The Association Between Bacteria, Plant Establishment and Metal(Loid) Immobilization in Metalliferous Mine Tailings

Rhizosphere Bacteria and Phytostabilization Success: The Association Between Bacteria, Plant Establishment and Metal(loid) Immobilization in Metalliferous Mine Tailings

Item Type text; Electronic Dissertation

Authors Honeker, Linnea Katherine

Publisher The University of Arizona.

Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Download date 07/10/2021 03:05:59

Link to Item http://hdl.handle.net/10150/624161

RHIZOSPHERE BACTERIA AND PHYTOSTABILIZATION SUCCESS: THE ASSOCIATION BETWEEN BACTERIA, PLANT ESTABLISHMENT AND METAL(LOID) IMMOBILIZATION IN METALLIFEROUS MINE TAILINGS

Linnea Honeker

______

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF SOIL, WATER, AND ENVIRONMENTAL SCIENCE

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2017

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Linnea Honeker, titled “Rhizosphere Bacteria and Phytostabilization Success: The Association Between Bacteria, Plant Establishment and Metal(loid) Immobilization in Metalliferous Mine Tailings”, and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

______Date: 2/2/17 Raina M. Maier, Ph.D.

______Date: 2/2/17 Jonathan D. Chorover, Ph.D.

______Date: 2/2/17 Virginia I. Rich, Ph.D.

______Date: 2/2/17 Donata Vercelli, M.D.

______Date: 2/2/17 Gayatri Vedantam, Ph.D.

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: 2/2/17 Dissertation Director: Raina M. Maier, Ph.D.

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of the requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that an accurate acknowledgement of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Linnea Honeker

ACKNOWLEDGEMENTS

I have had a tremendous amount of support over the last 5 years, and couldn’t have completed this journey without some key individuals. First, I’d like to acknowledge and thank my advisor Dr. Raina Maier. Thank you for granting me the opportunity to be a graduate student in your lab and mentoring me. With patience and encouragement, you have taught me so much about the research process, writing, and how to push myself to my limits. I couldn’t have asked for a better advisor to help mold me into a professional scientist prepared for a post-graduate career and beyond.

I’d also like to thank my committee members, Dr. Virginia Rich, Dr. Jon

Chorover, Dr. Gayatri Vedantam, and Dr. Donata Vercelli. Thank you for making the commitment and taking the time from your busy schedules to play such a critical role in the completion of my graduate degree. I appreciate any advice and suggestions you have given me regarding my research along the way.

Regarding the implementation and maintenance of the Iron King Mine and

Humboldt Smelter Superfund field site, there are several people to thank. Thank you

Scott White, who devoted endless hours setting up and maintaining the field site. Thank you Dr. Juliana Gil-Loaiza for all your effort in collecting and processing many samples from the field site and for the critical data I used in my research. Without the effort of these researchers, I would not have had the field site from which to collect my samples.

There are many others that I appreciate for their time and effort in helping me with my research. Our lab manager Dr. Julie Neilson was vital to my success with our many talks about my research and her help in preparing and writing my manuscripts. Her brilliant ideas and input helped me tremendously. It was a pleasure working with my

5 colleague Dr. Robert Root in my first research project who did metal analysis on my root samples at the Stanford Synchrotron. Also, I’d like to thank Patricia Jansma who trained me on the confocal microscope and was always available if I needed any help or advice regarding sample analysis. And finally, I’d like to thank all my lab mates who were so much fun and made this journey more light-hearted and enjoyable.

DEDICATION

First, I’d like to dedicate this dissertation to my mother, Diane Herbertson.

Despite sometimes challenging conditions during my childood, my mom always provided for me and did whatever she could to make sure I got everything I needed and that I was in good schools so I could succeed. She instilled in me perserverance and the will to overcome challenges. During the darkest times of my life, she never left my side, and I wouldn’t have made it to where I am now if it weren’t for her.

I’d also like to dedicate this dissertation to my husband Craig Honeker and daughter Audrey. My husband has been with me through all of the ups and downs of my entire graduate career, and never gave up on me. He helped me to laugh and have fun, and took me on many adventures, giving me a break from my school worries and stress.

And finally, my daughter Audrey is a shining light in my life who gives me endless hope and motivation to be the best I can be. I want her to know she can acheive whatever her dreams may be.

TABLE OF CONTENTS ABSTRACT……………………………………………………………………………....8

CHAPTER 1: INTRODUCTION………………………………………………………..10

1. Literature Review………………………………………………………….…..10

2. Explanation of Dissertation Format…………………………………………...31

3. References………………………………………………………………….….33

4. Figure Legend…………………………………………………………………51

5. Figures………………………………………………………………………....52

APPENDIX A: EFFECT OF RE-ACIDIFICATION ON BUFFALO GRASS

RHIZOSPHERE AND BULK MICROBIAL COMMUNITIES DURING

PHYTOSTABILIZATION OF METALLIFEROUS MINE TAILINGS...... 54

APPENDIX B: BACTERIAL ROOT COLONIZATION PATTERNS OF

BUCHLOE DACTYLOIDES GROWING IN METALLIFEROUS MINE

TAILINGS REFLECT PLANT STATUS AND GEOCHEMICAL

CONDITIONS………………………………………………………….…...... 124

APPENDIX C: RESOLVING COLOCALIZATION OF BACTERIA AND

METAL(LOID)S ON PLANT ROOT SURFACES BY COMBINING

FLUORESCENCE IN SITU HYBRIDIZATION (FISH) WITH

MULTIPLE-ENERGY MICRO-FOCUSED X-RAY FLUORESCENCE

(ME μXRF)……………………………………..……………………………..173

ABSTRACT Phytostabilization offers a less expensive alternative to traditional cap and plant methods for containing metalliferous mine tailings to prevent wind erosion and contamination of nearby communities and the environment. However, plant establishment during phytostabilization of pyritic legacy mine tailings in semiarid regions is challenging due to particularly extreme conditions including low pH, low organic carbon, low nutrients, and high toxic metal(loid) concentrations. Microorganisms drive major biogeochemical cycles in soils, however, the roles microorganisms play at the root

– soil interface during phytostabilization, particularly in relation to plant health and metal immobilization, are not yet fully understood. The aims of this dissertation are to focus on bacterial communities associated with the roots of buffalo grass used in the phytostabilization of pyritic metalliferous mine tailings to: i) characterize bacterial diversity and communities of rhizosphere and bulk substrate, ii) delineate associations between rhizoplane bacterial colonization patterns and environmental and plant status parameters, and iii) develop an in situ method to visually assess associations between roots, bacteria, and metals. Key findings indicate that after addition of a compost amendment to alleviate the plant-growth inhibiting characteristics of mine tailings, rhizosphere and bulk substrate contain a diverse, plant-growth supporting bacterial community. As substrate re-acidifies due to compost erosion, an emergence of an iron

(Fe)- and sulfur (S)-oxidizer and Fe-reducer dominated, less diverse community develops in the bulk and rhizosphere substrate, thus posing a threat to successful plant establishment. However, even at low pH, some plant-growth-promoting bacteria are still evident in the rhizosphere. On the rhizoplane (root surface), the relative abundance of

9 metabolically active bacteria was positively correlated with plant health, verifying the strong association between plant health and bacteria. Furthermore, pH showed a strong association with the relative abundance of Alphaproteobacteria and

Gammaproteobacteria on the rhizoplane. In relation to microbe-metal interactions on the root surface, results showed that Actinobacteria and Alphaproteobacteria colocalized with Fe-plaque and arsenic (As) contaminant on the root surface, indicating their potential role in adsorbing or cycling of these metal(loid)s. Developing a more thorough understanding of bacteria-root-metal interactions in relation to plant health and metal immobilization can help to improve phytostabilization efforts and success.

CHAPTER 1 INTRODUCTION

1. Literature Review

1.1 Introduction

Since plants appeared on earth as early as 700 million years ago (Kenrick and

Crane, 1997) they have been co-evolving with microbes in the soils of the earth’s surface.

This has led to multi-faceted, diverse, and complex root-microbe interactions that still leave much mystery as to what is taking place at the microscopic scale. Research addressing this topic has begun to uncover both the micro-scale mechanisms behind these interactions as well as how this influences microbial dynamics of root-associated compartments, including the inside the root (endosphere), on the root surface

(rhizoplane), and in the immediate zone surrounding the root that is influenced by root exudates (rhizosphere) (Fig. 1). The influences of the root on these compartments make them unique compared to the surrounding bulk soil in terms of microbial communities, diversity, and activity. The plant-microbe interactions that occur here are important to understand for a multitude of reasons including how they affect plant health and survival in not only typical conditions, but also in stressful or extreme environments caused by both biotic (e.g., pathogens) and abiotic (e.g., drought, flooding, fire, salinity, metal contamination, nutrient deficiency) stressors. Stressed or extreme environments can induce changes in root-microbe interactions. For example, conditions such as drought and nutrient deficiencies have been shown to affect root exudation (Badri et al., 2009) which in turn can alter rhizosphere microbial community (Haichar et al., 2008; Doornbos et al.,

2012; Huang et al., 2014). Understanding the feedbacks between the plant health and

11 rhizosphere-associated microbial communities will allow identification of indicator microbial populations that correlate with plant health (or plant stress) as well as methods for improving plant health which is applicable to phytoremediation efforts, revegetation of disturbed lands, and even agriculture.

This review will focus on metal-contaminated environments and how this affects rhizosphere microbial dynamics and plant health. Metal-contamination of soils can be caused by anthropogenic activities such as farming, smelting, and urban runoff. Another major source of metal-contamination is mining, particularly from metals left behind after the mining process resulting in metal-contaminated mine tailings, or waste. Pyritic mine tailings also create an added stressor of acidic pH. Plants and their rhizosphere microbial communities growing in metal-contaminated environments from soils to mine tailings will be discussed in this review.

1.2. Background: Root-microbe interactions and plant health

Before delving into plant-microbe interactions in specific metal-contaminated conditions, it is important to have an understanding of the general relationships between microbes and plant health. Also, to understand the patterns of microbial communities and diversity in the rhizosphere, it is necessary to first examine the plant- or microbe-driven mechanisms that drive these patterns. Plant-driven mechanisms include the production of root exudates and border cells each of which impacts the behavior of microorganisms in the surrounding rhizosphere. Microbially-driven mechanisms can include interactions that improve plant health by plant-growth promoting microbes and that are detrimental to plant health by microbial pathogens.

1.2.1 Plant-driven influences on microbial diversity

1.2.1.1 Plant root exudates

Root exudation provides not only a food source, but these molecules also participate in a system of communication with surrounding microbes in the soil via chemoattraction. Exudates are comprised of organic compounds including polysaccharides, sugars, amino acids, and fatty acids (Somers and Vanderleyden, 2004;

Bais et al., 2006; Uren, 2007; Neumann and Römheld, 2007; Badri et al., 2009; Badri and

Vivanco, 2009). Evidence for the assimilation of organic compounds originating from the plant exudates by bacteria was found by following the incorporation of isotopic carbon

13 originating from carbon compounds produced by plants grown in the presence of CO2 into bacterial biomass (Haichar et al., 2008). As conducers of chemoattraction, specific exudates, such as malic acid, flavonoids, and strigolactones signal for and attract beneficial bacteria and fungi (Somers and Vanderleyden, 2004; Akiyama et al., 2005;

Rudrappa et al. 2008) (Fig. 2). More specifically, flavonoids are often produced by legume species to attract nitrogen-fixing rhizobia (Hassan and Mathesius, 2012) which produce nodules in the plant roots, forming a mutual symbiotic interaction. Some plants exude strigolactones, such as sesquiterpenes, to attract arbuscular mycorrhizal fungi

(Akiyama et al., 2005). Such exudates are one means for plants to participate in creating an individualized “microbiome” (Berendsen et al., 2012). Here we refer to microbiome as the rhizosphere-associated microbes, genetic elements, and their interactions, similar to the microbiome within the guts of humans (Dove, 2013). In both cases, human and plant, the metagenome of the microbiome acts in addition to the host genomes, effectively

13 expanding the host’s functional capacity to create a more beneficial environment that maximizes plant health.

Research has shown that exudates differentially select for bacterial communities depending on molecular structure (Berendsen et al., 2012). This is supported by evidence showing that there is a difference in root colonizers between species of plants, each of which produced a different array of exudates (Haichar et al., 2008; Berendsen et al.,

2012). This suggests that plant species distribution is a major driver for development of the rhizosphere-associated microbiome (Berg and Smalla, 2009). Not only do rhizosphere communities differ between plant species, but there is also variation within a species.

Specifically, variation among plant genotypes, including wild type vs. transgenic, can result in different exudation patterns which in turn selects for different rhizobacterial populations. For example, Micallef et al., (2009) examined Arabidobsis thaliana rhizosphere bacterial communities using T-RFLP analysis and found different patterns between eight accessions. HPLC analysis also revealed that exudates produced by each accession were significantly different. In examining transgenic vs. wild type Eucalyptus,

Andreote et al., (2009) found differences in Alphaproteobacteria communities in both the rhizosphere and rhizoplane. This shows how important plant exudates are in shaping the rhizosphere microbiome.

1.2.1.2 Plant root border cells

Located on the root tip, border cells in a moist environment can detach from the root while maintaining metabolic activity (Hawes et al., 2000). These active border cells excrete a matrix containing polysaccharides, proteins, and nucleic acids that trap pathogens to protect the plant and/or attract beneficial microbes to promote plant health

(Hawes et al., 2012). Extracellular DNA plays a particularly important role in trapping bacteria as evidenced by DAPI (nucleic acid stain taken into live cells) staining of root border cells and trapped microbial cells being associated with SYTOX green (nucleic acid stain not taken into live cells) stained extracellular DNA (Hawes et al., 2012).

Proteins excreted by border cells can include antimicrobial enzymes that help to protect the root tip from pathogenic infection (Wen et al., 2007; Haichar et al., 2014). Wen et al.,

(2007) identified over 100 extracellular proteins excreted by pea plant root border cells which protected the plant from infection by the soilborne pea pathogen Nectria haematococca. Border cell trapping of microbes is so effective that root tips are often free of microbial colonization (Hawes et al., 2012). Therefore, plants have a strong influence on the microbial communities surrounding their roots, creating a means for corresponding feedback from the microbial components of the soil.

1.2.2 Microbially-driven influences on plant health

1.2.2.1 Plant growth promoting bacteria

The corresponding microbially-driven interactions with plants range from beneficial to harmful. The beneficial relationship between roots and microbes will be focused on here. Bacteria that have a beneficial role in plant growth and health are termed

“plant growth promoting bacteria” or PGPB. In addition to residing in the rhizosphere and on the root surface, some PGPB are endophytic, living inside the root itself, the endosphere (Fig. 1) (Szderics et al., 2007; Dimpka et al., 2009). Well-studied general services offered by a wide variety of PGPB include (Tilak, 2005; Hayat et al., 2010;

Dobbelaere et al., 2003; Oliveira et al., 2009):

 solubilizing phosphorous for plant uptake

 providing phytohormones to increase plant growth

 nitrogen-fixation either by diazotrophic free living organisms or in a symbiosis

that occurs between specific bacteria and plants (e.g., legume-rhizobium)

 production of iron-chelating siderophores

 decreasing effects of toxic heavy metals

 providing protection from pathogens.

1.2.2.2 Plant growth promoting fungi

“Plant growth promoting fungi” (PGPF) can also confer beneficial effects to plant health. General benefits conferred by fungal association with plants include a more immediate activation of host plant stress response (Redman et al., 1999) and by fungal production of anti-stress biochemicals (Bacon and Hill, 1996; Rodriguez et al., 2004).

While the only known anti-stress biochemicals found to be produced by endophytic fungi are alkaloids that decrease plant herbivory in response to stress (Siegel and Bush, 1997), it is assumed that more examples like this exist due to the long co-evolutionary lineage of plant-fungi symbioses and the ability of fungi to activate plant host stress responses

(Rodriguez et al., 2004). The endophytic fungus Piriformospora indica is an example of a fungus that improves its host’s response to abiotic and biotic stressors such as drought, salinity, and pathogens by colonizing the root surface and entering into the root tissue via root hairs (Singh et al., 2011). Furthermore, some fungi themselves have bacteria or virus symbionts that aid in the symbiosis with plants (Rodriguez et al., 2008). For example,

Dichanthelium lanuginosum, a tropical panic grass from geothermal soils, has a symbiotic relationship with the endophytic fungus Curvularia protuberate that allows the

16 organisms to grow at high soil temperatures in Yellowstone National Park. C. protuberate can only confer heat tolerance to the host plant when it is infected with the

Curvularia virus (CThTV) (Márquez et al., 2007).

In addition to these more general benefits, there are two types of fungi that participate in wide-spread mutualistic fungal-plant interactions; ectomycorrhizal and endomycorrhizal fungi. In both cases, the fungus benefits from access to plant photosynthates while the plant receives protection from desiccation and increased access to limiting nutrients. These fungi form extensive associations with the plant roots.

Ectomycorrhizal fungi do not invade the plant cells of the root tissue itself, but can invade root tissue in the extracellular region with the formation of a hartig net system

(Hou et al., 2012). In contrast, endomycorrhizal fungi extend hyphae into the plant root cells, thus directly connecting the root with the extensive hyphal system in the soil. This allows for increased water and nutrient (especially phosphorous [P] and nitrogen [N]) uptake (Altomare et al., 1999). One type of endomycorrhiza are the arbuscular mycorrhizal fungi (AMF) which form arbuscules within plant root cells and also aid in water and nutrient uptake as well as improved soil structure (Miransari, 2010).

1.2.3 Patterns of microbial communities and diversity

Plant- and microbe-driven forces, as described above, influence microbial communities and diversity. In relating this to plant health, these patterns can change depending on if the plant is healthy, under stressed conditions, or dying. It has been suggested, that certain bacterial phyla could be classified overall as representing primarily r-strategists or k-strategists (Smit et al., 2001; Torsvik et al., 2002; Fierer et al.,

2007; Bulgerri et al., 2012), which typically are associated with copiotrophic and

17 oligotrphic lifestyles, respectively. Copiotrophic bacteria have higher growth rates and shorter generation times, thriving in environments with readily available and easily metabolized sugars, such as those present in exudates, whereas oligotrophic bacteria predominate in environments with low nutrient availability, have slower growth rates, and longer generation times (Torsvik et al., 2002). From hence forward, the pairs of terms r-strategist and copiotroph as well as K-strategist and oligotroph will be used interchangeablely. The bacteria phyla Proteobacteria and Bacteroidetes represent primarily r-strategists and being found to predominate in soils with a high C content. On the other hand Actinobacteria and Acidobacteria, representing primarily K-strategists, are more predominant in soils with low organic C and nutrient content (Torsvik et al., 2002).

Therefore, it can be hypothesized that the rhizosphere of a healthy plant, which should be rich in sugars and other organic carbon, should have a rhizosphere community dominated by copiotrophic bacteria. This is exemplified by Bulgarelli et al., (2012) who found that colonization of roots by Betaproteobacteria, which tend to be copiotrophic soil bacteria, signify their ability to proliferate in the presence of polysaccharide-containing exudates.

Additionally, the presence of specific types of microbes can be associated with stressed roots. For example, Yu et al., (2012) found a distinctive difference between the compositions of root-associated fungi associated with healthy versus stressed plants, with the AMF fungus Glomus caledonium prevalent in healthy plant roots compared to the pathogenic fungus Phoma sojicola that was associated with stressed roots.

Another evident pattern is the shift in bacterial communities across the different root-associated compartments (endosphere, rhizoplane, and rhizosphere) (Fig. 1). One major observation is that diversity decreases from the bulk soil (region not influenced by

18 plant) to the rhizoplane (Marilley and Aragno, 1999; Thirup et al., 2003; Ehrenfeld et al.,

2005; Buée et al., 2009; Bulgarelli et al., 2012; Lundberg et al., 2012; Edwards et al.,

2015). As bacterial diversity decreases from the bulk to the rhizosphere compartment, bacterial biomass increases (Ehrenfeld et al., 2005), presumably due to root exudates attracting and providing a food source for bacteria. This pattern suggests that the plant

(exudates) are selecting for the growth of certain types of bacteria over others, in effect creating a selective media. Another pattern discovered in the different root-associated compartments is a difference in community composition (Tilak et al., 2005; Bulgarelli et al., 2012; Lundberg et al., 2012; Edwards et al., 2015). For example, beta diversity analysis of the bulk, rhizosphere, rhizoplane, and endosphere compartments of

Arabidopsis thaliana grown in the field shows that plant-associated compartment is the biggest driver of microbial composition (Lundberg et al., 2012) and a similar pattern was found for rice (Edwards et al., 2015). Edwards et al., (2015) found an increase in

Proteobacteria and decrease in Acidobacteria from the bulk to the endosphere of rice.

Lundberg et al., (2012) found an increase in Proteobacteria from the bulk to the rhizosphere, then a decrease from the rhizosphere to the endosphere compartments while

Actinobacteria had a significant increase from the rhizosphere to the endosphere.

Bulgarelli et al., (2012) also found an increase in Actinobacteria from the rhizosphere to the endosphere of A. thalinana. This emphasizes that microbial communities shift from the rhizosphere to bulk compartments, and also that this pattern can be different between plant species.

A stressed or dying plant can potentially have a community composition in the root zone different than a healthy plant, due its changing exudates in response to stress

19 and possible presence of pathogens. A dead or dying plant with roots not actively exudating, or older parts of roots where exudation has decreased or stopped, would likely have a predominant oligotrophic community (Nacamulli et al., 2004; Buée et al., 2009).

Oligotrophic bacteria have slower growth rates and longer generation times, predominating in environments with more recalcitrant carbon sources, such as the sloughed cells from plant roots, which would be the primary food source provided by a root that is not exudating. Actinobacteria communities, which are primarily oligotrophic, are found to be associated with older roots producing less exudates (Thirup et al., 2001;

Watt et al., 2006). Not only are Actinobacteria considered primarily oligotrophic, they are also considered to be saprophytic, in that they obtain their nutrients from dead organic matter (Simao-Beaunoir, 2009), similar to saprophytic fungi, which may also be more closely associated with dying roots. Alternatively, a plant experiencing stress but not yet dying may respond by changing its exudates, rather than stopping exudation, thus attracting different bacteria to the rhizosphere. Additionally, pathogenic microbes can be associated with stressed or dying plant roots. Bais et al., (2006) reviews ways in which exudates can vary and mentions physiological state of the plant and nutrient availability to be two major factors, both related to the health of the plant, which is affected by environmental stresses, such as metal contamination.

1.3 Influence of metal contamination on microbial diversity and plant health

Metal contamination is an abiotic stressor on plant health that can result from naturally high levels of metals in a soil or, most commonly, from human activities such as mining. Many metals are essential nutrients, however, when in excess can be toxic.

The most widespread and problematic metal contaminants are lead (Pb), mercury (Hg),

20 cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), zinc (Zn) and a common metalloid contaminant is arsenic (As) (Purves et al., 1985). The extent of toxicity is determined by a combination of the bioavailability of the metal and the specific dose- response between a microbial species and given metal. The bioavailability of metals is dependent on factors that govern metal speciation such as pH, redox potential, cation exchange, and metal adsorption and complexation with either organic ligands or other functional groups such as (oxyhydr)oxides (Rieuwerts et al., 1998).

Negative effects of metals on plants are largely due to direct effects, such as nutritional imbalances and oxidative stress, and indirect effects, such as impacting microbial diversity. Nutritional imbalances occur when a contaminant metal replaces an essential nutrient in important enzymes, proteins, and even DNA. Metals can cause oxidative stress in plants by eliciting a dramatic increase in reactive oxygen species

(ROS). The normal metabolism of oxygen results in the production of ROS, however, the overproduction of ROS caused by metal toxicity can lead to lipid peroxidation, depletion of sulfhydryls, altered calcium homeostasis, and even DNA damage (Kanoun-Boulé et al., 2008). In addition, plants can respond to metal stress by producing ethylene

(Gamalero et al., 2009), which as discussed previously, can inhibit cell growth and increase cell wall rigidity. In summary, both direct and indirect effects of metal contamination affects the growth of plants and importantly, in some cases, limits the species of plants that are able to grow in the presence of toxic metals.

Recent studies have shown that heavy metal contamination in soil can cause a decrease in microbial diversity (Bennisse et al., 2004; Shentu et al., 2008; Zhan et al.,

2012; Xie et al., 2016), presumably due to the toxic effects of the metals on

21 microorganisms. Therefore, microbes that are able to persist in metal-impacted soils are those that are resistant to their toxic effects. Metal-resistance genes are often located on plasmids which can be shared between bacteria via horizontal gene transfer (Bruins et al.,

1999; Sørenesen et al., 2005; Roosa et al., 2014). Soil health refers to the soil’s capacity to sustain plant growth and perform essential functions, and can be partly assessed by measuring microbial species diversity (Alkorta et al., 2003). Therefore, a decrease in microbial diversity can have an indirect negative impact on plant health.

1.3.1 Microbial mediation of metal toxicity

Microbes able to survive in metal contaminated conditions, or those introduced via addition of amendments, can mediate the toxic effects of metals and promote growth in plants. Because metals are persistent contaminants and cannot be eliminated from the environment biologically, microbes have developed detoxification mechanisms that in effect decrease metal bioavailability, which in turn can decrease metal uptake by plants.

Bacteria are capable of several mechanisms for metal detoxification. First, bacteria can immobilize metals by adsorbing metals to their outer cell membrane (Gadd et al., 1990; Bennisse et al., 2004; Fein, 2016) and/or extracellular polysaccharides (EPS)

(Ernst, 1996). Bennisse et al., (2004) found that the degree of heavy metal toxicity in the rhizosphere is lower than in the bulk soil, which he attributed to the complexation of heavy metals with the cell wall of rhizobacteria. Bacteria in the rhizosphere can also change the speciation of metals (Blake et al., 1993; Khan et al., 2009; Oves et al., 2010).

Toxic hexavalent chromium can be reduced to a less toxic trivalent form by several species of bacteria including Pseudomonas sp. (Oves et al., 2013), Bacillus sp. (Wani et al., 2010), and Mesorhizobium (Wani et al., 2008). Additionally, Fe-oxidizing bacteria

22 can play a role in Fe-plaque formation on plant roots (St-Cyr et al, 1993; Emerson et al.,

1999), which in turn can adsorb metals (Seyfferth et al., 2011), thereby preventing them from being taken in by the plant.

Fungi can also decrease metal uptake by plants in several ways (Jeffries et al.,

2003). The mycelium of fungi have metal binding capacities similar to those described for bacteria (Joner et al., 2000; Christie et al., 2004). Most AMF produce copious amounts of glomalin, a glycoprotein that can form complexations with heavy metals such as Cu, Pb, and Cd (González-Chávez et al., 2004). AMF can also translocate metals to their cell wall and vesicles, thus sequestering metals from the environment (Hildebrandt et al., 2004; Miransari, 2010). The mycelium of fungi have metal binding capacities as well (Joner et al., 2000; Christie et al., 2004). Fungi can also mediate toxic effects of heavy metals by inducing the expression of specific genes in their hosts that confer metal stress tolerance (Gamalero et al., 2009; Rivera-Beccerril et al., 2005) For example, AMF colonization of Medicago truncatula roots induces a downregulation of the Zn transporter gene (Burleigh et al., 2003) Therefore, fungi play important roles in metal immobilization thereby decreasing metal uptake into plants.

