An Interdisciplinary Graduate Course for Engineers, Plant Scientists, and Data Scientists in the Area of Predictive Plant Phenomics

This paper describes the development and first offering of a new graduate course entitled Fundamentals of Predictive Plant Phenomics, which is part of a recently awarded National Science Foundation Graduate Research Traineeship (NRT) award to Iowa State University. The focus of this particular NRT award is to train engineering, plant science, and data science graduate students in the area of predictive plant phenomics (P3), with the goal to develop researchers who can design and construct crops with desired traits to meet the needs of a growing population and that can thrive in a changing environment. To meet this goal, the P3 NRT program will train next generation crop scientists to have broad technical skillsets as well as strong soft skills in communication and collaboration. A companion paper (Dickerson et al., 2017) provides an overview of the P3 NRT program, whereas this paper focuses on a new course developed as part of the P3 NRT. One of the challenges associated with providing the students in the P3 NRT program with the needed multidisciplinary skills to thrive is to ensure that all students have a common knowledge base in engineering, plant sciences, and data sciences, no matter their background. The goal is to get all students communicating in the same language. The course Fundamentals of Predictive Plant Phenomics was developed to meet this challenge. The course planning took nearly one year and incorporated input from faculty with various disciplinary backgrounds. The actual course is coordinated by an engineering faculty member and taught through a series of guest lecturers covering various plant science, data science, and engineering topics over a 15-week period. In addition to the three 50-minute lectures per week, a 3-hour laboratory each week provides an experiential learning opportunity where students can apply the knowledge they learn in the lectures. The first offering of this course occurred in fall 2016, with 16 enrolled students, 7 from engineering disciplines, and 9 from plant and data science programs. Lessons learned from the first offering of this course are summarized in this paper. The course is providing the needed background so students can develop a successful research topic in the area of predictive plant phenomics and communicate with others in this broad multidisciplinary field. Because the course is a leveling or survey of three disciplines, and each student has a good background in at least one of the three, it has been challenging to keep all students interested and engaged for all lectures (but not labs). To address this challenge, expanding the application of Inquiry-Based Learning approaches during the lecture period in future years is proposed

Training Students with T-shaped Interdisciplinary Studies in Predictive Plant Phenomics

Modern sensors and data analysis techniques make it feasible to develop methods to predict plant growth and productivity based on information about their genome and environment. The NSF Research Traineeship (NRT) Predictive Plant Phenomics (P3) Specialization implements the T-training model proposed by the American Society of Plant Biology (ASPB) and described in “Unleashing a Decade of Innovation in Plant Science: A Vision for 2015-2025.”[1] The goal of the P3 program is to prepare graduate students with the understanding and tools to design and construct crops with desired traits that can thrive in a changing environment. Students with “T-shaped” experiences will differ from traditional STEM graduate programs that produce students with deep disciplinary knowledge in at least one area. This depth represents the vertical bar of the T . The horizontal bar represents their ability to effectively collaborate across a variety of different disciplines [2], which is the focus of P3. The first cohort of students began their training in August 2016 with a two-week “boot camp” short course to introduce the students to the basic topics they will need to succeed. The four-credit P3 core graduate course (Fundamentals of Predictive Plant Phenomics) taken the first year of the program expands upon the boot camp and is comprised of classroom and hands-on laboratory components. The P3 core course has two key objectives: 1) bring all students’ knowledge up to the same level for issues that pertain to plant phenomics, sensor engineering, and data analysis, and 2) begin the process of teaching students the needed terminology to speak across disciplines. A companion paper submitted to the ASEE Graduate Studies Division discusses the first offering of this course. Additionally, the collaborative spirit required for students to thrive will be strengthened through the establishment of a community of practice to support collective learning (i.e., a P3 graduate learning community). The P3 program is being evaluated both internally and externally. The internal evaluation focuses on metrics such as student recruitment and retention, program outcomes, and student performance. The external evaluation includes pre-test and post-test designs for quantitative assessments of how well the program is developing scientists and engineers with broad skillsets to address the research needs to increase understanding of agricultural production. Qualitative measures include in-depth interviews and focus groups of student students. Evaluation activities follow a recursive design so that the project can be continually informed and improved by the evaluation findings in real time. This evaluation has already been applied to the initial boot camp activities. The overall view of the activities was positive from both the trainees and program administrators. However, the students felt that the introductory sessions should be more hands-on and structured more for beginners in the field. This input will be applied to future designs. 1. American Society of Plant Biologists, Unleashing a Decade of Innovation in Plant Science - A Vision for 2015-2025, in Plant Science Decadal Vision. 2013, American Society of Plant Biologists,. p. 36. 2. T-Summit 2016, “What is the T?”, http://tsummit.org/t, viewed October 2016

