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Abundance and diversity of nitrifying microbes in sediments impacted by acid mine drainage

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Title:
Abundance and diversity of nitrifying microbes in sediments impacted by acid mine drainage
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Ramanathan, Bhargavi ( author )
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English
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1 electronic file (78 pages) : ;

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Subjects / Keywords:
Acid mine drainage ( lcsh )
Soil chemistry ( lcsh )
Nitrification ( lcsh )
Acid mine drainage ( fast )
Nitrification ( fast )
Soil chemistry ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Extremely acidic and metal-rich acid mine drainage (AMD) waters can have severe toxicological effects on aquatic ecosystems. AMD has been shown to completely halt nitrification, which plays an important role in transferring nitrogen to higher organisms and in mitigating nitrogen pollution. We evaluated whether AMD differentially impacts three groups of microorganisms involved in nitrification: ammonia-oxidizing archaea (AOA), ammonia–oxidizing bacteria (AOB), and nitrite–oxidizing bacteria (NOB). Sediment and water samples were collected from AMD-impacted aquatic sites during June and August/September 2013 and 2014 in the Iron Springs Mining District (Ophir, Colorado). Many of the sites were characterized by low pH (<5), low dissolved oxygen concentrations (<6 mg/L), and high metal concentrations. Community 16S rRNA gene sequencing revealed the presence of AOA (Nitrososphaera and Cenarchaeota), AOB (Nitrosomonas), and NOB (Nitrospira) at multiple AMD-impacted sites. The overall abundance of AOA, AOB and NOB were examined using quantitative PCR (qPCR) amplification of the amoA and nxrB functional genes and 16S rRNA genes. The total gene copy numbers across the 2013 and 2014 samples ranged from 2.0x103-4.9x107 archaeal amoA copies/µg DNA, 1.5x103-5.3x105 AOB 16S rRNA copies/µg DNA, and 7.3x105-7.7x107 Nitrospira nxrB copies/µg DNA. Overall, Nitrospira nxrB genes were found to be more abundant than AOB 16S rRNA and archaeal amoA genes in most of the sample sites across 2013 and 2014. Archaeal amoA, AOB 16S rRNA, and Nitrospira nxrB genes were quantified in sediments with pH as low as 3.2. Statistical analyses showed a significant correlation between AOB 16S rRNA gene abundance and the surface sediment pH, temperature, and dissolved oxygen. Archaeal amoA gene abundance was significantly correlated with dissolved calcium and sodium concentrations. These findings extend our understanding of the relationship between AMD and nitrifying microbes and provide a platform for further research.
Thesis:
Thesis (M.S.)-University of Colorado Denver.
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Includes bibliographic references
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System requirements: Adobe Reader
Statement of Responsibility:
by Bhargavi Ramanathan.

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University of Florida
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953837080 ( OCLC )
ocn953837080
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LD1193.E547 2016m R36 ( lcc )

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Full Text
ABUNDANCE AND DIVERSITY OF NITRIFYING MICROBES IN SEDIMENTS
IMPACTED BY ACID MINE DRAINAGE
by
BHARGAVIRAMANATHAN
Bachelor in Technology, Anna University, 2012
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Masters of Science
Environmental Science Program
2016


2016
BHARGAVIRAMANATHAN
ALL RIGHTS RESERVED


This thesis for the Master of Science degree by
Bhargavi Ramanathan
has been approved for the
Environmental Science Program
by
Annika C. Mosier, Chair
Timberley M. Roane
Frederick Chambers
April 26, 2016
m


Ramanathan, Bhargavi (M.S., Environmental Sciences)
Abundance and Diversity of Nitrifying Microbes in Sediments Impacted by Acid Mine
Drainage
Thesis directed by Assistant Professor Annika C. Mosier
ABSTRACT
Extremely acidic and metal-rich acid mine drainage (AMD) waters can have severe
toxicological effects on aquatic ecosystems. AMD has been shown to completely halt
nitrification, which plays an important role in transferring nitrogen to higher organisms
and in mitigating nitrogen pollution. We evaluated whether AMD differentially impacts
three groups of microorganisms involved in nitrification: ammonia-oxidizing archaea
(AOA), ammonia-oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB).
Sediment and water samples were collected from AMD-impacted aquatic sites during
June and August/September 2013 and 2014 in the Iron Springs Mining District (Ophir,
Colorado). Many of the sites were characterized by low pH (<5), low dissolved oxygen
concentrations (<6 mg/L), and high metal concentrations. Community 16S rRNA gene
sequencing revealed the presence of AOA (Nitrososphaera and Cenarchaeota), AOB
(Nitrosomonas), and NOB (Nitrospira) at multiple AMD-impacted sites. The overall
abundance of AOA, AOB and NOB were examined using quantitative PCR (qPCR)
amplification of the amoA and nxrB functional genes and 16S rRNA genes. The total
gene copy numbers across the 2013 and 2014 samples ranged from 2.0xl03-4.9xl07
archaeal amoA copies/pg DNA, 1.5xl03-5.3xl05 AOB 16S rRNA copies/pg DNA, and
7.3x105-7.7x107 Nitrospira nxrB copies/pg DNA. Overall, Nitrospira nxrB genes were
found to be more abundant than AOB 16S rRNA and archaeal amoA genes in most of the
IV


sample sites across 2013 and 2014. Archaeal amoA, AOB 16S rRNA, and Nitrospira
nxrB genes were quantified in sediments with pH as low as 3.2. Statistical analyses
showed a significant correlation between AOB 16S rRNA gene abundance and the
surface sediment pH, temperature, and dissolved oxygen. Archaeal amoA gene
abundance was significantly correlated with dissolved calcium and sodium
concentrations. These findings extend our understanding of the relationship between
AMD and nitrifying microbes and provide a platform for further research.
The form and content of this abstract are approved. I recommend its publication.
Approved: Annika C. Mosier
v


ACKNOWLEDGMENTS
I would like to express my deepest gratitude and immeasurable appreciation to my
research thesis advisor, Dr. Annika C. Mosier for her full support, expert guidance,
encouragement and understanding, through every step of my study and research. Without
her persistent help, guidance, expertise and patience this thesis would have not been
possible. I would also like to thank my thesis committee members: Dr. Timberley M.
Roane, for your collaboration, field work, time, mentorship and sharing your knowledge
with me, and Dr. Frederick Chambers for your support and time. I would like to specially
thank Dr. Christopher S. Miller for helping me in bioinformatics analysis and sharing his
knowledge, and Dr. Charles Patterson for sharing with me his expertise on acid mine
drainage, for his encouragement and support throughout. This thesis wouldnt have been
the same without their thoughtful questions and valuable comments.
I would like to thank Robyn Blackburn, Environmental Protection Agency Region
8, for her aid in the field and sharing her insights on the sample sites regions, and the U.S.
Forest Services Abandoned Mines Program. I would like to thank the following institutes
for their role in biological and chemical analyses: United States Environmental Protection
Agency Region 8 Laboratory, and the Roy J. Carver Biotechnology Center at the
University of Illinois.
I would like to express my sincerest gratitude to the following individuals: Joshua
D. Sackett for helping me with the samples and also sharing his knowledge and project
details, Ashley Joslin for her help with the sample preparation for the second year,
Adrienne Narrowe and Sladjana Subotic for taking their time to assist in bioinformatics
analysis, Andrew M. Boddicker for your assistance in the sequence data analysis, and for
vi


being an amazing friend and colleague throughout the way, to my parents, Ramanathan
Srinivasan and Nalini Ramanathan for you support, encouragement and your belief in me
and to many other family member and friends who supported me every step of the way. I
wouldnt be where Im today without these people in my life.
Lastly, I would like to offer this endeavor to the God Almighty for this
opportunity, the wisdom, the strength, peace of mind, and good health in order to finish
this research.
Vll


TABLE OF CONTENTS
CHAPTER
I. PROJECT OVERVIEW...................................................1
II. INTRODUCTION.......................................................2
III. MATERIALS AND METHODS.............................................5
Site Description and Sample Collection...........................5
Environmental Parameters.........................................6
DNA Extraction...................................................6
Quantitative PCR.................................................7
Illumina MiSeq Library Preparation...............................8
Community Composition Analyses..................................11
Statistical Analyses............................................13
IV. RESULTS...........................................................15
Chemistry.......................................................15
Overall Abundance of AO A and AOB...............................22
Overall Abundance of NOB........................................26
Relative Abundance and Diversity................................27
Correlation with Environmental Parameters.......................39
V. DISCUSSION........................................................46
Conclusions.....................................................49
SUPPLEMENTAL TABLES....................................................51
REFERENCES.............................................................60
viii
APPENDIX.
52


LIST OF TABLES
TABLE
3.1: Chemistry data for sample sites across June and August 2013 in the Iron Springs
Mining District....................................................................18
3.2: Chemistry data for sample sites across June and September 2014 in the Iron Springs
Mining District....................................................................19
3.3: Total recoverable metal from water samples across 2013 in the Iron Springs Mining
District...........................................................................20
3.4: Total recoverable metal from water samples across 2014 in the Iron Springs Mining
District...........................................................................21
3.5: Summary of environmental parameters for each mine/region compared to each other.
...............................................................................22
SI: Gene copy numbers of AOA amoA, AOB 16S rRNA and Nitrospira nxrB (based on
qPCR amplification). All data are expressed in copies/pg of DNA extract. Data for the
sample site NDGP was not included since there was no amplification for any genes
across 2013 and 2014...............................................................51
IX


LIST OF FIGURES
FIGURE
1. Map study sites within the Iron Springs Mining District near Ophir, Colorado.6
2. The overall abundance of (A) archaeal amoA genes, and (B) AOB 16S rRNA genes
across 2013 and 2014 samples sites in the Iron Springs Mining District..........24
3. Log ratio of AOA amoA AOQ 16S rRNA copy numbers across sample sites during
2013 and 2014 in the Iron Springs Mining District...............................26
4. The overall abundance of Nitrospira nxrB genes across 2013 and 2014 samples sites in
the Iron Springs Mining District................................................27
5. Number of observed Thaumarchaeota OTUs at each site (based on taxomonic identiy
of the archaeal 16S rRNA sequences with the Greengenes database)................29
6. Relative abundance of Thaumarchaeota taxa within the archaeal 16S rRNA gene
sequence dataset (depth of 60 sequences per sample).............................29
7. Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S rRNA
gene sequencing) color-coded by the three major mines/regions. (A) unweighted UniFrac
distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites
from both 2013 and 2014.......................................................31
8. Weighbor weighted Neighbor-Joining phylogenetic tree showing the affiliation of
Thaumarchaeota taxa-specific archaeal 16S rRNA sequences (highlighted in red) and
NCBI sequences from other environments. Only significant bootstrap values (>50) are
shown at the branch nodes.....................................................33
9. Number of observed Nitrosomonadales OTUs at each site (based on taxomonic identiy
of the AOB 16S rRNA sequences with the Greengenes database).....................34
10. Relative abundance of Nitrosomonadales taxa within the AOB 16S rRNA gene
sequence dataset (depth of 630 sequences per sample)............................34
11. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA
sequencing) color-coded by the three major mines/regions. (A) unweighted UniFrac
distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites
from both 2013 and 2014.......................................................36
12. Weighbor weighted Neighbor-Joining phylogenetic tree showing the affiliation of
Nitrosomonadales taxa-specific AOB 16S rRNA sequences (highlighted in red) and
NCBI sequences from other environments. Only significant bootstrap values (>50) are
shown at the branch nodes.....................................................38
x


13. Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S
rRNA gene sequencing) color-coded by the three major pH ranges. (A) unweighted
UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample
sites from both 2013 and 2014................................................41
14. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA
sequencing) color-coded by the three major temperature ranges (1C increment based on
sample measurements). (A) unweighted UniFrac distance matrices. (B) weighted UniFrac
distance matrices. Plots contain sample sites from both 2013 and 2014........43
15. Principal component analysis plots of Nitrosomonadales taxa (from AOB gene 16S
rRNA sequencing) color-coded by the three major pH ranges. (A) unweighted UniFrac
distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites
from both 2013 and 2014......................................................44
16. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA
sequencing) color-coded by the three major TRW manganese ranges (low, medium and
high). (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distance
matrices. Plots contain sample sites from both 2013 and 2014.................45
xi


CHAPTER I
PROJECT OVERVIEW
The overall goal of this study was to determine the abundance and diversity of
three groups of nitrifying microbes in acid mine drainage (AMD) impacted sediments in
Colorados Iron Springs Mining District: ammonia-oxidizing archaea (AOA), ammonia-
oxidizing bacteria (AOB), and nitrite-oxidizing bacteria (NOB). The specific objectives
of this study were to determine:
Overall abundance of ammonia oxidizers and nitrite oxidizers in AMD-impacted
sediments (Objective 1);
Relative abundance of specific groups of ammonia oxidizers and nitrite oxidizers in
AMD-impacted sediments (Objective 2);
Diversity of ammonia oxidizers and nitrite oxidizers in AMD-impacted sediments
(Objective 3); and
Relate the abundance and diversity of ammonia oxidizers and nitrite oxidizers to
environmental factors in AMD-impacted sediments (Objective 4).
Understanding AMD-associated disturbances on nitrification and its associated microbes
will strengthen our understanding of the environmental limits for nitrification and will
help inform management decisions (e.g., site restoration, minimizing harmful effects of
nitrogen).
1


CHAPTER II
INTRODUCTION
Nitrification, a central part of the nitrogen cycle, is globally important because it
transfers nitrogen to higher organisms and mitigates nitrogen pollution when coupled
with denitrification and anammox. Nitrification is the two-step aerobic oxidation of
ammonia to nitrate through nitrite. Ammonia oxidation is mediated by ammonia-
oxidizing bacteria and ammonia-oxidizing archaea, while nitrite oxidation is mediated by
bacteria from the Nitrobacter and Nitrospira lineages. The study of the microbial
communities involved in ammonia oxidation and nitrite oxidation is commonly based on
functional markers such as the amoA gene (encoding the alpha subunit of the ammonia
monooxygenase enzyme) (Francis eta/., 2005) and nxrB gene (encoding the beta subunit
of the nitrite oxidoreductase enzyme) (Pester eta/., 2014).
Nitrification has been documented in virtually every environment across the earth
(e.g., soil, marine, freshwater). Nonetheless, very little is known about nitrification in
systems impacted by acid mine drainage (AMD), which refers to acidic and metal-rich
waters that flow out of coal and metal mines. One study found that nitrification was
completely halted in some AMD-impacted streams with pH <5.3 (Niyogi eta/., 2003).
However, it is unknown whether the findings are a general phenomenon in other streams
or whether AMD differentially impacts the various groups of microorganisms involved in
nitrification.
Though the impacts of AMD on nitrification are largely unknown, other research
has evaluated individual environmental factors that are often associated with AMD. For
instance, nitrification is highly sensitive to acidic pH. Low pH presents physiological
2


challenges to ammonia-oxidizing microbes through reduced bioavailability of ammonia
(Suzuki eta/., 1974; Stein eta/., 1997). Previous studies found significant nitrification
rates in soils with acidic pH, including forest, tea plantation, and heath systems (Boswell,
1955; De Boer eta/., 1989; 1992; Pennington and Ellis, 1993; Tietema eta/., 1992; N
Walker and Wickramasinghe, 1979). In acidic soils, ammonia oxidation rates are
optimum at pH 5 (Hayatsu, 1993) and AOA tend to dominate over AOB (He eta/., 2007;
Stopnisek eta/., 2010; Isobe eta/., 2012; Yao eta/., 2011; He eta/., 2012). In marine
water columns and artificially acidified lakes, ammonia oxidation ceased at pH values
lower than 5.7-6.5 (Kitidis eta/., 2011; Liu eta/., 2013; Rudd et a/., 1988). The lower pH
limits for ammonia oxidation recorded to date were as low as 3.5 in soils (woodlands,
grasslands and agriculture) (Booth eta/., 2005) and 4.5 in a wastewater batch reactor
(Jimenez et a/., 2012). Bacterial and archaeal amoA genes (no measured rates) were
recovered in soils with pH values as low as 3.7 (He eta/., 2007). Very little is known
about the effects of acidic pH on nitrite oxidation or its associated microbes. One study
found that nitrite oxidation rates ceased at pH values lower than 6.5 in wastewater batch
reactors (Jimenez et a/., 2011). Nitrite oxidation rates by a Nitrospira enrichment culture
significantly decreased at pH 6, compared to the pH optimum of 8.2 (Blackburne eta/.,
2007).
Nitrification is also extremely sensitive to heavy metal concentrations. Heavy
metals such as copper, zinc, lead, cadmium, metal sulfides, and nickel are known to
inhibit nitrification in soils (Yan eta/., 2013; Cela and Sumner, 2002). Lab experiments
on freshwater sediments indicated that ammonia and nitrite oxidation rates were not
completely inhibited, but were delayed at high cadmium (5400 pg g'1 dw), copper (18800
3


pg g'1 dw), zinc (19600 pg g'1 dw) and lead (36500 pg g'1 dw) concentrations (Broberg,
1984). This suggested that some ammonia oxidizers and nitrite oxidizers were able to
adapt to the metal rich environments (Broberg, 1984). AOB recovered from long-term
zinc contaminated soils were more tolerant to elevated zinc concentrations than AOB
from uncontaminated control soils (Mertens etal., 2006). AOA were shown to be more
tolerant to copper than AOB in copper contaminated soils (Li etal., 2009). To the best of
our knowledge, no studies have been conducted to understand the effect of heavy metals
on nitrite oxidation.
In the Colorado Rocky Mountains, AMD is a particularly common threat due to
the large number of mines in the area. In the present study, we used high-throughput
sequencing and quantitative PCR (qPCR) to examine the abundance and diversity of
AOA, AOB and NOB in AMD-impacted sediments in the Iron Springs Mining District
near Ophir, Colorado. We found that AOA and Nitrospira were very abundant across
most sites, but AOB and Nitrobacter were rarely detected.
4


CHAPTER III
MATERIALS AND METHODS
Site Description and Sample Collection
The Iron Springs Mining District located in Ophir, Colorado consists of several
abandoned mines (Figure 1). Since 1890, the Iron Springs Mining District was
predominantly mined for metals such as silver, gold and lead, and also to some extent for
iron (pigment) and tungsten (Nash, 2002). AMD from these mines drains directly into the
Howard Fork River. Composite sediment samples (approximately two inches deep) were
collected from 13 sampling sites during June and August 2013, and from 11 sampling
sites during June and September 2014 at Iron Springs. Samples were stored on dry ice
until permanent storage at -20C in the laboratory freezer. For total recoverable metal
analysis (TRW), 500 mL of surface water sample was collected, acidified to pH <2 with
concentrated HNO3 and stored at 4C. For dissolved metal analysis (DM), 500 mL of
surface water sample was collected, filtered with a cellulose nitrite membrane filter
(Thermo Scientific, Waltham, MA), acidified to pH <2 with concentrated HNO3 and
stored at 4C (Roane & Sackett, in prep). An additional ~50 grams of composite surface
sediments was collected for total recoverable metal analysis from sediments (TRS) and
was stored at 4C (Roane & Sackett, in prep).
5


Iron Springs Mining District, Colorado
Figure 1. Map study sites within the Iron Springs Mining District near Ophir, Colorado.
Environmental Parameters
Temperature, pH, conductivity and dissolved oxygen were measured at the
sediment surface using a Thermo Scientific Orion 5-Star Multiparameter Meter Kit
(Thermo Fisher Scientific, Inc., Waltham, MA) (Roane & Sackett, in prep). Total
recoverable metal concentration analysis of the Iron Springs water and sediment samples
were done at the EPA Region 8 lab (Golden, CO) using Inductively-coupled Plasma
Mass Spectrometry (ICP-MS) following EPA method 200.8. Dissolved metal
concentration analysis of the Iron Springs water and sediment samples were done at the
EPA Region 8 lab (Golden, CO) using Inductively-coupled Plasma Optical Emission
Spectrometry (ICP-OES) following EPA method 200.7.
DNA Extraction
The MO-BIO PowerMax Soil DNA Isolation Kit (MO BIO Laboratories, Inc.,
Carlsbad, CA) was used to isolate total DNA from sub-samples of mechanically
6


homogenized saturated composite sediment (~10 grams) from each sample site (Roane &
Sackett, in prep). DNA extracts were quantified using the Qubit dsDNA HS Assay Kit
with the Qubit 2.0 Fluorometer (Life Technologies Corporation, Carlsbad, CA).
Quantitative PCR
The overall abundance of archaeal amoA, AOB 16S rRNA, Nitrospira nxrB, and
Nitrobacter 16S rRNA genes were quantified using the StepOnePlus Real-Time PCR
system (Life Technologies Corporation, Carlsbad, CA). The primers used were Arch-
amoAA and Avch-amoA R for archaeal amoA (Francis et al., 2005); NitA and CT0654r
for AOB 16S rRNA (Voytek and Ward, 1995; Kowalchuk et al., 1997; Sonthiphand et
al., 2013); nxrB\69F and //X/A638R for Nitrospira nxrB (Pester etal., 2014); and Nitro-
1198f and Nitro-1423r for Nitrobacter 16S rRNA (Graham et al., 2007; Huang et al.,
2010). qPCR conditions and reactions were carried out as previously described for
archaeal amoA (Mosier and Francis, 2008); for AOB 16S rRNA (Sonthiphand et al.,
2013) with the addition of a plate read step at 80C for 10s; and Nitrobacter 16S rRNA
qPCR (Graham et al., 2007). Nitrobacter 16S rRNA qPCR was carried out using a
TaqMan assay due to its high sensitivity over SYBR Green. The previously described
PCR conditions for Nitrospira nxrB (Pester et al., 2014) were applied to qPCR with the
addition of a plate read step at 84C for 10s. The reactions were performed in a 20 pi
reaction mixture with 1 pi of template DNA, 0.4 pi of 25 pM passive reference dye, and
the following for the respective primer pairs: 10 pi of Failsafe Green Premix E
(Epicentre, Madison, WI), 2 pM of MgCb, 0.4 pM of each primer, 0.04 pM of BSA, and
0.05 pM of AmpliTaq DNA Polymerase (Applied Biosystems) for archaeal amoA, 10 pi
of Failsafe Green Premix F (Epicentre, Madison, WI), 0.3 pM of each primer, 0.3 pM of
7


BSA, and 0.05 pM of AmpliTaq DNA polymerase (Applied Biosystems) for AOB 16S
rRNA; 10 pi of Failsafe Green Premix F (Epicentre, Madison, WI), 1 pM of each primer,
and 0.025 pM of AmpliTaq DNA polymerase (Applied Biosystems) for Nitrospira nxrB',
and 10 pi of Failsafe Green Premix F (Epicentre, Madison, WI), 0.9 pM of each primer,
0.25 pM of Nitro-1374Taq probe (Life Technologies Corporation, Carlsbad, CA), and
0.038 pM of AmpliTaq DNA polymerase (Applied Biosystems) for Nitrobacter 16S
rRNA. The amount of DNA used in each reaction ranged from 0.2 53.3 ng of DNA for
2013 samples and from 0.3-18.3 ng of DNA for 2014 samples.
Standard curves were generated using synthesized genes (Life Technologies
Corporation, Carlsbad, CA) or linearized plasmid DNA. The standard gene copies for the
assays ranged from 6 1.2 x 107for archaeal amoA, 16 3.3 x 107for AOB 16S rRNA, 7
- 1.5 x 107 for Nitrospira nxrB, and 18.9 3.8 x 107 for Nitrobacter 16S rRNA.
Triplicate reactions were carried out for all the samples and standards, and average values
were calculated. In some cases, one outlier was removed from some triplicate
measurements to maintain a standard deviation of less than 15% for each sample. Melt
curves were generated after each SYBR assay to check the specificity of amplification.
PCR efficiencies ranged from 87.3% 97.2% for archaeal amoA reactions, 86.1% -
89.1% for AOB 16S rRNA reactions, 72% 83.3% for Nitrospira nxrB reactions and
93.7% 95.1% for Nitrobacter 16S rRNA reactions. The correlation coefficients (R2) for
all the assays were >0.99.
Illumina MiSeq Library Preparation
DNA extracts were sequenced using the Fluidigm Access Array and Illumina
MiSeq platform at the Roy. J. Carver Biotechnology Center (University of Illinois,
8