1.3.2 Microbial plant growth promoting activities

General plant growth promoting qualities of microbes can improve plant survival in heavy metal stressed environments in several ways. The plant growth promoting activities of bacteria and fungi described above become very important in stressed, metal contaminated environments. In fact, Epelde et al., (2010) found that in metalliferous soils, microbial parameters (biomass, soil enzyme activities, and functional diversity) had a stronger effect on plant biomass than the other way around. This was attributed to the

23 necessity of microbial activity to aid in plant survival in such harsh conditions. As

Ehrenfeld et al., (2005) puts it, PGPB and PGPF really “step up to the plate” in stressful, extreme environments where plant feedback with the microbial component of the soil is most pronounced. These examples suggest that the role of microbes in stressful environments are even more vital to plant health than in undisturbed environments, because their services are often essential for plant survival. Below are some specific examples of how bacteria and fungi promote the growth of plants in metal contaminated soils and mine tailings.

1.3.2.1 Heavy-metal contaminated soils

Ethylene is produced by plants as a response to plant stress, such metal toxicity, and has the effect of inhibiting plant growth (Glick, 2004). Bacteria can produce ACC deaminase which breaks down plant-produced ethylene in order to obtain ammonia, a nitrogen source for the bacteria (Belimov et al., 2001; Glick, 2004; Arshad et al., 2007).

A decrease in ethylene results in decreased growth inhibition to the plant. Also, bacterial ferri-siderophores bind and deliver iron to the plant, which is important because often plants growing in high metal concentrations experience iron-deficiency symptoms in the often iron-limiting environment of metal contaminated soils (Dimpka et al., 2008, 2009).

As mentioned previously, fungi can increase P availability, a phenomenon that has also been linked to increased heavy metal tolerance by improving plant growth in heavy-metal contaminated soils (Christie et al., 2004).

1.3.2.2 Heavy-metal contaminated mine tailings

Studies in mine tailings, which are an extreme example of a metal-contaminated system, have shown the importance of plant-growth-promoting microbes in mitigating the stress of the tailings environment (Petrisor et al., 2004; Glick, 2003; Grandlic et al.,

2008; De-Bashan et al., 2010a and b; Tak et al., 2013). Heavy metal-contaminated mine tailings can be either neutral to alkaline depending on the pyrite vs. carbonate mineral content, with low pyrite and high carbonate content resulting in alkaline conditions and high pyrite and low to no carbonate resulting in acidic conditions (Balistrieri et al., 1999).

Other characteristics include high metal content, lack of soil structure, low carbon content, hypersalinity, and minimal nutrients (Mendez and Maier, 2008; Mendez et al.,

2008a,b; Hayes et al., 2014; Valentín-Vargas et al., 2014) Grandlic et al (2008) found increased growth in plants inoculated with isolates chosen for their ability to solubilize phosphate, strains which included Clavibacter, Microbacterium, and Streptomyces from the phylum Actinobacteria, and Azospirillum from the class Alphaproteobacteria.

Similarly, the presence of the PGPF AMF has also been shown to increase plant growth.

Inoculation of mesquite roots with AMF in Zn-contaminated mine-tailings (Sólis-

Dominguez 2011) and Pisum sativum roots with Glomus intraradices in Cd stressed soils both resulted in increased plant growth (Rivera-Bacerril et al., 2005)

1.3.3 Patterns of microbial communities and diversity

1.3.3.1 Establishing vegetation

There are several interesting patterns in rhizosphere and bulk microbial communities in metal contaminated soils and mine tailings in relation to plant health.

This can be looked at from a couple viewpoints. First, in order for plant life to be sustained, there is a minimum requirement of the microbial communities in the heavy

25 metal contaminated soils. As Griffiths et al. (1997) puts it, there is a critical level of species richness necessary that if not attained, impairs function. In this case, bacteria and fungi may not be able to “step up to the plate” and provide required services to the plant.

Plant health and/or establishment in phytostabilization studies in heavy-metal contaminated mine tailings was positively correlated with heterotrophic bacteria counts in alkaline (Rosario et al 2007) and acidic (Moynahan et al., 2002; Mendez et al

2007,2008a; Solís-Dominguez et al., 2012; Gil-Loaiza et al., 2016) conditions. Mendez et al., (2008) suggests that the bacterial community composition of metal contaminated mine tailings is an indicator of how successful revegetation will be: a predominance of heterotrophs indicating successful and a predominance of autotrophs, such as iron and sulfur-oxidizers, indicating unsuccessful future plant establishment. The predominance of heterotrophs vs. autotrophs in this case was also related to the pH of the conditions with the most acidic mine-tailings (pH 2.7) having the highest number of autotrophic iron and sulfur-oxidizers and the lowest number of neutrophilic heterotrophic counts.

Furthermore, in acidic conditions, metal bioavailability increases (Rieuwerts et al., 1998;

Jurjovec et al., 2002), thus also affecting the outcome of plant establishment. Therefore, it is likely a combination of multiple factors influencing plant establishment and health

(microbes, pH, metal bioavailability, organic carbon, salinity, moisture, etc.). In order to achieve successful plant cover in heavy metal impacted soils or waste, such as mine tailings, adding an amendment such as compost or biosolids may be necessary (Mendez et al., 2008b,c) as an inoculum of heterotrophic microbes, source of nutrients, and a buffer to neutralize acidity (if present).

1.3.3.2 Maintaining vegetation

Once plants have been introduced and/or established in metal impacted areas, other patterns relating microbial communities to plant health develop. These patterns are often related to whether the metal impacted areas are otherwise typical soils or mine tailings, which are characterized by very different conditions. During revegetation of alkaline heavy metal impacted mine tailings, after 13 months the community structures of four-wing saltbush rhizosphere-influenced tailings were more similar to each other than to that of an unplanted control (Rosario et al 2007). This signifies that the plants are affecting and changing the microbial community of the soil around them. As the plants drive changes in the microbial communities of the rhizosphere, changes in communities between the rhizosphere and bulk soil are created (Gremion et al 2003; Bennisse et al.

2004; Navarro-Noya et al. 2010).

Although some studies showed a predominance of Proteobacteria in the rhizosphere, one study showed that Actinobacteria made up a majority of the active population. In a study conducted by Navarro-Noya (2010), rRNA gene clone library results showed that the rhizospheres of two pioneer plants Bahia xylopoda and Viguiera linearis growing in heavy metal contaminated tailings from a Ag mine contained

Betaproteobacteria, Acidobacteria, and Alphaproteobacteria, in proportions of 57, 18, and 9% respectively. In contrast, the community in the bulk soil consisted of primarily

Acidobacteria and Actinobacteria in proportions of 20 and 25%. These results provide evidence that the copiotrophic Alphaproteobacteria and Betaproteobacteria are thriving in the rhizosphere environment rich in exudates whereas the oligotrophic Actinobacteria are more prominent in the bulk tailings environment with less carbon and nutrients, a pattern hypothesized to occur with healthy plants in a typical soil, as discussed above. In

a bacterial community analysis of the hyper-accumulator Thlaspi caerulescens growing in a heavy metal contaminated soil, Gremion et al. (2003) found that although 16s rRNA gene clone libraries indicated a rhizosphere community of Alphaproteobacteria and

Actinobacteria at about 30% and 3%, rRNA clone libraries indicated levels at < 10 and

60% respectively. These results could indicate that the Actinobacteria community were the most metabolically active phylum, assuming that the rRNA clone library represents metabolically active populations, however, this is not always the case (Blazewicz et al.,

2013). If the Actinobacteria are more metabolically active, it could be due to several reasons such as the plants responding to heavy metal sress by decreasing exudation or an increased heavy metal resistance of Actinobacteria compared to Alphaproteobacteria.

From a species diversity standpoint, other interesting patterns have been observed.

Several researchers have found that species diversity is lower in the rhizosphere versus non-plant influenced soil, as was the case in heavy metal contaminated soils examined by

Navarro et al., (2010). Additionally, this pattern was observed when assessing the diversity of free-living nitrogen fixers in copper mine tailings (Zhan et al., 2012). Similar to a typical soil, the species diversity may be lower in the rhizosphere due to selecting effect of exudates, which presumably select for certain r-strategists that are higher in abundance in the carbon rich rhizosphere of healthy plants. For instance, Kozdrój et al

(2000) found a decrease in diversity in the rhizosphere with an increased predominance of copiotrophic (r-strategist) bacteria in metal contaminated soils. Interestingly, the decreased diversity in the rhizosphere was more pronounced in high vs. low metal contaminated soils. Other researchers have found the opposite trend. Li et al. (2011) found that species richness is higher in samples taken from the rhizosphere than from

28 non-rhizosphere soil in lead-zinc mine tailings. Similarly, Callender et al., (2016) found that the alpha diversity was higher in the rhizosphere than the bulk of alkaline gold mine waste rock. However, species richness can also be affected by root age, regardless of the health of the plant. As mentioned previously, aging roots can also produce less exudates

(Thirup et al., 2001, Bais et al., 2006). Therefore, patterns in diversity from the rhizosphere to the bulk can depend on multiple factors including the degree of metal- contamination, whether the plants are growing in soil or tailings, health of the plant, and age of the plant.

Examining microbial colonization of the root surface itself in metal contaminated environments can also provide clues into how plant health relates to microbial ecology. In focusing on bacterial coverage of the root, Iverson and Maier (2008) studied Buchloe dactyloides (Buffalo grass) roots from compost amended heavy metal impacted mine tailings and uncontaminated Vinton soils. Using fluorescence in situ hybridization

(FISH), it was found that roots grown in 0, 5, and 10% compost amended tailings had about 3.6, 12.8, and 18.9% coverage by bacteria compared to roots grown in Vinton soil which had 5-7% coverage at all compost ratios. This indicates that the roots in the more stressful metal-contaminated soils may be producing more exudates in order to attract more bacteria, or that in the inhospitable environment, the bacteria are attracted to the root surface as a more hospitable place to live. In a study looking at AMF colonization of

Fragaria vesca roots growing in Zn, Pb, Cd, and As contaminated soils using a combination of trypan blue staining of roots to detect AMF colonization and PCR for species identifications, naturally occurring AMF populations could be identified (Turnau

29 et al. 2001). A total of five Glomus species were detected at differing colonization rates, with Glomus sp. HMCL4 being the most abundant.

Overall, patterns of microbial diversity and abundance in the rhizosphere of plants growing in metal(loid) contaminated sites is correlated with plant health and successful plant establishment. This trend has strong implications for revegetation of areas devoid of plant cover which is especially important during phytoremediation, which is the use of a plant cover to remediate metal contamination.

1.4. Limits of Our Current Knowledge

Despite much research into plant-microbe interactions and stressed environments, there are still several areas where there are gaps in knowledge in this interesting topic. As described above, there are numerous studies describing the specific benefits that microbes can confer to plants in general and in stressful conditions, like metal contamination.

However, while some general trends of microbial ecology in contaminated environments have been researched, studies examining the correlation between microbial communities and plant health are lacking. Specifically, there are several areas which need to be further studied, including looking at plants at varying degrees of health in metal contaminated studies and their associated microbial communities, including the rhizoplane, rhizosphere, and bulk soils, in comparison to a typical, uncontaminated soil.

Yu et al. (2012) touched on this idea when he compared the fungal communities from stressed versus healthy seedlings in an uncontaminated soil. Similar studies in metal contaminated soils are lacking. Also, whereas some studies looked at the effects of adding PGPB or PGPF to seeds on plant growth, as mentioned above, actual studies

30 looking at the natural populations of bacteria and fungi are sparse, particularly when examining the continuum from the rhizosphere to the bulk. Information regarding microbial communities and their relation to plant health can provide a lot of useful information such as identifying potential bio-indicators of plant health and stress, specific microbial species that may improve plant health, and plant-associated microbial populations that may help in bio-remediation of metals. Information such as this can be extremely useful and applicable to phytoremediation and the clean-up of metal contaminated sites.

Regarding microbial colonization and species composition on root surfaces of plants growing in metal contaminated soils, there is also a gap in information. There are several studies regarding bacteria colonization of plant roots in metal contaminated soils with regard to bacterial root coverage of the entire domain Bacteria (Iverson and Maier,

2009), presence of specific introduced PGPB (de-Bashan et al., 2010a and b) and presence of AMF (Turnau et al., 2001) in relation to plant health. However, these do not look at the specific groups of naturally occurring microbial communities, which would also be important to know to identify overall trends in relation to geochemistry and plant condition. Additionally, because of the close interactions between microbes, plants, and metals in metal-contaminated environments, viewing the root colonizing bacteria and fungi that may be accumulating or associated with metals may help us to really understand the patterns of plant-microbe-metal interactions and how this affects plant health.

2. Explanation of Dissertation Format

This goals of this dissertation were to address some of the gaps of knowledge presented above in order to improve our understanding of how rhizosphere-associated bacterial communities relate to plant-health and metal immobilization during phytostabilization of pyritic metalliferous mine tailings. Samples analyzed were from buffalo grass root-associated rhizoplane, rhizosphere, and bulk compartments from a phytostabilization study being carried out at the Iron King Mine and Humboldt Smelter

Superfund Site (IKMHSS) located in Dewey-Humboldt, AZ. The research studies carried out are presented in this dissertation as 2 chapters and an appendix consisting of a published article.

To begin, Appendix A presents a novel method for assessing the colocalization of bacteria and metal(loid)s on the root surface (rhizoplane) of buffalo grass. This method combined fluorescence in situ hybridization (FISH) for the detection of bacteria and multiple energy micro-focused x-ray fluorescence (ME μXRF) for the detection of metals(loids) (Fe and As) on the same region of a root. Using colocalization analysis,

Manders coefficients representing the percentage of bacteria overlapping with metals and the percentage of metals overlapping with bacteria were calculated. This method can be a helpful tool in the development of hypotheses regarding the role of bacteria in metal(loid) immobilization or mobilization which can be studied further. This paper was recently published: Honeker, L. K., Root, R., Chorover, J., Maier, R. M. (2016) Resolving

Colocalization of Bacteria and Metal(loid)s on Plant Root Surfaces by Combining

Fluorescence in Situ Hybridization (FISH) with Multiple-Energy Micro-Focused X-Ray

Fluorescence (ME μXRF). Journal of Microbiological Methods. 131:22-33.

Chapter 3 presents the next study which examines patterns of bacterial colonization on the rhizoplane of buffalo grass in relation to plant condition and geochemical parameters. Rhizoplane Alphaproteobacteria, Gammaproteobacteria, and

Actinobacteria were analyzed using FISH. Additionally, iTag 16S rRNA gene sequencing analysis of rhizosphere bacterial communities was included to determine the bacterial pool available for colonization on the root surface. The findings of this study help us to better understand the relationship between rhizoplane bacteria and plant health in stressful conditions. This paper was recently submitted to Microbial Ecology and is under consideration for publication after minor revisions.

Finally, Chapter 2 presents a study that builds upon the previous study by examining the microbial communities and diversity in the rhizosphere and bulk compartments of buffalo grass across a pH gradient. DNA and RNA extractions of rhizosphere and bulk samples were analyzed using iTag 16S rRNA gene sequencing to characterize the abundance and activity of bacterial communities. Findings suggest that re-acidification drives significant microbial community and diversity changes, particularly in relation to plant-growth-promoting bacteria (PGPB) and Fe/S oxidizing and Fe reducing bacteria, in the rhizosphere and bulk compartments. These results have strong implications for the management of pH during phytostabilization efforts.

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4. Figure Legend

Figure 1. Root-associated compartments include the endosphere, the region within the root; the rhizoplane, the root surface; and the rhizosphere, the region surrounding the root influenced by root exudates (including but not limited to sugars, fatty acids, amino acids, chemoattractants, and polysaccharides). The bulk region represents soil that is minimally or non-influenced by plant root exudates. Within the continuum from the bulk to the rhizosphere, microbial biomass increases while diversity decreases, in a typical soil.

Figure 2. Examples of chemoattractants that can be constituents of plant root exudates.

A) Malic acid can attract beneficial rhizosphere bacteria such as Bacillus subtilis. B)

Flavonoids are often produced by legume species to attract nitrogen-fixing rhizobia. C)

Strigolactones can attract arbuscular mycorrhizal fungi (AMF).

5. Figures

Figure 1.

Figure 2.

A .

Malic acid

B .

Flavonoid backbone structure, flavone (2, phenyl-1,4-

benzopyrone)

C .

Strigolactone

APPENDIX A

EFFECT OF RE-ACIDIFICATION ON BUFFALO GRASS RHIZOSPHERE AND

BULK MICROBIAL COMMUNITIES DURING PHYTOSTABILIZATION OF

METALLIFEROUS MINE TAILINGS

Effect of Re-Acidification on Buffalo Grass Rhizosphere and Bulk Microbial

Communities During Phytostabilization of Metalliferous Mine Tailings

Linnea K. Honeker1,2, Julia W. Neilson1,3,, Jon Chorover1,4, and Raina M. Maier1,5,*

1Department of Soil, Water, and Environmental Science, P.O. Box 210038, University of Arizona, Tucson, AZ, 85721, [email protected], [email protected], [email protected], [email protected]

*Corresponding author

Email: [email protected]; Phone: 520-621-7231; Department of Soil, Water, and

Environmental Science, P.O. Box 210038, University of Arizona, Tucson, AZ 85721

1. Introduction

Effective remediation strategies are needed to mitigate the human-induced metal- contamination of the environment. One particular area of concern are legacy mine wastes which are often contaminated with high levels of metal(loid)s, left behind as a byproduct of the mining process. Phytostabilization is one possible remediation strategy that involves direct planting into the mine tailings. Due to the often inhospitable conditions of mine tailings such as acidic pH, low carbon and nutrient content, poor soil structure, and autotrophic dominated microbial community (Mendez et al., 2008), successful establishment of a plant cover can be challenging. To encourage plant growth, an organic amendment, such as compost or biosolids, is often added to provide a heterotrophic bacterial inoculum, carbon and nutrients, and a neutralizing effect on the acidic mine tailings (Mendez and Maier, 2008). Once a vegetative cover is established, the plants can further promote neutralization of tailings and development of a heterotrophic dominated bacterial community, which can replace the acid-generating autotrophic resident community of pyritic tailings, thus inhibiting bacterial-mediated pyrite oxidation

(Mendez et al., 2007; Mendez et al., 2008; Mendez and Maier, 2008; Solís-Dominguez et al., 2011; Li et al., 2016). As primary drivers of biogeochemical cycles, microbes can have a direct effect on plant health, which is of key importance particularly in stressed conditions like those present during phytostabilization of metalliferous mine tailings.

The resident microbial community of pyritic mine tailings is dominated by autotrophic iron (Fe) and sulfur (S) oxidizers that promote acidic conditions inhibitory to plant growth (Mendez et al., 2008, Schippers et al., 2010) via oxidation of sulfidic pyrite which produces acidity (Schippers et al., 2004) . The addition of organic amendments,

57 like compost, not only provides an inoculum of carbon-cycling heterotrophs, but it also facilitates positive plant-microbe feedbacks by providing a source of plant growth promoting bacteria (PGPB). PGPB enhance plant growth by 1) increasing nutrient availability through phosphorous solubilization, production of iron-chelating siderophores, and nitrogen fixation; 2) decreasing the plant-stress hormone ethylene through production of ethylene-cleaving ACC (1-aminocyclopropane-1-carboxylate) deaminase; 3) providing protection from pathogens; and 4) decreasing metal bioavailability to plants (Bennisse et al., 2004; Tilak et al., 2005; Hayat et al., 2010).

Specifically, inoculation with the PGPB Arthrobacter spp., Bacillus pumilus,

Azospirillum brasiense, and Azotobacter chroococcum, has been shown to improve plant growth in metal(loid) contaminated tailings (Petrisor et al., 2004; Grandlic et al., 2008,

2009; de-Bashan et al., 2010a and b; Tak et al., 2013), thus, verifying the importance of

PGPB to plant growth and establishment under extreme conditions of the mine tailings environment.

In characterizing plant root-associated bacteria, it is helpful to consider the rhizoplane and the rhizosphere compartments in comparison to the bulk soil. The rhizoplane is the root surface, and the rhizosphere is the soil immediately surrounding the root that is directly influenced by root exudates, causing an increase in microbial biomass. In a typical soil, the rhizosphere represents an enrichment of species found in the bulk (Edwards et al., 2015; van der Heijden and Schlaeppi, 2015) and, in general, the species diversity decreases from the bulk to the rhizosphere (Thirup et al., 2003; Buée et al., 2009; García-Salamanca et al., 2012; Edwards et al., 2015; Hacquard et al., 2015).

While the rhizosphere microbial community has direct interactions with the plant,

58 understanding who is colonizing the bulk soil can also have strong implications for better understanding the current and potential future rhizosphere microbial communities, because the bulk soil is the source of the enriched rhizosphere microorganisms.

Characterizing both the bulk and rhizosphere microbial communities is key in determining whether a community conducive to plant growth is present with 1) sufficient

PGPB and 2) low occurrence of acid-generating microorganisms. In an extreme environment, such as during phytostabilization of acid-generating mine tailings, this is especially informative in assessing the capability of successful plant establishment.

As previous research has shown, establishing a plant cover on pyritic mine tailings can cause a shift in microbial populations (Moynahan, et al., 2002; Mendez et al.,

2007; Li et al., 2015, Li et al., 2016), however, there is limited data on how a shift back to acidic conditions in turn shapes plant-associated microbial communities. Honeker et al.

(submitted) examined bacteria communities on the rhizoplane compartment of buffalo grass growing in pyritic metalliferous mine tailings and found that pH was strongly associated with the relative abundance of Alphaproteobacteria and

Gammaproteobacteria on the root surfaces. The current study will focus on the bulk and rhizosphere compartments of buffalo grass growing in metalliferous mine tailings across a range of pH (2.35 – 7.76). Specifically, the objectives are to 1) compare microbial communities and diversity between rhizosphere and bulk compartments and 2) to characterize how re-acidification affects the abundance and activity of the most abundant putative PGPB/N2-fixing and Fe/S oxidizing and Fe reducing OTUs, as representative of the plant-growth supporting and acid generating communities, respectively. The results of this study will tell us whether a plant supporting community has developed and how re-

59 acidification affects microbial communities, which will have implications for the management of phytostabilization sites.

2. Methods

2.1 IKHMSS field site and study description

The Iron King Mine and Humboldt Smelter Superfund (IKMHSS) site is located adjacent to the town of Dewey-Humboldt, AZ. Mining operations were carried out between the late 1800s until 1969 extracting Cu, Ag, Au, Pb, and Zn (Creasey, 1952). A

64 ha residual tailings pile was left behind after the mining process containing high concentrations of toxic metal(loid)s including As and Pb, at 3.1 and 2.2 g kg-1 tailings respectively (Root et al., 2015). Oxidation of the pyritic tailings in the top 25 cm has produced extremely acidic conditions (pH of 2.3 – 2.7) (Hayes et al., 2014). Additionally, the tailings are characterized by low organic carbon content (0.14 g kg-1), hypersalinity

(EC 6.5-9.0 ds m-1), and poor substrate structure (Hayes et al., 2014; Valentín-Vargas, et al., 2014) contributing to the complete absence of vegetation.

The IKHMSS field trial consists of three phases implemented in consecutive years starting in 2010 to evaluate multiple direct planting strategies for native plant species in compost-amended tailings. Phase 1, described in detail in Gil-Loazia, et al.

(2016), was initiated in May 2010 to study different compost amendment rates and was seeded with six native plant species in semi-random plots. For the current study, rhizosphere and bulk samples associated with buffalo grass grown in the 15% compost

(w/w in the top 20 cm) treatment were examined. Phase 3, initiated in June 2012, was

60 designed to study the effectiveness of buffalo grass or quailbush monocultures situated in rows. The entire area was amended with 15% compost (w/w in the top 20 cm with lime

(2.0622 kg m-2). For this study, rhizosphere and bulk samples associated with buffalo grass plants from the 3 replicates of the buffalo grass treatment were examined. For

-1 Phase 1, the acid generating potential (AGP) of the tailings was 35.1 kg CaCO3 ton at

-1 time of implementation (2010) and 23.4 kg CaCO3 ton in the year of sample collection

(2013) (data not published). This indicates that 15% compost amended tailings had the potential to produce acid. AGP was not calculated for Phase 3.

2.2 Sample collection and processing

Rhizosphere and bulk samples were collected from eight buffalo grass plants from

Phase 1 (Plot 5a and b, Plot 10a and b, Plot 19a and b, and Plot 24a and b) and eight from

Phase 3 (Row 2a and b; Row 4a, b, and c; and Row 6a, b, and c) in 2013. Briefly, the top of the plant was cut off at the surface of the substrate, and a corer measuring 10 cm in length and 2 cm in diameter was inserted directly over a truncated root and the substrate core was placed into a sterile bag for rhizosphere DNA and RNA analysis. An additional substrate core was collected 10-15 cm from the plant, in a direction with no plant cover, for bulk DNA and RNA analysis. For substrate geochemical analysis, a surface tailings sample from the top 20 cm was collected for each plant between the rhizosphere and bulk sampling locations. Samples were stored on ice for the duration of sampling and for the trip back to the lab (5-15 hours). Immediately upon arrival to the lab, 0.5 g and 0.25 g subsamples from the cores were placed into lysis tubes for DNA and RNA extractions, respectively. For RNA preservation, 0.5 mL of Life Guard Soil Preservation Solution

(MO BIO Laboratories, Inc., Carlsbad, CA, USA) was added to the lysis tubes for RNA

61 extraction. Unfortunately, a subsample for Row2b.R for RNA extraction was missed.

Lysis tubes with substrate material were stored at -80 °C until extraction.

2.3 Evaluation of plant condition and establishment

The following field measurements were used to quantify plant condition and establishment. First, leaf chlorophyll levels were measured for each plant as an indication of individual plant stress (Lichtenthaler et al., 1988; Baker et al., 2008; Pavlovic et al.,

2014) at the time of root and rhizosphere collection using a handheld portable chlorophyll meter (atLEAF; Wilmington, DE) which measures light transmittance. Higher measured values correspond to healthier plants within the same species. Second, as a measure of plant establishment, plant canopy cover was estimated using transect and quadrat sampling in October 2013, 2 months after buffalo grass root and rhizosphere collections, for each Phase 1 field plot and Phase 3 row section from which a plant was harvested

(Lutes et al., 2006; Swanson et al., 2006) by Gil-Loaiza et al. (2016).

2.4 Geochemical analysis

Rhizosphere tailings were analyzed for pH, total organic carbon (TOC), total nitrogen (TN), and electrical conductivity (EC). Each rhizosphere sample was sieved at 2 mm and dried at 65 ° for 72 h. The pH and EC were measured from a 1:2 mass ratio extract of tailings to ultrapure DI water (18.2 MΩ, Milli-Q) that was mixed for 30 minutes prior to measurement. The pH was measured in the homogenized extract and the

EC was determined from the supernatant after the substrate was allowed to settle for 10-

15 minutes. Analyses of TOC and TN were performed on milled subsamples of the dried rhizosphere material (Shmiadzu TOC analyzer, Columbia, MD) using a solid state module (SSM), which utilizes a dry combustion under oxygen with detection by a non- dispersive infrared (NDIR) for total carbon and chemoluminescence for TN.

