Nickel Speciation, Microbial Community Structure, And

University of Kentucky UKnowledge

Theses and Dissertations--Plant and Soil Sciences Plant and Soil Sciences

2020

NICKEL SPECIATION, MICROBIAL COMMUNITY STRUCTURE, AND CHEMICAL ATTRIBUTES IN THE RHIZOSPHERE OF NICKEL HYPERACCUMULATING AND NON-ACCUMULATING PLANTS GROWING IN SERPENTINE SOILS

James W. Morris University of Kentucky, [email protected] Digital Object Identifier: https://doi.org/10.13023/etd.2020.154

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Recommended Citation Morris, James W., "NICKEL SPECIATION, MICROBIAL COMMUNITY STRUCTURE, AND CHEMICAL ATTRIBUTES IN THE RHIZOSPHERE OF NICKEL HYPERACCUMULATING AND NON-ACCUMULATING PLANTS GROWING IN SERPENTINE SOILS" (2020). Theses and Dissertations--Plant and Soil Sciences. 131. https://uknowledge.uky.edu/pss_etds/131

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The document mentioned above has been reviewed and accepted by the student’s advisor, on behalf of the advisory committee, and by the Director of Graduate Studies (DGS), on behalf of the program; we verify that this is the final, approved version of the student’s thesis including all changes required by the advisory committee. The undersigned agree to abide by the statements above.

James W. Morris, Student

Dr. David H. McNear Jr., Major Professor

Dr. Mark S. Coyne, Director of Graduate Studies NICKEL SPECIATION, MICROBIAL COMMUNITY STRUCTURE, AND CHEMICAL ATTRIBUTES IN THE RHIZOSPHERE OF NICKEL HYPERACCUMULATING AND NON-ACCUMULATING PLANTS GROWING IN SERPENTINE SOILS

DISSERTATION

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the College of Agriculture, Food and Environment at the University of Kentucky

NICKEL SPECIATION, MICROBIAL COMMUNITY STRUCTURE, AND CHEMICAL ATTRIBUTES IN THE RHIZOSPHERE OF NICKEL HYPERACCUMULATING AND NON-ACCUMULATING PLANTS GROWING IN SERPENTINE SOILS

Serpentine soils are formed from weathering of serpentinite, the ultramafic parent material that provides serpentine soils with their unique identity. Weathering of serpentinite results in a plethora of edaphic factors that impose strong selection pressures on plant life, with high magnesium (Mg) to calcium (Ca) ratio, low fertility, and high levels of geologically derived metals such as cobalt (Co), chromium (Cr), and nickel (Ni). Appropriately, plant life on serpentine soils is often specialized, sparse, and endemic which in turn affects the ecosystems that develop on serpentine soils from the landscape scale down to the physiology of their inhabitants. At the landscape scale, selection by serpentine edaphic factors may result in sharply delineated changes in plant community structure, resulting in abrupt changes in ground cover and habitat. At the physiological scale, plant adaptation to ultramafic soils is thought to have resulted in the emergence of heavy metal hyperaccumulation. Interestingly, plant species closely related to hyperaccumulators may be found living adjacent to them on serpentine soils, but the opposite strategy for coping with elevated levels of heavy metals whereby heavy metals are excluded from the vascular system. Studying the development and differentiation of the rhizosphere of serpentine adapted plants may lead to a better understanding of how plants have evolved to cope with the varied abiotic stresses posed by, but not exclusive to, serpentine soils. This research seeks to contribute to the understanding of hyperaccumulation by asking, what are the fundamental differences between the rhizospheres of hyperaccumulating and non-accumulating serpentine adapted plants, and could those differences be related to these contrasting strategies for inhabiting serpentine soils? In the research reported here, rhizospheres of Ni hyperaccumulating and non- hyperaccumulating plants growing in two serpentine soils, Neshaminy silt loam and Chrome silt loam, were interrogated using a multidisciplinary approach including x-ray absorption spectroscopy for in-situ Ni speciation, ratiometric fluorescent imaging for in- situ spatially resolved O2 concentrations and pH, and bacterial community structure via 16S rRNA gene amplicon sequencing and phospholipid fatty acid analysis. Overall, the results of this research suggest that Ni speciation in the rhizosphere of some Ni hyperaccumulators may contain a greater abundance of mineral sored species compared to a non-accumulator, hypertolerant plant. Oxygen content and pH did not differ among plant types and does not appear to explain differences in Ni speciation among rhizospheres. In Neshaminy silt loam soils, a clear differentiation in rhizosphere microbial community structure was observed, while in Chrome silt loams the differences were more subtle. Due to the lack of evidence for differing pH and redox environments across the rhizosphere of Ni hyperaccumulating and non-accumulating plants presented in this study and others, along with support for the differentiation of these environments along biological lines, it appears more likely that differences in Ni speciation and availability are driven by differences in root and microbial biochemistry.

KEYWORDS: Nickel, Hyperaccumulator, Rhizosphere, Microbiome, Serpentine, XAFS

______James W. Morris______

5/11/2020 Date RHIZOSPHERE ATTRIBUTES AND SURVIVAL STRATEGIES: A UNIQUE LOOK AT NICKEL SPECIATION, MICROBIAL COMMUNITY STRUCTURE, AND CHEMICAL ATTRIBUTES IN THE RHIZOSPHERE OF NICKEL HYPERACCUMULATING AND NON-ACCUMULATING PLANTS GROWING IN SERPENTINE SOILS

James W. Morris

______David H. McNear Jr____. _ Director of Dissertation

______Mark Coyne _ Dr. Mark Coyne Director of Graduate Studies

______5/11/2020 _ Date The desire to explain is a natural impulse that follows in the wake of curiosity.

To my wife, my parents, my mentors, and all those that encouraged my curiosity—and allowed me to bob in its wake.

ACKNOWLEDGEMENTS

To all committee members, I extend my deepest gratitude for their service and their continued support throughout the work that culminated in this dissertation. I would like to thank my advisor, Dr. David H. McNear Jr. for his guidance, expertise, and all the opportunities to learn and experience new things over the years. Special thanks to Dr. Mark S. Coyne for his excellent counsel, patience, and entertaining all manner of questions and curiosities during my time as a student. I thank Dr. Robert S. Boyd for providing me with Streptanthus polygaloides seeds, his kindness, and for entertaining my thoughts on Ni hyperaccumulators. Also, thanks to Dr. Luke Moe for his guidance and always welcoming me into his laboratory when needed.

Further, I would like to offer thanks to Joseph V. Kupper for his unfailing support, all the laughs, and the free food—and special thanks for his help at Beamline 10.3.2. I would not have survived graduate school without him. To my lab mate and friend, Dr. Sunendra Joshi, who dug through the stony soils of Soldiers Delight with me in the freezing cold, and never failed to ask a good question—many thanks. Likewise, I appreciate the intellectual and moral support, the laughs, and the new vocabulary provided by my friend and lab mate Rebecca McGrail.

Additional thanks must be extended to Mathew Marcus and Sirine Fakra at Beamline 10.3.2 of the Advanced Light Source, Berkeley National Laboratory, Berkeley, CA for their assistance in the collection of x-ray absorbance spectroscopy (XAS) data. Dr. Mathew Siebecker of the Department of Plant and Soil Science at Texas Tech University also aided in the XAS endeavor. His willingness to share data from his own work in the support of this dissertation is greatly appreciated. I would like to express my gratitude for the financial support provided to me through the National Science Foundation by way of the Graduate Research Fellowship Program and the financial support provided to me in my final year by The Department of Plant and Soil Sciences at the University of Kentucky—with special thanks to our chair, Dr. Rebecca McCulley.

iii

Lastly, to my wife and family—whose wisdom and support have seen me through many tough times over the past 4 years, and will see me through the years beyond, I cannot thank enough.

TABLE OF CONTENTS

Acknowledgments...... iii

List of Tables ...... ix

List of Figures ...... xii

Chapter 1: Biogeochemistry of Nickel in Soils, Plants, and the Rhizosphere

1.1 Introduction ...... 1

Chemical Properties of Ni ...... 1

Introduction of Ni into Soils and the Aquatic Environment ...... 4

Biological Ni Requirements and Toxicity Threshold ...... 6

1.2 Serpentine Soils...... 7

Nickel-Bearing Parent Materials ...... 7

Properties of Serpentine-Derived Soils ...... 8

Selective Pressure of Serpentine Soils ...... 11

1.3 Nickel Chemistry in Soil ...... 11

Ni Chemistry in Soils, Aquifers, and Sediments ...... 11

Ni Chemistry in the Rhizosphere ...... 17

Ni Uptake ...... 18

Physiological Role of Ni in Plants ...... 20

Ni Toxicity and Tolerance in Plants ...... 22

1.4 Ni Hyperaccumulation in Plants ...... 23

Ni Hyperaccumulation Defined ...... 23

Physiology of Ni Hyperaccumulation ...... 24 v

Evolution of the Metal Hyperaccumulating Phenotype ...... 29

1.5 Ni–Microbe Relationships ...... 31

Ni Influx ...... 33

Ni Efflux ...... 34

Ni Tolerance ...... 35

1.6 The Serpentine Microbiome and Hyperaccumulator Rhizobiome ...... 37

Chapter 2: Ni speciation and rhizosphere microbial communities differ among Ni hyperaccumulating and non-accumulating plants

2.1 Abstract ...... 42

2.2 Introduction ...... 43

2.3 Methods ...... 45

2.3.1 Soil Sampling and Characterization ...... 45

2.3.2 Plant Propagation ...... 47

2.3.3 In-situ X-ray Absorbance Spectroscopy ...... 47

2.3.4 Phospholipid Fatty Acid Analysis ...... 50

2.3.5 DNA Sampling, Sequencing, and Analysis ...... 51

2.3.6 Ni Concentration in Plant Tissues ...... 52

2.4 Results ...... 53

2.4.1 Ni in Plant Tissues ...... 53

2.4.2 Phosphlipid Fatty Acid Analysis ...... 55

2.4.3 Bacterial Community (16S) Analysis ...... 60

2.4. 4 In-situ Ni Speciation ...... 71

2.5 Discussion ...... 73

2.6 Conclusion ...... 79

2.7 Acknowledgements ...... 80

Chapter 3: A Comparison of Microbial Community Structure, Ni speciation, pH, and Oxygen Content within the Rhizosphere of the Ni-hyperaccumulator, S. polygaloides and its relative, S. glandulosus

3.1 Abstract ...... 82

3.2 Introduction ...... 83

3.3 Methods ...... 86

3.3.1 Soil Sampling and Characterization ...... 86

3.3.2 In-situ X-ray Absorbance Spectroscopy ...... 88

3.3.3 Ni Concentration in Plant Tissues ...... 89

3.3.4 In-situ pH and O2 ...... 90

3.3.5 Phospholipid Fatty Acid Analysis ...... 91

3.4 Result ...... 92

3.4.1 Ni Concentration in plant tissues ...... 92

3.4.2 In-situ Ni speciation ...... 95

3.4.3 In situ pH and O2 ...... 97

3.4.4 Microbial Community Structure ...... 104

3.5 Discussion ...... 110

vii

3.5.1 Shoot Ni content and distribution in leaves ...... 110

3.5.2 In-situ Ni speciation ...... 110

3.5.3 In situ pH and O2 ...... 112

3.5.4 Microbial Community Structure ...... 114

3.6 Conclusion ...... 115

3.7 Acknowledgements ...... 115

References ...... 117

Vita ...... 137

viii

LIST OF TABLES

Table 1.1, Ni-bearing minerals most commonly found in economically important, global nickel deposits ...... 2

Table 2.1, Chemical characteristics of the Neshaminy silt loam serpentine soil ...... 46

Table 2.2, Shoot Ni on an oven dry tissue basis (mg Ni/kg dry tissue), total shoot Ni (mg), and the translocation factor (shoot [Ni]/root [Ni]) are shown for Alyssum murale, Noccaea caerulescens, Streptanthus polygaloides and Streptanthus glandulosus ...... 54

Table 2.3, Results of the Benjamini-Hochberg corrected mulitple regression permutation procedure performed on PLFA data gathered from the rhizosphere of A. murale, N. caerulescens, S. polygaloides, S. glandulosus and unplanted control ...... 57

Table 2.4, Average PLFA biomarker concentrations along with the standard deviation (pmol per gram of soil) gathered from the rhizosphere of A. murale, N. caerulescens, S. polygaloides, S. glandulosus and unplanted control ...... 58

Table 2.5, Results of the Benjamini-Hochberg corrected mulitple regression permutation procedure performed on PLFA data gathered from the rhizosphere of A. murale, N. caerulescens, S. polygaloides, S. glandulosus and unplanted control ...... 62

Table 2.6, Bacterial orders identified by NMDS as influencing separation of Ni-hyperaccumulator rhizosphere bacterial communities from those of non-accumulator and unplanted control ...... 63

Table 2.7, ANOVA results from the comparison of bacterial orders identified by NMDS as influencing the separation of Ni-hyperaccumulator

ix rhizosphere bacterial communities from those of the non-accumulator and unplanted control ...... 64

Table 2.8, Bacterial orders identified by NMDS as influencing the separation of non-accumulator and unplanted control bacterial communities from those of the Ni-hyperaccumulators ...... 65

Table 2.9, ANOVA results from the comparison of bacterial orders identified by NMDS as influencing the separation of non-accumulator and unplanted control bacterial communitiesfrom those of Ni hyperaccumulators...... 66

Table 3.1, Chemical characteristics of the Chrome silt loam serpentine soil ...... 87

Table 3.2, Shoot and root Ni on an oven dry tissue basis (mg Ni/kg dry tissue), along with the translocation factor (shoot [Ni]/root [Ni]) are shown for the Ni hyperaccumulator, S. polygaloides, and the non- accumulator, S. glandulosus at 3, 6, and 9 weeks post germination ...... 93

Table 3.3, Average pH values in an unplanted control and the rhizosphere of the Ni hyperaccumulator S. polygaloides and the non-accumulator S. glandulosus at 3, 6, and 9 weeks post germination ...... 98

Table 3.4, ANOVA results for the analysis of soil pH in an unplanted control and the rhizosphere of the Ni hyperaccumulator S. polygaloides and the non-accumulator S. glandulosus at 3, 6, and 9 weeks post germination as well as across all time points ...... 99

Table 3.5, Average percent air saturation values in an unplanted control and the rhizosphere of the Ni hyperaccumulator S. polygaloides and the non-accumulator S. glanulosus at 3, 6, and 9 weeks post germination ...... 101

Table 3.6, ANOVA results for the analysis of soil oxygen content in an unplanted control and the rhizosphere of the Ni hyperaccumulator S.

x polygaloides and the non-accumulator S. glandulosus at 3, 6, and 9 weeks post germination as well as across all time points ...... 102

Table 3.7, The results of the MRPP performed on the 3, 6, and 9 week rhizosphere and control PLFA data, where T represents the test statistic, A represents the chance-corrected within group agreement and p represents the probability of obtaining a smaller or equal weighted mean within group distance (Bray-Curtis). The last three columns show the results of the Benjamini-Hochberg Procedure ...... 108

LIST OF FIGURES

Figure 1.1, Global distribution of economically important sources of Ni ore as laterite and sulfide deposits ...... 3

Figure 1.2, Ni consumed (imported and mined) and Ni released to the environment as Ni metal or Ni compounds in the United States from 1996–2016 ...... 5

Figure 1.3, The global distribution of ophiolites with approximate ages serving as a proxy for the patchy, global distribution of ultramafic soils ...... 10

Figure 1.4, Eh–pH diagrams for Ni at 25°C ...... 13

Figure 1.5, Ni sorption as a function of pH in the presence of a hypothetical soil system with iron and manganese oxides reflective of the crustal abundance of these elements (a). Same conditions as in (a), but with 10 μM EDTA added ...... 15

Figure 1.6, Ni, Fe, and Zn fluorescence computed microtomography images of a leaf, stem, coarse and fine root cross sections from A. murale “Kotodesh.” Inset in root tomogram is of a finer root ...... 28

Figure 2.1, Diagram depicting the general setup and collection of XAS data using specially designed rhizoboxes ...... 49

Figure 2.2, Two-dimensional non-metric multidimensional scaling ordination depicting the separation of rhizosphere microbial communities as estimated by PLFA 21 days post germination for plants grown in Neshaminy silt loam soil ...... 56

Figure 2.3, Bar chart depicting the relative abundance of PLFA biomarker groups as observed in the rhizosphere of the Ni hyperaccumulators Alyssum murale, Noccaea caerulescens, and Streptanthus polygaloides

xii along with the non-accumulator Streptanthus glandulosus and an unplanted control ...... 59

Figure 2.4, Two-dimensional non-metric multidimensional scaling ordination depicting the separation of rhizosphere bacterial communities, at the taxonomic level of order, as estimated by 16S amplicon sequencing 21 days post germination for plants grown in Neshaminy silt loam soil ...... 61

Figure 2.5, Principal components analysis plot showing the separation of rhizosphere bacterial community functional profiles created using PICRUSt, and analyzed using STAMP ...... 69

Figure 2.6, Bar plot showing the differences in mean proportion of orthologs involved in various KEGG pathways that could be related to Ni tolerance or Ni exposure among bacteria ...... 70

Figue 2.7, XAS spectra and the average, best fit, along with the composition of Ni species observed for each spectrum, collected from the rhizosphere of Ni hyperaccumulating plants and a non-accumulator growing in Neshaminy silt loam soil. Unknowns were fit to a maximum of 3 standards, as determined by PCA, using linear least squares fitting...... 72

Figure 3.1, X-ray absorption spectroscopy maps showing Ni distribution in leaves of S. polygaloides and S. glandulosus. (a) Adaxial Ni distribution in a mature leaf of S. polygaloides with Ni accumulating in the leaf tip (b) Cross section of youngleaf from S. polygaloides showing Ni compartmentalized within the epidermal tissues and more evenly distributed within the epidermis (c) Abaxial Ni distribution in S. glandulosus; the Ni signal was not strong enough to produce a usable map, and so Ca, K, and Mn were used...... 94

xiii

Figure 3.2, Bar charts representing the composition of Ni species observed for each spectrum collected from the rhizosphere of S. polygaloides, S. glandulosus, and an unplanted control at 3, 6, and 9 weeks. Three spectra were collected from each rhizobox. Unknowns were fit to a maximum of 4 standards, as determined by PCA, using linear least squares fitting ...... 96

Figure 3.3, Representative planar optode images of the 2-dimensional distribution of pH in the unplanted control, S. glandulosus rhizosphere, and S. polygaloides rhizosphere at 3, 6, and 9 weeks post germination for plants grown in Chrome silt loam soil ...... 100

Figure 3.4, Representative planar optode images of the 2-dimsensional distribution of air saturation in the unplanted control, S. glandulosus rhizosphere, and S. polygaloides rhizosphere after 3, 6, and 9 weeks post germination for plants grown in Chrome silt loam soil ...... 103

Figure 3.5, Two-dimensional non-metric multidimensional scaling ordination depicting the separation of rhizosphere microbial communities as estimated by PLFA after 3, 6, and 9 weeks post germination for plants grown in Chrome silt loam soil ...... 105

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CHAPTER 1

Biogeochemistry of Nickel in Soils, Plants, and the Rhizosphere

1.1 Introduction

1.1.1 Chemical Properties of Ni

Nickel (Ni) was first recognized as a unique element in 1751 upon its isolation by the Swedish mineralogist Axel Fredrik Cronstedt. It is a d-block transition metal with an electron structure of [Ar] 3d84s2. The grand majority of available Ni occurs as one of five stable isotopes: 58Ni, 60Ni, 61Ni, 62Ni, and 64Ni, with the majority occurring as 58Ni (68%) and 60Ni (26%) (Japan Atomic Energy Agency 2016). However, there are several natural and synthetic isotopes of Ni that are unstable, with many having short half-lives (Japan Atomic Energy Agency 2016). Ni is recognized as one of the primary constituents of Earth’s core, making it the fifth most abundant element on Earth. Despite its overall abundance, however, Ni is a relatively minor constituent of the Earth’s crust, having an average concentration of less than 0.01% by weight and ranking 24th in terms of abundance. Ni is very heterogeneously distributed among crustal rocks, ranging from less than 0.0001% in sandstone and granite to 4% in coveted ore deposits (Duke 1980). Ni can be found in igneous, sedimentary, and metamorphic rocks as well as Ni ores. While there are many Ni-bearing minerals, most are considered rare. The most common Ni-bearing minerals occur in tandem with economically important ore deposits where Ni concentrations are relatively high (Table 1.1), with such deposits being globally distributed (Figure 1.1). In soils, Ni ranges from 5 to 500 mg kg−1 (Lindsay 1979). Serpentine clay-rich soils are noted for being geologically enriched in Ni and have been the focus for use of hyperaccumulating plants to phytomine Ni (Chaney et al. 1995).

Table 1.1 Ni-bearing minerals that are most commonly found in economically important Ni deposits. Name Group Formula Most common mode of Example deposits occurance Pentlandite Sulphide (Fe, Ni)9S8 In mafic intrusions or Noril’sk, Russia. Bushveld, South remobilized phase after Africa. Voisey’s Bay, Canada. metamorphism Kambalda, Western Australia. Ni Sulphide Fe1-xSx In mafic intrusions Noril’sk, Russia. Bushveld, South replacement Africa. Voisey’s Bay, Canada. in pyrrhotite Kambalda, Western Australia. Garnierite Hydrous (Ni, Mg)3Si2O5(OH)4 In laterites related to New Caledonia. Sulawesi, nickel ultramafic rocks Indonesia. Cerro Matoso, Columbia. silicate Nickeliferous Hydroxide (Fe, Ni)O(OH) In laterites related to New Caledonia. Sulawesi,

2 limonite ultramafic rocks Indonesia. Euboea, Greece.

Millerite Sulphide NiS Mafic intrusions where Silver Swan, Western Australia. metasomatism has Sudbury, Canada. remobilized Ni and S from pentlandite Niccolite Nickel NiAs Hydrothermal replacement of Cobalt, Ontario. Widgiemooltha arsenide pentlandite in mafic intrusions Dome and Kambalda, Western or metasomatisms of low Australia. sulfur mafic rocks. Nickeliferous Hydrated (Fe, Ni)O(OH) In laterites related to Koniambo Massif, New Caledonia. goethite oxide ultramafic rocks Siegenite Sulphide (Ni, Co)3S4 Hydrothermal veins Siegen, Germany. Jachymov, Cech Republic. Source: Richter, R. O. and T. L. Theis. 1980. Nickel speciation in a soil/water system. In: Nickel in the Environment, ed. J. O. Nriagu, pp. 189–202, New York, NY: John Wiley & Son.

Figure 1.1 Global distribution of economically important sources of Ni ore as laterite and sulfide deposits. Note the high degree of complementarity between the above map and that displaying the global distribution of ophiolites, which approximates the distribution of ultramafic soils. (From Richter, R. O. and T. L. Theis. 1980. Nickel speciation in a soil/water system. In: Nickel in the Environment, ed. J. O. Nriagu, pp. 189–202, New York, NY: John Wiley & Sons.)

1.1.2 Introduction of Ni into Soils and the Aquatic Environment

Industrial and agricultural activity, the burning of fossil fuels, and the weathering of geologic sources of Ni have led to the introduction of Ni into soil and aquatic environments (Duke 1980; Richter and Theis 1980). World production of Ni was 2.25 million metric tons in 2016, with the Philippines being the world’s top producer of Ni ore followed by Russia and Canada (United States Geological Survey [USGS] 2017). World Ni reserves are estimated to be approximately 78 million metric tons, with Australia, Brazil, and Russia holding approximately 47% of the total (USGS 2017). It is further estimated that there may be as many as 130 million metric tons of available Ni world-wide, with 40% residing in sulfide deposits and the remaining 60% in laterite deposits (USGS 2017). Globally, the production of Ni-bearing stainless steel consumes 60% of the world’s Ni, followed by nonferrous alloys at 12%, surface finishing at 11%, and alloy steels at 10%. The remaining 7% is consumed by a variety of chemical applications, such as the production of batteries, catalysts, and reagents (USGS 2016). According to the USGS and United States Environmental Protection Agency (USEPA), 210,000 metric tons of Ni consumed and 18,282 metric tons of Ni and Ni compounds were released to the environment in the United States in 2016 (USEPA 2017; USGS 2017). Over the past 20 years, it is estimated that approximately 401,665 metric tons of Ni and Ni compounds have been released to the environment in the United States alone, with about 39% of those releases occurring between 1998 and 2002, while the amount of Ni and Ni compounds released from 2003 to 2016 has been relatively stable at 16,358 ± 1890 metric tons per year (Figure 1.2) (USEPA 2017; USGS 1997–2017). The majority of Ni released into the environment in the United States is deposited onto land or into the atmosphere, with surface waters and injection wells being the other important sinks for anthropogenic Ni (USEPA 2017).

Figure 1.2 Ni consumed (imported and mined) and Ni released to the environment as Ni metal or Ni compounds in the United States from 1996–2016 (USEPA 2017; USGS 1997–2017). Data for both series are given in Megagrams (Mg), which are equivalent to metric tons. In order to mitigate the loss of resolution due to scaling, the data are plotted on primary and secondary axes.

1.1.3 Biological Ni Requirements and Toxicity Thresholds

Ni is a micronutrient that is essential to the health of higher plants, microbes, and some animals, possibly including humans, and so it maintains ecological and agricultural, as well as industrial, significance (Brown et al. 1987; USEPA 2000). In plants, Ni is required in extremely small quantities (0.01–5 mg Ni/kg dry plant weight) and so Ni deficiency is seldom a problem; however, Ni-enriched soils such as ultramafic and Ni- polluted soils may impose strong selection pressures on plant and microbial communities, where selection by Ni pollution may have uncertain ecological consequences. With respect to humans, the USEPA has not established a reference concentration for Ni, and only has medium confidence in its reference dose of 0.02 mg per kg body weight per day for soluble Ni salts (USEPA 2000). Other thresholds for Ni exposure set by the USEPA include no more than 0.1 mg/L Ni in drinking water and exposure to no more than 1 mg of Ni per cubic meter air during a standard work week (8 hours per day, 40 hours per week) (Agency for Toxic Substances and Disease Registry 2005). Humans are most frequently exposed via ingestion of food, where it is estimated that adults ingest an average of 100–300 μg Ni per day while the estimated daily requirement for a 70 kg adult is roughly 3.5 mg Ni per day, or 50 μg Ni per kg body mass (USEPA 2000). Other routes of exposure may include inhalation of Ni released into the atmosphere by fossil fuel combustion, biosolid incineration, manufacturing, occupational exposure where Ni is processed or used, the smoking of tobacco, and contact with household items such as cooking/eating utensils and jewelry, drinking of water containing Ni, and exposure to soil containing Ni (Registry 2005). Such exposures may result in a wide variety of acute and chronic health effects that can range from mild dermatitis in the case of dermal exposure to cancer in the case of chronic inhalation of Ni refinery dust (USEPA 2000). Thus, the widespread contact, redistribution, and release of Ni by human activities is of increasing global concern, and along with it, the need to better understand Ni’s place within terrestrial ecosystems.

The following chapter will provide a brief discussion of Ni as it relates to soils, soil microbiology, and plants with some perspective scattered throughout. While the 6

information contained herein is exhaustive, it should be noted that there is very little research aimed at understanding the complex interactions that occur at the Ni–soil– plant–microbe interface. As such, the reader is cautioned to accept the following body of information as a snapshot of the current state of the science on Ni–soil–plant– microbe interactions—a science that is still in its infancy and must overcome the highly contingent nature of soils, biological systems, and the interplay between them, in order to grow. The hope is that this chapter will inspire thoughtful creativity and embolden researchers to engage such challenges in new ways so as to strengthen the claim on knowledge regarding Ni and the terrestrial systems that control its fate, which may create consequential feedbacks that in turn influence the ecological fate of such systems. In that context, it seems only appropriate to begin the discussion of Ni in soils and plants with the most notorious of geogenically Ni-enriched ecosystems—the serpentine soils.

1.2 Serpentine Soils

1.2.1 Nickel-Bearing Parent Materials

Serpentine soils are part of a larger, distinct group of soils known as ultramafic soils. Ultramafic soils form from the weathering of igneous or metamorphic rocks composed of 70% or greater ferromagnesian (mafic) minerals, thus the designation of “ultramafic” (Brady et al. 2005). Serpentinization occurs when peridotite, a rock composed mostly of the mineral olivine, undergoes anaerobic oxidation followed by hydrolysis within oceanic crust giving rise to serpentine minerals, a subgroup of the kaolinite–serpentine group of minerals. The serpentine subgroup minerals are 1:1 phyllosilicates, making them less susceptible to high rates of weathering compared to olivine. Thus, Ni is thought to be more readily available in soils formed from partially serpentinized peridotite as opposed to soils formed from more highly serpentinized parent materials (Kierczak et al. 2016; Pędziwiatr et al. 2017). There are several known serpentine subgroup minerals represented by the generic chemical formula X2-3[Si2O5]

2+ 2+ 3+ 2+ 2+ 3+ 2+ (OH)4, where X may be represented by Mg , Fe , Fe , Ni , Mn , Al , or Zn in

octahedral coordination. Chrysotile, lizardite, and antigorite are the most abundant of the serpentine subgroup minerals, with the grand majority of the rest being considered rare in soils. Serpentinite is a catch-all term referring to rocks composed of one or more serpentine minerals. Serpentinite is found within geological formations known as ophiolites (Tashakor et al. 2013). Ophiolites form at convergent tectonic plate boundaries where oceanic crust has been obducted, forcing the ultramafic rocks within it, such as serpentinite, to the continental surface where chemical and physical weathering shape them into some of the most interesting soils studied to date. Given the unique circumstances of their formation, the distribution of serpentine soils is global, but patchy, with potential ages ranging from 1 billion (start of Neoproterozoic) to about 34 million years (end of Eocene) old (Tashakor et al. 2013; Vaughan and Scarrow 2003). A map showing the global distribution of ophiolites and their predicted ages can be seen in Figure 1.3 (Vaughan and Scarrow 2003).

1.2.2 Properties of Serpentine-Derived Soils

The weathering of serpentinite results in a plethora of unique edaphic factors that impose strong selection pressures on plant life (Brady et al. 2005). This suite of properties is characteristic of serpentine soils and may include low Ca-to-Mg ratio; minimal clay and silt content resulting in low cation exchange capacity (CEC), reduced water retention, ready leaching of nutrient cations; low levels of fundamental macronutrients such as N, P, and K; increased temperature relative to surrounding soils; and high levels of geologically derived metals such as cobalt (Co), chromium (Cr), and Ni (Ni), with levels of Ni commonly being in excess of 1000 parts per million (ppm) (Brady et al. 2005). However, not all serpentine soils are created equal, and any given serpentine taxon may exhibit various combinations of the above characteristics, with low Ca:Mg ratio and enrichment of trace metals being considered the common denominator among them. So effective are serpentine soils at withering the Darwinian fitness of plant life that Hans Jenny coined the term “serpentine syndrome” to describe the harsh chemical and physical impositions suffered by plant life attempting to stake a claim on serpentine territory. Appropriately, plant life on serpentine soils is often 8

sparse and endemic. Thus, serpentine ecosystems can be open and sharply delineated in contrast to the surrounding landscape, especially where boundaries between adjacent soil types are less gradual.

Figure 1.3 The global distribution of ophiolites with approximate ages provides a proxy for the patchy, global distribution of ultramafic soils. (From Vaughan, A. P. M. and J. H. Scarrow. 2003. Ophiolite obduction pulses as a proxy indicator of

superplume events? Earth Planet. Sci. Lett. 213 (3–4):407–416. doi: http://dx.doi.org/10.1016/S0012-821X(03)00330-3 .

1.2.3 Selective Pressure of Serpentine Soils

Serpentine-adapted plants are generally characterized by a few key morphological traits that separate them from closely related, non–serpentine- adapted relatives. Typically, serpentine-adapted species will exhibit reduced stature, a more developed root system, and xeromorphic foliage (Brady et al. 2005). Serpentine-associated molecular adaptations, however, are less well characterized. As serpentine syndrome is multifaceted, the exact properties of a serpentine system that select for, or against, a given plant species is likely to be specific to the species and site in question. Not all serpentine sites may exhibit each constituent of serpentine syndrome, nor are all plants sensitive to each constituent alone with equal intensity. Selection is generally thought to occur as a result of Mg toxicity, Ca or other nutrient deficiencies, drought sensitivity, trace metal toxicity, or a combination of these (Brady et al. 2005). Elevated quantities of exchangeable trace metals pose one of the most unique selection pressures within serpentine systems. The broad, nonspecific mechanisms of trace metal toxicity affect the most minute single-celled soil organisms to the most intricate evolutions of botany, and can therefore be significant modifiers of plant and microbial community structure and ecosystem dynamics. While the effects of elevated levels of trace metals on biological entities are well characterized in a general sense, less is known about the precise effects of specific trace metals such as Ni on plants and soil microorganisms. A logical starting point in the attempt to understand what such effects might be, is with an examination of the chemical behavior of Ni in bulk and rhizosphere soil.

1.3 Nickel Chemistry in Soil

1.3.1 Ni Chemistry in Soils, Aquifers, and Sediments

Nickel may be immobilized through formation of pure Ni precipitates such as hydroxides, silicates, or sulfides (Mattigod et al. 1997; Merlen et al. 1995; Peltier et al. 2006; Scheidegger et al. 1998; Scheinost and Sparks 2000; Thoenen 1999) or through coprecipitation with other soil-forming minerals such as silicates, iron oxides/sulfides, or carbonates (Ford et al. 1999a; Hoffmann and Stipp 2001; Huerta-Diaz and Morse 1992; Manceau 1986; Manceau et al. 1985). Predicted Ni concentrations in the absence of 11

sulfide for several potential pure Ni precipitates suggest that phyllosilicate and layered double hydroxide (LDH) precipitates (incorporating aluminum) may result in dissolved Ni concentrations below most relevant regulatory criteria over a pH range typical for groundwater. These data also point to the limited capability of pure Ni carbonates and hydroxides in controlling dissolved Ni concentrations to sufficiently low values except under very alkaline conditions. The Eh–pH conditions under which these solubility- limiting phases may form is shown in Figure 1.4. According to these data, Ni-bearing phyllosilicate and/or LDH precipitates possess large stability fields indicating their relative importance to controlling Ni solubility under a range of conditions. These calculations point to the importance of dissolved aluminum and silicon concentrations in soil water relative to the potential sequestration of Ni via precipitation (Ford et al. 1999b; Scheinost et al. 1999).

Figure 1.4 Eh–pH diagrams for Ni at 25°C. (a) System Ni-H2O-Ca-Al-NO3-HCO3- SO4 (2 mg Ni/L; 40 mg Ca/L; 3 mg Al/L; 6 mg NO3/L; 60 mg HCO3/L; 100 mg SO4/L). Stability fields for solids are shaded green (Vaesite = NiS2). (b) Same system plus 3 mg Si/L. Thermodynamic data for Ni3Si4O10(OH)2 and Ni0.63Al0.33(OH)2(SO4)0.125 are from Peltier et al. (2006). [Note that the solubility of the Ni-Al-SO4 LDH was adjusted to correct for charge imbalance for the chemical structure published in Peltier et al. (2006).]

Retention of Ni may also occur via coprecipitation during the formation of (hydr)oxides or sulfides of iron. These minerals have been observed to form at the boundaries between oxidizing and reducing zones within groundwater plumes. There are numerous laboratory and field observations that demonstrate the capacity of these precipitates for Ni uptake (Coughlin and Stone 1995; Ford et al. 1997, 1999a; Huerta- Diaz and Morse 1992; Schultz et al. 1987). Under these circumstances, the solubility of Ni will depend on the stability of the host precipitate phase. For example, iron oxide precipitates may alternatively transform to more stable forms (Ford et al. 1997), stabilizing coprecipitated Ni over the long term, or these precipitates may dissolve concurrent with changes in groundwater redox chemistry (Zachara et al. 2001).

Adsorption of Ni in soil environments is dependent on pH, temperature, and type of sorbent (minerals or organic matter), as well as the concentration of aqueous complexing agents, competition from other adsorbing cations, and the ionic strength in groundwater. Ni has been shown to adsorb onto many solid components encountered in aquifer sediments, components that may be analogous to those found in surface soils, including iron/manganese oxides, clay minerals (Bradbury and Baeyens 2005; Dähn et al. 2003), and solid organic matter (Nachtegaal and Sparks 2003). Sorption to iron/manganese oxides and clay minerals has been shown to be of particular importance for controlling Ni mobility in subsurface systems. The relative affinity of these individual minerals for Ni uptake will depend on the mass distribution of the sorbent minerals as well as the predominant geochemical conditions (e.g., pH and Ni aqueous speciation). For example, the pH-dependent distribution of Ni between iron and manganese oxides [hydrous ferric oxide (HFO) and a birnessite-like mineral (nominally MnO2)] for a representative groundwater composition is shown in Figure 1.5a. Based on the available compilations for surface complexation constants onto these two solid phases (Dzombak and Morel 1990; Tonkin et al. 2004), one would project the predominance of Ni sorption to MnO2 at more acidic pH and the predominance of HFO (or ferrihydrite) at more basic pH. With increasing mass of MnO2, the solid-phase speciation of Ni will be progressively dominated by sorption to this phase. There are

Figure 1.5 (a) Ni sorption as a function of pH in the presence of a hypothetical soil system with iron and manganese oxides reflective of the crustal abundance of these elements (Schulze, 2002; assumed 30% porosity with 185.0 g HFO/L and 1.66 g MnO2/L). (b) Same conditions as in (a), but with 10 μM EDTA added. Nominal groundwater composition: 0.005 mol/L NaCl, 0.001 mol/L K2SO4, 0.001 mol/L MgNO3, 0.001 mol/L CaCO3, and 34 μ mol/L Ni (2 mg Ni/L). Model predictions using Visual MINTEQ Version 2.50 (based on MINTEQA2 described in Allison et al. 1991) with available surface complexation parameters derived from Dzombak and Morel (1990) and Tonkin et al. (2004); kaolinite set as an “infinite” solid for pH titration.

examples of the relative preference of Ni sorption to manganese oxides over iron oxides for natural systems (Kjøller et al. 2004; Larsen and Postma 1997; Manceau et al. 2002, 2007). As shown in Figure 3.5b, Ni adsorption may be inhibited (or Ni desorption enhanced) through the formation of solution complexes with organic ligands such as EDTA or natural organic matter (Bryce and Clark 1996; Nowack et al. 1997).

As previously noted, adsorption of Ni onto mineral surfaces may serve as a precursory step to the formation of trace precipitates that reduce the potential for desorption with changes in groundwater chemistry. This may be realized through the nucleation and growth of surface precipitates on clay mineral surfaces due to continued uptake of Ni (Dähn et al. 2002; Scheckel et al. 2000; Scheckel and Sparks 2000, 2001). This type of process may compete with other adsorption processes, such as ion exchange, depending on the prevailing groundwater chemistry and characteristics of the clay mineral (Elzinga and Sparks 2001).

Nickel is one of the most mobile of the heavy metals in the aquatic environment. The mobility of Ni in the aquatic environment is controlled largely by competition between various sorbents to scavenge it from solution and ligands to form non-sorptive complexes. Although data are limited, it appears that in pristine environments, hydrous oxides and phyllosilicates control Ni mobility via coprecipitation and sorption. In polluted environments, the more prevalent organic compounds will keep Ni soluble by ligand complexation. In reducing environments, insoluble Ni sulfide may form. Ni chloride is water soluble and would be expected to release divalent Ni into the water. The atmosphere is a major conduit for Ni as particulate matter. Contributions to atmospheric loading come from both natural sources and anthropogenic activity, with input from both stationary and mobile sources. Various dry and wet precipitation processes remove particulate matter as washout or fallout from the atmosphere with transfer to soils and waters. Soil-borne Ni may enter waters by surface runoff or by percolation into groundwater. Once Ni is in surface and groundwater systems, physical and chemical interactions (complexation, precipitation/dissolution, adsorption/desorption, and oxidation/reduction) occur that will determine its fate and that of its constituents. 16

In ambient aqueous systems, Ni exists in the divalent oxidation state and is not subject to oxidation state transformations under typical conditions. Ni predominantly exists as a cationic species (Ni2+) or various hydrolysis species (e.g., NiOH+) at near-neutral pH (Baes and Mesmer 1986). However, Ni may also form dissolved complexes in the presence of high concentrations of inorganic ions such as carbonate/bicarbonate and sulfate (Chen et al. 2005; Hummel and Curti 2003) or organic ligands such as natural/synthetic carboxylic acids and dissolved humic compounds (Baeyens et al. 2003; Bryce and Clark 1996; Strathmann and Myneni 2004). It is anticipated that Ni may form complexes with dissolved sulfide under sulfate-reducing conditions, although the current state of knowledge is insufficient to ascertain the relative importance of these species in aqueous systems (Thoenen 1999). The formation of solution complexes, especially with organic ligands, may limit sorption of Ni to mineral surfaces in aquifer sediments.

1.3.2 Ni Chemistry in the Rhizosphere

While Ni chemistry in the rhizosphere of plants is poorly understood, the above discussion provides some meaningful insight. As in bulk surface soils, aquifers, and sediments, Ni mobility in the rhizosphere may be modulated by well-established edaphic factors such as clay mineralogy, CEC, soil texture, moisture content, redox potential, pH, soil organic matter (SOM) content and composition, and the species and concentrations of competing ions. However, the relative mobility of Ni in rhizosphere soil compared to other trace metals seems to show no distinct pattern when examined in the context of electronegativity, stability of hydrolysis products, soil type, and various sequential extractions (Antoniadis et al. 2017). Aside from edaphic factors, nickel mobility could be altered by rhizosphere microorganisms and root exudates; however, such effects are difficult to differentiate from each other and from abiotic factors, as the activity of roots and associated microorganisms may alter the physical, chemical, and biological state of the soil environment. Root exudates can be defined by three general

+ categories: ions, such as CO3 , H , NH4, PO4 , and SO4 ; low-molecular-weight organics such as amino acids, fatty acids, flavonoids, organic acid phenolics, and sugars; and high

molecular weight organics such as enzymes, mucilage, and polysaccharides (Antoniadis et al. 2017). In addition to root exudates, the rhizosphere is likely to contain a multitude of microbial exudates as well. Such compounds may foster Ni–plant–microbe interactions by altering the pH, redox state, and ion exchange dynamics in the rhizosphere and via the formation of Ni–organic complexes (Antoniadis et al. 2017). In many cases, the mechanisms by which exudates such as low-molecular-weight organic acids play a role in Ni chemistry are difficult to elucidate, as such molecules are capable of serving a dual role in Ni–plant interactions: they may serve as a source of acidity resulting in the solubilization of Ni, or as a chelator, or potentially both. As previously mentioned, a prevalence of organic ligands may keep Ni in solution via complexation, which might explain why increases in bioavailable Ni have been noted in the rhizosphere despite variability in pH and presumably reducing conditions (due to plant and microbial respiration) that may otherwise favor the formation of insoluble species. Much more research is needed in this area, and powerful methodologies for investigating in situ Ni speciation, distribution, and mobility, along with controlling factors like pH and redox conditions in the rhizosphere, are available. For example, by combining planar optode sensors that measure pH and O2 with density gradient thin films infused with a cation binding agent, the distribution of phytoavailable Ni and variable that may influence it can be measured using rhizotrons (Hoefer et al. 2017; Santner et al. 2015). Likewise, highly tunable x-rays produced by synchrotrons provide an excellent means to produce two dimensional maps of Ni distribution while allowing for the determination of Ni speciation across the bulk-rhizosphere continuum and within plant tissues (McNear et al. 2005a,b).

1.3.3 Ni Uptake

The uptake of Ni by plant roots is modulated at two course levels—the rhizosphere level and the plant soma level. Thus, uptake may be controlled, in part, by edaphic factors in the rhizosphere such as clay mineralogy, CEC, soil texture, moisture content, redox potential, pH, SOM content and composition, and the species and concentrations of competing ions; the latter five properties can be directly influenced by 18

plant biology and that of the rhizosphere microbiome. Likewise, it is possible that root exudates affect microbial ecology so as to alter the mobility and subsequent uptake of Ni. At the plant soma level, uptake is presumed to occur via facilitated diffusion or active transport, where the mechanism responsible depends largely on individual genetics, plant species, the concentration of phytoavailable Ni, and the species of phytoavailable Ni present in the rhizosphere (Chen et al. 2009; Seregin and Kozhevnikova 2006). Antoniadis et al. (2017) provide an interesting list of soil-to-plant transfer coefficients for Ni across a variety of plant species that exemplifies this point. Currently, it is generally thought that the free Ni2+ ion is the most phytoavailable form in soils (Deng et al. 2017). When free, ionic Ni2+ is available in high quantities, in the context of the biology of a given plant species, passive diffusion is likely to be the mechanism of entry into the root body. Uptake of Ni via passive diffusion may occur through nonselective, low-affinity cation channels through which plants absorb other elements such as Ca2+, Co2+, Cu2+, Fe2+, Mn2+, and Zn2+. Thus, it is intuitive that the uptake of Ni may be competitively inhibited by the presence of other divalent cations and vice versa. However, the modulation of Ni uptake via competition between Ni and other divalent cations has been observed to be largely nonspecific in nature and may be highly contingent on the plant species in question. Generally speaking, Ca2+, Cu2+, Mg2+, and Zn2+ have been shown to inhibit Ni2+ uptake. Additionally, studies in Arabidopsis thaliana have demonstrated that Ni2+ may be transported into the root through IRT1, a known Fe3+ transporter, and Ni2+ uptake increases under times of Fe3+ deficiency (Nishida et al. 2011, 2012). It has also been suggested that Ni2+ may be absorbed by plant roots by way of Mg2+ transport systems due to chemical similarities between the Ni2+ and Mg2+ ions. However, experimental verification of Ni absorption into roots via Mg2+ specific transporters is lacking, and much of what is known about Ni uptake in plants is inferred from the results of experiments focusing on competitive uptake, where reductions in the uptake of other cations is correlated with increased uptake of Ni and the mechanism presumed to be as described above.

When phytoavailable Ni exists in the rhizosphere primarily as Ni-organic complexes, especially when the ligand is of lower molecular weight, it is possible that Ni may enter the root body via active transport (Chen et al. 2009). As Ni is a required micronutrient that typically exists in low concentrations in the soil environment, active transport may play a more important role in Ni acquisition than is currently recognized; however, experimental evidence for the active transport of Ni into root cells is extremely scarce. Like other nutrients, when Ni is scarce, plants may rely on phytosiderophores or low-molecular-weight organic acids for the release and/or uptake of Ni2+. Likewise, other chelators such as nicotianomine, histidine, cysteine, and metallothionein may play an important role in Ni acquisition by plants. As an example, the uptake of Ni2+ along with other metals in conjunction with nicotianamine and phytosiderophores was demonstrated to occur in yeast via ZmYS1, a transmembrane transport protein in maize (Schaaf et al. 2004). However, it is suspected that in plants chelators may play a more direct role in vascular transport of Ni once it is loaded into the xylem.

1.3.4 Physiological Role of Ni in Plants In plants, the only widely accepted physiological role of Ni is as an essential component of the active site in urease—a protein that catalyzes the hydrolysis of urea, resulting in the release of ammonia and hydroxamic acid, which then spontaneously hydrolyzes to release another ammonia along with bicarbonate (Carlini and Ligabue- Braun 2016; Fabiano et al. 2015; Polacco et al. 2013). There are two major forms of plant urease currently recognized by the plant science community—one is found in the seeds of some plants and the other is a ubiquitous form thought to be present in the vegetative tissues of all plants (Fabiano et al. 2015; Seregin and Kozhevnikova 2006). Two Ni atoms are necessary at the active site for proper catalytic activity of the ubiquitous form of urease (Seregin and Kozhevnikova 2006). Urease may be active in intracellular and extracellular environments. While Ni is typically present in plant vegetative tissues in low concentrations, and the ubiquitous form of urease is thought to have low activity in such tissues, the N released from urea hydrolysis may serve as an

important source of N for the synthesis of N-containing biomolecules under times of N stress. Active extracellular ureases are relatively stable and may affect rhizosphere conditions by increasing soil pH and facilitating the precipitation of carbonates, which in turn has the potential to alter nutrient dynamics (Carlini and Ligabue-Braun 2016). Further, extracellular ureases may decrease the efficiency of urea fertilization in agroecosystems resulting in release of ammonia to the atmosphere (Carlini and Ligabue- Braun 2016). A wide variety of additional roles for urease in plants and plant–microbe–soil interactions are present in the literature. However, such phenomena do not constitute additional, unique roles for Ni in plant biology, as the relationship between Ni and the urease enzyme does not change from one scenario to the next. Nevertheless, such scenarios may provide further insight into the necessity of Ni in plant biology, as Ni may be a necessary structural component of urease with respect to both urealytic and nonurealytic functions. Thus, some of these additional urease functions are noteworthy and are briefly mentioned here. Urease and urease-derived peptides have been observed to exhibit antifungal activity against filamentous fungi and yeasts, and have also been observed to have insecticidal properties in natural environments, suggesting potential roles for urease and urease products in defense (Carlini and Ligabue-Braun 2016). A variant of jack bean urease, known as canatoxin, utilizes one Ni atom and one Zn in its active site, and is proposed to play a primarily non-enzymatic, defensive role against phytopathogenic fungi and herbivorous insects (Carlini and Ligabue-Braun 2016). Additionally, ureases may be involved in microbe–microbe signaling, as well as plant– microbe signaling in important processes such as nodulation (Polacco et al. 2013).

