2019 Agriculture & Food Tech Projects

Profiles of Chemical Contaminants and Antibiotic Resistance in Surface Soil of Urban Community Gardens

Chunrong Jia

Community gardens promote healthy communities and contribute to food security for low-income populations in urban areas. Concerns have arisen regarding chemical contamination in soil, mainly focusing on heavy metals, e.g., lead (Pb), arsenic (As) and cadmium (Cd), and toxic organic chemicals, e.g., pesticides and polycyclic aromatic hydrocarbons (PAHs). Although some studies have measured lead and other metals, few studies have addressed organic chemicals in garden soils. Heavy metals and other chemicals are known to impart selective pressure on soil microorganisms which results in the emergence of resistance in a sub-population of these microbes, and thus garden soil is a significant potential reservoir of antibiotic resistance (and metals and other biocides). Antibiotic-resistant bacteria persist in soil for years even after discontinuation of contamination. There is a knowledge gap regarding the extent of antibiotic resistance and its association with chemical contaminants in garden soil, and how these factors interact to compromise food safety aspect related to urban agriculture.
The overall objective of this study is to characterize chemical contamination and antibiotic resistance genes (ARGs) in surface soil of community gardens in urban areas through a partnership among university researchers, community stakeholders, and government agencies. There are three specific aims:
Specific Aim 1: Establish analytical capabilities for assessing soil contaminants and microbiome through multidisciplinary partnership within the University of Memphis.
Specific Aim 2: Characterize heavy metals and pesticides and determine the prevalence of ARGs in surface soil of community gardens in the Memphis Metropolitan Area.
Specific Aim 3: Determine the association of metals and organics with the prevalence of ARGs.
The long-term goal of this project is to establish a multidisciplinary team and laboratories at the University of Memphis that conduct cutting-edge research on urban agricultural environments, food security, and food safety hazard and public health.

Phylogenetics and population genetic structure in the African oilseed crop, Centrapalus pauciflorus, and related taxa

Jennifer Mandel

An understanding of the levels of genetic variation and the phylogenetic relationships of related taxa is crucial for identifying species to target for breeding. In addition, the gene pools (primary, secondary, and tertiary) of crops are described in terms of their genetic
relatedness and cross-compatibility to the crop species of focus, and thus a knowledge of evolutionary relationships provides information for the determination of gene pool classes. We propose to:
1) Obtain additional genetic material from all varieties, cultivars of Centrapalus pauciflorus, as well as, from all putative related taxa as proposed in recent phylogenies of the tribe Vernonieae;
2) Use phylogenomic approaches for Asteraceae pioneered in the Mandel Lab to gain a better understanding of evolutionary relationships in Centrapalus and related taxa;
3) Employ population genetic methods to investigate the amount and distribution of
genetic diversity within Centrapalus and related taxa.

Harvesting fungal endophytes for integrative bioherbicidal management of an invasive plant to reduce legume pathogen spillover events

Shawn Brown

Leguminous crops, primarily soybean production, are a major industry in the state in Tennessee. Within Tennessee, 1.7 million acres of soy was planted in 2018 with over 76 million bushels harvested with a current market value of over $683 million (based on futures market values of March 27, 2019). In fact, acreage of soybeans planted in Tennessee in 2018 was over 6% of the total land area of the state of Tennessee. With any crop, final harvestable yields of soybeans are impacted by plant health as well as forces uncontrollable by producers including climatic and anthropogenic factors. The healthier a plant is, the more energy the plant can allocate to fruit production and the larger the yield. The largest threats to soybean health impacting yields are pathogenic agents. Numerous pathogens attack soybeans, which combine to reduce harvestable yields by more than 10% annually, but individual field loss percentages are often drastically higher. Major agents that impact harvestable yields of soybean include bacterial pathogens (e.g. Psuedomonas savastanoi pv. glycinea – causal agent of bacterial blight; Xanthomonas axonopodis pv. glycines – causal
agent of bacterial pustule) and viral agents including Soybean Mosaic Virus and Tobacco Ringspot Virus. However, fungal pathogens account for the majority of crop damage causing soybean losses valued over $113 million (as of 2014, ~10 mil. Bushels;). There are numerous fungal disease that cause mass yield loss or entire plant failure including Anthracnose (which elicits spontaneous seed abortions and is caused by Colletotrichum truncatum and can locally reduce yields by as much as 25%, Brown Spot (caused by Sphaerulina glycines {formally Septoria glycines} and other Sphaerulina and Mycosphaerellaceae sp.), and perhaps the most infamous, Soybean Rust (Phakospora pachyrhizi) which can locally reduce yields by up to 80%. Soybean rusts, and all fungal rusts, are obligate plant pathogens (thus cannot be cultured) and has been spreading globally since it was identified in Japan in 1902 and was first documented in Tennessee in 2004. Due to the potential crop losses, soybean rust is the major concern, particular in the southeastern United States as spore viability after overwintering of P. pachyrhizi (required for the rust lifecycle) is particularly sensitive to long-term freezing conditions that are not often met in the southeast US. While many plant pathogens are specific to certain hosts, the majority of pathogens evolved pathogenicity cassettes that can act upon many hosts, but still often constrained by plant relatedness. Consequently, often is the case that fungal pathogens of soybean (Fabaceae – legume) can also infect and impact other leguminous plants. There is general evidence that nonnative species (including crops or wild exotics) are more likely to be threatened by generalist pathogens, likely due to absence of co-evolutionary dynamics. Many generalist pathogens can symptomatically or asymptomatically colonize plants across the Fabaceae but not all pathogens attack all Fabaceae; given the economic importance of this family regionally and nationally, reduction of pathogen incidences can translate to drastically increased yields. One exotic and invasive member of the Fabaceae that has widespread occurrence across the southeastern United States is Kudzu (Pueraria montana var. lobata Willd.). Kudzu is readily colonized by numerous fungal pathogens of soybean and other legumes including Septoria sp., Diaporthe sp., and most often studied, soybean rust (P. pachyrhizi). Kudzu acts as a pathogen reservoir whereby many economically important 2 pathogens can persist and maintain a strong regional propagule/spore pool facilitating infection of leguminous crops. Crops are heavily treated with fungicides to suppress, prevent, or mitigate disease severity, yet nearby Kudzu acts as a reservoir making transmission and future infections of soybean diseases a practical certainty. These conditions, in which nearby alternative hosts to soybean pathogens (namely Kudzu) which are infected and prevalent, facilitates pathogen spillover events and increases the probability of transmission to and epidemics on soy. It has been suggested that Kudzu often acts as the initial inoculum source of rust on soybeans and may have actively facilitated the rapid spread of soybean rust across the US, even at a distance over 100km due to spore dispersal. Further, it has been predicted using landscape-scale pathogen spillover models that even small infected patches of Kudzu are sufficient to maintain active epidemic levels of soybean rust.

Together, this suggests two main conclusions: 1) as long as Kudzu density remains high and Kudzu remains infected, pathogen spillover will occur and infect soybeans, and 2) this leads to consistent and annual treatment by massive amounts of
fungicides by producers. Chemical fungicides to suppress soybean rust alone cost producers ~$2 billion annually and require at least three application per growing season. While commercial fungicides generally do a good job reducing pathogen loads, these fungicides are often non-specific and may also suppress beneficial fungal endophytes and epiphytes, leading to poorer plant growth overall and may have unforeseen environmental impacts. Kudzu is notoriously difficult to control due to fast vegetative growth (twining vine growth as much as 30cm per day), clonal nature (through genets), deep starchy tap roots that provide ample energy reserves that allows for rapid regrowth after control treatments, and general resistance to common herbicidal treatments (e.g. glyphosate). Due to all these factors it can take up to a decade of repeated treatments to induce Kudzu mortality. Besides
mechanical and herbicidal treatments, which have general poor long-term results, bioherbicidal methods have been developed. Interestingly, while herbicides have in general poor performance on Kudzu control, sublethal doses of herbicides may sufficiently stress plant immune responses allowing for increased pathogen susceptibility. It is this synergistic approach, chemical and
bioherbicidal treatments in combination, that has been a major focus of Kudzu control research in recent years. Still, widespread Kudzu suppression programs are rare and fungal bioherbicides are potentially problematic due to cross-reactivity. The fungal bioherbicide Myrothecium verrucaria (Mv - Hypocreales; Stachybotryaceae) has emerged as a promising control tool for Kudzu due to: 1) it is not known to causes symptoms on soybean, and 2) germination of conidia (asexual spores) is uncommon in the absence of a strong surfactant allowing for highly targeted applications of this bioherbicide. Still, applications of Mv alone does not fully suppress Kudzu and only provides high mortality with concurrent chemical herbicidal treatments. Chemical herbicidal treatments for pest and weed management is a major expenditure. US farmers alone spend more than $6 billion on herbicidal weed management annually. While herbicidal use is generally safe and has low environmental impacts, there is a strong movement to reduce application loads.

Recently, there has been greater public awareness about herbicidal applications (namely glyphosate) due to recent litigation in California about potential carcinogenic effects. Despite this court ruling, there has been no demonstrated link between glyphosate application and cancer. This, coupled with some evidence that herbicide runoff in water systems may suppress microalgal primary productivity leading to trophic interruptions and loss of crustacean biodiversity which are particular sensitive to may common herbicides and the potential development of herbicidal resistance in plants, alternative biocontrol methods development is in demand. Here, we propose to use fungal endophytes in concert with pathogens to exacerbate pathogenicity and reduce Kudzu fitness and increase mortality. Fungal plant endophytes, fungi 3 associated with plant tissues in inconspicuous infections that are asymptomatic, are hyperdiverse and are much more abundant than pathogens in the plant microbiome. Endophytic roles in plant defenses are poorly known but fungal endophytes have been demonstrated to modify plant diseases buy acting as direct pathogen antagonists, pathogen facilitators, or act as agents increasing quantitative resistance in hosts. Despite increasing research on endophyte-pathogen interactions, few studies explicitly examine pathogen facilitation and even fewer directly test such interactions in vitro or in plantae, instead relying of correlative and network analyses of next generation sequencing (NGS) plant mycobiome data. While this method is incredibly useful for developing a more holistic understanding of endophyte assembly and as hypotheses generations tools, direct relationships cannot be determined without experimental manipulations. Further, current sequencing technologies limits amplicon sizes, thereby limiting confidence in OTU taxonomic assignments. Also, NGS surveys do not allow for morphological descriptions, which are required for positive fungal identifications. We have used NGS community surveys to generate testable hypotheses on pathogen-endophyte interactions (see preliminary data). In this proposed work, we will test these hypotheses in addition to generating a substantive culture library of Kudzu specific and leguminous generalist pathogens and endophytes that will be used to: 1) test endophytic fungi for synergistic action and facilitation of the established bioherbicidal fungi Mv, thus alleviating or reducing the need to concurrent chemical herbicidal treatments, 2) identify and develop novel bioherbicidal agents and novel pathogen-endophyte co-inoculate compounds to be tested in greenhouse and field trials, and 3) using this same pathogen-endophyte culture library, endophytes antagonistic to generalist pathogens (that infect legumes widely, including

economically important crops like soy, bean, and alfalfa) can be identified for potential in plantae tests to reduced disease incidence and severity.

Increasing Crop Production in Rural Haiti

Debra Bartelli

A Plant-Derived Polyphenol Modulates Immunity Through Metabolic Reprogramming of Innate Immune Cells