2.5 Extraction of Nucleic Acids

DNA extractions were performed using the FastDNA Spin Kit for Soil (MP

Biomedicals; Santa Ana, CA, USA) following the manufacturer’s protocol with modifications to enhance DNA yield, as outlined in Valentín-Vargas et al. (2014).

Substrate samples were thawed on ice prior to extraction, and all tubes, pipettes, and pipette tips were UV sterilized for 30 minutes. DNA concentrations were measured using the Qubit 3.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA). To maximize success of downstream sequencing, we found a combination of clean-up using the ZR

Genomic Clean and Concentrator-10 kit (ZymoResearch Corporation, Irvine, CA, USA) and dilution, if needed, to a concentration of less than 10 ng/μL to be optimal.

RNA extractions were preformed using the ZR Soil/Fecal RNA MicroPrep kit

(ZymoResearch Corporation, Irvine, CA, USA) following modifications outlined in

Nelson et al. (2015). Prior to RNA extraction, all surfaces and pipettes were pre-treated with RNaseZap Wipes (Ambion, Grand Island, NY, USA). Substrate samples were thawed on ice prior to extraction and centrifuged to remove the LifeGuard Soil

Preservation solution. Residual DNA was removed with a 35 minute DNase treatment at

37 °C, as described in Neilson et al., 2010. RNA quality was checked with gel electrophoresis and quantified using the TBS-380 Fluorometer (Turner BioSystems,

Sunnyvale, CA, USA) in conjunction with the Quanit-it RNA RiboGreen quantification

63 kit (Invitrogen, Carlsbad, CA, USA) following the manufacturers’ protocols. Due to undetectable concentrations, RNA samples for Plot24b.R (rhizosphere), Plot24b.B (bulk),

Row2c.R, and Row2c.B were not included in further analysis.

2.6 Reverse transcriptase PCR (RT-PCR)

First strand cDNA synthesis was performed on extracted RNA using the

SuperScript III First Strand Synthesis System for RT-PCR (Invitrogen-ThermoFisher

Scientific, Waltham, MA, USA) following the manufacturer’s protocol. This kit uses random hexamer primers. Second strand synthesis was performed using the NEB mRNA

Second Strand Synthesis Module (New England BioLabs, Ipswich, MA, USA), following the manufacturer’s protocol. Double-stranded cDNA was cleaned up using the Qiagen

Mini Elute PCR Purification Kit (Qiagen, Hamburge, Germany), following the manufacturer’s protocol. Final double-stranded cDNA was quantified using the Quibit

3.0 Fluorometer (ThermoScientific, Waltham, MA, USA). Due to overall low concentrations of double-stranded cDNA (< 10ng/μL), no dilutions were required.

2.7 16S rRNA gene amplicon sequencing (iTag)

Final DNA and double-stranded cDNA samples were sent to Argonne National

Laboratory (Chicago, IL) where library preparation was performed. Library preparation followed a single-step PCR protocol presented by Caporaso et al., (2012) using primers

505f and 806r to amplify the V4 region of the 16S rRNA gene. Sequencing was carried out in a single lane on an Illumina Mi-Seq using v2 chemistry and bidirectional amplicon sequencing (2 x 151bp).

Raw sequence reads were processed using the open source software package

QIIME v1.9 (Quantitative Insights into Microbial Ecology; www.qiime.org). Forward and reverse reads were joined, using a minimum overlap of 30 bp followed by quality filtering using default QIIME parameters and demulitplexing. Default QIIME quality filtering parameters include: removal of the primer/barcode from the sequence, and removal of all sequences 1) with a Phred quality score <4, 2) containing any ambiguous

(N) base calls, and 3) having a length of < 0.75 of original length post quality filtering.

After quality filtering, a total of 7,150,517 sequences reads remained, with an average of

121,195 ± 27,274 sequence reads per sample and a median sequence length of 253 bp.

Using a sequence cutoff of 30,000 reads, one sample (Row4b.R RNA) was removed from further analysis. Sequences were clustered into 553,627 operational taxonomic units

(OTUs) at 97% or greater sequence similarity using UCLUST (Edgar, 2010).

Representative sequences from each OTU were aligned using PyNAST (Caporaso et al.,

2010) and assigned taxonomy using the UCLUST classifier and Greengenes 16S rRNA gene database (DeSantis et al., 2006). During DNA extraction, blanks that contained no tailings sample were extracted alongside each batch of samples for quality control and subsequently sequenced. For DNA samples, OTUs contained in the sequenced blanks were removed from the associated samples extracted in the same batch to minimize influence of contamination. For RNA samples, due to high contamination in the blanks,

OTUs present in the blanks were individually assessed and if they were not present in the associated DNA samples (post-contaminant screening), then they were removed from the associated samples extracted in the same batch. It was assumed that if the DNA extraction step isolated the same OTUs, then they must really be present in the

65 community and not be a contaminant. It was also concluded that the high contamination present in the RNA blanks were acquired from nearby samples during either the extraction or cDNA library construction steps, and not due to contaminants present in any of the reagents in the extraction kit. Low abundant (less than 0.001% of total) OTUs were removed and OTU tables were rarefied to 50,000 sequences, prior to downstream analyses.

2.8 rRNA:rRNA gene ratios

The 16S rRNA and 16S rRNA gene datasets were used to calculate rRNA:rRNA gene ratios for specific OTUs that represented the most abundant (out of the top 100

OTUs) putative PGPB (N2-fixing and general PGPB) and Fe/S oxidizers and Fe-reducers

(Fe-reducing, Fe-oxidizing, Fe/S-oxidizing, and S-oxidizing). Putative function was assessed by BLAST (National Institute of Health; https://blast.ncbi.nlm.nih.gov/Blast.cgi) searches of the OTU sequences to determine the taxonomy of the closest match and a subsequent literature search for functional capabilities of the closest match.

Determination of functional capacity includes experimental verification of the function and/or the presence of the gene capable of the function, as published in the literature.

rRNA:rRNA gene ratios have been used as a proxy for activity or growth rate in prior studies (Kerkoh and Ward, 1993; Muttray and Mohn, 2000; Ka et al., 2001; Pérez-

Osorio et al., 2010; Campbell et al., 2011; Gaidos et al., 2011; Brettar et al., 2012; Hunt et al., 2012; Campbell and Kirchman, 2013; Gómez-Silván et al., 2014; Mei et al., 2016) with the assumption that the rRNA in a cell is proportional to how active it is and that the number of rRNA genes are consistent across taxa being compared. Because these

66 assumptions are more accurately applied to a single taxon, rRNA:rRNA gene ratios were used to assess activity for a single OTU across different samples. rRNA:rRNA gene ratios were calculated by dividing the rarefied number of sequence reads of each OTU from the DNA dataset by number of sequences of the same OTU from the RNA dataset.

2.9 nifH gene qPCR

Bacterial nifH gene was quantified to assess the nitrogen-fixing potential of the bacterial communities. The bacterial nifH gene, encoding the Fe-subunit of the nitrogenase enzyme, was amplified using primers PolF/PolR (Poly et al., 2001). These primers were designed to target diazotrophic soil bacteria (Poly et al., 2001) and have been shown to successful target nifH from a diverse range of diazotrophic bacteria

(Wartianinen et al., 2008). Furthermore, this primer pair has been shown to be effective in conjunction with qPCR (Nelson et al., 2015; Bouffaud et al., 2016). Representative sequences for qPCR calibration curves were previously determined using clone libraries, by selecting clones from the most abundant taxonomic group for each gene and preserving the plasmids (Nelson et al., 2015). Calibration curves were prepared by 10- fold serial dilutions of plasmids. Triplicate 10 μL reactions were amplified using a

CFX96 Real-Time Detection System (Bio-Rad Laboratories, Hercules, CA, USA) containing: 1 μL template, 5 μL SsoFast EvaGreen Supermix (Bio-Rad Laboratories,

Hercules, CA, USA), and 0.4 μM of each primer. Reaction conditions were as follows: 95

°C for 1 min followed by 50 cycles of 95 °C for 6 s and 59 °C for 6 s. Following amplification, a melt curve was created under the following conditions: 95 °C for 10 s, 5 s cycles increasing from 65 – 90 °C in 0.5 °C increments, and a final extension at 72 °C for 5 min. The melt curve was performed to confirm product specificity and aided in

67 determination of detection limit in conjunction with standard curve. Minimum and maximum detection thresholds were set based on the cycle numbers (Cq) of the highest and lowest reproducible standard curve, the Cq of the DNA extraction blanks processed with respective samples, the of the qPCR negative controls, and deviations from a melt curve representing nifH amplification. Samples below the detection limit were assigned a

Cq value equal to the average of the Cq for the DNA extraction blank to account of extraction error

2.10 Statistical analysis

For correlation analysis between microbial communities and geochemical conditions, the spearman correlation coefficients (ρ) and p-values were calculated using the program PAST (Hammer et al., 2001) with a subsequent false discovery rate (FDR) analysis using an α of 0.05. A Linear Discriminant Analysis (LDA) Effect Size (LEfSe)

(Segata et al, 2010; https://huttenhower.sph.harvard.edu/galaxy) method was performed to determine significant enrichments in rhizosphere versus bulk microbial communities at high pH (> 4) and low pH (< 4). Beta diversity was evaluated using principal coordinate analysis (PCoA) based on the unweighted UniFrac distance matrices, and analysis was done in QIIME. Significant differences between high and low pH bulk and rhizosphere samples was determined using a one-way analysis of variance (ANOVA) followed by a

Tukey test for means comparisons, using the program OriginPro 2016.

3. Results

3.1 Geochemical and plant cover/condition analysis

The pH, TOC, TN, EC, leaf chlorophyll, and plant cover data associated with each buffalo grass plant is reported in Table 1. Recall that Phase 1 represents 3 years of plant growth and Phase 3 represents 1 year of plant growth. For reference, Phase 1 at time zero and after 1 year of growth (2011) had an average pH of 6.45 ± 1.11 (range: 5.08 –

7.75) and 6.99 ± 0.72 (range: 5.93 – 7.52), respectively (see 15% compost and seeds treatment in Gil-Loaiza et al., 2016). In comparison, Phase 3 at time zero and after 1 year of growth had an average pH of 7.37 ± 0.27 (range: 6.96 – 7.62) (unpublished data) and

6.17 ± 1.87 (range: 2.56 – 7.76) (Table 1), respectively. After 3 years of growth, Phase 1 had an average pH of 3.71 ± 1.32 (Table 1). This illustrates three important patterns in pH: 1) Phase 3 after one year of growth had a comparable pH to Phase 1 after one year of growth; 2) Phase 3 had begun to undergo some re-acidification after one year of growth which is due to certain regions flooding causing compost to erode away; and 3) Phase 1 had undergone extensive re-acidification after 3 years of growth. This is evidence of not only the presence of a pH gradient, but also that re-acidification of the planted mine tailings has occurred over time both in Phase 1 and Phase 3. For many of the analyses in this paper, the samples are divided into two groups: high pH (>4) and low pH (<4), between which pH is significantly different (t test; p < 0.001). Samples that fall into the high pH category are Plot10a, Plot10b, Row2a, Row2c, Row4a, Row4c, Row6a, and

Row6b. Samples that fall into the low pH category are Plot5a, Plot5b, Plot19a, Plot19b,

Plot24a, Plot24b, and Row2b.

The geochemical parameters TOC, TN, and EC (Table 1) also represented a range of values (Table 1). TOC and TN were also both significantly greater from the high pH

69 samples compared to low pH samples (t-test; p < 0.05; p < 0.001, respectively). For the rest of the paper, pH will be focused on.

Chlorophyll and plant cover, representing plant condition and establishment, did not show any associations with geochemical conditions (pH, TOC, TN, or EC). However, plant cover was positively correlated with leaf chlorophyll levels (ρ = 0.53; p < 0.05).

3.2 Phylogenetic profiles

Rhizosphere and bulk samples associated with buffalo grass plants were collected from the IKHMSS phytostabilization field trial from Phases 1 and 3 for 16S rRNA gene amplicon sequencing. Phylogenetic profiles for Phases 1 and 3 are shown in Fig. 1a and b, both ordered in ascending pH. Many of the phyla/subphyla were correlated with pH, as is visible in the phylogenetic profiles, and statistically confirmed with Spearman correlation coefficients shown in Fig. 2. In the rhizosphere and bulk, pH was significantly positively correlated with Bacteroidetes, Chlamydia, Chloroflexi, Gemmatimondetes,

Deltaproteobacteria, Verrucomicrobia, and [Thermi] and negatively correlated with

Euryarchaeota and Nitrospirae. Additionally, in the bulk, pH was significantly positively correlated with Cyanobacteria, Planctomycetes, Betaproteobacteria, and TM6, and negatively correlated with Firmicutes. Alphaproteobacteria did not show any significant changes across the pH spectrum, however, at the family level there was a large shift from a dominance of Acetobacteraceae (primarily Acidiphilium) at low pH to a more diverse community dominated by Rhizobiales including Hyphomicrobiaceae, Phyllobacteraceae,

Bradyrhizobiaceae, and Rhizobiaceae (see Honeker et al., submitted).

Overall, differences in rhizosphere and bulk microbial communities were more pronounced at low pH (pH < 4) than high pH (pH > 4) (Fig. 3). At high pH, there were no significant differences between communities at the phylum/subphylum level, whereas at low pH, Bacteroidetes, Verrumicrobia, and [Thermi] were significantly more abundant in the rhizosphere compared to the bulk (t-test, p < 0.05). This same pattern is supported by

LEfSe analysis which revealed less phylogenetic groups at the order and family level enriched in the rhizosphere of high pH (Fig. 4) than low pH (Fig. 5). At high pH, top groups that showed enrichment in the rhizosphere (p < 0.05) included several groups within the subphylum Alphaproteobacteria, including Sphingomonadales,

Rhodobacterales, Rhizobiales, and Caulobacterales at the order level and

Sphingomonadaeae, Rhodospirillaceae, Rhodobacteraceae, and Caulobacteraceae at the family level. Other groups that were enriched in the rhizosphere were Sphingobacteria,

Pseudomonadales, and Myxococcales (Fig. 4a and b). Altogether, this represented 4 distinct phylogenetic lineages (Fig. 4b). A couple groups showed enrichment in the bulk, thereby being excluded from the rhizosphere, including the Fe-oxidizing

Acidithiobacillaceae of the order Acidithiobacillales and Euryarchaeota. (Fig. 4a and b).

At low pH, over 100 phylogenetic groups were significantly discriminative between the rhizosphere and bulk, more than could be handled by the program. Again, this is an example of the higher discrepancies between rhizosphere and bulk samples in the low pH samples. To decrease the number of significantly discriminative microbial groups, the analysis was repeated using a more stringent p-value cutoff of 0.005. At low pH, phylogenetic groups that showed enrichment in the rhizosphere that were also enriched at high pH were Rhizobiales and Sphingobacteriales. Specific Rhizobiales families that

71 were enriched in the rhizosphere at low pH that were not enriched at high pH were

Hyphomicrobiaceae, Phyllobacteraceae, and Bradyrhizobiaceae. Other groups enriched in the rhizosphere at low pH were Saprospirae, Cytophagia, Intrasporangiaceae,

Opitutatae, Xanthomonadaceae, Burkholderiales, and Pirellulales (Fig. 5a and b). In total, 9 distinct phylogenetic lineages were enriched in the rhizosphere (Fig 5b). At low pH, there were no microbial groups that displayed exclusion from the rhizosphere. Even using the more stringent p-value cutoff for the low pH rhizosphere and bulk samples, there were still more groups that were enriched in the rhizosphere compared to the high pH.

Beta diversity analysis on rhizosphere and bulk samples from Phase 1 and Phase 3 confirmed that pH was a major driver of microbial communities and that there was higher variation between rhizosphere and bulk samples at low pH vs. high pH (Fig 6). PCoA of beta diversity revealed that samples from low to high pH separated out from left to right across the x-axis, which explains 37.5% of variation. Samples from a lower pH exhibited a higher variation than samples from a higher pH. Additionally, at a higher pH, the bulk and rhizosphere samples were clustered together, whereas at a lower pH, the bulk and rhizosphere samples were separated, showing the higher variability. Again, this exemplified the higher variation between rhizosphere and bulk samples from low pH as compared to those from high pH. It shuld be noted that the rhizosphere and bulk samples from Phase 1 with a pH of 6.01 (Plot10a.R and B) clustered with the rhizosphere and bulk samples from Phase 3 in the 6-8 pH range. Likewise, the rhizosphere and bulk samples from Phase 3 with a pH of 2.56 (Row2b.R and B) fell within the range of acidic

Phase 1 rhizosphere and bulk samples. This finding supports combining Phase 1 and

Phase 3 within the low and high pH categories despite differences in treatments and plant age (as described above).

Alpha diversity in rhizosphere and bulk samples from high and low pH showed a distinct pattern. At high pH (< 4), the alpha diversity, as measured by the number of observed species, was the same between the rhizosphere and bulk samples (ANOVA, p >

0.05) (Fig. 7). At low pH (>4), however, the alpha diversity was significantly higher in the rhizosphere compared to the bulk (ANOVA, p < 0.05). Also, the alpha diversity in both the rhizosphere and bulk at high pH were significantly higher than the rhizosphere and bulk at low pH (p < 0.05).

3.3 Fe/S-oxidizers and Fe-reducers

3.3.1 Abundance vs. activity

OTUs representing putative Fe/S-oxidizers and Fe-reducers were selected from the top 100 most abundant OTUs (Table 2). As a proxy for activity, rRNA:rRNA gene ratios were calculated for this subset of the most abundant OTUs, and the abundance and activity of these OTUs are displayed in Fig. 8.

OTUs that are putative Fe/S-oxidizers and Fe-reducers, were almost all more abundant in the rhizosphere and bulk at low pH. This is supported by a large proportion of the Fe/S-oxidizers and Fe-reducers that exhibited a significant negative correlation between their relative abundance and pH in the rhizosphere and bulk, however, more

OTUs showed a significant negative correlation in the rhizosphere compared to the bulk

(24 vs. 13, respectively) (Fig. 11). Some exceptions included OTUs that showed a positive association with pH in the bulk and a negative association in the rhizosphere (the

S-oxidizing Thiomonas and Dyella, and Fe-reducing Metallibacterium [81089]) and

OTUs that showed a positive association with pH in the bulk and rhizosphere (S- oxidizing Thioprofundum and Fe-reducing Paliudibaculum, Acidobacter [1099090], and

Aciditerrimonas [741311]). Additionally, there were some OTUs that appeared to have an optimal pH range of 3 - 6 (Metallibacterium [540036] and some Acidibacter [228030,

4301944, and 1110303]) in the bulk, yet were present in the rhizosphere of plants growing in substrate with a pH in the 2 range (Plot19b.R and Plot5a.R) (Fig. 8), indicating that buffalo grass plants were creating a buffering effect in the rhizosphere, as has been shown previously (Solís-Dominguez et al., 2011). However, acidophilic metal- cyclers that were also present in the rhizosphere of plants growing in acidic conditions showed that despite the neutralizing effect of buffalo grass on the rhizosphere providing neutral micro-niches, this did not exclude acidophilic organisms from inhabiting the rhizosphere.

There were no significant correlations between OTU activity and pH in the bulk, however, there were several associations in the rhizosphere. In the rhizosphere, the activity of Acidiphilium (227409) was significantly negatively correlated with pH (Fig.

12). Two OTUs closely related to Aciditerrimonas (160500 and 741708) had activities which were negatively associated with pH, however this was not significant. These patterns correspond to the acidophilic lifestyle of these organisms. The OTU closely related to Leptospirillum (251679), also acidophilic with an abundance that was negatively correlated with pH, had an activity that was positively associated with pH (not significant) (Fig. 12). Two other OTUs that were closely related to Thioprofundum and

Acidibacter and had a positive correlation with pH had activities that were also positively correlated with pH (not significant) (Fig. 12).

3.4 N2-fixing and general PGPB

3.4.1 Abundance vs activity

N2-fixing bacteria and PGPB were present in the bulk at higher pH and almost absent in the bulk at low pH, however, in the rhizosphere, some OTUs were present at both the higher and lower pH ranges (for example, OTU 1053775 [Devosia]) (Fig. 8).

This indicated that these high abundant OTUs were neutrophilic, and that they may be inhabiting circum-neutral microniches created by the buffering effect produced by buffalo grass roots (Solis-Dominguez et al., 2011). Overall, putative N2-fixing bacteria and PGPB were more abundant at high pH. This is supported by a significant positive correlation between all of the OTUs in this group, except for OTU 1061815

(Arthrobacter), and pH in the rhizosphere and the bulk (Fig. 9).

The activity of N2-fixing bacteria and PGPB appeared to be similar between the bulk and rhizosphere for each of the OTUs. Additionally, the activities did not show any significant correlation with pH. The activity of Arthrobacter showed a positive association with pH in the bulk and the activity of Pseudomonas showed a positive association with pH in the rhizosphere, however, these trends were not significant.

Comparing the presence of PGPB/N2-fixers with Fe/S-oxidizers and Fe-reducers at high versus low pH also revealed a distinct pattern. At high pH, the rhizosphere had more PGPB/N2-fixers than Fe/S-oxidizers and Fe-reducers while at low pH, both groups were present. In the bulk at high pH there were more PGPB/ N2-fixers than Fe/S- oxidizers and Fe-reducers, however, at low pH, there were very few PGPB/ N2-fixers and a dominance of Fe/S-oxiders and Fe-reducers.

3.4.2 nifH gene

qPCR was performed to quantify the nifH gene as a measure of the N2-fixing potential of the microbial communities from the rhizosphere and bulk samples across a pH gradient. NifH gene abundance was significantly greater in the high pH vs. the low pH bulk compartments (ANOVA p < 0.05), however, no significant difference was observed between the high and low pH rhizosphere compartments (Fig. 13). Similarly, there was a higher correlation between the nifH gene abundance and pH in the bulk (ρ =

0.64, p < 0.01) than the rhizosphere (ρ = 0.51, p < 0.05).

The lower limit of detection was determined based on the average Cq of the extraction blanks, which was 34.42. Two samples, Row2b.B and Row4a.B were below this detection limit (average Cq of 35.92 and 36.06 respectively). To account for extraction error, they were assigned a Cq equivalent to the lower detection limit (34.42).

This equated to 3.5 x 10.2 copy# μL-extract-1, or 5.95 x 104 copy# g-soil-1.

4. Discussion

Establishment of a plant cover is vital to the success of phytostabilization, but can be difficult in the extreme conditions of acid-generating pyritic metalliferous mine tailings. Microbial communities colonizing the rhizosphere and bulk compartments of the plants used during phytostabilization can be a large factor in plant survival and establishment because of the large role they play in biogeochemical cycling and plant health. In this study, bacterial communities were characterized with a focus on putative

N2-fixing/PGPB, Fe/S-oxidizing, and Fe-reducing bacteria from rhizosphere and bulk compartments of buffalo grass used to phytostabilize mine tailings exhibiting a pH

76 gradient, from 2.35 – 7.76. The pH gradient was caused by re-acidification due to factors such as: 1) the presence of acid generating potential after only a single compost amendment was added at inception, and 2) regular (irrigation) and extreme (rain storm) events leading to compost erosion. The stark shift in rhizosphere and bulk microbial communities, changing from a plant-supporting to an Fe/S-oxidizer and Fe-reducer dominated community with decreasing pH, has strong implications for the ability of the site to sustain long-term plant growth.

The rhizosphere and bulk microbial communities showed varying patterns in composition and diversity between high and low pH, with a transition zone at around pH

4. At high pH (> 4), both alpha and beta diversity of the rhizosphere and bulk were very similar, as was the community composition. A typical soil capable of supporting plant growth is often characterized by a rhizosphere and bulk with similar alpha diversity

(Edwards et al., 2015) or an alpha diversity of the bulk greater than the rhizosphere

(García-Salamanca et al., 2012). Even in some metal-polluted soils the alpha diversity of the bulk can be greater than the rhizosphere (Navarro-Noya et al., 2010). Similarly, beta diversity patterns of the rhizosphere and bulk in a typical soil reveal an overlapping of the rhizosphere and bulk microbial communities (Lundberg et al., 2012; Edwards et al.,

2015). Therefore, the diversity patterns of the rhizosphere and bulk at high pH are more similar to that of a typical soil capable of supporting plants.

In contrast, at low pH the community composition and diversity patterns differed between the rhizosphere and bulk. First, the community composition of the rhizosphere was different from the bulk, which could have several explanations. In the more stressed conditions, the buffalo grass may be recruiting different species by the production of

77 different exudates compared to at high pH. Plant root exudates can have a large effect on the rhizosphere microbial community (Berendsen et al., 2012). Another explanation is that the buffering effect of buffalo grass on the rhizosphere (Solís-Dominguez et al.,

2011) has delayed acidification in that region. Next, the alpha diversity not only significantly decreased in both the rhizosphere and bulk in low pH compared to high pH, but the alpha diversity in the rhizosphere was significantly higher than in the bulk. If the bulk is the source of microbes for the rhizosphere, and select species are enriched in the rhizosphere (Smalla et al., 2001; Edwards et al., 2015), then it follows that a bulk soil that has lost its species diversity has less of a diverse pool of microorganisms for the rhizosphere to recruit from. This pattern suggests that the buffalo grass has created a neutral micro-niche via its buffering capacity that not only supports a higher species diversity, but also may protect some species from the more acidic conditions of the surrounding substrate. This is further supported by the LEfSe analysis which showed that at least 9 phylogenetic linages were significantly higher in the rhizosphere compared to the bulk (Fig. 5b), compared to only 4 at high pH (Fig. 4b). As a follow up study, it would be informative to measure the pH of the rhizosphere and bulk separately to assess whether the species richness is tightly linked to the pH, or if there is a strong plant

(rhizosphere) effect as well.

In order for phytostabilized mine tailings to support plant health like a typical, healthy soil, one of the goals is to achieve ecosystem health (Epelde et al., 2009; Gómez-

Sagasti et al., 2012) which can partly be assessed by measuring microbial species diversity (Alkorta et al., 2003). The re-acidification of the mine tailings has caused a significant decrease in species diversity, which would indicate a decrease in ecosystem

78 health. This decrease in species diversity in acidic conditions can be due to the increase in metal bioavailablity at acidic pH. Aluminum (Al) is particularly toxic to microorganisms

(Piña and Cervantes, 1996) and is abundant in the mine tailings at IKMHSS (41 g kg-1)

(Hayes et al., 2014). Al3+ is solubilized into the soil solution when the pH drops below 5

(Kochian et al., 2004), which is close to the transition zone found at pH 4 in this study.

The presence of microbes isn’t the only factor in plant health in these stressful conditions. One of the most detrimental constraints of plant’s ability to withstand acidic pH is Al and manganese (Mn) toxicity from the resulting increase in bioavailability of metals (Foy et al., 1998). Mechanisms that plants have developed to withstand Al toxicity include the exudation of organic acids or other substances that chelate Al, and increasing the pH of the rhizosphere to decrease Al3+ activity (Kochian et al., 2004), the latter of which has been found to occur in buffalo grass (Solís-Dominguez et al., 2011).