The Agricultural Genome to Phenome Initiative (AG2PI): creating a shared vision across crop and livestock research communities

Predicting phenotype from genotype is a central challenge in biology. By understanding genomic information to predict and improve traits, scientists can address the challenges and opportunities of achieving sustainable genetic improvement of complex, economically important traits in agriculturally relevant species. Converting the enormous, recent technical advances in all areas of genomics and phenomics into sustained and ecologically responsible improvements in food and fuel production is complex. It will require engaging agricultural genome to phenome (G2P) experts, drawing from a broad community, including crop and livestock scientists and essential integrative disciplines (e.g., engineers, economists, data and social scientists). To achieve this vision, the USDA NIFA-funded project inaugurating the Agricultural Genome to Phenome Initiative (AG2PI) is working to: Develop a cohesive vision for agricultural G2P research by identifying research gaps and opportunities; advancing community solutions to these challenges and gaps; and rapidly disseminating findings to the broader community. Towards these ends, this AG2PI project is organizing virtual field days, conferences, training workshops, and awarding seed grants to conceive new insights (details at www.ag2pi.org). Since October 2020, more than 10,000 unique participants from every inhabited continent have engaged in these activities. To illustrate AG2PI’s scope, we present survey results on agricultural G2P research needs and opportunities, highlighting opinions and suggestions for the future. We invite stakeholders interested in this complex but critical effort to help create an optimal, sustainable food supply for society and challenge the community to add to our vision for future accomplishments by a fully actualized AG2PI enterprise

Response to Persistent ER Stress in Plants: a Multiphasic Process that Transitions Cells from Prosurvival Activities to Cell Death

The unfolded protein response (UPR) is a highly conserved response that protects plants from adverse environmental conditions. The UPR is elicited by endoplasmic reticulum (ER) stress, in which unfolded and misfolded proteins accumulate within the ER. Here, we induced the UPR in maize (Zea mays) seedlings to characterize the molecular events that occur over time during persistent ER stress. We found that a multiphasic program of gene expression was interwoven among other cellular events, including the induction of autophagy. One of the earliest phases involved the degradation by regulated IRE1-dependent RNA degradation (RIDD) of RNA transcripts derived from a family of peroxidase genes. RIDD resulted from the activation of the promiscuous ribonuclease activity of ZmIRE1 that attacks the mRNAs of secreted proteins. This was followed by an upsurge in expression of the canonical UPR genes indirectly driven by ZmIRE1 due to its splicing of Zmbzip60 mRNA to make an active transcription factor that directly upregulates many of the UPR genes. At the peak of UPR gene expression, a global wave of RNA processing led to the production of many aberrant UPR gene transcripts, likely tempering the ER stress response. During later stages of ER stress, ZmIRE1\u27s activity declined as did the expression of survival modulating genes, Bax inhibitor1 and Bcl-2-associated athanogene7, amidst a rising tide of cell death. Thus, in response to persistent ER stress, maize seedlings embark on a course of gene expression and cellular events progressing from adaptive responses to cell death

Assessing plant performance in the Enviratron

Background: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. Results: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. Conclusions: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ—in that the rover takes sensors to the plants rather than moving plants to the sensors

The effect of artificial selection on phenotypic plasticity in maize

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements