The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.

ARF transcriptional activity is controlled by the large intrinsically disordered ����������������middle region��������������� and ARF activity in a yeast-based system has been well characterized. We will exploit the deep evolutionary conservation of ARF function, combined with the characterized middle region transcriptional activity to identify and characterize ARF ADs from 79 ARFs across a spectrum of species, including Arabidopsis thaliana (22), Zea mays (35), Physcomitrella patens (19), and Marchantia polymorpha (3). From this data, we will examine the conservation of number and positioning of ADs within ARF IDRs. We will validate these ADs by AD mutant variant analysis. We will use ML approaches to generate the ����������������rules��������������� for ARF AD features, then test our models on species such as tomato. This portion of the project will benefit from the highly characterized activity of ARF proteins from an evolutionarily diverse set of species.

A major challenge for humankind is to feed the increasing human population in a sustainable manner. According to UN������������������s development programme extreme hunger and malnutrition is a major barrier to development in many countries: 795 million people are estimated to be chronically undernourished as of 2014, often as a direct consequence of environmental degradation, drought and loss of biodiversity. The Sustainable Development Goals (SDGs) aim to end hunger and malnutrition by 2030. Improved agricultural productivity is a critical part of achieving the SDG goal 2, Zero Hunger. Currently more than one third of crop yields are lost due to abiotic and biotic stress factors, such as drought, salinity, pests and disease. To minimize this yield gap and to simultaneously reduce the environmental impact of current agricultural practices, future crop production needs to be achieved on sub-optimal soils with reduced input of fertilizers and pesticides (���������������more with less������������������). These challenges have increased the awareness of the importance of the plant microbiome for improved agricultural practices. Plants are colonized by an astounding number of microorganisms that can have profound effects on seed germination, seedling vigour, plant growth and development, nutrition, diseases and productivity. Thus, the plants can be viewed as holobionts that benefits from its microbiome in terms of specific functions and traits. In return, plants transfer a substantial part of their photosynthetically fixed carbon directly into symbionts and into their immediate surroundings thereby supporting the microbial community and influencing its composition and activities. For the vast majority of plant-associated microorganisms, however, there is little knowledge of their specific impact on crop growth and crop resilience and the mechanisms underlying microbiome-plant interactions. Hence, a critical step in developing new microbiome-assisted approaches to quantitatively and predictably improve crop resilience management strategies is deciphering the hyperdiverse plant microbiome. In particular, we need to identify keystone microorganisms and mechanisms involved in plant growth promotion and protection against biotic and abiotic stresses. To that end, systems-based analyses combined with deep-learning and modelling are essential to decode the taxonomic diversity and functional potential of plant microbiomes. The overall aim of this multidisciplinary research program is to develop a scalable system-based strategy to harness the functional potential of plant microbiomes for improving crop resilience. More specifically, we will focus on experimental analyses and modelling of the phyllosphere microbiome of wheat (Triticum aestivum), one of the most important cereal food crops worldwide. The phyllosphere microbiome is defined here as the collective microbial communities inhabiting both the leaf surface as well as the internal leaf tissue. We will zoom in on the microbiome of flag leaves of wheat, as the flag leaf is a major determinant (up to 45%) of wheat yield. To do this, we combine renowned academic expertise in microbiology, chemistry, DNA and RNA sequencing, bioinformatics, machine-learning and modelling with company support in plant breeding and agronomy to deliver novel approaches and technologies.

Minimizing crop loss and increasing output, across the food supply chain, will increase the economic viability of US growers and the global economic competitiveness of industry and stakeholder partners. We have assembled a diverse team across different National and International Universities with faculty that have track records of convergent research, education, and outreach. We will be well positioned to implement a Networks of Networks with diverse backgrounds, ethnicities, genders, experiences, and disciplines to drive research and innovation. Students and postdocs will be exposed to hands-on learning, on-farm technology training, cooperative extension, commercialization, industry engagement, and transdisciplinary education to create a highly trained workforce that is equipped to address food security and safety challenges.

Heat stress reduces soybean yield and can affect market-critical traits like protein and oil concentration. Developing new, heat tolerant soybean varieties through conventional breeding strategies is extremely difficult, because of the logistical constraints inherent in conducting heat stress experiments. In a prior project supported in part by NCSPA, we used a strategy to identify protein markers, hereafter referred as phosphomarkers, that can be used for breeding heat-tolerant soybeans. We conducted growth chamber experiments and field experiments that measured agronomically relevant traits, including yield, protein concentration, and oil concentration, upon heat stress. In addition to the identified phosphomarkers, we now aim to complement this research to identify genetic markers, giving us a holistic view of heat stress responses in soybean. To this end, we will leverage the available leaf samples and perform transcriptomics to identify genes that regulate heat stress responses in heat-tolerant and heat-sensitive soybean genotypes. Using the tissue stored from experiments conducted in 2020 and 2021, we will measure gene expression, identify genes that are central to regulating heat stress tolerance, and link those genes with agronomic outcomes using machine learning-based analytical pipelines aimed at predicting causal-effect relationships.