4.1 Fe/S-oxidizers and Fe-reducers

Across the pH gradient from high to low, the bacterial community shifts from a diverse community containing N2-fixing bacteria and other PGPB to one dominated by not only autotrophic Fe- and S-oxidizers, but also heterotrophic Fe-reducers. This shift in community is largely driven by a decrease in pH (Table 1), as illustrated by a primarily negative correlation between dominant Fe/S-oxidizers and Fe-reducers with pH (Fig. 11).

As mentioned previously, the decrease in pH is attributed to compost depletion from water erosion caused by flooding due to extreme rain events and regular irrigation. The erosion of compost caused exposure of the bare tailings and a decrease of the buffering capacity that the compost provided. This has led to a change in the microbial community,

79 particularly of the bulk, to one dominated by putative autotrophic Fe/S-oxidizers and heterotrophic Fe-reducers, many of which have been identified previously from acidic pyritic mine tailings or acid mine drainage (AMD) (Baker and Banfield, 2003; Schippers et al., 2004; Mendez et al., 2007; Méndez-Garcia et al., 2015; Chen et al.., 2016).

Fe- and S-oxidizers can accelerate pyrite oxidation in acidic pH (Evangelou and

Zhang, 1995), thus leading to further acidification. It has been reported that acidophilic heterotrophic Fe-reducers can also contribute to this acidification, as discussed below. As illustrated in Fig. 8, at lower pH, Fe/S-oxidizers and Fe-reducers were overall more abundant and active in the bulk vs. the rhizosphere. Therefore, this increase in abundance and activity of Fe/S-oxidizers and Fe-reducers in the bulk can promote further re- acidification, and as these communities overtake the buffalo grass rhizosphere, the decrease in pH can be detrimental to plant growth in the long-term.

In this study, OTU 251679 was closely related to Leptospirillum ferriphilum, an

Fe oxidizer that is ubiquitously found in acidic pyritic mine tailings and AMD with an optimal growth range of pH 1 to 2 (Baker and Banfield, 2003; Mendez et al., 2007; Chen et al., 2016). OTU 251679 was significantly negatively correlated with pH in the rhizosphere and bulk and the activity showed a positive trend with pH in the rhizosphere, however, this correlation was not significant (Fig. 12). This pattern suggests that L. ferriphilum at low abundance can still be highly active, particularly in the rhizosphere compartment. At high pH, buffalo grass can actually acidify the rhizosphere (Solís-

Dominguez, et al., 2011). This could make ferrous Fe more available for L. ferriphilum to oxidize, therefore, even at low abundance it may be actively oxidizing Fe. Overall,

80 however, OTU 251679 was most abundant and active at the lowest pH (2.47 – 2.56) in the bulk substrate.

Abundant putative Fe- and S-oxidizers that were present were closely related to

Acidiferrobacter thiooxydans (Hallberg et al., 2011) and Sulfobacillus acidophilus (Baker and Banfield, 2003; Chen et al., 2016). OTU 4484442, closely related to the gammaproteobacterium A. thiooxydans, was present and active in the bulk and rhizosphere at low pH (Fig. 8). Honeker et al., (submitted) found that at low pH, a higher relative abundance of Gammaproteobacteria colonized the root surface. It is possible that some of these Gammaproteobacteria comprise A. thiooxydans and oxidize Fe contributing to Fe-plaque formation. Using the method for quantifying metal(loid) and bacteria colocalization on root surfaces presented in Honeker et al. (2016), it was found that 12.4 - 47.9% of Gammaproteobacteria colocalized with Fe on roots from buffalo grass grown in acidic conditions (Honeker et al., submitted), which supports the aforementioned hypothesis.

Putative S-oxidizers in high abundance were all are neutrophilic, and included

Dyella thiooxydans (Anandham et al., 2008; Anandham et al., 2014), Thioprofundum hispidum (Mori et al., 2011) and Thiomonas arsenovirans (Battaglia-Brunet et al., 2011).

Thp. hispidum has not been characterized from acidic mine tailings previously, and was originally isolated from a deep sea vent (Mori et al., 2011). The OTUs closely matched to

Thp. hispidum (589587 and NROTU388) were absent from the bulk and rhizosphere at low pH, while the OTUs closely matched to Dyella and Thiomonas, 842284 and

NROTU1193, respectively, were also present in the rhizosphere of plants growing in the acidic substrate (Fig. 8). Dyella has previously been found associated with the

81 rhizosphere (Anandham et al., 2008, Anandham et al., 2011) and was even found to promote the growth of crop plants by providing S in the form of sulfate (Anandham et al.,

2008). In the current study, there was no indication that OTU 842284 (Dyella) promoted plant growth, and there was even a negative correlation between the relative abundance of OTU 842284 and plant cover (ρ = -0.71), however this was not significant.

Putative Fe-reducers by far dominated the top 100 OTUs in the rhizosphere and bulk. Ferric iron respiration is widespread among heterotrophic acidophiles because ferric iron is more bio-available at low pH and the redox potential is more positive compared to more neutral pH (Coupland and Johnson, 2007). These OTUs were closely related to

Paludibaculum fermentans (Kulichevskaya et al., 2014), Acidobacterium encapsulatum

(Kishimoto et al., 1991), Metallibacterium scheffleri (Ziegler et al., 2013), Acidibacter ferrireducens, Aciditerrimonas ferrireducens, and Acidiphilium sp. All of these species are facultative anaerobes and are capable of Fe-reduction coupled with heterotrophic growth in low-oxygen or anaerobic conditions. Therefore, it is important to note that since the mine tailings have overall oxic conditions, Fe-reduction may be spatially limited to anoxic niches or temporally limited to times of flooding (Tiedje et al., 1984).

Within the genus Acidiphilium, close matches include Acdp. angustum, Acdp. multivorum, and Acdp. acidiphilium. Acdp. acidiphilium also been shown to grow autotrophically and oxidize S as well (Hiraishi et al., 1998; Rohwerder and Sand, 2003).

Fe-reducers, like Acidiphilium spp., can promote Fe oxidation by autotrophic Fe- oxidizers by consuming organic compounds which are toxic to autotrophs (Schippers et al, 2000; Méndez-Garcia et al., Baker and Banfield, 2003; Liu et al., 2011). Acidiphilium spp. can also cause rapid cycling of Fe in combination with Acidothiobacillus

82 ferrooxidans (also present at low abundance) (Küsel et al., 2003) and potentially with other Fe-oxidizers such as L. ferriphilum. This could further contribute to acidification by contributing a constant source of ferrous Fe for Fe-oxidizers. The OTUs closely related to

Acidiphilium are all negatively correlated with pH in the rhizosphere, and the activity of the OTU closely related to Acdp. angustum (227409) was significantly positively correlated with pH (Fig. 8; Fig. 12). This activity in the rhizosphere at lower pH could facilitate further acidification. While overall Acidiphilium spp. are more abundant in the bulk, their presence in the rhizosphere at low pH indicates they are potential root- colonizers. Honeker et al., (submitted) found that at low pH, there was an increase in relative abundance of Alphaproteobacteria on the root surface, and it was hypothesized that Acidiphilium may be key players in this Alphaproteobacteria community, participating in Fe-reduction of the ferrihydrite coating the root surfaces (Honeker et al.,

2016). Previous research has confirmed that Acidiphilium spp. are capable of reductive dissolution of ferric Fe minerals, such as ferrihydrite (Bridge and Johnson, 2000).

Five out of the top 100 OTUs match closely with Acdb. ferrireducens, an obligate heterotroph isolated from a pit lake at an abandoned metal mine in Southwest Spain

(Falagán and Johnson, 2014) (Fig. 8). In addition to being As-tolerant, Acdb. ferrireducens reduces Fe in the mineral phase, particularly Scwertmannite (Falagán and

Johnson, 2014) which is found as a secondary phase following oxidative dissolution of pyrite at IKHMSS (Hayes et al., 2014). This could explain why this OTU was in high abundance and activity in this system at lower pH (Fig. 8). Moreover, this gammaproteobacterium, which was present in the rhizosphere at low pH, could be colonizing the root surface. As mentioned previously, a higher relative abundance of

Gammaproteobacteria was found on the root surface of buffalo grass growing in more acidic conditions (Honeker et al., submitted). Gammaproteobacteria Fe-oxidizers

(Acidiferrobacter thiooxydans) and Fe-reducers (Acdb. ferrireducens) may both be contributing to Fe-cycling and formation of and dissolution of Fe-plaque minerals.

4.2 N2-fixing and other PGPB

For long-term success of phytostabilization, it is desirable for a plant-growth supporting microbial community containing N2-fixing bacteria and other PGPB to develop, which play important roles in supporting plant survival in these harsh conditions. In this study, top most abundant OTUs that are putative N2-fixing and general

PGPB are all present in the high pH rhizosphere and bulk samples (Fig. 8). While this is not an exhaustive list of all the PGPB in the system, it does signify the presence of a plant-growth supporting community. Most of these OTUs also show presence in the rhizosphere of the low pH samples, however, most are absent in the low pH bulk samples

(Fig. 8). This pattern is supported by a significant positive correlation between all of these groups and pH in the bulk and rhizosphere, except for OTU closely related to

Arthrobacter globiformis (Fig. 9) and suggests that as pH decreases, some PGPB can survive in the circum-neutral microniches created by the buffalo grass roots (Solís-

Dominguez, et al., 2011). However, the depletion of PGPB in the bulk at low pH indicate that future plants may struggle with no PGPB to recruit from the bulk.

Putative PGPB OTUs closely match Pseudomonas putida (829851) and

Arthrobacter pascens (1081815). P. putida is well-known PGPB that lowers the plant stress hormone ethylene by producing ACC-deaminase which cleaves ACC, the

84 precursor to ethylene (Glick et al., 2004). This species also produces indoleaceteic acid

(IAA) which is a plant phytohormone that improves plant growth (Patten and Glick,

2002; Glick et al., 2014). OTU 829851 (P. putida), is more abundant in the rhizosphere than the bulk (Fig. 8). This pattern is supported by LEfSe analysis indicating that the family Pseudomonadaceae is enriched in the rhizosphere at high pH (Fig. 4). At low pH, there is a very sharp decrease in OTU 829651 from the rhizosphere and a complete depletion in the bulk. The activity and abundance of OTU 829651 is positively correlated with pH in the rhizosphere, however, the correlation of activity and pH is not significant trend (Fig. 10). The other PGPB A. pascens not only has the ability to fix nitrogen, but it also has other PGPB traits such as siderophore and IAA production (Langella et al.,

2014) and has been shown to significantly increase plant height, shoot dry weight, and root dry weight (Yu et al., 2012). Furthermore, Grandlic et al., (2008) found that buffalo grass grown from seeds inoculated with Arthrobacter spp. significantly improved plant biomass and also increased plant survival during the phytostabilization of mine tailings.

OTU 1081815 (A. pascens), was the only one in the group of N2-fixing bacteria and

PGPB that was not positively correlated with pH in the rhizosphere or bulk, however, its activity was positively correlated with pH in the bulk, though not significantly.

A large proportion of the N2-fixing/PGPB OTUs in the rhizosphere and bulk closely matched species within the subphylum Alphaproteobacteria, including Tistlia consotensis, Sinorhizobium medicae, Hyphomicrobium aestaurii, Pyllobacterium loti and

Devosia nepuniae. Sinorhizobium, Hyphomicrobium, Phyllobacterium, and Devosia all are genera within the order Rhizobiales. LEfSe analysis finds that Rhizobiales are enriched in the rhizosphere at high and low pH, and furthermore, that the specific

85 families Hyphomicrobiaceae and Phyllobacteriaceae are enriched in the rhizosphere at low pH. This signifies that the OTUs that closely match these species were plant- associated and enriched in the rhizosphere, particularly at low pH. While Tistlia consotensis is a free-living N2-fixing bacterium (Díaz-Cárdenas et al., 2010),

Sinorhizobium medicae typically form nodules on legumes (Rome et al., 1996) and

Devosia nepuniae forms a symbiotic relationship with Neptunia plants (Rivas et al.,

2002). However, Sinorhizobium spp. have been found to have a plant growth promoting effect on non-leguminous plants as well (Galleguillos et al., 2000). Honeker et al.,

(submitted) found a positive association between the relative abundance of

Alphaproteobacteria colonizing the root surface of buffalo grass and pH in Phase 3, which was hypothesized to be N2-fixing bacteria/PGPB. The results from this study support this hypothesis, however, further studies are needed.

For a quantitative measure of N2-fixing capacity of the substrate, the nifH gene was quantified. Huang et al., (2011) showed that nifH gene copy number was significantly correlated with actual N2-fixation, which supports the use of the nifH gene as an indicator of N2-fixing potential of the microbial communities. Quantification of the nifH gene showed a similar pattern as the abundance N2-fixing bacteria and PGPB in the bulk vs. the rhizosphere at high and low pH. In the bulk, the nifH gene was significantly more abundant at high pH than in low pH, showing that the N2-fixing capacity in the bulk at high pH was greater than at lower pH. However, in the rhizosphere at high vs. low pH, there was no significant difference in nifH gene copy number. This shows that 1) the rhizosphere at low pH has a buffering effect creating micro-niches supporting the growth of neutrophilic N2-fixing bacteria and 2) the capacity for N2-fixation in the bulk decreases

with re-acidification. This decrease in N2-fixing capacity of the bulk substrate after re- acidification reiterates the decreased plant-growth promoting capabilities of microbial communities that are present.

4.3 Implications for phytostabilization management

A sharp shift in microbial diversity and community composition happened around pH 4 in the current system in both the bulk and rhizosphere. In the bulk, this is probably a majority microbially-driven process with little plant influence. In the rhizosphere, however, it is difficult to determine based on the current study how much of this shift is microbially-driven or plant-driven, but it is most likely a combination of both. For instance, as the plant experiences stress in the acidic conditions, they can release different exudates, thereby affecting the rhizosphere microbial community (Bais et al., 2006).

Counter to that, the microbial community can shift based on their own ability to withstand acidic conditions and the associated metal toxicity. Also, it cannot be determined from this study whether this pattern is observed with all plants or just buffalo grass. The overall pattern apparent in the bulk is most likely similar across most plant species, however, a shift in rhizosphere microbial communities may be different.

Assuming similar overall trends in microbial community dynamics during re- acidification, there are several management strategies that could be explored. Several approaches include 1) re-applying compost, 2) adding lime, 3) inoculating with a probiotic (in addition to either 1 or 2), and 4) preventing compost erosion via erosion control. Regarding the addition of compost or lime to increase the pH, application should be done before the pH reaches 4 to prevent a transition in microbial communities. To

87 possibly boost plant health, a probiotic consisting of the PGPB identified in the buffalo grass rhizosphere could be added along with compost or lime. Maintaining the pH above

4 will help in preserving a plant-growth supporting microbial community and suppressing the Fe/S-oxidizing and Fe-reducing acid-generating community, thus fostering continued plant health and establishment.

4.3 Conclusions

A drastic change in microbial diversity and community composition of buffalo grass rhizosphere and bulk substrate was observed across a pH gradient during phytostabilization of metalliferous pyritic mine tailings. The microbial community shifted from one characterized by higher diversity and an abundance of PGPB and N2-fixers in the rhizosphere and bulk at high pH to a less diverse community dominated by Fe/S- oxidizers and Fe-reducers, particularly in the bulk compartment, at low pH. This combination of autotrophic Fe/S oxidizers and heterotrophic Fe reducers can promote the continued acidification of the substrate, which would be detrimental for long-term phytostabilization. PGPB and N-fixers are almost completely absent in the bulk at low pH conditions, however, some are still present in the rhizosphere presumably due to the buffering effect produced by buffalo grass roots. While this may be helpful to plant survival in the extreme conditions, as the plants die and the buffered micro-niches disappear, eventually these regions may lose their ability to support plant growth due to the lack of abundant PGPB and N2-fixing populations in the bulk substrate.

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6. Tables

Table 1. Geochemical and plant condition parameters for tailings substrate collected next to each buffalo grass plant.

TOC TN EC Plant cover Sample Phase pH Chlorophyll (g kg-1) (g kg-1) (dS m-1) (%) Plot24b 1 2.35 47.6 3.60 10.20 10.5 3.5 Plot19b 1 2.47 51.1 3.46 4.46 11.4 49.3 Plot5a 1 2.88 35.2 3.07 3.42 35.5 46.6 Plot19a 1 3.46 44.6 3.66 2.97 38.6 49.3 Plot24a 1 3.49 57.1 3.76 2.93 49.3 3.5 Plot5b 1 3.66 39.6 3.29 3.17 12.8 46.6 Plot10b 1 5.38 49.4 4.50 5.47 15.4 37.9 Plot10a 1 6.01 72.2 5.22 7.94 32.3 37.9 Row2b 3 2.56 47.7 4.52 14.56 7.5 29.4 Row4b 3 4.71 48.7 5.78 5.76 11.6 29.5 Row2c 3 5.22 51.9 5.62 4.30 8.7 29.4 Row6a 3 6.54 99.2 9.04 8.10 50.0 73.2 Row6b 3 7.11 68.4 7.13 9.46 8.3 29.5 Row4c 3 7.72 126.8 9.54 9.83 31.1 65.7 Row2a 3 7.73 153.6 9.53 4.10 40.5 48.4 Row4a 3 7.76 140.8 8.68 8.86 44.2 53.6 TOC = total organic carbon; TN = total nitrogen; EC = electrical conductivity

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Table 2. Identities of putative N2-fixing, PGBP, Fe/S oxidizing, and Fe reducing within the top 100 most abundant OTUs

Closest BLAST match % Putative OTU no. Phylum (accession no.) Identity function Pseudomonas putida 829851 99 Gammaproteobacteria PGPB (NR_11365.1) Arthrobacter pascens 1081815 100 Actinobacteria PGPB (NR_026191.1) Phyllobacterium loti 610753 98 Alphaproteobacteria PGPB (NR_133818.1) Tistlia consotensis NROTU688 98 Alphaproteobacteria N Fixation (NR_116437.1) 2 Sinorhizobium medicae 829523 99 Alphaproteobacteria N Fixation (NR_104719.1) 2 Devosia neptuniae 1053775 97 Alphaproteobacteria N Fixation (NR_028838.1) 2 Hyphomicrobium aestuarii 1056070 99 Alphaproteobacteria N Fixation (NR_104954.1) 2 Thiomonas arsenivorans S/As NROTU1193 96 Betaproteobacteria (NR_115341.1) Oxidation Dyella thiooxydans S 842284 99 Gammaproteobacteria (NR_116006.1) Oxidation Thioprofundum hispidum S 589587 98 Gammaproteobacteria (NR_112620.1) Oxidation Thioprofundum hispidum S NROTU388 96 Gammaproteobacteria (NR_112620.1) Oxidation Alicyclobacillus cycloheptanicus Fe/S 253241 98 Firmicutes (NR_118875.1) Oxidation Acidiferrobacter thiooxydans Fe/S 4484442 97 Gammaproteobacteria (NR_114629.1) Oxidation Sulfobacillus acidophilus Fe/S 247206 96 Fimicutes (NR_074758.1) Oxidation Leptospirillum ferriphilum Fe 251679 99 Nitrospirae (NR_028818.1) Oxidation Paludibaculum fermentans Fe 544426 92 Acidobacteria (NR_134120.1) Reduction Acidibacter ferrireducens Fe 1099090 92 Gammaproteobacteria (NR_126260.1) Reduction Acidibacter ferrireducens Fe 228030 98 Gammaproteobacteria (NR_126260.1) Reduction Acidibacter ferrireducens Fe 4301944 98 Gammaproteobacteria (NR_126260.1) Reduction Acidibacter ferrireducens Fe NROTU1218 98 Gammaproteobacteria (NR_126260.1) Reduction

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Acidibacter ferrireducens Fe 1110303 100 Gammaproteobacteria (NR_126260.1) Reduction Aciditerrimonas ferrireducens Fe 741311 91 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe NROTU23 91 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe 160500 93 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe 741708 93 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe 1109815 94 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe 221609 94 Actinobacteria (NR_112972.1) Reduction Aciditerrimonas ferrireducens Fe 235014 96 Actinobacteria (NR_112972.1) Reduction Acidobacterium capsulatum Fe 220544 96 Acidobacteria (NR_074106.1) Reduction Acidobacterium capsulatum Fe 1003326 97 Acidobacteria (NR_074106.1) Reduction Metallibacterium scheffleri Fe 545036 100 Gammaproteobacteria (NR_118103.1) Reduction Metallibacterium scheffleri Fe 81089 94 Gammaproteobacteria (NR_118103.1) Reduction Acidiphilium angustum Fe 562968 98 Alphaproteobacteria (NR_025850.1) Reduction Acidiphilium angustum Fe 227409 96 Alphaproteobacteria (NR_025850.1) Reduction Acidiphilium angustum Fe NROTU462 96 Alphaproteobacteria (NR_025850.1) Reduction Acidiphilium multivorum Fe 219194 (NR_074327.1) 100 Alphaproteobacteria Reduction/ As xoidation Acidiphilium acidiphilium Fe 4497 (NR_036831.1) 97 Alphaproteobacteria Reduction/S Oxidation

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7. Figure Legend

Figure. 1. Phylogenetic profiles of (A) Phase 1 and (B) Phase 3 rhizosphere and bulk samples collected from buffalo grass plants used to phytostabilize pyritic metalliferous mine tailings. Samples are ordered in increasing pH. R = Rhizosphere; B = Bulk

Figure 2. Heatmap of Spearman correlation coefficients between relative abundance of phyla/subphyla and geochemical/plant status parameters in the (A) bulk and (B) rhizosphere of buffalo grass growing in pyritic metalliferous mine tailings. The black box encompasses the correlations with pH, which is the focus of this paper. * p < 0.05 (raw);

** significant after FDR analysis (α = 0.05); TOC = total organic carbon; TN = total nitrogen; EC = electrical conductivity

Figure 3. Average phylogenetic profiles across low pH (< 4) and high pH (> 4) rhizosphere and bulk samples collected from buffalo grass used to phytostabilize pyritic metalliferous mine tailings. R = Rhizosphere; B = Bulk

Figure 4. Microbial groups that show enrichment in the rhizosphere (green) or in the bulk (red) at high pH (> 4) based on LEfSe results with a LDA threshold of 3.0 (p <

0.05). (A) Bar chart of enriched groups. (B) Cladogram depicting taxonomic lineages of enriched microbial groups where concentric rings represent hierarchical taxonomy levels.

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Figure 5. Microbial groups that show enrichment in the rhizosphere (green) or in the bulk (red) at low pH (> 4) based on LEfSe results with a LDA threshold of 3.0 (p <

0.005). (A) Bar chart of enriched groups. (B) Cladogram depicting taxonomic lineages of enriched microbial groups where concentric rings represent hierarchical taxonomy levels.

Figure 6. PCoA plot of beta diversity of bacterial communities from buffalo grass bulk and rhizosphere substrate collected from Phase 1 and Phase 3. Beta diversity was based on unweighted UniFrac distances.

Figure 7. Alpha diversity of microbial communities from buffalo grass bulk and rhizosphere samples from low pH (< 4) and high pH (> 4) substrate. Different letters indicate significant difference (ANOVA, p < 0.05). R = Rhizosphere; B = Bulk

Figure 8. Bubble plot showing the relative abundance (depicted by size) and activity

(depicted by color) of top abundant OTUs present in the bulk and rhizosphere of buffalo grass that are putative plant-growth promoting, nitrogen-fixing, sulfur-oxidizing, iron- oxidizing, and iron-reducing bacteria. Putative functions of OTUs are based on known functions of species that each OTUs matches with the closest based on BLAST searches.

Samples are ordered in ascending pH of bulk (on the left) and rhizosphere (on the right).

Activity values are only comparable within each OTU and not between OTUs.

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Figure 9. Heatmap of Spearman correlation coefficients between relative abundance of putative N2-fixing and PGPB OTUs and geochemical/plant status parameters in the (A) bulk and (B) rhizosphere of buffalo grass growing in pyritic metalliferous mine tailings.

The black box encompasses the correlations with pH, which is the focus of this paper. * p

< 0.05 (raw); ** significant after FDR analysis (α = 0.05); TOC = total organic carbon;

TN = total nitrogen; EC = electrical conductivity

Figure 10. Heatmap of Spearman correlation coefficients between activity (calculated based on 6S rRNA:16S rRNA gene ratio) of putative N2-fixing and PGPB OTUs and geochemical/plant status parameters in the (A) bulk and (R) rhizosphere of buffalo grass growing in pyritic metalliferous mine tailings. The black box encompasses the correlations with pH, which is the focus of this paper. * p < 0.05 (raw); ** significant after FDR analysis (α = 0.05); TOC = total organic carbon; TN = total nitrogen; EC = electrical conductivity

Figure 11. Heatmap of Spearman correlation coefficients between relative abundance of putative Fe/S oxidizing and Fe reducing OTUs and geochemical/plant status parameters in the (A) bulk and (B) rhizosphere of buffalo grass growing in pyritic metalliferous mine tailings. The black box encompasses the correlations with pH, which is the focus of this paper. * p < 0.05 (raw); ** significant after FDR analysis (α = 0.05); TOC = total organic carbon, TN = total nitrogen, EC = electrical conductivity

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Figure 12. Heatmap of Spearman correlation coefficients between activity (calculated based on 16S rRNA:16S rRNA gene ratio) of putative Fe/S oxidizing and Fe reducing

OTUs and geochemical/plant status parameters in the bulk and rhizosphere of buffalo grass growing in pyritic metalliferous mine tailings. Some OTUs are missing because of multiple ratios being uncalculatable due to a 16S rRNA abundance of zero. The black box encompasses the correlations with pH, which is the focus of this paper. * p < 0.05

(raw); ** significant after FDR analysis (α = 0.05); TOC = total organic carbon; TN = total nitrogen; EC = electrical conductivity

Figure 13. nifH gene abundance in buffalo grass bulk and rhizosphere samples from low pH (< 4) and high pH (> 4) substrate. Different letters indicate significant difference

(ANOVA, p < 0.05). R = Rhizosphere; B = Bulk

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8. Figures

Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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Figure 5.

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Figure 6.

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Figure 7.

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Figure 8

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Figure 9.

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Figure 10.

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Figure 11.

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Figure 12.

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Figure 13.