Urbana, IL). The Fluidigm Access Array system uses microfluidic technology to perform
sequencing library preparation simultaneously across multiple samples and multiple
genes. Barcoded samples were then pooled for an Illumina MiSeq run with 300 base pair,
paired-end reads.
DNA extracts from 38 sample sites were chosen for sequencing, along with one
sample replicate and one sample of nuclease-free water as a control. The DNA extracts
from the remaining 14 sample sites were not sequenced due to either one or both of the
following reasons: no qPCR amplification for the primers sets of interest or extremely
low DNA concentration.
Prior to the Fluidigm Access Array amplification, all DNA samples were
quantified on Qubit (Life Technologies Corporation, Carlsbad, CA) using the High
Sensitivity dsDNA assay kit and each sample was diluted to a concentration of 2 ng/pl.
The master mix for amplification was prepared using the Roche High Fidelity Fast Start
Kit and 20x Access Array loading reagent according to Fluidigm protocols. The reagents
for the master mix per reaction included the following: 0.5 pi of 10X FastStart Reaction
Buffer without MgCl2, 0.9 pi of 25 mM MgCl2, 0.25 pi of DMSO, 0.1 pi of 10 mM PCR
grade Nucleotide Mix, 0.05 pi of 5 U/pl FastStart High Fidelity Enzyme Blend, 0.25 pi of
20X Access Array Loading Reagent and 0.95 pi of water. The master mix (3 pi) was
aliquoted to 48 wells of a PCR plate and then 1 pi of DNA sample and 1 pi of Fluidigm
Illumina linkers with unique barcodes were added to each well. AO A, AOB, and NOB
were sequenced with primers used for qPCR amplification: Arch-rmoHF and Arch-
amoAK for archaeal amoA, NitA and CT0654r for AOB 16S rRNA, nxrBl 69F and
rari?638R for Nitrospira nxrB, and FGPS872F and FGPS1269R for Nitrobacter 16S
9


rRNA. Total Bacterial and Archaeal 16S rRNA genes were sequenced using general
primers for the V4-V5 region of the 16S rRNA gene (modifications of 515/926R primers;
Earth Microbiome Project; (Parada etal., 2015; Quince eta/., 2011)). Archaea were also
sequenced with primers specific for archaeal 16S rRNA genes (Arch349F/Arch806;
Takai and Horikoshi, 2000; Wang and Qian, 2009). All primers were synthesized by IDT
Corporation (Coralville, IA). 20X primer solutions for each primer pair were prepared in
a separate plate by adding 2 pi of each forward and reverse primer, 5 pi of 20X Access
Array Loading Reagent and water to a final volume of lOOpl.
Each sample mixture (4 pi) was loaded into the sample inlets and 4 pi of the 20X
primers were added to the primer inlets of a previously primed Fluidigm 48.48 Access
Array Integrated Fluidic Circuit (IFC). The microfluidic loading of all the sample/primer
combinations in the IFC plate were carried out with an AX Controller (Fluidigm
Corporation, South San Francisco, CA). Following the loading, the IFC plate was placed
on the Fluidigm Biomark HD PCR machine for amplification. The samples were
amplified using the following Access Array cycling program: one cycle of 50C for 2
minutes, 70C for 20 minutes and 95C for 10 minutes; 10 cycles of 95C for 15 seconds
and 60C 30 seconds and 72C for 1 minute; 2 cycles of 95C for 15 seconds, 80C for 30
seconds, 60C for 30 seconds and 72C for 1 minute; 8 cycles of 95C for 15 seconds,
60C for 30 seconds and 72C for 1 minute; 2 cycles of 95C for 15 seconds, 80C for 30
seconds, 60C for 30 seconds and 72C for 1 minute; 8 cycles of 95C for 15 seconds,
60C for 30 seconds and 72C for 1 minute; and 5 cycle of 95C for 15 seconds, 80C for
30 seconds, 60C for 30 seconds and 72C for 1 minute.
10


Following amplification, the PCR products were harvested by loading 2 pi of
Fluidigm Harvest Buffer into the sample inlets and placing the IFC on the AX controller.
Harvested PCR products were then transferred to a new 96 well plate, quantified on a
Qubit Fluorometer (Life Technologies Corporation, Carlsbad, CA) and stored at -20C.
All samples were run on a Fragment Analyzer (Advanced Analytics, Ames, IA) to
confirm amplicon regions and expected sizes. Samples were pooled in equal amounts
according to product DNA concentration. The pooled products were then size selected on
a 2% agarose E-gel (Life Technologies, Carlsbad, CA) and extracted with a Qiagen gel
extraction kit (Qiagen, Hilden, Germany). Cleaned, size-selected products were run on
an Agilent Bioanalyzer to confirm an appropriate profile and to determine an average
size. The library pool was quantified by qPCR and loaded on to an Illumina MiSeq
flowcell for 2x300 paired-end sequencing (Illumina, Inc. San Diego, CA). Illumina
sequencing technology allows parallel sequencing of DNA, resulting in approximately 25
billion reads using MiSeq sequencer. The flowcell was sequenced for 301 cycles from
each end of the fragments using a MiSeq 600-cycle sequencing kit version 3. PhiX DNA
(genome length: 3kb) was used as a spike-in control. For the FASTQ quality score, the
ASCII offset of 33 (Sanger scores) was used instead of the 64 offset using Casava 1.8.
The sequence data was demultiplexed using bcl2fastq vl.8.4 Conversion Software
(Illumina, Inc. San Diego, CA) and PCR primer sorted by the Roy. J. Carver Center for
Biotechnology. Reads matching the PhiX control were removed using Casava v. 1.8.
Community Composition Analyses
Relative sequence abundance and diversity analyses (Appendix I) were conducted
in the QIIME (Quantitative Insights into Microbial Ecology) open package software
11


(Caporaso etal., 2010). The paired-ends were joined and quality filtered at Phred quality
score of 20. The V4-V5 combined 16S rRNA primer sequencing reads were clustered
into operational taxonomical units (OTUs) at 97% sequence identity. For taxa-specific
analyses, the AO A, AOB, and NOB-like 16S rRNA gene sequencing reads were
clustered at 99% OTUs. The taxonomy was assigned using a BLAST based method in
QIIME and searched against the Greengenes 13 8 rep set database (DeSantis el al.,
2006). The final OTUs were checked for chimeras using the DECIPHER web tool with
the short sequences option (Wright et al., 2012). Putative chimeras were removed from
the OTU table using QIIME. The final OTUs were filtered for specific taxa for further
analyses: Archaeal 16S rRNA reads were filtered for the class Thaumarchaeota and AOB
16S rRNA reads were filtered for the order Nitrosomonadales (no other AOB sequences
were identified in the dataset).
Diversity analyses were done to understand the community composition within
sample sites (alpha diversity) and between sample sites (beta diversity). Alpha diversity
analyses (Chaol, Faiths phylogenetic diversity and observed OTUs) were determined
based on multiple rarefied depths and collated for creating rarefaction plots. Rarefaction
of all the samples for Thaumarchaeota taxa-specific archaeal 16S rRNA and
Nitrosomonadales taxa-specific AOB 16S rRNA was performed at multiple depths with
size of 10 sequences at each step between the minimum and maximum number of
sequences. For beta diversity analyses, the sampling depth was rarefied to 16,000
sequences for the combined V4-V5 region of total 16S rRNA, 630 sequences for
Nitrosomonadales taxa-specific AOB 16S rRNA, and 60 sequences for Thaumarchaeota
taxa-specific archaeal 16S rRNA to avoid bias of various sampling depth. Principal
12


component analysis (PCoA) plots were generated using both unweighed and weighed
UniFrac distance metrics (Lozupone and Knight, 2005).
Weighbor weighted Neighbor Joining phylogenetic trees were constructed for
representative sequences of each observed OTU using the Ribosomal Database Project
(RDP) Tree Builder. Distance (Jukes-Cantor Correction) and bootstrap analyses (100
replicates) were performed using Tree Builder (RDP). Edits were made to the tree using
the online software Interactive Tree of Life v3.1 (iTOL) (Letunic and Bork, 2006).
Statistical Analyses
Analysis of Similarity (ANOSIM) was performed to determine the statistical
significance of site-specific microbial community clusters by environmental variables
(e.g., location, pH) using 999 permutations. Spearman correlations between
environmental parameters and the relative abundance of taxa-specific OTUs
(Thaumarchaeota and Nitrosomonadales) were carried out using QIIME
(observationmetadatacorrelation.py) with />values corrected using the Benjamini-
Hochberg FDR procedure. Spearman correlations between environmental parameters and
total gene abundances (based on qPCR) were performed using the IBM SPSS Statistics
software package version 23 (IBM, Armonk, NY) with />values corrected using
Bonferroni correction (critical /i-value/number of correlations; the adjusted significant p-
value was set to < 0.002). The environmental parameters from surface sediments included
in the statistical analyses were: pH, temperature (C), conductivity (pS/cm) and dissolved
oxygen (mg/L). Environmental parameters from surface water included in statistical
analyses were: total recoverable metals (TRW in pg/L) including Al, As, Ba, Cd, Ca Cu,
13


Fe, Pb, Mg, Mn, Ni, Na, TI, and Zn; and dissolved metals (DM in gg/L) including Al, Ba,
Cd, Ca, Cu, Fe, Pb, Mg, Mn, Ni, Na, and Zn.
14


CHAPTER IV
RESULTS
Chemistry
Sample sites covered four different mines/regions within the greater Iron Springs
Mining District: Caribbeau Mine (CaribOl, Carib02 & Carib03), Iron Bog/Fen (FenIBOl,
FenIB02, HFIBE & Opp03), New Dominion Mine (FenNDOl, FenND03, NDMD02,
NDCS02, CS1B & NDGP), and Howard Fork River (HF04). The measured
environmental parameters varied by sampling date and sampling location (Table 3.1 &
3.2) . Across surface sediments from all the sites in 2013 and 2014, temperature ranged
from 7.8C 22.4C and dissolved oxygen ranged from 1.1 mg/L 10 mg/L (Table 3.1 &
3.2) . Conductivity ranged from 296 pS/cm 1608 pS/cm (data not shown).
During 2013, pH was lowest at the NDGP (pH 3.6) and CS1B (pH 3.2 and 4.1)
sites at both time-points. At the Iron Bog/Fen sites, pH ranged from 4.1 4.8 at the
drainage site (FenIBOl & FenIB02) and gradually increased to 6.1 6.4 at the confluence
of the AMD drainage and the river (HFIBE). pH values of the New Dominion Mine
(FenNDOl, FenND03 & NDMD02) sites and Caribbeau Mine sites, ranging from 6.2 -
7.8, were higher than other sites, possibly due to inputs from the nearby wetland systems.
Similar to 2013, the pH was at its lowest at CSIB-Jun (pH 3.2) and highest at the
Caribbeau Mine sites (pH 6.9 7.3) in 2014. In 2014, pH followed a similar trend with
Iron Bog/Fen sites (FenIBOl, FenIB02 & HFIBE) ranging from 3.5 5.6 and New
Dominion Mine (FenNDOl & FenND03) ranging from 5.7 7.1. The September time
points for NDCS02 and NDMD02 had low pH (pH 3.8 and 3.7) when compared to the
June time points (pH 6).
15


The total recoverable metal concentrations from surface water (TRW; including
both dissolved and suspended metals) were measured for all samples sites and time
points. The sediments contain high concentrations of metals commonly found in AMD
environments including cadmium, copper, iron, lead, and zinc (Table 3.3 and 3.4).
Concentrations of certain metals including arsenic, barium, cadmium, nickel and thallium
were below the detection limit of the instrument for the majority of the sample sites
during 2013 and 2014. During 2013, total recoverable metal concentrations (TRW)
ranged from 0.94 48.7 pg/L for cadmium, 10.8 382 pg/L for copper, 0.38 36.8 mg/L
for iron, 0.97 110 pg/L for lead, and 32.6 18,300 pg/L zinc. During both sample
points in 2013, CS1B and NDGP sample sites had the highest metal concentrations for
the majority of the metals including aluminum (Al), calcium (Ca), copper (Cu), iron (Fe),
lead (Pb), magnesium (Mg), manganese (Mn), sodium (Na), and zinc (Zn). During 2014,
total recoverable metal concentrations ranged from 1.02 25.4 pg/L for cadmium, 32.4 -
620 pg/L for copper, 1.08 139 mg/L for iron, 1.29 74.4 pg/L for lead, and 154 -
11,800 pg/L for zinc. Metal concentrations (Al, Ca, Fe, Pb, Mn, Na and Zn) were high in
New Dominion Mine sites overall when compared to other mines/regions. Overall, metal
concentrations during 2013 were higher in comparison with 2014.
The dissolved metal concentrations from surface water in sites across 2013 ranged
from 70.7 380 mg/L for calcium, 0.13 40.3 mg/L for iron, 5.6 15.4 mg/L for
magnesium, and 19.4 19,100 pg/L for zinc (data not shown). Dissolved metal
concentrations for Al, Cd, Cu, Pb, and Ni were below the detection limit of the
instrument for most of the sites, with the exception of CS1B, FenIBOl, FenIB02,
NDCS02, NDGP and Opp03. CS1B and NDGP had high metal concentrations for the
16


majority of the metals such as Al, Fe, Mg, Mn, Na and Zn, at both time-points in 2013.
Across 2014, the dissolved metal concentrations ranged from 58.1 368 mg/L for
calcium, 0.63 10 mg/L for iron, 3.5 12.9 mg/L for magnesium, and 112 12,100 pg/L
for zinc. Overall, dissolved metals concentrations for Ca, Fe, Mg, Mn, Na, and Zn were
high in New Dominion Mine sites and Caribbeau Mine sites in 2014 when compared to
other mines/regions. Each mine/region was different when characterized and compared
with each other based on measured environmental parameters (Table 3.5).
Metals were also measured in surface sediments (in addition to surface water
reported above) in 2013 and ranged from 1,600- 237,000 ppb for arsenic, 131 311,000
ppb for cadmium, 33.2 5,690 ppb for copper, 5,590 372,000 ppb for iron, 36,000 -
1,660,000 ppb for lead, 681 521,000 ppb for nickel, and 22.1 5,140 ppb for zinc. On
average, the Caribbeau Mine sites and New Dominion Mine sites had the highest
sediment metal concentrations for the majority of the metals.
17


Table 3.1: Chemistry data for sample sites across June and August 2013 in the Iron
Springs Mining District.___________________________________________________________
Summer 2013
Sample Region Sample Sites Date Collected pH Temp. CO DO (mg/L)
CaribOl 6.25.13 7.3 7.9 8.8
a 8.05.13 7.1 7.8 7.7
Carib02 6.25.13 7.2 8.4 8.0
.=
'C § 8.05.13 7.8 8.0 8.5
U Carib03 6.25.13 6.6 13.1 7.9
8.05.13 7.0 7.9 10
$ 6.25.13 3.2 14.9 5.1
g- e CS1B
u £ 8.05.13 4.2 21.2 6.6
FenIBOl 6.25.13 4.5 13.4 1.4
c
o s- WD 8.05.13 4.8 10.4 1.1
c pa FenIB02 6.25.13 4.1 18.5 6.4
Pm 8.05.13 4.3 19.8 3.5
FenNDOl 6.25.13 7.3 11.0 8.1

New lomir -on Fen 8.05.13 6.25.13 6.2 6.4 13.3 14.3 6.0 6.6
FenND03
M
8.05.13 6.5 18.7 5.9
-o 6.25.13 5.7 8.9 7.8

S o HF04
c u. a 8.05.13 8.3 10.9 7.9
? 6.25.13 6.1 17.4 6.3
Sh M I -as pi O i- " HFIBE
£ O tM 8.05.13 6.4 14.6 7.5
2 ^ g fi 5^ £ S a S NDCS02 6.25.13 4.6 20.3 4.5
Z o 's CO
P U 8.05.13 4.1 18.0 5.2
^ fl S ^ |! 1 £! NDGP 6.25.13 3.6 14.2 5.0
Z 1 0 &H 8.05.13 3.6 22.4 2.2
1 6.25.13 4.7 14.0 3.9
§ s 3 Opp03 8.05.13
2 2
5.9 8.4 3.8
3 (DA 6.25.13 6.8 9.5 8.9
? 2 fi fi C S B .§ NDMD02
^ 5 z 2 p 8.05.13 7.4 8.7 8.1
18


Table 3.2: Chemistry data for sample sites across June and September 2014 in the Iron
Springs Mining District.
Summer 2014
Sample Region Sample Sites Date Collected pH Temp. fC) DO (mg/L)
CaribOl 6.24.14 7.1 n.d 8.4
3 9.30.14 6.9 8.0 5.9
£ 1 Carib02 6.24.14 7.3 n.d 8.5
-fi .5
'C § A ^ 9.30.14 7.1 7.9 6.7
u Carib03 6.24.14 7.2 n.d 8.0
9.30.14 7.1 8.4 7.8
-d 6.24.14 3.2 10.7 7.0
g- C4 o o CS1B
S 1* H u 9.30.14 4.9 10.2 7.6
FenIBOl 6.24.14 3.6 9.3 7.1
g § o 9.30.14 4.3 13.7 6.9
^ HH M FenIB02 6.24.14 3.5 15.9 5.7
n.s n.s n.s n.s
6.24.14 5.7 15.1 9.2
a FenNDOl
s o a a 9.30.14 6.9 8.4 9.2
p o ii ^ £ 6.24.14 7.1 n.d 6.9
z FenND03 n.s n.s n.s n.s
-o £ * n.s n.s n.s n.s
i HF04
o u. a n.s n.s n.s n.s
? o 6.24.14 5.5 9.5 8.1
S HH M s ^ ^ HFIBE
X 9.30.14 5.6 8.7 8.5
a "O .S & P'Sao.S' £ g o. 3 NDCS02 6.24.14 6.0 15.0 6.1
Z o 1 a aj P U 9.30.14 3.8 10.6 6.9
6.24.14 6.5 16.0 8.9
^ S3 S3 ^ |! I £! NDGP
9.30.14 7.4 9.3 8.4
1 n.s n.s n.s n.s
a g '3 Opp03
£ n.s n.s n.s n.s
HH Q
_ 2 ten 6.24.14 6.0 9.0 5.7
New Domin -on Mine Drain a; NDMD02 9.30.14 3.7 8.5 5.9
n.s: not sampled
n.d.: not determined.
*Temperature readings not available for some sites due to an equipment failure.
19


Table 3.3: Total recoverable metal from water samples across 2013 in the Iron Springs
Mining District.____________________________________________________________________
Summer 2013
Sample Region Sample Sites Date Collected Cadmium (Pg/L) Copper (Pg/L) Iron (mg/L) Lead (Pg/L) Zinc (Pg/L)
CaribOl 6.25.13 12 132 5.2 57 104
a 8.05.13 BDL 51.5 7.3 5.6 166
£ g Carib02 6.25.13 BDL 32.8 5.5 3.7 102
-& .E
'C s U 8.05.13 BDL 43 6.4 5.0 150
Carib03 6.25.13 BDL 28.8 4.6 3.2 98.5
8.05.13 BDL 39.7 6.1 6.6 160
-d 6.25.13 19.1 194 36.8 39.4 16200
Q, S' S M So P- £ £ CS1B
S M H u 8.05.13 48.7 382 12.4 42.3 18300
FenIBOl 6.25.13 1.9 308 4.5 4.3 430
a o 6D 8.05.13 2 281 16.3 12.7 538
u- ca FenIB02 6.25.13 1.3 45.7 0.7 1.8 236
8.05.13 1.5 37.4 0.8 1.8 325
FenNDOl FenND03 6.25.13 BDL BDL 2.5 1.0 55.4
New )omin -on Fen 8.05.13 6.25.13 BDL BDL BDL BDL 2.8 BDL BDL BDL BDL 88.7
8.05.13 BDL BDL BDL 1.2 279
-o S £ 6.25.13 BDL BDL 0.4 BDL 32.6
HF04
o t£
a 8.05.13 BDL BDL BDL BDL BDL
-o 6.25.13 BDL 75.5 2.1 1.8 169
S S M
s s: a HFIBE 8.05.13 0.9 90.9 5.0 4.2 191
i -a it a a. 5 '3 S g- i-T & ft. 0) NDCS02 6.25.13 BDL BDL 0.7 3.6 158
A O S 1/5
£5 U 8.05.13 BDL 54 16.3 110 503
6.25.13 5.46 271 15400 47.5 1860
New Domin -on Green Pond NDGP 8.05.13 13.2 225 15600 40.6 4230
u Sv 6.25.13 1.7 277 15900 5.18 374
e § 1 Opp03
A a 8.05.13 1.79 292 26400 6.98 422
.5 Ts s s £ a a <5JD ce a NDMD02 6.25.13 BDL 10.8 6.2 1.2 BDL
z g S 8.05.13 BDL BDL 6.5 1.1 BDL
BDL: Values were measured, but below the detection limit of the instrument.
20


Table 3.4: Total recoverable metal from water samples across 2014 in the Iron Springs
Mining District.____________________________________________________________________
Summer 2014
Sample Sample Date Cadmium Copper Iron Lead Zinc
Region Sites Collected (Pg/L) (Pg/L) (mg/L) (Pg/L) (Pg/L)
CaribOl 6.24.14 BDL 92 13.9 33.7 192
a ce 9.30.14 BDL 74.6 10.3 10.9 231
Carib02 6.24.14 BDL 88 13.7 33.5 198
-& .£ 232
g s 9.30.14 BDL 111 10.5 11.8
u Carib03 6.24.14 1.02 39.8 7.2 36.2 249
9.30.14 BDL 32.4 4.9 5.6 154
-d a S" S ^ 5T O O CS1B 6.24.14 22.9 277 7.6 36.3 11800
^5 N u 9.30.14 10.9 82.3 1.9 27.7 4150
FenIBOl 6.24.14 2.5 254 10.2 5.1 570
C C JD 9.30.14 5.2 428 BDL 3.3 730
u- CO F enIB02 6.24.14 2.9 94.3 BDL 2 536
n.s n.s n.s n.s n.s n.s
FenNDOl 6.24.14 BDL BDL 1.6 BDL BDL
New omin -on Fen 9.30.14 6.24.14 BDL BDL BDL BDL 3.6 BDL BDL BDL 271 201
P FenND03
n.s n.s n.s n.s n.s n.s
n.s n.s n.s n.s n.s n.s
S HF04
O ti K n.s n.s n.s n.s n.s n.s
a i- k> £ 09 6.24.14 BDL 85.3 6.0 12.6 208
£ a HFIBE
o^o a a 9.30.14 1.0 62.6 1.8 2.6 166
.s "2 o. £ a S NDCS02 6.24.14 2.4 BDL BDL 18 555
Z 1 aj
P U 9.30.14 25.4 206 2.9 26.7 10100
fl 6.24.14 BDL BDL 7520 1.29 BDL
New Dominit Green Pond NDGP 9.30.14 BDL BDL 13900 3.26 269
n.s n.s n.s n.s n.s n.s
a C *2 Opp03
2 n.s n.s n.s n.s n.s n.s
c v 6.24.14 20.9 82 1.1 74.4 495
1.a NDMD02
* § S 2
p o 9.30.14 13.7 620 7.7 57.7 4040
n.s: not sampled.
BDL: Values were measured, but below the detection limit of the instrument.
21


Table 3.5: Summary of environmental parameters for each mine/region compared to each
other.
Parameters
Sites pH Temperature CO Dissolved Oxygen (mg/L) Conductivity (pS/cm) Metal Concentrations (TRW & DM)
Caribbeau Mine 6.6-7.8 7.8-13.1 5.9-10 814-1139 Low
Iron Bog/Fen 3.5-6.4 8.4-19.8 1.1-8.5 479-1018 High
New
Dominion 3.2-7.4 8.4-22.4 2.2-9.2 889.6-1608 High
Mine
Howard Fork River 5.7-8.3 8.8-10.9 7.8-7.9 296-335 Low
Overall Abundance of AOA and AOB
The overall abundance of AOA and AOB genes was determined across 13 AMD-
impacted sites from June 2013 and August 2013 and 11 AMD-impacted sites from June
2014 and September 2014 from the Iron Springs Mining District. AOA were quantified
using archaeal amoA genes. AOB were quantified using AOB-specific 16S rRNA gene
primers since AOB amoA genes did not amplify in our samples (using primers amoA-
lF/amoA-2R (Rotthauwe et al., 1997) or amoA-lF*/amoA-2R (Stephen el al., 1998)).
Archaeal amoA gene copies across 2013 and 2014 ranged from 2 x 103 4.9 x 107
copies/pg of sediment DNA (Figure 2A). During 2013, archaeal amoA genes were most
abundant in the Howard Fork River site (HF04, ~106 copies/pg of sediment DNA) and
the Iron Bog/Fen sites (HFIBE & FenIBOl, ~105-106 copies/pg of sediment DNA),
followed by the Caribbeau Mine sites (CaribOl, Carib02 & Carib03, ~104-105 copies/pg
of sediment DNA). In 2014, the archaeal amoA genes were most abundant in the Iron
Bog/Fen sites (HFIBE & FenIBOl, ~107 copies/pg of sediment DNA) and one of the New
22