It has also been reported that Ni may be involved in the activation of malate dehydrogenase in the cytoplasm of plant cells (Polacco et al. 2013). Malate dehydrogenase has been implicated in the synthesis of NADH that is subsequently used by nitrate reductase, potentially providing another link between Ni and N metabolism in plants (Polacco et al. 2013). Ni has also been reported to possibly play a role in glyoxalase I activation in rice (Fabiano et al. 2015). While a first for plants, Ni and Zn requirements for

glyoxalase activation is well known in microorganisms. In both plants and microorganisms, glyoxalase enzymes are responsible for degrading the toxic compound methylglyoxal, which may increase oxidative stress and interfere with cellular division when present at elevated levels (Fabiano et al. 2015). Furthermore, the process by which methylglyoxal is degraded requires the consumption of reduced glutathione (glyoxalase I) and its subsequent regeneration (glyoxalase II) (Fabiano et al. 2015). Other reports suggest that glyoxalase I enzymes may be induced in response to a variety of biotic and abiotic stressors that are often associated with reactive oxygen species (ROS) generation and oxidative stress (Fabiano et al. 2015). These relationships—between glyoxalase I, glutathione, and oxidative stress response—suggest that Ni may also function in glutathione homeostasis and oxidative stress tolerance in some plants (Fabiano et al. 2015).

1.3.5 Ni Toxicity and Tolerance in Plants

Symptoms of Ni toxicity in plants may be elicited by concentrations as low as 10– 15 μg of Ni per gram of dry plant tissue (Rascio and Navari-Izzo 2011). Physiologically excess Ni may have a wide variety of toxic effects on plants depending on the intensity of the exposure, plant species exposed, stage of plant development at the time of exposure, the overall condition of the plant at the time of exposure, and growth conditions. In general, plants challenged with Ni stress experience nutrient imbalances; perturbations in photosynthesis; wilting, chlorosis, and/or necrosis of leaf tissue; and the inhibition of normal growth and developmental patterns (Chen et al. 2009). Likewise, plants may exhibit a diverse array of responses to such challenges that are difficult to generalize. Thus far, information regarding plant responses to Ni stress, and the investigation thereof, is largely centralized around a theory of detoxification by chelation. Such studies report elevated levels of metallothioneins, phytochelatin precursors such as glutathione and thiol glutathione, O-acetyl-L-serine, nicotianamine (NA), nicotianamine synthase, free histidine, Ni2+–NA, and Ni2+–organic acid complexes in response to Ni2+ exposure—all responses commonly associated with detoxification of

heavy metals in a general sense (Chen et al. 2009). However, it is unclear at this time what, if any, mechanisms plants may have for coping with Ni2+ stress specifically.

1.4 Ni Hyperaccumulation in Plants

1.4.1 Ni Hyperaccumulation Defined

One of the most interesting outcomes of ultramafic selection pressure on plant life has been the evolution of species that hyperaccumulate Ni within their leaves to normally toxic concentrations. These hyperaccumulator plants are generally defined as wild varieties that accumulate Ni and other trace metals within their leaf tissues to concentrations at least two to three orders of magnitude more than that of other plants growing on non-ultramafic soils, and at least one order of magnitude more than plants growing on ultramafic soils (Pollard et al. 2014; van der Ent et al. 2013). Generally accepted threshold values for Ni hyperaccumulation (μg Ni/g dry plant tissue) are >1000 ppm Ni; however, concentrations >10,000 ppm have been observed, with the term “hypernickelophore” having been suggested as a special designation for such plants (van der Ent et al. 2013). However, Ni hyperaccumulation is simply considered to occur on a spectrum across which no special categorization scheme has been applied at this time. Although there is some debate as to whether there are “obligate” and “facultative” hyperaccumulators, where facultative hyperaccumulators may occur on metal enriched and non-enriched soils but only hyperaccumulate when growing on metal enriched soils (Pollard et al. 2014; van der Ent et al. 2013). Plants categorized as Ni hyperaccumulators should exhibit hyperaccumulation within wild, self-sustaining populations not excluding those discovered on sites anthropogenically enriched with Ni, in order to avoid assigning hyperaccumulation status to species in which it occurs only as an artifact of experimentation (van der Ent et al. 2013). Ni hyperaccumulation was first discovered in the serpentine endemic species Alyssum bertolonii in 1948 (Kramer 2010). Now, there are roughly 500 species of hyperaccumulator plants described to date, with approximately 400 of those described as Ni hyperaccumulators, occurring across 42 different plant families. The Brassicaceae and Euphorbiaceae families together comprise

the majority of known Ni hyperaccumulators, with each containing 25% of known species (Kramer 2010). Hyperaccumulation has evolved in several distantly related plant families. Regardless of the evidence suggesting that hyperaccumulation has evolved multiple times independently, the study of hyperaccumulation has been largely relegated to two species within the Brassicaceae family, Arabidopsis halleri and Noccaea caerulescens, which are now widely regarded as model species for the study of hyperaccumulation, with N. caerulescens serving as a model Ni hyperaccumulator (Rascio and Navari-Izzo 2011). Thus, evidence supporting the accepted mechanisms of hyperaccumulation should be interpreted and extrapolated to other species with caution.

Most plant species that are capable of tolerating elevated levels of trace metals do so by complexing them with cell wall components within the apoplastic spaces of the root. Plants with this phenotype are often referred to as “excluders” of Ni and other trace metals, as the complexation to cell wall components within the epidermis and cortex of the root excludes metals from entering the vascular system. However, hyperaccumulators differ from most other plant species in four distinguishing features: enhanced absorption of heavy metals from the soil into the root system; efficient root- to-shoot translocation; enhanced tolerance and sequestration of heavy metals within the shoot tissue, usually in the cell walls or vacuoles of leaves; and efficient systems for cell-to-cell distribution of hyperaccumulated metals in shoots (Kramer 2010; Rascio and Navari-Izzo 2011). The current consensus is that these three processes are not mediated by novel genes, but rather the overexpression of commonly occurring genes involved in metal homeostasis as a result of mutated promoters and duplicate copies (Kramer 2010; Rascio and Navari-Izzo 2011). Most of these genes are involved in transport of cations across the plasma membrane or tonoplast.

1.4.2 Physiology of Ni Hyperaccumulation

Following is a brief summary of the widely accepted information regarding the physiology of Ni hyperaccumulation in plants. It is hypothesized that trace metals such

as Ni may enter the plant through transporters for physiologically important metal cations, such as Mg2+ and Zn2+. Constitutive overexpression of ZIP (zinc-regulated transporter iron-regulated transporter) genes may serve an important role in trace metal uptake into the roots of hyperaccumulators, especially as this overexpression indicates the loss of metal regulated, or induced, metal transport into the root system (Rascio and Navari-Izzo 2011). Thus, it is suggested that the feedback mechanisms for regulating the entry of trace metal ions into the root system has been compromised in hyperaccumulator plants. Once in the roots, Ni must be efficiently translocated across the casparian strip and into the xylem. The overexpression of HMAs (heavy metal transporting ATPases) and MATE (multidrug and toxin efflux) proteins, such as FDR3 (overexpressed in the pericycle of N. caerulescens and A. halleri) has been implicated as important to efficient xylem loading of some trace metals (Rascio and Navari-Izzo 2011). In the case of Ni, some evidence suggests that, along with the overexpression of transporters, complexation to histidine or nicotinamine may be important for keeping Ni in the cytosol, thus allowing for efficient passage from root cells into the xylem. In contrast to HMAs and MATE proteins, the overexpression of YSL (Yellow Strip1-Like) family of proteins in the roots and shoots of hyperaccumulators may be involved in the both the loading and unloading of the xylem nicotinamine–metal (Ni-nicotinamine complexes in N. caerulescens (Kramer 2010; Rascio and Navari-Izzo 2011). Additionally, there is some evidence that suggests that like uptake, Zn2+ and Fe3+ efflux proteins may be important in the xylem loading of Ni (Deng et al. 2017). It is not yet clear whether Ni undergoes translocation primarily as hydrated metal ions or complexed with organic ligands, and the evidence suggests that it may be plant species specific rather than a generalizable phenomenon. Regardless, Ni has been observed to be translocated in complex with an organic ligand, such as citrate, histidine, nicotinamine, or phytate, as hydrous Ni2+, and as a mixture of free and complexed forms (Deng et al. 2017; McNear et al. 2005, 2010). While the specific ligands used to transport Ni within the vascular system of plants remain largely unknown, in a general sense it can be expected that Ni

will be bound to those with reactive groups containing nitrogen and oxygen (McNear et al. 2010).

Once in the leaf tissues, hyperaccumulator strategies consist of complexing Ni with ligands or storing them in compartments with low physiological activity, such as the cell wall and vacuole, in order to protect sensitive biochemical processes (Kramer 2010; Rascio and Navari-Izzo 2011). In the case of Ni, hyperaccumulators primarily sequester Ni in the vacuoles of epidermal cells as Ni–organic or Ni–carboxylic acid complexes; however, the cuticle and trichomes could possibly serve as Ni sinks, especially when exceptionally large quantities of Ni are present in the leaf (Deng et al. 2017; Rascio and Navari-Izzo 2011). CDF (cation diffusion facilitator) proteins, also named MTPs (metal transport proteins), may mediate the transport of divalent cations from the cytosol into the vacuole (Rascio and Navari-Izzo 2011). As an example, MTP1 (localized at tonoplast) has been observed to be overexpressed in leaves of some Zn/Ni hyperaccumulators (Deng et al. 2017; Rascio and Navari-Izzo 2011). Additionally, V-

ATPase may facilitate transport of Ni2+ from the cytoplasm into the vacuole via N2+/H+ antiporter activity (Deng et al. 2017; Rascio and Navari-Izzo 2011). Ni has also been observed to be translocated by the phloem, where the speciation in the phloem may differ from that of the xylem. Nickel has been observed to be translocated from old to young leaves, from shoots to roots, and to reproductive organs and seeds; however, very little is known about these processes (Deng et al. 2017). Figure 1.6 provides a snapshot of how Ni, along with Fe and Zn, is distributed within the Ni hyperaccumulator Alyssum murale, where Ni can easily be seen to be concentrated within the vascular system and the leaves (McNear et al. 2005a).

Ni hyperaccumulating plants have garnered interest over the years for their potential use as a means to remediate Ni-contaminated soils. Theoretically, such plants could be used to “mine” Ni out of Ni-contaminated soils. The Ni-rich aerial tissues could be harvested and the Ni retrieved via incineration or other means, and recycled. However, given the slow rate of growth, small stature, ultramafic endemism, and/or

climate tolerances of many known hyperaccumulators, the economic feasibility of such methods of remediating soil has not yet panned out. As we learn more about the molecular mechanisms behind Ni hyperaccumulation, biotechnological applications could provide a remedy to this problem by allowing for the transfer of the capacity to hyperaccumulate Ni to faster-growing, more robust, and easier to harvest plants. However, caution is needed, as fostering Ni-rich crops could result in the transfer of Ni from soils into the food chain. Interestingly, many Ni hyperaccumulators and their closely related, non-hyperaccumulator counterparts often inhabit the same ultramafic soils. Thus, it is difficult not to ask, why do closely related species utilize opposite strategies to inhabit the same niche, and what has driven this divergence in strategy? Moreover, these plants are often endemic to ultramafic soils, or at least have a high affinity for them. As ultramafic soils often impose a range of abiotic challenges to plants growing on them, Ni hyperaccumulators and other ultramafic specialists could be good plant systems for studying plant adaptation to abiotic stress.

Figure 1.6 Ni, Fe, and Zn fluorescence computed microtomography images of a leaf, stem, coarse and fine root cross sections from A. murale “Kotodesh.” Inset in root tomogram is of a finer root. The colorimetric scale maps region-specific relative metal concentrations (μg g−1) for each element, with brighter colors indicating areas of higher enrichment. The yellow scale bar represents ~500 μm; the white scale bar (root inset) represents ~100 μm. [Plant figure adapted from Plant Physiology, 3rd ed., Taiz and Zeiger (eds.) with permission from Sinauer Associates, Inc., Publishers.] (From McNear, D. H., E. Peltier, J. Everhart, R. L. Chaney, S. Sutton, M. Newville, M. Rivers, and D. L. Sparks. 2005a. Application of quantitative fluorescence and absorption-edge computed microtomography to image metal compartmentalization in Alyssum murale. Environ. Sci. Technol. 39(7):2210–2218. doi: 10.1021/es0492034.)

1.4.3 Evolution of the Metal Hyperaccumulating Phenotype There are several hypotheses as to how heavy metal hyperaccumulation increases plant fitness. However, as Boyd aptly points out, when assigning function to a trait in the context of evolution, one must be keen to remember that the currently observed function of a trait may not be the function for which the trait was originally selected for millions of years ago (Boyd 2004). In other words, it is important in such endeavors to be able to differentiate between adaptation and exaptation, where exaptation may occur as a means of acclimating to new challenges without evolving new traits (Boyd 2004). Additionally, it would be wise to keep in mind that evolution is not goal oriented—in order for genes associated with survival on ultramafic soils to increase in frequency within the population of a given species, they had to be present in the population of their ancestor in the first place. In other words, plants did not evolve to inhabit ultramafic soils, but rather the ultramafic environment imposed selection pressures that shaped the genetic makeup of the plant populations that inhabit them. Taking the prior two points together, the differentiation between what adaptations were originally selected for when land plants first encountered Ni-enriched, ultramafic soils, and how such traits have since been enhanced, diminished, or put to novel uses, is a daunting task. However, as previously mentioned, it has been suggested that Ni hyperaccumulation may be the result of constitutive overexpression of common genes, rather than the accumulation of novel ones, suggesting that the adaptations required for Ni hyperaccumulation may have been exacted on the regulation of the metal homeostasis system, rather than the alteration of the system itself, which could simplify the problem considerably and help explain the independent evolution of hyperaccumulation across distantly related plant families.

The tolerance hypothesis suggests that heavy metal hyperaccumulation evolved simply as a means of dealing with high concentrations of metallic elements in the rhizosphere by translocating them to aerial tissues and sequestering them away from critical physiological processes, such as photosynthetic centers (Boyd 2004; Boyd and Martens 1998). This hypothesis is certainly plausible, as an abundance of heavy metal

cations may outcompete comparatively scarce nutrient cations for binding and uptake sites along roots, exacerbating problems of nutrient deficiency in soils already low in plant nutrients. Additionally, this strategy may mitigate any negative effects that free metal ions impose directly on the roots or on plant growth-promoting rhizobacteria communities. The drought resistance hypothesis suggests that the accumulation of heavy metals within leaf tissues may reduce transpiration across the cuticle, resulting in greater water retention within the plant body in landscapes that are often drought prone and subject to reduced water holding capacity (Boyd and Martens 1998). The hypothesis of elementary allelopathy puts forth the idea that metal-rich leaf tissue further enriches the topsoil in metals after senescence and decay, reducing competition within the hyperaccumulator’s zone of influence (Boyd and Martens 1998).

The defense hypothesis suggests that by sequestering toxic levels of metallic trace elements within leaf tissues, hyperaccumulators are able to discourage herbivorous insects from feeding on leaves, which are particularly important to a plant of small stature (Boyd 2012). This is the most widely researched hypothesis and consequently the most widely accepted, but it requires further investigation. Additionally, it is also posited that use of metals as a defense mechanism is beneficial to the plant, as it reduces the need to synthesize defense compounds. Metals such as Ni may also provide constant protection, whereas defense compounds are often synthesized in response to an attack. However, it is unclear as to whether or not the cost of synthesizing defense compounds is greater than the cost of constitutively overexpressing the genes needed in order hyperaccumulate and sequester toxic levels of metals, especially if a given plant is never preyed upon to any significant measure. This requires special attention, especially in the case where overexpressed transporters are ATPases or symporters, as overexpression of transporters that require ATP for function is metabolically costly. The overexpression of symporters is also costly, as symport usually involves the disruption of electrical potential or the H+ gradient across the membrane, which requires energy to correct. Furthermore, there is no evidence to suggest that herbivorous insects posed such a great selection pressure on serpentine plant communities, as excluders of heavy metals are more prevalent on the

landscape than hyperaccumulators. Last, the joint effects hypothesis claims that elemental defense and organic defense mechanisms act in tandem, reducing the metabolic costs of defense to the plant overall; however, this argument requires close consideration of the previously mentioned points regarding the defense hypothesis (Boyd 2012).

One difficulty shared across all of the above hypotheses is that hyperaccumulated Ni may exhibit variations in temporal and spatial distribution through plant tissues over the duration of the plant’s life. The benefits described by any given hypothesis may only increase fitness if the event they are proposed to defend against coincided regularly with certain patterns of Ni hyperaccumulation (Boyd et al. 2008). While these hypotheses seem to address the aforementioned question of why closely related species may have adapted different strategies for inhabiting similar niches, a key element—the rhizosphere—seems to absent from all of them. The physicochemical properties of the rhizosphere, along with the genetic capacity and biochemical activity of the soil microbiota, can differ greatly from that of bulk soil. How might the relationship between rhizosphere microorganisms and soil Ni have influenced plant biology with respect to Ni? As microorganisms often serve as the gatekeepers between geochemistry and biochemistry, the consortium of microorganisms that are active in the rhizosphere of plants growing in Ni-enriched soil may hold the key to Ni cycling and bioavailability—the front lines of Ni hyperaccumulation. Last, Boyd points out that an understanding of hyperaccumulator ecology is only in its infancy. While it is important to understand how hyperaccumulation takes place at the plant molecular level, and how it is affected by rhizosphere processes, that understanding must be placed in the context of the ecology of Ni hyperaccumulating species. Only then will we be able to speculate with success as to why plants may have developed this interesting trait.

1.5 Ni–Microbe Relationships

Like plants, certain metals are required by prokaryotic microorganism in order for them to carry out critical physiological processes. As microorganisms have

presumably always lived in environments containing metal ions and have always needed them, it is suggested that genetic mechanisms for regulating metal ions evolved at the same time as those involved in carbon metabolism. Over their 3.5-billion-year history, bacteria in particular have honed their ability to protect themselves against environmental toxins. Thus, regulation and tolerance of both essential and nonessential metals have an extensive evolutionary history, resulting in multiple mechanisms of regulation and tolerance along with the partitioning of these mechanisms among genetic compartments. Essential metals are largely regulated by chromosomal genes, while nonessential metals are often regulated by genes contained within plasmids and other mobile genetic elements, allowing for such traits to be passed among bacteria via transduction, conjugation, or transformation.

Essential metals serve critical functions in osmoregulation, cell wall and protein stability, the catalytic activity of metalloenzymes, and biologically mediated redox chemistry (Macomber and Hausinger 2011). Ni in particular serves as a cofactor in nine enzymes, eight of which are exclusive to microorganisms, with the exception being urease, which is also found in plants and some invertebrates (Boer et al. 2014). Within these enzymes, Ni is often coordinated with histidine or cysteine, and sometimes with aspartate, glutamate, and lysine carbamate (urease) (Boer et al. 2014; Nies 1999). Like all elements, physiologically excessive levels of Ni can be toxic to microorganisms. Evidence regarding the mechanisms of Ni toxicity in microorganisms is sparse; however, some evidence exists for the competitive Zn and Fe metalloenzymes and noncompetitive inhibition of enzymes, oxidative stress, and the disruption of electron transport by the replacement of redox-reactive elements such as Fe and Cu in key enzymes with Ni2+, which is relatively redox stable (Macomber and Hausinger 2011). As Ni is an essential micronutrient for many microorganisms, they have developed tools for sensing Ni and regulating its intake and efflux, some of which depend on Ni-regulated gene expression.

1.5.1 Ni Influx

Ni influx may occur across transmembrane proteins that are either specific or nonspecific to Ni. When Ni concentrations are relatively high, Ni may enter microbial cells nonspecifically via the passive Mg2+ transport protein, CorA (Macomber and Hausinger 2011; Nies 1999). As exceedingly high concentrations of Mg and Ni may occur together in serpentine systems, it is possible that regulation of the CorA transport system in order to mitigate the influx of Mg may serve a dual role by also limiting Ni uptake. There are three known systems for the specific uptake of Ni in microorganisms. The most well-known and understood is the nik operon, which encodes a Ni-specific ATP-binding cassette transport system and a protein, NikR, which represses the expression of the operon when Ni2+ concentrations are adequate (Macomber and Hausinger 2011). Interestingly, the NikR protein contains two Ni binding sites, a high- affinity site in the C-terminal domain and a low-affinity site in the N-terminal domain (Macomber and Hausinger 2011). Satisfying the high-affinity sites is enough to facilitate repression; however, the affinity between NikR and its DNA target is increased by as much as a 1000-fold when both the high- and low-affinity sites are occupied, with the low-affinity N-terminal being the DNA binding domain (Macomber and Hausinger 2011). NikR homologs are widely distributed in archaea and bacteria and the exact mechanisms of action vary; notably, NikR has been shown to regulate urease expression in some microorganisms (Macomber and Hausinger 2011).

Uptake also occurs via the Ni-cobalt transporter family proteins (NiCoT), namely, HoxN, a Ni permease with high affinity for Ni, and NhlF, a permease that transports Ni and Co (Macomber and Hausinger 2011; Nies 1999). Additionally, transport of Ni across the outer membrane of Gram-negative organisms may be modulated by two proteins— FecA3 and FrpB4 of the TonB-dependent transport family of proteins (Macomber and Hausinger 2011). Aside from Ni and Ni complexes, these proteins are known to be involved in the transport of siderophores, carbohydrates, and vitamin B(12) (Macomber and Hausinger 2011).

1.5.2 Ni Efflux

As Ni is needed in minute quantities and can be toxic when these quantities are surpassed, it is no surprise that Ni efflux systems are more numerous than influx systems. There are six to seven known Ni efflux systems, three of which are known to be plasmid encoded (Macomber and Hausinger 2011). Among the plasmid-encoded Ni efflux systems are cnrCBA (cobalt-Ni resistance), nccCBA (Ni-cadmium-cobalt resistance), and nreB. Both cnrCBA and nccCBA are members of the resistance– nodulation–division family (RND) and are common among bacteria resistant to Ni. Another member of the RND family, cznCBA (cadmium-zinc-Ni resistance cluster), occurs on the bacterial chromosome and is also commonly associated with metal tolerance (Macomber and Hausinger 2011; Nies 1999). NreB and related proteins are part of the major facilitator superfamily of efflux proteins and have been noted to pump Ni out of the cytoplasm and into the periplasm of many bacteria using the proton motive force (Macomber and Hausinger 2011).

One of the more recently discovered systems for Ni efflux is that facilitated by the protein RcnA. Discovered in Escherichia coli, RcnA is an efflux pump that moves Ni and cobalt out of the cytoplasm (Macomber and Hausinger 2011). The expression of RcnA is governed by a transcriptional repressor known as RcnR. In this case, transcription is de-repressed in the presence of Ni, meaning that RcnR is released from its DNA target once it is bound with Ni, allowing transcription and translation of the efflux pump (Macomber and Hausinger 2011). Potential homologs of RcnA have been noted in a wide variety of prokaryotes, including archaea; alpha, beta, gamma proteobacteria; and cyanobacteria (Macomber and Hausinger 2011). Additionally, high levels of expression via plasmids containing many copies of RcnA have been demonstrated to dramatically increase bacterial resilience when challenged with Ni, while deletion of RcnA has the reverse effect—a two- to threefold increase in Ni sensitization (Macomber and Hausinger 2011). Other chromosomal Ni transporters can be found in the cation diffusion facilitator (Cdf) family and include DmeF (Divalent metal efflux), CzcD, and NepA. DmeF was discovered within the chromosome of Cupriavidus 34

metallidurans and is capable of removing iron, zinc, cobalt, cadmium, and Ni out of the cytoplasm and appears to be constitutively expressed in C. metallidurans. CzcD and NepA were discovered in Bacillus subtilis and Rhizobium etli, respectively. Members of the Cdf family have been noted in archaea, bacteria, and eukaryotes. Lastly, two transcriptional repressors, NmtR and KmtR, were observed to sense Ni and cobalt in Mycobacterium tuberculosis and may regulate the transcription of nmtA, a gene encoding a Ni efflux pump (Macomber and Hausinger 2011). Like RcnR, these appear to be repressors in their native form, releasing from their DNA targets and allowing transcription once Ni concentrations within the cell are great enough.

1.5.3 Ni Tolerance

There are six generally accepted mechanisms by which microorganisms tolerate excessive quantities of metals: (1) avoidance, (2) exclusion by a physical barrier with reduced permeability to metals, (3) active transport (efflux) of the metal out of the cell, (4) sequestration (intra- or extracellular), (5) enzymatic detoxification, and (6) desensitization of cellular targets of metallic elements (Bruins et al. 2000). A given microorganism may utilize one or more of these strategies depending on its particular suite of genes.