Brandt Pence

Polyphenols are a class of organic compound which are found naturally in plants and which coordinate a variety of biological processes in these organisms. In addition to roles in cellular signaling and growth regulation, polyphenols notably have been implicated in immune defense in plants and in regulation of metabolism and antioxidant functions. As such, these compounds have sparked an interest in researchers in determining their efficacy in humans and other animals in modulating similar physiological responses, as polyphenols are readily available in many human-consumed foods.
In health-related research, polyphenols have been investigated for a wide variety of physiological and anti-disease effects. Importantly for research in my laboratory, there is a large body of literature linking various polyphenols to the modulation both of immunity and of metabolism. Our studies focus on immunometabolism, the regulation of immune function by metabolic processes, and its role in conditions such as aging and chronic disease. The ability of polyphenolic compounds to regulate immunometabolism is therefore an obvious extension of our current research, and has the potential to link important natural agricultural products to nutritional treatments for human disease.
During my postdoctoral work I investigated epigallocatechin gallate (EGCG), a polyphenolic compound which is the major bioactive component of green tea and which has been investigated for a wide variety of health impacts. Among several investigated outcomes, EGCG was demonstrated to reduce mortality in older mice through an unknown mechanism. Due to my background in EGCG research and existing evidence linking EGCG to anti-inflammatory and immunoenhancing effects, I am keen to investigate the impact of EGCG on immunometabolism using our existing innate immune function models. Indeed, EGCG has recently been shown to inhibit PI3K-mTOR signaling in preadipocytes, a pathway that is also critical to immune and inflammatory responses in innate immune cells such as monocytes and macrophages, the cells in which I am primarily interested.
Therefore, the hypothesis that EGCG modulates inflammation and immune function through altering immunometabolic responses in innate immune cells is well-justified by existing literature. Indeed, my laboratory has collected preliminary data further supporting this contention. In isolated human monocytes, a critical circulating innate immune cell, pretreatment with EGCG prior to inflammatory activation with the bacterial molecule lipopolysaccharide (LPS) blocks the characteristic increase in glycolysis which has been shown to be crucial for the innate response to LPS in these cells. Extracellular acidification rate (ECAR) is a proxy measure of glycolysis and rises during an inflammatory response proportional to the rate of utilization of glucose by immune cells. In our hands, LPS induces the expected increase in glycolysis in isolated human monocytes. However, pre-treatment with EGCG blocks this, and indeed reduces to near 0 over time. The area under the curve for the ECAR response to LPS therefore is drastically increased in the LPS condition but is not significantly different from media in the EGCG+LPS condition. This suggests a strong EGCG
effect on moderating inflammation-related metabolism in innate immune cells.
In this application I propose to develop in vitro applications to determine the mechanism by which EGCG alters cellular metabolism in innate immune cells undergoing an inflammatory stimulus. This study has obvious applications to human health as described above. Additionally, given the general research interest in polyphenols, the methods and results developed from these experiments will have broad applications to similar fields of study. Further, as polyphenols are important aspects of plant immune defenses as previously stated, the work supported by this study can be leveraged to examine fundamental questions in several agricultural sciences, especially in plant pathology.

Investigate Heavy Metals Uptake by Microplastics Present in Agricultural Fields

Maryam Salehi Esfandarani

This is an interdisciplinary and multidisciplinary engineering and scientific research project aimed to examine agricultural plastics residuals photodegradation and heavy metals transport. The primary scientific goal is to illuminate the coupled physical and chemical processes that govern the inorganic pollutants uptake by microplastics. This research project will be conducted through well-controlled bench-scale experiments, and a combination of analytical approaches from water chemistry, polymer, and material science. The specific objectives are to examine the kinetics of heavy metals uptake by soil and microplastics, investigate the role of microplastics photodegradation on their heavy metal uptake, and explore heavy metals desorption kinetics for microplastics. Our proposed work is needed to better understand fundamental mechanisms that control heavy metal uptake by microplastics left in agricultural fields. Understanding heavy metals fate in agricultural soil, will assist future evaluation of bioavailable fraction of heavy metals for the crop uptake.
Hypothesis: Our hypothesis is that polymers such as LDPE undergo chemical oxidation (>C=O, -OH formation) due to photodegradation and they become more vulnerable to metal oxide association, and thus heavy metals accumulation. Plastics and soil metal uptake and subsequent release from plastic surface will follow the Pseudo 1st order reversible reaction kinetics model.

GIS Mapping and Multispectral Imaging Camera Drones in Urban Farming

Esra Ozdenerol

The specific objectives of the project is to 1) aggregate all of this soil data and present it as a report, including online mapping, that would be useful for soil testing interpretation as well as for the Memphis Tilth organization to present data to the public for educational purposes and further potential grant opportunities. 2) The soil testing data from laboratory will be interpreted with the output of the multispectral drone imaging to be able to see how they complement each other (lab testing and imagery output spectral reflectance of soil and vegetation) on interpreting and understanding soil quality and plant health.

Deep Learning based Autonomous Detection, Classification and Localization of Weed using Unmanned Aerial Vehicles (UAVs)

Eddie Jacobs

To summarize, this project aimed to provide a low-cost and automated solution of early weed detection, classification and localization using UAVs. As a final product, we will generate annotated geo-referenced weed infestation 3D maps of selected annual crops by using images collected from multispectral and ToF cameras mounted on a UAV. Using the geo-referenced maps, a UGV or UAV can autonomously perform site-specific weed elimination by selectively spraying herbicide on the exact weed locations, thereby, reducing large scale herbicide application, crop damage and labor costs while increasing agricultural yield.

