select a part of a panicle. Typically, one or two clicks suffice to annotate a panicle, and we

derive panicle counts based on these annotations. The alignment between super-pixels and panicles

depend on how well the panicle stands out from the background. The preliminary annotation

performance is promising. Errors can be further reduced by allowing variation in super-pixel sizes. The

super-pixels in the first few images is then used in a template matching algorithm to automate the

annotation of future images. The user will then simply need to correct errors.

406 Predicting Cleavage Activity of CRISPR-Cas9 Across Diverse Maize Germplasm using Reference

Genome Analysis

Ian Braun, Iowa State University

Carolyn Lawrence-Dill, Iowa State University; Dipali Sashital, Iowa State University; Kan Wang, Iowa

State University; Jeffrey Wolt, Iowa State University

The CRISPR-Cas9 system enables sequence-specific editing at target sites within a genome. However,

editing may also occur at other points in the genome (off-target sites) where sequences are similar. In

particular, off-target cleavage presents a problem for specific editing in large, polyploid, or redundant

eukaryotic genomes such as that of maize. Exacerbating this problem, target sites for CRISPR-Cas9 often

are selected within the context of a reference genome, and then applied to different germplasm

altogether. In such cases, it would be valuable to estimate the number of expected off-target sites

unique to the new line. We compared the maize reference genome B73 with the genomes of four

additional sequenced lines (B104, W22, Mo17, and PH207) to estimate this relationship. The predicted

ratios between unique off-target sites and the number of off-target sites in the reference genome are as

large as 0.15, depending on the specific germplasm analyzed. These findings likely are influenced by

various confounding factors, including the relative quality of genomes analyzed. The extent to which

predicted off-target rates vary based on genuine genomic differences versus such artifacts is discussed.

Implications for experimental design are described.

407 An Efficient, Scalable Data Store for Automated High Throughput Phenotyping

Upendra Chitnis, International Business Machines

Karthikeyan Ramamurthy, International Business Machines; Upendra Chitnis, International Business

Machines

Modern crop breeding experiments generate several petabytes of data in different formats. UAVs and wheel-based systems collect petabytes of image and environment data that encode important phenomic characteristics of crops across spatio-temporal dimensions. Manual phenotyping and other surveys generate substantial amount of structured data. A mechanism to efficiently store and query these disparate data sets is necessary for developing downstream exploratory and predictive analytic approaches that make use of it. Such a data store, among other things, must have: 1. Scalable approaches for ingesting and processing petabytes of data in a reasonable amount of time. 2. Scalable file system for storing thousands to millions of files that vary widely in their sizes from a few MBs to 10s of GBs. 3. Data indexing features for efficient queries across spatio-temporal and other dimensions . 4. Data join capabilities across spatio-temporal and other dimensions. 5. Support for distributed computing architectures used by downstream exploratory and predictive analytics algorithms. No single off-the-shelf product supports all these features. Data stores such as MongoDB are scalable but provide very limited join capabilities for raster datasets. Big data stores such as HBase are very good at horizontal scaling, but do not provide spatial query capabilities. Some relational data stores, such as Postgres, provide good support for spatial queries but do not have join capabilities for raster images. In this presentation we will describe a scalable data platform solution for efficiently storing and querying high throughput phenotyping data. It is built on big data stack and supports distributed computing architecture necessary for downstream analytic applications. The solution includes linearly scalable custom algorithms to join different data layers such as soil, weather, elevation with custom phenotypic and image data sets. These data layers could be at different spatio-temporal resolutions and use different geographic coordinate systems (datums).

408 Quantitative Analysis of Cotton Canopy Growth and Development in Field Conditions using

Consumer-grade RGB-D Imaging

Yu Jiang, University of Georgia

Changying Li, University of Georgia; Tariq Shehzad, University of Georgia; Andrew Paterson, University of

Georgia

Field-based high throughput phenotyping (FB-HTP) systems with imaging capabilities can rapidly acquire

various imaging data of plants in field conditions, which enables the possibility of quantifying plant

canopies. The goal of this study was to develop approaches to quantitatively analyze cotton plant

canopy by extracting morphological traits from images collected in the field. An imaging-based FB-HTP

system (GPhenoVision) was used to scan a field that was planted with cotton plants in Watkinsville, GA,

in 2016. The field included four cotton cultivars of 24 plots each. Color and depth images with GPS

information were acquired in the field on four days covering growth stages from plant germination to

boll maturity. Based on GPS information, the collected color and depth images were segregated and

reconstructed into colored point clouds of individual plots. In the colored point clouds, cotton plant

canopies were segmented from the background using an excess-green (ExG) based color filter, and the