Dominion sites (NDMD02, ~107 copies/pg of sediment DNA), followed by the
Caribbeau Mine site (Carib02, ~106-107 copies/pg of sediment DNA). The archaeal
amoA gene copy numbers were lowest at the New Dominion Mine site (NDCS02-Septl4,
~105 copies/pg of sediment DNA) with a ~ 100-fold difference when compared to the
most abundant sites. Archaeal amoA genes did not amplify in FenND03-Jun, NDGP-Jun,
NDGP-Aug, and Opp03-Jun samples for 2013, and CaribOl-Jun, Carib03-Jun, CS1B-
Sept, FenIB02-Jun, FenNDOl-Jun, FenNDOl-Sept, NDCS02-Jun, NDGP-Jun, and
NDGP-Sept samples for 2014 possibly suggesting that AOA were absent in those
selected sites, were present in low numbers, or did not properly amplify with the PCR
primers.
The overall abundance of AOB 16S rRNA genes across 2013 and 2014 samples
sites ranged from 1.5 x 103- 5.3 x 105 copies/pg of sediment DNA (Figure 2B). During
2013, the AOB 16S rRNA genes were most abundant in the Caribbeau Mine sites
(CaribOl, Carib02 & Carib03, ~104-105 copies/pg of sediment DNA) followed by the
Howard Fork River site (HF04, ~104 copies/pg of sediment DNA) and one of the New
Dominion Mine sites (NDMD02, ~104 copies/pg of sediment DNA). Similar to 2013,
AOB were most abundant in Caribbeau Mine sites (Carib01-Septl4, Carib02 & Carib03-
Septl4, ~104-105 copies/pg of sediment DNA) in 2014. The AOB 16S rRNA gene copy
numbers were lowest in one of the Iron Bog/Fen site (FenIB02-Junl4). AOB 16S rRNA
genes did not amplify in FenIBOl-Jun, FenIBOl-Aug, FenNDOl-Jun, FenND03-Jun,
NDCS02-Aug, NDGP-Jun, NDGP-Aug, Opp03-Jun, and Opp03-Aug samples for 2013,
and Carib03-Jun, CSIB-Jun, FenIBOl-Jun, FenNDOl-Jun, FenND03-Jun, HFIBE-Sept,
23


NDCS02-Jun, NDCS02-Sept, NDGP-Jun, NDGP-Sept, NDMD02-Jun, and NDMD02-
Sept samples for 2014.
A:
<
z
Q
O
X
o.
o
CJ
c
X
<
o
5
5
-fl
a
<
6.E+07
5.E+07 -
4.E+07 -
3.E+07 -
2.E+07 -
l.E+07 -
0.E+00
1 Jun-13
River Iron Bog/Fen
i Aug-13
iJun-14
New Dominion Mine
n-----1 ---r
O
IJ-H
a
o
CQ
d
cn
O
Qh
Qh
O
O
CQ
d
IJ-H
ii
w
m
E
K
O
Q
Z
%
o
o
CO
o
m
So
u
Ph
1 Sep-14
Carribeau Mine
J i
cn
o
1)
IJ-H
o
o o
B:
<
z
Q
o
X
o.
o
(J
CJ
c
X
Tfl
O
6.E+05
l Jun-13
River Iron Bog/Fen
5.E+05 -
4.E+05 -
3.E+05 -
2.E+05 -
l.E+05 -
0.E+00 -H
o
PH
X
Aug-13 *100-14
New Dominion Mine
lSep-14
Carribeau Mine
o
m
E
1)
IJ-H
o
Qh
Qh
O
O
CQ
d
IJ-H
w
e
IJ-H
X
O o O O O o O O
GO So &
§ z § U u z c cd c cd c cd
| 1) IJ-H § o IJ-H U U O
Figure 2. The overall abundance of (A) archaeal amoA genes, and (B) AOB 16S rRNA
genes across 2013 and 2014 samples sites in the Iron Springs Mining District.
24


We compared the relative abundance of AOA and AOB in our samples using
AOA amoA genes and AOB 16S rRNA genes, since AOB amoA genes did not amplify in
our system and both genes are thought to exist in single copies within each genome
(Aakra eta/., 1999; Kowalchuk and Stephen, 2001; Hermansson and Lindgren, 2001;
Norton eta/., 2008). The qPCR results indicated that archaeal amoA gene copies were
greater than the AOB 16S rRNA gene copies at most sites and time-points (Figure 3). On
average, the archaeal amoA gene was 71 times more abundant than AOB 16S rRNA in
2013 (average logio AOA amoA:AOB 16S rRNA ratio = 1.5) and 356 times more
abundant in 2014 (average logio AOA amoA: AOB 16S rRNA ratio = 1.9). However, the
AOB 16S rRNA gene is on average half as abundant than archaeal amoA at the CS IB and
NDCS02 (June) sites during 2013 (average logio AOA amoA:AOB 16S rRNA ratio = -
0.4). Archaeal amoA gene amplification was observed in many sites with no AOB 16S
rRNA gene amplification, including FenIBOl, FenNDOl-Jun, NDCS02-Aug and Opp03-
Aug in 2013, and CSIB-Jun, FenIBOl-Jun, FenND03-Jun, NDCS02-Sept and NDMD02
during 2014. Similarly, AOB 16S rRNA gene amplification was observed in 2014
samples sites (Carib03-Sept, CSIB-Sept, FenIB02-Jun and FenNDOl-Sept) where there
was no archaeal amoA amplification.
25


Jun-13
Aug-13
Jun-14
Sep-14
15.0
10.0
IS1
\o
as
o
<
%
&
<
o
<
5.0
0.0
#J3
O
-5.0
-10.0
Figure 3. Log ratio of AOA amoA: AOB 16S rRNA copy numbers across sample sites
during 2013 and 2014 in the Iron Springs Mining District.
Overall Abundance of NOB
The overall abundance of Nitrospira nxrB was quantified using qPCR across 13
AMD-impacted sites from June 2013 and August 2013 and 11 AMD-impacted sites from
June 2014 and September 2014, from the Iron Springs Mining District (Figure 4). The
total number of Nitrospira nxrB gene copies across 2013 and 2014 samples sites ranged
from 7.3x105-7.7x107 copies/pg of sediment DNA. During 2013, Nitrospira nxrB genes
were most abundant at the Howard Fork River sites (HF04, ~107 copies/pg of sediment
DNA) and Caribbeau Mine sites (CaribOl, Carib02 & Carib03, ~106-107 copies/pg of
sediment DNA). Similar to 2013, Nitrospira nxrB genes were most abundant at the
Caribbeau Mine sites (Carib02 & Carib03, ~106-107 copies/pg of sediment DNA)
followed by FenIBOl (~107 copies/pg of sediment DNA) and FenNDOl sites (~106-107
26


copies/gg of sediment DNA) in 2014. Nitrospira nxrB genes were not amplified in
CSIB-Jun, CSIB-Aug, FenIB02-Aug, FenND03-Jun, FenND03-Aug, NDCS02-Aug,
NDGP-Jun, NDGP-Aug, and Opp03-Aug samples for 2013, and CaribOl-Jun, CaribOl-
Sept, CSIB-Sept, Fenlb02-Jun, HFIBE-Sept, NDCS02-Jun, NDCS02-Sept, NDGP-Jun,
NDGP-Sept, and NDMD02-Jun samples for 2014.
Nitrobacter genes did not amplify in any of the samples using nxrB-lF/nxrB-lR
primers (Vanparys el al., 2007), Nitrobacter-specific 16S rRNA gene primers Nitro-
1198f/Nitro-1423r (Huang et al., 2010), or Nitrobacter-specific 16S rRNA gene primers
FGPS872f/FGPS1269r (Degrange and Bardin, 1995).
1 Jun-13
i Aug-13
iJun-14
1 Sep-14
< z 9.E+07 -I
o 8.E+07 -
o X s 7.E+07 -
& 6.E+07 -
o 5.E+07 -
c X 4.E+07 -
3.E+07 -
& 2.E+07 -
l.E+07 -
1 0.E+00 -
Figure 4. The overall abundance of Nitrospira nxrB genes across 2013 and 2014 samples
sites in the Iron Springs Mining District
Relative Abundance and Diversity
The relative abundance and diversity of the V4-V5 combined region of total 16S
rRNA, archaeal 16S rRNA, AOB 16S rRNA, and Nitrobacter 16S rRNA genes were
evaluated in 38 samples from AMD-impacted sites (2013 and 2014). Preliminary
27


analyses performed on total 16S rRNA revealed that the bacterial communities in these
sites were dominated by Proteobacteria (47%), Bacteroidetes (10.6%), Chloriflexi (8%),
Acidobacteria (7.0%) and Actinobacteria (6.3%) (data not shown). The archaeal
communities consisted of organisms belonging to the phylum Crenarchaeota,
Euyarchaeota and Parvarchaeota. Thaumarchaeota accounted for 0.1%, Nitrospira
accounted for 0.7%, and Nitrosomonadales specific betaproteobacteria accounted for
<0.1% of the total community. Beta diversity analyses based on unweighted and
weighted Unifrac distance matrices revealed that the sample mines/regions did not cluster
together, except for the Caribbeau Mine sites (data not shown).
Twenty samples contained >10 Thaumarchaeota sequences within the archaeal
16S rRNA gene sequence dataset. Of those, a total of 11 unique Thaumarchaeota OTUs
(clustered at 99% identity) were recovered, with 1-7 OTUs observed within each sample
(Figure 5). The Thaumarchaeota OTUs were taxonomically related to Nitrosopumilus,
SAGMA-X, Cenarchaeaceae, and Candidatus Nitrososphaera (Figure 6).
28


Jun 13
Aug -13
Jun -14
Sept -14
Figure 5. Number of observed Thaumarchaeota OTUs at each site (based on taxomonic
identiy of the archaeal 16S rRNA sequences with the Greengenes database).
f__Cenarchaeaceae; g_
(15.1%)
g_Nitrosopumilus (50.7%)
f SAGMA-X; g_ (23.1%)
g_Candidatus Nitrososphaera
(11.1%)
Figure 6. Relative abundance of Thaumarchaeota taxa within the archaeal 16S rRNA
gene sequence dataset (depth of 60 sequences per sample)
29


Beta diversity analyses on the Thaumarchaeota taxa-specific archaeal 16S rRNA,
were conducted for 17 sample sites each with a sampling depth of 60 sequences.
Unweighted UniFrac analyses, based on the presence and absence of observed OTUs,
revealed no statistically significant correlation (Ranosim = 0.01, p = 0.37, 999
permutations) between the three major mines/regions (Caribbeau Mines, New Dominion
Mine Site and Iron Bog/Fen) (Figure 7A). There was also no statistically significant
correlation between the three mines/regions when considering OTU abundance in
weighted UniFrac analyses (Ranosim = 0,009, p = 0.42, 999 permutations) (Figure 7B).
30


Figure 7. Principal component analysis plots of Thaumarchaeota taxa (from archaeal
16S rRNA gene sequencing) color-coded by the three major mines/regions. (A)
unweighted UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots
contain sample sites from both 2013 and 2014.
31


Phylogenetic analyses of Thaumarchaeota taxa-specific archaeal 16S rRNA
revealed that a proportion of the OTUs belonging to unassigned genus of Nitrososphaera
fell under Group I. lb (3 of 11) and OTUs belonging to the genus Nitrosopumilus fell
under mine soil environments (3 of 11) (Figure 8). When combined, approximately 55%
of the Thaumarchaeota OTUs fell under the soil group. One OTU was closely related to
coastal AOA and two OTUs were closely related to AOA from freshwater environments.
Two OTUs were not closely related to any other database sequences.
32


Tree scale: 0.01
Environments



Soil
Coastal
Mine Soil
Freshwaters
uncultured archaeon| CaR3s.26'
'uncultured archaeon| ARCHDBF4'
uncultured archaeon| T-RF|a| NG-W-080829 2-11'
'Candidatus Nitrosoarchaeum koreensis MY1'
Candidatus Nitrosoarchaeum limnia BG20'
Candidatus Nitrosoarchaeum limnia SFB1'
Candidatus Nitrosopelagicus brevis strain CN25'
'FenlB01Junel4 148805'
T
*.r
Cenarchaeum symbiosum A'
'Thaumarchaeota archaeon MY2'
Thaumarchaeota archaeon N4'
'Candidatus Nitrosopumilus sp. NF5'
'Candidatus Nitrosopumilus sp. NM25'
'Candidatus Nitrosopumilus salaria BD31'
W'Candidatus Nitrosopumilus koreensis AR1'
r

Candidatus Nitrosopumilus sp. AR2'
Nitrosopumilus maritimus SCM1'
'Candidatus Nitrosopumilus sp. D3C'
Carib01Augl3 1908'
'uncultured crenarchaeote| JG35-TR-Arl4'
Carib02Septl4 19492'
'Carib03Augl3 25010'
I'Car
Â¥
'uncultured archaeon| CASN22'
'uncultured archaeon| A P3K8f
'uncultured archaeon| A2 29'
* 'uncultured archaeon| K1 3A9'
uncultured archaeon| HJARA B36'
'uncultured archaeon| A0610R002 B05'
uncultured archaeon| IH2-2b'
uncultured crenarchaeote| DGGE band HMLNll'
p'uncult
t
r'FenlB01Septl4 151732'
*-'FenlB01Septl4 154950'
'Carib03Septl4 74885'
Candidatus Nitrososphaera gargensis Ga9.2'
'Candidatus Nitrososphaera evergladensis SRI'
'Nitrososphaera viennensis strain EN76'
'unidentified archaeon SCAll'
| I-uncultured crenarchaeote| S2A-6'
68|('uncultured archaeon| ZYsA-1'
Carib03Augl3 24719'
'Carib03Augl3 32550'
Uncultured crenarchaeote genomic fragment 54d9'
'Candidatus Nitrosocaldus yellowstonii strain HL72'
'Methanosarcina mazei| N2M9705'
Figure 8. Weighbor weighted Neighbor-Joining phylogenetic tree showing the affiliation
of Thaumarchaeota taxa-specific archaeal 16S rRNA sequences (highlighted in red) and
NCBI sequences from other environments. Only significant bootstrap values (>50) are
shown at the branch nodes.
Fifteen samples contained at least ten AOB sequences within the AOB 16S rRNA
sequences sequence dataset. Of those, a total of 29 unique OTUs (clustered at 99%
identity) were recovered belonging to the order Nitrosomonadales, with 2-25 OTUs
observed within each sample (Figure 9). An unassigned genus of the family
Nitrosomonadaceae accounted for 99.7% of OTUs, followed by 0.2% of Other
33


classified genus of the family Nitrosomonadaceae, and 0.1% Nitrosomonas (Figure 10).
No other AOB sequences were recovered (e.g., Nitrosospira).
Jun -13 Aug -13 Jun 14 Sept 14
-H o o O a O
P-H CQ CQ Q
a E E a §
o Ph o Ph |
m
GO
U
o
cd
U
O
"S
cd
U
CO
O
cd
U
Figure 9. Number of observed Nitrosomonadales OTUs at each site (based on taxomonic
identiy of the AOB 16S rRNA sequences with the Greengenes database).
hi hi 2 hi -3- ^r
3 < e 3 B o C/2 *5) 3 < 3 3 C/5 "Si 3 < B C/3 B CT>
O O o O O O O O O O
£ .£ .£ .£ .0 .0
c3 c3 5 c3 c3
U U U U u U U U O
rn
bp
<
a
z
£
Q
Z
f Nitrosomonadaceae;
Other (0.2%)
f Nitrosomonadaceae;
g_ (99.7%)
g Nitrosomonas (0.1%)
Figure 10. Relative abundance of Nitrosomonadales taxa within the AOB 16S rRNA
gene sequence dataset (depth of 630 sequences per sample)
34


Beta diversity analyses of Nitrosomonadales were conducted on 13 sample sites
each with a sampling depth of 630 sequences. Unweighted UniFrac analyses revealed
that the majority of the Caribbeau Mine sites (except for Carib03-Septl4 and the
replicate) were clustered together and were separated from the three New Dominion Mine
sites (NDMD02-Junl3, NDMD02-Augl3 and NDMD02-Septl4) (Ranosim = 0.6, p =
0.01, 999 permutations) (Figure 11 A). Taking the observed abundance into consideration,
the weighted UniFrac analyses also revealed that Carribeau Mine sites were clustered
together and separated from the New Dominion Mine site cluster (Ranosim = 0.82, p =
0.007, 999 permutations) (Figure 11B).
35


A:
Figure 11. Principal component analysis plots of Nitrosomonadales taxa (from AOB
16S rRNA sequencing) color-coded by the three major mines/regions. (A) unweighted
UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain
sample sites from both 2013 and 2014.
36


Phylogenetic analyses of Nitrosomonadales taxa-specific AOB 16S rRNA
revealed that the majority of OTUs belonging to the unassigned genus of
Nitrosomonadales were closely related to sequences from a gold mine (13 of 29) (Figure
12). OTUs belonging to the unassigned genus of Nitrosomonadales, Other assigned
genus of Nitrosomonadales and genus Nitrosomonas were closely related to sequences
from freshwater environments (11 of 29). Two OTUs were unrelated to other sequences
in the database sequences.
For NOB, initial analyses revealed 0-53,910 Nitrospira nxrB sequences per
sample, but no Nitrobacter-like 16S rRNA gene sequences. This supports the qPCR
results where Nitrospira genes were very abundant, but Nitrobacter genes did not
amplified.
37


Tree scale: 0.01 i-----1
Environments
I I Coastal
I I Mine
I I Freshwaters
100
100
'Nitrosospira briensis'
'Nitrosospira briensis strain C-128'
I ^'Nitrosospira sp. isolate AF'
100^'Nitrosovibrio tenuis'
'Nitrosomonas sp. Nml43'
'Uncultured Nitrosospira sp. clone LD2-2'
'Nitrosospira sp.'
^'Nitrosospira sp. Nsp65'
'Nitrosospira sp. Nsp57'
'Nitrosospira sp. Ka3'
'Nitrosospira sp. APG3'
'Nitrosospira sp. isolate 40KI'
'Carib03Augl3 89819'
r'Ni
k
£'Carib01Augl3 386'
'Carib01Augl3 4959'
'Carib01Augl3 583'
'Carib01Septl4 12698'
'Carib01Septl4 12792'
-'Carib01Junl3 6433'
''Carib01Augl3 11'
'Carib01Augl3 2808'
*'Carib01Augl3 2641'
2arib01Augl3 409'
"'Carib01Augl3 344'
*'Carib02Junel4 65747'
'uncultured gold mine bacterium D14'
rC
'Carib03Septl4 95303'
'Carib03Septl4 109722'
-----Carib03Septl4 110666' __________
'Nitrosomonas cryotolerans isolate Nm55'
*'Carib03Septl4 112812'
'Carib03Septl4 109254'
'Nitrosomonas nitrosa'
'Nitrosomonas communis'
'Nitrosomonas marina'
'Nitrosomonas aestuarii isolate Nm36'
'Nitrosococcus mobilis Nc2'
'Nitrosomonas halophila'
'Nitrosomonas europaeaATCC 19718'
'Nitrosomonas eutropha C91'
'Nitrosomonas stercoris'

2
Nitrosomonas ureae'
Nitrosomonas sp. AL212'
'Nitrosomonas sp. ls79A3'
Nitrosomonas oligotropha'
--------Carib03Septl4 95550'
--------------NDMD02Augl3 134006'
-----------------------Carib03Septl4 98061'
'Carib03Septl4 95402'
|"'Carib03Septl4 98505'
------NDMD02Junl3 141175'
----------NDMD02Junl3 134626'
'Carib03Septl4 99710'
'Carib03Septl4 100974'
'Carib03Septl4 105602'
'Carib03Septl4 100712'
^'uncultured ammonia-oxidizing bacterium| CL1-1/E'
'Nitrosospira multiformis ATCC 25196'
'uncultured ammonia-oxdizing bacterium| DGGE gel band SYA9'
'Nitrosococcus watsonii strain C-113'
'Bradyrhizobium japonicum| NA6086'
Figure 12. Weighbor weighted Neighbor-Joining phylogenetic tree showing the
affiliation of Nitrosomonadales taxa-specific AOB 16S rRNA sequences (highlighted in
red) and NCBI sequences from other environments. Only significant bootstrap values
(>50) are shown at the branch nodes.
38


Correlation with Environmental Parameters
Spearman correlations between the surface sediment environmental parameters
and each of the gene abundances (archaeal amoA, AOB 16S rRNA, and Nitrospira nxrB)
were made to determine potential drivers of nitrifying microbes. Archaeal amoA gene
abundance was not strongly correlated (p<0.002) with pH, temperature, dissolved
oxygen, or conductivity in 2013 or 2014, or when both years were combined. Moderate
negative correlations was observed between archaeal amoA gene abundance and
conductivity (p = -0.59, p = 0.003, N = 24), but were not statistically significant. AOB
16S rRNA gene abundance for 2013 was moderately correlated with dissolved oxygen (p
= 0.66,p = 0.002, N = 19), pH (p = 0.64,p = 0.003, N = 19), and temperature (p = -0.63,
p = 0.004, N = 19). For 2013 and 2014 combined, the AOB 16S rRNA gene abundances
were statistically correlated with pH (p = 0.57, p = 0.001, N = 29) and temperature (p = -
0.69, p = 0.0006, N = 27). The Nitrospira nxrB gene abundance was statistically
correlated with pH (p = 0.69,/? = 0.002, N = 18) for 2013. For 2013 and 2014 combined,
Nitrospira nxrB gene abundance was negatively correlated with temperature (p = -0.49,/?
= 0.009, N = 27).
Archaeal amoA, AOB 16S rRNA, and Nitrospira nxrB gene abundances were not
significantly correlated with metal concentrations (total recoverable metals, TRW;
dissolved metals, DM) in 2013 or 2014. When both years were combined, archaeal
amoA gene abundance was negatively correlated (but not statistically significant) to DM
calcium (p = -0.54,/? = 0.001, N = 37), DM sodium (p = -0.52,/? = 0.001, N = 37), TRW
calcium (p = -0.46,/? = 0.005, N = 37), and TRW sodium (p = -0.45,/? = 0.005, N = 37).
39


Spearman correlations showed no relationship between the relative abundance of
observed Thaumarchaeota OTUs and pH, temperature, DO, conductivity, or surface
water metal concentrations (TRW and DM) for 2013 and 2014 combined. When samples
in the PCoA plots were color-coded by pH ranges (<4, 5-7 and >7), there was no
obvious clustering and correlation according to pH (Unweighted UniFrac: Ranosim =
0.16,/) = 0.05; Weighted UniFrac: Ranosim = 0.14,/) = 0.08, 999 permutations) (Figure
13 A and 13B).
40


IZZI
pH <5
pH 5-7
IZZI
pH >7
IZZI
pH <5
pH 5-7
IZZI
pH >7
Figure 13. Principal component analysis plots of Thaumarchaeota taxa (from archaeal
16S rRNA gene sequencing) color-coded by the three major pH ranges. (A) unweighted
UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain
sample sites from both 2013 and 2014.
41


Correlations between environmental parameters and each observed
Nitrosomonadales OTU were evaluated using Spearman correlation. The majority of
OTUs observed for the unassigned genus of the family Nitrosomonadaceae had a positive
correlation with pH (p range = 0.49 to 0.52, p < 0.05) and a strong negative correlation
with temperature (p range = -0.58 to -0.74, p < 0.01). Unweighted UniFrac analyses
revealed that the sample sites with temperature ranging from 7C 7.9C were clustered
together, but not in weighted UniFrac analyses (Figure 14A and 14B). In both
unweighted and weighted UniFrac analyses, there were no statistical significant
clustering observed by sample location (Unweighted UniFrac: Ranosim = 0.14,/) = 0.16;
Weighted UniFrac: Ranosim = 0.05, p = 0.33, 999 permutations). Sample sites were not
clustered by pH ranges (Unweighted UniFrac: Ranosim = 0,15, p = 0.18; Weighted
UniFrac: Ranosim = 0.29,/) = 0.07, 999 permutations) (Figure 15A and 15B). For surface
water metal concentrations (TRW and DM), the majority of OTUs observed for the
unassigned genus of the family Nitrosomonadaceae had a moderate correlation with
TRW manganese (p range = 0.41 to 0.53,/) < 0.05). However, unweighted and weighted
UniFrac analyses based on the entire community structure revealed that the sample sites
were not clustered with TRW manganese concentrations (Unweighted UniFrac: Ranosim
= -0.013, p = 0.42; Weighted UniFrac: Ranosim = -0.008, p = 0.43, 999 permutations)
(Figure 16A and 16B).
42


Figure 14. Principal component analysis plots of Nitrosomonadales taxa (from AOB
16S rRNA sequencing) color-coded by the three major temperature ranges (1C
increment based on sample measurements). (A) unweighted UniFrac distance matrices.
(B) weighted UniFrac distance matrices. Plots contain sample sites from both 2013 and
2014.
43


Figure 15. Principal component analysis plots of Nitrosomonadales taxa (from AOB
gene 16S rRNA sequencing) color-coded by the three major pH ranges. (A) unweighted
UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain
sample sites from both 2013 and 2014.
44


Figure 16. Principal component analysis plots of Nitrosomonadales taxa (from AOB
16S rRNA sequencing) color-coded by the three major TRW manganese ranges (low,
medium and high). (A) unweighted UniFrac distance matrices. (B) weighted UniFrac
distance matrices. Plots contain sample sites from both 2013 and 2014.
45