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APPENDIX B

BACTERIAL ROOT COLONIZATION PATTERNS OF BUCHLOE

DACTYLOIDES GROWING IN METALLIFEROUS MINE TAILINGS REFLECT

PLANT STATUS AND GEOCHEMICAL CONDITIONS

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Bacterial Root Colonization Patterns of Buchloe Dactyloides Growing In

Metalliferous Mine Tailings Reflect Plant Status and Geochemical Conditions

Linnea K. Honeker1,2, Julia W. Neilson1,3,*, Juliana Gil-Loaiza1,4, Jon Chorover1,5, and Raina M. Maier1,6

1Department of Soil, Water, and Environmental Science, P.O. Box 210038, University of Arizona, Tucson, AZ, 85721, [email protected], [email protected], [email protected], [email protected], [email protected]

In revision Microbial Ecology

Submitted 11/2/16

Running Head:

Root colonization patterns in mine tailings

Keywords: Phytostabilization, Rhizosphere, Root surface, Plant-microbe associations, FISH, iTag,

*Corresponding author

Email: [email protected]; Phone: 520-621-9759; Department of Soil, Water, and

Environmental Science, P.O. Box 210038, University of Arizona, Tucson, AZ 85721

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Abstract

Plant establishment during phytostabilization of legacy mine tailings in semiarid regions is challenging due to extreme conditions including low pH, low organic carbon, low nutrients, and high toxic metal(loid) concentrations. Plant-associated bacterial communities are particularly important in these harsh conditions because of their beneficial services to the plant. We hypothesize that the bacterial colonization profiles on root surfaces are not random. The aim of this study was to identify potential associations between the bacteria colonizing surfaces of buffalo grass (Buchloe dactyloides) roots and both plant status (leaf chlorophyll and plant cover) and substrate geochemistry (pH, electrical conductivity, total organic carbon, and total nitrogen). Buffalo grass roots were analyzed from mesocosm- and field-scale phytostabilization studies conducted with tailings from the Iron King Mine and Humboldt Smelter Superfund Site in Dewey- Humboldt, Arizona. These tailings are extremely acidic and have total arsenic and lead concentrations of 2 – 4 g kg-1 substrate. Bacterial communities on root surfaces and rhizosphere-associated substrate were characterized using fluorescence in situ hybridization and 16S rRNA gene amplicon sequencing, respectively. The results indicate that the metabolic status of bacterial root-colonizers is significantly related to plant health. Principal component analysis revealed that root surface Alphaproteobacteria relative abundance was associated most strongly with substrate pH and Gammaproteobacteria relative abundance associated strongly with substrate pH and plant cover. These factors also affected the phylogenetic profiles of the associated rhizosphere communities. In summary, bacterial colonization patterns on the root surfaces are plant specific and influenced by plant status and rhizosphere geochemical conditions.

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1. Introduction

Metal(loid) contamination due to anthropogenic activities, such as mining, poses a threat to the surrounding environment and human health. In particular, legacy mine tailing ponds or piles, which consist of the residual ore left behind after mining at historic sites, are susceptible to wind and water erosion, facilitating contaminant transport.

Revegetation can be exploited to reduce the spread of contaminants via wind and water erosion, but technologies must be developed to facilitate plant growth in these harsh materials. Typically a barrier such as a soil or plastic cap has been used to isolate mine tailings from plants [1], however, phytostabilization provides an alternative method in which a plant cover is established directly into the tailings without a soil cap to promote the immobilization of metal(loid)s in the root zone [2]. In addition to being less costly

[2,3], phytostabilization is less destructive because it does not necessitate soil excavation from surrounding lands. However, plant establishment during phytostabilization can be difficult, particularly in pyritic mine tailings that weather under semiarid conditions, due to acidic pH, low organic carbon content, low nutrients, hypersalinity, low moisture, poor substrate structure, and an autotroph-dominated, acid-generating, microbial community

[2,4,5,6,7]. Often, the addition of organic amendment is required, such as compost, manure, or biosolids, which offers many benefits including a source of organic carbon and nutrients, pH neutralization, increased water holding capacity, improved aggregate structure, and the addition of a heterotrophic microbial inoculum [2,4,8,9]

Research concerning plant-associated microbial communities present under stressed conditions, such as those characteristic of pyritic mine tailings, is key to understanding how plant-microbe interactions can be exploited to help sustain plants in

128 stressed environments. Direct interaction between plants, microorganisms, and tailings materials occurs in the rhizosphere, which is defined as the root surface and the root- exudate impacted area surrounding the root. Root exudates consisting of organic compounds such as polysaccharides, sugars, amino acids, and fatty acids [10,11,12,13] create the “rhizosphere effect” that is characterized by a 10- to 100-fold greater abundance of microbes in the rhizosphere as compared to the bulk soil [14]. Previous research does indicate that rhizosphere bacteria, specifically plant growth promoting bacteria (PGPB), are particularly important for plant growth and establishment under extreme conditions of the mine tailings environment [15,16,17,18,19,20,21]. PGPB enhance plant growth by 1) increasing nutrient availability through phosphorous solubilization, production of iron-chelating siderophores, and nitrogen fixation; 2) decreasing the plant-stress hormone ethylene through production of ethylene-cleaving

ACC (1-aminocyclopropane-1-carboxylate) deaminase; 3) providing protection from pathogens; and 4) decreasing metal bioavailability to plants [22,23,24]. These services are crucial in the nutrient-limited, stressed conditions of mine tailings and facilitate positive plant-microbe feedbacks.

We contend that understanding the factors controlling microbial colonization patterns on the root surface will provide important insights into the significance of plant- microbe associations to plant survival in acidic mine tailings. Despite the importance of rhizosphere bacteria to plant health and establishment, studies examining bacterial colonization on the root surfaces of plants grown in mine tailings are lacking.

Fluorescence in situ hybridization (FISH) in combination with confocal scanning electron microscopy (CLSM) has been used to study bacterial root-colonization in typical soils

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[25,26,27] and has been adapted for roots grown in compost-amended mine tailings

[18,19,28,29]. In a comparison of Buchloe dactyloides (buffalo grass) grown in unamended vs compost-amended (10%) mine tailings, increases in bacterial root coverage and plant biomass of 3.6 to 18.9% and 0.045 g pot-1 to 0.18 g pot-1, respectively, were observed with compost amendment [28]. These results show an association between domain-level bacterial root colonization and either external substrate or plant condition. However, phylum/subphylum-level bacterial root colonization patterns by bacteria during phytostabilization of mine tailings are poorly understood, especially at the field scale.

In this study, we combined FISH and CLSM to analyze bacterial root colonization of buffalo grass used in the phytostabilization of metalliferous, pyritic mine tailings, and we found associations between colonization patterns and geochemical and plant conditions. We hypothesized that bacterial root colonization is not random, but reflects both plant status and external geochemical parameters. We differentiate between the rhizosphere microbial populations colonizing the root surface to be referred to as root- colonizers and those present in the root-impacted soil surrounding the root which are labelled rhizosphere bacteria. Therefore, the aims of this study were to: i) quantify bacterial root colonization of buffalo grass at the domain and phylum/subphylum-level; ii) identify associations between bacterial root colonization and both plant status (leaf chlorophyll and plant cover) and geochemical parameters (pH, total organic carbon

[TOC], total nitrogen [TN], and electrical conductivity [EC]); and iii) evaluate associations between rhizosphere microbial community composition, profiled by high throughput sequencing of 16S rRNA gene amplicons, and root colonization patterns. We

130 first examined bacterial colonization patterns from buffalo grass roots grown in a controlled 12 month mesocosm experiment, and then extended this analysis to a field- scale phytostabilization trial. This study provides new insights into associations between bacteria colonizing the root surface and plant status in the stressful mine tailing environment.

2. Methods

2.1 Iron King Mine and Humboldt Smelter Superfund (IKMHSS) site description

The Iron King Mine and Humboldt Smelter Superfund (IKMHSS) site is located in the town of Dewey-Humboldt, AZ. Mining operations were carried out between 1906 and 1969 extracting Cu, Ag, and Au [30], but leaving high residual tailings concentrations of toxic metal(loid)s including As and Pb, at 3.1 and 2.2 g kg-1 tailings respectively [31]. Oxidation of the pyritic tailings in the top 25 cm has produced acidic conditions (pH of 2.3 – 2.7) [6]. The tailings are characterized by low organic carbon content (0.14 g kg-1), hypersalinity (EC 6.5-9.0 ds m-1), poor substrate structure [6,7], and the complete absence of vegetation.

2.2 Mesocosm Study

2.2.1 Controlled greenhouse experiment

A controlled greenhouse experiment conducted to evaluate phytostabilization of mine tailings from IKHMSS was exploited for the current study to assess bacterial root colonization patterns in a controlled system undergoing defined acidification. As described previously [7,32], the 12-month greenhouse experiment was conducted at the

131

Controlled Environment Agriculture Center at the University of Arizona in highly instrumented polypropylene mesocosms that were 1 m in diameter and 0.5 m deep and instrumented with pore water samplers at 10 cm intervals from 5 – 35 cm along the depth of the mesocosm. For this bacterial root colonization study, buffalo grass grown in tailings amended with 15% compost (w/w) was examined. Buffalo grass germination was observed within the first week of the experiment and plants grew well for 3 months. After

3 months, plants showed signs of stress and severe die-back was observed by 12 months.

2.2.2 Root sample collection

Roots from each of the three buffalo grass replicate mesocosms were collected at

3, 6, and 9 months following planting. To collect the roots, the top of the plant was cut off at the surface of the substrate and then a corer measuring 2 cm in diameter and 10 cm in length was inserted directly over the truncated plant to enable sampling of the root plus rhizosphere substrate [7]. A small section of root sample was cut off and treated to fix root-colonizing bacteria, as described previously [28,29]. Briefly, the root sections were placed into 4 °C, sterile 4 % paraformaldehyde (PFA) in phosphate-buffered saline solution (PBS; 7 mM Na2HPO4, 3 mM NaHPO4 and 130 mM NaCl, pH 7.2) and processed in the laboratory within 12 h of collection as follows. Roots were rinsed twice in phosphate buffered saline (PBS), then stored in a 1:1 mixture of PBS and 100 % ethanol at -20 °C until FISH analysis.

2.2.3 Mesocosm plant status (plant cover) and pore water geochemical parameters

Plant cover was determined for each of the buffalo grass mesocosms at each of the collection times (3, 6, and 9 months). A photograph of each mesocosm at the

132 specified time was imported into the program ImageJ [33] for digital analysis of percent plant cover.

Pore water was collected continuously at 5 and 15 cm depths and the depths were consolidated for chemical analysis. Pore water subsamples collected at 0, 3, 6, and 9 months were analyzed for pH, dissolved organic carbon (DOC), dissolved nitrogen (DN), and electrical conductively (EC) as described previously [7,34].

2.3 Field study

2.3.1 IKHMSS Phase 1 and Phase 3

The IKHMSS field trial, consisting of 3 individual experiments, or phases, was designed to evaluate multiple direct planting strategies for native plant species in compost-amended tailings [35]. Phase 1, initiated in May 2010, was designed to study different compost amendment rates and seeded with six native plant species in semi- random plots. For the current bacterial root colonization study, buffalo grass roots from the 15% compost (w/w in the top 20 cm) treatment were examined. Phase 3, initiated in

June 2012, was designed to study the effectiveness of buffalo grass or quailbush monocultures situated in rows. The entire area was amended with 15% compost (w/w in the top 20 cm) with lime (2.0622 kg m-2). For the current bacterial root colonization study, buffalo grass plants from the 3 replicates of the buffalo grass treatment were examined.

2.3.2 Sample collection

Eight buffalo grass plants were sampled from Phase 1 (Plot 5a and b, Plot 10a and b, Plot 19a and b, and Plot 24a and b) and eight from Phase 3 (Row 2a and b; Row 4a, b,

133 and c; and Row 6a, b, and c) in August 2013. After collection, root samples were fixed immediately, as described above. In addition, following the removal of the root sample, the entire core was placed in a sterile bag for rhizosphere DNA analysis. Corers were sterilized with 95% ethanol before each sample collection. Additionally, a surface tailings sample from the top 20 cm was collected next to the root and stored in a plastic bag for substrate chemical analysis.

2.3.3 Field study plant status and substrate geochemical analysis

The following field measurements were used to quantify plant establishment and status. First, as a measure of plant establishment, plant canopy cover was estimated using transect and quadrat sampling in October 2013, 2 months after buffalo grass root and rhizosphere collections, for each Phase 1 field plot and Phase 3 row section from which a plant was harvested [36,37] by Gil-Loaiza et al. [35]. Second, leaf chlorophyll levels were measured for each plant as an indication of individual plant stress [38,39,40] at the time of root and rhizosphere collection using a handheld portable chlorophyll meter

(atLEAF; Wilmington, DE) which measures light transmittance. Higher measured values correspond to healthier plants within the same species.

Rhizosphere tailings were analyzed for pH, TOC, TN, and EC. Each rhizosphere sample was sieved at 2 mm and dried at 65 ° for 72 h. The pH and EC were measured from a 1:2 mass ratio extract of tailings to ultrapure DI water (18.2 MΩ, Milli-Q) that was mixed for 30 minutes prior to measurement. The pH was measured in the homogenized extract and the EC was determined from the supernatant after the substrate was allowed to settle for 10-15 minutes. Analyses of TOC and TN were performed on milled subsamples of the dried rhizosphere material (Shmiadzu TOC analyzer, Columbia,

134

MD) using a solid state module (SSM), which utilizes a dry combustion under oxygen with detection by a non-dispersive infrared (NDIR) for total carbon and chemoluminescence for TN.

2.4 Fluorescence in situ hybridization (FISH)

Bacterial colonization of buffalo grass roots was assessed with FISH using probes chosen to target major phyla/subphyla present in the IKHMSS buffalo grass rhizosphere, as presented in Honeker et al., [29]. The FISH protocol was adapted from Watt et al. [25] and Iverson and Maier [28] and is described in detail in Honeker et al. [29]. Briefly, a fixed root was immobilized on a glass slide and incubated in hybridization buffer with fluorescently-labelled probes (Eub338-CY5 mix to target the domain Bacteria plus one of the following specific probes: HGC69a-CY3 for Actinobacteria, Alf968-CY3 for

Alphaproteobacteria, or Gam42a-CY3 for Gammaproteobacteria; probe sequences are from ProbeBase [41]) for 2-2.5 h at 46 °C followed by incubation in wash buffer for 15 min at 48 °C. Slides with roots were rinsed in ice-cold DI water, dried using compressed air, and then placed in the dark at room temperature until completely dry. Slides were stored in a dark slide box with desiccant at -20 °C until viewing.

2.5 Confocal laser scanning microscopy (CLSM)

Prior to viewing, slides were allowed to equilibrate to room temperature. As a counterstain, 40 µL of 500 μM SytoBC nucleic acid stain (Molecular Probes, OR) in 10

μM Tris-HCl at pH 8.0 was added to each root and incubated at room temperature for 20 min. SytoBC was rinsed off with 10 μM Tris-HCl and excess liquid was removed by

135 wicking with a Kimwipe and gently blowing with condensed air. The slides were then placed in the dark at room temperature until completely dry.

Once dry, 1 drop of AF1 antifadent mountant (Citifluor Ltd., London) was added to the root and then a coverslip (size 1.5, VWR) was placed over the root. Slides were viewed on a Zeiss 510 Meta Confocal Laser Scanning Microscope (CLSM) using a 63x objective (Plan Apochromat, NA 1.3). For viewing SytoBC, CY3 labeled probes, and

CY5 labeled probes, a 488nm Argon laser with a BP 505-570 filter, 543nm HeNe laser with an LP 560 filter, and 633 nm HeNe laser with an LP 650 filter were used, respectively. The captured image consisted of three channels, each representing a different wavelength, and hence, a different probe or the nucleic acid stain (see Fig. 1 for a representative set of images). Each probe has a specific excitation and emission wavelength, and the three laser wavelengths and filters listed were optimally chosen to ensure excitation and image collection of the appropriate probe while avoiding any overlap of probe excitation and emission wavelengths. Images were collected at 1 µm intervals from the surface of the root to a depth of 6 μm.

2.6 Quantitative image analysis

Quantitative analysis of bacterial colonization was performed on FISH images following a stepwise procedure using ImageJ software complete with the MBF

(McMaster Biophotonics Facility) plugin collection [42] specifically designed for analysis of microscopy images. For each of the three channels within the original file representing the SytoBC live-dead stain, the domain Bacteria probe, and the specific group probe, quantification was performed at depths of 2, 4 and 6 μm, that were averaged together after analysis. The SytoBC live-dead stain channel, was used to differentiate

136 fluorescence produced by bacterial cells from that of the background fluorescence or autofluorescence. Bacterial cells and colonies were detected and outlined using the nucleus counter plug-in (a part of the MBF ImageJ plugin collection) with the following settings: particle size set to 5 – 200, automatic subtraction of background, and application of the watershed filter. The outlined “particles”, or in this case bacterial cells, were saved to the ROI (region of interest) manager. Each image was manually checked to confirm that cells selected by the plugin were in fact bacterial cells and to add cells missed by the plugin. The root surface area covered by cells stained with the SytoBC live-dead stain was then calculated. This cell coverage calculation represented both live and dead bacterial cells. Second, channel 2, the bacteria probe channel, was analyzed using the library of stored cells identified based on the SytoBC stain and saved to the ROI manager. Each cell was individually queried for detection with the domain Bacteria probe. Cells not detected with the Bacteria probe were deleted. Remaining data from channel 2 were used to calculate the total area of metabolically active bacteria cells.

Third, the same step was repeated with channel 3 images for the phylum/subphylum specific probe. This step-wise colocalization strategy decreased the chance of including autofluorescence into the analysis and ensured that specific cells (Alphaproteobacteria,

Gammaproteobacteria, and Actinobacteria) were also stained with the Bacteria probe and SytoBC nucleic acid stain.

Three quantitative measurements were then recorded as follows. The relative abundances of Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria were calculated by dividing the area of cells stained by each specific probe by the total area of cells stained with Bacteria probe. The percentage of metabolically active bacteria was

137 calculated by dividing the area of cells stained by the Bacteria probe by the area of cells stained by the SytoBC nucleic acid stain. It should be noted that this may be an over- estimate of metabolically active cells, as was found to be the case by [43]. While active cells can contain abundant rRNA, some species of dormant cells can also contain abundant rRNA, as they prepare for conditions conducive for cellular activity [44]. In this study, we assume that the proportion of potentially dormant cells containing high rRNA levels remain constant across samples, therefore, allowing comparison of metabolically active cells between samples. And finally, bacterial root coverage was calculated by dividing the area of cells stained by the Bacteria probe by the area of the root surface, thus this measurement represents the percent of the root surface colonized by metabolically active bacteria. The above calculations at 2, 4, and 6 μm depths were averaged to get a final value for each root sample.

2.7 PCA of plants from IKMHSS field site

Principal component analysis (PCA) was performed on Phase 1 and Phase 3 samples, separately, in order to identify potential drivers of the species composition colonizing the root surfaces under different field conditions. The PCA analyses were performed using CANOCO 4.5 (Microcomputer Power, Ithaca, NY).

2.8 Microbial phylogenetic analysis (iTag) of plant rhizosphere substrates from

IKMHSS field site

Rhizosphere microbial phylogenetic analysis was used to evaluate the influence of rhizosphere populations on bacterial root colonization patterns of plants harvested from the IKMHSS field site. Microbial community profiles were generated from 16S rRNA

138 gene amplicons from rhizosphere-influenced material collected from each plant harvested from Phase 1 and Phase 3, as described previously [29]. From the rhizosphere-influenced sample, a 0.5 g subsample was subjected to DNA extraction using the FastDNA Spin Kit for Soil (MP Biomedicals; Santa Ana, CA) following the manufacturer’s protocol with modifications to enhance DNA yield as outlined in [7]. Library preparation and sequencing were performed at Argonne National Laboratory (Chicago, IL). Library preparation followed a single-step PCR protocol presented by Caporaso et al. [45] using primers 515f and 806r to amplify the V4 region of the 16S rRNA gene. Sequencing was carried out in a single lane on an Illumina Mi-Seq using v2 chemistry and bidirectional amplicon sequencing (2 x 151bp).

Raw sequence reads were processed using the open source software package

(N) base calls, and 3) having a length of < 0.75 of original length post quality filtering.

After quality filtering, a total of 1,863,169 high quality sequences, with an average number of 116,448 ± 30,667 sequence reads per sample remained. Using a sequence cutoff of 30,000 sequences, one sample (Row4b) from Phase 3 was removed from further analysis. Sequences were clustered into operational taxonomic units (OTUs) of greater than or equal to 97% similarity using UCLUST [46]. During DNA extraction, blanks that contained no tailings sample were extracted alongside each batch of samples for quality

139 control and subsequently sequenced. OTUs contained in the sequenced blanks were removed from the associated samples extracted in the same batch to minimize influence of contamination. Representative sequences from each OTU were aligned using PyNAST

[47] and assigned taxonomy using the UCLUST classifier and Greengenes 16S rRNA gene database [48]. Sequence data have been deposited in the NCBI Sequence Read

Archive (SRA) database under the accession number SRR4462508.

3. Results

3.1 Mesocosm study

Buffalo grass plants grown in mesocosms during a highly controlled greenhouse experiment were analyzed to examine bacterial root colonization during acidification of metalliferous pyritic tailings. A significant temporal decrease in both the relative abundance and root coverage of metabolically active bacteria was observed at 6 months

(p < 0.05) (Table 1). A concurrent increase in the relative abundance of

Gammaproteobacteria occurred at 6 months (p < 0.05) while Alphaproteobacteria and

Actinobacteria relative abundance did not change significantly with time (Table 1). These observed temporal changes in bacterial root colonization were associated with significant decreases in percent plant cover at 6 months (p < 0.05) and pH at 9 months (p < 0.05)

(Table 1). Pore water DOC, DN, and EC showed no significant change with time (Table

1). These results reveal that temporal changes in relative abundance and root coverage of metabolically active bacteria, and the relative abundance of Gammaproteobacteria on the root surface were associated with changes in substrate pH and plant growth status.

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3.2 Root colonization of buffalo grass from the IKHMSS field trial

After using FISH to observe buffalo grass root bacterial colonization patterns during acidification of 15% compost amended (w/w) IKHMSS mine tailings in a controlled meoscosm study, the bacterial colonization of buffalo grass roots from

IKHMSS Phase 1 and Phase 3 field trials (15% compost amended [w/w]) was characterized. At the time of sampling, the Phase 1 and Phase 3 field trials represented three and one years of growth, respectively. Eight plants from each site were sampled, but one Phase 3 root degraded during transport and could not be analyzed (Row 2c).

Chlorophyll levels from each plant were used to separate the plants into two categories; high chlorophyll with values of 32 – 50, and low chlorophyll with values of 8 – 16.

Chlorophyll level was used as a proxy for plant status at the time of sampling with high chlorophyll levels representing robust plants and low chlorophyll levels representing stressed plants (Table 2). The relative abundance of metabolically active bacteria on the root surface was significantly higher for the plants with high chlorophyll levels compared to that of the plants with low chlorophyll levels (p < 0.05). This pattern is consistent with the mesocosm observation that the relative abundance of metabolically active bacteria on root surfaces is positively associated with plant cover. The average root coverage for metabolically active bacteria was also higher on the roots from the plants with high chlorophyll levels, however this trend was not significant. An examination of the specific phyla colonizing the root surfaces showed no significant trend associated with chlorophyll level.

A comparison was then made between Phase 1 and Phase 3 plants. Recall that major differences between phases include: 1) difference in time of implementation, and

141 therefore age of plants and compost amendment, with Phase 1 starting in May 2010 and

Phase 3 starting in June 2012; 2) addition of lime in Phase 3 versus no lime in Phase 1; and 3) Phase 1 seeded with mixed plant species while Phase 3 seeded with a monoculture of buffalo grass. Plants sampled from each phase included a broad range of chlorophyll content (Table 3) indicating that a comparable range of plant health was sampled from both field trials. Further results show that the relative abundance of Actinobacteria was significantly higher on the roots of Phase 1 plants compared to Phase 3 (p < 0 .05). The other bacterial root colonization measurements were not significantly different between the two experiments. There was high variability in both the percent root coverage as well as the relative abundance of specific phyla/subphyla colonizing root surfaces within

Phase 1 and Phase 3 (CV = 0.5 - 0.8). The percent of metabolically active bacteria varied little between Phase 1 and 3 (CV = 0.1 for Phase 1 and Phase 3) (Table 3). Nutrient status

(TOC and TN) and pH were significantly higher (p < 0.05), and more favorable, in Phase

3 than Phase 1 (Table 3). EC was also significantly higher in Phase 3 than phase 1 (p <

0.05) (Table 3), which can be attributed to the more recent addition of compost in Phase

3. Plant cover was lower and more variable (coefficient of variation [CV] = 0.6) in Phase

1 than Phase 3 (CV = 0.4), but the difference was not significant (Table 3). This trend may reflect the observed difference in geochemical parameters between Phase 1 and

Phase 3.

Next, the impacts of rhizosphere geochemistry and plant condition were evaluated as explanatory variables for the high within treatment variability in root colonization patterns for different plants from Phases 1 and 3. PCA was performed using the relative abundances of the different phyla/subphyla on the root surfaces. In Phase 1,

142

Alphaproteobacteria relative abundance was negatively associated with TN, pH, and plant cover along PC1 (x axis; explaining 72.9% of the variation) (Figure 2a).

Gammaproteobacteria relative abundance was positively associated with EC and negatively associated with plant cover. Actinobacteria were not highly associated with any of the plant status or geochemical parameters. Overall for Phase 1, the geochemical and plant status variables explained 97.7% of the total variation in bacterial composition

(Figure 2a) with plant cover followed by pH being the most significant explanatory variables. In contrast, in Phase 3, Alphaproteobacteria relative abundance on root surfaces was positively associated with TN, TOC, pH, chlorophyll, and plant cover along

PC1 (83.3% of the variation). Gammaproteobacteria and Actinobacteria were positively associated with EC and negatively associated with pH. In Phase 3, the geochemical and plant status parameters explained 95.8% of the total variation in bacterial composition

(Figure 2b), with pH, TOC, and EC being the largest drivers. In summary,

Alphaproteobacteria relative abundance on root surfaces was positively associated with pH, TN, and plant cover in Phase 3, but negatively associated with the same parameters in Phase 1. Second, strong negative associations are observed for Gammaproteobacteria relative abundance with plant cover in Phase 1 and with pH in Phase 3 (Figures 2a and

2b). These results suggest that Gammaproteobacteria root colonization increases under conditions of stress, a finding that is consistent with results from the mesocosm study which showed a significant increase in relative abundance of Gammaproteobacteria concurrent with a decrease in buffalo grass plant cover from 73% to 10%.

3.3 Associations between the rhizosphere microbial community and root surface colonization

143

Phylogenetic profiles of the rhizosphere bacterial communities were explored as potential drivers of the observed root colonization patterns. The rhizosphere communities represent the source from which the root-colonizing bacteria might migrate or be recruited by the plant to the root surface. In contrast to root surfaces which had high variability in the relative abundance of Alphaproteobacteria colonizing the root surface, the relative abundance of rhizosphere Alphaproteobacteria did not vary significantly with pH in Phase 1 or Phase 3; however, the phylogenetic profiles at the family level were strikingly different between the two phases and varied significantly with pH (Figure 3).

In Phase 1, the buffalo grass rhizosphere was dominated by the family Acetobacteraceae at low pH (Figure 3c). Overall, the Phase 3 buffalo grass rhizospheres had a significantly lower relative abundance of Acetobacteraceae (p < 0.05) than Phase 1 and, at lower pH, had the highest relative abundance of Sphingomonadaceae. At higher pH (> 6), there was a consistent pattern in both Phase 1 and Phase 3 of increased relative abundance

Hyphomicrobiaceae, Phyllobacteraceae, and Rhizobiales as well as an unknown

Alphaproteobacteria in the rhizosphere (Figures 3c and 3d). Taken together, these results suggest that Acetobacteraceae was the dominant family influencing Alphaproteobacteria root colonization under low pH conditions, whereas Hyphomicrobiaceae,

Phyllobacteraceae, Rhizobiales, and the unknown Alphaproteobacteria were the primary groups potentially influencing root colonization at pH > 6.