Harnessing Data to Predict Tree Species Diversity - Application of Deep Learning Techniques

Youngsang Kwon

The ability to measure biodiversity is critically important, particularly given the increasing rates of human alteration of natural habitats and the recent trends of rapid climate change. Species richness, the number of different species represented in an ecological community, landscape or region, is an important component of biodiversity, and the spatial variation of taxonomic terrestrial fauna/flora species richness has received considerable attention over the past two decades (Wu and Liang 2018; Yap et al. 2015). Tree species (woody plants) in forest, however, have received little attention even though they are one of the most prominent organisms sustaining biodiversity and functions of ecosystems. Previous studies have focused on spatial variations in climate and topography or altitude to understand disparities of species biodiversity, but the driving forces of spatial patterns are complex (non-linear and highly dimensional with intense interaction effect, Kwon, Larsen, and Lee 2018; Wang et al. 2010) and scale dependent (Whittaker, Willis, and Field 2001; Frazier and Kedron 2017). Thus, there remains a lack of basic knowledge about the spatial patterns of biodiversity and its driving forces, which limits our ability to target conservation efforts and locate biodiversity hotspots (Moser et al. 2002).
The emergence of landscape ecology, over the past few decades, provided a new perspective in traditional biodiversity research, considers spatial patterns of interacting landscape patches or ecosystems. It is now widely acknowledged that the future status of a forest is constrained by spatial variation of landscape characteristics at multiple scales. One component of spatial variation that has received little attention in the literature is the impact of the spatial configuration of landscape patches on tree species richness. Spatial patterns are a product of the composition and configuration of landscape patches. Specifically, spatial configuration refers to the arrangement, position, orientation, and spatial character of landscape patches (McGarigal, Cushman, and Ene 2012). In this regard, landscape metrics have the potential to be useful indicators of species richness (Griffiths and Lee 2000; Lindenmayer and Franklin 2002), but results have been inconsistent, and many conclusions have been drawn based on a single scale or a single taxon (Schindler et al. 2015). At the local to regional, landscape metrics have been used to indicate habitat functions (biodiversity, habitats), landscape regulating functions (fire control, microclimate control, etc.), and information functions (landscape aesthetics). The scientific premises of landscape ecology theory suggest that, at a higher spatial level, the composition and structure of the landscape mosaic also influences biotic processes and hence influence species richness (Honnay et al. 2003).
Recent developments in ML algorithm have expanded modelling capabilities, allowing researchers to maximize the utility of 'big data'. Tree species richness and environmental data show complex spatial patterns that reflect environmental states and processes that originate at different spatial scales (Kanevski et al. 2004). At broad scales, non-linear spatial trends in species richness and mixture of continuous and discrete types of environmental variables make traditional statistical (multivariate regression) and geostatistical (kriging) models inefficient, consequently, new approaches must be considered. Deep learning, a state-of-the-art ML methods, has been highly effective in learning complex non-linear models that performs accurate predictions in several domains such as image recognition (Krizhevsky, Sutskever, and Hinton 2012; R. Wu et al. 2015), natural language processing (Mikolov et al. 2013), etc. However, deep learning approaches in forest ecology is still in its infancy.
We predict how spatial configuration impacts the composition of species living in a particular landscape patch (or nearby patches) by altering the quantity and quality of habitat. For example, the distance between two forest patches may affect the likelihood that dispersing seeds are able to establish in a suitable habitat. Similarly, the shape of an individual forest patch will determine the amount of sunlight reaching the interior areas, which in turn dictates what species are able to survive or thrive in that patch (Schindler et al. 2015). Thus, the objective of this study is to test the relationship between landscape metrics and tree species richness using deep learning approach across eastern USA. We set to determine the relative importance of landscape structure, through the use of over 200 landscape metrics, as indicators of tree species richness. We also model the performance of traditional climate and topographic variables that have been found to provide good indications of biodiversity.

Wastewater treatment via Ag-C composite nanostructure derived using "green" technique

Sanjay Mishra

The objective of this study is to assess the usability of silver nanoparticles loaded amorphous carbon (Ag-C) nanospheres for the removal of organic pollutant molecules from water. The amorphous carbon nanospheres are derived using environmentally friendly "green" synthesis technique via hydrothermal treatment of glucose (sugar). As derived carbon nanospheres with different graphitic structures will be decorated with silver nanoparticles. The efficacy of silver decorated carbon nanospheres (Ag-C) composite
particles for wastewater treatment will be assessed in the presence of UV and natural sunlight. The study will focus on studying the fundamental and applied aspect of the as-synthesized of Ag-C nanospheres and its efficacy in organic pollutant removal from waste-water.