CHAPTER V
DISCUSSION
AMD is a serious threat to freshwater systems and can devastate a river and its
aquatic life for hundreds to thousands of years through its acidic pH and high metal
concentrations. In this study, we determined the abundance and diversity of archaeal
amoA, AOB 16S rRNA, and Nitrobacter nxrB gene sequences in AMD-impacted
sediments at the Iron Springs Mining District.
Very little is known about the community structure of nitrifying microbes in
AMD environments. In this study, AO A, AOB, and NOB communities had low numbers
of observed OTUs (1-11 for AO A; 1-29 for AOB and none for Nitrobacter). AOA
OTUs were related to three major groups: (1) SAGMA-Xfirst observed in a deep South
African gold mine (Takai et al., 2001) and sometimes associated with soil environments
(Pesaro and Widmer, 2002); (2) Nitrosopumilus commonly observed in coastal and
terrestrial environments (Konneke et al., 2005; Moin et al., 2009; C B Walker et al.,
2010; Jung et al., 2011; Matsutani et al., 2011; Bartossek et al., 2012; Pester et al., 2012;
Mosier etal., 2012; Park et al., 2012; Horak etal., 2013; Lebedeva et al., 2013; Newell
et al., 2013); and (3) Nitrososphaera commonly observed in soil (Prosser and Nicol,
2008; Offre et al., 2009; Zhang et al., 2010; Pratscher et al., 2011) and freshwater
environments (Herrmann et al., 2008; van der Widen et al., 2009; Lliros etal., 2010).
AOB observed in these AMD-impacted sites belonged to the family Nitrosomonadaceae,
which are generally well distributed in both terrestrial and aquatic environments (Stein et
al., 2007; Norton et al., 2008; Zheng et al., 2013). NOB in these AMD-impacted sites
were related to Nitrospira species, but not Nitrobacter species. Although Nitrobacter are
46


typically the most common NOB in aquatic systems, Nitrospira are often numerically
dominant in sediments (Altmann et al., 2004; Coci et al., 2005; Freitag et al., 2006;
Cebron and Gamier, 2005; Satoh et al., 2007) and water treatment plants (Watson eta/.,
1986; Juretschko eta/., 1998; Daims eta/., 2001; Lebedeva eta/., 2005; Huang eta/.,
2010). Overall, the AO A, AOB, and NOB community structure within these AMD-
impacted freshwater sediments appears to be phylogenetically distinct from other
unimpacted freshwater sites. Further sequence analyses will provide a finer level of
resolution for comparing sequence types between different systems.
Metal concentrations (including Al, Cu, Fe, and Zn) at all of the sites exceeded
the allowable concentrations as determined by the Colorado Department of Public Health
and Environment (CDPHE) Regulation 31. Additionally, the majority of sites with pH
<4 had the highest concentrations of metals (TRW & DM) (e.g., Al, Cd, Cu, Fe, Pb, Mg,
Mn, Ni, and Zn). Low pH (3-5) causes metals to be readily dissolved and may affect the
solubility and toxicity of total and dissolved metal concentrations. Many of these metals
are toxic to aquatic life at high levels (Nash, 2002).
Nitrification is extremely sensitive to heavy metal concentrations. Heavy metals
such as copper, zinc, lead, cadmium, sulfide, and nickel are known to inhibit the
nitrification process in soils (Yan eta/., 2013; Cela and Sumner, 2002). Here, we found
high numbers of AOA, AOB, and NOB genes at sites with high metal concentrations,
suggesting that these organisms are tolerant to high metal-rich environments, as shown in
soil studies (Broberg, 1984, Mertens eta/., 2006, Li eta/., 2009). For most of the metals,
there was no clear correlation between metal concentrations and gene abundances or
community structure, in contrast to previous studies that showed decreased abundance or
47


changes in community composition with increasing metal concentrations (Mertens etal.,
2006; Li etal., 2009; Morel and Price, 2003; Cao etal., 2011; Principi etal., 2008). In
cases where correlations were found to be significant in the present study, specific metals
did not equally impact gene abundances of AO A, AOB, or NOB, possibly suggesting that
different metals have different effects on each of these groups.
Nitrification is highly sensitive to acidic pH. Low pH presents physiological
challenges to ammonia-oxidizing and nitrite-oxidizing microbes, as seen for other
microbes. In addition, pH affects the balance of ammonia (NH3) and ammonium (NH4+)
in water. At low pH, NH3 concentrations are very low, which is problematic for
ammonia oxidizers because NH3 is the substrate of ammonia oxidation (but not NH4+)
(Bowen et al., 2013). However, it was recently proposed that AOA might possess
ammonium transporters (Offre et al., 2014), which could facilitate the conversion of
ammonium into ammonia once inside the cytoplasm at circumneutral pH. Because
decreased pH reduces the bioavailability of NH3 acidic habitats are predicted to favor
AOA over AOB because AOA have a higher affinity for NH3 (Martens-Habbena et al.,
2009). Archaeal amoA genes have generally been shown to dominate at low pH
conditions when compared to bacterial amoA genes (Krause et al., 2012; Nicol et al.,
2008; Hu et al., 2013). Here, we found that AOA amoA genes were more abundant than
AOB 16S rRNA genes at most sites and time-points, regardless of pH. Several of the
AOA OTUs at the acidic sites were related to Nitrososphaera, which are widely
distributed in acidic soils (Gubry-Rangin etal., 2011; Pester et al., 2012; Lehtovirta-
Morley etal., 2011; Tourna etal., 2011; Zhang etal., 2012; Nicol etal., 2008). Though
there was no significant correlation with pH, archaeal amoA gene abundances (2.0xl03to
48


4.9xl07 copies per pg DNA) and Nitrospira nxrB gene abundances (1 .2 xlO6 to 4.1xl07
copies per pg DNA) were high at sites with pH <4.5, and were detected at sites with pH
as low as 3.19. AOB 16S rRNA gene abundance decreased with decreasing pH. The
presence of AOA, AOB, and NOB genes suggests the potential for nitrification in these
acidic sediments, but further research is necessary to confirm activity.
The Nitrospira nxrB genes were more abundant in the majority of the sites than
archaeal amoA and AOB 16S rRNA during both years. However, the gene copy numbers
per cell varies for each organism: AOA have one amoA copy/cell (Hallam etal., 2006),
AOB have one 16S rRNA copy/cell, and Nitrospira have 2-6 copies/cell (Liicker el al.,
2010; Pester etal., 2014). When accounting for these differences in gene copy numbers
and when summing the total ammonia oxidizing community (AOA + AOB) and nitrite
oxidizing community (.Nitrospira + Nitrobacter), the ammonia oxidizers were more
abundant than the nitrite oxidizers for both years separately and when combined. These
community abundance estimates are very speculative, but may provide some insight into
the relative differences between ammonia-oxidizing and nitrite-oxidizing populations.
Conclusions
In summary, we found numbers of observed AOA, AOB, and NOB OTUs in
AMD-impacted sediments, relative to other more mesophilic environments. Though not
diverse, these AOA, AOB, and NOB genes were very abundant in AMD-impacted
sediments with high metal concentrations and low pH. These nitrifying microbes may be
well adapted to multiple, simultaneous stresses experienced in these environments. We
identified specific environmental parameters that were correlated with gene abundances
or community structure (e.g., pH and AOB abundance). However, many other factors not
49


measured here could influence nitrification, including other chemical variables or
organismal interactions. This study provides a foundation for future work to determine
rates of nitrification in AMD-impacted systems, and how AMD runoff impacts nitrogen
cycling within streams and the transfer of nitrogen to higher organisms.
50


Supplemental Table SI: Gene copy numbers of AOA amoA, AOB 16S rRNA and Nitrospira nxrB (based on qPCR
amplification). All data are expressed in copies/pg of DNA extract. Data for the sample site NDGP was not included since
there was no amplification for any genes across 2013 and 2014.
Summer 2013 Summer 2014
Sample Region Sample Sites Sample Date AOA amoA AOB 16S rRNA Nitrospira nxrB Sample Date AOA amoA AOB 16S rRNA Nitrospira nxrB
6.25.13 7.88E+05 2.16E+05 1.66E+07 6.24.14 _ _ _
CaribOl 8.05.13 1.35E+06 2.03E+05 2.24E+07 9.30.14 7.05E+05 5.29E+05 -
6.25.13 3.22E+04 1.36E+04 6.06E+06 6.24.14 5.55E+06 1.26E+05 2.75E+07
Caribbeau Mines Carib02 8.05.13 9.09E+05 1.20E+05 2.80E+07 9.30.14 1.23E+07 3.67E+05 3.75E+07
6.25.13 4.38E+04 3.46E+03 5.28E+06 6.24.14 _ 3.65E+03 8.70E+06
Carib03 8.05.13 2.37E+05 1.88E+04 1.36E+07 9.30.14 6.59E+06 4.50E+04 6.42E+07
Capped Seap Iron CS1B 6.25.13 5.39E+03 7.47E+03 - 6.24.14 8.66E+06 - 1.24E+06
Bog 8.05.13 1.96E+03 2.01E+04 - 9.30.14 - 3.39E+03
6.25.13 1.41E+06 _ _ 6.24.14 1.30E+07 _ 3.75E+07
Fen Iron Bog FenIBOl 8.05.13 5.42E+05 - 3.19E+06 9.30.14 4.92E+07 4.04E+04 4.15E+07
FenIB02 6.25.13 4.11E+05 2.08E+03 1.94E+06 6.24.14 - 1.72E+03 -

8.05.13 6.49E+05 5.10E+03 - n.s n.s n.s
6.25.13 2.96E+05 _ 7.12E+06 6.24.14 . . 8.5/1 ()(>
FenNDOl 8.05.13 3.03E+05 1.51E+03 6.52E+06 9.30.14 2.05E+04 2.04E+07
New Dominion Fen FenND03 6.25.13 - - - 6.24.14 1.53E+06 - 9.25E+06
8.05.13 4.85E+04 1.54E+03 - n.s n.s n.s n.s
6.25.13 5.21E+05 1.24E+04 1.55E+07 n.z n.s n.s n.s
Howard Fork HF04 8.05.13 2.49E+06 2.55E+04 2.90E+07 n.z n.s n.s n.s
Howard Fork Iron 6.25.13 3.64E+06 1.51E+04 1.62E+07 6.24.14 1.32E+07 1.92E+04 7.66E+07
HFIBE
Bog 8.05.13 1.57E+06 1.14E+04 2.17E+06 9.30.14 1.08E+07 - -
New Dominion NDCS02 6.25.13 5.82E+03 7.82E+03 7.45E+05 6.24.14 - - -
Capped Seap 8.05.13 1.26E+04 - - 9.30.14 6.37E+05 - -
New Dominion Mine NDMD02 6.25.13 1.38E+05 1.60E+04 2.00E+06 6.24.14 1.01E+06 - -
Drainage 8.05.13 4.27E+05 3.43E+04 3.82E+06 9.30.14 2.05E+07 _ 3.76E+07
n.s: not sampled
no amplification
SUPPLEMENTAL TABLES


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59


APPENDIX
Appendix I: QIIME scripts used for processing high-throughput Illumina MiSeq
sequencing data. All file/folder names are user-specific. Each command associated with
the process is denoted by >\
Renaming the files to be QIIME compatible:
Renames the sequence names in the raw data files so that the formatting is compatible
with QIIME analyses. Example for one dataset:
> mkdir IronSpringsArch349F
> cd IronSpringsArch349F/
> Is /home/bhargavi/RawData/Arch349F/ > a
> grep fastq a | cut -f 2,3 -d a > b
> sort b | uniq > labels.txt
> while read i; do sed "A@MISEQFGXT/sA@/@$i /l"
/home/bhargavi/RawData/Arch349F/Arch349F_$i*Rl.fastq > ${i}.Rl.fastq ; done <
labels.txt
> while read i; do sed "A@MISEQFGXT/sA@/@$i /l"
/home/bhargavi/RawData/Arch349F/Arch349F_$i*R2.fastq > ${i}.R2.fastq ; done <
labels.txt
> cat *R1 .fastq > combinedRl.fastq
> cat *R2.fastq > combined_R2.fastq
> grep combined Rl .fastq | head
> grep combined Rl .fastq | head -100
> sed's/_//' combined Rl.fastq > relabeledRl.fastq
> sed's/_//' combined_R2.fastq > relabeled_R2.fastq
> awk '{if(((NR-1 )%4)==0) print $l"_"(NR-l)/4" "$2" "$3; else print;}'
relabeled Rl.fastq > allIronSpringsArch349F_Rl.fastq
> awk 1 {if(((NR-1 )%4)==0) print $l"_"(NR-l)/4" "$2" "$3; else print;}'
relabeled_R2.fastq > allIronSpringsArch349F_R2.fastq
Quality Plots for Reads:
Allows viewing the quality of reads through plots.
> /home/chris/software/bin/qa2.py type fastq allIronSpringsl6SrRNA_Rl.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsl6SrRNA_R2.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsAOB16SrRNA_Rl.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsAOB16SrRNA_R2.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsArchl6SrRNA_Rl.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsArchl6SrRNA_R2.fastq
> /home/chris/software/bin/qa2.py type fastq allIronSpringsNB16SrRNA_Rl.fastq
60


> /home/chris/software/bin/qa2.py type fastq allIronSpringsNB16SrRNA_R2.fastq
Joining paired-ends for all sequencing runs:
(http://qiime.org/scripts/ioin paired ends.html)
Merge the forward and reverse reads for each paired-end.
> join_paired_ends.py -f allIronSpringsl6SrRNA_Rl.fastq -r
allIronSpringsl6SrRNA_R2.fastq -o $PWD/fastq-joinjoined
> join_paired_ends.py -f allIronSpringsAOB16SrRNA_Rl .fastq -r
allIronSpringsAOB16SrRNA_R2.fastq -o $PWD/fastq-join joined
> join_paired_ends.py -f allIronSpringsArchl6SrRNA_Rl.fastq -r
allIronSpringsArchl6SrRNA_R2.fastq -o $PWD/fastq-joinjoined
> join_paired_ends.py -f allIronSpringsNB16SrRNA_Rl .fastq -r
allIronSpringsNB16SrRNA_R2.fastq -o $PWD/fastq-join joined
Quality Filtering:
(http://qiime.org/scripts/split libraries fastq.html)
Filters sequences based on Phred Quality score q < 20. Phred Quality score < 20 has a
maximum error rate at lbp in lOObp.
> split libraries jhstq.py -i /home/bhargavi/IronSpringsl6SrRNA/fastq-
j oin j oined/fastqj oin.j oin.fastq -o
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/ -r 3 -p 0.75 -q 20 -n 0 barcode type 'not-barcoded' sample ids ironsprings
> split libraries jhstq.py -i /home/bhargavi/IronSpringsAOB16SrRNA/fastq-
j oin j oined/fastqj oin.j oin. fastq -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/ -r 3 -p 0.75 -q 20 -n 0 barcode type 'not-barcoded' sample ids
ironsprings
> split libraries jhstq.py -i /home/bhargavi/IronSpringsArchl6SrRNA/fastq-
j oin j oined/fastqj oin.j oin. fastq -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/ -r 3 -p 0.75 -q 20 -n 0 barcode type 'not-barcoded' sample ids
ironsprings
> split libraries jhstq.py -i /home/bhargavi/IronSpringsNB16SrRNA/fastq-
j oin j oined/fastqj oin.j oin. fastq -o
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
ltered_q20/ -r 3 -p 0.75 -q 20 -n 0 barcode jype 'not-barcoded' sample ids ironsprings
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Picking OTUs:
(http://qiime.org/scripts/pick open reference otus.html)
Picks OTUs by clustering reads against the Greengenes 16S rRNA and assigns taxonomy
database based on 97% sequence identity for the total (bacterial + archaeal) 16S rRNA
and 99% sequence identity for archaeal 16S rRNA, and AOB 16S rRNA sequencing
reads.
> pick_open_reference_otus.py -i /home/bhargavi/IronSpringsl6SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/joined.fasta/renamed.seqs.fna -r
/opt/qiime-1,7.0/gg_13_8_otus/rep_set/97_otus.fasta -o
/home/bhargavi/IronSprings 16SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/pick_otus_97/ -a08
> pick_open_reference_otus.py -i /home/bhargavi/IronSpringsAOB16SrRNA/
IronSpringsAOB 16SrRNA_all_analyses_qual_filtered_q20/j oined.fasta/renamed. seqs.fna
-r /opt/qiime-1.7.0/gg_13_8_otus/rep_set/99_otus.fasta -o
/home/bhargavi/IronSpringsAOB 16SrRNA/
IronSpringsAOB 16SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ -a08
> pick_open_reference_otus.py -i /home/bhargavi/IronSpringsArchl6SrRNA/
IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/j oined.fasta/renamed.seqs.fna
-r /opt/qiime-1.7.0/gg_13_8_otus/rep_set/99_otus.fasta -o
/home/bhargavi/IronSpringsArchl6SrRNA/
IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ -a08
> pick_open_reference_otus.py -i /home/bhargavi/IronSpringsNB16SrRNA/
IronSpringsNB 16SrRNA_all_analyses_qual_filtered_q20/joined.fasta/renamed.seqs.fna -
r /opt/qiime-1.7.0/gg_13_8_otus/rep_set/99_otus.fasta -o
/home/bhargavi/IronSpringsNB 16SrRNA/
IronSpringsNB 16SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ -a08
Filters the OTU table:
(http://qiime.org/scripts/filter otus from otu table.html)
Removes OTUs that have a fraction of total observation count < 0.00005, which is
applied as the minimum total observation count for each OTU.
> filterotusfromotutable.py -i
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/pick_otus_97/otu_table_mc2_w_tax.biom -o
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/pick_otus_97/otu_table_filter_min_frac.biom mincountfraction 0.00005
> filter otus from otu table.py -i
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
62


I_filtered_q20/pick_otus_99/otu_table_mc2_w_tax.biom -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom mincountfraction
0.00005
> filterotusfromotutable.py -i
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/pick_otus_99/otu_table_mc2_w_tax.biom -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom mincountfraction
0.00005
> filter otus from otu table.py -i
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
Itered_q20/pick_otus_99/otu_table_mc2_w_tax.biom -o
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
Itered_q20/pick_otus_99/otu_table_filter_min_frac.biom min count fraction 0.00005
Chimeric Sequences:
(http://qiime.org/scripts/filter otus from otu table.html)
Identifies chimeric sequences based on the representative sequences obtained from
picking OTUs using DECIPHER. Removes chimeric sequences from OTU table
> filter otus from otu table.py -i
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/pick_otus_97/otu_table_filter_min_frac.biom -o
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/IronSpringsl6SrRNA_otu_table_non_chimeric.biom -e
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/IronSpringsl 6SrRNA_chimera.txt
> filter otus from otu table.py -i
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/IronSpringsAOB 16SrRNA_otu_table_non_chimeric.biom -e
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/IronSpringsAOB 16SrRNA_chimera.txt
> filter otus from otu table.py -i
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/IronSpringsArchl6SrRNA_otu_table_non_chimeric.biom -e
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/IronSpringsArchl 6SrRNA_chimera.txt
63


> filterotusfromotutable.py -i
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
ltered_q20/pick_otus_99/otu_table_filter_min_frac.biom -o
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
ltered_q20/IronSpringsNB 16SrRNA_otu_table_non_chimeric.biom -e
/home/bhargavi/IronSpringsNB 16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi
ltered_q20/IronSpringsNB 16SrRNA_chimera.txt
Summarize Taxa through Plots:
(http://qiime.org/scripts/summarize taxa through plots.html)
Summarizes taxa and generates taxonomic bar charts for each sample based on
taxonomic levels.
> summarize_taxa_through_plots.py -i /home/bhargavi/IronSpringsl6SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/
IronSpringsl6SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSprings 16SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu97
> summarize_taxa_through_plots.py -i /home/bhargavi/IronSpringsAOB16SrRNA/
IronSpringsAOB 16SrRNA_all_analyses_qual_filtered_q20/
IronSpringsAOB 16SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSpringsAOB 16SrRNA/
IronSpringsAOB 16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99
> summarize_taxa_through_plots.py -i /home/bhargavi/IronSpringsArchl6SrRNA/
IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/
IronSpringsArchl6SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSpringsArchl6SrRNA/
IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99
> summarize_taxa_through_plots.py -i /home/bhargavi/IronSpringsNB16SrRNA/
IronSpringsNB 16SrRNA_all_analyses_qual_filtered_q20/
IronSpringsNB 16SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSpringsNB 16SrRNA/
IronSpringsNB 16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99
Filtering the OTU Table for Specific Taxa:
(http://qiime.org/scripts/filter taxa from otu table.html)
Pulls out AOB and AOA sequences into a separate file (without other bacterial/archaeal
sequences) based on taxonomy defined in the OTU picking step (based on Greengenes).
> filtertaxafromotutable.py -i
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/IronSpringsAOB 16SrRNA_otu_table_non_chimeric.biom -o
64


/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/otu_table_nitrosomonadalesl6SrRNA_only.biom -p o__Nitrosomonadales
> filtertaxafromotutable.py -i
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/IronSpringsArchl6SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/otu_table_thuamarchaeotal6SrRNA_only.biom -p c__Thaumarchaeota
Summarize Biom Table:
(http://biom-format.org/documentation/summarizing biom tables.html)
Summarizes the relative abundance of each OTU.
> biom summarize-table -i /home/bhargavi/IronSpringsl6SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/
IronSpringsl6SrRNA_otu_table_non_chimeric.biom -o
/home/bhargavi/IronSprings 16SrRNA/
IronSpringsl6SrRNA_all_analyses_qual_filtered_q20/IronSpringsl6SrRNA_biomtables
ummary.txt
> biom summarize-table -i /home/bhargavi/IronSpringsAOB16SrRNA/
IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/otu_table_nitrosomonadalesl
6SrRNA_only.biom -o /home/bhargavi/IronSpringsAOB16SrRNA/
IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/nitrosomonadales_biomtable
_summary.txt
> biom summarize-table -i
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/otu_table_thaumarchaeotal6SrRNA_only.biom -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/thaumarchaeota_biomtable_summary.txt
Mapping File Validation:
(http://qiime.org/scripts/validate mapping file.html)
Validates mapping file to make sure that the formatting is QIIME compatible.
> validate mapping file.py -m IronSpringsMappingFile.txt -o validate-
mappingfileoutput
Core Diversity Analyses:
(http://qiime.org/scripts/core diversity analyses.html)
Computes core diversity analyses (beta diversity, summarize taxa through plots and alpha
rarefaction). Sequencing depth varied for each analysis (sequence depth selected to
capture greater than 50% of the OTUs predicted in the rarefaction curve): 16,000
sequences per sample for the total (bacterial + archaeal) 16S rRNA sequences, 630
65


sequences per sample for the AOB 16S rRNA sequences, and 60 sequences per sample
for the archaeal 16S rRNA sequences.
> core_diversity_analyses.py -o
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/ IronSpringsl6SrRNACoreAnalyses recoverfromfailure -i
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/IronSpringsl6SrRNA_otu_table_non_chimeric.biom -m
/home/bhargavi/IronSpringsMappingFile.txt -t
/home/bhargavi/IronSprings 16SrRNA/IronSprings 16SrRNA_all_analyses_qual_filtered_
q20/pick_otus_97/rep_set.tre -e 16000
> core_diversity_analyses.py -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/IronSpringsAOB16SrRNACoreAnalyses630 recover from failure -i
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/otu_table_nitrosomonadalesl6SrRNA_only.biom -m
/home/bhargavi/IronSpringsMappingFile.txt -t
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/pick_otus_99/rep_set.tre -e 630
> core_diversity_analyses.py -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/IronSpringsArchl6SrRNACoreAnalyses60 recover from failure -i
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/otu_table_thaumarchaeotal6SrRNA_only.biom -m
/home/bhargavi/IronSpringsMappingFile.txt -t
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/pick_otus_99/rep_set.tre -e 60
Spearman Correlations Between the Relative Abundance of OTUs and
Environmental Parameters:
(http://qiime.org/scripts/observation metadata correlation.html)
Correlates the relative abundance of individual OTUs to the environmental parameters
(metadata in the mapping file). Example for one dataset:
> observationmetadatacorrelation.py -i
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/otu_table_nitrosomonadalesl6SrRNA_only.biom -m
/home/bhargavi/IronSpringsMappingFilenounits.txt -c pH -s spearman -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/Spearman_correlation_AOB16SrRNA/spearman_otu_gradient_nitrosomo
nadales_only_pH.txt
Filter Sequences for Phylogeny:
(http://qiime.org/scripts/filter fasta.html)
66


Pulls out AOB and AOA sequences into a separate file (without other bacterial/archaeal
sequences) based on taxonomy defined in the OTU picking step (based on Greengenes).
> filter fasta.py -f
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/pick_otus_99/rep_set.fna -o
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/nitrosomonadales 16SrRNAonly_biom_filtered_seqs.fna -b
/home/bhargavi/IronSpringsAOB 16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua
l_filtered_q20/otu_table_nitrosomonadales 16SrRNA_only .biom
> filter fasta.py -f
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/pick_otus_99/rep_set.fna -o
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/thaumarchaeotal6SrRNAonly_biom_filtered_seqs.fna -b
/home/bhargavi/IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual
_filtered_q20/otutablethaumarchaeota 16 SrRNAonly .biom
ANOSIM:
(http://qiime.org/scripts/compare categories.html)
Analysis of the strength and statistical significance of site-specific sample groups based
on environmental variables using distance matrices.
> comparecategories.py method anosim -i
IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/Ir
onSpringsArchl6SrRNACoreAnalyses60/bdiv_even60/unweighted_unifrac_dm.txt -m
IronSpringsMappingFile-Location.txt -c Location -o
IronSpringsArchl6SrRNA/IronSpringsArchl6SrRNA_all_analyses_qual_filtered_q20/an
osim out location
67