Gammaproteobacteria relative abundance on root surfaces was influenced most by plant cover in Phase 1 and pH in Phase 3 (Figures 2a and 2b). The greatest relative abundance was observed in areas of low plant cover (Figure 4a) during Phase 1, and low pH in Phase 3 (Figure 5a). Similar to Alphaproteobacteria, the high variability of

144

Gammaproteobacteria on the root surface does not parallel the relative abundance of

Gammaproteobacteria in the rhizosphere microbial community (Figures 4b and 5b).

However, major differences were observed in the phylogenetic composition of the rhizosphere Gammaproteobacteria communities of Phase 1 and Phase 3 plants.

Xanthomondaceae and Sinobacteraceae comprised between 71.7 – 99.8% of total

Gammaproteobacteria for all Phase 1 plant rhizospheres with the exception of the plant harvested from Plot 10a (30.4% relative abundance) (Figure 4b). In Phase 3, the

Gammaproteobacteria were more diverse and also had large representation from the

Alteromonadaceae and Pseudomonadaceae families and the Chromatiales order (Figure

5b). Of interest is the unique Gammaproteobacteria rhizosphere community profile for the plant harvested from Row 2b that is associated with the lowest rhizosphere pH, the highest relative abundance of root colonization, and the highest relative abundance of

Acidithiobacillaceae in the rhizosphere. The observed variation in rhizosphere

Gammaproteobacteria community phylogenetic profiles for Phase 1 and Phase 3 confirms that distinct groups of Gammaproteobacteria influence colonization of the root surfaces of buffalo grass plants under the different conditions in these two field trials.

4. Discussion

This study provides the first evidence for a deterministic rather than random pattern of bacterial root colonization of plants grown in metalliferous mine tailings. The results show that the relative abundance and root coverage of metabolically active bacteria and the relative abundance of specific subphyla reflect substrate geochemistry and rhizosphere community composition. As will be discussed below, these findings suggest key insights into potential roles that the bacteria colonizing the root surface play

145 in plant adaptation and survival in the extreme conditions of the mine tailings.

Knowledge of these relationships can be exploited to develop novel technologies to advance the long-term success of mine-tailing phytostabilization.

4.1 Metabolically active bacteria

It is a significant finding that the relative abundance of metabolically active bacteria on the root surface correlates with plant health. In the mesocosm study, as plant cover significantly decreased between 3 and 6 months, there was a coinciding decrease in relative abundance of metabolically active bacteria colonizing the root surface (Table 1).

We hypothesize that the degree of plant cover is an indicator of plant health and establishment, suggesting a possible link between plant health and presence of metabolically active bacteria. To further test this hypothesis, plants harvested from the field study were categorized according to chlorophyll levels as a measure of plant status.

More vigorous plants, with high leaf chlorophyll levels, were found to have a significantly higher relative abundance of metabolically active bacteria (p < 0.05), supporting our hypothesis that the relative abundance of metabolically active bacteria on the root surface correlates with plant health. This correlation is likely driven by a combination of increased plant root exudation of healthy plants creating a more abundant food source for bacteria, and in exchange, more active bacteria providing beneficial services that improve plant fitness. In fact, previous studies have shown that plant health and growth can lead to an increase in root exudation which has been found to positively correlate with rhizosphere microbial activity [49,50,51].

4.2 Subphyla

146

The significant relationship observed between plant health and root-colonizing bacteria led to a more in-depth interrogation into relationships between the relative abundance of specific subphyla colonizing the root surface and geochemical and plant status parameters. Most pronounced is the contrasting pattern of Alphaproteobacteria, which is negatively associated with pH and TN in Phase 1, and is positively associated with pH, TOC, and TN in Phase 3. This interesting contrast in pattern reflects the significantly different geochemical conditions of Phase 1 and Phase 3 (p < 0.05) (Table

3), most notable between pH, TOC, and TN. As mentioned previously, Phase 1 was implemented in 2010 and Phase 3 in 2012. At the time of sample collection in 2013, the compost amendment in Phase 3 was still abundantly present helping to maintain a buffering effect and source of organic matter. However, Phase 1 compost levels were lower, leading to lower organic matter content and a decrease in buffering capacity that was associated with re-acidification of the tailings material (Table 3). A second key pattern is the increase in relative abundance of Gammaproteobacteria on the root surface under stressed conditions, as evidenced by the negative association with plant cover in

Phase 1 and pH in Phase 3.

The phylogenetic composition of the Alphaproteobacteria and

Gammaproteobacteria within the rhizosphere substrate material reveals striking patterns that correspond with root colonization patterns. The relative abundance of both subphyla colonizing the root surface shows high variation; however, this variation is not explained by differences in the respective abundances of these groups in the Phase 1 and Phase 3 rhizosphere material. Despite this, the phylogenetic profiles of the rhizosphere

Alphaproteobacteria and Gammaproteobacteria in Phase 1 and 3 are quite distinct and

147 vary with geochemical conditions suggesting that differential root colonization patterns could be explained by either selective recruitment from the distinct rhizosphere communities by the plant or preferential colonization of the plant root by rhizosphere bacteria. Whether differential bacterial root colonization is primarily plant-driven or microbially-driven is difficult to determine based on this experimental study.

4.2.1 Alphaproteobacteria

The relative abundance of Alphaproteobacteria root-colonization is negatively associated with pH in Phase 1 and positively associated with pH in Phase 3.

Phylogenetically distinct rhizosphere Alphaproteobacteria communities were also observed in Phase 1 vs. Phase 3. Alphaproteobacteria communities in the Phase 1 rhizosphere are increasingly dominated by acidophilic Acetobacteraceae as the pH declines, of which Acidiphilium is the dominant genus (39.5 – 91.7%). Species within the genus Acidiphilium are known to reduce Fe, and at least one species, Acidiphilum mulitvorum, is known to oxidize As(III) [52,53]. Therefore, we hypothesize that

Acetobacteraceae, including Acidiphilium, are colonizing the root surface and participating in Fe and/or As cycling, and possibly even immobilizing these metal(loid)s on the root surface. In a previous study, up to 27.4% of Alphaproteobacteria were colocalized with Fe/As plaques on buffalo grass roots from Plot 24 in Phase 1 [29]. In contrast, Alphaproteobacteria phylogenetic communities in Phase 3 are dominated by

Hyphomicrobiaceae, Phyllobacteraceae, and other Rhizobiales at higher, more neutral, pH, when the relative abundance of Alphaproteobacteria on the root surface is high.

According to iTag sequencing results, the family Hypohomicrobiaceae primarily consists of the genus Devosia (43.0 – 92.6%), a known PGPB with the capacity for nitrogen-

148 fixation [52,54]. The family Phyllobacteraceae also contains many PGPB species capable of nitrogen-fixation [52]. We hypothesize that these PGPB are the

Alphaproteobacteria colonizing the root surfaces in Phase 3, which would explain the positive association of Alphaproteobacteria on the root surface with the favorable nutrient status, as indicated by TN and TOC, and more neutral conditions. The stark difference in Alphaproteobacteria phylogenetic profiles in Phase 1 and Phase 3 coupled with the opposing trend of root-colonizing Alphaproteobacteria relative abundance in relation to pH suggest that plant-microbe associations fluctuate with changing geochemical conditions.

4.2.2 Gammaproteobacteria

Phylogenetic profiles within Gammaproteobacteria rhizosphere communities in

Phase 1 and Phase 3 also reveal an interesting trend in relation to Gammaproteobacteria root colonization. In the more stressed conditions of low pH and low plant cover where there is a higher relative abundance of Gammaproteobacteria colonizing the root surface,

Acidithiobacillaceae (100% belonging to the genus Acidithiobacillus) are present in the rhizosphere at a higher relative abundance, indicating that these may be selectively recruited by the plant or preferentially colonizing the root surface under these conditions.

Species within the genus Acidithiobacillus, most notably A. ferrrooxidans and A. thiooxidans, are known to oxidize Fe and S, respectively [52]. We hypothesize

Acidithiobacillus are colonizing the root surface and contributing to ferric Fe plaque formation. Using the method for quantifying metal(loid) and bacteria colocalization on root surfaces presented in Honeker et al. [29], we found that 12.4 - 47.9% of

Gammaproteobacteria colocalized with Fe on roots from the mesocosm study at 6

149 months (Supplementary Figure 1) characterized by low plant cover (11.9%), which supports this hypothesis. It is interesting to point out that root sample Plot 24b, which was associated with an acidic pH and low plant cover, had high abundance of

Alphaproteobacteria and Gammaproteobacteria (37.8 and 50.3%, respectively). Abiotic

Fe oxidation kinetically occurs relatively slowly at a low pH, whereas bacterially- mediated transformation is favored. Under these conditions, poorly crystalline Fe(III) oxides are created, such as ferrihydrite, which we have previously shown to be the mineral phase constituting the Fe plaque on roots from IKMHSS [29]. Together, these bacteria groups can create a rapid cycling of Fe in the root zone, involving the alphaproteobacterium Acidiphilium as the Fe reducer and Gammaproteobacteria

Acidithiobacillus as the Fe oxidizer [55].

4.3 Conclusions

The results of this study reveal that bacterial root colonization patterns are variable within a single plant species in reflection of geochemical and plant status parameters. The combined use of iTag bacterial community analysis of rhizosphere substrate populations and FISH profiling of root surface bacteria enabled a characterization of the bacterial taxa colonizing the root surface, with possible explanations for influences that these plant-microbe associations may have on plant health under harsh environmental conditions. Taken together, the results of this study suggest a model in which buffalo grass roots from neutral compost-amended tailings material are colonized predominantly by PGPB, whereas roots from plants surviving in a more highly acidic, weathered tailings/compost mixture with significantly lower TOC and TN levels have colonization patterns dominated by metal-cycling bacteria. The

150 significance of these patterns is not known, but we hypothesize that they influence plant survival. The latter field conditions result over time following the erosion and mineralization of compost amendments which lead to re-acidification and reduced nutrient levels. Based on the current study, it is difficult to determine whether the bacterial colonization patterns observed are plant-driven (i.e. due to changing exudates) or are driven by changes in geochemical conditions which directly affect the bacterial communities both on the root surface and in the rhizosphere. Most likely, it is a combination of both, as has been reported previously [56].

The characterization of specific plant-microbe associations during phytostabilization provides cues for understanding plant survival in the extreme environment of metalliferous mine tailings that can be exploited to develop bacterial inoculum-based technologies to help boost plant health under various suboptimal field conditions. Furthermore, these associations may provide insights for evaluating the success and establishment of plants during phytostabilization of metalliferous mine tailings.

5. Acknowledgements

We thank Scott White for his work in designing and implementing the IKHMSS field study. We thank Steven Schuchardt, president of North American Industries, for providing access to the IKMHSS site and help with irrigation. We thank all volunteer students from Environmental Microbiology, Environmental Biochemistry, and

Contaminant Transport Labs for their help during field implementation and sampling

151 trips, and especially Corin Hammond for her dedicated work in helping to organize as well as participate in these sampling trips. We also thank Alexis Valentín-Vargas for collecting and fixing buffalo grass root samples from the mesocosm study that were used in the current study. This work was supported by the National Institute of Environmental and Health Sciences (NIEHS) Superfund Research Program (SRP) grant P42 ES004940 and R01 ES01709 and the National Science Foundation Graduate Research Fellowhip

Program (NSF GRFP) grant DGE-1143953.

152

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7. Tables Table 1. Mesocosm study geochemical, plant status, and bacterial root colonization data. Values represent averages of buffalo grass mesocosm replicates with standard deviations (n=3 at each time point) and coefficients of variation in parentheses. Parameters 3 months 6 months 9 months pH 5.7 ± 0.5 (0.1)a 6.3 ± 0.5 (0.1)a 4.1 ± 0.2 (0.0)b DOC (mg L-1) 1934.0 ± 1635.0 (0.8) 844.0 ± 305.0 (0.2) 252.0 ± 57.0 (0.5) Geochemicalc DN (mg L-1) 172.6 ± 175.1 (1.0) 222.3 ± 104.1 (0.5) 104.2 ± 68.9 (0.7) EC (dS m-1) 30.4 ± 23.8 (0.8) 34.3 ± 6.5 (0.2) 30.5 ± 4.3 (0.1) Plant Cover Plant Status 72.8 ± 16.2 (0.2)a 9.7 ± 7.8 (0.8)b 17.7 ± 23.4 (1.3)b (%) MABd (%) 45.8 ± 4.0 (0.09)a 25.2 ± 8.1 (0.3)b 19.6 ± 7.6 (0.4)b RCe (%) 0.4 ± .1 (0.3)a 0.1 ± 0.1 (0.7)b 0.1 ± 0.1 (0.7)b Bacterial root Alapha.f (%) 21.4 ± 21.2 (0.8) 13.7 ± 1.3 (0.2) 20.9 ± 10.1 (0.4) colonization Gamma.g (%) 8.4 ± 3.3 (0.4)a 19.8 ± 2.6 (0.1)b 16.9 ± 1.7 (0.1)b Actino.h (%) 30.9 ± 20.8 (0.8) 46.4 ± 7.3 (0.2) 43.7 ± 15.5 (0.4) DOC = dissolved organic carbon, DN = dissolved nitrogen, EC = electrical conductivity a,b Different letters represent significant differences between single variables at different time points (ANOVA, p < 0.05; Tukey-Kramer HSD test). c Geochemical parameters characterize pore water samples collected at depths of 5 and 15 cm d MAB (metabolically active bacteria) % = area of Bacteria (probe Eub338 mix) / area of total live and dead bacteria (StytoBC nucleic acid stain) e RC (root coverage) % = area of Bacteria (probe Eub338 mix) / area of root surface f Alpha. (Alphaproteobacteria) % = area of Alpha. (probe Alf968) / area of Bacteria (probe Eub338 mix) g Gamma. (Gammaproteobacteria) % = area of Gamma. (probe Gam42a) / area of Bacteria (probe Eub338 mix) hActino. (Actinobacteria) % = area of Actino. (probe HGC69a) / area of Bacteria

162

Table 2. Field study root colonization data for plants with high (32-50) and low (8-16) chlorophyll levels. Values represent averages with standard deviation and coefficient of variation in parentheses.

Bacterial root colonization High chlorophyll Low chlorophyll n=8 n=7 MABa (%) 66.3 ± 4.3 (0.1)* 59.3 ± 6.9 (0.1)* RCb (%) 1.2 ± 0.7 (0.6) 0.7 ± 0.3 (0.5) Alapha.c (%) 23.1 ± 14.8 (0.6) 18.7 ± 18.0 (1.0)

Gamma.d (%) 13.7 ± 10.7 (0.8) 20.1 ± 17.5 (0.9) e Actino. (%) 6.8 ± 5.2 (0.8) 10.8 ± 6.3 (0.6) * Significant difference between high and low chlorophyll plants (t-test, p < 0.05) a MAB (metabolically active bacteria) % = area of Bacteria (probe Eub338 mix) / area of total live and dead bacteria (StytoBC nucleic acid stain) b RC (root coverage) % = area of Bacteria (probe Eub338 mix) / area of root surface c Alpha. (Alphaproteobacteria) % = area of Alpha. (probe Alf968) / area of Bacteria (probe Eub338 mix) d Gamma. (Gammaproteobacteria) % = area of Gamma. (probe Gam42a) / area of Bacteria (probe Eub338 mix) eActino. (Actinobacteria) % = area of Actino. (probe HGC69a) / area of Bacteria (probe Eub338 mix)

163

Table 3. Field study 2013 geochemical, plant status, and bacterial root colonization data for Phase 1 (initiated in 2010) and Phase 3 (initiated in 2012). Values represent averages with standard deviation and coefficient of variation in parentheses.

Parameters Phase 1 Phase 3 n=8 n=7 pH 3.7 ± 1.3 (0.4)* 6.3 ± 2.0 (0.3)* TOC (g/kg) 49.8 ± 11.8 (0.2)* 97.9 ± 44.0 (0.5)* Geochemicala TN (g/kg) 3.8 ± 0.7 (0.2)* 7.7 ± 2.0 (0.3)* EC (dS m-1) 5.0 ± 2.7 (0.5)* 8.9 ± 3.3 (0.4)* Plant Cover (%) 30.5 ± 17.4 (0.6) 48.2 ± 17.2 (0.4) Plant status Leaf Chlorophyll 25.7 ± 15.0 (0.6) 26.3 ± 16.6 (0.6) MABb (%) 62.1 ± 5.3 (0.1) 64.1 ± 8.0 (0.1) RCc (%) 0.8 ± 0.4 (0.6) 1.2 ± 0.7 (0.6) Bacterial root Alpha.d (%) 22.8 ± 18.2 (0.8) 19.0 ± 14.1 (0.7) colonization Gamma.e (%) 19.5 ± 15.9 (0.8) 13.5 ± 12.2 (0.6) Actino.f (%) 12.0 ± 5.6 (0.5)* 4.8 ± 3.5 (0.7)* TOC = total organic carbon, TN = total nitrogen, EC = electrical conductivity

* Significant difference between Phase 1 and Phase 3 (t-test, p < 0.05) a Geochemical parameters characterize surface substrate sample from top 20cm b MAB (metabolically active bacteria) % = area of Bacteria (probe Eub338 mix) / area of total live and dead bacteria (StytoBC nucleic acid stain) c RC (root coverage) % = area of Bacteria (probe Eub338 mix) / area of root surface d Alpha. (Alphaproteobacteria) % = area of Alpha. (probe Alf968) / area of Bacteria (probe Eub338 mix) e Gamma. (Gammaproteobacteria) % = area of Gamma. (probe Gam42a) / area of Bacteria (probe Eub338 mix) f Actino. (Actinobacteria) % = area of Actino. (probe HGC69a) / area of Bacteria (probe Eub338 mix)

164

8. Figure Legend

Figure. 1. Buffalo grass root processed using (A) nucleic acid stain (SytoBC) to target all

Bacteria, (B) fluorescence in situ hybridization (FISH) probe to target metabolically active Bacteria (Eub338), and (C) FISH probe to target Alphaproteobacteria (Alf968).

(D) Overlay of fluorescence and light images, where Alphaproteobacteria cells appear white due to colocalization between the SytoBC nucleic acid stain and the Eub338 and

Alf968 FISH probes. Roots were viewed and imaged with a Zeiss 510 Meta confocal laser scanning microscope at 63x magnification. Scale bars represent 10μm.

Figure 2. Principle Component Analysis (PCA) showing relationships between bacterial community composition on buffalo grass roots from (A) Phase 1 and (B) Phase 3. Red vectors depict the Actinobacteria (Actino), Alphaproteobacteria (Alpha), and

Gammaproteobacteria (Gamma) colonizing the root surfaces and the blue arrows depict the relationship between the geochemical (pH, electrical conductivity [EC], total organic carbon [TOC], and total nitrogen [TN]) and plant status (plant cover and chlorophyll) parameters and bacterial community composition. Overall, the direction and magnitude of the red and blue vectors represent the presence of and strength of associations between specific phyla/subphyla, geochemical, and plant status parameters. The total variance explained by each axis is shown in parentheses.

Figure 3. Relative abundance of Alphaproteobacteria on buffalo grass root surface and in the rhizosphere, as assessed using fluorescence in situ hybridization and 16S rRNA

165 gene amplicon sequencing, respectively for (A) Phase 1 and (B) Phase 3.

Alphaproteobacteria phylogenetic profiles of buffalo grass rhizosphere for (C) Phase 1 and (D) Phase 3. Samples arranged from left to right in order of increasing pH within each phase. pH values are representative of the surface substrate collected directly adjacent to each plant.

Figure 4. (A) Phase 1 relative abundance of Gammaproteobacteria on buffalo grass root surfaces and in the rhizosphere, as assessed using fluorescence in situ hybridization and

16S rRNA gene amplicon sequencing, respectively. (B) Phase 1 Gammaproteobacteria phylogenetic profile of buffalo grass rhizosphere. Samples are arranged from left to right in order of increasing plant cover of the plot from which plant was collected.

Figure 5. (A) Phase 3 relative abundance of Gammaproteobacteria on buffalo grass root surfaces and in the rhizosphere, as assessed using fluorescence in situ hybridization and

16S rRNA gene amplicon sequencing, respectively. (B) Phase 3 Gammaproteobacteria phylogenetic profile of buffalo grass rhizosphere. Samples arranged from left to right in order of increasing pH of surface substrate collected directly adjacent to each plant.

Supplementary Figure 1. Images of two buffalo grass roots (root 1: A-F and root 2: G-

L) grown in IKHMSS metalliferous mine tailings with low plant cover (11.9%).

Fluorescence in situ hybridization (FISH) and multiple energy micro-focused x-ray fluorescence (ME μXRF) were combined to calculate co-localization of bacteria and Fe

166 using the technique described in Honeker et al., (2016). Results indicate that 47.9% and

12.4% of Gammaproteobacteria co-localized with Fe on the root surfaces for root1 and root2, respectively. (A,G) light microscopy image of root; (B,H) all live and dead cells targeted with SytoBC Nucleic acid stain; (C,I) FISH image showing subphylum

Gammaproteobacteria labeled with Gamm42a probe; (D, J) ME μXRF image showing

Fe (post alignment to confocal image); (E, K) overlay of all live/dead bacterial cells and

Fe images; and (F, L) overlay of Gammaproteobacteria and Fe images. Scale bars represent 20um.

167

9. Figures

Figure 1.

168

Figure 2.

169

Figure 3.

170

Figure 4.

171

Figure 5.

172

Supplementary Figure 1.

173

APPENDIX C

RESOLVING COLOCALIZATION OF BACTERIA AND METAL(LOID)S ON

PLANT ROOT SURFACES BY COMBINING FLUORESCENCE IN SITU

HYBRIDIZATION (FISH) WITH MULTIPLE-ENERGY MICRO-FOCUSED X-

RAY FLUORESCENCE (ME μXRF)

174

Resolving Colocalization of Bacteria and Metal(loid)s on Plant Root Surfaces by

Combining Fluorescence in Situ Hybridization (FISH) with Multiple-Energy Micro-

Focused X-Ray Fluorescence (ME μXRF)

Linnea K. Honeker1,2, Robert A. Root1,3, Jon Chorover1,4, and Raina M. Maier1,5,*

1Department of Soil, Water, and Environmental Science, P.O. Box 210038,

University of Arizona, Tucson, AZ, 85721, [email protected],

[email protected], [email protected], [email protected]

Published: December 2016

Journal of Microbiological Methods. 131: 22–33.

Running Head:

Bacteria and Metal(loid) Colocalization on Plant Roots

Keywords: Phytostabilization; Root-colonizing bacteria; Root iron plaques; FISH; iTag sequencing; XRF

*Corresponding author

Email: [email protected]; Phone: 520-621-7231; Fax: 520-626-6782;

Department of Soil, Water, and Environmental Science, P.O. Box 210038, University of

Arizona, Tucson, AZ 85721

175

Abstract

Metal(loid)-contamination of the environment due to anthropogenic activities is a global problem. Understanding the fate of contaminants requires elucidation of biotic and abiotic factors that inﬂuence metal(loid) speciation from molecular to ﬁeld scales.

Improved methods are needed to assess micro-scale processes, such as those occurring at biogeochemical interfaces between plant tissues, microbial cells, and metal(loid)s. Here we present an advanced method that combines fluorescence in situ hybridization (FISH) with synchrotron-based multiple-energy micro-focused X-ray fluorescence microprobe imaging (ME μXRF) to examine colocalization of bacteria and metal(loid)s on root surfaces of plants used to phytostabilize metalliferous mine tailings. Bacteria were visualized on a small root section using SytoBC nucleic acid stain and FISH probes targeting the domain Bacteria and a specific group (Alphaproteobacteria,

Gammaproteobacteria, or Actinobacteria). The same root region was then analyzed for elemental distribution and metal(loid) speciation of As and Fe using ME μXRF. The

FISH and ME μXRF images were aligned using ImageJ software to correlate microbiological and geochemical results. Results from quantitative analysis of colocalization show a signiﬁcantly higher fraction of As colocalized with Fe-oxide plaques on the root surfaces (fraction of overlap 0.49 ± 0.19) than to bacteria (0.072 ±

0.052) (p < 0.05). Of the bacteria that colocalized with metal(loid)s, Actinobacteria, known for their metal tolerance, had a higher correlation with both As and Fe than

Alphaproteobacteria or Gammaproteobacteria. This method demonstrates how coupling these micro-techniques can expand our understanding of micro-scale interactions

176 between roots, metal(loid)s and microbes, information that should lead to improved mechanistic models of metal(loid) speciation and fate.

1. Introduction

Metal(loid)s are naturally present in the environment, but can accumulate above background levels due to agriculture, mining, and other anthropogenic activities, resulting in potential toxicity to surrounding microbial communities and ecosystems.

Concentration and bioaccessibility of metal(loid) contamination are routinely measured using bulk geochemical analytical approaches. Similarly, bulk microbiological characterizations can be performed to elucidate microbial community structure and its response to added metal(loid)s. However, tools are lacking that can be used to assess the micro-scale biotic and abiotic interactions that affect bulk-scale biogeochemistry. Such information is needed to better understand both short- and long-term impacts of metal(loid) contamination in the environment as well as remediation outcomes of efforts that seek to stabilize contaminant metal(loid)s in situ rather than remove them. Such technologies include the use of wetlands to treat contaminated water and phytostabilization that seeks to use plants for in situ stabilization of metal(loid)s in contaminated soils (Mendez and Maier, 2008; Närhi et al., 2012).

One area where the need for micro-scale information is particularly important to resolving such interactions is the rhizosphere, i.e., the plant root surface and the root zone in general. In this zone, root border cells, exudates, and rhizo-deposits promote a 10 to

100-fold increase in the prevalence of bacteria and associated metabolic activity, a

177 process known as the “rhizosphere effect” (Rovira, 1956). Relatively little information is currently available concerning micro-scale root-metal-microbe interactions and the impact on metal(loid) sequestration and transformation. Resolving the intricacies of this micro-environment requires micro-focused methodologies, including scale-appropriate mapping and quantiﬁcation of both metal(loid)s and microorganisms on the root surface.