segmented canopy points were used to extract six morphological traits including canopy length, width,

height histogram, projection area, coverage ratio, and volume on each day. Changing rates of the six

traits were calculated as growth rates between two consecutive scanning dates, and thus a total of 42

phenotypic traits (6 traits per day by 4 days plus 6 growth rates per period by 3 periods) were extracted

to quantify canopy growth and development of cotton plants. Statistical analyses were conducted

between the extracted traits and cotton cultivars, and the results showed that certain traits were

statistically different among cultivars, indicating the efficacy of the proposed approach for quantification

of canopy growth and development. Additionally, the trait differences among cultivars could be

correlated with fiber yield for selecting genotypes with high yields.

409 Single Cell DNA-sequencing to Monitor Homoeologous Recombination Frequency in Wheat

Venkatesh Bollina, National Research Council Canada

Pankaj Bhowmik, National Research Council Canada; Erin Higgins, Agriculture and Agri-Food Canada;

Isobel Parkin, Agriculture and Agri-Food Canada; Andrew Sharpe, National Research Council Canada;

Sateesh Kagale, National Research Council Canada

To exploit the potential of wild relatives of bread wheat carrying beneficial traits to address biotic and

abiotic factors rely on successful crossover formation of homoeologous chromosomes during meiosis.

Meiotic recombination plays a vital role in acquiring new traits and genetic variation into commercial

crops. However, polyploidy crops like wheat and Brassica have a strict homologous pairing mechanism

controlled by a pairing homoeologous gene 1 (Ph1) and Ph1-like genes. Ph1 locus impedes the process

of modulation of homoeologous chromosomes between alien donor and a cultivated wheat variety.

There are several attempts to develop a Ph1 deletion mutant line through chemical and physical

mutagenesis and gene silencing approaches. Conventionally, laborious and time-consuming cytological

methods have been employed to study the homoeologous chromosomal pairing behaviour. Lack of a

fast and vigorous method to study the homoeologous recombination frequency hinders the progress of

exploiting genetic variation and elite traits in wild relatives. The main objective of this study is to

establish an easy and efficient method for monitoring the impact of modulation of recombination in

plants. We have devised a strategy to assess homoeologous recombination frequency in an F1 plant

which leverages the combined advantages of single cell whole genome sequencing technology. Single

cell haploid microspore from an F1 plant of an interspecific cross is the ideal material to quickly assess

homoeologous recombination frequencies as it is relatively easy to isolate thousands of microspores

carrying segregating genotypes. Capturing the F1 plant microspores in a high throughput manner using

fluorescence-activated cell sorting (FACS) or Fluidigm C1 single cell model followed by DNA isolation and

amplification workflow. And subsequent genotyping of multiple segregating microspores facilitates

assessment of the frequency of homoeologous recombination. The successful implementation of this

novel F1 microspore sequencing approach will provide a cost-effective breeding tool for genotyping and

assessment of recombination frequency.

410 The TERRA REF Hyperspectral Imagery Workflow

Jiarun Mao, University of California, Irvine

David LeBauer – University of Illinois at Urbana-Champaign, National Center for Supercomputing

Applications; Charles Zender – University of California, Irvine

Hyperspectral imaging provides insight into plant functioning that would be difficult or impossible to

measure using traditional field measurements. While there is much to be learned from these sensors,

widespread use requires expensive hardware as well as high technical expertise. The TERRA Reference

Phenotyping Platform (TERRA REF) is creating a high resolution public reference data set including

hyperspectral imaging sensors.

The TERRA REF team has deployed two Headwall hyperspectral sensors on a Lemnatec Field scanner in

Maricopa, AZ. These sensors cover the visible and infrared (300-2500 nm) portion of the light spectrum

at mm spatial and nm spectral resolution over a one acre field. Throughout the growing season, these

sensors scan diverse sorghum lines. Key features of the pipeline include self-describing output metadata

based on Climate and Forecasting (CF) standards, calibration from hyperspectral exposure image to

reflectance data, lossless compression to the raw imagery and self quality assurance. Using parallel

computing and optimal memory use in High Performance Computing environments (HPC), the pipeline

is capable of processing fifty 64 GB scans, each producing 256 GB of calibrated reflectance data. The

hyperspectral data pipeline requires the netCDF Operators (NCO) and Python netCDF4 libraries and uses

netCDF as the format of final products. The source code of the pipeline is available at

github.com/terraref/extractors-hyperspectral.

