Studying Methanotrophic Bacterial Diversity in Ohio Soils

Using High-Throughput Sequence Analysis

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

By

Aditi Sengupta

Graduate Program in Environment and Natural Resources

The Ohio State University

2015

Dissertation Committee:

Dr. Warren A. Dick, Advisor

Dr. Richard P. Dick

Dr. Brian H. Lower

Dr. Renukaradhya Gourapura

Copyrighted by

Aditi Sengupta

2015

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ABSTRACT

The net flux of methane (CH4), a biogenic greenhouses gas, into the atmosphere is dependent on feedbacks that exist between the atmosphere and the soil. Aerobic methanotrophic bacteria in soil oxidize CH4 and use it as their sole source of carbon and energy, thereby allowing soils to serve as the only know biological sink of atmospheric methane. However, a clear understanding of the diversity and community composition of these bacteria, as affected by land-use and land-management practices is lacking. The objective of this study was to characterize the diversity of methanotrophs in two contrasting soils in Ohio using the sequencing-by-synthesis technique. In addition to location, the effect of rotation, tillage, and management of soils under grass and forest areas on methanotrophic community was also studied.

Several molecular-based high-throughput sequencing techniques were employed.

Following a pilot pyrosequencing study, Illumina’s sequencing-by-synthesis approach was used to generate millions of sequences targeting the methanotrophic bacteria. A combination of four primer sets targeting the whole community 16S rRNA, the 16S rRNA gene region of Type I and Type II methanotrophs, and the functional pmoA gene (a

ii subunit of the particulate methane monooxygenase gene) were used. Software packages including Mothur, QIIME (Quantitative Insights Into Microbial Ecology), and R were used to study community diversity and abundance in soils under no-till continuous corn, no-till corn-soybean, plow-till continuous-corn, plow-till corn-soybean, grass, and forest.

A variety of methanotrophic bacterial operational taxonomic units (OTUs) were identified across different land-uses and management, representing diverse genera of methanotrophs. On average, 2% of sequences represented methantrophs OTUs in the 16S rRNA datasets, while the pmoA dataset was compared to a reference database which classified all the sequences as methanotrophic OTUs. Community diversity estimators showed that a combination of community richness and evenness contributed to the methanotrophic diversity. In addition to commonly reported methanotrophic genera, this study also noted the presence of Verrucomicrobial methanotrophic OTUs, non- methanotrophic methylotroph OTUs, and OTUs representing Upland Soil Clusters.

For most datasets, no-till soils had higher diversity than plow-till soils. The community composition of both agricultural practices were distinctly different from forest and grass areas. Due to the fact that forest soils were undisturbed, the highest number of different species was generally recovered from these soils. Among the variables analyzed, location was dominant, followed by tillage and rotation. The pmoA dataset showed that even over a long period of time (>50 years), soil methanotrophy function was governed by soil type. It can be concluded that despite soil disturbance, the

iii inherent functioning of microbes in these soils is possibly more impacted by soil type, that is a reflection of its geographical location, followed by land-use.

This dissertation adds to knowledge of land-use and land-management practices that can be employed on a long-term basis to increase biological fixation of CH4 gas. On a broader level, this study of methanotrophic diversity in soils has the potential to help develop climate change mitigation strategies with respect to globally shifting soils to become increasingly active as CH4 sinks.

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This dissertation is dedicated to my parents Vaswati and Arunabha Sengupta, and to my

brother Anirban and sister-in-law Ruchika, for their unconditional love, support, and

encouragement.

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ACKNOWLEDGMENTS

This has been an exhilarating journey full of challenges, excitement, highs, and lows. Through it all, I have had the support of a number of people. To begin with, I want to thank my advisor, Dr. Warren A. Dick for believing in me. His support has been unwavering, as has been his guidance. He taught me to aim high and step out of my comfort zone. His approach towards science and life is inspiring, and for which I will forever be grateful.

I want to thank Dr. Richard P. Dick for allowing me to work in his lab during the initial days of my research and for his support over these years. I am thankful to Dr.

Brian H. Lower for being my mentor when I sought him out and for his encouraging words. I am grateful to Dr. Renukaradhya Gourapura for his motivation and support as I tried to get my experiments going.