Active transport, or efflux, of metal ions out of the cell is the most commonly observed and well-understood strategy, especially in reference to Ni homeostasis and tolerance. The primary genes involved in this strategy are described above. However, there is some evidence that prokaryotic organisms may employ sequestration and enzymatic detoxification strategies. One method of detoxification, reduction of ionic Ni to metallic Ni via H+ metabolism, has been observed in Thiocapsa roseopersicina (Bruins et al. 2000). Thus, it is reasonable to assume that other anaerobic microorganisms (obligate or facultative) may be able to mitigate Ni toxicity via reduction. However, the redox potential of Ni2+ (−678 mV) is beyond the range of most organisms growing in aerobic conditions (between the proton–hydrogen redox couple [−421 mV] and the oxygen–hydrogen redox couple [+808 mV]), and so is not likely to be a widely utilized

mechanism (Nies 1999). Some sulfur-reducing bacteria have been observed to form extracellular Ni precipitates by forming Ni–protein complexes and Ni–sulfides (Bruins et al. 2000). Interestingly, Pseudomonas aeruginosa is able to sequester Ni in the periplasm by trans- forming it into crystalline forms of Ni carbide and Ni phosphide (Bruins et al. 2000). Several other instances of Ni-inducible sequestration via protein complexes have been observed and may occur in the cytoplasm and the periplasm, including the possible formation of Ni-containing inclusion bodies in Streptomyces coelicolor (Bruins et al. 2000). On this note, many have speculated that Ni chaperones that are involved in the transport of Ni and its placement within Ni-metalloenzymes may aid in Ni tolerance by transiently sequestering Ni away from sensitive components; however, there is no strong evidence to support such claims. Last, motile bacteria may avoid Ni exposure via a negative chemotactic response they sense Ni in their environment, as has been observed in E. coli and Helicobacter pylori (Macomber and Hausinger 2011).

Interestingly, Ni toxicity may be intertwined with Fe metabolism and toxicity. Some prokaryotes exhibit a Ni-dependent increase in Fe uptake when challenged with excessive quantities of Ni (Macomber and Hausinger 2011). It is proposed that Ni may replace Fe in important metalloenzymes, thus the greater need for Fe under Ni stress (Macomber and Hausinger 2011). This is an interesting prospect, as serpentine soils are formed from Fe- and Mg-rich minerals. An abundance of Fe may aid in mitigating Ni toxicity in serpentine systems; however, Fe is also more redox reactive. Thus, a Ni- dependent increase in Fe uptake may also result in significant ROS production. As mentioned before, the exceedingly high levels of Mg in serpentine may also result in the downregulation of CorA, and so mitigate nonspecific influx of Ni. Together, these two edaphic factors (excess Mg leading to less nonspecific uptake and excess Fe needed to mitigate Ni toxicity) may play an important role in Ni homeostasis and tolerance for prokaryotes living in serpentine systems.

1.6 The Serpentine Microbiome and Hyperaccumulator Rhizobiome

While our understanding of the genetic and molecular mechanisms of Ni hyperaccumulation remains minimal, our understanding of how the rhizobiome contributes to the hyperaccumulator phenotype is even less so. Furthermore, the effects of serpentine syndrome on the serpentine microbiome has been largely ignored, and the rhizobiome of hyperaccumulator plants have only been tentatively investigated. The rhizobiome of only about 10% of hyperaccumulator plants have been investigated, with majority of these being Cd and Zn hyperaccumulators despite the fact that Ni hyperaccumulators are the most abundant representatives of the hyperaccumulator community (Visioli et al. 2014). However, there is consensus among the hyperaccumulator and serpentine communities about the increase in available metal ions and the selection of metal-tolerant microorganisms within the rhizosphere of hyperaccumulator plants, including Ni. Additionally, it is commonly accepted that metal- tolerant rhizosphere microorganisms may facilitate plant growth in metal-enriched soils by secretion of phytohormones and beneficial enzymes, increasing the solubility of metals, and increasing root surface area by inducing hairy root development (Visioli et al. 2014). Non–metal-specific genera that seem to be common occupants of the hyperaccumulator rhizobiome include members of Arthrobacter, Bacillus, Curtobacterium, Microbacterium, Pseudomonas, Sphingomonas, and Variovorax, with Arthrobacter, Bacillus, and Microbacterium appearing to be common members of the Ni hyperaccumulator rhizobiome (Visioli et al. 2014), although the supporting evidence is mainly derived from culture-dependent studies carried out in metal-contaminated soils, which may be biased, especially considering the stark differences between serpentine conditions and compromised soil conditions, as well as disparities between soil conditions and culture conditions. Also, it is exceptionally important to note that simply because a microorganism is culturable from the rhizosphere does not mean that it plays an important role in the rhizosphere. In the same vein, culture-dependent studies tend to focus only on fast-growing heterotrophic organisms, which represent only a portion

of the rhizosphere community and may have less influence in serpentine systems where carbon and nutrients are often limiting.

Ultramafic soil microbial communities are even less well characterized. However, there is some evidence to suggest some interesting relationships between ultramafic soils, the microbial communities living within them, and ultramafic adapted plant communities. Bordez et al. (2016) found that bacterial and fungal communities in serpentine soils located in New Caledonia inhabited by different plant communities (across different topographies) were not limited compared to non-serpentine systems. Using 16S rRNA gene pyrosequencing, 3477 ± 317 bacterial operational taxonomic units (OTUs) were observed per habitat, with Proteobacteria, Acidobacteria, Actinobacteria, Planctomycetes, Verrucomicrobia, and Chloriflexi making up the majority of the bacterial community across all plant communities (Bordez et al. 2016). Fungal communities were dominated by Basidiomycota, Ascomycota, Zygomycota, and unknown fungi, with an average of 712 ± 43 OTUs observed in each habitat based on internal transcribed spacer (ITS) pyrosequencing. Additionally, it was found that plant cover was the primary driver of microbial community structure, as opposed to edaphic factors (Bordez et al. 2016).

Pessoa et al. (2015) assessed soil microbiological functioning, diversity, richness, and community structure in Brazilian ultramafic soils populated by tropical savannah vegetation. It was found that soil microbiological functioning was correlated with SOM content more so than soil Ni. Also, Ni did not appear to affect microbial enzyme activities related to the C, P, and S cycles or microbial biomass (Pessoa et al. 2015). While differences in bacterial community structure were observed between serpentine and non-serpentine sites, there was no correlation between community structure and function (based on very limited methods). Acidobacteria and Actinobacteria were found to be the most abundant phyla in the ultramafic soils. There were no differences in diversity or richness among the sites (Pessoa et al. 2015).

Schipper and Lee (2004) investigated soil microbial biomass, respiration, and diversity under six different plant communities growing on ultramafic soils located in New Zealand. Once again, it was found that vegetation type and total carbon, rather than edaphic factors, were the primary drivers of differences between microbial biomass, respiration, and catabolic evenness (Schipper and Lee 2004). Metal enrichment had no direct effect on microbial diversity, respiration, or biomass, although the authors suggest that the increasing metal concentrations exert an effect on soil microbial communities by altering the plant community structure (Schipper and Lee 2004).

DeGrood et al. (2005) investigated differences in soil microbial communities from non-serpentine soils and human disturbed or non-disturbed serpentine soils in Central California, near San Francisco. It was found that microbial biomass was greater in non-serpentine soils when compared to both disturbed and non-disturbed serpentine soils (DeGrood et al. 2005). The most highly disturbed serpentine sight had the lowest microbial biomass and a greater proportion of fungi, and all sites had distinct microbial community structures based on phospholipid fatty acid (PLFA) analysis (DeGrood et al. 2005). It was also found that slow growth and actinomycetes biomarkers were more prominent in the serpentine reference soil than other soils, potentially suggesting an important role for k selected organisms in serpentine systems. Also of note, pH, organic matter, and potassium levels were suggested to play a role in shaping serpentine microbial communities (DeGrood et al. 2005).

Oline (2006) investigated differences among bacterial communities inhabiting serpentine and non-serpentine soils across Northern California and Southern Oregon. It was found that serpentine and non-serpentine communities tended to be different, with geologically separate serpentine communities being more similar to each other than non-serpentine communities, and vice versa (Oline 2006). Serpentine soils were dominated by Actinobacteria, Acidobacteria, Alphaproteobacteria, Verrucomicrobia, Green-nonsulfur-bacterium related, Gemmatimonadetes, Planctomycetes, and Bacteroidetes (in that order), while non-serpentine soils were dominated by Actinobacteria, Alphaproteobacteria, Acidobacteria, Betaproteobacteria, 39

Planctomycetes, Bacteroidetes, and Deltaproteobacteria (Oline 2006). Additionally, members of the OP8 division were exclusive to serpentine soils, while members of the OP10 division, Firmicutes, and Nitrospirae were found to be exclusive to non-serpentine systems. Community diversity was not observed to be different by sample, site, or soil type (Oline 2006).

Fitzsimons and Miller (2010) found that the microbial communities associated with the roots of Avenula sulcata were not affected by serpentine soil properties, based on PLFA analysis, and found that there were no differences in arbuscular mycorrhizal fungi (AMF) communities (using 18S rDNA) or colonization among serpentine and non- serpentine soils. As noted above, this may indicate that serpentine soils do not pose as much of a challenge for AMF and bacteria as they do for plants. Likewise, Daghino et al. (2012) found that chemical and mineral differences between serpentine soils had no effect on fungal diversity within serpentine systems. On the contrary, Kohout et al. (2015) found that, in serpentine grasslands of the Czech Republic, serpentine soil characteristics and high Fe concentrations had a negative effect on root colonization by AMF. Additionally, it was observed the high K and Cr concentrations, as well as low pH, had a negative effect on AMF richness. AMF community structure was also found to be correlated with Ni concentration and plant life stage.

In summary, it appears that serpentine syndrome may not pose as much of a challenge to bacteria and fungi as compared to plant life, given that richness and diversity of serpentine microbial communities is often unaffected compared to non- serpentine systems. However, there is some evidence to suggest that while serpentine microbial communities may be just as diverse, there might be some key differences between the community structure of serpentine and non-serpentine systems driven by vegetative cover, pH, and soil carbon. However, other evidence indicates that serpentine soils may pose some unique limitations, as indicated by reduced microbial biomass and respiration in some serpentine soils despite a rich microbial diversity. While these results are interesting, it cannot be ignored that many of these studies utilized methods with course resolution and the number of studies focusing on 40

serpentine microbial communities remains few, indicating a need for more robust studies on ultramafic microbial communities. The fact that serpentine systems are just as diverse as non-serpentine systems and contain similar groups of organisms may be indicative of novel organisms or novel genetic capacity. Additionally, it should be noted that many studies find groups of organisms such as Actinobacteria, Acidobacteria, Proteobacteria, Firmicutes, Cyanoacteria, Bacteroidetes, and Spirochetes representing the vast majority of data within genomic databases, presenting the possibility of biased results from molecular ecology studies like those above (Land et al. 2015).

CHAPTER 2

Ni speciation and rhizosphere microbial communities differ among Ni hyperaccumulating and non-accumulating plants 2.1 Abstract

It is unknown how rhizosphere attributes may contribute to survival strategies adapted by serpentine flora, and how such strategies might be related to the availability of trace metals. This research investigated how rhizosphere microbial community structure and Ni speciation may contribute to the survival strategies of the Ni hyperaccumulating plants Alyssum murale, Noccaea caerulescens, and Streptanthus polygaloides along with the non-accumulator Streptanthus glandulosus growing in serpentine soil. Rhizosphere microbial communities were analyzed via phospholipid fatty acid analysis (PLFA) and 16S rRNA gene amplicon sequencing. Ni speciation was analyzed in-situ using x-ray absorbance spectroscopy. Alyssum murale (3162 mg Ni/kg dry shoot tissue), N. caerulescens (1239 mg Ni/kg dry shoot tissue), and S. polygaloides (1377 mg Ni/kg dry shoot tissue) exhibited high Nickel uptake and translocation was significantly less in the non-accumulator S. glandulosus than all Ni hyperaccumulators, with only 5 mg Ni/kg dry shoot tissue being detected. PLFA profiles indicated that rhizosphere community structure among Ni hyperaccumulators were similar to each other but different from the non-accumulator and unplanted control communities, with the main difference being an increase in fungal biomarkers and a decrease in gram positive lipid markers in rhizosphere communities of Ni hyperaccumulators. Nickel speciation trended toward more mineral sorbed forms in the Ni-hyperaccumulator rhizosphere compared to the unplanted control and non-accumulator rhizosphere, in which Ni species trended toward more structurally bound Ni species.. Microbial and plant driven bioweathering could be responsible for the release of Ni in the Ni- hyperaccumulator rhizosphere. The release of Ni from phyllosilicates and amorphous iron oxides in the Ni-hyperaccumulator rhizosphere may correspond to Ni uptake and the partitioning of Ni to iron oxide sorbed forms.

2.2 Introduction

Serpentine soils are formed over ultramafic parent materials, with serpentinized peridotite, also called serpentinite, providing serpentine soils with their unique identity. Soil formation over ultramafic parent materials such as serpentinite often results in a suite of characteristic edaphic factors, including low calcium (Ca) concentration, high magnesium (Mg) concentration, low fertility, and enrichment of trace metals such as cobalt (Co), chromium (Cr), and nickel (Ni) (Gordon and B Lipman 1926, Proctor and R.J. Woodell 1975, Vlamis and Jenny 1948, Whittaker 1954). It has long been recognized that these edaphic factors impose strong selection pressures on plants living on serpentine soils, which in turn affects ecosystems that develop on serpentine soils from the landscape scale down to the physiology of their inhabitants. At the landscape scale, selection by serpentine edaphic factors may result in sharply delineated changes in plant community structure, resulting in abrupt changes in the amount of ground cover and types of habitat present, which can lead to further differences in soil moisture and temperature compared to adjacent soil types. At the physiological scale, plant adaptation to ultramafic soils is thought to have resulted in the emergence of heavy metal hyperaccumulation. Heavy metal hyperaccumulation is a plant trait exhibited by some species living on serpentine soil, whereby wild varieties that form self-sustaining populations accumulate 10 times more trace metals than other plants growing in trace metal enriched soil and 100-1000 times more than other plants growing on non- enriched soil (Bernal et al. 1994, Brooks et al. 1977, Jaffre et al. 1976). Hyperaccumulation of trace metals is typified by the translocation of trace metals to the shoots, particularly the leaves, and sequestration in cell walls or vacuoles. To date, there are more than 500 species of such plants identified, about 25% of which belong to the Brassicaceae family and most of which hyperaccumulate Ni (Rascio and Navari-Izzo 2011, van der Ent et al. 2013). Interestingly, plant species that are closely related to hyperaccumulators may be found living on serpentine soils, but they utilize the opposite strategy for coping with elevated levels of trace metals. These plants are often deemed to be hypertolerant and consist of wild varieties within self-sustaining populations which

grow and reproduce normally in the presence of high amounts of heavy metals without accumulating them in shoot tissues.

In a recent review, the authors reflected on the evolutionary trajectory of the hyperaccumulator phenotype, suggesting that current research supports the idea that hyperaccumulator and hypertolerant, non-accumulating, species are likely the result of distinct evolutionary trajectories (Goolsby and Mason 2015, Chaney et al. 1997, Hanikenne et al. 2008, Verbruggen, Hermans, and Schat 2009). While such plants may have indeed experienced unique evolutionary histories, the causal history that underwrites their evolution may contain many common elements. In the context of many hyperaccumulating and hypertolerant plants, the serpentine environment, including the microbial component and the associated selection pressures, are among those common elements. As the physiology of hyperaccumulating and hypertolerant plants are the result of their evolutionary trajectory, the physiology alone cannot explain why their trajectories diverged in spite of sharing the same environment. Therefore, more attention must be given to the environment itself, and how serpentine adapted species interact with and modulate the serpentine environment and vice-versa. We suggest that selection of Ni hyperaccumulating phenotypes may have been the result of rhizosphere attributes which cause differences in Ni dynamics within the rhizosphere. System wide analysis of the rhizosphere of serpentine adapted species may explain why some species with serpentine affinity evolved hyperaccumulation, while others exclude heavy metals from the vascular system and exhibit hypertolerance to elevated levels of heavy metals in their rhizosphere. By characterizing a suite of rhizosphere attributes, including root system architecture, microbial community structure and biochemical potential, root exudate profiles, pH and redox conditions, and their effect on Ni bioavailability, we begin to understand how the rhizosphere may have shaped these fascinating phenotypes.

This research investigates how rhizosphere attributes may contribute to survival strategies evolved by serpentine flora. Specifically, we are interested in (i) do Ni hyperaccumulators growing in serpentine soils select for a similar rhizosphere microbial 44

community, (ii) is hyperaccumulator rhizosphere microbial community different from that associated with the hypertolerant genotype, and (iii) do these differences correlate with shifts in Ni speciation and Ni availability? To this end, we chose to analyze Ni speciation and microbial community structure in the rhizosphere of three Ni- hyperaccumulating plants, Alyssum murale, Noccaea caerulescens, and Streptanthus polygaloides, in conjunction with a non-accumulator, Streptanthus glandulosus subsp. glandulosus, which is a relative of S. polygaloides. Both S. polygaloides and S. glandulosus are endemic to California and can be found on serpentine soils within the same general region. Alyssum murale and N. caerulescens are well documented Ni hyperaccumulators. We hypothesized that more bioavailable Ni would be present the rhizosphere of Ni hyperaccumulating plants, and that microbial community structure and bacterial community profiles would not only differ between hyperaccumulating and non-accumulating plants, but that the communities among the Ni hyperaccumulating plants would be similar.

2.3 Methods

2.3.1 Soil Sampling and Characterization

Neshaminy silt loam soil were collected from an upland and moderately forested site adjacent to a utility corridor in Baltimore County, Maryland using a shovel to a depth of approximately 8 cm. Several cores gathered with a push probe along with National Resource Conservation Service (NRCS) soil maps and soil series descriptions, were used to definitively identify the soil. Collected soil was air dried, mixed, ground, and sieved to 2 mm. Soil chemical parameters including pH, total carbon and nitrogen, and Mehlich I and Mehlich III extractable nutrients and non-physiological metals, were measured by the University of Kentucky Division of Regulatory Services Soil Testing Laboratory (http://www.rs.uky.edu/soil/tests/methods.php). Soil parameters can be seen in Table 2.1.

Table 2.1 Chemical characteristics of the Neshaminy silt loam

serpentine soil. Data represent the mean ± one standard deviation from the mean (n=5).

Soil Parameter Observed values

pH 6.09 ± 0.03 CEC* 19.46 ± 0.69 (cmol(+)/kg)

Total C (mg/kg) 4.91 ± 4.36 Total N (%) 0.30 ± 0.01

P 4.2 ± 0.76 K 85 ± 6.08

) Ca 1861 ± 56.60 Mg 546.8 ± 17.34

(mg/kg Mn 137.8 ± 4.49 IMehlich

Fe 142 ± 4.09 Al 356 ± 11.52

Cd 0.15 ± 0.01

Cr 0.51 ± 0.01 Ni 36.69 ± 3.03

Pb 16.40 ± 0.72

(mg/kg)

Mehlich IIIMehlich Zn 10.12 ± 0.36

Cu 2.18 ± 0.13 *Cation exchange capacity

2.3.2 Plant Propagation

Seeds of Streptanthus polygaloides (obtained from Dr. Robert S. Boyd, Auburn University, AL, USA), Streptanthus glandulosus (Seedhunt, CA, USA) and Noccaea caerulescens (University of Kentucky Rhizosphere Laboratory, KY, USA) were placed in petri dishes containing approximately 30 g of air dried, sieved Neshaminy silt loam soil and watered to 70% water holding capacity. Seeds were then incubated in a Conviron growth chamber operating on the following schedule: 16 h light on light setting 3 at 22°C followed by 8 h dark at 16°C with 60% relative humidity throughout. Seeds were watered to 70% water holding capacity three times per week. After germination, seedlings were transplanted into specially designed rhizoboxes containing approximately 65 g air dried, sieved Neshaminy silt loam soil. For Alyssum murale, clones were generated as cuttings from a single mother plant using a hydroponics system and quarter strength Hoaglands nutrient solution. Once cuttings had produced roots they were transplanted into rhizoboxes. Plants in rhizoboxes were maintained in the same growth chamber used for germination operating on the same schedule. After transplanting, plants were grown for approximately 21 days prior to analysis via x-ray absorbance spectroscopy.

2.3.3 In-situ X-ray Absorbance Spectroscopy

Plants were grown in rhizoboxes with an open face that was covered with Kapton film and then covered with an opaque cover plate to omit light from the root zone during growth. Rhizoboxes were filled with approximately 65 g soil and into each rhizobox was transplanted either an Alyssum murale, Noccaea caerulescens, Streptanthus polygaloides, or Streptanthus glandulosus plant, or left unplanted as a ‘bulk’ soil control. Three rhizoboxes were constructed per treatment. Rhizoboxes were maintained at an approximate 35-40° angle to ensure that roots would grow along the kapton film-soil interface. After approximately 21 days of growth, the rhizoboxes were transported to the X-ray Microprobe Beamline, 10.3.2, at the Advanced Light Source, Lawrence Berkeley National Laboratory (Berkeley, CA).

The general setup and process for x-ray data collection can be seen below in Figure 2.1. The opaque cover plate was removed from a given rhizobox which was then mounted with the Kapton film-soil interface at a 45° angle to the incident x-ray beam. Nickel distribution was mapped (Ni K-edge; 20 μm x 20 μm pixel size) within the root zone in order to identify regions of interest for the collection of micro x-ray absorbance fine structure spectroscopy μXAFS data. For the collection of XRF maps and μXAFS spectra, fluorescence was detected using a Ge solid-state multi-element detector. Due to the presence of an overwhelming Fe signal, three pieces of Al foil were placed on the detector which was operated with no beryllium (Be) window. The resulting spectra were aligned, merged when appropriate, and deglitched using the program Athena. Spectra were then cut so that all features between 2.8 and 8.6 Å-1 were maintained and used for further analysis. All standard spectra were trimmed to the same specifications. Principal component analysis (PCA) was performed using software written for use by beamline 10.3.2 users. The resulting indicator value was used to determine the maximum number of standards to use during linear least squares fitting, where the number of components at which the indicator value reached its minimum was chosen; in this case a maximum of three standards were chosen for fitting. Software written for use by beamline 10.3.2 users was then used to perform linear least squares fitting. Beginning with a single standard, each subsequent fit that included an additional standard was required to exhibit an increase in the fit statistic by at least 10% to be considered an improvement over the more simplistic fit. After the best fit was chosen, the top three fits for that number of standards was used to create an average fit, which was fitted back against the standards. The average fit was then compared to the top three best fits for reasonableness. Post fitting, the standards used were grouped into four classes: those where Ni is structurally bound, those where Ni is precipitated as an insoluble complex, those where Ni is sorbed to a mineral surface, and those where Ni is complexed to an organic compound.

Sample Ni hotspots Ni -XAFS X-ray source Rhizobox across rhizosphere

N. caerulescens FeCrNi 3 0 1 2

Roots

4 LLS Fitting

49 -synchrotron XRF root map showing Ni distribution and hot spots (green)

Solid state detector Rhizobox

Figure 2.1 Diagram depicting the general setup and collection of XAS data using the rhizoboxes built for this experiment. On the left, the rhizobox is mounted at a 45 degree angle to the x-ray source and solid state detector. Next, a rhizobox containing N. caerulescens is shown, with the root system visible through the Kapton film window. The red square in the rhizobox indicates an area of interest that was mapped by rastering across the area of interest, line by line, creating the XRF map shown to the right of the rhizobox. Nickel distribution is shown in green, with color intensity proportional to the intensity of the Ni signal received by the detector. Roots are outlined in purple. Spectra were collected within the rhizosphere, processed, and fit to standards using linear least squares fitting. Spectra and maps were collected the same way from unplanted control boxes, where Ni hot spots with reasonable Ni signal throughout the box were chosen to collect spectra.