Full Text

PAGE 1

ABUNDANCE AND DIVERSITY OF NITRIFYING MICROBES IN SEDIMENTS IMPACTED BY ACID MINE DRAINAGE by BHARGAVI RAMANATHAN B achelor in Tech nology Anna University, 2012 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Masters of Science Environmental Science Prog ram 2016

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2016 BHARGAVI RAMANATHAN ALL RIGHTS RESERVE D

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! iii This thesis for the Master of Science degree by Bhargavi Ramanathan has been approved for the Environmental Science Program by Annika C. Mosier, Chair Timberley M. Roane Frederick Chambers April 26 2016

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! iv Ramanathan, Bhargavi (M.S. Environmental Sciences) Abundance and Diversity of Nitrifying Microbes in Sediments Impacted by Acid Mine Drainage Thesis directed by Assistant Professor Annika C. Mosier ABSTRACT Extremely acidic and metal rich acid mine drainage (AMD) waters can have severe toxicological effect s on aquatic ecosystems AMD has been shown to completely halt nitrification which plays an important role in transferring nitrogen to higher or ganisms and in mitigating nitrogen pollution. We evaluated whether AMD differentially impacts three groups of microorganisms involved in nitrification: ammonia oxidizing archaea (AOA), ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) Sediment and water samples were collected from AMD impacted aquatic sites during June and August/September 2013 and 2014 in the Iron Springs Mining District (Ophir, Colorado). Many of the sites were characterized by low pH (<5), low dissolved oxygen co ncentrations (<6 mg/L), and high metal concentrations. Community 16S rRNA gene sequencing revealed the presence of AOA ( Nitrososphaera and Cenarchaeota ) AOB ( Nitrosomonas ), and NOB ( Nitrospira ) at multiple AMD impacted sites The overall abundance of AOA, AOB and NOB were examined using quantitative PCR (qPCR) amplification of the amoA and nxrB functional genes and 16S rRNA genes The total gene copy numbers across the 2013 and 2014 samples ranged from 2 .0 x10 3 4.9 x 10 7 archaeal amoA copies / g DNA 1.5x10 3 5.3 x10 5 AOB 16S rRNA copies/! g DNA and 7.3x10 5 7.7 x 10 7 Nitrospira nxrB copies/ g DNA Overall, Nitrospira nxrB genes were found to be more abundant than AOB 16S rRNA and archaeal amoA genes in most of the

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! v sample sites across 2013 and 2014. Archaeal amoA AOB 16S rRNA and Nitrospira nxrB genes were quantified in sediments with pH as low as 3.2. Statistical analyses showed a significant correlation between AOB 16S rRNA gene abundance and the surface sediment pH, temperature and dissolved oxy gen. A rchaeal amoA gene abundance wa s significantly correlated with dissolved calcium and sodium concentrations These findings extend our understanding of the relationship between AMD and nitrifying microbes and provide a platform for further research. The form and content of this abstract are approved. I recommend its publication. Approved: Annika C. Mosier

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! vi ACKNOWLEDGMENTS I would like to express my deepest gratitude and immeasurable appreciation to my research thesis advisor, Dr. Annika C. Mosier for her full support, expert guidance, encouragement and understanding through every step of my study and research. Without her persistent help, guidance expertise and patience this thesis would have not been possible. I would also like to thank my thesis committ ee members: Dr. Timberley M. Roane, for your collaboration, field work, time, mentorship and sharing your knowledge with me, and Dr. Frederick Chambers for your support and time. I would like to specially thank Dr. Christopher S. Miller for helping me in bioinformatics analysis and sharing his knowledge, and Dr. Charles Patterson for sharing with me his expertise on acid mine drainage, for his encouragement and support throughout. This thesis wouldn't have been t he same without their thoughtful questions and valuable comments. I would like to thank Robyn Blackburn, Environmental Protection Agency Region 8 for her aid in the field and sharing her insights on the sample sites regions and the U.S. Forest Services Abandoned Mines Program I would like to thank the following institutes for their role in biological and chemical analyses: United States Environmental Protection Agency Region 8 Laboratory, and the Roy J. Carver Biotechnology Center at the University of I llinois. I would like to express my sincerest gratitude to the following individuals: Joshua D. Sackett for helping me with the samples and also sh aring his knowledge and project details, Ashley Joslin for her help with the sample preparation for the seco nd year, Adrienne Narrowe and Sladjana Subotic for taking their time to assist in bioinformatics analysis, Andrew M. Boddicker for you r assistance in the sequence data analysis, and for

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! vii being an amazing friend and colleague throughout the way to my parents, Ramanathan Srinivasan and Nalini Ramanathan for you support, encouragement and your belief in me and to many other family member and friends who supported me every step of the way. I wouldn't be where I'm today without these people in my l ife. Lastly I would like to offer this endeavor to the God Almighty for this opportunity, the wisdom, the strength, peace of mind, and good health in order to finish this research.

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! viii TABLE OF CONTENTS CHAPTER I. PROJECT OVERVIEW ................................ ................................ ................................ ........ 1 II. INTRODUCTION ................................ ................................ ................................ ................. 2 III. MATERIALS AND METHODS ................................ ................................ .......................... 5 Site Description and Sample Collection ................................ ............................... 5 Environmental Parameters ................................ ................................ .................... 6 DNA Extraction ................................ ................................ ................................ .... 6 Quantitative PCR ................................ ................................ ................................ .. 7 Illumina MiSeq Library Preparation ................................ ................................ .... 8 Community Composition Analyses ................................ ................................ .... 11 Statistical Analyses ................................ ................................ ............................. 13 IV. RESULTS ................................ ................................ ................................ ............................ 15 Chemistry ................................ ................................ ................................ ........... 15 Overall Abundance of AOA and AOB ................................ ............................... 22 Overall Abundance of NOB ................................ ................................ ............... 26 Relative Abundance and Diversity ................................ ................................ ..... 27 Correlation with Environmental Parameter s ................................ ...................... 39 V. DISCUSSION ................................ ................................ ................................ ...................... 46 Conclusions ................................ ................................ ................................ ........ 49 SUPPLEMENTAL TABLES ................................ ................................ ................................ ......... 51 REFERENCES ................................ ................................ ................................ ............................... 60 APPENDIX ................................ ................................ ................................ ................................ ..... 52

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! ix LIST OF TABLES TABLE 3.1: Chemistry data for sample sites across June and August 2013 in the Iron Springs Mining District. ................................ ................................ ................................ ................. 18 3.2: Chemistry data for sample sites across June and September 2014 in the Iron Springs Mining District. ................................ ................................ ................................ ................. 19 3.3: Total recoverable metal from water samples across 2013 in the Iron Springs Mining District. ................................ ................................ ................................ .............................. 20 3.4: Total recoverable metal from water samples across 2014 in the Iron Springs Mining District. ................................ ................................ ................................ .............................. 21 3.5: Summary of environmental parameters for each mine/region compared to each other. ................................ ................................ ................................ ................................ ........... 22 S1: Gene copy numbers of AOA amoA AOB 16S rRNA and Nitrospira nxrB (based on qPCR amplification). All data are expressed in copies/ g of DNA extract. Data for the sample site NDGP was not i ncluded since there was no amplification for any genes across 2013 and 2014. ................................ ................................ ................................ ....... 51

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! x LIST OF FIGURES FIGURE 1. Map study sites within the Iron Springs Mining District near Ophir, Colorado. ............ 6 2. The overall abundance of (A) archaeal amoA genes, and (B) AOB 16S rRNA genes across 2013 and 2014 samples sites in the Iron Springs Mining District. ......................... 24 3. Log ratio of AOA amoA :AOB 16S rRNA copy numbers across sample sites during 2013 and 2014 in the Iron Springs Mining District. ................................ .......................... 26 4. The overall abundance of Nitrospira nxrB genes across 2013 and 2014 samples sites in the Iron Springs Mining District ................................ ................................ ........................ 27 5. Number of observed Thaumarchaeota OTUs at each site (based on taxomonic identiy of the archaeal 16S rRNA sequences with the Greengenes database). ............................. 29 6. Relative abundance of Thaumarchaeota taxa within the archaeal 16S rRNA gene sequence dataset (depth of 60 sequences per sample) ................................ ....................... 29 7. Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S rRNA gene sequencing) color coded by the three major mines/regions. (A) unweighted UniFrac distance matrices. (B) we ighted UniFrac distance matrices. Plots contain sample sites from both 2013 and 2014. ................................ ................................ ................................ 31 8. Weighbor weighted Neighbor Joining phylogenetic tree showing the affiliation of Thaumarchaeota taxa specific archaeal 16S rRNA sequences (highlighted in red) and NCBI sequences from other environments. Only significant bootstrap values (>50) are shown at the branch nodes. ................................ ................................ ................................ 33 9. Number of observed Nitrosomonadales OTUs at each site (based on taxom onic identiy of the AOB 16S rRNA sequences with the Greengenes database). ................................ .. 34 10. Relative abundance of Nitrosomonadales taxa within the AOB 16S rRNA gene sequenc e dataset (depth of 630 sequences per sample) ................................ ..................... 34 11. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color coded by th e three major mines/regions. (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites from both 2013 and 2014. ................................ ................................ ................................ 36 12. Weighbor weighted Neighbor Joining phylogenetic tree showing the affiliation of Nitrosomonadales taxa specific AOB 16S rRNA sequences (highlighted in red) and NCBI sequences from other environments. Only significant bootstrap values (> 50) are shown at the branch nodes. ................................ ................................ ................................ 38

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! xi 13. Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S rRNA gene sequencing) color coded by the th ree major pH ranges. (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites from both 2013 and 2014. ................................ ................................ ......................... 41 14. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color coded by the three major temperature ranges (1C increment based on sample measurements). (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distan ce matrices. Plots contain sample sites from both 2013 and 2014. .......................... 43 15. Principal component analysis plots of Nitrosomonadales taxa (from AOB gene 16S rRNA sequen cing) color coded by the three major pH ranges. (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites from both 2013 and 2014. ................................ ................................ ................................ 44 16. Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color coded by the three major TRW manganese ranges (low, medium and high). (A) unweighted UniFrac distance matrices. (B) weighted UniFrac distance matrices. Plots contain sample sites from both 2013 and 2014. ................................ ........ 45

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! 1 CHAPTER I PROJECT OVERVIEW The overall goal of this study was to determin e the abundance and diversity of three gro ups of nitrifying microbes in acid mine drainage (AMD) impacted sediments in Colorado 's Iron Springs Mining District : ammonia oxidizing archaea (AOA), ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB). The specific objectives of this study were to determine : O verall abundance of ammonia oxidizers and nitrite oxidizers in AMD impacted sediments (Objective 1); R elative abundance of specific groups of ammonia oxidizers and nitrite oxidizers in AMD impacted sediments (Objective 2); Diversity of ammonia oxidizers an d nitrite oxidizers in AMD impacted sediments (Objective 3) ; and Relate the abundance and diversity of ammonia oxidizers and nitrite oxidizers to environmental factors in AMD impacted sediments (Objective 4). Understanding AMD associated disturbances on nitrification and its associated microbes will strengthen our understanding of the environmental limits for nitrification and will help inform management decisions (e.g. site restoration, minimiz ing harmful effects of nitrogen ).

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! 2 CHAPTER I I INTRODUCTION Nitrification a central part of the nitrogen cycle, is globally important because it transfers nitrogen to higher organisms and mitigates nitrogen pollution when coupled with denitrification and anammox. N itrification is the two step aerobic oxidation of ammonia to nitrate through nitrite Ammonia oxidation is mediated by ammonia oxidizing bacteri a and ammonia oxidizing archaea while n itrite oxidation is mediated by bacteria from the Nitrobacter and Nitrospira lineages. The study of the microbial communities involved in ammonia oxidation and nitrite oxidation is commonly based on functional markers such as the amoA gene ( encoding the alpha subunit of the ammonia monooxygenase enzyme ) (Francis et al. 2005) and nxrB gene ( encoding the beta subunit of the nitrite oxidoreductase enzyme ) (Pester et al. 2014) Nitrification has been documented in virtually every envir onment across the earth (e.g., soil, marine, freshwater ). Nonetheless very little is known about nitrification in systems impacted by acid mine drainage (AMD) which refers to acidic and metal rich water s that flow out of coal and metal mines One study found that n itrification was completely halted in some AMD impacted streams with pH <5.3 (Niyogi et al. 20 03) However, it is unknown whether the findings are a general phenomenon in other streams or whether AMD differentially impacts the various groups of microorganisms involved in nitrification. Though the impacts of AMD on nitrification are largely unknown, other research has evaluated individual environmental factors that are often associated with AMD. For instance, n itrification is highly sensitive to acidic pH. Low pH presents physiological

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! 3 challenges to a mmonia oxidiz ing microbes through reduced bioavailability of ammonia (Suzuki et al. 1974; Stein et al. 1997) Previous studies found significant nitrification rates in soils with acidic pH, including forest, tea plantation, and heath s ystems (Boswell, 1955; De Boer et al. 1989; 1992; Pennington and Ellis, 1993; Tietema et al. 1992; N Walker and Wickramasinghe, 1979) In acidic soils, ammonia oxidation rates are optimum at pH 5 (Hayatsu, 1993) and AOA tend to dominate over AOB (He et al. 2007; Stopnisek et al. 2010; Isobe et al. 2012; Yao et al. 2011; He et al. 2012) In marine water columns and artificially acidified lakes, a mmonia oxidation ceased at pH values lower than 5.7 6.5 (Kitidis et al. 2011; Liu et al. 2013; Rudd et al. 1988 ) The lower pH limits for a mmonia oxidation recorded to date were as low as 3.5 in soils ( woodlands, grasslands and agriculture ) (Booth et al. 2005) and 4.5 in a wastewater batch reactor (JimÂŽnez et al. 2012) Bacterial and archaeal amoA genes (no measured rates) were recovered in soils with pH values as low as 3.7 (He et al. 2007) Very little is known about the effects of acidic pH on nitrite oxidation or its associated microbes. One study found that nitrite oxidation rates c eased at pH values lower than 6.5 in wastewater batch reactors (JimÂŽnez et al. 2011) Nitrite oxidation rates by a Nitrospira enrichment culture significantly decreased at pH 6, compared to the pH optimum of 8.2 (Blackburne et al. 2007) Nitrification is also extremely sensitive to heavy metal concentr ations H eavy metals such as copper, zinc, lead, cadmium, metal sulfide s and nickel are known to inhibit nitrification in soils (Yan et al. 2013; Cela and Sumner, 2002) Lab experiments on freshwater sediments indicated that ammonia and nitrite oxidation rates were not completely inhib ited but were delayed at high cadmium (5400 g g 1 dw ), copper (18800

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! 4 g g 1 dw ), zinc (19600 g g 1 dw ) and lead (36500 g g 1 dw ) concentrations (Broberg, 1984) This suggested that some ammonia oxidizers and nitrite oxidizers were able to adapt to the metal rich environments (Broberg, 1984) AOB recovered from long term zinc contaminated soil s were more tolerant to elevated zinc concentrations than AOB from uncontaminated control soil s (Mertens et al. 2006) A OA were shown to be more tolerant to copper than AOB in copper contaminated soil s (Li et al. 2009) To the best of our knowledge, n o studies have been conducted to understand the effect of hea vy metals on nitrite oxidation. In the Colorado Rocky Mountains, AMD is a particularly common threat due to the large number of mines in the area. In the present study, we used high throughput sequencing and q uantitative PC R (qPCR) to examine the abundance and diversity of AOA, AOB and NOB in AMD impacted sediments in the Iron Springs Mining District near Ophir, Colorado We found that AOA and Nitrospira w ere very abundant a cross most sites, but AOB and Nitrobacter were rarely detected

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! 5 CHAPTER II I MATERIALS AND METHODS Site Description and Sample Collection The Iron Springs Mining District located in Ophir, Colorado consists of several abandoned mines (Figure 1) Since 1890, the Iron Spring s Mining District was predominantly mined for metals such as silver, gol d and lead, and also to some extent for iron (p igment) and tungsten (Nash, 2002) AMD from these mines drains directly into the Howard Fork River Composite sediment samples (approximately two inches deep) were collected from 13 sampling sites during June and August 2013 and from 11 sampling sites during June and September 2014 at Iron Springs Samples were stored on dry ice until permanent storage at 20 ¡ C in the laboratory freezer. F or total recoverable metal analysis (TRW) 500 mL of surface water sample was collected, acidified to pH <2 with concentrated HNO 3 and stored at 4 ¡ C F or dissolved metal analysis (DM) 500 mL of surface water sample was collected, filtered with a cellulose nitrite membrane filter (Thermo Scientific, Waltham, MA), acidified to pH <2 with concentrated HNO 3 and stored at 4 ¡ C (Roane & Sackett, in prep ) An additional ~50 g rams of composite surface sediments was collected for total recoverable metal analysis from sediments (TRS) and was stored at 4 ¡ C (Roane & Sackett, in prep ).

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! 6 Figure 1. Map study sites within the Iron Springs Mining District near Ophir, Colorado. Environmental Parameters T emperature, pH, conductivity and dissolved oxygen were measured at the sediment surface using a Thermo Scientific Orion 5 Star Multiparameter Meter Kit (Thermo Fisher Scientific, Inc., Waltham, MA) (Roane & Sackett, in prep ) Total recoverable metal concentration analysis of the Iron Springs water and sediment sample s were done at the EPA Region 8 lab (Golden, CO) using Inductively coupled Plasma Mass Spectrometry (ICP MS) following EPA method 200.8 Dissolved metal concentration analysis of the Iron Springs water and sediment sample s were done at the EPA Region 8 lab (Golden, CO) using Inductively coupled Plasma Optical Emission Spectrometry (ICP OES) following EPA method 200.7. DNA Extraction The MO BIO PowerMax Soil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA) was used to isolate total DNA from sub samples of mechanically

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! 7 homogenized saturated composite sediment (~10 grams) from each sample site (Roane & Sackett, in prep ). DNA extracts were quantified using the Qubit dsDNA HS Assay Kit with the Qubit 2.0 Fluorometer ( Life Technologies Corporation, Carlsbad, CA ). Quantitative PCR The overall abundance of archaeal amoA AOB 16S rRNA Nitrospira nxrB and Nitrobacter 16S rRNA genes were quantified using the StepOnePlus Real Time PCR system (Life Technologies Corporation, Carlsbad, CA ). The primers used were Arch amoA F and Arch amoA R for archaeal amoA (Francis et al. 2005) ; NitA and CTO654r for AOB 16S rRNA (Voytek and Ward, 1995; Kowalchuk et al. 1997; Sonthiphand et al. 2013) ; nxrB 169F and nxrB 638R for Nitrospira nxrB (Pester et al. 2014) ; and Nitro 1198f and Nitro 1423r for Nitrobacter 16S rRNA (Graham et al. 2007; Huang et al. 2010) q PCR conditions and reactions were carried out as previously described for a rchaeal amoA (Mosier and Francis, 2008) ; for AOB 16S rRNA (Sonthiphand et al. 2013) with the addition of a plate read step at 80C for 10s ; and Nitrobacter 16S rRNA qPCR (Graham et al. 2007) Nitrobacter 16S rRNA qPCR was carried out using a TaqMan assay due to its high sensitivity over SYBR Green. The previously described PCR conditions f or Nitrospira nxrB (Pester et al. 2014) were app lied to qPCR with the addition of a plate read step at 84C for 10s The reactions were performed in a 20 l reaction mixture with 1 l of template DNA, 0.4 l of 25 M passive reference dye, and the following for the respective primer pairs : 10 l of Failsafe Green Premix E (Epicentre Madison, WI ), 2 M of MgCl 2 0.4 M of each primer, 0.04 M of BSA and 0.05 M of AmpliTaq DNA Polymerase (Applied Biosystems) for archaeal amoA ; 10 l of Failsafe Green Premix F (Epicentre Madison, WI ), 0.3 M of each primer, 0.3 M of

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! 8 BSA, and 0.05 M of AmpliTaq DNA polymerase (Applied Biosystems) for AOB 16S rRNA; 10 l of Failsafe Green Premix F (Epicentre Madison, WI ), 1 M of each primer, and 0.025 M of AmpliTaq DNA polymerase (Applied Biosystems) for N itrospira nxrB ; and 10 l of Failsafe Green Premix F (Epicentre Madison, WI ), 0.9 M of each primer, 0.25 M of Nitro 1374Taq probe (Life Technologies Corporation, Carlsbad, CA) and 0.038 M of AmpliTaq DNA polymerase (Applied Biosystems) for Nitrobacter 16S rRNA. The amount of DNA used in each reaction ranged from 0. 2 53.3 ng of DNA for 2013 samples and from 0. 3 18. 3 ng of DNA for 2014 samples. S tandard curves were generated using synthesized genes (Life Technologies Corporation, Carlsbad, CA ) or linearized plasmid DNA The standard gene copies for the assays ranged from 6 1.2 x 10 7 for archaeal amoA 16 3.3 x 10 7 for AOB 16S rRNA, 7 1.5 x 10 7 for Nitrospira nxrB and 18.9 3.8 x 10 7 for Nitrobacter 16S rRNA Triplicate reactions were carried out for all the samples and standard s, and a verage values were calculated In some cases, one outlier was remove d from some triplicate measure ments to maintain a standard deviation of less than 15% for each sample. Melt curves were generated after each SYBR assay to check the specificity of amplification. PCR efficiencies ranged from 87.3% 97.2% for archaeal amoA reactions, 86.1% 89.1% for AOB 16S rRNA reactions, 72% 83.3% for Nitrospira nxrB reactions and 93.7% 95.1% for Nitrobacter 16S rRNA reactions The correlation coefficients ( R 2 ) for all the assays were > 0.99. Illumina MiSeq Library Preparation DNA extracts were sequenced using the Fluidigm Access Array and Illumina MiSeq platform at the Roy. J Carver Biotechnology Center ( University of Illinois

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! 9 Urbana, IL) The Fluidigm Access Array system uses microfluidic technology to perform sequencing library preparation simultaneously across multiple samples and multiple genes Barcoded samples were then pooled for an Illumina MiSeq run with 300 base pair, paired end reads. DNA extracts from 38 sample sites were chosen for sequencing, along with one sample replicate and one sample of nuclease free water as a control. The DNA extracts from the remaining 14 sample sites were not seque nced due to either one or both of the following reasons: no qPCR amplification for the primers sets of interest or extremely low DNA concentration. Prior to the Fluidigm Access A rray amplification, all DNA samples were quantified on Qubit (Life Technolog ies Corporation, Carlsbad, CA) using the H igh Sensitivity dsDNA assay kit and e ach sample was diluted to a concentration of 2 ng/ l. The master mix for amplification was prepared using the Roche High Fidelity Fast Start Kit and 20x Access Array loading reagent according to Fluidigm protocols The reagents for the master mix per reaction included the following: 0.5 l of 10X FastStart Reaction Buffer without MgCl 2 0.9 l of 25 mM MgCl 2 0. 25 l of DMSO 0.1 l of 10 mM PCR grade Nucleotide Mix 0.05 l of 5 U/ l FastStart High Fidelity Enzyme Blend 0.25 l of 20X Access Array Loading Reagent and 0.95 l of w ater T he master mix (3 l ) was aliquoted to 48 wells of a PCR plate and then 1 l of DNA sample and 1 l of Fluidigm Illumina linkers with unique barcode s were added to each well AOA, AOB, and NOB were sequenced with primers us ed for qPCR amplific ation : Arch amoA F and Arch amoA R for archaeal amoA NitA and CTO654r for AOB 16S rRNA, nxrB 169F and nxrB 638R for Nitrospira nxrB and FGPS 872F and FGPS1269R for Nitrobacter 16S

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! 10 rRNA. Total B acterial and Archaeal 16S rRNA genes were sequenced using general primers for the V4 V5 region of the 16S rRNA gene (modifications of 515/926R primers; Earth Microbi ome Project; (Parada et al. 2015; Quince et al. 2011) ) Archaea were also sequenced with primers specific for archaeal 16S rRNA genes ( Arch349F/Arch 806; Takai and Horikoshi, 2000; Wang and Qian, 2009) All primers were synthesized by IDT C orporation (Coralville, IA). 20X primer solutions for each primer pair were prepared in a separate plate by adding 2 l of each forward and reverse primer, 5 l of 20X Access Array Loading Reagent and water to a final volume of 100 l. E ach sample mixture (4 l) was loaded in to the sample inlet s and 4 l of the 20X primers were added to the primer inlet s of a previously primed Fluidigm 48.48 Access Array Integrat ed Fluidic Circuit (IFC). The microfluidic loading of all the sample/primer combinations in the IFC plate were carried out with an AX Controller (Fluidigm Corporation, South San Francisco, CA). Following the loading, the IFC plate was placed on the Fluidi gm Biomark HD PCR machine for amplification. The samples were amplified using the following Access Array cycling program: one cycle of 50C for 2 minutes, 70 C for 20 minutes and 95 C for 10 minutes; 10 cycles of 95 C for 15 seconds and 60 C 30 seconds and 72C for 1 minute; 2 cycle s of 95C for 15 seconds, 80C for 30 seconds, 60 C for 30 seconds and 72 C for 1 minute ; 8 cycle s of 95 C for 15 seconds 60 C for 30 seconds and 72C for 1 minute; 2 cycle s of 95 C for 15 seconds 80 C for 30 seconds, 60 C for 30 seconds and 72 C for 1 minute; 8 cycle s of 95 C for 15 seconds, 60 C for 30 seconds and 72C for 1 minute; and 5 cycle of 95 C for 15 seconds, 80 C for 30 seconds 60 C for 30 seconds and 72 C for 1 minute