For metal(loid) analysis, multiple energy micro-focused X-ray ﬂuorescence imaging (ME μXRF) can be used to identify and create a micro-scale (2–500 μm) spatial rendering of metal(loid)s and speciation in a sample. This synchrotron technique can assess the extent of metal(loid) association with a surface as well as the juxtaposition of different metal(loid)s on the surface. For example, this micro-scale technique has been applied in wetland systems to detect As and Pb adsorption to Fe root plaques (Hansel and

Fendorf, 2001; Blute et al., 2004; Seyfferth et al., 2011; Zimmer et al., 2011), which are coatings of Fe-oxides formed on a root surface due to oxidation of Fe(II) to Fe(III) as an abiotic process when O2 is released from the roots, or as a biotic process facilitated by

Fe-oxidizing bacteria (FeOB) (St-Cyr et al., 1993; Emerson et al., 1999). Further metal(loid) characterization using X-ray absorption spectroscopy (XAS), which includes

X-ray absorption near-edge structure (XANES) and extended X-ray absorption ﬁne structure (EXAFS), can provide information on metal(loid) oxidation state and local bonding environment, also referred to as molecular speciation. For instance, XRF combined with XAS data indicated As(III) and As(V) juxtaposed with Fe plaques composed primarily of ferrihydrite (ca. 50–100%) with lesser amounts of lepidocrocite

(28 ± 1%) on the roots of medium-grained Clarose rice (Oryza sativa L., cv. M-206)

(Seyfferth et al., 2011). These X-ray techniques can directly measure the chemical

178 speciation of contaminants while also identifying the structure or mineral form of Fe and

Mn plaques to help better understand the geochemical processes and cycles, potentially mediated by microbial activities, occurring in discrete locations such as the root zone.

Identiﬁcation and spatial resolution of bacteria on a micro-scale (2–500 μm) can be done using H, 2009), and it was reported that compost had a positive effect on bacterial colonization, with bacterial root coverage increasing from 3.6 to 18.9% as compost amendment was increased from 0 to 10%. Compared to other methods of analyzing microbial communities in the environment, the beneﬁts of FISH include the ability to spatially and quantitatively resolve microbial communities directly on the root surface.

Here we describe a new method that combines ME μXRF and FISH to allow micro-scale examination, on a single sample, of both metal(loid)s and microorganisms on root surfaces. The method is tested on root surfaces harvested from an on-going phytostabilization study of metalliferous mine tailings to: i) determine its effectiveness for identifying colocalization between particular metal(loid)s and bacteria, and ii) quantify the degree of colocalization in order to evaluate biogeochemical interactions that may contribute to stabilizing metal(loid)s in the root zone. This method can be used to detect either all bacteria or speciﬁc groups of bacteria of interest, but the number of different bacterial groups examined in the same sample is limited by the number of

ﬂuorescent probe wavelengths available, as will be discussed further. A related approach was employed previously (Mitsunobu et al., 2012) using a combination of EXAFS and

FISH to identify FeOB associations with geochemical formations on thin sections of natural Fe bio-mats. This group thin-sectioned the bio-mats they were analyzing and then

179 used alternating thin sections for EXAFS or FISH analysis. This allowed inference of bacteria-metal(loid) colocalization by combining results from different thin sections. In the current method we combine FISH with ME μXRF on the same intact section of root surface to directly examine the extent of bacteria and metal(loid) colocalization.

2. Materials and methods

2.1 Site description

The Iron King Mine and Humboldt Smelter Superfund (IKMHSS) site, in Dewey-

Humboldt, AZ, supported mining operations from 1906 to1969. Copper, Au and Ag were extracted (Creasey, 1952) leaving behind high concentrations of metal(loid)s including

As and Pb, at 3.1 and2.2 g kg−1 respectively (Root et al., 2015). Oxidation of the pyritic tailings in the top 25 cm of the tailings has produced acidic conditions (pH 2.3–2.7)

(Hayes et al., 2014). Other conditions, including a semi-arid climate, low organic carbon content (0.14 g kg− 1), hypersalinity (EC 6.5–9.0 ds m− 1) and poor soil structure (Hayes et al., 2014; Valentín-Vargas et al., 2014), cause the surface of the tailings to remain barren and susceptible to wind and water erosion as vehicles for potential contaminant transport. A ﬁeld trial, initiated in May 2010, is being performed to evaluate the effectiveness of compost-assisted phytostabilization as a remediation technology for the mine tailings (Gil-Loaiza et al., 2016). In this study we focus on Buchloe dactyloides

(buffalograss) roots collected from the treatment containing 15% compost (w/w) (tilled into the top 15 cm of the tailings) seeded with a total of six native plant species, including

B. dactyloides. The compost amendment had at least ﬁve immediate impacts (Valentín-

180

Vargas et al., 2014): 1) the addition of nutrients, 2) the addition of a microbial inoculant more suited to carbon and nitrogen cycling than the resident community, 3) pH neutralization, 4) increased water holding capacity, and 5) improved aggregate structure.

2.2 Sample collection and ﬁxation

Three B. dactyloides roots were collected from the 15% compost (w/w) field trial plots to be used for both FISH and subsequent ME μXRF analysis. For each root, a different phylum specific probe was used, which is reflected in the sample label: Actino1,

Actino2, Alpha1, Alpha2, Alpha3, and Gamma1. For the roots analyzed with the

Actinobacteria and Alphaproteobacteria probes, more than one region of the root was analyzed, as indicated by the numeric part of the label. Roots were collected by cutting off the top of the plant at the surface of the soil and then inserting a corer measuring 2 cm in diameter and 10 cm in length directly over the truncated stem (Valentín-Vargas et al.,

2014). Next, a root sample was harvested from the core and treated to ﬁx root-colonizing bacteria by immediately placing it into 4 °C, sterile 4% paraformaldehyde (PFA) in phosphate-buffered saline solution (PBS) (7 mM Na2HPO4, 3 mM NaHPO4, and 130 mM NaCl, pH 7.2) (Iverson and Maier, 2009). The corer was sterilized with 95% ethanol before each sample collection. The samples were then transported back to the laboratory where they were processed for storage within 12 h of collection. The ﬁrst step of processing was to remove the root from the PFA and place it into ice-cold sterile PBS.

The sample was then shaken vigorously by hand to remove adhered mine tailing particles, and incubated for 10 min. This step was repeated once and then roots were placed into a 1:1 mixture of PBS and 100% ethanol for storage at − 20 °C (Iverson and

Maier, 2009). Four additional B. dactyloides root samples and adhered soil were collected

181 from the ﬁeld for rhizosphere DNA analysis, using the same collection procedure described above and placing the entire soil/root sample from the corer into sterile bags.

These samples were kept on ice during transport back to the lab, and then stored at − 80

°C until DNA extraction.

2.3 Microbial community analysis to determine FISH probes

The choice of FISH probes used to identify root-colonizing bacteria was informed by determining the bacterial phyla that were most abundant in the B. dactyloides root zone. Abundance was determined using Illumina sequencing analysis of 16S rRNA gene amplicons (iTags) generated from 4 samples of rhizosphere-inﬂuenced soil collected from different locations in the ﬁeld site. A 0.5 g sample from each collection site was subjected to DNA extraction using the FastDNA Spin Kit for Soil (MP Biomedicals;

Santa Ana, CA) following the manufacturer's protocol with some modiﬁcations, as outlined in Valentín-Vargas et al. (2014). Library preparation and sequencing were performed at Argonne National Laboratory (Chicago, IL). Library preparation followed a single-step PCR protocol using primers 515f and 806r to amplify the V4 region of the

16S rRNA (Caporaso et al., 2012) and sequencing was carried out in a single lane on an

Illumina Mi-Seq using v2 chemistry and bidirectional amplicon sequencing (2 × 151 bp).

Raw sequence reads were processed using the open source software package

QIIME (Quantitative Insights into Microbial Ecology; www. qiime.org). Brieﬂy, forward and reverse reads were joined, requiring a minimum overlap of 30 bp followed by quality

ﬁltering using default QIIME parameters and demulitplexing. Default QIIME quality

ﬁltering parameters included removing the primer/barcode from the sequence in addition

182 to removing all sequences characterized by a Phred quality score b 4, containing any ambiguous (N) base calls, and having a length of b 0.75 of original length post quality

ﬁltering. After quality ﬁltering, sequences were clustered into operational taxonomic units (OTUs) of greater than or equal to 97% similarity using UCLUST (Edgar, 2010).

Representative sequences from each OTU were aligned using PyNAST (Caporaso et al.,

2010) and assigned taxonomy using the UCLUST classiﬁer and Greengenes 16S rRNA gene database (DeSantis et al., 2006). Taxonomic results were used to identify the most abundant taxonomic phyla/classes for subsequent development of FISH probes.

Sequence data have been deposited in the NCBI Sequence Read Archive database under the experimental run accession number SRR3476550 as part of BioProject number

PRJNA309329.

2.4 FISH

The FISH protocol was adapted from previous studies that performed FISH on roots (Watt et al., 2006; Iverson and Maier, 2009). A ﬁxed root section 10–20 mm long and b 0.2 mm thick was placed onto a quartz slide in the middle of a modiﬁed

FastWells™ Reagent Barrier (Grace Bio-Labs, CA) with a 1–2 mm section cut-out to enable buffer ﬂow to the root. Low melting point agarose (0.2%) was added to keep the root adhered to the slide for the duration of the hybridization process. The slide was then placed in a 37 °C hybridization oven (VWR International, PA) until the agarose set, usually about 1 h. To dehydrate the root prior to hybridization, the slide was suspended for 3 min each in 50, 80, and 98% ethanol.

183

Probe hybridization was performed in 40 μL of hybridization buffer (HB) (2% 1

M Tris-HCl, 18% 5 M NaCl, 0.25% SDS, and formamide [see Table 1], in ultrapure deionized [DI] water). All roots received 4 μL of Eub338 mix (20 ng μL− 1 each of

Eub338, Eub338II, and Eub338III) and 4 μL of a group speciﬁc probe for either

Actinobacteria (30 ng μL−1 of HGC69a [roots Actino1, Actino2]), Alphaproteobacteria

(30 ng μL-1 Alf968 [roots Alpha1, Alpha2, and Alpha3]), or Gammaproteobacteria (30 ng μL-1 Gam42a [root Gamma1]). Due to the limited number of available channels, constrained by the available fluorophores and corresponding excitation wavelengths as described below, only one group specific probe could be used per root section. For a summary of probe sequences and specific stringencies, see Table 1. All probe sequences are from ProbeBase (Loy et al., 2007) and were designed to target regions of the 16S or

23S rRNA genes. A Hybrislip (Grace Bio-Labs, CA) was placed on each hybridization frame and the entire slide was inserted into a moisture chamber (50 mL centrifuge tube with a Kimwipe saturated with HB) for a 2–2.5 h incubation at 46 °C in the hybridization oven. After incubation, the Hybrislip was removed so that the root could be rinsed in wash buffer (WB) (2% 1 M Tris-HCl, 1% 0.5 M EDTA, 5 M NaCl [see Table 1], in ultrapure DI water) and then the Hybrislip was replaced before immersing the entire slide in a 50 mL tube containing WB and incubating for 15 min in a 48 °C water bath. Slides with roots were rinsed in ice-cold ultrapure DI water, dried using compressed air, and then placed in the dark at room temperature until completely dry. Slides were stored in a dark slide box with desiccant at − 20 °C until viewing.

EDTA is a multi-dentate carboxylate ligand that forms strong complexes with cationic metals, therefore, a small experiment was performed in order to quantify the

184 effects of EDTA, present in the WB, on metal release from the roots. The experiment was performed in triplicate by placing a small section of root, similar to those used in this study, into a microcentrifuge tube containing 1 mL of either ultrapure DI water or 5 mM

EDTA. Control treatments containing no root section were also included. Tubes were placed in a 48 °C water bath for 15 min, similar to the wash step in the FISH process, and then roots were immediately removed. The remaining liquid was analyzed for Fe and As concentration to assess the extent of metal release from the roots by the Arizona

Laboratory for Emerging Contaminants (ALEC) at the University of Arizona in Tucson,

AZ. Roots were dried at 40 °C for 48 h to obtain a dry weight for normalization of metal release to root mass.

2.5 Confocal laser scanning microscopy (CLSM)

Prior to viewing, slides were allowed to equilibrate to room temperature. As a counterstain, 40 μL of 500 μM SytoBC nucleic acid stain (Mo- lecular Probes, OR) in 10

μM Tris-HCl at pH 8.0 were added to each root and incubated at room temperature for 20 min. This was just long enough for the stain to target nucleic acids within bacterial cells but not long enough for it to permeate plant cells. The counterstain was useful as a control that stained all live and dead bacterial cells on the root and also helped distinguish bacterial cells from background ﬂuorescence in the FISH analysis. SytoBC was rinsed off with 10 μM Tris-HCl and excess liquid was removed by wicking with a Kimwipe and gently blowing with condensed air. The slides were then placed in the dark at room temperature until completely dry.

185

Once dry, 1 drop of AF1 antifadent mountant (Citiﬂuor Ltd., London) was added to the root and a coverslip (size 1.5, VWR) was carefully placed over the root. Slides were viewed on a Zeiss 510 Meta Confocal Laser Scanning Microscope (CLSM) using a

40 × (Plan Apochromat [Apo.] numerical aperture [NA] 1.3) or 63 × (Plan Apo. NA 1.4) objective. For viewing SytoBC, CY3 labeled probes, CY5 labeled probes, and auto-

ﬂuorescence, a 488 nm Argon laser with a BP 505–570 ﬁlter, 543 nm HeNe laser with an

LP 560 ﬁlter, 633 nm HeNe laser with an LP 650 ﬁl- ter, and a 405 nm diode laser with

BP 120–480 filter were used, respectively. Autofluorescence was captured with the 405 nm laser and later subtracted from all of the other laser lines to reduce overall autofluorescence for image analysis. The captured image consisted of four channels obtained sequentially at each interval representing a different wavelength. Each probe has a specific excitation and emission wave-length, and the four laser wavelengths and filters listed were optimally chosen to ensure excitation and image collection of the appropriate probe while avoiding an overlap of probe excitation and emission wavelengths. Images were collected at intervals of 1 μm from the surface of the root to a depth that allowed for full outline of root to be depicted, usually about 30–50 μm, creating an image stack within each channel. Images were collected from conspicuous regions on the root such as root tips, branching nodes, or protrusions that could be easily reidentified during ME

μXRF analysis. After viewing, slides were stored at 4 °C until transport to the Stanford

Synchrotron Radiation Lightsource (SSRL) for ME μXRF analysis.

186

2.6 Multiple-energy micro-focused X-ray ﬂuorescence imaging (ME μXRF)

Following FISH analysis, ME μXRF imaging across the Fe and As K-edge absorption energies was used to investigate the micron scale (2 μm spot size) spatial distribution of elements, minerals, and chemical species associated with the B. dactyloides roots. Similar to the elemental maps that can be collected by scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS), high- energy synchrotron-source X-rays can be used to collect images of elemental abundance.

Without the necessity for subjecting samples to high vacuum conditions, synchrotron- based XRF data can be collected at near in situ conditions with minimal sample preparation or manipulation. Thus, ME μXRF spectromicroscopy is a non-invasive chemical imaging technology that provides elemental and chemical species information at micrometer spatial resolution in a heterogeneous and complex matrix (Jones and

Gordon, 1989; Tokunaga et al., 1994; Pickering et al., 2006).

In ME μXRF spectromicroscopy, tuning the incident X-ray energy to exceed core level electron binding energy (e.g., 1S or Kα) of an element of interest results in ionization and creation of a core hole. The core hole is short lived and the subsequent radiative relaxation of a lower energy electron (e.g. e− transition of L to K) results in a measurable X-ray photon of matching energy. All elements with binding energies below the energy of the incident X-ray will be ionized and emit characteristic X-rays that can be monitored. However, low energy X-rays from low Z elements such as C, N, and O (e.g. Z b 16) are attenuated in ambient air, generally not energetic enough to travel to the detector, and therefore are not measured. For an element with an absorption edge sensitive to oxidation state (e.g., As) or interatomic coordination (e.g., Fe), the incident

187 energy of the X-ray can be finely tuned at multiple energies across the absorption edge to exploit the fluorescent response of different species. Stacking the images collected at multiple energies across the absorption edge produces an image of spatially-resolved element speciation, oxidation state, or mineral phase in a complex sample matrix. The normalized fluorescence of the elements with binding energies below the probed element are effectively invariable at the multiple energies across the absorption edge, and speciation analysis is limited to the probed element, e.g., here Fe or As (Root et al.,

2015).

X-ray ﬂuorescence imaging of the roots was performed at Beamline 2-3 at the

Stanford Synchrotron Radiation Lightsource (SSRL). The bending magnet beamline is optimized for X-ray microprobe analysis with a tunable Si (111) double crystal monochromator that allows for accurate and precise incident X-ray energy calibration with resolution ﬂuorescence detection. Coverslips used for confocal microscopy during

FISH analysis were removed and excess mountant was wicked away with a Kimwipe, while keeping the root fixed in position on the slide. To assure a representative image in the ME μXRF map, coarse maps up to 1 cm2 area were collected with large step sizes and short dwell times to assure homogeneity and representative micrographs and aid in the selection of representative points of interest for detailed analysis. The incident X-ray energy was calibrated with Fe and As metal foils with the assigned energy of 7112 eV for the first derivative of the Fe edge, and 11,867 eV for the first derivative of the As edge.

The ME μXRF images were processed using the analysis toolkit software package

SMAK (http://smak.sams-xrays.com). Principal component analysis (PCA) was applied to the 60–80k pixel images to locate regions of unique components or chemical

188 differences (see Mayhew et al., 2011). The unique components highlighted with PCA were probed with micro-focused X- ray absorption near edge structure (μXANES) to conﬁrm the species or mineralogy and analyze the largest variety of different chemical species in the afforded beam-time. The μXANES was at the same spot-size, stage and detector position as image collection.

2.7 Quantitative analysis of colocalization

Quantitative analysis of metal(loid)-bacteria spatial overlap, or colocalization, was performed by combining separate FISH and ME μXRF images using ImageJ software (Schneider et al., 2012). Each original FISH file consisted of four channels composed of image stacks representative of 1 μm intervals of root depth (about 30–40 layers) showing fluorescence from the SytoBC live-dead stain, the domain Bacteria probe, a specific group probe, and autofluorescence. To process the original file, it was

first split into the four separate channels. Each channel was then compressed from an image stack to a single image representing the entire depth. To correct for autofluorescence, the image representing the background autofluorescence of the root collected with the 405 laser was subtracted from each of the other three images. This resulted in three single images from each original image file: a SytoBC live-dead stain image, a domain Bacteria image, and a specific group (Actinobacteria,

Alphaproteobacteria, or Gammaproteobacteria) image.

The three FISH images were then each aligned with a corresponding ME μXRF image (As or Fe). Alignment was required due to parallax from the ME μXRF angle of collection. The plugin TurboReg (Thévenaz et al., 1998) was used for image alignment.

189

TurboReg requires four corresponding landmarks to be selected on each image, such as prominent features on the root, to align one image to the other. Following alignment, the

JACoP (Just Another Colocalization Plugin) colocalization tool was used for quantitative analysis (Bolte and Cordelières, 2006). One quantitative measure of the degree of overlap of bacteria-metal(loid) colocalizations on the root are Manders coefficients M1 and M2, where M1 (Eq. (1)) is defined as the ratio of the summed intensities of pixels in which there is a signal from both the bacteria and metal(loid) images to the total intensity of pixels in the metal(loid) image, and M2 (Eq. (2)) is defined conversely for bacteria

(Manders et al., 1993).

∑푖 푚푖,푐표푙표푐 푀1 = (Eq. 1) ∑푖 푚푖

where: mi,coloc = is the colocalization of metal(loid)s and bacteria, mi = metal(loid)s with no overlap; and

∑푖 푏푖,푐표푙표푐 푀2 = (Eq. 2) ∑푖 푏푖

where: bi,coloc is the colocalizatiation of bacteria and metal(loid)s; bi, bacteria with no metal(loid) overlap. In this case, M1 represents the fraction of overlap of metal(loid)s with bacteria, and M2 the fraction of overlap of bacteria with metal(loid)s, each proportional to the amount of per pixel colocalized objects. Coefﬁcients of colocalization can be determined even where the signals from the two components differ strongly

190

(Manders et al., 1993). Because the Manders coefﬁcient is sensitive to background noise

(Bolte and Cordelières, 2006), background evaluation was carefully completed by selecting a region for analysis that did not include empty space and appropriate threshold values were set prior to analysis to adjust for the effects of autoﬂuorescence.

3. Results

3.1. Microbial community analysis to determine FISH probes

A phylogenetic analysis of the 16S rRNA gene from B. dactyloides rhizosphere- associated samples was performed to help choose the probes for FISH analysis.

Following Illumina sequencing, a total of 9,600,618 sequences were passed through quality ﬁltering and demultiplexing, after which a total of 8,826,415 sequences remained, of which 481,478 sequences belonged to the B. dactyloides rhizosphere-associated samples focused on in this study. Taxonomic classiﬁcation of OTUs revealed

Proteobacteria and Actinobacteria as the two most dominant phyla in abundances of 42.7

± 8.0 and 15 ± 5.3% respectively (Fig. 1). The classes Alphaproteobacteria and

Gammaproteobacteria dominated the Proteobacteria making up 52.8 ± 4.3 and 33.6 ±

10.0% of this phylum respectively (and 22.4 ± 3.8 and 14.2 ± 4.1% of total abundance).

Based on these abundance results, as well as the reported presence of families within these groups that exhibit plant and metal(loid) interactions (see Table 2 for summary), group speciﬁc FISH probes were chosen to target Alphaproteobacteria,

Gammaproteobacteria, and Actinobacteria.

191

3.2. FISH

FISH was performed on six sections from a total of three B. dactyloides roots from the IKMHSS ﬁeld trail to spatially and phylogenetically characterize bacterial colonization. Representative results are shown in Fig. 2. The section of the root labeled

Actino2 that was processed using FISH (and later ME μXRF) is shown in Fig. 2A (white box). SytoBC nucleic acid staining resulted in ﬂuoresence of all cells, both live and dead

(Fig. 2B). Conversely, only metabolically active cells were targeted with the domain

Bacteria probe Eub338 (Fig. 2C). These images show significant, but incomplete, overlap between the live/ dead and metabolically active cells. Cells that fluoresce with the live/ dead stain but not the Bacteria probe may be dormant or dead, since probes can only create enough detectable fluorescence in active cells with sufficient ribosomes. Cells targeted by the Actinobacteria probe (Fig. 2D) were only a small subset of the live/dead cells shown in Fig. 2B. A prominent Actinobacteria colony indicated by the white arrow

(Fig. 2D) was also observed with the Syto BC stain (Fig. 2A) and the domain Bacteria probe (Fig. 2B) confirming that this is an actual colony and not autofluorescence. Silicate clay particles can also appear as autofluorescence very strongly with the Syto BC stain

(Fig. 2B, yellow arrow) and slightly with Eub338 probe (Fig. 2C). However, based on the size and shape of these particles, they are not easily confused with bacterial cells.

Comparable results were observed for roots Actino1, Alpha1, and Gamma1, which were processed for Actinobacteria, Alphaproteobacteria, and

Gammaproteobacteria, respectively (Fig. S1). Of interest on root Alpha1 is a prominent bacterial colony indicated by the white arrow that is seen both in the SytoBC nucleic acid stained image (Fig. S1H) and with the domain Bacteria probe (Fig. S1I). However, this

192 colony did not hybridize with the Alphaproteobacteria probe although

Alphaproteobacterial cells are visibly distributed over the root surface (Fig. S1J).

Additional FISH results for Alpha2 and Alpha3, processed for Alphaproteobacteria are shown in Fig. S2.

Roots from similar plants to those used in this study were tested to assess the effects of EDTA (contained in the WB) compared to DI water on metal release from root surfaces. After root addition to plain water, the mass of Fe and As released normalized to the mass of root assayed was 0.56 ± 0.43 μg mg− 1 and 0.12 ± 0.036, respectively. In comparison, the mass of Fe and As released from roots subjected to a WB rinse containing EDTA, was 2.9 ± 1.6 μg mg− 1 and 0.14 ± 0.070 μg mg− 1, respectively.

There was no signiﬁcant difference between the water and the WB-EDTA treatments and thus, a minimal effect of EDTA on metal release from root surfaces during the FISH process.

3.3 Multiple-energy micro-focused X-ray ﬂuorescence absorption near edge structure (μXANES)

Following FISH analysis, each sample was analyzed by ME μXRF for elemental and mineralogic spatial association. A bicolor map of Fe and As distribution and intensity for root Actino2 (Fig. 2E, F) shows abundant Fe plaques (in red) on the root surface with color intensity proportional to relative concentrations; each pixel in the image is 2 μm2, and the scale bar is 20 μm. Arsenic (shown in blue) is less abundant than Fe and in many areas is present in association with Fe plaques (displayed as purple). Similar results were

193 observed for all other roots examined and other examples can be seen in Figs. S1E, S1K,

S1Q, S2E, and S2K.

ME μXRF mapping shows that As is primarily associated with ferric oxyhydroxide coatings at the surface of the roots (Fig. 3). It is known that the ﬂuorescent yield varies between elements (O'Day et al., 2004); however, plotting the per pixel

fluorescence counts of As versus Fe reveals three distinct stoichiometries of As/Fe co- association (Fig. 3C), most pixels having a low As:Fe ratio (ca. 1:60), a few pixels with a high As:Fe ratio (ca. 1:12) and a final set with no observed As. Most of the fluorescence data show a ratio of As:Fe close to the mole ratio of As:Fe in the bulk tailings (1:56)

(Root et al., 2015). The ME μXRF map for Fe shows a predominance of ferrihydrite with isolated Fe silicate clay (chlorite) on the roots. Arsenic is only associated with the areas mapped as ferrihydrite. Areas mapped as chlorite show no colocalized As.

Regions of interest were probed with As and Fe XANES. Iron XANES fine structure is sensitive to Fe-bearing mineral types (O'Day et al., 2004), and As XANES is sensitive to oxidation state (Foster and Kim, 2014). Arsenic XANES spectroscopy confirms that the As on the root surfaces was As(V) (Fig. S5). Two Fe phases were confirmed with μXANES at spots of interest selected based on PCA (Fig. S4): i) a ferrihydrite-like (nominally Fe(OH)3) ferric oxyhydroxide; and ii) a mixed valent Fe-rich clay, matching closely with reference ripidolite [CCa-2 chlorite group;

(Ca0.5)(Mg4.44,Fe[III]3.47,Fe[II]3.02,Al0.60,Mn0.01,Ti0.06)(Si4.51,Al3.49)O20(OH)1

6] (Fig. 3B). The ripidolite reference, from the Clay Mineral Society Source Clay

Repository, has diagnostic XANES spectral features that differentiate it from other clay references, and it was identiﬁed in the tailings previously (Hayes et al., 2014). The roots

194 were washed prior to ME μXRF analysis suggesting that clay association on the root surface is likely due to root mucilage adhesion. The XANES identiﬁed ferrihydrite phase is a ferric (oxy)hydroxide plaque that remained on the root surface after washing, and was not the result of bulk tailings particles.

3.4. FISH/ME μXRF quantiﬁcation and colocalization

Root coverage by metal(loid)s and bacteria were separately estimated from the

ME μXRF and FISH images, respectively (Table 3). Root coverage by Fe (19–20%) on

Actino 1, Actino2, and Alpha1 was higher than for the other three roots (4.7–11%). We note that this is possibly due to the region of the root that was examined including: two elongation zones; a broken elongation zone; two lateral root junctions; and a root tip

(Table 3). The two intact root elongation zone samples had lower Fe coverage than the other regions examined. In contrast, As coverage varied widely, ranging from 0.6 to 15% and did not display any patterns related to root region examined. Root coverage estimated from the SytoBC live/dead stain was consistent across all of the roots (6.3– 8.2%).