2.3.4 Phospholipid Fatty Acid Analysis

After x-ray absorbance spectroscopy, plants were allowed to recover for approximately one week after which the Kapton film was removed from the rhizobox and soil was sampled from the rhizosphere of planted rhizoboxes, as well as soil from the unplanted controls. The soil mass within the rhizobox was gently broken apart, allowing the roots to be extracted and gently shaken. Soil that adhered to roots was considered rhizosphere soil. Rhizosphere soil was separated from roots by holding the roots in hand, using gloves sterilized with 70% ethanol, and gently tapping the roots. Soil samples were mixed, flash frozen in liquid nitrogen, freeze dried and stored at -80°C prior to extraction and analysis. Soil microbial community structure was determined by phospholipid fatty acid (PLFA) analysis according to the high throughput protocol described by Buyer and Sasser (Buyer and Sasser 2012). Briefly, PLFAs were extracted from soil with a Bligh-Dyer (chloroform/methanol/phosphate buffer) extractant (4.0 ml. 1:2:0.8, v/v/v, 50 mM, pH 7.4) containing an internal standard. Lipid classes were separated by solid phase extraction (SPE) using a 96-well SPE plate containing 50 mg of silica per well (Phenomenex, Torrance, CA, USA). The samples were then dissolved in 75 μL of hexane, transferred to glass inserts in GC vials, and analyzed using a MIDI system (Microbial Identification System Inc., Newark, DE) consisting of an Agilent 7890 gas chromatograph (Agilent Technologies, Wilmington, DE, USA) fitted with a 100 place auto-sampler, an Agilent 7693 Ultra 2 column, and a flame ionization detector. Fatty acid methyl esters (FAMEs) were identified using the MIDI PLFA calibration mix and MIDI peak naming table from which concentrations and relative abundance of each FAME were calculated. Data were normalized to the average of the total concentration of observed FAMEs across the unplanted control samples. The Hellinger transformation was then used to relativize the data, after which a distance matrix was calculated using Bray-Curtis distances, and non-metric multidimensional scaling (NMS) ordination was performed on that distance matrix. Multiple regression permutation procedure (MRPP) was used to test for differences in community composition among treatments and the

results were corrected for multiple comparisons using the Benjamini-Hochberg method. Both NMS and MRPP were performed using the software PCORD (McCune 2011).

2.3.5 DNA Sampling, Sequencing, and Analysis

DNA was extracted from 0.25 g of freeze-dried soil using the Qiagen DNeasy PowerSoil Kit (Qiagen, Germantown, MD, USA) according to the manufacturer’s protocol. The quality and quantity of DNA extracted from soil samples was determined spectrophotometrically. Amplification of the V4 region of the 16S rRNA gene, library preparation, and sequencing were carried out by The Microbial Systems Molecular Biology Laboratory (MSMBL) at the University of Michigan. The V4 region of the 16S rRNA gene was amplified via PCR using the primer set 515F/806R and methods developed by Kozich et al. (Caporaso et al. 2012, Kozich et al. 2013). Each reaction contained 2 μL 10x AccuPrime PCR Buffer II (Invitrogen, Carlsbad, CA, USA), 11.85 μL PCR grade water, 0.15 μL AccuPrime high fidelity Taq polymerase (Invitrogen, Carlsbad, CA, USA), 1.0 μL template DNA, and 5.0 μL primer mixture, for a total reaction volume of 20 μL. The PCR conditions used were: initial denaturation for 2 min at 95 °C; 30 cycles of amplification which consisted of 20 s denaturation at 95°C, 15 s annealing at 55°C, and 5 min of extension at 72°C with a final 10 min extension period at 72°C; samples were held at 4°C until checked for quality and quantity. PCR products were visualized using 2% agarose gel-electrophoresis and SYBR Safe DNA Gel Stain (Life technologies cat# G7208-02). PCR products were normalized using SequalPrep Normalization Plate Kit (Life technologies, Carlsbad, CA, USA; cat # A10510-01) following the manufacturer’s protocol. The concentration of pooled samples was then measured using the Kapa Biosystems Library Quantification Kit for Illumina platforms (Kapa Biosystems KK4824). Amplicon size was determined using the Agilent Bioanalyzer High Sensitivity DNA analysis kit (cat# 5067-4626). The final library consisted of equal molar amounts from each of the plates, normalized to the pooled plate at the lowest concentration. Libraries were then denatured and diluted for sequencing according to Illumina’s protocol (part# 15039740 Rev. D) for 2nM or 4nM libraries. The final load consisted of 5.5 pM of library, a 15% PhiX spike in, and no genome spike in. Sequencing reagents, including custom 51

read and index primers, were prepared according to MSMBL protocols developed by Patrick Schloss. Sequencing was performed on the Illumina MiSeq platform using the Illumina MiSeq Nano V2 500 cycle chemistry. The resulting paired-end sequence data were denoised, quality filtered, assigned taxonomy, and analyzed for differences in alpha and beta diversity using standard methods in QIIME2 (Bolyen et al. 2018). Differences among 16S community profiles were assessed using NMS and MRPP in PCORD as described for the PLFA data in the above section.

The prediction of the bacterial community metagenome and corresponding biochemical capacity was carried out using the Phylogenetic Investigation of Communities by Reconstructing Unobserved States (PICRUSt) software package (Langille et al. 2013). Briefly, OTUs were picked at 100% identity using a closed reference picking method against the GreenGenes database using QIIME2. The resulting OTU table was normalized by copy number and then used to predict biochemical functionality according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) in PICRUSt. Data were then grouped into two groups in accordance with the results of MRPP and function was analyzed using the STAMP software package (Parks and Beiko 2010). Further analysis of differences between groups regarding various KEGG pathways was carried out using STAMP and utilized Welch’s t-test, with confidence intervals calculated as the inversion of Welch’s t-test (Parks and Beiko 2010).

2.3.6 Ni Concentration in Plant Tissues

Shoots were excised from the roots at the time of soil sampling. After rhizosphere soil was removed from roots, roots where rinsed multiple times with double deionized water to remove as much residual soil as reasonably possible. Roots and shoots were then oven dried at 60°C for 48 h. Dried shoots were ground for 40 s in a ball mill. Roots were not ground due to the low amount of root mass recovered, their fine structure, and their high amount of surface area. To ensure that Ni was not being introduced into the samples from the operation of the ball mill, tomato leaf standards were also passed through ball mill in triplicate and were analyzed in tandem with a set

that was not. Dried tissues were then subjected to microwave assisted acid digestion according to EPA method 6020a and Ni concentration assessed via inductively coupled plasma mass spectrometry (United States Environmental Protection Agency 1998).

2.4 Results

2.4.1 Ni in Plant Tissues

Alyssum murale (3162 mg Ni/kg dry shoot tissue), N. caerulescens (1239 mg Ni/kg dry shoot tissue), and S. polygaloides (1377 mg Ni/kg dry shoot tissue) exhibited high Ni shoot concentrations as expected (Table 2.2). Shoot Ni quantity was statistically unique in A. murale and statistically similar across N. caerulescens and S. polygaloides (Table 2.2). Nickel uptake and translocation was significantly less in the non- accumulator S. glandulosus than all Ni hyperaccumulators, with only 5 mg Ni/kg dry shoot tissue being detected (Table 2.2). Within the shoots of each Ni hyperaccumulator, Ni concentration was observed to be well over 200 times greater than that of S. glandulosus, meeting the criteria of Ni hyperaccumulation whereby Ni must be present at quantities 10 times greater compared to other plants growing on metal enriched soils. The translocation factor (Tf) for each plant was calculated as a simple ratio of mg/kg Ni per in dry shoot divided by mg/kg Ni in dry root. As illustrated in Table 2.2, Ni was present in the shoots of the Ni hyperacculators at levels approximately 2.3 – 5.3 times greater than roots, whereas the Tf for S. gandulosus was less than one, indicating that more Ni was present in roots than shoots—a common phenomonon in plants that tolerate but do not accumulate or hyperaccumulate trace metals (Table 2.2). Ball milling did not contribute any measurable quantites of Ni (Table 2.2)

Table 2.2 Average Shoot Ni concentration on an oven dry tissue basis (mg Ni/kg dry tissue), total shoot Ni (mg), and the translocation factor (shoot [Ni]/root [Ni]) are shown for three Ni-hyperacumulators (H) and one non-accumulator (N), along with standard deviation of the mean. Average Ni concentration (mg Ni/kg dry tissue) for ball milled and non-milled tomato leaf standards are also shown. Lower case letters denote groups determined by Tukey’s HSD test, where the treatment was determined to be significant using ANOVA. (a=0.05; n=3).

Plant Shoot Nickel Translocation factor Plant Total Shoot Ni (mg) Type (mg Ni/kg dry tissue) (shoot [Ni]/root [Ni]) H 3162 ± 692 a 50.90 ± 16.9 a 5.33 ± 1.56 a A. murale N. caerulescens H 1239 ± 287 b 4.80 ± 3.13 b 4.98 ± 1.65 a

S. polygaloides H 1377 ± 292 b 12.32 ± 4.77 b 2.33 ± 0.34 b S. glandulosus N 4.83 ± 1.19 c 0.05 ± 0.01 b 0.015 ± 0.0048 c

54 Tomato Leaf Standard Control 1.15 ± 0.00005 NA NA Ball Milled Tomato Leaf Control 1.20 ± 0.000008 NA NA Standard

2.4.2 Phospholipid Fatty Acid Analysis

Rhizosphere microbial community structure generally differed across plant types and the unplanted control, but NMS ordination showed rhizosphere microbial community structure associated with Ni hyperaccumulating plants (Am, Nc, and Sp) was more similar than that of the non-accumulator (Sg) and the unplanted control (Ctrl) treatments (Figure 2.2). According to Benjamini-Hochberg corrected MRPP, rhizosphere community structures associated with A. murale (Am) and N. caerulescens (Nc) did not significantly differ, but all other community structures significantly differed from each other (p<0.05; False Discovery Rate (FDR)=0.10; Table 2.3). Although the rhizosphere microbial communty associated with the Ni hyperaccumulator S. polygaloides (Sp) was statistically different from the other Ni hyperaccumulator communities, it still clustered with them along the x-axis of the NMS ordination, which accounted for 90% of the variability within the model. This suggests that while statistically different, Sp rhizosphere microbial community structure was more similar to that of the other Ni hyperaccumulators than to the other treatments. Biplot analysis (r cutoff=0.30) indicated that arbuscular mycorrhizal fungi (AMF) and general fungal biomarkers influenced differentiation of Ni hyperaccumulator associated microbial communities, while gram positive bacteria influenced differentiation of non-accumulator and unplanted control associated communities (Figure 2.2). Indeed, both AMF and general fungal lipid biomarker concentrations were greater among the Ni hyperaccumulator associated communities compared to the non-accumulator and unplanted control, with the Ni hyperaccumulator microbial communities forming a distinct statistical group, and the non-accumulator and unplanted control forming a second statistically distinct group (p<0.05; Tukey’s HSD). Likewise, concentrations of lipid biomarkers indicative of gram positive bacteria were significantly greater among non-accumulator and unplanted control microbial communities, which formed a statistically distinct group as compared to the Ni hyperaccumulator communities (Table 2.4 and Figure 2.3; p<0.05; Tukey’s HSD).

Axis 2 8.6% 2 Axis

Axis 1 90%

Figure 2.2 Two-dimensional non-metric multidimensional scaling

ordination depicting the separation of rhizosphere microbial communities as estimated by PLFA after 21 days post germination for plants grown in Neshaminy silt loam soil (n=3; 2-dimensional solution;

51 iterations; stress=4.19685). Data are normalized to the control to account for differences in total biomarker concentration. The rhizosphere microbial community structure associated with the Ni-

hyperaccumulators Alyssum murale (Am), Noccaea caerulescens (Nc), and Streptanthus polygaloides (Sp) cluster apart from the non- accumulator Streptanthus glandulosus (Sg) and the unplanted control

(Ctrl) which separate primarily along the x-axis. Biplot rays indicate the direction and magnitude by which each biomarker group contributes to the dissimilarity of groups.

Table 2.3 The results of the Benjamini-Hochberg corrected MRPP performed on PLFA data across all treatments are shown below. For the calculation of Benjamini-Hochberg corrected values, I was the rank fo the comparison by p value, p was the p value calculated by MRPP, m was the total number of comparisons, and Q was the false discovery rate (Q=0.10; n=3). Am = A. murale; Nc = N. caerulescens; Sp = S. polygaloides; Sg = S. glandulosus; and Ctrl = unplanted control.

Rank (i) Comparison T A p (p*m)/i (i/m)*Q 1 Nc vs. Sg -2.99 0.467 0.022 0.216 0.010 2 Nc vs. Ctrl -2.98 0.702 0.022 0.108 0.020 3 Sp vs. Ctrl -2.94 0.612 0.022 0.073 0.030 4 Sp vs. Nc -2.92 0.416 0.022 0.055 0.040 5 Am vs. Ctrl -2.87 0.627 0.022 0.045 0.050 6 Sp vs. Sg -2.83 0.379 0.022 0.037 0.060 7 Ctrl vs. Sg -2.60 0.300 0.024 0.035 0.070 8 Am vs. Sg -2.62 0.379 0.024 0.030 0.080 9 Sp vs. Am -1.68 0.277 0.059 0.066 0.090 10 Am vs. Nc -1.03 0.090 0.146 0.146 0.100

Table 2.4 The average PLFA biomarker concentrations along with the standard deviation can be seen below (n=3). Data are reported in picomoles of biomarker per gram of soil sampled.

Average PLFA Biomarker Concentration (pmol g-1)

Gram Negative Gram Positive Arbuscular Treatment General Fungi Actinobacteria Protozoa Bacteria Bacteria Mycorrhizal Fungi

S. polygaloides 60643 ± 2169 30648 ± 1658 6825 ± 282 4318 ± 764 21375 ± 1100 715 ± 41 A. murale 66851 ± 1227 32998 ± 620 7276 ± 69 6984 ± 1555 22120 ± 151 812 ± 159 N. caerulescens 64549 ± 2927 34258 ± 2344 7276 ± 51 6770 ± 475 23278 ± 1656 871 ± 78 S. glandulosus 41263 ± 3030 24009 ± 1939 3441 ± 615 2700 ± 412 12348 ± 1488 490 ± 100

58 Control 32521 ± 2675 20445 ± 1499 2736 ± 619 1244 ± 177 11178 ± 972 582 ± 104

G- G+ AMF Fungi Actinobacteria Protozoa

Sp Treatment Sg

Ctrl

59 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Relative Abundance of Representative PLFA Biomarkers

Figure 2.3 Bar chart depicting the relative abundance of each PLFA marker group as observed in the rhizosphere of the Ni-hyperaccumulators Alyssum murale (Am), Noccaea caerulescens (Nc), and

Streptanthus polygaloides (Sp) along with the non-accumulator Streptanthus glandulosus (Sg) and an unplanted control (Ctrl). Data are normalized to the unplanted control to adjust for differences in total biomarker concentration. PLFA was performed at 21 days post germination with growth in

Neshaminy silt loam soil. ANOVA indicated a significant treatment affect for all markers (n=3; a=0.05), however the Ni-hyperaccumulator communities were most similar to each other, and different from the other treatments, with respect to fungal lipid markers, which were greater in the Ni-hyperaccumulator rhizosphere, and those indicative of gram positive bacteria which were lesser in the Ni-hyperaccumulator rhizosphere (Tukey’s HSD).

2.4.3 Bacterial Community (16S) Analysis

The NMS ordination revealed that the Ni hyperaccumulator rhizosphere bacterial communities, analyzed at the taxonomic level of order, clustered together and separated from those of the non-accumulator and unplanted control along the x-axis of a 2-dimensional ordination, which accounted for approximately 76% of the variability in the ordination (Figure 2.4). Benjamini-Hochberg corrected MRPP analysis indicated that the rhizosphere bacterial communities associated with the three Ni hyperaccumulating plants were statistically indistinguishable from each other but were significantly different from the non-accumulator and unplanted control bacterial communities (p<0.05; FDR=0.10; Table 2.5). Non-accumulator and unplanted control communities were also indistinguishable from one another. As such, data were grouped accordingly, with the Ni hyperaccumulator bacterial communities composing one group and those of the non-accumulator and control composing another group, prior to further statistical analysis. Neither alpha diversity nor community evenness differed among treatments. Beta diversity, as assessed by both weighted and unweighted UniFrac distances, showed a similar trend as that observed with NMS and MRPP. Biplot analysis (r cutoff=0.50) suggested that organisms within the orders CCU221, iii1-15, Sva0725, RB41, Xanthomonadales, and an unknown order were primary drivers of separation in the direction of the Ni hyperaccumulator groups within the ordination and exhibted greater abundances that were significantly different compared to those in the non-acumulator rhizosphere (Figure 2.4; Table 2.6, 2.7). Orders CCU221, iii1-15, Sva0725, and RB41 are contained within the phylum Acidobacteria, while Xanthomonadales resides within Proteobacteria, and the unknown order was of the Chloroflexi phylum. Separation of groups in the direction of the non-accumulator and unplanted control were primarily driven by organisms in the orders of Actinomycetales, Burkholderiales, Gemmatimonadales, Nitrosomonadales, Sphingobacteriales, and an unkown order within the phylum Gemmatimonadetes, all of which were more abundant in the non- accumulator and control treatments relative to the Ni hyperaccumulator rhizosphere (Figure 2.4; Table 2.8, 2.9). Actinomycetales is of the phylum Actinobacteria,

9.9%

Axis 2 Axis

Axis 1 70.4%

Figure 2.4 Two-dimensional non-metric multidimensional scaling ordination depicting the separation of rhizosphere bacterial

communities, at the taxonomic level of order, as estimated by 16S amplicon sequencing 21 days post germination of plants grown in Neshaminy silt loam soil (n=3; 2-dimensional solution; 85 iterations;

stress=12.245). Data are normalized to the control. The rhizosphere microbial community structure associated with the Ni- hyperaccumulators Alyssum murale (Am), Noccaea caerulescens (Nc),

and Streptanthus polygaloides (Sp) cluster apart from the non- accumulator Streptanthus glandulosus (Sg) and the unplanted control (Up), which also separate primarily along the x-axis. Biplot rays

indicate the direction and magnitude by which bacterial orders contribute to the dissimilarity of groups.

Table 2.5 The results of the Benjamini-Hochberg corrected MRPP performed on PLFA data across all treatments are shown below. For the calculation of Benjamini-Hochberg corrected values, I was the rank fo the comparison by p value, p was the p value calculated by MRPP, m was the total number of comparisons, and Q was the false discovery rate (Q=0.10). Am = A. murale; Nc = N. caerulescens; Sp = S. polygaloides; Sg = S. glandulosus; and Ctrl = unplanted control.

Rank (i) Compared T A P (p*m)/i (i/m)*Q 1 Sg vs. Nc -2.95 0.232 0.0218 0.218 0.010 2 Sg vs. Sp -2.92 0.165 0.0220 0.110 0.020 3 Ctrl vs. Nc -2.82 0.210 0.0226 0.075 0.030 4 Sg vs. Am -2.77 0.167 0.0230 0.057 0.040 5 Ctrl vs. Am -2.69 0.172 0.0232 0.046 0.050 6 Ctrl vs. Sp -2.67 0.141 0.0232 0.039 0.060 7 Ctrl vs. Sg -2.07 0.041 0.0300 0.043 0.070 8 Am vs. Nc -1.37 0.037 0.0920 0.115 0.080 9 Sp vs. Nc -0.38 0.008 0.2731 0.303 0.090 10 Sp vs. Am -0.42 0.014 0.3070 0.307 0.100

Table 2.6 Bacterial orders identified by NMDS as influencing the separation of Ni-hyperaccumulator (H) bacterial communities from those of the non-accumulator and unplanted control (Ctrl-N). The orders were always more abundant in the H group than the Ctrl-N group (a=0.05; nNHA=9; nNAC=6).

Key Orders influencing separation of Ni Hyperaccumulators (H) p compared Ctrl-N O13 k__Bacteria;p__Acidobacteria;c__Acidobacteria-6;o__CCU21 < 0.0001

O22 k__Bacteria;p__Acidobacteria;c__Sva0725;o__Sva0725 0.0092

O28 k__Bacteria;p__Acidobacteria;c__[Chloracidobacteria];o__RB41 < 0.0001

O77 k__Bacteria;p__Chloroflexi;c__Ellin6529;o__ 0.0062

O184 k__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Xanthomonadales 0.0008

Table 2.7 ANOVA results from the comparison of bacterial orders identified by NMDS as influencing the separation of Ni-hyperaccumulator (H) bacterial communities from those of the non- accumulator and unplanted control (Ctrl-N). The below bacterial orders were always more abundant in the H group than the Ctrl-N group (a=0.05; nNHA=9; nNAC=6).

Bacterial Oder ANOVA Results Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 3712.0444 3712.04 9.336 0.0092 Sva_0725 Error 13 5168.8889 397.61

C. Total 14 8880.9333 Source DF Sum of Squares Mean Square F Ratio Prob > F

64 Plant_Type 1 90440.1 90440.1 41.2175 <0.0001 RB41

Error 13 28524.83 2194.2 C. Total 14 118964.93 Source DF Sum of Squares Mean Square F Ratio Prob > F

Plant_Type 1 17556.1 17556.1 10.6258 0.0062 Unknown Order Error 13 21478.833 1652.2 C. Total 14 39034.933

Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 18662.4 18662.4 18.8783 0.0008 Xanthomonadales Error 13 12851.333 988.6

C. Total 14 31513.733

Table 2.8 Bacterial orders identified by NMDS as influencing the separation of non-accumulator and unplanted control (Ctrl-N) bacterial communities from those of Ni hyperaccumulators (H). The orders were always more abundant in the Ctrl-N group than the H group (a=0.05; nNHA=9; nNAC=6).

Key Orders influencing separation of non-accumulator and control (Ctrl-N) from H p compared to H

O33 k__Bacteria;p__Actinobacteria;c__Actinobacteria;o__Actinomycetales 0.0004 O52 k__Bacteria;p__Bacteroidetes;c__Sphingobacteriia;o__Sphingobacteriales < 0.0001

O110 k__Bacteria;p__Gemmatimonadetes;c__Gemm-5;o__ <0.0001

O114 k__Bacteria;p__Gemmatimonadetes;c__Gemmatimonadetes;o__Gemmatimonadales 0.0006

O160 k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Burkholderiales <0.0001

O164 k__Bacteria;p__Proteobacteria;c__Betaproteobacteria;o__Nitrosomonadales 0.0043

Table 2.9 ANOVA results from the comparison of bacterial orders identified by NMDS as influencing the separation of non-accumulator and unplanted control (Ctrl-N) bacterial communities from those of Ni hyperaccumulators (H). The orders were always more abundant in the Ctrl-N group than the H group (a=0.05; nNHA=9; nNAC=6).

Bacterial Order ANOVA Results Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 88297.34 88297.3 22.1608 0.0004 Actinomycetales Error 13 51797.06 3984.4 C. Total 14 140094.4

66 Source DF Sum of Squares Mean Square F Ratio Prob > F

Plant_Type 1 32262.4 32262.4 40.873 <0.0001 Sphingobacteriales Error 13 10261.333 789.3 C. Total 14 42523.733 Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 677.87778 677.878 32.4317 <0.0001 Unknown Order Error 13 271.72222 20.902 C. Total 14 949.6 Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 4522.7111 4522.71 20.0241 0.0006 Gemmatimonadales Error 13 2936.2222 225.86 C. Total 14 7458.9333

Table 2.9 (continued)

Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 628504.9 628505 34.55732 <0.0001 Burkholderiales Error 13 236435.5 18187 C. Total 14 864940.4 Source DF Sum of Squares Mean Square F Ratio Prob > F Plant_Type 1 1345.6 1345.6 11.9378 0.0043 Nitrosomonadales Error 13 1465.3333 112.72 C. Total 14 2810.9333

Sphingobacteriales is of the phylum Bacteroidetes, with Burkholderiales resides in the phylum Proteobacteria. The potential biochemical function of the community was also found to be different according to PICRUSt analysis and principal components analysis (PCA). Like the 16S bacterial communities, the samples separated into two primary clusters, with the Ni hyperaccumulators composing one cluster and the non- accumulator and control comprising the other (Figure 2.5). Separation of rhizosphere bacterial community functional profiles was primarily attributed to principal component one (Figure 2.5), while principal components two and three contributed to some minor separation of individual treatments and within treatment variability (data not shown). Differences in KEGG orthologs abundances were mostly small in magnitude across a broad array of pathways, sugggesting that the communities were different because of small differences across many pathways. KEGG orthologs associated with Ni tolerance were not found to be different in abundance among the two different groups (data not shown). Other general pathways that could be related to Ni exposure and tolerance were examined, with few resulting in significant differences among the two groups (Figure 2.6). Pathways involved in lipopolysaccharide biosynthesis, DNA repair, glutamine biosynthesis, and other transporters were slightly elevated in bacteria among the rhizospheres of the Ni hyperaccumulator group. Pathways involved in various transporter systems were slightly elevated among the bacterial community associated with the non-accumulator and control treatments.

Ctrl- H N

Figure 2.5 Principal components analysis plot showing the separation of rhizosphere bacterial community functional profiles created using PICRUSt, and analyzed using STAMP. The data separated into two primary groups, with the Ni- hyperaccumulator (H; n=9) functional profile forming one group and the non- accumulator and control (Ctrl-N; n=6) forming a second group, the separation of which is primarily attributed to principal component one (PC1). Some separation among individual treatments can be attributed to principal components 2 and 3 (data not shown).