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! 11 Following amplification the PCR product s were harvested by loading 2 l of Fluidigm Harvest Buffer in to the sample inlets and placing the IFC on the AX controller. Harvested PCR product s were then transferred to a new 96 well plate quantified on a Qubit Fluoro meter ( Life Technologies Corporation, Carlsbad, CA) and stored at 20 C. All samples were run on a Fragment Analyzer (Advanced Analytics, Ames, IA) to confirm ampl icon regions and expected sizes Samples were pooled in equal amounts according to product DNA concentration. The poo led products were then size selected on a 2% agarose E gel (Life Technologies Carlsbad, CA ) and extracted with a Qiagen gel extraction kit (Qiagen Hilden, Germany ). Cleaned size selected products were run on an Agilent Bioanalyzer to confirm an appropriate profile and to determine an average size. The library pool was quantified by qPCR and loaded on to an Illumina MiSeq flowcell for 2x300 pair ed end sequencing (Illumina, Inc. San Diego, CA) Illumina sequencing technolo gy allows parallel sequen cing of DNA, resulting in approximately 25 billion reads using MiSeq sequencer. The flowcell was sequenced for 301 cycles from each end of the fragments using a MiSeq 600 cycle sequencing kit version 3. Ph iX DNA (genome length: 3kb) was used as a spike in control. For the FASTQ quality score, the ASCII offset of 33 (Sanger scores) was used instead of the 64 offset using Casava 1.8. The sequence data was demultiplexed using bcl2fastq v1.8.4 Conversion Software (Illumina Inc. San Diego, CA ) and PCR primer sorted by the Roy. J. Carver Center for Biotechnology. Reads matching the PhiX control were removed using Casava v. 1.8. Community Composition Analyses Relative sequence abundance and diversity analyses (Appendix I) were conducted in the QII ME (Quantitative Insights in to Microbial Ecology) open package software

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! 12 (Caporaso et al. 2010) The paired ends were joined and quality filtered at Phred quality score of 20 The V4 V5 combined 16S rRNA primer sequencing reads were clustered into operational taxonomical units (OTUs) at 97% sequence identity For taxa specific analyses the AOA AOB and NOB like 16S rRN A gene sequencing reads were clustered at 99% OTUs The taxonomy was assigned using a BLAST based method in QIIME and search ed against the Greengenes 13_8 rep set database (DeSantis et al. 2006) The final OTUs were checked for chim eras using the DECIPHER web tool with the short sequences option (Wright et al. 2012) Putative chimeras were removed from the O T U table using QIIME. The final OT Us were filtered for spe cific taxa for further analyses : Archaeal 16S rRNA reads were filtered for the class Thaumarchaeota and AOB 16S rRNA reads were filtered for the order Nitrosomonadales (no other AOB sequences were identified in the dataset) Diversity analyses were done to understand the community composition within sample sites (alpha diversity) and between sample sites (beta diversity). Alpha diversity analyses (ChaoI, Faith's phylogenetic diversity and observed OTUs) were determined based o n multiple rarefied depths and collated for creating rarefaction plots. Rarefaction of all the samples for Thaumarchaeota taxa specific archaeal 16S rRNA and Nitrosomonadales taxa specific AOB 16S rRNA was performed at multiple depths with size of 10 seque nces at each step between the minimum and maximum number of sequences. For beta diversity analyses, the sa mpling depth was rarefied to 16 0 00 sequences for the combined V4 V5 region of total 16S rRNA, 6 3 0 sequences for Nitrosomonadales taxa specific AOB 16S rRNA, and 60 sequences for Thaumarchaeota taxa specific archaeal 16S rRNA to avoid bias of various sampling depth. Principal

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! 13 component analysis (PCoA) plots were generated using both unweighed and weighed UniFrac distance metrics (Lozupone and Knight, 2005) Weighbor weighted Neighbor Joining phylogenetic trees were constructed for representative sequences of each observed O T U using the Ribosomal Database Project (RDP) Tree Builder. Distance (Jukes Cantor Correction) and bootstrap analyses (100 replicates) were performed using Tree Builder (RDP). Edits were made to the tree using the online software Interactive Tree of Life v3.1 (iTOL) (Letunic and Bork, 2006) Statistical Analyses Analysis of Similarity (ANOSIM) was performed to determine the statistical significance of site specific microbial community clusters by environmental variables (e.g., location, pH) using 999 permutations. S pearman correlations between environmental parameters and the relative abundance of taxa specific OTUs ( Thaumarchaeota and Nitrosomonadales ) were carried out using QIIME (observation_metadata_correlation.py) with p values corrected using the Benjamini Hochberg FDR procedure. Spearman c orrelation s between environmental parameters and total gene abundance s ( based on qPCR) were performed using the IBM SPSS Statistics software package version 23 (IBM, Armonk, NY ) with p values corrected using Bonferroni correction (critical p value/number of correlations ; the adjusted significant p value was set to 0.002 ). The environment al parameters from surface sediments included in the statistical analyses were: pH, temperature (C), conductivity ( S/cm ) and dissolve d oxygen (mg/L). Environmental parameters from surface water included in statistical analyses were: total recoverable metals (TRW in g/L ) including Al As Ba Cd Ca Cu,

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! 14 Fe Pb, Mg, Mn Ni, Na, TI, and Zn ; and dissolved metals (DM in g/L ) including Al Ba, Cd, Ca Cu, Fe, Pb, Mg, Mn, Ni, Na, and Zn

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! 15 CHAPTER IV RESULTS Chemistry S ample sites covered four different mines/regions within the greater Iron Springs Mining District : Caribbeau Mine (Carib01, Carib02 & Carib03), Iron Bog/Fen (FenIB01, FenIB02, HFIBE & Opp03), New Dominion Mine (FenND01, FenND03, NDMD02, NDCS02 CS1B & NDGP) and Howard Fork River (HF04). The measured environmental parameters varied by sampling date and sampling location (Table 3.1 & 3.2 ). A cross surface sediments from all the sites in 2013 and 2014 temperature ranged f rom 7.8C 22.4C and dissolved oxygen ranged from 1.1 mg/L 10 mg/L (Table 3.1 & 3.2 ). Conductivity ranged from 296 S/cm 1608 S/cm (data not shown). During 2013, pH was lowest at the NDGP (pH 3.6) and CS1B (pH 3.2 and 4.1) site s at both time points. A t the Iron Bog/Fen sites pH ranged from 4.1 4.8 at the drainage site (FenIB01 & FenIB02) and gradually increased to 6.1 6.4 at the confluence of the AMD drainage and the river (HFIBE). pH values of the New Dominion Mine (FenND01, FenND03 & NDMD02) sites and Caribbeau Mine sites ranging from 6.2 7.8, were higher than other sites, possibly due to inputs from the nearby wetland s ystems Similar to 2013, the pH was at its lowest at CS1B Jun ( pH 3.2) and highest at the Caribbeau Mine sites ( pH 6.9 7.3) in 2014 In 2014, pH fo llowed a similar trend with Iron Bog/Fen sites (FenIB01, FenIB02 & HFIBE) ranging from 3.5 5.6 and New Dominion Mine (Fen ND01 & FenND03) ranging from 5.7 7.1 The September time points for NDCS02 and NDMD02 had low pH ( pH 3.8 and 3.7) when compared to the June time points (pH 6).

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! 16 The total recoverable metal concentrations from surface water (TRW ; includ ing both dissolved and suspended metals ) were measured for all samples sites and time points. The sediments contain high concentrations of metals commonly found in AMD environment s including cadmium, copper, iron, lead and zinc (Table 3.3 and 3.4) Concentrations of certain metals including arsenic, barium, cadmium, nickel and thallium were below the detection limit of the instrument for the majo rity of the sample sites during 2013 and 2014. During 2013, total recoverable m etal concentrations (TRW) r anged from 0.94 48.7 g/L for cadmium 10.8 382 g/L for copper, 0.38 36 .8 m g/L for iron, 0.97 110 g/L for lead, and 32.6 18 300 g/L zinc During both sample points in 2013 CS1B and NDGP sample site s had the highest metal concentrations for the majority of the metals including aluminum (Al) calcium ( Ca ) copper ( Cu ) iron ( Fe ) lead ( Pb ) magnesium ( Mg ) manganese ( Mn ) sodium ( Na ) and zinc ( Zn ) During 2014, total recoverable metal concentratio ns ranged from 1.02 25.4 g/ L for cadmium 32.4 620 g/L for copper, 1.0 8 139 m g/L for iron, 1.29 74.4 g/L for lead, and 154 11 8 00 g/L for zinc. M etal concentrations ( Al, Ca, Fe, Pb, Mn, Na and Zn ) were high in New Dominion Mine sites overall when compared to other mines/regions Overall, m etal concentrations during 2013 were higher in comparison with 2014. The dissolve d metal concentrations from surface water in sites across 2013 ranged from 70.7 380 m g/L for calcium, 0.13 40 3 m g/L for iron, 5.6 15 4 mg/L for magnesium and 19.4 19 100 g/L for zinc (data not shown) Dissolved metal concentrations for Al, Cd, Cu, Pb and Ni were below the detection limit of the instrument for most of the sites with the except ion of CS1B, FenIB01, FenIB02, NDCS02, NDGP and Opp03. CS1B and NDGP had high metal concentrations for the

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! 17 majority of the metals su ch as Al, Fe, Mg Mn, Na and Zn, at both time points in 2013 Across 2014, the dissolved metal concentrations ranged from 58.1 368 mg/L for calcium, 0.63 10 mg/L for iron, 3.5 12.9 mg/L for magnesium and 112 12 100 g/L for zinc. Overall, d issolved metals concentrations for Ca, Fe, Mg, Mn, Na and Zn were high in New Dominion Mine sites and Caribbeau Mine sites in 2014 when compared to other mines/regions Each mine/region was different when characterized and compared with each other based on measured environmental parameters (Table 3.5) Metals were also measured in surface sedim ents ( in addition to surface water reported above) in 2013 and ranged from 1 600 237 000 ppb for arsenic, 13 1 311 000 ppb for cadmium, 33.2 5 690 ppb for copper, 5 590 372 000 ppb for iron, 36 000 1 660 000 ppb for lead, 68 1 521 000 ppb for nickel and 22.1 5 140 ppb for zinc. On average, the Caribbeau Mine sites an d New Dominion Mine sites had the highest sediment metal concentrations for the majority of the metals.

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! 18 Table 3. 1: Chemistry data for sample sites across June and August 2013 in the Iron Springs Mining District Summer 2013 Sample Region Sample Sites Date Collected pH Temp. (C) DO (mg/L) Caribbeau Mines Carib01 6.25.13 7.3 7.9 8.8 8.05.13 7.1 7.8 7.7 Carib02 6.25.13 7.2 8.4 8 .0 8.05.13 7.8 8 .0 8.5 Carib03 6.25.13 6.6 13.1 7.9 8.05.13 7 .0 7.9 10 Capped Seap Iron Bog CS1B 6.25.13 3.2 14.9 5.1 8.05.13 4.2 21.2 6.6 Fen Iron Bog FenIB01 6.25.13 4.5 13.4 1.4 8.05.13 4.8 10.4 1.1 FenIB02 6.25.13 4.1 18.5 6.4 8.05.13 4.3 19.8 3.5 New Domini on Fen FenND01 6.25.13 7.3 11 .0 8.1 8.05.13 6.2 13.3 6 .0 FenND03 6.25.13 6.4 14.3 6.6 8.05.13 6.5 18.7 5.9 Howard Fork HF04 6.25.13 5.7 8.9 7.8 8.05.13 8.3 10.9 7.9 Howard Fork Iron Bog HFIBE 6.25.13 6.1 17.4 6.3 8.05.13 6.4 14.6 7.5 New Domini on Capped Seap NDCS02 6.25.13 4.6 20.3 4.5 8.05.13 4.1 18 .0 5.2 New Domini on Green Pond NDGP 6.25.13 3.6 14.2 5 .0 8.05.13 3.6 22.4 2.2 Iron Bog Drainage Opp03 6.25.13 4.7 14 .0 3.9 8.05.13 5.9 8.4 3.8 New Domini on Mine Drainage NDMD02 6.25.13 6.8 9.5 8.9 8.05.13 7.4 8.7 8.1

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! 19 Table 3.2: Chemistry data for sample sites across June and September 2014 in the Iron Springs Mining District Summer 2014 Sample Region Sample Sites Date Collected pH Temp. (C) DO (mg/L) Caribbeau Mines Carib01 6.24.14 7.1 n.d 8.4 9.30.14 6.9 8 .0 5.9 Carib02 6.24.14 7.3 n.d 8.5 9.30.14 7.1 7.9 6.7 Carib03 6.24.14 7.2 n.d 8 .0 9.30.14 7.1 8.4 7.8 Capped Seap Iron Bog CS1B 6.24.14 3.2 10.7 7 .0 9.30.14 4.9 10.2 7.6 Fen Iron Bog FenIB01 6.24.14 3.6 9.3 7.1 9.30.14 4.3 13.7 6.9 FenIB02 6.24.14 3.5 15.9 5.7 n.s n.s n .s n.s New Domini on Fen FenND01 6.24.14 5.7 15.1 9.2 9.30.14 6.9 8.4 9.2 FenND03 6.24.14 7.1 n.d 6.9 n.s n.s n.s n.s Howard Fork HF04 n.s n.s n.s n.s n.s n.s n.s n.s Howard Fork Iron Bog HFIBE 6.24.14 5.5 9.5 8.1 9.30.14 5.6 8.7 8.5 New Domini on Capped Seap NDCS02 6.24.14 6 .0 15 .0 6.1 9.30.14 3.8 10.6 6.9 New Domini on Green Pond NDGP 6.24.14 6.5 16 .0 8.9 9.30.14 7.4 9.3 8.4 Iron Bog Drainage Opp03 n.s n.s n.s n.s n.s n.s n.s n.s New Domini on Mine Drainage NDMD02 6.24.14 6 .0 9 .0 5.7 9.30.14 3.7 8.5 5.9 n.s: not sampled n.d : not determined. *Temperature readings not available for some sites due to an equipment failure

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! 20 Table 3.3 : Total recoverable metal from water samples across 2013 i n the Iron Springs Mining District Summer 2013 Sample Region Sample Sites Date Collected Cadmium (g/L) Copper (g/L) Iron (mg/L) Lead (g/L) Zinc (g/L) Caribbeau Mines Carib01 6.25.13 12 132 5.2 57 104 8.05.13 BDL 51.5 7.3 5.6 166 Carib02 6.25.13 BDL 32.8 5.5 3.7 102 8.05.13 BDL 43 6.4 5.0 150 Carib03 6.25.13 BDL 28.8 4.6 3.2 98.5 8.05.13 BDL 39.7 6.1 6.6 160 Capped Seap Iron Bog CS1B 6.25.13 19.1 194 36.8 39.4 16200 8.05.13 48.7 382 12.4 42.3 18300 Fen Iron Bog FenIB01 6.25.13 1.9 308 4.5 4.3 430 8.05.13 2 281 16.3 12.7 538 FenIB02 6.25.13 1.3 45.7 0.7 1.8 236 8.05.13 1.5 37.4 0.8 1.8 325 New Domini on Fen FenND01 6.25.13 BDL BDL 2.5 1.0 55.4 8.05.13 BDL BDL 2.8 BDL BDL FenND03 6.25.13 BDL BDL BDL BDL 88.7 8.05.13 BDL BDL BDL 1.2 279 Howard Fork HF04 6.25.13 BDL BDL 0.4 BDL 32.6 8.05.13 BDL BDL BDL BDL BDL Howard Fork Iron Bog HFIBE 6.25.13 BDL 75.5 2.1 1.8 169 8.05.13 0.9 90.9 5.0 4.2 191 New Domini on Capped Seap NDCS02 6.25.13 BDL BDL 0.7 3.6 158 8.05.13 BDL 54 16.3 110 503 New Domini on Green Pond NDGP 6.25.13 5.46 271 15400 47.5 1860 8.05.13 13.2 225 15600 40.6 4230 Iron Bog Drainage Opp03 6.25.13 1.7 277 15900 5.18 374 8.05.13 1.79 292 26400 6.98 422 New Domini on Mine Drainage NDMD02 6.25.13 BDL 10.8 6.2 1.2 BDL 8.05.13 BDL BDL 6.5 1.1 BDL BDL : V alues were measured, but below the detection limit of the instrument.

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! 21 Table 3.4 : Total recoverable metal from water samples across 2014 in the Iron Springs Mining District Summer 2014 Sample Region Sample Sites Date Collected Cadmium (g/L) Copper (g/L) Iron (mg/L) Lead (g/L) Zinc (g/L) Caribbeau Mines Carib01 6.24.14 BDL 92 13.9 33.7 192 9.30.14 BDL 74.6 10.3 10.9 231 Carib02 6.24.14 BDL 88 13.7 33.5 198 9.30.14 BDL 77.7 10.5 11.8 232 Carib03 6.24.14 1.02 39.8 7.2 36.2 249 9.30.14 BDL 32.4 4.9 5.6 154 Capped Seap Iron Bog CS1B 6.24.14 22.9 277 7.6 36.3 11800 9.30.14 10.9 82.3 1.9 27.7 4150 Fen Iron Bog FenIB01 6.24.14 2.5 254 10.2 5.1 570 9.30.14 5.2 428 BDL 3.3 730 FenIB02 6.24.14 2.9 94.3 BDL 2 536 n.s n.s n. s n.s n.s n.s New Domini on Fen FenND01 6.24.14 BDL BDL 1.6 BDL BDL 9.30.14 BDL BDL 3.6 BDL 271 FenND03 6.24.14 BDL BDL BDL BDL 201 n.s n.s n.s n.s n.s n.s Howard Fork HF04 n.s n.s n .s n.s n.s n.s n.s n.s n.s n.s n.s n.s Howard Fork Iron Bog HFIBE 6.24.14 BDL 85.3 6.0 12.6 208 9.30.14 1.0 62.6 1.8 2.6 166 New Domini on Capped Seap NDCS02 6.24.14 2.4 BDL BDL 18 555 9.30.14 25.4 206 2.9 26.7 10100 New Dominion Green Pond NDGP 6.24.14 BDL BDL 7520 1.29 BDL 9.30 .14 BDL BDL 13900 3.26 269 Iron Bog Drainage Opp03 n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s n.s New Dominion Mine Drainage NDMD02 6.24.14 20.9 82 1.1 74.4 495 9.30.14 13.7 620 7.7 57.7 4040 n.s : no t sampled BDL : V alues were measured, but below the detection limit of the instrument.

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! 22 Table 3.5: Summary of environmental parameters for each mine/region compared to each other. Sites Parameters pH Temperature (C) Dissolved Oxygen (mg/L) Conductivity ( S/cm) Metal Concentrations (TRW & DM) Caribbeau Mine 6.6 7.8 7.8 13.1 5.9 10 814 1139 Low Iron Bog/Fen 3.5 6.4 8.4 19.8 1.1 8.5 479 1018 High New Dominion Mine 3.2 7.4 8.4 22.4 2.2 9.2 889.6 1608 High Howard Fork River 5.7 8.3 8.8 10.9 7.8 7.9 296 335 Low Overall Abundance of AOA and AOB T he overall abundance of AOA and AOB gene s was determined across 13 AMD impacted sites from June 2013 and August 2013 and 11 AMD impacted sites from June 2014 and September 2014 from the Iron Springs Mining District AOA were quantified using archaeal amoA genes. AOB were quantified using AOB specific 16S rRNA gene primers since AOB amoA genes did not amplify in our samples (using primers amoA 1F/amoA 2R (Rotthauwe et al. 1997) or amoA 1F*/amoA 2R (Stephen et al. 1998 ) ) A rchaeal amoA gene copies across 2013 and 2014 ranged from 2 x 10 3 4.9 x 10 7 copies / g of sediment DNA (Figure 2 A) During 2013, archaeal amoA genes were most abundant in the Howard Fork R iver site (HF04, ~10 6 copies/! g of sediment DNA ) and the Iron Bog/Fen sites (HFIBE & FenIB01, ~10 5 10 6 copies/! g of sediment DNA ) followed by the Caribbeau Mine sites (Carib01, Carib02 & Carib03, ~10 4 10 5 copies/! g of sediment DNA ). In 2014, the archaea l amoA genes w ere most abundant in the Iron Bog/Fen sites (HFIBE & FenIB01, ~10 7 copies/ g of sediment DNA ) and one of the New

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! 23 Dominion sites (NDMD02, ~10 7 copies/! g of sediment DNA ) foll owed by the Caribbeau Mine site (Carib02, ~10 6 10 7 copies/! g of sediment DNA ). The archaeal amoA gene copy numbers were lowest a t the New Dominion Mine site ( NDCS02 Sept14 ~10 5 copies/! g of sediment DNA ) with a ~100 fold difference when compared to the most abundant sites. A rchaeal amoA genes did not amplify in FenND03 Jun, NDGP Jun, NDGP Aug, and Opp03 Jun samples for 2013 and Carib01 Jun, Carib03 Jun, CS1B Sept FenIB0 2 Jun, FenND01 Jun, FenND01 Sept, NDCS02 Jun NDGP Jun, and NDGP Sept samples for 2014 possibly suggesting that AOA were absent in those selected sites were present in low numbers, or did not properly amplify with the PCR primers The overall abundance of AOB 16S rRNA genes across 2013 and 2014 samples sites ranged from 1.5 x 10 3 5.3 x 10 5 copies/! g of sediment DNA (Figure 2 B) During 2013, the AOB 16S rRNA genes were most abundant in the Caribbeau Mine sites (Carib01, Carib02 & Carib03, ~10 4 10 5 copies/! g of sediment DNA ) followed by the Howard Fork River site (HF04, ~10 4 copies/! g of sediment DNA ) and one of the New Dominion Mine sites (NDMD02, ~10 4 copies/! g of sediment DNA ). Similar to 2013, AOB were most abundant in Caribbeau Mine sites ( Carib01 Sept14, Carib02 & Carib03 Sept14 ~10 4 10 5 copies/! g of sediment DNA ) in 2014. The AOB 16S rRNA gene copy numbers were lowest in one of the Iron Bog/Fen site (FenIB02 Jun14). AOB 16S rRNA gene s did not amplify in FenIB01 Jun, FenIB01 Aug FenN D01 Jun, FenND03 Jun, NDCS02 Aug, NDGP Jun, NDGP Aug, Opp03 Jun, and Opp03 Aug samples for 2013 and Carib03 Jun, CS1B Jun, FenIB01 Jun, FenND01 Jun, FenND03 Jun, HFIBE Sept,

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! 24 NDCS02 Jun, NDCS02 Sept, NDGP Ju n, NDGP Sept, NDMD02 Jun and NDMD02 S ept samples for 2014 A: B: Figure 2 The overall abundance of ( A ) archaeal amoA genes, and ( B ) AOB 16S rRNA genes across 2013 and 2014 samples sites in the Iron Springs Mining District. 0.E+00 1.E+07 2.E+07 3.E+07 4.E+07 5.E+07 6.E+07 HF04 FenIB01 Opp03 FenIB02 HFIBE NDMD02 FenND01 NDGP NDCS02 CSIB FenND03 Carib01 Carib02 Carib03 Archaeal amoA gene copies/ g of DNA Jun-13 Aug-13 Jun-14 Sep-14 River Iron Bog/Fen New Dominion Mine Carribeau Mine "#$%""! &#$%"'! (#$%"'! )#$%"'! *#$%"'! '#$%"'! +#$%"'! HF04 FenIB01 Opp03 FenIB02 HFIBE NDMD02 FenND01 NDGP NDCS02 CSIB FenND03 Carib01 Carib02 Carib03 AOB 16S rRNA gene copies/ g of DNA ,-./&)! 0-1/&)! ,-./&*! 234/&*! River Iron Bog/Fen New Dominion Mine Carribeau Mine

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! 25 We compared the relative abundance of AOA and AOB in our samples using AOA amoA genes and AOB 16S rRNA genes since AOB amoA genes did not amplify in our system and both genes are thought to exist in single cop ies with in each genome (Aakra et al. 1999; Kowalchuk and Stephen, 2001; Hermansson and Lindgren, 2001; Norton et al. 2008) The q PCR results indicate d that archaeal amoA gene copies were greater than the AOB 16S rRNA gene copies at most sites and time points (Figure 3 ). On average, the archaeal amoA gene was 71 times more abundant than AOB 16S rRNA in 2013 (average log 10 AOA amoA :AOB 16S rRNA ratio = 1.5) and 356 times more abundant in 2014 (average log 10 AOA amoA :AOB 16S rRNA ratio = 1.9). However the AOB 16S rRNA gene is on average half as abundant than archaeal amoA at the CS1B and NDCS02 ( June) sites during 2013 (average log 10 AOA amoA :AOB 16S rRNA ratio = 0.4). Archaeal amoA gene amplification was observed in many sites with no AOB 16S rRNA gene amplification including FenIB01, FenND01 Ju n, NDCS02 Aug and Opp03 Aug in 2013 and CS1B Jun, FenIB01 Jun, FenND03 Jun, NDCS02 Sept and NDMD02 during 2014 Similarly AOB 16S rRNA gene amplification was observed in 2014 samples sites ( Carib03 Sept, CS1 B Sept, FenIB02 Jun and FenND01 Sept) where there was no archaeal amoA amplification.