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

Although an invaluable workhorse for research and training, the current 3D bioprinters available at NC State, such as the 3D Bioplotter (EnvisionTec), BioAssemblyBot (Advanced Solutions), BioX (Cellink), and Allevi 3 (3D Systems) are primarily based on the extrusion printing mechanisms. These systems are well suited for macro-geometric structures, but their micro-scale and cellular level control and precision are limited. Furthermore, these systems lack in-process monitoring abilities. This severely impedes fundamental research about cellular-level functional interactions in bioprinting and the potential to develop new manufacturing strategies and applications that can benefit from single cell-level control across layers of bulk constructs. The proposed multi-modal, high-resolution Next-Generation Bioprinter-Research (NGB-R) system [1] will address this gap and make a huge impact on on-going and future research and training at NC State. The comprehensive standalone BSL-2 system is equipped with micro-extrusion and inkjet bioprinting modalities along with the one-of-its-kind laser induced forward transfer (LIFT) mechanism. The uniqueness of the NGB-R system is further enhanced by the embedded microscope driven by machine learning algorithms, which enables high-throughput, in-line, real-time quality monitoring of bioprinted constructs.

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers������������������ and growers������������������ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project������������������s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU������������������s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.

Stem cells are present in all multicellular organisms and are considered the building blocks for different cell types and tissues. Deciphering the processes by which stem cells are specified and differentiate is critical to producing specific cell-types and even organ-like structures from induced pluripotent stem cells in both animals and plants. However, in most mammalian systems, the lack of definitive stem cell markers, the inaccessibility of these cells, and cell movement confound analysis of these cells. These limitations can be overcome by using the model plant Arabidopsis as an experimental system. Moreover, because plant cells do not move and stem cells divide in a stereotypical manner, the root offers a spatially oriented lineage from stem cells to their differentiated progeny and provides an excellent system in which to identify emergent properties underlying cell specification, determination, and differentiation. Furthermore, disruption of the stereotypical cellular arrangement of the root via either physical, mechanical, or laser ablation, can inform us about the underlying rules of cellular reprogramming and reestablishment of morphogen patterning. Interrogating this self-organizing capacity, however, has thus far been limited by the challenge of manipulating individual cells within their local microenvironment. Nowadays, the precise placement of cellular materials can be programmatically assembled in 3D space in nearly any arrangement using 3D-bioprinting capabilities. Despite this great advantage, 3D-printing technologies have been limited to animal cells, and have not been yet exploited to plant cells, which offer an amenable system (i.e plant cells can be easily isolated and manipulated and their tissues are organized into cell layers where entire cell lineages are spatially restricted). Thus, we propose that by arranging, at high-resolution, plant cells in predetermined architectures (e.g precise -through 3d printing- placement of cell types, which recapitulate gradients and influence cell behavior), using fluorescent reporters to quantify small molecule gradients (e.g auxin and gibberellin biosensors) and performing single cell gene expression profile and network modeling we will be able to: 1) Understand, simplify, and test the critical spatial and temporal establishment of patterning and interactions among cells; 2) Determine the rules by which morphogen gradients are established across cells to predict differentiation and growth; 3) Identify mechanisms regulating stem cell regulation and the differentiation of their progeny into specific tissues.

More than a third of crop yields are currently lost due to abiotic and biotic stressors such as drought, pests, and disease. These stressors are expected to worsen in a warmer, drier future, resulting in crop yields further declining ~25%; however, breeding is only expected to rescue 7-15% of that loss [1]. The plant microbiome is a new avenue of plant management that may help fill this gap. All plants have fungi living inside their leaves (����������������foliar fungal endophytes���������������). This is an ancient and intimate relationship in which the fungi affect plant physiology, biotic and abiotic stress tolerance, and productivity. For example, some foliar fungi prevent or delay onset of major yield-limiting diseases caused by pathogens such as Fusarium head blight [2]. Foliar endophytes also reduce plant water loss by up to half and delay wilting by several weeks [3, 4]. Endophyte effects on plants occur via diverse genes and metabolites, including genes involved in stress responses and plant defense [5]. Genes and metabolites also predict how interactions in fungal consortia affect host stress responses, which is important for developing field inoculations [6]. Because newly emergent leaves lack fungi, endophytes are also an attractive target for manipulation (particularly compared to soils, where competition with the existing microbial community inhibits microbial additives). We propose to address the role of endophytic ����������������mycobiomes��������������� in stress tolerance of five North Carolina food, fiber, and fuel crops (corn, hemp, soybean, switchgrass, wheat), and to develop tools that can push this field beyond its current limits. Our major objectives (Fig. 1) are to: 1. Identify key microbiome scales to optimally manage endophytes 2. Determine fungal mechanisms via greenhouse tests, modeling, and genetic engineering 3. Build tools for field detection of endophytes 4. Understand the regulatory environment and engage diverse stakeholders Results of these objectives will allow us to make significant progress in both understanding the basic biology of plant-fungal interactions and managing those interactions in real-world settings. Our extension efforts will also bring these ideas to the broader community. Finally, we will also be well positioned to pursue several future research endeavors supported by federal granting agencies.