Ctrl-N H

Figure 2.6 Bar plot showing the differences in mean proportion of orthologs involve in various KEGG pathways that could 70 be related to Ni tolerance or Ni exposure among bacteria. Differences in mean proportions were calculated in STAMP

using Welch’s t-test to account for unequal variances and sample sizing between the two groups (Ctrl-N=unplanted control + non-accumulator, n=6; H=Ni-Hyperaccumulator, n=9; alpha=0.05). Pathways involved in lipopolysaccharide biosynthesis, D-glutamine and D-glutamate metabolism, other transporters, and DNA repair were slightly elevated

among bacteria in the rhizosphere of the Ni hyperaccumulator group. These pathways could be involved in Ni tolerance, where lipopolysaccharides may form barriers to Ni entering the cell, glutamine may act as a chelating agent, and elevated levels of DNA repair proteins may be indicative of oxidative stress, often associated with heavy metal exposure.

Pathways involved in other ion-coupled transporters, ABC transporters, and pores ion channels where slightly elevated in the bacterial among the unplanted control and non-accumulator group. With respect to Ni, a slight elevation in this group could indicate simple influx and efflux associated with Ni tolerance without significant exposure.

2.4.4 In-situ Ni Speciation

Control spectra were primarily composed of structurally bound Ni species, indicating a high proportion of Ni enclosed within the crystal structure of soil minerals, along with a minor occurrence of organically complexed Ni. Conversely, spectra collected from the rhizosphere systems were more varied, with mostly structurally bound, mineral-sorbed, insoluble precipitates and organic species occurring in that order (Figure 2.7). Of the rhizosphere systems, organically complexed Ni only occurred within the non-accumulator rhizosphere and was 18% of one spectrum. Mineral sorbed species occurred within the rhizosphere of all plants, however, the proportion of mineral sorbed species tended to be higher within the rhizosphere of Ni hyperaccumulating plants (Figure 2.7). Mineral sorbed species occurred at their highest levels within the rhizosphere of A. murale, where one spectrum was composed of 100% mineral sorbed Ni species, with the second greatest abundance occurring within the rhizosphere of the Ni hyperaccumulator S. polygaloides at 66.6% of the total fit (Figure 2.7). The lowest abundance of mineral sorbed species occurred in the rhizosphere of the non-accumulator, with one spectrum containing 28% mineral sorbed Ni. By comparison, the lowest abundance of mineral sorbed Ni species among the Ni hyperaccumulators was 32.7% and occurred within the rhizosphere of S. polygaloides. Insoluble Ni precipitates also occurred among the plant systems, however, its occurrence was less frequent than that of mineral sorbed Ni species. Where insoluble Ni precipitates were observed, they were also more abundant within the rhizosphere of Ni hyperaccumulators than the rhizosphere of the non-accumulator. Of the two occurrences of insoluble Ni precipitates within the Ni hyperaccumulator rhizosphere, these Ni species comprised 54% and 27% of the total fit within one spectrum each from the rhizospheres of N. caerulescens and A. murale, respectively (Figure 2.7). Comparatively, the non-accumulator rhizosphere contained one spectrum in which insoluble Ni precipitates were observed whereas 13% of the spectra was comprised of insoluble Ni species. Across all treatments, mineral sorbed species were primarily represented by either Ni-geothite or Ni-birnessite—Ni sorbed to iron oxyhydroxide or

-1 Inverse Angstroms (Å ) Proportion of Ni types comprising spectra

Figure 2.7 XAS spectra (black line) and the average, best fit (red dotted line), along with the composition of Ni species observed for each spectrum. Unknowns were fit to a maximum of 3 standards, as determined by PCA, using linear least squares fitting. Ctrl=unplanted control; Sg=Streptanthus glandulosus; Sp=Streptanthus polygaloides; Nc=Noccaea caerulescens; Am=Alyssum murale.

manganese oxide. Organically complexed Ni species were always represented by Ni- citrate or Ni-malate—or Ni complexed with an oxygen donor ligand that is likely low in molecular weight. Structurally bound Ni species were represented ultramafic rocks collected during soil sampling, or ultramafic minerals or phyllosilicates provided by Dr. Mathew Siebecker of the Department of Plant and Soil Science at Texas Tech University. Insoluble Ni species were always represented by Ni oxide.

2.5 Discussion

The extent of Ni hyperaccumulation corresponded well with the findings of other researchers. The Ni concentration of A. murale shoots was approximately 3,162 mg Ni/kg dry tissue, which was at the lower end of the range observed across several studies in which A. murale sequestered Ni in shoots at levels from 3,320 – 11,300 mg Ni/kg dry tissue (Bani et al. 2007, Broadhurst and Chaney 2016, Li et al. 2003). Likewise, S. polygaloides sequestered Ni in shoots to a level of 1,377 mg Ni/kg dry tissue, with the literature citing observations in the range of 1,100 -16,400 mg Ni/kg dry tissue in Ni enriched soils, and ranges as low as 16 – 230 mg Ni/kg dry tissue in soils containing lesser quantities of Ni (Boyd, Shaw, and Martens 1994, Jhee, Boyd, and Eubanks 2005, Reeves, Brooks, and Macfarlane 1981). Nickel quantities observed in the tissues of N. caerulescens, approximately 1,239 mg Ni/kg dry tissue, corresponded well with the range of 1000-1700 mg Ni/kg dry tissue reported in other studies (Mari et al. 2006, Marqu et al. 2004). Several researchers have reported that Ni hyperaccumulating plants absorb Ni from the same soluble pools as other plants, and the soluble Ni pool is typically quite small relative to the total Ni present in serpentine soils (Hammer et al. 2006, Massoura et al. 2004, Shallari et al. 2001). Further, several researchers have concluded that neither acidification of the rhizosphere nor the release of Ni via chelation seem to play a major role in making Ni available to hyperaccumulating plants (Bernal et al. 1994, McGrath, Shen, and Zhao 1997, Zhao, Hamon, and McLaughlin 2001). This has led to the conclusion that Ni availability must be tied to Ni solubility at moderate pH values, with relatively soluble species comprising the labile Ni pool. In the current study, the Mehlich III extractable Ni quantities were estimated at 37 mg Ni/kg 73

soil, making for a total labile Ni pool in each rhizobox (~ 65 g soil) of roughly 2.4 mg Ni (Table 1). However, extraction by the Ni hyperaccumulating plants far exceeded this pool. Alyssum murale, S. polygaloides, and N. caerlescens extracted, on average, 51, 12, and 5 mg of Ni respectively (Table 2). This suggests that as labile Ni pools were depleted in the rhizosphere of the Ni hyperaccumulators, Ni was released from elsewhere. This could be the result of changing Ni equilibria due to the loss of Ni from the soil solution in tandem with the release of Ni due to the metabolic activities of plants and microorganisms. A study by Centofanti et al. (2011) reported that Ni species such as Ni- phyllosilicate, Ni-bernessite, and Ni-humate exhibited greater uptake by A. murale than one would predict from solubility alone—indicating that even in a hydroponic system, without the action of rhizosphere microorganisms and edaphic factors, some Ni hyperaccumulators are accessing pools of trace metals that are traditionally characterized as ‘non-labile’ based on chemical parameters of those pools alone, or operationally defined paradigms (Centofanti et al. 2012). In the current study, XAS spectra collected from the unplanted control indicated an overwhelming prevalence of structurally bound Ni species and modest amounts of Ni-organic complexes, while sorbed Ni species were often found within the rhizosphere and tended to occur in higher abundance within that of the Ni hyperaccumulating plants (Figure 2.4). While Ni- birnessite-one of the sorbed species identified in this study-may provide accessible Ni to plants such as A. murale, Ni sorbed to goethite often forms inner sphere complexes, making it unclear how available this sorbed Ni is to the plants in our system. Regardless, the increased occurrence of sorbed Ni in the rhizosphere of Ni hyperaccumulating plants may be indicative of the release of Ni from phyllosilicates and amorphous iron oxides, which then partition to mineral surfaces such as that of goethite. We take this as evidence in support of our hypothesis that available Ni species would be more abundant in the rhizosphere of Ni hyperaccumulators. As previously mentioned, the instantaneous labile pool of Ni is typically minute compared to the total Ni in serpentine soils, and so it is reasonable to assume that one could not measure it using μ-XRF and collecting spectra from hotspots in serpentine soil where the Ni signal is great enough to

overcome the Fe signal. Given this assumption, the overwhelming presence of structural Ni species in the unplanted control is expected (Figure 2.4). However, there is a clear shift towards a greater prevalence of sorbed species in the plant systems, which may represent the release of Ni from amorphous phyllosilicates, native organic matter, and amorphous iron oxides, resulting in an increase in the exchangeable Ni pool due to the activities of the plants and soil microorganisms, particularly fungi, which were noted as a driver of community dissimilarity within our analysis of microbial community structure based on PLFA. Indeed, many of the spectra that were comprised of sorbed species were also comprised of a Ni-phyllosilicate standard or a standard obtained from weathering rock gathered from the field sites. Furthermore, there have been many reports of Ni resistant microorganisms being enriched within the rhizosphere of hyperaccumulating plants (Visioli, D'Egidio, and Sanangelantoni 2015), which leaves one to wonder: if Ni hyperaccumulators are only accessing the same labile pool as other plants, why would there be a selection for Ni resistant microorganisms in the rhizosphere of these plants? Moreover, many microorganisms found within the Ni hyperaccumulator rhizosphere have been implicated in Ni mobilization (Visioli, D'Egidio, and Sanangelantoni 2015), leading to the realization that a pure soil minerology and chemistry approach will not be sufficient for understanding Ni dynamics in the hyperaccumulator rhizosphere and the selection for the hyperaccumulating phenotype.

It was initially hypothesized that the rhizosphere microbial community structures would differ among Ni hyperaccumulating and non-accumulating plants, and would be shared to some degree among the rhizosphere of Ni hyperaccumulators, which was supported by our PLFA analysis. The overall Ni hyperaccumulator microbial community structure, when normalized to the control, largely differed from that of the other treatments due to a higher fungal biomass with respect to AMF and general fungi while the non-accumulator and control treatments were more enriched with gram positive bacteria (Figure 2.1). The enrichment of the AMF biomarker in the Ni hyperaccumulator system is unusual, as many members of the Brassicaceae family are thought to be non- mycorrhizal. However, there are sparse observations of some Brassicaceae interacting

with AMF. Regvar et al. (2003) detected poor AMF colonization of pennycress species, potentially by Rhizophagus irregularis, in metal enriched soils in the field, but was unable to detect AMF colonization in greenhouse experiments (Regvar et al. 2003). Veiga et al, (2013) observed AMF colonization in the roots of Arabidopsis thaliana when grown in conjunction with an AMF host species, with the subsequent infection of A. thaliana by Rhizophagus irregularis being detrimental to plant growth (Veiga et al. 2013). While such observations certainly do not verify the presence of AMF in our system, especially given the paucity of data on the subject, they do lend credibility to the possibility of AMF interactions with non-host plants in the Brassicaceae family. In the present study, the small magnitude in the differences among AMF biomarker abundance could be indicative of poor colonization in the Ni hyperaccumulator rhizosphere. Regardless of this possibility, however, this seems unlikely in light of methodological considerations. Several researchers have noted issues with the use of 16:1ω5 as a biomarker for AMF due to overlap with soil bacterial species, the presence of which may result in background levels of bacterially derived 16:1ω5 that interfere with AMF detection (Ngosong, Gabriel, and Ruess 2012, Nichols et al. 1986, Zelles 1997). Given the few observations of AMF colonization among Ni hyperaccumulating members of Brassicaceae, it is more reasonable to attribute the difference in AMF biomarker abundance to the non-specificity of the 16:1ω5 biomarker, where the Ni hyperaccumulator rhizosphere microbial community structure in this study is characterized by an increase in general fungal biomarkers and that of some unidentified bacteria. However, we cannot rule out the possibility of AMF-plant interaction in the Ni hyperaccumulator rhizosphere of the plants used in this study. Further, the PLFA data highlight the potential role that fungi may play in the Ni hyperaccumulator rhizosphere—a topic that is woefully understudied, but has great potential for understanding how serpentine adapted plants may cope with abiotic, edaphic stressors with through plant-microbe relationships. While examples of Ni hyperaccumulators interacting with fungi are scarce, several studies have demonstrated that the serpentine soil environment is not limiting to fungal abundance and diversity, that fungal

inhabitants of serpentine soils are not specialized, and that in some case, serpentine conditions may inhibit AMF colonization (Branco 2010, Branco and Ree 2010, Daghino et al. 2012, Kohout et al. 2015). Likewise, several researchers have explored the ability of fungi to release metals from soil minerals. Daghino and associates found that in many instances, Verticillium leptobactrum, a fungal species native to serpentine soils, engaged in the bioweathering of chrysotile asbestos, releasing siderophores and so extracting structural ions such as iron and magnesium (Daghino, Martino, and Perotto 2010, Daghino et al. 2008, Daghino et al. 2012, Daghino et al. 2009). Teng et al. (2016) found that when the fungus Talaromyces flavus interacted with the serpentine mineral lizardite, the fungus reduced the pH and released siderophores in the microenvironment where attachment to the mineral occurred, extracted Fe, and destroyed the crystal mineral structure. They concluded that dissolution at the cell-mineral interface may account for up to 50% of bioweathering (Li et al. 2016). Many serpentine minerals contain Ni2+, and it is reasonable to assume that fungal bioweathering may release Ni into the soil solution as a result of the destruction of mineral structures, or because of dissolution fronts at the sites where mineral structure has been disturbed. Further, considering the physical scale at which fungi operate, as opposed to bacteria, their effects on bioweathering and Ni availability in serpentine soils may be overlooked and underrated. While the PLFA results point towards an enrichment in fungi in the rhizosphere of Ni hyperaccumulators, it does not lend insight into specifically how the bacterial communities may differ in their structure and functional capabilities.

Our inspection of bacterial community structure and function supported our hypothesis that rhizosphere microbial communities would differ among Ni hyperaccumulators and non-accumulators, with aspects of the community being shared among Ni hyperaccumulators. However, the functional analysis was less conclusive. With respect to the rhizosphere bacterial community, the bacterial orders that were most strongly associated with Ni hyperaccumulating plants consisted of poorly documented orders, with the exception of Xanthomonadales, which is itself poorly documented among the rhizosphere of Ni hyperaccumulating plants but commonly

occurring among soil bacterial communities. Analysis of the bacterial community functional profiles using PCA suggested that the majority of variability among the treatments could be explained by the same grouping scheme identified by MRPP, indicating the Ni hyperaccumulator rhizosphere bacterial communities share functionality, while the non-accumulator and control communities shared functionality. KEGG orthologs associated with Ni tolerance were identified and analyzed, however, there were no differences in the abundance of these orthologs among treatments (data not shown). This may be explained in the context of the serpentine soil environment; Ni has been present in serpentine soils over evolutionary time periods, making Ni exposure a ubiquitous phenomenon among serpentine microbial communities. Thus, the presence of genes involved in tolerating Ni exposure are expected to be widely distributed among the serpentine microbial community. Additionally, as Ni solubilization and uptake happen over time, with labile Ni likely being transient in the rhizosphere of Ni hyperaccumulating plants, Ni concentrations at any given time may not be elevated enough to cause significant selection among bacterial communities—a consideration that may be better suited for endophytes of Ni hyperaccumulators. Idris et al. (2004) for example, found that bacteria isolated from the rhizosphere of Thlaspi goesingense exhibited lower Ni tolerance than those isolated from shoots (Idris et al. 2004). An observation of non-enrichment in Ni tolerance orthologs within bacterial communities associated with the Ni hyperaccumulator rhizosphere may also be attributed to methodological considerations, where picking OTU’s at 100% identity within the GreenGenes database resulted in a suite of organisms across treatments that shared commonalities simply because they were represented within the database, where differences among unrepresented organisms is likely to play an important role in differentiating the communities. This may be especially true of soil microbial communities, which are often poorly characterized and underrepresented among genetic databases due to the difficulties in characterizing their microbial constituents. Other pathways that could be associated with Ni exposure and tolerance were analyzed, with few resulting in significant differences among treatments (Figure 2.6). Pathways

associated with lipopolysaccharide biosynthesis, glutamine metabolism, DNA repair, and other transporters were slightly elevated in the Ni hyperaccumulator rhizosphere, and may be indicative of Ni exposure and tolerance. Lipopolysaccharides that are anchored into the outer membranes or cell walls of bacteria, for example, can serve as a barrier to entry of unwanted heavy metals and other charged ions via biosorption (Nocelli et al. 2016, Langley and Beveridge 1999). Glutamine is known to form complexes with Ni and other metals, which may serve to immobilize the metal in the immediate environment of a bacterium, or simply keep the metal from causing oxidative damage or otherwise interfering with cellular functions (Bhatia, Walsh, and Baker 2005, Alves et al. 2011). Likewise, a slight elevation in the presence of genes involved in DNA repair may indicate selection of bacteria that can cope with the damage to nucleic acids via oxidative stress and other stresses that result in DNA breakage and inhibition of typical DNA repair pathways, however, much of the work in this area has been done on mammalian cells in regard to cancer formation (Morales et al. 2016, Dally and Hartwig 1997). Most of the other pathways that could be related to Ni exposure and tolerance among bacteria that were elevated among the non-accumulator and control group were simply involved in transport systems, which may indicate some tolerance as a result of simple import and export dynamics. Regardless of statistical significance, however, the magnitude of the differences between groups with respect to these pathways was small, and the correlations to Ni exposure are largely speculative. While instances of bacterial involvement in metal solubilization and dynamics in the rhizosphere have been noted, such notions are difficult to grasp from the above data with any degree of confidence (Becerra-Castro et al. 2013, Ma, Rajkumar, and Freitas 2009, Sessitsch et al. 2013).

2.6 Conclusion

In conclusion, the data supported our hypotheses regarding Ni speciation and microbial community structure within the rhizosphere of Ni hyperaccumulators and a hypertolerant, non-accumulator. The tendency towards a greater prevalence of sorbed Ni species in the rhizosphere of the Ni hyperaccumulating plants compared to the non- accumulator and unplanted control treatments indicates release of Ni to a greater 79

degree from the crystal structure of soil minerals into a more available, mineral-sorbed form in the Ni hyperaccumulator rhizosphere. In the system presented here, we suggest that this release may be a result of bioweathering by fungi within the rhizosphere of Ni hyperaccumulators, in conjuction with bioweathering that may be directly facilitated by the plants through exudation of organic acids and siderophores. This enrichment and/or activity may be associated with plant-microbe relationships, whereby Ni hyperaccumulating plants select for fungi, and possibly bacteria, that are involved in the active release of Ni from serpentine minerals, potentially in their search for Fe, Mg, and other ions. The evidence provided by PLFA and 16S amplicon sequencing and functional analysis provided no evidence that bacteria are heavily involved in the dynamics that may differentiate the Ni hyperaccumulator rhizosphere from the other systems. However, it cannot be ruled out that bacteria are also influencing the mobilization of Ni within the rhizosphere of Ni hyperaccumulator plants, and we were simply unable to assess their involvement with the methods employed. Overall, we suggest that selection of Ni hyperaccumulating phenotypes may have been the result of rhizosphere attributes which result in differences in Ni dynamics within the rhizosphere. Where possible, future work should focus on a more thorough assessment of the biochemical contributions made by bacterial and fungal communities through metagenomic sequencing in combination with RNASeq, allowing a more complete picture of genetic potential and the portion of that potential that is being put to use in the rhizosphere of Ni hyperaccumulators versus non-accumulators. This is an important distinction from common methods, which rely on indirect assessments of function from community composition and diversity, which may not be directly related to biological activity. In addition, in-situ Ni speciation, along with spatial resolution of chemical parameters such as pH and redox conditions, as well as root system architecture, should be assessed in tandem with biochemical functionality to provide a holistic view of the rhizosphere.

2.7 Acknowledgements

This research was supported by the National Science Foundation Graduate Research Fellowship Program. Any opinions, findings, and conclusions or recommendations 80

expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank Sirine Fakra and Mathew Marcus for their assistance with data collection at the Advanced Light Source, beamline 10.3.2, Lawrence Berkeley National Laboratory (Berkeley, CA). The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We thank Mathew Seibecker of Texas Tech University for sharing μXAFS spectra obtained from Ni-bearing minerals and field samples with us for use in this research. This research was further supported by work performed by The University of Michigan Microbial Systems Molecular Biology Laboratory.

CHAPTER 3

Comparison of Microbial Community Structure, Ni speciation, pH, and Oxygen Content within the Rhizosphere of the Ni hyperaccumulator, S. polygaloides and its relative, S. glandulosus

3.1 Abstract

How rhizosphere attributes associated with hyperaccumulating plants compare to their non-accumulating relatives growing in serpentine soil is understudied, and comparison of these rhizosphere attributes may provide insight into why these plants developed divergent mechanisms for inhabiting the same niche. This research compared microbial community structure and soil pH, oxygen content, and Ni speciation, between rhizospheres of a Ni hyperaccumulator (Streptanthus polygaloides,) and a non- accumulator (Streptanthus glandulosus) growing in serpentine soil over 3, 6, and 9 weeks post germination. Rhizosphere microbial communities were analyzed via phospholipid fatty acid analysis (PLFA). Ni speciation was analyzed in situ using x-ray absorbance spectroscopy (XAS). Oxygen content and pH were measured in situ using planar optodes. Hyperaccumulation occurred within S. polygaloides shoots, whereas no appreciable accumulation occurred in S. glandulosus shoots, as expected. No differences were observed in pH among treatments. Oxygen content in the unplanted control and S. glandulosus rhizosphere were distinct from each other, however, they were not distinct from that of S. polygaloides. Microbial community structures associated with the unplanted control at 3 and 9 weeks were statistically unique, while all others were not. Nickel speciation showed no temporal trends in the rhizosphere of either plant, but was far more consistent and primarily consisted of mineral sorbed Ni and organically complexed Ni in the rhizosphere of S. glandulosus. Nickel speciation was less consistent in the rhizosphere of S. polygaloides, with organically complexed Ni occurring less frequently and with less abundance compared to the S. glandulosus rhizosphere. Mineral sorbed Ni also occurred less frequently in the rhizosphere of S. polygaloides

compared to S. polygaloides, but when it did occur, it tended to be greater than that of the S. glandulosus rhizosphere. Nickel bound within the crystal structure of soil minerals occurred more frequently and to a greater extent in the rhizosphere of S. polygaloides. Nickel availability did not appear to differ among the rhizospheres of the Ni hyperaccumulator and its non-accumulating relative, however, the effects of Ni uptake were evident. Changes in Ni speciation compared to the control in this study were attributed to the biological activity of the plants and their associated microbial communities. Even though their microbial community structures were similar, we hypothesized that their genetic potential and species composition would differ. Similarities among pH and percent air saturation may be the result of methodological issues, in which not enough time was allowed post-watering to discern differences in treatments. Our hypotheses regarding the chemical environment, the abundance of available Ni species, and the microbial community structure within the rhizosphere of the Ni hyperaccumulator, S. polygaloides, and the related non-accumulator, S. glandulosus were ultimately rejected. Nickel hyperaccumulation occurred as expected, however, there was no evidence to suggest that more bioavailable species of Ni were present in the rhizosphere. Evidence suggested that Ni dynamics in the rhizosphere of these two plants, whereby Ni is released by the weathering of serpentine phyllosilicates and amorphous iron oxides, resulted in distinct patterns of partitioning that could be explained by plant uptake. This may be further supported by the similarities among pH and redox conditions of the rhizosphere, along with similarities among the microbial communities, which could not be used to explain any differences observed in Ni speciation. Although differences among the rhizosphere community composition between treatments cannot be ruled out, we cannot make such conclusions based on this study.

3.2 Introduction

Weathering of ultramafic parent materials such as serpentinite often results in a predictable suite of edaphic factors that include low calcium (Ca) to magnesium (Mg) ratio, low fertility, and enrichment in trace metals such as cobalt (Co), chromium (Cr), 83

and nickel (Ni), which are typical of serpentine soils (Gordon and B Lipman 1926, Proctor and R.J. Woodell 1975, Vlamis and Jenny 1948, Whittaker 1954). These edaphic factors impose such strong selection pressures on plant communities, that Hans Jenny coined the term ‘serpentine syndrome’ to describe the complex, mixed effects that serpentine soils have on plant growth and adaptation (Jenny 1980). One of the most unique outcomes of such selection pressures is the adaptation of heavy metal hyperaccumulation in plants. Hyperaccumulation of heavy metals is typified by translocation of trace metals to shoots, particularly leaves, and sequestration of metal in cell walls or vacuoles at normally cytotoxic concentrations with no pathological effects. Heavy metal hyperaccumulation is exhibited by some species living on soils geologically enriched in heavy metals, whereby self-sustaining populations composed of wild varieties accumulate 10 times more trace metals than other plants growing in trace metal enriched soil and 100-1000 times more than other plants growing on non- enriched soil (van der Ent et al. 2013, Brooks et al. 1977, Jaffre et al. 1976). To date, there are over 500 species of heavy metal hyperaccumulating plants identified, most of which hyperaccumulate Ni (Rascio and Navari-Izzo 2011, van der Ent et al. 2013). Plant species that are closely related to hyperaccumulators may also inhabit serpentine soils, but utilize the opposite strategy for coping with elevated levels of heavy metals. These plants are often deemed to be excluders or hypertolerant and consist of self-sustaining populations composed of wild varieties whose fitness is unaffected by the presence of high amounts of heavy metals and do not accumulate heavy metals in shoot tissues (Goolsby and Mason 2015).