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! 26 Figure 3 Log ratio of AOA amoA :AOB 16S rRNA copy numbers across sample sites during 2013 and 2014 in the Iron Springs Mining District Overall Abundance of NOB The overall abundance of Nitrospira nxrB was quantified using qPCR across 13 AMD impacted sites from June 2013 and August 2013 and 11 AMD impacted sites from June 2014 and September 2014, from the Iron Springs Mining District (Figure 4 ) The total number of Nitrospira nxrB gene copies across 201 3 and 2014 samples sites ranged from 7.3x10 5 7.7 x 10 7 copies/ g of sediment DNA During 2013, Nitrospira nxrB genes were most abundant at the Howard Fork River sites (HF04, ~10 7 copies/! g of sediment DNA ) and Caribbeau Mine sites (Carib01, Carib02 & Carib03, ~10 6 10 7 copies/! g of sediment DNA ). Similar to 2013, Nitrospira nxrB genes were most abundant at the Caribbeau Mine sites (Carib02 & Carib03, ~10 6 10 7 copies/! g of sediment DNA ) followed by FenIB01 (~10 7 copies/! g of sediment DNA ) and FenND01 sites (~10 6 10 7 /&"#"! /'#"! "#"! '#"! &"#"! &'#"! HF04 FenIB01 Opp03 FenIB02 HFIBE NDMD02 FenND01 NDGP NDCS02 CS1B FenND03 Carib01 Carib02 Carib03 log 10 (AOA amoA :AOB 16S rRNA) Jun-13 Aug-13 Jun-14 Sep-14 River Iron Bog/Fen New Dominion Mine C aribbeau Mine

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! 27 copies/! g of sediment DNA ) in 2014 Nitrospira nxrB genes were not amplified in CS1B Jun CS1B Aug, FenIB02 Aug, FenND03 Jun F enND03 Aug, NDCS02 Aug NDGP Jun NDGP Aug, and Opp03 Aug samples for 2013 and Carib01 Jun, Carib01 Sept, CS1B Sept, FenIb02 Jun, HFIBE Sept, NDCS02 Jun, NDCS02 Sept N DGP Jun, NDGP Sept, and NDMD02 Jun samples for 2014 Nitrobacter genes did not amplify in any of the samples using nxrB 1F/nxrB 1R primers (Vanparys et al. 2007) Nitrobacter specific 16S rRNA gene primers Nitro 1198f/Nitro 1423r (Huang et al. 2010) or Nitrobacter specific 16S rRNA gene primers FGPS872f/FGPS1269r (Degrange and Bardin, 1995) Figure 4 The overall abundance of Nitrospira nxrB genes across 2013 and 2014 samples sites i n the Iron Springs Mining District Relative Abundance and Diversity T he relative abundance and diversity of the V4 V5 combined region of total 16S rRNA, archaeal 16S rRNA, AOB 16S rRNA and Nitrobacter 16S rRNA genes were evaluated in 38 samples from AMD impacted sites ( 2013 and 2014 ) Preliminary 0.E+00 1.E+07 2.E+07 3.E+07 4.E+07 5.E+07 6.E+07 7.E+07 8.E+07 9.E+07 HF04 FenIB01 Opp03 FenIB02 HFIBE NDMD02 FenND01 NDGP NDCS02 CSIB FenND03 Carib01 Carib02 Carib03 Nitrospira nxrB gene copies/ug of DNA Jun-13 Aug-13 Jun-14 Sep-14 River Iron Bog/Fen New Dominion Mine Carribeau Mine

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! 28 analyses per formed on total 16S rRNA revealed that t he bacterial communities in these sites were dominated by Proteobacteria (47%), Bacteroidetes (10.6%), Chloriflexi (8%), Acidobacteria (7.0%) and Actinobacteria (6.3%) (data not shown) The archaeal communities consisted of organisms belonging to the phylum Crenarchaeota Euyarchaeota and Parvarchaeota Thaumarchaeota accounted for 0.1%, Nitrospira accounted for 0.7%, and Nitrosomonadales specific betaproteobacteria accounted for <0.1% of the total community. Beta diversity analyses based on unweighted and weighted Unifrac distance matrices revealed that the sample mines/regions did not cluster together except for the Caribbeau Mine sites (data not shown). Twenty samples contained > 10 Thaumarchaeota sequences within the archaeal 16S rRNA gene sequence dataset. Of those, a total of 11 unique Thaumarchaeota OTUs (clustered at 99% identity) were recovered with 1 7 OTUs observed within each sample (Figure 5 ) The Thaumarchaeota OTUs were taxonomically related to Nitrosopumilus SAGMA X Cenarchaeaceae and Candidatus Nitrososphaera (Figure 6 ).

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! 29 Figure 5. Number of observed Thaumarchaeota OTUs at each site (based on taxomonic identiy of the archaeal 16S rRNA sequences with the Greengenes database). Figure 6 Relative abundance of Thaumarchaeota taxa within the archaeal 16S rRNA gene sequence dataset ( depth of 60 sequences per sample) 0 1 2 3 4 5 6 7 8 HF04 FenIB01 FenIB02 HFIBE NDMD02 FenND01 CS1B FenND03 Carib01 Carib02 Carib03 No. of Observed OTUs Jun 13 Aug 13 Jun 14 Sept 14 River Iron Bog/Fen New Dominion Mine Caribbeau Mine

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! 30 B eta diversity analyses on the Thaumarchaeota taxa specific archaeal 16S rRNA, were conducted for 17 sample sites each with a sampling depth of 60 sequences Unweighted UniF rac analyses, based on the presence and absence o f observed OTUs reveal ed no statistically significant correlation (R ANOSIM = 0.01, p = 0.37 999 permutations ) between the three major mines/regions (Caribbeau Mi nes, New Dominion Mine Site and Iron Bog/Fen) (Figure 7 A). There was also no statistically significant correlation between the three mines/regions when considering OTU abundance in weighted UniFrac analyses (R ANOSIM = 0.009, p = 0.42 999 permutations ) (Figure 7 B).

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! 31 A: Iron Bog/Fen New Dominion Mine Caribbeau Mines B: Iron Bog/Fen New Dominion Mine Caribbeau Mines Figure 7 Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S rRNA gene sequencing) color coded by the three major mines/regions. (A) unweighted UniFrac distance matrices ( B) weighted UniF rac distance matrices. Plots contain sample sites from both 2013 and 2014.

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! 32 Phylogenetic analyses of Thaumarchaeota taxa specific archaeal 16S rRNA revealed that a proportion of the OTUs belonging to unassigned genus of Nitrososphaera fell under Group I.1b (3 of 11) and OTUs belonging to the genus Nitrosopumilus fell under mine soil environments (3 of 11) (Figure 8 ) When combined, approximately 55% of the Thaumarchaeota OTUs fell under the soil group. One OTU was closely related to coastal AOA and t wo OTUs were closely related to AOA from freshwater environments. Two OTUs were not closely related to any other database sequences

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! 33 Figure 8 Weighbor weighted Neighbor Joining phylogenetic tree showing the affiliation of Thaumarchaeota taxa specific archaeal 16S rRNA sequences (highlighted in red) and NCBI sequences from other environments. Only significant bootstrap values (>50) are shown at the branch nodes. Fifteen samples contained at least ten AOB sequences within the AOB 16S rRNA sequences sequence dataset. Of those, a total of 29 unique OTUs (clustered at 99% identity) were recovered belonging to the order Nitrosomonadales with 2 25 OTUs observed within each sample (Figure 9 ) An unassigned genus of the family Nitrosomonadaceae accounte d for 99.7% of OTUs followed by 0.2% of Other'

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! 34 classified genus of the family Nitrosomonadaceae and 0.1% Nitrosomonas (Figure 10 ). No other AOB sequences were recovered (e.g., Nitrosospira ). Figure 9. Number of observed Nitrosomonadales OTUs at each site (based on taxomonic identiy of the AOB 16S rRNA sequences with the Greengenes database). Figure 10 Relative abundance of Nitrosomonadales taxa within the AOB 16S rRNA gene sequence dataset ( depth of 630 sequences per sample) "! '! &"! &'! ("! ('! )"! HF04 FenIB01 FenIB02 HFIBE NDMD02 FenND01 CS1B Carib01 Carib02 Carib03 No. of Observed OTUs Jun 13 Aug 13 Jun 14 Sept 14 River Iron Bog/Fen New Dominion Mine Caribbeau Mine

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! 35 Beta diversity analyses of Nitrosomonadales were conducted on 1 3 sample sites each with a sampling depth of 6 3 0 sequences Unweighted UniFrac analyses revealed that the majority of the Caribbeau Mine sites (except for Carib03 Sept14 and the repli cate) were clustered together and were separated from the three New Dominion Mine sites (NDMD02 Jun 13 NDMD02 Aug13 and NDMD02 Sept14) (R ANOSIM = 0.6, p = 0.01, 999 permutations) (Figure 11 A) Taking the observed abundance into consideration the weighted UniFrac analyses also revealed that Carribeau Mine sites were clustered together and separated from the New Dominion Mine site cluster (R ANOSIM = 0.82, p = 0.007, 999 permutations) (Figure 11 B)

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! 36 A: New Dominion Mine Caribbeau Mines B: New Dominion Mine Caribbeau Mines Figure 11 Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color c oded by the three major mines/regions ( A) unweighted UniFrac distance matrices. ( B) weighted UniF rac distance matrices. Plots contain sample sites from both 2013 and 2014.

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! 37 Phylogenetic analyses of Nitrosomonadales taxa specific AOB 16S rRNA revealed that the majority of OTUs belonging to the unassigned genus of Nitrosomonadales were closely related to sequences from a gold mine (13 of 29) (Figure 12 ). OTUs belonging to the unassigned genus of Nitrosomonadales Other' assigned genus of Nitrosomonadales and genus Nitrosomonas were closely re lated to sequences from freshwater envir onments (11 of 29). Two OTUs were unrelated to other sequences in the database sequences For NOB, initial analyses revealed 0 53,910 Nitrospira nxrB sequences per sample, but no Nitrobacter like 16S rRNA gene sequences. This supports the qPCR results where Nitrospira genes were very abundant, but Nitrobacter genes did not amplified.

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! 38 Figure 12 Weighbor weighted Neighbor Joining phylogenetic tree showing the affiliation of Nitrosomonadal es taxa specific AOB 16S rRNA sequences (highlighted in red) and NCBI sequences from other environments. Only significant bootstrap values (>50) are shown at the branch nodes.

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! 39 Correlation with Environmental Parameters Spearman c orrelations between the surface sediment environment al parameters and each of the gene abundances ( archaeal amoA AOB 16S rRNA and Nitrospira nxrB ) were made to determine potential drivers of nitrifying microbes A rchaeal amoA gene abundance was not strong ly correlated ( p <0.002) with pH, temperature, dissolved oxygen or conductivity in 2013 or 2014, or when both years were combined Moderate negative correlations was observed between archaeal amoA gene abunda nce and conductivity (# = 0.59 p = 0.003, N = 24), but were not statistically significant. AOB 16S rRNA gene abundance for 2013 was moderately correlated with dissolved oxygen ( # = 0.66 p = 0.002, N = 19) pH (# = 0.64 p = 0.003, N = 19) and temperature (# = 0. 63, p = 0.004, N = 19). For 2013 and 2014 combined, the AOB 16S rRNA gene abundances were statistically correlated with pH (# = 0.57, p = 0.001, N = 29 ) and temperature (# = 0.69 p = 0.00 06 N = 27). The Nitrospira nxrB gene abundance was statistically correlated with pH (# = 0.69, p = 0.002, N = 18) for 2013. For 2013 and 2014 combined, Nitrospira nxrB gene abundance was negatively correlated with temperature ( # = 0.49, p = 0.009, N = 27) A rchaeal amoA AOB 16S rRNA and Nitrospira nxrB gene abundances were not significantly correlated with metal concentrations ( total recoverable metal s T R W ; dissolved metal s DM ) in 2013 or 2014. When both years were combined, archaeal amoA gene abundance was negatively correlated ( but not statistically significant ) to DM calcium (# = 0.54, p = 0.001, N = 37) DM sodium (# = 0.52 p = 0.001, N = 37) TRW calcium (# = 0.46, p = 0.005, N = 37), and TRW sodium (# = 0.45, p = 0.005, N = 37)

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! 40 Spearman c orrelations showed no relationship between the relative abundance of observed Thaumarchaeota OTUs and pH, tem perature, DO conductivity or surface water metal concentrations (TRW and DM) for 2013 and 2014 combined. When samples in the PCoA plots were color coded by p H ranges (<4, 5 7 and >7) the re was no obvious clustering and correlation according to pH (Unweighted UniFrac: R ANOSIM = 0.16 p = 0.05 ; Weighted UniFrac: R ANOSIM = 0.14 p = 0.08 999 permutations) (Figure 13A and 13 B)

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! 41 A: pH < 5 pH 5 7 pH > 7 B: pH < 5 pH 5 7 pH > 7 Figu re 13 Principal component analysis plots of Thaumarchaeota taxa (from archaeal 16S rRNA gene sequencing) color coded by the three major pH ranges. ( A) unweighted UniFrac distance matrices ( B) weighted UniF rac distance matrices P lots contain sample sites from both 2013 and 2014.

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! 42 Correlations between environmental parameters and each observed Nitrosomonadales OTU were evaluated using Spearman correlation. The majority of OTUs observed for the unassigned genus of the family Nitrosomonadaceae had a positive correlation with pH ( # range = 0.49 to 0.52 p < 0.05 ) and a strong negative correlation with temperature ( # range = 0.58 to 0.74 p < 0.01 ). U nweighted UniFrac analyses revealed that the sample sites with temperature ranging from 7C 7.9C were clustered together but not in weighted UniFrac analyses (Figure 14A and 14 B) In both unweighted and weighted UniFrac analyses, there were no statistical significant c lustering observed by sample location (Unweighted UniFrac: R ANOSIM = 0.14, p = 0.16; Weighted UniFrac: R ANOSIM = 0.05, p = 0.33, 999 permutations). Sample sites were not clustered by pH range s (Unweighted UniFrac: R ANOSIM = 0.15, p = 0.18; Weighted UniFrac : R ANOSIM = 0.29, p = 0.07, 999 permutations) (Figure 15A and 15 B) For surface water metal concentrations (TRW and DM), the majority of OTUs observed for the unassigned genus of the family Nitrosomonadaceae had a moderate cor relation with TRW manganese ( # range = 0.41 to 0.53, p < 0.05 ). However unweighted and weighted UniFrac analyses based on the entire community structure revealed that the sample sites were not clustered wit h TRW man ganese concentrations (Unweighted UniFrac: R ANOSIM = 0.013, p = 0.42; Weighted UniFrac: R ANOSIM = 0.008, p = 0.43, 999 permutations) (Figure 16A and 16 B).

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! 43 A: 7C 7.9C 8 C 8. 9C 9 C 9 .9C B: 7C 7.9C 8 C 8. 9C 9 C 9 .9C Figure 14 Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color coded by the three major temperature ranges (1C increment based on sample measurements). ( A) unweighted UniFrac distance matrices (B) weighted UniF rac distance matrices Plots contain sample sites from both 2013 and 2014.

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! 44 A: pH < 5 pH 5 7 pH > 7 B: pH < 5 pH 5 7 pH > 7 Figure 15 Principal component analysis plots of Nitrosomonadales taxa (from AOB gene 16S rRNA sequencing) color coded by the three major pH ranges. ( A) unwei ghted UniFrac distance matrices. ( B) weighted UniF rac distance matrices. Plots contain sample sites from both 2013 and 2014.

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! 45 A: 1100 1500 g/L 1550 2000 g/L 2000 2200 g/L B: 1100 1500 g/L 1550 2000 g/L 2000 2200 g/L Figure 16 Principal component analysis plots of Nitrosomonadales taxa (from AOB 16S rRNA sequencing) color coded by the three major TRW manganese ranges (low, medium and high). ( A) unweighted UniFrac distan ce matrices. (B) weighted UniF rac distance matrices Plots contain sample sites from both 2013 and 2014.

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! 46 CHAPTER V DISCUSSION AMD is a serious threat to freshwater systems and can devastate a river and its aquatic life for hundreds to thousands of years through its acidic pH and high metal concentrations. In this study, we determined the abundance and diversity of archaeal amoA AOB 16S rRNA and Nitrobacter nxrB gene sequences in AMD impacted sediments at the Iron Springs Mining District. Very little is known about the community structure of nitrifying microb es in AMD environments. In this study, AOA, AOB, and NOB communities had low numbers of observed OTUs (1 11 for AOA; 1 29 for AOB and none for Nitrobacter ). AOA OTUs were related to three major groups: (1) SAGMA X first observed in a deep South African gold mine (Takai e t al. 2001) and sometimes associated with soil environments (Pesaro and Widmer, 2002) ; (2) Nitrosopumilus commonly observed in coastal and terrestrial environments (Kšnneke et al. 2005; Moin et al. 2009; C B Walker et al. 2010; Jung et al. 2011; Matsutani et al. 2011; Bartossek et al. 2012; Pester et al. 2012; Mosier et al. 2012; Park et al. 2012; Horak et al. 2013; Lebedeva et al. 2013; Newell et al. 2013) ; and (3) Nitrososphaera commonly observed in soil (Prosser and Nicol, 2008; Offre et al. 2009; Zhang et al. 2010; Pratscher et al. 2011) and freshwater environments (Herrmann et al. 2008; van der Wielen et al. 2009; Llir—s et al. 2010) AOB observed in these AMD impacte d sites belonged to the family Nitrosomonadaceae which are generally well distributed in both terrestrial and aquatic environments (Stein et al. 2007; Norton et al. 2008; Zheng et al. 2013) NOB in these AMD impacted sites were related to Nitrospira species, but not Nitrobacter species. Al though Nitrobacter are

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! 47 typically the most common NOB in aquatic systems, Nitrospira are often numerically dominant in sediments (Altmann et al. 2004; Coci et al. 2005; Freitag et al. 2006; CÂŽbron and Garnier, 2005; Satoh et al. 2007) and water treatment plants (Watson et al. 1986; Juretschko et al. 1998; Daims et al. 2001; Lebedeva et al. 2005; Huang et al. 2010) Overall, the AOA, AOB, and NOB community structure within these AMD impacted freshwater sediments appears to be phylogenetically distinct from other unimpacted freshwater sites. Further sequence analyses will provide a finer level of resolution for comp aring sequence types between different systems. Metal concentrations (inclu ding Al, Cu, Fe, and Zn ) at all of the sites exceeded the allowable concentrations as determined by the Colorado Department of Public Health and Environment (CDPHE) Regulation 31. Additionally, the majority of sites with pH <4 had the highest concentrations of metals (TRW & DM) (e.g., Al, Cd, Cu, Fe, Pb, Mg, Mn, Ni and Zn). Low pH (3 5) causes metals to be readily dissolved and may affect the solubility and toxicity of total and di ssolved metal concentrations. Many of these metals are toxic to aquatic life at high levels (Nash, 2002) Nitrification is extremely sensitive to heavy metal concentrations. Heavy metals such as copper, zinc, lead, cadmium, sulfide, and nickel are known to inhibit the nitrification process in soils (Yan et al. 2013; Cela and Sumner, 2002) Here, we found high numbers of AOA, AOB, and NOB genes at sites with high metal concentrations, suggesting that these organisms are tolerant to high metal rich environments as shown in soil studies ( Broberg, 1984, Mertens et al. 2006, Li et al. 2009) For most of the metals, there was no clear correlation between metal concentrations and gene abundances or community structure, in contrast to previous studies that showed decreased abundance or

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! 48 change s in community composition with increasing metal concentrations (Mertens et al. 2006; Li et al. 2009; Morel and Price, 2003; Cao et al. 2011; Principi et al. 2008) In cases where correlations were found to be significant in the present study specific metals did not equally impact gene abundances of AOA, AOB, or NOB, possibly suggesting that different metals have different effects on each of these groups. Nitrification is highly sensitive to acidic pH. Low pH presents physiological challenges to a mmonia oxidiz ing and nitrite oxidizing microbes, as seen for other microbes. In addition, pH affects the balance of ammonia ( NH 3 ) and ammonium ( NH 4 + ) in water. At low pH, NH 3 concentrations are very low, whic h is problematic for ammonia oxidizers because NH 3 is the substrate of ammonia oxidation (but not NH 4 + ) (Bowen et al. 2013) However, i t was recently proposed that AOA might possess ammonium transporters (Offre et al. 2014) which could facilitate the conversion of ammonium into ammonia once inside the cytoplasm at circumneutral pH. Because d ecreased pH reduces the bioavailability of NH 3, acidic habitats are predicted to favor AOA over AOB because AOA have a hi gher affinity for NH 3 (Martens Habbena et al. 2009) Archaeal amoA genes have generally been shown to dominate at low pH conditions when compared to bacterial amoA genes ( Krause et al. 2012; Nicol et al. 2008; Hu et al. 2013) Here, we found that AOA amoA genes were more abundant than AOB 16S rRNA gene s at most sites and time points, regardless of pH. S everal of the AOA OTUs at the acidic sites were related to Nitrososphaera which are widely distributed in acidic soils (Gubry Rangin et al. 2011; Pester et al. 2012; Lehtovirta Morley et al. 2011; Tourna et al. 2011; Zhang et al. 2012; Nicol et al. 2008) Though there was no significant correlation with pH, archaeal amoA gene abundances (2.0x10 3 to

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! 49 4.9 x10 7 copies per g DNA) and Nitrospira nxrB gene abundances ( 1.2 x10 6 to 4.1 x10 7 copies per g DNA) were high at sites with pH "4.5, and were detected at sites with pH as low as 3.19. AOB 16S rRNA gene abundance decreased with decreasing pH. The presence of AOA, AOB, and NOB genes suggests the potential for nitrification in these acidic sediments, but further research is necessary to confirm activity. The Nitrospira nxrB genes were more abundant in the majority of the sites than archaeal amoA and AOB 16S rRNA during both years However, the gene cop y number s per cell varies for each organism: AOA have one amoA copy/cell (Hallam et al. 2006) AOB have one 16S rRNA copy/ cell and Nitrospira have 2 6 copies/cell (LŸcker et al. 2010; Pester et al. 2014) When accounting for these differences in gene copy numbers and when summing the total ammonia oxidizing community (AOA + AOB) and nitrite oxidizing community ( Nitrospira + Nitro bacter ) the ammonia oxidizers were more abundant than the nitrite oxidizers f or both years separately and when combined. Th ese community abundance estimates are very speculative but may provide some insight into the relative differences between ammonia oxidiz ing and nitrite oxidiz ing populations Conclusions In summary, we found numbers of observed AOA, AOB, and NOB OTUs in AMD impacted sediments relative to other more mesophilic environments. Though not diverse, these AOA, AOB, and NOB genes were very abundant in AMD impacted sediments with high metal concentrations and low pH. The se nitrifying microbes may be well adapted to multiple, simultaneous stresses experienced in these environments. We identified specific environmental parameters that were correlated with gene abundances or community structure (e.g., pH and AOB abundan ce). However, many other factors not

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! 50 measured here could influence nitrification, including other chemical variables or organismal interactions. This study provides a foundation for future work to determine rates of nitrification in AMD impacted systems, and how AMD runoff impacts nitrogen cycling within streams and the transfer of nitrogen to higher organisms.