411 A Graphical Approach to Data Modeling and Storage

Mallory Lai, University of Wyoming

Brent Ewers, University of Wyoming

One of the great difficulties facing scientists today is the integration and curation of large sets of

disparate, yet intertwined, data. We propose a graph-centered approach to ‘omics data integration that

will provide scientists with effective models for analysis and the tools to manage large and complex data

sets. Here, we illustrate how we can use probabilistic graphical models, known as Dynamic Bayesian

Networks (DBN), to capture regulatory relationships using temporal transcriptomic and phenomic data

from Brassica rapa. These networks capture the data probabilistically and allow us to make predictions

about the plants using conditional and joint probabilities. Its graphical framework allows for subsequent

integration and storage into the open-source graph database Neo4j. As a graph database, Neo4j is

capable of handling diverse, heterogeneous data as directed graphs which can be accessed by

collaborators and other scientists. Once stored in Neo4j, the network can be easily queried and its

results returned in a visually intuitive form—a graph!

412 A Meteorological Data Format that Makes Research Easier and More Reliable will also Rork for

Imaging Sensors.

David LeBauer, University of Illinois

Elizabeth Cowdery, Boston University; Ankur Desai, University of Wisconsin; Michael Dietze, Boston

University; Rob Kooper, National Center for Supercomputing Applications; Shawn Serbin, Brookhaven

National Laboratory; Alexey Shiklomanov, Boston University; James Simkins, University of Wisconsin

Standard data formats make it easier to share and reuse data. There are dozens of formats for climate

data and dozens of algorithms with bespoke requirements. After we specified the Climate and

Forecasting standard data format for climate data, we could reduce the problem of matching n weather

data types to m weather-driven crop and ecosystem models from n × m to n + m. This means that we

can focus on fewer conversion, downscaling, gap-filling, and other algorithms while making them more

reliable and accessible. These tools have greatly reduced the technical overhead required to use

computer simulation models in plant and ecosystem research, and are available in the Predictive

Ecosystem Analyzer (PEcAn) R package for atmospheric data.

This talk will describe the PEcAn meteorological workflow and propose an analogous framework for

sensor data pipelines. The TERRA REF and PEcAn teams are extending the CF data format to cover

sensor data sets, starting with hyperspectral imagery. We suggest that this will make life easier for the

community. A common interchange format makes it easier to exchange data without requiring that all

data meets a single standard or specification.

413 Metadata Standards for a Large-scale Plant Phenotyping System

David LeBauer, University of Illinois

Craig Willis, NCSA/University of Illinois

A central goal of the TERRA-REF integrated plant phenotying system is to produce reference data that

are interoperable, extensible, and understandable by users. The TERRA-REF system combines

information from a variety of domains including agricultural field experiments, manually collected field

data, sensor-missions, derived trait data, and genome sequences.

This poster describes our approach combining multiple domain-specific vocabularies and ontologies

using the JSON-LD (JSON-based Serialization for Linked Data) format. This allows us to maximize

reusability and interoperability across domains, while enabling different levels of implementation

complexity. The TERRA-REF metadata standards are composed from the ICASA, Crop/Agronomy

Ontology, Open Geospatial Consortium (OGC), Climate & Forecast, and Gene Ontology families of

standards. JSON-LD combines the flexibility of the ubiquitous JSON data format for programmatic access

with the power of standard vocabularies and ontologies to fulfill our goals of interoperability,

extensibility, and understandability.

414 Plant Comparative Phenomics

George Gkoutos, University of Birmingham

Within the biomedical community, one of the most successful strategies for achieving standardization

and integration of biomedical knowledge, data and associated experiments was proposed more than a

decade ago with the advent the advent of the Gene Ontology. Since then, the biomedical community

has invested a considerable amount of effort, research and resources in the development of ontologies

that are now becoming and increasingly successful as information management and integration tools.

This presentation will focus on how phenotype data ontology-based standardisation can enable a variety

of applications. It will employ examples stemmed from the biomedical domain and attempt to provide

directions of how these could be applied in the plant domain.

415 Maize - GO Annotation Methods Evaluation and Review (Maize-GAMER)

Kokulapalan Wimalanathan, Iowa State University

Carson Andorf, USDA - ARS; Iddo Friedberg, Iowa State University; Carolyn Dill, Iowa State University

Making a genome sequence accessible and useful involves three basic steps: genome assembly,

structural annotation, and functional annotation. The quality of data generated at each step influences

the accuracy of inferences that can be made, with high-quality analyses producing better datasets that

result in stronger hypotheses for downstream experiments. Here we report a new, high-confidence

functional annotation of genes for the maize B73 reference genome. To develop this annotation set, we

used sequence similarity- and protein domain-based methods as well as mixed methods developed for

the Critical Assessment of Function Annotation (CAFA) competition. Individual annotation sets as well as

combined outputs of multiple methods were compared both to each other, and to the existing datasets

from Gramene (release 49) and Phytozome (release 12). Our new functional annotation increases the

number of genes that are assigned at least one functional annotation (GO term) as well as the quality of

functional assignments on average (based on F-measure). Annotations derived from the GO Annotation

Methods and Evaluation (GAME) pipeline will be made accessible via MaizeGDB

(http://www.maizegdb.org).