While there has been much interest in the reasons behind the selection of the hyperaccumulator phenotype, less attention is paid to those plants that are closely related to hyperaccumulators but exhibit hypertolerance instead. Goolsby and Mason (2015) speculated that adaptation of hyperaccumulator and hypertolerant, non- accumulating, species are due to discrete evolutionary trajectories (Chaney et al. 1997, Goolsby and Mason 2015, Hanikenne et al. 2008, Verbruggen, Hermans, and Schat 2009). While these different strategies may have been shaped by distinct evolutionary

histories, the causal history that underpins their evolution may contain common features, such as the serpentine environment and the common suite of selection pressures that characterize it. As the unique physiologies of hyperaccumulating and hypertolerant plants are the result of their evolutionary trajectory, physiology alone cannot explain why their trajectories diverged in spite of sharing the same environment. Therefore, more attention must be given to the environment itself, and how serpentine adapted species interact with and modulate the serpentine environment. We suggest that selection of the Ni hyperaccumulating phenotype may have been the result of rhizosphere attributes which result in differences in Ni dynamics within the rhizosphere—forcing plants to adapt different strategies for surviving in the same niche. System wide analysis of the rhizosphere of both hyperaccumulators and closely related non-accumulating plants that share an affinity for the serpentine environment is necessary to gain an understanding of how their rhizosphere attributes are related to strategies for surviving on serpentine soils.

This research examines the rhizosphere attributes associated with the two aforementioned strategies. While one such examination alone will not answer the question of how the rhizosphere may be involved in the adaptation of one strategy or another, the goal of this research was to facilitate a paradigm that begins to approach the question while producing some information that contributes to an eventual understanding. While many researchers have analyzed individual aspects of the rhizosphere of serpentine flora such as pH, redox conditions, Ni availability, and microbial community profiles, few studies have analyzed these rhizosphere attributes in a single experimental system to understand how the rhizosphere may have influenced the selection for hyperaccumulation in some species but not others. We investigated the pH, oxygen content, nickel speciation as it relates to availability, and microbial community structure within the rhizosphere of the Ni hyperaccumulator Streptanthus polygaloides and its relative, the non-acccumulator Streptanthus glandulosus subsp. glandulosus (hereafter referred to as S. glandulosus). We hypothesized that the Ni hyperaccumulator rhizosphere would be host to a unique microbial community

structure, greater abundance of available Ni species, and a unique chemical environment compared to the non-accumulator.

3.3 Methods

3.3.1 Soil Sampling and Characterization

Chrome silt loam soil were collected from a site within a utility corridor running through Soldiers Delight Natural Environmental Area in Baltimore County, Maryland. Soil was sampled from the A horizon using a shovel, to a depth of approximately 10 cm. The site was in an upland position with dense grassland vegetation adjacent to a moderately forested area. Several cores gathered with a push probe were used along with Natural Resource Conservation Service (NRCS) soil maps and soil series descriptions to identify the soil as Chrome silt loam. Soils were air dried, mixed, ground, and sieved to 2 mm. Soil chemical parameters including pH, total carbon and nitrogen, and Mehlich extractable nutrients and metals, were measured by the University of Kentucky Division of Regulatory Services Soil Testing Lab (Table 3.1.).

Table 3.1 Chemical characteristics of the Chrome silt loam serpentine soil. Data represent the mean and one

standard deviation from the mean (n=5).

Soil Parameter Observed values pH 5.00 ± 0.05

CEC* 19.93 ± 0.44 (cmol(+)/kg) Total C (g/kg) 50.1 ± 1.7 Total N (%) 0.28 ± 0.01

P 4.00 ± 0.35

) K 147.7 ± 5.63

Ca 1067 ± 39.15 (mg/kg IMehlich Mg 183 ± 6.93

Mn 411.2 ± 12.93 Fe 153 ± 6.44 Al 685 ± 33.07 Cd 0.07 ± 0.01 Cr 0.29 ± 0.01

Ni 23.15 ± 1.25 Pb 5.69 ± 0.29 Zn 5.05 ± 0.18

(mg/kg)

Mehlich IIIMehlich Cu 1.80 ± 0.13 *Cation exchange capacity

3.3.2 In-situ X-ray Absorbance Spectroscopy

Plants were germinated and grown in rhizoboxes designed to have an open face which was covered with Kapton film using double sided tape and then covered with an opaque cover plate to omit light from the root zone during plant growth as described in Chapter 2. Rhizoboxes were filled with approximately 65 g soil and planted with either S. polygaloides (obtained from Dr. Robert S. Boyd, Auburn University, AL, USA), S. glandulosus (Seedhunt, CA, USA), or left unplanted as a ‘bulk’ soil control. Rhizoboxes were maintained at an approximate 35-40° angle to ensure that roots would grow along the Kapton film-soil interface. Planting was staggered through time so that plants were aged approximately 3, 6, or 9 weeks post germination at the time of analysis, with unplanted controls also being prepared for each time point. Plants were grown in a Conviron growth chamber operating on the following schedule: 16 h light on light setting 3 at 22°C followed by 8 h dark at 16°C with 60% relative humidity throughout. The rhizoboxes were watered to 70% water holding capacity 3 times per week. The rhizoboxes were transported to the X-ray Microprobe Beamline, 10.3.2, at the Advanced Light Source, Lawrence Berkeley National Laboratory (Berkeley, CA) for further analysis. To interrogate Ni speciation in the rhizosphere, the opaque cover plate was removed from a given rhizobox which was then mounted with the Kapton film-soil interface at a 45° angle to the incident x-ray beam. Nickel distribution was mapped (Ni K-edge; 20 μm x 20 μm pixel size) within the root zone to identify regions of interest for the collection of μXAFS spectra. For the collection of XRF maps and μXAFS spectra, fluorescence was detected using a germanium (Ge) solid-state multi-element detector. Due to the presence of an overwhelming Fe signal, three pieces of Al foil were placed on the detector which was operated with no beryllium (Be) window. The resulting spectra were aligned, merged when appropriate, and deglitched using the program Athena (Bruce Ravel 2005). Spectra were then cut such that all features between 2.8 and 11.0 Å-1 were maintained and used for further analysis. All standard spectra were trimmed to the same specifications. Principal component analysis (PCA) was performed using software written for beamline 10.3.2 users. The resulting indicator value was used to determine

the maximum number of standards used during linear least squares fitting, where the number of components at which the indicator reached its minimum was chosen. A maximum of four standards were fitted, but more than 3 were rarely used. Software written for use by beamline 10.3.2 users was then used to perform linear least squares fitting. Beginning at a single standard, each subsequent fit that included an additional standard was required to exhibit an increase in the fit statistic by at least 10% to be considered an improvement over the more simplistic fit. As the top 3 fits were often very similar statistically, an average of the top three fits was calculated, which was then back fit to the corresponding unknown spectrum and standard database for goodness of fit statistics and species identification. For the mapping of Ni distribution in leaves, leaves of either S. polygaloides or S. glandulosus were excised from the plant, flash frozen in liquid nitrogen and mounted to a Peltier cold stage maintained at -25 °C. Leaf maps were then created using the machine settings described above for mapping Ni distribution in soils, with the exception that no Al foil was required on the detector face.

3.3.3 Ni Concentration in Plant Tissues

After analysis via x-ray absorption spectroscopy, shoots were excised from the roots at the soil surface using scissors. After rhizosphere soil was removed from roots, the roots where rinsed multiple times with double deionized water to remove as much residual soil as reasonably possible. Roots and shoots were then oven dried at 60 °C for 48 h. Dried shoots were then ground for 40 s in a ball mill. Due to the low amount of root mass recovered and the high amount of surface area due to their fine structure, roots were not ground. To ensure that Ni was not being introduced into the samples from the operation of the ball mill, tomato leaf standards were also passed through the ball mill and were analyzed in tandem with a set that was not. Dried tissues were then subjected to microwave assisted acid digestion according to EPA method 6020a and Ni content assessed via inductively coupled plasma mass spectrometry (United States Environmental Protection Agency 1998).

3.3.4 In-situ pH and O2

Streptanthus polygaloides, S. glandulosus, and unplanted controls were propagated in the same rhizoboxes and under the same growth conditions as described in the above section, with one exception. The open face of the rhizobox was covered with a transparent acrylic plate bearing a 3 cm x 4 cm self-adhesive, planar sensor foil that was sensitive to either pH or molecular oxygen (PreSens Precision Sensing GmbH, Regensburg, Germany). The plate was attached to the rhizobox using double sided tape with the sensor foil on the inside of the box such that it was in contact with the soil. The transparent plate bearing the sensor foil was then covered with a black, opaque plate to omit light from the root zone during plant growth. The rhizoboxes were then maintained at an angle under the same growth conditions described above. Measurements were taken for spatially distributed pH and molecular oxygen at 3, 6, and 9 weeks using the VisiSens TD Basic System coupled with the Big Area Imaging Kit (PreSens Precision Sensing GmbH, Regensburg, Germany). The resulting images were analyzed against calibration curves with the PreSens Scientifical software (PreSens Precision Sensing GmbH, Regensburg, Germany). For molecular oxygen, a two point calibration curve was created in which the lowest point in the curve was considered to be oxygen free water and the highest point in the curve was considered to be the highest observed oxygen content from the unplanted control boxes. Aqueous, oxygen free conditions were created by adding sodium sulfite to double deionized water until all measurable oxygen was depleted as determined by a dissolved oxygen meter (Strathkelvin Instruments ltd, Motherwell, Scotland). A six point calibration curve ranging from pH 4.99-7.52 in 0.5 increments was created for the analysis of pH using the PreSens CaliPlate system (PreSens Precision Sensing GmbH, Regensburg, Germany), in which all pH solutions were maintained at a standard ionic strength as recommended by the manufacturer’s protocol.

3.3.5 Phospholipid Fatty Acid Analysis

Seeds of S. polygaloides and S. glandulosus were germinated in conical tubes containing Chrome silt loam soils that had been previously homogenized, sieved, and air dried. Plants were allowed to grow for 3, 6, or 9 weeks post germination, and each time point also included an unplanted control treatment. Each planted treatment was composed of triplicates, in which four seeds of each plant were sown into each tube and culled to a single plant at approximately 1 week post germination. For the 3 week treatment, plants were germinated in 15 mL conical tubes containing approximately 12 g of soil. For the 6 and 9 week treatments, plants were germinated in 50 mL conical tubes containing approximately 45 g of soil. All tubes had a hole bored into the bottom that was covered with glass wool to allow for gas exchanged and water drainage if needed. The outer walls of all conical tubes were covered with aluminum foil to exclude light from the root zone. Plants were propagated in the same growth conditions as described in previous sections. Soil clinging to the roots after gentle shaking was considered to be rhizosphere soil, and was removed by gently tapping by hand. Soil samples were placed in new sterile tubes, mixed, flash frozen in liquid nitrogen, lyophilized and stored at -80°C prior to analysis. Soil microbial community structure was determined using phospholipid fatty acid (PLFA) analysis according to the high throughput protocol described by Buyer and Sasser (Buyer and Sasser 2012). The PLFAs were extracted from soil with a Bligh-Dyer (chloroform/methanol/phosphate buffer) extractant (4.0 ml. 1:2:0.8, v/v/v, 50 mM, pH 7.4) containing an internal standard (19:0). Lipid classes were separated by solid phase extraction (SPE) using a 96-well SPE plate containing 50 mg of silica per well (Phenomenex, Torrance, CA, USA). The samples were then dissolved in 75 μL of hexane, transferred to glass inserts in GC vials, and analyzed using a MIDI system (Microbial Identification System Inc., Newark, DE) consisting of an Agilent 7890 gas chromatograph (Agilent Technologies, Wilmington, DE, USA) fitted with a 100 place auto-sampler, an Agilent 7693 Ultra 2 column and flame ionization detector. The FAMEs were identified using the MIDI PLFA calibration mix and MIDI peak naming table from which the concentrations and relative abundance were calculated. The data were normalized to

the average of the total concentration of observed FAMEs in the unplanted control samples. The Hellinger transformation was then used to relativize the data, after which a distance matrix was calculated using Bray-Curtis distances, and non-metric multidimensional scaling ordination (NMDS) was performed on that distance matrix. Multiple regression permutation procedure (MRPP) was used to test for differences among community structure among treatments and the results corrected for multiple comparisons using the Benjamini-Hochberg method. Both NMDS and MRPP were performed using the software PCORD (McCune 2011). Individual biomarker concentrations were subsequently analyzed with the JMP software package (JMP®).

3.4 Results

3.4.1 Ni Concentration in Plant Tissues

Nickel hyperaccumulation occurred as expected within the shoots of S. polygaloides, increasing in the shoots from 521 to 1182 ppm over the 9 weeks of post germination growth. In contrast, Ni in the shoots of S. glandulosus only increased from 9 to 15 ppm over the duration of the experiment (Table 3.2). Nickel concentrations also increased in the roots of S. polygaloides from 343-478 ppm over the 9 week growth period, while that of S. glandulosus decreased slightly from 277-215 ppm over the same period. Translocation factors for S. polygaloides increased with time, from 1.52-2.47, while the translocation factor for S. glandulosus never reached beyond 0.1 (Table 3.2). The hyperaccumulation of Ni was also confirmed via XAS mapping, where the presence of Ni at elevated levels within the tissues of S. polygaloides compared to S. glandulosus can be seen in Figure 3.1. The XAS mapping revealed that Ni is primarily sequestered in the epidermal tissue and accumulates at the leaf tip in S. polygaloides. Nickel did not accumulate within the leaves of S. glandulosus to the extent that an XAS map could be generated using Ni, so the leaf is shown mapped via the distribution of Ca, Mg, and K instead (Figure 3.1). Additionally, the leaves of S. glandulosus were thick and often curled at the edges, making it difficult to map the adaxial side without substantial destruction of the leaf after flash freezing and mounting to the cold stage, and so the

Table 3.2 Shoot and root Ni concentrations on an oven dry tissue basis (mg Ni/kg dry tissue), along with the translocation factor (shoot [Ni]/root [Ni]) for the Ni hyperaccumulator, S. polygaloides, and the non-accumulator, S. glandulosus. These results were derived directly from the individuals used to obtain rhizosphere XAFS Ni data, thus replications are not included.

Treatment Time Shoot Nickel (ppm) Root Nickel (ppm) Translocation Factor 3 weeks 521 343 1.52 S. polygaloides 6 weeks 955 421 2.27 9 weeks 1182 478 2.47 3 weeks 9 277 0.033

S. glandulosus 6 weeks 16 251 0.063 9 weeks 15 218 0.073 1.15 ± 0.00005

93 Tomato Leaf Standard NA NA NA

Ball Milled Tomato Leaf 1.20 ± 0.00008 NA NA NA Standard

NiK a NiK b

500 µm

CaKMn c

1 mm 1 mm

Figure 3.1 X-ray absorption spectroscopy maps showing Ni distribution in leaves of S. polygaloides and S. glandulosus. (a) Adaxial Ni distribution in a mature leaf of S. polygaloides with Ni accumulating in the leaf tip, blue represents K, green represents Ni. (b) Cross section of young leaf from S. polygaloides showing Ni compartmentalized within the epidermal tissues and more evenly distributed within the epidermis; blue represents K, green represents Ni. (c) Abaxial Ni distribution in S. glandulosus; the Ni signal was not strong enough to produce a usable map, and so Ca, K, and Mn were used. The abaxial side was mapped due to leaf curling.

abaxial side was shown (Figure 3.1). However, Ni distributions were no different on the adaxial side (data not shown).

3.4.2 In-situ Ni Speciation

Nickel species among the unplanted control treatment exhibited distinct shifts in response to microbial community activity with time. At 3 weeks, Ni in the unplanted control system was overwhelmingly structurally bound, or enclosed within the crystal structure of soil minerals, with a moderate organically complexed fraction (approximately 22-33%) appearing in two of the three spectra (Figure 3.2). At 6 weeks, structurally bound Ni represented approximately 30 and 78% of two out of three spectra, with one spectrum being composed of exclusively of mineral sorbed and organically complexed species. Mineral sorbed species represented 25 and 70% of two spectra, while organically complexed species represented 22 and 76% of two spectra (Figure 3.2). By 9 weeks, Ni speciation was its most diverse, with insoluble Ni precipitates, organically complexed, mineral sorbed, and structurally bound species being observed (Figure 3.2). No temporal trends in Ni speciation were observed in the rhizosphere of the non-accumulator, S. glandulosus. Interestingly, Ni speciation was primarily organically complexed and mineral sorbed across all time points, with structurally bound species making minor contributions across five spectra distributed among time points, and insoluble Ni precipitates occurring only as a small fraction of one spectra at the 3 weeks (Figure 3.2). Likewise, Ni speciation in the rhizosphere of the Ni hyperaccumulator, S. polygaloides, exhibited no temporal trends. The Ni speciation was less consistent in the Ni hyperaccumulator rhizosphere, with structurally bound Ni species appearing in greater proportions in some spectra at 6 and 9 weeks than seen in the non-accumulator rhizosphere (Figure 3.2). The proportion of organically complexed Ni tended to be less in the Ni hyperaccumulator rhizosphere when considered over all time points, with organically complexed Ni species comprising 33.6% ± 10% (n=9) of the spectra in which they occurred in the rhizosphere of S. glandulosus, while only comprising 23.5% ± 2.7% (n=6) of that of S. polygaloides. In contrast, mineral-sorbed Ni species tended to be greater in the rhizosphere of S. polygaloides compared to that of S. 95

Unplanted Control Streptanthus glandulosus Streptanthus polygaloides

0% 50% 100% 0% 50% 100% 0% 50% 100% Ctrl 3wk 1 Sg 3wk 1 Sp 3wk 1 Ctrl 3wk 2 Sg 3wk 2 Sp 3wk 2 Sp 3wk 3 Ctrl 3wk 3 Sg 3wk 3 Sp 6wk 1 Ctrl 6wk 1 Sg 6wk 1 Sp 6wk 2 Ctrl 6wk 2 Sg 6wk 2 Sp 6wk 3 Ctrl 6wk 3 Sg 6wk 3 Sp 9wk 1 Ctrl 9wk 1 Sg 9wk 1 Sp 9wk 2

Ctrl 9wk 2 Sg 9wk 2 96 Sp 9wk 3

Ctrl 9wk 3 Sg 9wk 3 Sp 9wk 4

Figure 3.2 Bar charts representing the composition of Ni species observed for each spectrum collected from the rhizosphere of S. polygaloides, S. glandulosus, and an unplanted control at 3, 6, and 9 weeks Three spectra were collected from each rhizobox. Unknowns were fit to a maximum of 4 standards, as determined by PCA, using linear least squares fitting. Standards were then categorized into one of five Ni types relating to relative Ni availability.

glandulosus when they occurred, with mineral-sorbed Ni species comprising 60.4% ± 27.5% (n=8) of spectra in which they occurred in the rhizosphere of S. polygaloides compared to 54.3% ± 18.7% (n=9) in that of S. glandulosus. Across all treatments, organically complexed Ni was most often represented by Ni-citrate along with some instances of Ni-malate. Mineral sorbed Ni species were most often represented by Ni sorbed to the surface of crystalline iron oxyhydroxides and oxides such as goethite, hematite, and magnetite. Structurally bound Ni species were most often represented by ultramafic mineral samples gathered from the field sites where soil was collected for this study. Insoluble Ni precipitates were represented by Ni (II) oxide. The third spectrum collected from the S. polygaloidies (Sp 3wk 3) rhizosphere exhibited a high degree of misfit to the standards in the database used and so is not an accurate representation of Ni speciation at that point; this spectrum will be excluded from further discussions.

3.4.3 In-situ pH and O2

No statistically significant differences were found across treatments or time points with respect to pH (Table 3.3, 3.4). In the unplanted control, the average pH ranged only from 5.8-5.6 across the three observations made over 9 weeks (Table 3.3, 3.4). Similarly, the pH in the non-accumulator system ranged only from pH 5.9-5.8 while the Ni hyperaccumulator rhizosphere ranged from pH 5.7-5.8 on average throughout the duration of the experiment (Table 3.3, 3.4). Oxygen content tended to be lower in the rhizosphere of either plant compared to the control with time, however, oxygen content among treatments was not significantly different from one another at the 3 weeks (Table 3.5, 3.6). Representative images of the 2-dimensional distribution of pH and oxygen content can be seen in Figure 3.3 and Figure 3.4. The pH was often elevated near the bottom of the box by about 0.5 pH units, possibly due to the distribution of water within the rhizobox (Figure 3.3).

Oxygen content was always lower in the rhizosphere environment than the unplanted control because of the combined effects of plant and microbial respiration.

Table 3.3 Average pH values in an unplanted control and the rhizosphere of the Ni hyperaccumulator (HA) S. polygaloides and the non-accumulator (NA) S. glandulosus at 3, 6, and 9 weeks post germination. Data represent the mean and one standard deviation (n=3 for all treatments except S. polygaloides at 6 and 9 weeks where n=2 due to plant death. No treatment effect was observed across treatments or time. pH

Sampling Unplanted S. glandulsosus (NA) S. polygaloides (HA) Time Control 3 weeks 5.8 ± 0.00 5.9 ± 0.17 5.8 ± 0.06 6 weeks 5.7 ± 0.00 5.8 ± 0.17 5.7 ± 0.07 9 weeks 5.6 ± 0.00 5.8 ± 0.12 5.7 ± 0.07

Table 3.4 ANOVA results for the analysis of soil pH in an unplanted control and the rhizosphere of the Ni hyperaccumulator S. polygaloides and the non-accumulator S. glandulosus at 3, 6, and 9 weeks post germination as well as across all time points.

Time Point ANOVA Results Source DF Sum of Squares Mean Square F Ratio Prob > F Treatment 2 0.03 0.015 1.1538 0.3872 3wk Error 5 0.065 0.013 C. Total 7 0.095 Source DF Sum of Squares Mean Square F Ratio Prob > F Treatment 2 0.3 0.015 1.1538 0.3872 6wk Error 5 0.065 0.013 C. Total 7 0.095

99 Source DF Sum of Squares Mean Square F Ratio Prob > F

Treatment 2 0.043333 0.021667 3.4211 0.1158 9wk Error 5 0.031667 0.006333 C. Total 7 0.075 Source DF Sum of Squares Mean Square F Ratio Prob > F Across all time Treatment 8 0.193116 0.024139 2.0904 0.1087 points Error 14 0.161667 0.011548 C. Total 22 0.354783

Figure 3.3 Representative planar optode images of the 2-dimsensional distribution of pH in the unplanted control, S. glandulosus rhizosphere, and S. polygaloides rhizosphere after 3,6, and 9 weeks post germination for plants grown in Chrome silt loam soil. Scale bars for each image can be seen to the left of the image. The orange line shows the average air saturation for that particular image, and the red error bar displays one standard deviation.

100

Table 3.5 Average percent air saturation values in an unplanted control and the rhizosphere of the Ni hyperaccumulator (HA) S. polygaloides and the non-accumulator (NA) S. glandulosus at 3, 6, and 9 weeks post germination. Data represent the mean and one standard deviation. There was no treatment effect (ns) within the 3 weeks (ANOVA; n=3). A significant treatment effect was observed within the 6 and 9 week sampling times (ANOVA; n=3; 6 week p=0.048; 9 week p=0.049).

% Air Saturation

Sampling Unplanted S. glandulosus (NA) S. polygaloides (HA) Time Control

3 weeks 58.7 ± 0.90 ns 48.5 ± 3.11 ns 54.1 ± 7.38 ns 6 weeks 62.2 ± 0.40 a 49.7 ±3.15 b 54.8 ± 7.61 ab 9 weeks 61.9 ± 0.17 a 49.8 ± 3.15 b 54.7 ± 7.41 ab

101

Table 3.6 ANOVA results for the analysis of soil oxygen content in an unplanted control and the rhizosphere of the Ni hyperaccumulator S. polygaloides and the non-accumulator S. glandulosus at 3, 6, and 9 weeks post germination as well as across all time points.