Analysis of coverage by metabolically active bacteria in comparison to the live/dead bacteria suggests that 20 to 54% of the imaged cells are active (Table 3).

To quantify colocalization between bacteria and metal(loid)s, FISH SytoBC and

ME μXRF images were aligned using the imageJ plugin TurboReg (Figs. 2F, S1E, S1K,

S1Q, S2E, and S2K). A side by side comparison of the difference between the pre- and post-aligned ME μXRF images is shown in Fig. 2E and F, respectively. Alignment of the

ME μXRF and FISH images allows direct comparison of metal(loid) and bacterial coverage. Overlays of the SytoBC and ME μXRF images (Figs. 2G, S1F, S1L, S1R, S2F,

195 and S2L) show consistent evidence for colocalization of bacteria with metal(loid) plaques on root surfaces. For example, the white arrow in Fig. 2G points to a prominent

Actinobacteria colony associated with an Fe/As plaque.

3.4.1 Colocalizing metal(loid) overlap with bacteria

Manders coefﬁcients M1 and M2 were calculated from the analysis of aligned

FISH - ME μXRF images to quantify colocalization. M1 and M2 values represent the fraction of metal(loid) that overlap bacteria and vice versa, respectively, where a value of

1.0 represents complete overlap. Several interesting patterns emerged from this analysis.

The fraction of metal overlap with live/dead bacteria (M1 values) was generally low ranging from 0.015 to 0.13 for Fe and from 0.0040 to 0.17 for As (Fig. 4A and B).

Examining the overlap that occurred further, both Fe and As generally had higher overlap with all live/dead bacteria than just metabolically active bacteria, suggesting that metal(loid)s are associated with dormant/non-metabolically active cells as well as metabolically active bacteria. Indeed, prior work shows that live and dead cell interactions with (oxyhydr)oxide mineral surfaces are often mediated by strong and kinetically-irreversible covalent bonds formed between surface hydroxylated metal centers (e.g., Fe) and phosphoryl or carboxyl groups of cell surface biomolecules (Parikh and Chorover, 2006, 2008).

3.4.2. Colocalizing bacterial overlap with metal(loid)s

Examination of fraction of bacterial overlap with metals (M2 values) showed high variability with values ranging from 0.019 to 0.36 for the association of live/dead bacteria imaged with the SytoBC stain with Fe and values of 0.0010 to 0.37 for As (Fig. 4C and

196

D). A striking result from the group speciﬁc probes was that Actinobacteria consistently had a higher degree of association with Fe than did Gammaproteobacteria or

Alphaproteobacteria. For instance, M2 coefﬁcients for Actinobacteria overlapping with

Fe ranged from 0.24 to 0.66, while comparable values were 0.0010 for

Gammaproteobacteria and 0.019 to 0.27 for Alphaproteobacteria (Fig. 4C). We note that in this study, roots that had high Fe coverage (Actino1, Actino2, and Alpha1) also had higher values for both M1 and M2 (Fig. 4A and C).

3.4.3. Arsenic colocalization with Fe vs. with bacteria

Examination of colocalization of As with Fe using all six sections (from three roots), without consideration of bacteria, showed that the average fraction of overlap of

As with Fe plaques was 0.49 ± 0.19. This large association of As with Fe is signiﬁcantly higher (p < 0.05) than the association between As and bacteria which had fraction of overlap values of 0.072 ± 0.052 and 0.053 ± 0.033 with live/dead bacteria and metabolically active bacteria, respectively. This comparison suggests that the primary mechanism of As retention at the root surface is adsorption to Fe plaques rather than to bacterial surfaces.

4. Discussion

Herein we have presented a method that makes conjunctive use of FISH and ME

μXRF analyses to characterize the micro-scale associations of bacteria with metal(loid)s on a set of root surfaces grown in As-conaminated, sulﬁde-ore derived tailings. Applying both techniques to a common set of samples allows discrimination of distinct metal(-

197 loid)-bacterial group spatial associations that may reveal insights into biogeochemical mechanisms, such as biomineralization and biodissolution of neoformed metal(loid)- containing precipitates. Finally, this method allows quantitative analysis of colocalization between metal(loid)s and bacteria on the root surface. Such insights into the association between bacteria and metal(loid)s on root surfaces are needed to develop and test hypotheses concerning the extent to which, and the mechanisms whereby, bacteria and bacterial subgroups are potentially driving metal(loid) transformations.

Several related approaches have been employed previously to examine the micro- scale association of bacteria and metals. One approach used ﬂuorescent probes targeting metals in combination with a nucleic acid stain to assess the colocalization of bacteria, heavy metals, glycoconjugates, and Fe minerals in an aqueous live pure bacterial culture using CLSM (Hao et al., 2013, 2016). A drawback to the use of metal-targeting

fluorescent probes on a heterogeneous environmental sample is the inability to obtain detailed geochemical and speciation data of the metals present. Furthermore, due to the limited number of fluorescent probe wavelengths available, the number of metals that can be analyzed at the same time is limited. Several research groups have sought to resolve systematic variation in bacterial colonization of geological media using a combination of microbial and geochemical methods on the same samples, as done here. CLSM has been previously combined with synchrotron analysis, particularly scanning laser X-ray microscopy (SLXM), in the study of the structure and metal content of biofilms

(Lawrence et al., 2003; Dynes et al., 2006; Neu et al., 2010). Also, a combination of bulk

XANES and microbial community analyses (Martínez et al., 2007) as well as micro-scale interrogation of non-speciﬁc stained bacterial cells using confocal microscopy (Yoon et

198 al., 2012) was used to describe the biogeochemistry of metalliferous peats. These studies reported indirect evidence for bacterial roles in sulfur reduction with i) a correlation between the presence of DsrAB (dissimilatory sulfite reductase) genes, characteristic of sulfur-reducers, and regions of sulfide minerals, such as ZnS (Martínez et al., 2007; Yoon et al., 2012) and ii) confocal microscopy of bacterial cells (stained using a non-specific nucleic acid stain) colocated with presumed sulfide minerals, as detected by autofluorescence (Yoon et al., 2012). The ME μXRF - FISH method described here builds on these techniques by spatially mapping both distinct bacterial cell and metal(loid) types and species, in situ on a micro-scale, for direct correlation of bacterial association with neoformed mineral types. In the present case, the microbial role in biomineralization of As-sequestering Fe plaques in mine tailings was of particular interest, but the data do not provide unequivocal support for a direct microbial role.

Rather, they suggest that the root surface itself may govern plaque formation.

Finally, a previous study characterized Pb and Fe (oxyhydr)oxide complexes using EXAFS spectra analysis of Pb-O interatomic distances (Hansel and Fendorf, 2001).

Their results showed Pb complexed to an organic substrate with characteristics similar to those exhibited by extracellular polysaccharides (EPS) and/or bioﬁlms, rather than to Fe

(oxyhydr)oxides, indicating that Pb2+ formed inner-sphere complexes with electronegative oxygen-containing functional groups in bioﬁlm components rather than the Fe-oxide itself. The method used was not able to identify the type of bacteria involved, nor the spatial rendering of interaction. In contrast, results of the current study suggest that direct complexation with biomolecular components is not responsible for most of the As immobilization, an observation that is consistent with both arsenate and

199 most biomolecules exhibiting negative charge. Only a small fraction of As, 0.072 ±

0.052, was spatially-associated with live/dead bacteria. Despite a relatively small proportion of the total As being co-associated with bacterial cells, over large areas this amount can add up; therefore, it would be beneﬁcial to understand the potential role bacteria play in As-immobilization on root surfaces and identify the bacteria that participate in As transformations, such as As-oxidation.

DNA sequencing was used in this study to guide development of the phylum-level probes chosen for FISH-ME μXRF. In addition, these sequence data can be used to help identify populations at greater taxonomic speciﬁcity that may be of interest in the system being studied. For example, of the bacterial groups examined, Actinobacteria exhibited the highest association with As; with overall fraction of overlap values of 0.036 to 0.47

(Fig. 4D). Within the Actinobacteria, the family Microbacteraceae had the highest sequence abundance (2.8%). This family contains Microbacterium lacticum, a species reported to be capable of As oxidation (Table 2) (Mokashi and Paknikar, 2002). As a second example, we focused on one of the 3 root sections probed for

Alphaproteobacteria which also showed a high association with As (Alpha1; fraction of overlap 0.21) (Fig. 4D). In examining the Alphaproteobacteria, the families

Hyphomicrobiaceae and Acetobacteraceae (present in the rhizosphere at abundances of

2.4 ± 1.6 and 9.0 ± 6.7%, respectively) have been reported to exhibit speciﬁc interactions with metal(loid)s (Table 2). For instance, Acidiphilium multivorum, of the

Acetobacteraceae family, is a known As(III) oxidizer (Wakao et al., 1994).

Beyond an apparent lack of direct evidence for a major bacterial role in As sequestration, we also tested for the signiﬁcance of association between bacteria and Fe

200 plaques on the root surface. This is motivated by the fact that a majority of As was associated with poorly-crystalline Fe(III) plaques (fraction of overlap 0.55 ± 0.15) whose mineralization may be mediated microbially (Banﬁeld et al., 2000), and whose formation gives rise to inner-sphere arsenate surface complexes with well-known stability (e.g., Gao et al., 2013). While the method presented does not prove a bacterial role in plaque formation, it does help to uncover patterns to guide future studies of these roles of bacteria.

The involvement of FeOB in the formation of Fe plaques has been shown previously using acridine orange staining (Emerson et al., 1999) and electron microscopy

(St-Cyr et al., 1993). In these studies, bacterial cells were visualized embedded in Fe plaques on roots from aquatic wetland plants. Both studies reported evidence of FeOB after culturing the root tissue; although the cells were not identified in situ. However, microbially-mediated aqueous Fe(II) oxidation is not the principal mechanism expected for Fe(III) (oxyhydr)oxide formation in the current system (Hayes et al., 2014). Indeed, extensive presence of Fe oxide plaques on root surfaces combined with a relatively low colocalization of bacteria with Fe (fraction of overlap ranges between 0.015 and 0.13 for live/dead bacteria, Fig. 4A) would suggest that, in this particular mine tailings environment, Fe plaque formation may not be a microbiologically-driven process, and plaque nucleation at the root surface is potentially regulated by root surface biomolecules. We recognize, however, that the FISH-ME μXRF data set studied here represents a snapshot in time that cannot rule out the possibility of previous microbial involvement in the resident Fe root plaque formations, and that fluorescent remnants of microbial cells may have since disappeared. While FeOB-specific probes were not used

201 in this study, DNA sequencing results show that within the phylum Actinobacteria, the order Acidimicrobiales has a high relative abundance in the rhizosphere of 4.4 ± 2.3% and contains many FeOB species, including Acidimicrobium ferroxidans,

Ferrimicrobium acidiphilum, and Ferrithrix thermotolerans (Table 2) (Whitman et al.,

2012). One way to address this issue more thoroughly would be to develop a FISH probe for potential FeOB within Acidimicrobiales.

While FeOB promote active biomineralization, bacteria may also promote biomineralization passively if surﬁcial biomolecules promote nucleation and crystal growth following adsorption of Fe(III) to cell walls or EPS moeities that complex Fe

(Banﬁeld et al., 2000; De Yoreo and Vekilov, 2003). Since a high proportion of

Actinobacteria were associated with Fe (fraction of overlap 0.66) (Fig. 4C) we examined the Actinobacteria sequences and found that Acidimicrobiales contains a species,

Ferrithrix thermotolerans, capable of Fe-oxide precipitation on its cell surface (Whitman et al., 2012). While colocalization between bacteria and Fe plaques does not necessarily imply a direct bacterial role in Fe plaque formation, the method presented can be used to quantitatively survey potential interactions in development of hypotheses for future studies.

As suggested in the previous paragraphs, one of the next steps in using the FISH-

ME μXRF method would be to employ probes designed for speciﬁc species hypothesized to play a role in metal(loid) transformations. In addition, a future step could focus on the use of FISH probes that target functional genes in order to detect potential microbial- induced transformations, for instance As oxidation. While traditional FISH, as used in this study, is likely not sensitive enough to target functional genes, catalyzed reporter

202 deposition FISH (CARD-FISH) results in brighter ﬂuorescence and can be used to view functional genes (Pernthaler et al., 2002). Finally, while the analysis done in this study

(on roots grown in compost-amended acidic mine tailings in a semi-arid environment) do not provide unambiguous support for a signiﬁcant bacterial role in metal immobilization, further studies are needed. Repeating the described method in a controlled bench-scale experiment using roots grown in both sterile and non-sterile mine tailing media could help in further elucidating the extent of the role of bacteria in mediating metal transformations and immobilization on root surfaces. Similarly, roots grown in a variety of soils and environmental conditions could assess the effects of factors such as soil type, pH, and TOC content on the factors affecting metal transformations on the root surface.

4.1. Other method considerations

In order to successfully perform ME μXRF - FISH, there are some important details to consider with respect to the FISH and ME μXRF analyses. Regarding FISH, details to take into account include the limited number of channels available using CLSM

(the Zeiss 510 Meta CLSM used in this study has 5), autoﬂuorescence, and possible reagent-induced metal(loid) alterations/loss. The combination of probes used should be carefully selected and optimized due to the limited number of channels available using

CLSM. The channels representing the wave-lengths 488, 543, and 633 nm used in this study are one optimal combination. Probes should be labeled with ﬂuorophores having the same excitation wavelengths as the channels being used, in order to elicit a

ﬂuoresence signal (for a list of ﬂuorophores and corresponding excitation wavelengths see Morrison et al., 2003) and only one probe (or nucleic acid stain) can be used per channel. To overcome these challenges, newer confocal microscopes can have as many as

203

5 channels. Additionally, a supercontinuum white light source can be used which generates a continuous spectrum from 450 nm to over 950 nm for the use of multiple

ﬂuorochromes (Gratton and vandeVen, 2006).

Autoﬂuorescence can present a problem, especially when analyzing root samples because root surfaces and soil particles have the tendency to autoﬂuoresce. Watt et al.

(2006) found that using probes labeled with fluorophores in the far-red spectrum, for instance CY5, provided the least interference by autofluorescence, however, in this study autofluorescence was found to be most problematic using the Bacteria (Eub338) probe labeled with CY5. The SytoBC nucleic acid stain and the specific probes (Gamm42a,

Alf968, HGC69a) labeled with CY3 had relatively little autofluroescence (see Figs. 2, S1, and S2). Also, using one wavelength channel that has no associated probes (405 was used in this study) to take an image of the background fluorescence that can later be subtracted from the images taken using the other channels can also be helpful. Though this did not completely remove the autofluorescence in this study, it made a significant improvement in many cases. Finally, it is important to optimize the FISH method for the type of sample being analyzed (i.e., soil, water, mine tailings, rocks, roots, leaves) because different media have varying autofluorescence issues.

Certain reagents used during the FISH process, such as PFA (used to ﬁx roots), and EDTA (present in WB) can potentially alter or promote release of some metal(loid)s.

Prior to this study, the effect of PFA on metal(loid) release from the root was examined by measuring the concentration of metal(loid)s from the PFA solution before and after root ﬁxation. There was no signiﬁcant difference in metal(loid) concentration indicating that metal(loid)s were not released from the root (data not shown). The effects of EDTA,

204 a multi-dentate carboxylate ligand that forms strong complexes with cationic metals, on metal(loid) release from roots were minimized by employing a low (5 mM) concentration. This was veriﬁed by conducting a small experiment which showed that there was no signiﬁcant difference between the Fe and As released after root incubation in 5 mM EDTA compared to water. Furthermore, when using stringencies below 20%,

EDTA is not required at all (Daims et al., 2005). Despite these results, it should be recognized that some disturbance and release of metals and/or bacteria likely occur during the extensive processing of the root during ﬁxation and FISH.

With respect to ME μXRF, one major factor to consider when comparing the images to the FISH images is the penetration depth. The depth probed with ME μXRF is controlled by the penetration depth of the incident beam (proportional to the brightness and sample density) and the escape depth of the generated ﬂuorescing X-ray (related to Z and the bulk density). A plant root has relatively low bulk density and is mostly transparent to the incident X-ray. Generally, metal(loid)s of interest can be detected from

100 μm; therefore, the ﬂuorescence of excited metal(loid)s from the full volume of the root exposed to the X-ray is sent to the detector. This makes the distinction of interior versus exterior associations difﬁcult. In the case of the B. dactyloides roots used in this study, with diameters of about 100–200 μm, this can lead to an underestimation of M1 and M2 values because the FISH confocal image depths only ranged from 30 to 50 μm.

One way to overcome this would be to prepare histological thin-sections of the root before FISH and ME μXRF analysis, so that the same depth is analyzed for both. This is challenging for smaller roots, such as the B. dactyloides roots used in this study.

205

5. Acknowledgements

We thank Corin Hammond for her dedicated assistance in root analysis at the

SSRL. This work was supported by the National Institute of Environmental Health

Sciences (NIEHS) Superfund Research Program (SRP) grant P42 ES04940 and R01

ES1709 and the National Science Foundation Graduate Research Fellowship Program

(NSF GRFP) grant DGE-1143953. Use of the Stanford Synchrotron Radiation

Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Depart- ment of Energy, Ofﬁce of Science, Ofﬁce of Basic Energy Sciences under Contract No.

DE-AC02-76SF00515.

206

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Cosmochim. Acta 84, 165–176. http://dx.doi.org/10.1016/j.gca.2012.01.022.

Zimmer, D., Kruse, J., Baum, C., Borca, C., Laue, M., Hause, G., Meissner, R.,

Leinweber, P., 2011. Spatial distribution of arsenic and heavy metals in willow

roots from a contaminated ﬂoodplain soil measured by X-ray ﬂuorescence

spectroscopy. Sci. Total Environ. 409, 4094–4100.

http://dx.doi.org/10.1016/j.scitotenv.2011.06.038.

216

7. Table

Reference

Neef, 1997

Manz et al., 1992

Daims et al., 1999 Daims et al., 1999

Amann et al., 1990

Roller eet al., 1994

label

CY5 CY5 CY5 CY3 CY3 CY3

Fluorescent Fluorescent

)

/WB

Variable

25 / 3.0 20 / 4.3 35 / 1.4

Variable Variable

Stringency Stringency

(HB

TG CGC GTT

GT AGG TGT

Sequence (5’ to 3’) (5’ Sequence

GCT GCC TCC CGT AGG AGT AGG CGT TCC GCC GCT

GCC TTC CCA CAT CGT TT

TAT AGT TAC CAC CGC CGT

GGT AAG GTT C

GCT GCC ACC CGT AGG TGT

GCA GCC ACC C

specific probes. probes. specific

Target group Target Domain Bacteria Planctomycetales Verrucomicrobiales Actinobacteria Alphaproteobacteria Gammaproteobacteria

. Domain and group group and Domain .

Eub338 Eub338II Eub338III

Probe name Probe Eub Mix HGC69a Alf968 Gam42a

Table 1 Table buffer Wash WB, buffer; Hybridization HB, (%) in HB Formamide a. (%) WB in NaCl 5M b.

217

x x

oxid.

As(III)

Fe Fe

oxid.

S S

oxid.

Interactions Interactions with metals

x x

Accumulate

Mn depositsMn

oxidized Fe

x x x

Phyto

pathogenic

anti

(PGPB)

Produce

microbials

Interactions Interactions plantswith

x (D) x (D) x

fixing fixing

(PGPB)

x (D,x N)

Nitrogen Nitrogen

x x x

Root

colonizing

Environments

x x

x x x

Metal

conta

minated

ance

± 2.3± 1.4± 0.2± 0.6± 1.6± 6.7± 1.7± 4.0± 2.7±

(%)

91.3 ±

4.4 1.0 1.5 1.0 2.4 1 9.0 2.6 6.6 4.4

rhizosphere 16S rRNA gene amplicon iTag sequencing results showing families with sequence abundances of ≥1%. of abundances sequence families with showing results sequencing iTag amplicon gene rRNA 16S rhizosphere

Sequence Sequence

Abund

a,b a,b

c,d

B. dactyloides B.

. .

Orderor Family

Table 2 Table

Acidimicrobiales Cellulomonadaceae Microbacteriaceae Hyphomicrobiaceae Sphingomonadaceae Acetobacteraceae Caulobacteraceae Sinobacteraceae Xanthomonadaceae

Gammaproteobacteria Phylum orPhylum Class Actinobacteria Geodermatophilaceae Alphaproteobacteria

218

1.6 1.6 1.4 1.1

0.21 0.93

Specific Phyla/Class Specific

4.2 1.8 2.4 3.2 3.0 1.6

Metabolically Active Bacteria Active Metabolically

Root Coverage (%) Coverage Root

7.8 6.6 6.7 6.3 6.9 8.2

Live/Dead Bacteria Live/Dead

2.9 3.5 3.0 8.2

0.60

Fe 20 19 20 11

4.7 6.2

oken end oken

Region of root Region zone Elongation junction root Lateral tip Root junction root Lateral zone Elongation zone Elongation

. Percent root coverage by Fe, As, Live/Dead Bacteria, metabolically active bacteria, and specific phyla/class. specific and bacteria, active metabolically Bacteria, Live/Dead As, Fe, by coverage root Percent .

Root

Alpha1 Alpha2 Alpha3

Actino1 Actino2

Gamma1

Table 3 Table

219

8. Figure Legends

Figure 1. Average phyla distribution in B. dactyloides rhizosphere-influenced mine tailings samples (amended with 15% compost [w/w]; n=4) collected from the IKMHSS field site.

DNA was extracted and subjected to 16s rRNA gene amplicon sequencing using Illumina

Mi-Seq. Asterisks represent the three largest represented phyla chosen for FISH analysis.

Figure 2. FISH and ME µXRF images of B. dactyloides root Actino2 grown in 15% compost (w/w) amended tailings at IKMHSS. (A) Full root section adhered to slide with white box indicating region of root analyzed for FISH and ME µXRF, (B) all live and dead cells targeted with Syto BC nucleic acid stain, (C) domain Bacteria targeted with a

CY5 labeled universal Eub338 mix probe, (D) phylum Actinobacteria targeted with CY3 labeled HGC69a probe, (E) original ME µXRF image showing Fe and As, (F) ME µXRF image after alignment to FISH images using TurboReg plug-in with imageJ, (G) overlay of SytoBC image and aligned ME µXRF image showing colocalization between bacteria and metal(loid)s. White arrow points to a colony of Actinobacteria (B, D, G) and corresponding Fe plaque with As (E, F, G). Yellow arrow points to a silicate clay particle on root, visible only with Syto BC stain (B, G). Scale bars represents 500µm in A and

20µm in B-G.

Figure 3. B. dactyloides root. (A) Multiple-energy microfocused x-ray fluorescence (ME

µ-XRF) image showing arsenic (in red) associated with a ferrihydrite like ferric hydroxide phase (in green) and areas of iron rich clay, fit as chlorite (in blue, e.g. spot 3) that are not associated with arsenic. The ferrihydrite-like plaque coats the root. The color

220 intensity scale for As, Fe-rich clay (chlorite), and ferrihydrite are 0-50, 0-150, and 0-2500 counts respectively to highlight the As relative to the more abundant ferrous iron. (B)

Microfocused (2 µm spot) XANES of select spots on the root were fit to iron minerals,

1Fh = ferrihydrite (synthetic, Root et al., 2009) and 2Clay = chlorite clay (CCa-2, Clay

Minerals Society Source Clays Repository, spectra from O’Day et al., 2004), binary fit with standard deviation shown above in panel. (C) Per pixel correlation of As to ferrihydrite, showing regions of high (e.g. spot 1), low (e.g. spot 2) and no arsenic associated with the ferric mineral plaque. Dashed lines in C) point out that some pixels

(n=11925 pixels) contain no As, while many pixels have both As and ferrihydrite; at low

(1:60) and high (1:12) As:Fe ratios.

Figure 4. Manders coefficients (M1 and M2) for overlap of Fe and As with live/dead bacteria, metabolically active bacteria, and specific phyla/classes. (A) M1, representing the fraction of Fe overlapping with bacteria, (B) M1, representing fraction of As overlapping with bacteria, (C) M2, representing the fraction of bacteria overlapping with

Fe, (D) M2, representing fraction of bacteria overlapping with As. Note the difference in scales between A and B (M1 values) and C and D (M2 values).

Supplementary Figure 1. FISH and ME μXRF images of B. dactyloides roots Actino1,

Alpha1, and Gamma1: (A,G,M) root adhered to slide; (B,H,N) all live and dead cells targeted with SytoBC Nucleic acid stain; (C,I,O) domain Bacteria targeted with a CY5 labeled universal Eub338 mix probe; (D) phylum Actinobacteria targeted with CY3 labeled HGC69a probe; (J) class Alphaproteobacteria labeled with Alf968 probe; (P)

221 class Gammaproteobacteria labeled with Gamm42a probe; (E,K,Q) ME μXRF image showing Fe and As (post alignment to confocal image using TurboReg in ImageJ); and

(F,L,R) overlay of SytoBC image and aligned ME μXRF image. Scale bars represent

20um.

Supplementary Figure 2. FISH and ME μXRF images of B. dactyloides roots Alpha2 and Alpha 3: (A,G) root adhered to slide; (B,H) all live and dead cells targeted with

SytoBC Nucleic acid stain; (C,I) domain Bacteria targeted with a CY5 labeled universal

Eub338 mix probe; (D,J) class Alphaproteobacteria labeled with Alf968 probe; (E,K)

ME μXRF image showing Fe and As (post alignment to confocal image using TurboReg in ImageJ); and (F,L) overlay of SytoBC image and aligned ME μXRF image. Scale bars represent 20um.

Supplementary Figure 3. Individual maps of B. dactyloidesroot, energy maps in the top row (A-E) collected across the Fe absorption edge at 7114, 7121, 7126, 7130 and 7137 eV, the bottom row are total As and Fe (F,G) and inferred Fe phases the based on the multiple energy maps (H, I), and the combination of As and Fe species (J), with ferrihydrite in red, chlorite in green and total As in blue. The heat maps represent low to high abundance in colors blue to red. The scale bar is 40 μm.

Supplementary Figure 4. Principal component analysis (PCA) was performed on the multiple energy maps (n=5) from B. dactyloidesroot IK3. Correlation results for component 1 vs. component 2 (A) and component 2 and component 3 (B). Generally,

222 component 1 vs. 2 shows the distribution of intensities (i.e. concentration) and 2 vs. 3 shows differences in oxidation state. Component 1, the isolated pixels from the correlation of 1 vs. 2 , and component 3 are plotted in heat maps (D-F).

Supplementary Figure 5. Normalized arsenic Ka XANES from of B. dactyloides root and reference spectra. Multiple As XANES scans from multiple B. dactyloides roots indicated only As(V). The noisy BG data is due to low As counts.

223

9. Figures

Figure 1.

224

Figure 2.

225

Figure 3.

226

Figure 4.

227

Supplementary Figure 1.

228

Supplementary Figure 2.

229

Supplementary Figure 3.

230

Supplementary Figure 4.

231

Supplementary Figure. 5.