Plasticity in Plant Traits

500 Assessment of Plant Performance Traits in Controlled Environments and Translation to the Field

Astrid Junker, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)

Marc Heuermann, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Rongli Shi, Leibniz

Institute of Plant Genetics and Crop Plant Research (IPK); Henning Tschiersch, Leibniz Institute of Plant

Genetics and Crop Plant Research (IPK); Matthias Lange, Leibniz Institute of Plant Genetics and Crop

Plant Research (IPK); Daniel Arend, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK);

Jean-Michel Pape, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Rhonda Meyer,

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK); Kathleen Weigelt-Fischer, Leibniz

Institute of Plant Genetics and Crop Plant Research (IPK); Michael Grau, Leibniz Institute of Plant

Genetics and Crop Plant Research (IPK); Andreas Boerner, Leibniz Institute of Plant Genetics and Crop

Plant Research (IPK); Uwe Scholz, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK);

Thomas Altmann, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK)

To meet the challenges in global food security requires the development of strategies towards the

optimization of yield formation and resource efficiency of crop plants under future climate scenarios. To

enable this, a deeper understanding of processes underlying plant acclimation to changing

environments is necessary. Using high throughput automated plant phenotyping systems at IPK, the

dynamics of plant growth and photosynthetic efficiency have been studied in accession panels of

Arabidopsis and maize under controlled conditions. This led to the identification of Arabidopsis and

maize candidate accessions with different acclimation strategies to high light and variations in biomass

yield and photosynthetic efficiency, respectively. To evaluate and enhance the prospects of lab-to field

translation, candidate maize accessions have furthermore been phenotyped for root- and shoot traits

under different cultivation conditions in the glasshouse and in the field. Modification of the standard

cultivation procedures improved the lab-to-field translation of phenotypic trait expression which will be

further optimized in the Plant Cultivation Hall currently being erected at IPK. This building will enable to

run specifically designed and reproducible cultivation scenarios that mimic field conditions. In this way,

trait expression similar to that in the field will be triggered and monitored using automated phenotyping

installations. Investigations involving precise variation of environmental parameters will yield a deeper

understanding of acclimation processes and underlying molecular mechanisms and genetic

determinants and will support the prediction of idiotypes with improved performance under future

climate scenarios. Furthermore recent achievements in phenotype data management, standardized

metadata representation (e.g. MIAPPE), and data publication will be presented.

501 The Impact of Short Petiole Length in Soybean Yield

Ursula Ruiz-Vera, University of Illinois at Urbana-Champaign

Benjamin Campbell, University of Minnesota; Venkatraman Srinivasan, University of Illinois at Urbana-

Champaign; Robert Stupar, University of Minnesota; Aaron Lorenz, University of Minnesota; Donald Ort,

USDA-ARS and University of Illinois at Urbana-Champaign

At the current rate of progress in yield improvement, agricultural will fall to feed a ~34% larger

population by 2050. The maximum yield (Yp) obtained for a crop is determine by the total incident solar

radiation available during the growing season multiplied by the light interception efficiency (Ei), the

energy conversion efficiency (Ec) and the partitioning efficiency (Ep). Ec is primarily determined by net

photosynthesis, does not operate near its theoretical maximum and is the remaining factor with the

potential to increase Yp by maximizing the proportion of solar energy intercepted by the crop and used

to build biomass. Model simulations indicate that one way to improve Ec is by altering the canopy

architecture. An important component of the canopy architecture are petioles but it is not known how

their length may affect light penetration into the canopy and thereby affect yield. In this experiment, 4

soybean NIL (near-isogenic lines) pairs of short and normal petiole length were grown in a randomized

block design experiment (4 blocks and row spacing of 0.254m) to determine the effect of petiole length

on photosynthetic physiology and yield. We hypothesized a higher yield in the short petiole compared to

the standard petiole soybean reasoning that short petioles at the top of canopy would permit more light

penetration to lower canopy layers where light could be more efficiently used in photosynthesis. There

were no differences in leaf photosynthesis, above-ground biomass, seed weight or harvest-index

between the short and non-short petiole plants within any given NIL pair. However, there were

differences among the four NIL groups for seed weight and harvest-index. The determinations of Ei, Ec

and Ep are still in progress, but the preliminary results suggest that a petiole size up to half of normal did

not increase yield compared to standard petiole soybean.