Time Point ANOVA Results

Source DF Sum of Squares Mean Square F Ratio Prob > F Treatment 2 156.49556 78.2478 3.6138 0.0933 3wk Error 6 129.91333 21.6522 C. Total 8 286.40889 Source DF Sum of Squares Mean Square F Ratio Prob > F Treatment 2 237.17556 118.588 5.2357 0.0483 6wk Error 6 135.9 22.652

102 C. Total 8 373.07556 Source DF Sum of Squares Mean Square F Ratio Prob > F

Treatment 2 222.41556 111.208 5.1467 0.0499 9wk Error 6 129.64667 21.608 C. Total 8 352.06222 Source DF Sum of Squares Mean Square F Ratio Prob > F Across all time Treatment 8 660.84 82.605 3.7564 0.0094 points Error 18 395.8267 21.9904 C. Total 26 1056.6667

Figure 3.4 Representative planar optode images of the 2-dimsensional distribution of air saturation in the unplanted control, S. glandulosus rhizosphere, and S. polygaloides rhizosphere after 3,6, and 9 weeks post germination for plants grown in Chrome silt loam soil. Scale bars for each image can be seen to the left of the image. The orange bar shows the average air saturation for that particular image, and the red error displays standard deviation.

103

There was a significant treatment effect for oxygen content at the 6 and 9 week sampling times, in which the unplanted control and non-accumulator rhizosphere were distinctly different from one another, but oxygen content within the Ni hyperaccumulator rhizosphere was not distinct from either the control or the non- accumulator rhizosphere (Table 3.5, 3.6). There were no differences in oxygen content across time points, with the unplanted control ranging from 59-62% air saturation, the non-accumulator rhizosphere ranging from 49-50 % air saturation, and the Ni hyperaccumulator rhizosphere ranging from 54-55 % air saturation over 9 weeks (Table 3.5, 3.6). Two of three S. polygaloides plants exhibited stunted growth from about 2 weeks post germination on, resulting in average oxygen contents that were more similar to the control than the third S. polygaloides replicate; it was noted that the oxygen content in the rhizosphere of this replicate, which was growing normally, ranged from 46-49% air saturation during the experiment making it highly similar to that of the non- accumulator rhizosphere. Oxygen content was always higher near the edges of the rhizobox because the box was not constructed to be air-tight, allowing gaseous exchange from its joints (Figure 3.4).

3.4.4 Microbial Community Structure

Microbial community structures associated with the unplanted control separated temporally along Axis 1 of the NMDS ordination, which represented 81% of the variability within the ordination (Figure 3.5). The microbial community structure associated with the rhizosphere of the Ni hyperaccumulator, S. polygaloides, showed a similar trend, where the communities at the 6 and 9 week time points were more similar to each other than that of the community at 3 weeks (Figure 3.5). Rhizosphere microbial community structure in the hypertolerant, non-accumulator, S. glandulosus, exhibited little dissimilarity according to NMDS (Figure 3.5). Biplot analysis revealed that biomarkers representing Arbuscular mycorrhizal fungi (AMF) and gram positive bacteria were largely associated with community structure at 3 weeks, while those representing general fungi and gram negative bacteria were drivers of dissimilarity with respect to the S. polygaloides treatments at 6 and 9 weeks (Figure 3.5). Upon further analysis, 104

Axis 12.9% 2 Axis

Axis 1 80.7%

Figure 3.5 Two-dimensional non-metric multidimensional scaling ordination depicting the separation of rhizosphere microbial communities as estimated by PLFA after 3,6, and 9 weeks post germination for plants grown in Chrome silt loam soil (n=3; 2-dimensional solution; 68 iterations; stress=10.7). Data are normalized to the control to account for differences in total biomarker concentration. The rhizosphere microbial community structure associated with the Ni-hyperaccumulator S. polygaloides (Sp) and the unplanted control (Ctrl) communities separate along Axis 1 with time,

while that of S. glandulosus (Sg) cluster together. The microbial community structure within the rhizosphere of Sp at week 9 appears to be the most unique of the plant treatments. Biplot rays indicate the direction and magnitude by which each biomarker group contributes to the dissimilarity of groups.

105

fungal biomarker concentrations were not significantly different among treatments, but were considerably elevated among one of the S polygaloides samples taken at 9 weeks.

A significant treatment effect was observed for concentrations of gram negative bacterial biomarkers, with concentrations being elevated in the S. polygaloides rhizosphere at 9 weeks. However, comparison of the means using Tukey’s HSD test yielded no significant differences among treatments, likely due to the small magnitude in the differences among means. The AMF biomarker concentrations were highly elevated in the unplanted control at the 3 week time point, after which they declined considerably as expected, with the lowest concentrations in the unplanted control and

S. polygaloides rhizosphere at 9 weeks. A significant treatment effect was likewise observed for the gram positive bacterial biomarkers, where the most elevated concentrations were present in the unplanted control communities at the 3 and 6 week time points, while the lowest concentrations were observed in the S. polygaloides rhizosphere community at 6 and 9 weeks. AMF, gram negative, general fungi, and gram positive biomarkers remained relatively stable within the S. glandulosus rhizosphere.

Actinobacterial biomarkers were not different among treatments. Protozoan biomarkers exhibited a significant treatment effect, with concentrations being highest among the unplanted control and S. polygaloides rhizosphere at the 6 and 9 week time points, while they were lowest within the rhizosphere of the non-accumulator, S. glandulusos.

Benajamini-Hochberg corrected MRPP indicated that the unplanted control treatments at 3 and 9 weeks were statistically different from all other treatments. The unplanted control community at 6 weeks was different from all other treatments except the Ni

106

hyperaccumulator community at 3 weeks, and the non-accumulator community at 6 weeks (Table 3.7). All rhizosphere community structures were similar with the exception of the Ni hyperaccumulator community at 9 weeks, which was statistically different from the non-accumulator communities at 6 and 9 weeks (Table 3.7).

107

Table 3.7 The results of the MRPP performed on the 3, 6, and 9 week rhizosphere and control PLFA data, where T represents the test statistic, A represents the chance-corrected within group agreement and p represents the probability of obtaining a smaller or equal weighted mean within group distance (Bray-Curtis). The last three columns show the results of the Benjamini-Hochberg Procedure. rank Groups Compared T A P (p*m)/i (i/m)*Q (i) Ctrl 3wk vs. Sp 6wk -2.84028644 0.38095238 0.02236355 1 0.805088 0.002778 Ctrl 3wk vs. Sp 9wk -2.79751445 0.38095238 0.02269763 2 0.408557 0.005556 Ctrl 3wk vs. Sg 9wk -2.74710464 0.42857142 0.02314663 3 0.277760 0.008333 Ctrl 3wk vs. Sg 3wk -2.75280994 0.42857142 0.02324511 4 0.209206 0.011111 Ctrl 9wk vs. Sg 9wk -2.72255040 0.41269841 0.02328748 5 0.167670 0.013889 Ctrl 3wk vs. Ctrl 9wk -2.71910079 0.42857142 0.02347989 6 0.140879 0.016667

108 Sg 6wk vs. Ctrl 9wk -2.25876980 0.15873016 0.02408507 7 0.123866 0.019444 Ctrl 3wk vs. Ctrl 6wk -2.56555832 0.33333332 0.02414797 8 0.108666 0.022222

Sp 3wk vs. Ctrl 9wk -2.41943358 0.19047620 0.02418979 9 0.096759 0.025000 Ctrl 6wk vs. Sp 6wk -2.42694297 0.23809523 0.02467544 10 0.088832 0.027778 Sg 3wk vs. Ctrl 9wk -2.50640207 0.22222222 0.02477879 11 0.081094 0.030556 Ctrl 6wk vs. Ctrl 9wk -2.47487374 0.33333332 0.02536184 12 0.076086 0.033333 Ctrl 3wk vs. Sg 6wk -2.50096509 0.28571429 0.02555056 13 0.070755 0.036111 Ctrl 6wk vs. Sg 9wk -2.37473915 0.33333332 0.02620367 14 0.067381 0.038889 Ctrl 6wk vs. Sp 9wk -2.28217738 0.23809524 0.02839855 15 0.068157 0.041667 Sg 3wk vs. Ctrl 6wk -2.23353703 0.33333332 0.03007426 16 0.067667 0.044444 Sp 6wk vs. Sg 9wk -2.20564396 0.19047620 0.0310342 17 0.065719 0.047222 Ctrl 9wk vs. Sp 9wk -2.14529091 0.14285716 0.03344256 18 0.066885 0.050000 Sp 6wk vs. Ctrl 9wk -1.93649177 0.09523810 0.03913967 19 0.074159 0.052778 Sg 9wk vs. Sp 9wk -1.79028731 0.15873017 0.04922005 20 0.088596 0.055556 Ctrl 3wk vs. Sp 3wk -1.69822240 0.20634922 0.05542596 21 0.095016 0.058333

Table 3.7 (continued)

Ctrl 6wk vs. Sg 6wk -1.60611887 0.14285714 0.06463436 22 0.105765 0.061111 Sg 6wk vs. Sp 9wk -1.59363812 0.12698412 0.06464274 23 0.101180 0.063889 Sg 3wk vs. Sp 9wk -1.57027170 0.09523809 0.07155727 24 0.107336 0.066667 Sg 3wk vs. Sp 6wk -1.22977470 0.11111112 0.10840344 25 0.156101 0.069444 Sp 3wk vs. Ctrl 6wk -1.25683512 0.17460318 0.11096241 26 0.153640 0.072222 Sg 6wk vs. Sp 6wk -1.21122493 0.11111111 0.11775326 27 0.157004 0.075000 Sg 6wk vs. Sg 9wk -1.09544508 0.09523809 0.13238273 28 0.170206 0.077778 Sp 3wk vs. Sp 9wk -1.03362278 0.07936508 0.14920475 29 0.185220 0.080556 Sp 3wk vs. Sp 6wk -0.98437406 0.07936508 0.1608429 30 0.193011 0.083333 Sg 3wk vs. Sg 9wk -0.90219379 0.11111112 0.18003795 31 0.209076 0.086111 Sp 3wk vs. Sg 9wk -0.62017370 0.03174603 0.27415912 32 0.308429 0.088889

109 Sg 3wk vs. Sp 3wk 0.37932154 -0.0317460 0.60023002 33 0.654796 0.091667 Sp 3wk vs. Sg 6wk 0.51298923 -0.0317460 0.63613905 34 0.673559 0.094444

Sg 3wk vs. Sg 6wk 0.53033011 -0.0476190 0.63758208 35 0.655799 0.097222 Sp 6wk vs. Sp 9wk 1.13519156 -0.0793650 0.88422345 36 0.884223 0.100000

3.5 Discussion

3.5.1 Shoot Ni Concentration and Distribution in Leaves

The extent of Ni hyperaccumulation observed in S. polygaloides, 1182 mg Ni/kg oven dried shoots, was in agreement with other observations from the literature, where ranges from 1,100 -16,400 mg Ni/kg oven dried leaves among plants growing in Ni enriched soils have been reported, along with ranges as low as 16 – 230 mg Ni/kg oven dried shoots in soils containing lesser quantities of Ni (Boyd, Shaw, and Martens 1994, Jhee, Boyd, and Eubanks 2005, Reeves, Brooks, and Macfarlane 1981). Nickel distribution in the leaves of S. polygaloides correlated well with the findings of Sanchez- Mata et al. (Sanchez-Mata et al. 2014), who also found relatively high levels of Ni sequestered in the leaf epidermis using scanning electron microscopy along with an energy dispersive X-ray probe (SEM-EDX), with no differences between the adaxial and abaxial sides of the leaf (Sanchez-Mata et al. 2014).

3.5.2 In-situ Ni Speciation

While general temporal trends in Ni speciation were evident among the unplanted control treatments, no trends were observed in the rhizosphere of S. polygaloides or S. glandulosus (Figure 3.2). Nickel speciation within the rhizosphere of S. glandulosus was quite consistent with time, with organically complexed and mineral sorbed species being the majority across all points with small amounts of structurally bound Ni occurring throughout (Figure 3.2). Nickel species in the rhizosphere of S. polygaloides varied considerably with large proportions of structurally bound Ni species occurring at times within the 6 and 9 week time points. In all treatments, organically complexed Ni was represented by either Ni-citrate or Ni-malate standards, suggesting that Ni was complexed to oxygen donor ligands of low molecular weight. Additionally, the most commonly sorbed species identified were Ni-goethite, Ni-hematite, and Ni- magnetite, suggesting that the most common mineral sorbed species were those sorbed to crystalline Fe oxyhydroxides and Fe oxides. Trends in all three treatments can likely be explained by the bioweathering of Ni containing phyllosilicates and amorphous iron 110

oxides, resulting in the release of Ni2+. This labile, reactive Ni is then partitioned into soil organic matter, sorbed to mineral surfaces, chelated by organic compounds, taken up by a plant or microorganism, or under the right circumstances, precipitated. In the absence of a plant to stimulate the microbial community through the release of carbon and other nutrients into the rhizosphere, there is a slow release of Ni and a small fraction becomes organically complexed after 3 weeks. As microbial activity continues, Fe oxide sorbed species appear along with an organically complexed fraction at 6 weeks, and a heterogeneous mixture occurs after 9 weeks that includes insoluble Ni oxide (Figure 3.2). The microbial biodegradation of Ni-citrate complexes has been noted in the literature, and in the absence of significant plant uptake, microbial biodegradation of Ni- organic compounds will presumably result in the re-partitioning of previously complexed Ni (Francis, Dodge, and Gillow 1992, Francis, Joshi-Tope, and Dodge 1996, Joshi-Tope and Francis 1995, Qian et al. 2012). In the presence of the non-accumulator, S. glandulosus, the analysis yielded a fairly consistent trend where Ni is mostly either sorbed to Fe oxides or organically complexed across all time points. In the S. glandulosus rhizosphere, Ni is likely liberated from phyllosilicates and amorphous iron oxides by organic acids and other exudates released by a plant along with microbial activity at a much higher rate than the control, as indicated by the robust mineral sorbed and organically complexed Ni fractions at 3 weeks, versus that of the unplanted control. As S. glandulosus can tolerate Ni, but does not effectively remove it from the soil environment, the Ni partitions to stable forms such as inner-sphere complexes with Fe oxides and oxygen donor ligands on small organic molecules, which may themselves sorb to mineral surfaces. In this case, S. glandulosus or its associated microbial community may exude organic compounds that complex Ni as a means to reduce exposure and competition for other divalent nutrients. In contrast, Ni speciation in the rhizosphere of S. polygaloides was more erratic, and organically complexed Ni species tended occur less frequently and be slightly less than those occurring in the rhizosphere of the non-accumulator, and where mineral sorbed species were detected, tended to be slightly more than those of the non-accumulator rhizosphere (Figure 3.2). This may be

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the result of Ni uptake by S. polygaloides, as indicated by XAS mapping (Figure 3.1). As Ni is released from less stable soil mineral-Ni compounds in the rhizosphere of S. polygaloides, it is effectively removed from the rhizosphere via plant uptake and translocation, resulting in less Ni to partition. Spots where spectra contained more partitioned Ni may represent areas where more labile Ni was present, allowing for Ni to partition before uptake occurred. Likewise, spectra represented by mostly structurally bound Ni may represent areas where most of the labile Ni was effectively removed from the rhizosphere. Further, a slightly smaller organic fraction may be indicative of plant uptake in the Ni hyperaccumulator rhizosphere, as microorganisms are able to biodegrade low molecular weight Ni complexes, such as Ni-citrate, releasing Ni and making it available for plant uptake, as opposed to the non-accumulator rhizosphere where microbial release of organically complexed Ni would simply result in re- partitioning in the absence of plant uptake (Francis, Dodge, and Gillow 1992, Francis, Joshi-Tope, and Dodge 1996, Qian et al. 2012, Joshi-Tope and Francis 1995). Considerations of equilibrium chemistry come in to play here as well, in which the uptake of labile Ni in the rhizosphere of the Ni hyperaccumulator may drive the release of sorbed or complexed Ni into solution, making it available for uptake. As we hypothesized that the Ni hyperaccumulator rhizosphere would be characterized by a greater abundance of available Ni species, these results do not directly support our hypothesis. It may be that Ni is not any more or less available in the rhizosphere of non- accumulators or Ni hyperaccumulators growing in serpentine soil, but rather labile Ni partitions differently based on whether considerable plant uptake occurs. Given that Ni speciation was measured using XAS in which the Ni signal was high enough to gather data of reasonable quality over the Fe signal in the soil, it is unlikely that transient, freely available Ni would be detected unless it were more abundant than the background forms.

3.5.3 In situ pH and O2

In contrast to our hypothesis, this study did not identify any relevant differences between the pH and oxygen contents within the rhizosphere of S. polygaloides and S. 112

glandulosus (Table 3.3, Figure 3.3). Similar to our study, Bernal et. al (1994) observed no differences in pH between the Ni hyperaccumulator Alyssum murale and the non- accumulator Raphanus sativus (Bernal et al. 1994). The authors concluded that reducing potential and acidification in the rhizosphere of A. murale was always less than that of R. sativus, and these chemical properties were likely not involved in metal solubilization. McGrath et al. (1997) noted no differences in the intensity of acidification within the rhizospheres of the Ni hyperaccumulator, Noccaea caerulescens and Thlaspi ochroleucum (McGrath, Shen, and Zhao 1997). Likewise, several investigations of other hyperaccumulating plants have resulted in the rejection of the idea that rhizosphere pH can be implicated as a driving factor of metal solubilization in the hyperaccumulator rhizosphere, which may also be the case for the Ni hyperaccumulator S. polygaloides (HUTCHINSON et al. 2000, Knight et al. 1997, Luo, Christie, and Baker 2000, Puschenreiter et al. 2005). Interestingly, a recent study on the bioweathering of serpentine minerals by the fungus, Talaromyces flavus, revealed a significant decrease in pH at the fungus-mineral interface where the bioweathering of lizardite, a serpentine mineral, occurred (Li et al. 2016). Results of the aforementioned study highlight an important issue in rhizosphere science: the issue of scale and soil biochemistry, in which the chemistry of the rhizosphere is ultimately the cumulative result of microsite chemistry, or emergent phenomena rising from microsite chemistry, which we are unable to detect using readily available, and affordable methods (along with most other chemical parameters). Regardless, differences between the pH of the rhizosphere and the rest of the soil in the rhizobox were expected to be more distinct, but may have been ‘washed out’ during watering as measurements were taken approximately one hour after the rhizoboxes were watered to 70% water holding capacity. Alternatively, the size of the rhizobox may have resulted in relatively homogenous conditions, especially considering root density, however this would not account for the similarities between the plant treatments and the unplanted control. Along with pH, oxygen content was not considerably different among treatments. The rhizoboxes containing plants exhibited lower oxygen content than the unplanted control, as expected (Table

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3.5, Figure 3.4). This result is attributed to plant root respiration along with increased microbial respiration in the rhizosphere. While it was not expected that overall redox conditions would reach low enough to result in the reduction of Ni (II), it has been demonstrated that Ni can be released from iron oxides under conditions that are reducing enough to produce Fe (III) (Francis and Dodge 1990, Cambier and Charlatchka 1999, Burkhardt et al. 2011). Like pH, it was expected that some distinction between the rhizosphere and surrounding soil would be visible as a result of plant and microbial respiration, however, no such patterns were observed (Figure 3.5). This may also be the result of taking measurements too soon after watering.

3.5.4 Microbial Community Structure

Like rhizosphere pH and oxygen content, the microbial community structure among treatments in this study did not vary enough to say with confidence that they were statistically different. However, given the results of our previous study (Chapter 2), where S. polygaloides and S. glandulosus was shown to have distinct rhizosphere community structures, along with those of the current study, where the gradient of greatest variability appears to be time, with treatment playing a more significant role for the later sampling times in the rhizosphere of S. polygaloides, it seems unreasonable to conclude that the microbial community structures are indeed the same. Further, similar trends are observed between these studies, where biplot analysis suggests gram positive bacteria are associated with control treatments, and general fungal biomarkers with the Ni hyperaccumulator rhizosphere. However, due to the lack of statistical evidence yielded by this investigation, it is difficult to make specific conclusions with any confidence. The scarcity of other studies comparing the microbial community structures between closely related Ni hyperaccumulating and non-accumulating plants growing in serpentine soil makes it difficult to ascertain possibilities from the literature. Most studies on the microbial aspects of the Ni hyperaccumulator rhizosphere instead focus on metal resistant, metal mobilizing, and/or plant growth promoting microorganisms. While similarities among microbial community structure do not mean that the communities themselves are the same, PLFA does not provide sufficient resolution to 114

make such conclusions. Aside from considerations regarding the difficulties of sampling rhizosphere soil from plants with very fine root systems, as those used in this experiment, it is unknown why the microbial community structures were so similar. It is possible that given the small volume of rhizosphere soil sampled and the difficulties in removing it from such fine roots, that the rhizosphere samples were contaminated with ‘bulk’ soil in the process. However, given that the plants were grown in conical tubes, it stands to reason that while not all soil in the tube was rhizosphere soil, it was at least affected by the plant inhabiting it. Thus, the reason for the similarities among treatments remains unknown at this time.

3.6 Conclusions

Our hypotheses regarding the chemical environment, the abundance of available Ni species, and the microbial community structure within the rhizosphere of the Ni hyperaccumulator, S. polygaloides, and the related non-accumulator, S. glandulosus were ultimately rejected. Nickel hyperaccumulation occurred as expected, however, there was no evidence to suggest that more bioavailable species of Ni were present in the rhizosphere. However, evidence and arguments were provided for Ni dynamics in the rhizosphere of these two fascinating plants, whereby Ni was released by the weathering of serpentine phyllosilicates and amorphous iron oxides, resulting in distinct patterns of partitioning that could be explained by plant uptake. This may be further supported by the similarities among the pH and redox conditions of the rhizosphere, along with similarities among the microbial community structures, which could not be used to explain any differences observed in Ni speciation. However, differences among the rhizosphere community composition between treatments cannot be ruled out, and we cannot make such conclusions based on this study.

3.7 Acknowledgements

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not 115

necessarily reflect the views of the National Science Foundation. I thank Sirine Fakra and Mathew Marcus for their assistance with data collection at the Advanced Light Source, beamline 10.3.2, Lawrence Berkeley National Laboratory (Berkeley, CA). The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. I thank Mathew Seibecker of Texas Tech University for sharing μXAFS spectra obtained from Ni bearing minerals and field samples with us for use in this research.

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VITA

JAMES W. MORRIS

EDUCATION

B.S., summa cum laude, Natural Resources and Environmental Science University of Kentucky, Lexington, KY, 2015

A.S., high distinction, Natural Sciences Bluegrass Community and Technical College, Lexington, KY, 2013

A.A.S., high distinction, Environmental Science Technology Bluegrass Community and Technical College, Lexington, KY, 2013

A.A.S., high distinction, Biotechnology Bluegrass Community and Technical College, Lexington, KY, 2013

ACADEMIC EMPLOYMENT

Staff Instructor III in Chemistry and Environmental Science Technology, Bluegrass Community and Technical College, Lexington, KY, Fall 2019-present

Adjunct Instructor, Bluegrass Community and Technical College, Lexington, KY, Fall 2019

Instructor, University of Kentucky, Natural Resources and Environmental Science Program, Lexington, KY, May 2019

Research Assistant, University of Kentucky Rhizosphere Laboratory, Department of Plant and Soil Science, Lexington, KY, Fall 2018-Spring 2019

National Science Foundation Graduate Research Fellow, University of Kentucky, Department of Plant and Soil Science, Lexington, KY, Spring 2015-Fall 2018

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HONORS

National Science Foundation Graduate Research Fellowship Recipient, 2015-2018

PUBLICATIONS

Morris, J. W., K.G. Scheckel, D.H. McNear. 2019 “Biogeochemistry of Nickel in Soils, Plants, and the Rhizosphere.” Nickel in Soils and Plants. Ed. Christos D. Tsadilas, Ed. Jorg Rinklebe, Ed. H. Magdi Selim. Boca Raton: Taylor & Francis Group, pp. 51-86.

In Preparation:

Morris, J.W. and D. H. McNear Jr. In Preparation. “Ni speciation and rhizosphere microbial communities differ among Ni hyperaccumulating and non-accumulating plants”. Plant and Soil.

Morris, J.W. and D.H. McNear Jr. In Preparation. “Comparison of Microbial Community Structure, Ni speciation, pH, and Oxygen Content within the Rhizosphere of the Ni hyperaccumulator, S. polygaloides and its relative, S. glandulosus”. Plant and Soil.

Joshi, S.R., J.W. Morris, R.P. Young, M.M. Tfaily, and D.H. McNear Jr. In Review. “Low soil phosphorus availability triggers maize growth stage specific rhizosphere processes leading to mineralization of organic P”. Plant and Soil.

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