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! 51 SUPPLEMENTAL TABLES Supplemental Table S1 : G ene copy numbers of AOA amoA AOB 16S rRNA and Nitrospira nxrB (based on qPCR amplification ) All data are expressed in copies/ g of DNA extract. Data for the sample site NDGP wa s not included since there was no amplification for any genes across 2013 and 2014. Summer 2014 Nitrospira nxrB !"#$%&'# ( )"#$%&'# ( *"#'%&'+ ( +",!%&'# ( -"! ,%&'+ ( )"#$%&'# ( ,"-$%&'# ( n.s *")#%&'+ ( !"',%&'# ( ."!$%&'+ ( n.s n.s n.s #"++%&'# ( )"#+%&'# ( n.s: not sampled : no amplification AOB 16S rRNA / ( $"!.%&'$ ( -"!+%&'$ ( )"+#%&'$ ( )"+$%&') ( ,"$'%&', ( / ( )").%&') ( / ( ,"',%&', ( -"#!%&') ( n.s !"'$%&', ( / ( n.s n.s n.s -".!%&', ( AOA amoA / ( #"'$%&'$ ( $"$$%&'+ ( -"!)%&'# ( / ( +"$.%&'+ ( *"++%&'+ ( / ( -")'%&'# ( ,".!%&'# ( / ( n.s 1.53E+06 n.s n.s n.s -")!%&'# ( -"'*%&'# ( 6.37E+05 -"'-%&'+ ( !"'$%&'# ( Sample Date 6.24.14 9.30.14 6.24.14 9.30.14 6.24.14 9.30.14 6.24.14 9.30.14 6.24.14 9.30.14 6.24.14 n.s 6.24.14 9.30.14 6.24.14 n.s n.z n. z 6.24.14 9.30.14 6.24.14 9.30.14 6.24.14 9.30.14 Summer 2013 Nitrospira nxrB -"++%&'# ( !"!,%&'# ( +"'+%&'+ ( !"*'%&'# ( $"!*%&'+ ( -")+%&'# ( / ( / ( / ( )"-.%&'+ ( -".,%&'+ ( / ( #"-!%&'+ ( +"$!%&'+ ( / ( / ( -"$$%&'# ( !".'%&'# ( -"+!%&'# ( !"-#%&'+ ( #",$%&'$ ( / ( !"''%&'+ ( )"*!%&'+ ( AOB 16S rRNA !"-+%&'$ ( !"')%&'$ ( -")+%&', ( -"!'%&'$ ( )",+%&') ( -"**%&', ( #",#%&') ( !"'-%&', ( / ( / ( !"'*%&') ( $"-'%&') ( / ( -"$-%&') ( / ( -"$,%&') ( -"!,%&', ( !"$$%&', ( -"$-%&', ( -"-,%&', ( #"*!%&') ( / ( -"+'%&', ( )",)%&', ( AOA amoA #"**%&'$ ( -")$%&'+ ( )"!!%&', ( ."'.%&'$ ( ,")*%&', ( !")#%&'$ ( $").%&') ( -".+%&') ( -",-%&'+ ( $",!%&'$ ( ,"--%&'$ ( +",.%&'$ ( !".+%&'$ ( )"')%&'$ ( / ( ,"*$%&', ( $"!-%&'$ ( !",.%&'+ ( )"+,%&'+ ( -"$#%&'+ ( $"*!%&') ( -"!+%&', ( -")*%&'$ ( ,"!#%&'$ ( Sample Date 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 6.25.13 8.05.13 ( Sample Sites Carib01 Carib02 Carib03 CS1B FenIB01 FenIB02 FenND01 FenND03 HF04 HFIBE NDCS02 NDMD02 ( Sample Region Caribbeau Mines Capped Seap Iron Bog Fen Iron Bog New Dominion Fen Howard Fork Howard Fork Iron Bog New Dominion Capped Seap New Dominion Mine Drainage

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! 58 Satoh H, Nakamura Y, Okabe S. (2007). Influences of infaunal burrows on t he community structure and activity of ammonia oxidizing bacteria in intertidal sediments. Applied And Environmental Microbiology 73 : 1341 1348. Sonthiphand P, Cejudo E, Schiff SL, Neufeld JD. (2013). Wastewater effluent impacts ammonia oxidizing prokaryot es of the Grand River, Canada. Applied And Environmental Microbiology 79 : 7454 7465. Stein LY, Arp DJ, Berube PM, Chain PSG, Hauser L, Jetten MSM, et al. (2007). Whole genome analysis of the ammonia oxidizing bacterium, Nitrosomonas eutropha C91: implicati ons for niche adaptation. Environmental Microbiology 9 : 2993 3007. Stein LY, Arp DJ, Hyman MR. (1997). Regulation of the Synthesis and Activity of Ammonia Monooxygenase in Nitrosomonas europaea by Altering pH To Affect NH(inf3) Availability. Applied And En vironmental Microbiology 63 : 4588 4592. Stephen JR, Kowalchuk GA, Bruns M AV, Phillips CJ, Embley TM, Prosser JI. (1998). Analysis of $ Subgroup Proteobacterial Ammonia OxidizerPopulations in Soil by Denaturing Gradient Gel ElectrophoresisAnalysis and Hier archical Phylogenetic Probing. Applied And Environmental Microbiology 64 : 2958 2965. Stopnisek N, Gubry Rangin C, Hofferle S, Nicol GW, Mandic Mulec I, Prosser JI. (2010). Thaumarchaeal Ammonia Oxidation in an Acidic Forest Peat Soil Is Not Influenced by A mmonium Amendment. Applied And Environmental Microbiology 76 : 7626 7634. Suzuki I, Dular U, Kwok SC. (1974). Ammonia or ammonium ion as substrate for oxidation by Nitrosomonas europaea cells and extracts. J Bacteriol 120 : 556 558. Takai K, Horikoshi K. (2000). Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Applied And Environmental Microbiology 66 : 5066 5072. Takai K, Moser DP, DeFlaun M, Onstott TC, Fredrickson JK. (2001). Archaeal Diversity in Waters from Deep South African Gold Mines. Applied And Environmental Microbiology 67 : 5750 5760. Tietema A, De Boer W, Riemer L. (1992). Nitrate production in nitrogen saturated acid forest soils: vertical distribution and characteristics. Soil Biology and Biochemistry 24 : 235 240. Tourna M, Stieglmeier M, Spang A, Kšnneke M, Schintlmeister A, Urich T et al. (2011). Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. Proceedings of the National Academy of Sciences 108 : 8420 8425. van der Wielen PWJJ, Voost S, van der Kooij D. (2009). Ammonia oxidizing bacteria and archaea in groundwate r treatment and drinking water distribution systems. Applied And Environmental Microbiology 75 : 4687 4695.

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! 59 Vanparys B, Spieck E, Heylen K, Wittebolle L, Geets J, Boon N, et al. (2007). The phylogeny of the genusNitrobacterbased on comparative rep PCR, 16S rRNA and nitrite oxidoreductase gene sequence analysis. Syst Appl Microbiol 30 : 297 308. Voytek MA, Ward BB. (1995). Detection of ammonium oxidizing bacteria of the beta subclass of the class Proteobacteria in aquatic samples with the PCR. Applied And Envi ronmental Microbiology 61 : 2811. Walker CB, la Torre de JR, Klotz MG, Urakawa H, Pinel N, Arp DJ, et al. (2010). Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proceedi ngs of the National Academy of Sciences 107 : 8818 8823. Walker N, Wickramasinghe KN. (1979). Nitrification and autotrophic nitrifying bacteria in acid tea soils. Soil Biology and Biochemistry 11 : 231 236. Wang Y, Qian PY. (2009). Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies Field, D (ed). PLoS ONE 4 : e7401 9. Watson SW, Bock E, Valois FW, Waterbury JB, Schlosser U. (1986). Nitrospira marina gen. nov. sp. nov.: a chemolithotrophic nitrite oxidizing bacterium. Archives of Microbiology 144 : 1 7. Wright ES, Yilmaz LS, Noguera DR. (2012). DECIPHER, a search based approach to chimera identification for 16S rRNA sequences. Applied And Environmental Microbiology 78 : 717 725. Yan J, Quan G Ding C. (2013). Effects of the Combined Pollution of Lead and Cadmium on Soil Urease Activity and Nitrification. Procedia Environmental Sciences 18 : 78 83. Yao H, Gao Y, Nicol GW, Campbell CD, Prosser JI, Zhang L, et al. (2011). Links between Ammonia Oxi dizer Community Structure, Abundance, and Nitrification Potential in Acidic Soils. Applied And Environmental Microbiology 77 : 4618 4625. Zhang L M, Hu HW, Shen J P, He J Z. (2012). Ammonia oxidizing archaea have more important role than ammonia oxidizing b acteria in ammonia oxidation of strongly acidic soils. ISME J 6 : 1032 1045. Zhang L M, Offre PR, He J Z, Verhamme DT, Nicol GW, Prosser JI. (2010). Autotrophic ammonia oxidation by soil thaumarchaea. Proc Natl Acad Sci USA 107 : 17240 17245. Zheng Y, Hou L, Liu M, Lu M, Zhao H, Yin G, et al. (2013). Diversity, abundance, and activity of ammonia oxidizing bacteria and archaea in Chongming eastern intertidal sediments. Appl Microbiol Biotechnol 97 : 8351 8363.

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! 60 APPENDIX Appendix I: QIIME scripts used for processing high throughput Illumina MiSeq sequencing data. All file/folder names are user specific. Each command associated with the process is denoted by > '. Renaming the files to be QIIME compatible: Renames the sequence names in the raw data files so that the formatting is compatible with QIIME analyses. Example for one data set : > mkdir IronSpringsArch349F > cd IronSpringsArch349F/ > ls /home/bhargavi/RawData/Arch349F/ > a > grep fastq a | cut f 2,3 d "_" a > b > sort b | uniq > labels.txt > while read i ; do sed "/ \ @MISEQFGXT/s/ \ @/@$i /1" /home/bhargavi/RawData/Arch349F/Arch349F_$i*R1.fastq > ${i}.R1.fastq ; done < labels.txt > while read i ; do sed "/ \ @MISEQFGXT/s/ \ @/@$i /1" /home/bhargavi/RawData/Arch349F/Arch349F_$i*R2 .fastq > ${i}.R2.fastq ; done < labels.txt > cat *R1.fastq > combined_R1.fastq > cat *R2.fastq > combined_R2.fastq > grep "_" combined_R1.fastq | head > grep "_" combined_R1.fastq | head 100 > sed 's/_//' combined_R1.fastq > relabeled_R1.fastq > sed 's/_//' combined_R2.fastq > relabeled_R2.fastq > awk '{if(((NR 1)%4)==0) print $1"_"(NR 1)/4" "$2" "$3; else print;}' relabeled_R1.fastq > allIronSpringsArch349F_R1.fastq > awk '{if(((NR 1)%4)==0) print $1"_"(NR 1)/4" "$2" "$3; else print;}' relabeled_R2. fastq > allIronSpringsArch349F_R2.fastq Quality Plots for Reads : Allows viewing the quality of reads through plots. > /home/chris/software/bin/qa2.py -type fastq allIronSprings16SrRNA_R1.fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpring s16SrRNA _R2 .fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpringsAOB16SrRNA_R1.fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpringsAOB 16SrRNA_R2 .fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpringsArch16SrRNA_R1.fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpringsArch 16SrRNA_R2 .fastq > /home/chris/software/bin/qa2.py -type fastq allIronSpringsNB16SrRNA_R1.fastq

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! 61 > /home/chris/software/bin/qa2.py -type fastq allIronSpr ingsNB 16SrRNA_R2 .fastq Joining paired ends for all sequencing runs : ( http://qiime.org/scripts/join_paired_ends.html ) Merge the forward and reverse reads for each paired end. > join _paired_ends.py f allIronSprings16SrRNA_R1.fastq r allIronSprings16SrRNA_R2.fastq o $PWD/fastq join_joined > join_paired_ends.py f allIronSpringsAOB16SrRNA_R1.fastq r allIronSpringsAOB16SrRNA_R2.fastq o $PWD/fastq join_joined > join_paired_ends.py f allIronSpringsArch16SrRNA_R1.fastq r allIronSpringsArch16SrRNA_R2.fastq o $PWD/fastq join_joined > join_paired_ends.py f allIronSpringsNB16SrRNA_R1.fastq r allIronSpringsNB16SrRNA_R2.fastq o $PWD/fastq join_joined Quality Filtering: ( http://qiime.org/scripts/split_libraries_fastq.html ) Filters sequences based on Phred Quality score' q < 20 Phred Quality score < 20 has a maximum error rate at 1bp in 100bp > split _libraries_fastq.py i /home/bhargavi/IronSprings16SrRNA/fastq join_joined/fastqjoin.join.fastq o /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/ r 3 p 0.75 q 20 n 0 -barcode_type 'not barcoded' -sample_ids irons prings > split_libraries_fastq.py i /home/bhargavi/IronSpringsAOB16SrRNA/fastq join_joined/fastqjoin.join.fastq o /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/ r 3 p 0.75 q 20 n 0 -barcode_type 'not barc oded' -sample_ids ironsprings > split_libraries_fastq.py i /home/bhargavi/IronSpringsArch16SrRNA/fastq join_joined/fastqjoin.join.fastq o /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/ r 3 p 0.75 q 20 n 0 -barcode_type 'not barcoded' -sample_ids ironsprings > split_libraries_fastq.py i /home/bhargavi/IronSpringsNB 16SrRNA/fastq join_joined/fastqjoin.join.fastq o /home/bhargavi/IronSp ringsNB16SrRNA/IronSpringsNB 16SrRNA_all_analyses_qual_fi ltered_q20/ r 3 p 0.75 q 20 n 0 -barcode_type 'not barcoded' -sample_ids ironsprings

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! 62 Picking OTUs: ( http://qiime.org/scripts/pick_open_reference_otus.html ) Picks OTUs by clustering reads against the Greengenes 16S rRNA and assigns taxonomy database based on 97% sequence identity for the total (bacterial + archaeal) 16S rRNA and 99% sequence identity for archaeal 16S rRNA and AOB 16S rRNA sequencing reads. > pick_open_reference_otus.py i /home/bhargavi/IronSprings16SrRNA/ IronSprings16SrRNA_all_analyses_qual_filtered_q20/joined.fasta/renamed.seqs.fna r /opt/qiime 1.7.0/gg_13_8_otus/rep_set/97_otus.fasta o /home/bhargavi/IronSprings16SrRNA/ IronSprings16Sr RNA_all_analyses_qual_filtered_q20/pick_otus_97/ aO8 > pick_open_reference_otus.py i /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/joined.fasta/renamed.seqs.fna r /opt/qiime 1.7.0/gg_13_8_otus/rep_set/99_otu s.fasta o /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ aO8 > pick_open_reference_otus.py i /home/bhargavi/IronSpringsArch16SrRNA/ IronSpringsArch16SrRNA_all_analyses_qual_filtered_q20/joined.f asta/renamed.seqs.fna r /opt/qiime 1.7.0/gg_13_8_otus/rep_set/99_otus.fasta o /home/bhargavi/IronSpringsArch16SrRNA/ IronSpringsArch16SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ aO8 > pick_open_reference_otus.py i /home/bhargavi/IronSprings NB 16 SrRNA/ IronSprings NB 16SrRNA_all_analyses_qual_filtered_q20/joined.fasta/renamed.seqs.fna r /opt/qiime 1.7.0/gg_13_8_otus/rep_set/99_otus.fasta o /home/bhargavi/IronSprings NB 16SrRNA/ IronSprings NB 16SrRNA_all_analyses_qual_filtered_q20/pick_otus_99/ aO8 Filters the OTU table : ( http://qiime.org/scripts/filter_otus_from_otu_table.html ) Removes OTUs that have a fraction of total observation count < 0.00005 which is applied as the minimum total observation count for each OTU > filter_otus_from_otu_table.py i /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/pick_otus_97/otu_table_mc2_w_tax.biom o /home/bhargavi/IronSprings16SrRNA/IronSprings16Sr RNA_all_analyses_qual_filtered_ q20/pick_otus_97/otu_table_filter_min_frac.biom -min_count_fraction 0.00005 > filter_otus_from_otu_table.py i /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua

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! 63 l_filtered_q20/pick_otus_99/otu_table _mc2_w_tax.biom o /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom -min_count_fraction 0.00005 > filter_otus_from_otu_table.py i /home/bhargavi/IronSpringsArch16SrRNA/ IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/pick_otus_99/otu_table_mc2_w_tax.biom o /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom -min_count_fraction 0.00 005 > filter_otus_from_otu_table.py i /home/bhargavi/IronSprings NB 16SrRNA/IronSprings NB 16SrRNA_all_analyses_qual_fi ltered_q20/pick_otus_99/otu_table_mc2_w_tax.biom o /home/bhargavi/IronSprings NB 16SrRNA/IronSprings NB 16SrRNA_all_analyses_qual_fi ltered_q20 /pick_otus_99/otu_table_filter_min_frac.biom -min_count_fraction 0.00005 Chimeric Sequences: ( http://qiime.org/scripts/filter_otus_from_otu_table.html ) Identifies chi meric sequences based on the representative sequences obtained from picking OTUs using DECIPHER. Removes chimeric sequences from OTU table > filter_otus_from_otu_table.py i /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/pick_otus_97 /otu_table_filter_min_frac.biom o /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/IronSprings16SrRNA_otu_table_non_chimeric.biom e /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/Iro nSprings16SrRNA_chimera.txt > filter_otus_from_otu_table.py i /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom o /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16Sr RNA_all_analyses_qua l_filtered_q20/IronSpringsAOB16SrRNA_otu_table_non_chimeric.biom e /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/IronSpringsAOB16SrRNA_chimera.txt > filter_otus_from_otu_table.py i /home/bh argavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/pick_otus_99/otu_table_filter_min_frac.biom o /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/IronSpringsArch16SrRNA_otu_table_ non_chimeric.biom e /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/IronSpringsArch16SrRNA_chimera.txt

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! 64 > filter_otus_from_otu_table.py i /home/bhargavi/IronSprings NB 16SrRNA/IronSprings NB 16SrRNA_all_analyses_qu al_fi ltered_q20/pick_otus_99/otu_table_filter_min_frac.biom o /home/bhargavi/IronSprings NB 16SrRNA/IronSprings NB 16SrRNA_all_analyses_qual_fi ltered_q20/IronSprings NB 16SrRNA_otu_table_non_chimeric.biom e /home/bhargavi/IronSprings NB 16SrRNA/IronSprings NB 16Sr RNA_all_analyses_qual_fi ltered_q20/IronSprings NB 16SrRNA_chimera.txt Summarize Taxa through Plots: ( http://qiime.org/scripts/summarize_taxa_through_plots.html ) Summarizes taxa and generates taxonomic bar charts for each sample based on taxonomic levels > summarize_taxa_through_plots.py i /home/bhargavi/IronSprings16SrRNA/ IronSprings16SrRNA_all_analyses_qual_filtered_q20/ IronSprings16SrRNA_otu_table_non_chimeric.biom o /home/b hargavi/IronSprings16SrRNA/ IronSprings16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu97 > summarize_taxa_through_plots.py i /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/ IronSpringsAOB16SrRNA_otu_tab le_non_chimeric.biom o /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99 > summarize_taxa_through_plots.py i /home/bhargavi/IronSpringsArch16SrRNA/ IronSpringsArch16SrRNA_all_analyses_qual_fil tered_q20/ IronSpringsArch16SrRNA_otu_table_non_chimeric.biom o /home/bhargavi/IronSpringsArch16SrRNA/ IronSpringsArch16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99 > summarize_taxa_through_plots.py i /home/bhargavi/IronSprings NB 16SrRNA/ Iro nSprings NB 16SrRNA_all_analyses_qual_filtered_q20/ IronSprings NB 16SrRNA_otu_table_non_chimeric.biom o /home/bhargavi/IronSprings NB 16SrRNA/ IronSpringsNB 16SrRNA_all_analyses_qual_filtered_q20/taxasummary_otu_99 Filtering the OTU T able for Specific Taxa: ( http://qiime.org/scripts/filter_taxa_from_otu_table.html ) Pulls out AOB and AOA sequences into a separate file ( without other bacterial/archaeal sequences) b ased on tax onomy defined in the O TU picking step (based on Greengenes). > filter_taxa_from_otu_table.py i /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/IronSpringsAOB16SrRNA_otu_table_non_chimeric.biom o

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! 65 /home/bhargavi/IronSpringsAOB1 6SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/otu_table_nitrosomonadales16SrRNA_only.biom p o__Nitrosomonadales > filter_taxa_from_otu_table.py i /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/IronSpringsArch16SrRNA_otu_table_non_chimeric.biom o /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/otu_table_thuamar chaeota16SrRNA_only.biom p c__Thaumarchaeota Summarize Biom Table: ( http://biom format.org/documentation/summarizing_biom_tables.html ) Summarizes the relative abundance of each OTU. > biom summarize table i /home/bhargavi/IronSprings16SrRNA/ IronSprings16SrRNA_all_analyses_qual_filtered_q20/ IronSprings16SrRNA_otu_table_non_chimeric.biom o /home/bhargavi/IronSprings16SrRNA/ IronSprings16SrRNA_all_analyses_qual_filtered_q 20/IronSprings16SrRNA_biomtables ummary.txt > biom summarize table i /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpringsAOB16SrRNA_all_analyses_qual_filtered_q20/otu_table_nitrosomonadales1 6SrRNA_only.biom o /home/bhargavi/IronSpringsAOB16SrRNA/ IronSpring sAOB16SrRNA_all_analyses_qual_filtered_q20/nitrosomonadales_biomtable _summary.txt > biom summarize table i /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/otu_table_thaumarchaeota16SrRNA_only.biom o /home/bhar gavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/thaumarchaeota_biomtable_summary.txt Mapping File Validation : ( http://qiime.org/scripts/validate_mapping_file.html ) Validates mapping file to make sure that the formatting is QIIME compatible. > validate_mapping_file.py m IronSpringsMappingFile.txt o validate mapping_file_output Core Diversity Analyses: ( http://qiime.org/scripts/core_diversity_analyses.html ) Computes core diversity analyses ( beta diversit y, summarize taxa through plots and alpha rarefaction ) Sequencing depth varied for each analysis ( sequence depth selected to capture greater than 5 0 % of the OTUs predicted in the rarefaction curve ): 16 000 sequences per sample for the total (bacterial + archaeal) 16S rRNA sequences 630

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! 66 sequences per sample for the AOB 16S rRNA sequences and 60 sequences per sample for the archaeal 16S rRNA sequences > core_diversity_analyses.py o /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/ IronSprings16SrRNACoreAnalyses -recover _from_failure i /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/IronSprings16SrRNA_otu_table_non_chimeric.biom m /home/bhargavi/IronSpringsMappingFile.txt t /home/bhargavi/IronSprings16SrRNA/IronSprings16SrRNA_all_analyses_qual_filtered_ q20/pick_otus_97/rep_set.tre e 16000 > core_diversity_analyses.py o /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/IronSpringsAOB1 6SrRNACoreAnalyses630 -recover_from_failure i /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/otu_table_nitrosomonadales16SrRNA_only.biom m /home/bhargavi/IronSpringsMappingFile.txt t /home/bhargavi/IronSprings AOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/pick_otus_99/rep_set.tre e 630 > core_diversity_analyses.py o /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/IronSpringsArch16SrRNACoreAnalyses6 0 -recover_from_failure i /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/otu_table_thaumarchaeota16SrRNA_only.biom m /home/bhargavi/IronSpringsMappingFile.txt t /home/bhargavi/IronSpringsArch16SrRNA/IronSpri ngsArch16SrRNA_all_analyses_qual _filtered_q20/pick_otus_99/rep_set.tre e 60 Spearman C orrelations Between the Relative Abundance of OTUs and Environmental P arameters: ( http:/ /qiime.org/scripts/observation_metadata_correlation.html ) Correlates the relative abundance of individual OTUs to the environmental parameters (metadata in the mapping file). Example for one dataset : > observation_metadata_correlation.py i /home/bhargavi/IronSpringsAOB16SrRNA/IronSpringsAOB16SrRNA_all_analyses_qua l_filtered_q20/otu_table_nitrosomonadales16SrRNA_only.biom m /home/bhargavi/IronSpringsMappingFilenounits.txt c pH s spearman o /home/bhargavi/IronSpringsAOB16SrRNA/IronSprings AOB16SrRNA_all_analyses_qua l_filtered_q20/Spearman_correlation_AOB16SrRNA/spearman_otu_gradient_nitrosomo nadales_only_pH.txt Filter Sequences for Phylogeny: ( http://qiime.org/scripts/filter_fasta .html )

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! 67 Pulls out AOB and AOA sequences into a separate file (without other bacterial/archaeal sequences) based on taxonomy defined in the O TU picking step (based on Greengenes). > filter_fasta .py f /home/bhargavi/IronSpringsA OB16SrRNA/IronSpringsAOB 16SrRNA_all_analyses_qua l_filtered_q20/pick_otus_99/ rep_set.fna o /home/bhargavi/IronSpringsA OB 16SrRNA/IronSpringsA OB 16SrRNA_all_analyses_qua l_filtered_q20/ nitrosomonadales 16SrRNAonly_biom_filtered_seqs.fna b /home/bhargavi/IronSpringsAOB16SrRNA/IronSpr ingsAOB 16SrRNA_all_analys es_qua l_filtered_q20/otu_table_nitrosomonadales 16SrRNA_only.biom > filter_fasta .py f /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/pick_otus_99/ rep_set.fna o /home/bhargavi/IronSpri ngsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/ thaumarchaeota16SrRNAonly_biom_filtered_seqs.fna b /home/bhargavi/IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual _filtered_q20/otu_table_thaumarchaeota16SrRNA_only.biom ANOSIM: ( http://qiime.org/scripts/compare_categories.html ) Analysis of the strength and statistical significance of site specific sample groups based on environmental variables using distance matrices. > compare_categories.py -method anosim i IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual_filtered_q20/Ir onSpringsArch16SrRNACoreAnalyses60/bdiv_even60/unweighted_unifrac_dm.txt m IronSpringsMappingFile Location.txt c Location o IronSpringsArch16SrRNA/IronSpringsArch16SrRNA_all_analyses_qual_filtered_q20/an osim_out_location