Modeling

600 Testing a Whole-plant Biophysical Model with High Throughput Electrical Conductivity

Measurements of Plant Hydraulics

Brent Ewers, University of Wyoming

Timothy Aston, University of Wyoming; Carmela Guadagno, University of Wyoming; Mallory Lai,

University of Wyoming; Cynthia Weinig, University of Wyoming; David Mackay, University at Bufffalo;

Jonathan Pleban, University at Buffalo

Biophysical models of plant response to stress help bridge the gap between the genome and phenome

and improve predictive understanding. The processes behind organ and whole plant carbon, water and

nutrient use are generally known, but a priori parameters of the governing equations are poorly

predicted for novel genotypes or varying environmental conditions. Many biophysical parameters are

directly related to expression of functional genes. The means of quantifying parameters vary from easy

yet destructive measurements of biomass to difficult continuous gas exchange and transport between

organs. We test the hypothesis that electrical conductivity measurements of tissues and whole plants

provide temporally rigorous proxies of input parameters, e.g. whole plant hydraulic conductance, and

output time series, e.g. stomatal conductance and predictions of plant growth. Measurement and data

curation of these traits on 100s to 1000s plants has severely limited phenotyping throughput. We make

an initial test of high throughput electrical conductivity measurements of droughted and well-watered

plants with TREES, a mechanistic model of rhizosphere-plant-atmosphere integrated water, carbon and

nutrient processes. We combined open source data acquisition and processing with the graph database

Neo4j to set up a workflow that enables inexpensive yet rigorous collection of electrical conductivity and

model tests. We also measured at discrete time points leaf photosynthesis, biomass partitioning and

environmental conditions, which are also needed to test the model on the herbaceous plant, Brassica

rapa. We found that 1) electrical conductivity provides a continuous estimate of plant hydraulic stress

that has been missing from herbaceous plant studies, 2) the graphical database improved the efficiency

and accuracy of data to model testing, and 3) the workflow accommodates other –omics data such as

RNAseq, metabolomics and UAV-acquired spectral estimates. All of these improve predictive

understanding of the genome to phenome in response to varying genotypes and stress.

Education & Outreach

700 Building High-throughput Plant Phenotyping Devices while Training the Next-generation of Users.

David Mendoza, University of Missouri

High-throughput plant phenotyping is an emerging and fast-moving field that requires active

collaboration across disciplines, including plants sciences, engineering and computer sciences. Currently,

there are commercial instruments that can be acquired to pursue large-scale high-throughput

phenotyping (HTP) experiments. However, there are also small labs interested in pursuing HTP screens

using diversity panels available for some plant species or mutant collections that require very specific

conditions. Moreover, some labs may have needs - in terms of design - that may not be commercially

available. So who is going to build these devices and how can we ensure that our students are well

trained to take advantage of HTP technologies?

At the University of Missouri, we have started an interdisciplinary program where teams of students

from engineering and plant sciences work together to design, build and test automatic platforms for

high-throughput phenotyping of plants. Furthermore, during the entire academic year (2015-2016) an

MU journalist followed the team and conducted interviews at regular intervals to document how

members from distant fields exchange ideas to solve the different problems that appeared at different

stages of the project.

Our first cohort of interdisciplinary students graduated in 2016 after successfully assembling S.P.I.P.

v1.0 [Small Plant Imaging Platform v1.0].

Learn more about the project @ NSF Broader Impacts: https://goo.gl/cl580t

701 NSF Research Traineeship – P3, Predictive Plant Phenomics

Carolyn Lawrence-Dill, Iowa State University

Julie Dickerson, Iowa State University; Thedore Heinde, Iowa State University l; Patrick Schnable, Iowa

State University

New methods to increase crop productivity are required to meet anticipated demands for food, feed,

fiber, and fuel. Using modern sensors and data analysis techniques, it is now feasible to develop

methods to predict plant growth and productivity based on information about their genome and

environment. However, doing so requires expertise in plant sciences as well as computational sciences

and engineering. Through P3, we bring together students with diverse backgrounds, including plant

sciences, statistics, and engineering, and provide them with data-enabled science and engineering

training. The collaborative spirit required for students to thrive in this unique intellectual environment

will be strengthened through the establishment of a community of practice to support collective

learning. This traineeship anticipates preparing forty-eight (48) doctoral students, including twenty-eight

(28) NRT funded doctoral students, with the understanding and tools to design and construct crops with

desired traits that can thrive in a changing environment.