A majority of this research work was conducted at the Molecular and Cellular

Imaging Center (MCIC) at OARDC. I want to convey my gratitude to Jody Whittier,

Dr. Asela Wijeratene, Saranga Wijeratne, Maria Elena Hernandes Gonzales, and Fiorella

Cisneros Carter for providing assistance in sequencing and bioinformatics. I also want to thank Dr. Liming Chen, Dr. Dave Kost and Clayton Dygert for helping me in sampling.

The administrative staff in SENR provided me with all the support I needed during this time. A special thank you to Amy Schmidt for being my mother in Columbus vi when I first came. I also want to thank Beverly Winner in Wooster and Annie Bingman in Columbus for all their help.

Lastly, the journey would not have been half as enjoyable if not for my friends and colleagues at Ohio State. I am blessed to have Pranay Ranjan, Dr. Kshipra

Chandrashekhar, and Dr. Kuhuk Sharma stand by me during the brightest and the darkest hours. My heartfelt thank you to Joshua Kendall and Janani Hariharan for walking together with me as we all ventured into the world of bioinformatics. I want to thank Dr.

Taniya Roy Chowdhury and Dr. Jaideep Banerjee for being my family, Jennifer

Tvyergak for being my first friend in Columbus, and Jennifer Harrison for her timely reassurances. I also want to acknowledge the present and past members of the lab, especially Maninder Kaur Walia, Nghia Nguyen, Brittany Campbell, and Samer Al-

Saffar for their motivation and encouragement.

Funding Source

Funding for this study was provided by the USDA-NIFA, Award No. 2011-68002-30190

“Cropping Systems Coordinated Agricultural Project (CAP): Climate Change,

Mitigation, and Adaptation in Corn-based Cropping Systems”.

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VITA

2005...... Bharatiya Vidya Bhavan, New Delhi

2008...... B.S. Biochemistry, University of Delhi

2010...... M.S. Environmental Studies, TERI

University

2010 to present ...... Graduate Research Associate, School of

Environment and Natural Resources, The

Ohio State University

PUBLICATIONS

Sengupta, A. & Dick, W.A., Bacterial community diversity in soil under two tillage practices as determined by pyrosequencing, Microbial Ecology, May 2015. DOI:

10.1007/s00248-015-0609-4

Dick, W.A., Thavamani, B., Conley, S., Blaisdell, R. & Sengupta, A., Prediction of β- glucosidase and β-glucosaminidase activities, soil organic C, and amino sugar N in a diverse population of soils using near infrared reflectance spectroscopy, Soil Biology and

viii

Biochemistry, Volume 56, issue (January, 2013), p. 99-104. ISSN: 0038-0717. DOI:

10.1016/j.soilbio.2012.04.003.

FIELD OF STUDY

Major Field: Environment and Natural Resources

Specialization: Soil Science

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TABLE OF CONTENTS

Abstract ...... ii

Acknowledgments...... vi

Vita ...... ……..viii

Publications ...... ……..viii

Field of Study ...... ix

Table of Contents ...... x

List of Tables ...... xiv

List of Figures ...... xvi

CHAPTER 1: INTRODUCTION ...... 1

1. Rationale and Significance ...... 2

2. Hypothesis ...... 4

3. Objectives ...... 4

4. Data Analysis and Statistics ...... 5

x

5. Means of Applying Results ...... 6

6. References ...... 7

CHAPTER 2: LITERATURE REVIEW ...... 13

1. Soil Microorganisms ...... 13

2. Methane Budget ...... 17

3. Methanotrophy ...... 20

4. Identification of Methanotrophic Bacteria in Soil from Genes ...... 38

5. Future Research Needs ...... 42

6. References ...... 44

CHAPTER 3: BACTERIAL COMMUNITY DIVERSITY IN SOIL UNDER TWO

TILLAGE PRACTICES AS DETERMINED BY PYROSEQUENCING ...... 67

1. Abstract ...... 67

2. Introduction………………………………………………………………………… 69

3. Materials and Methods ...... 72

4. Results and Discussion ...... 75

5. Conclusion ...... 84

6. Acknowledgments ...... 84

7. Conflict of Interest ...... 84

8. References ...... 85 xi

9. Supplementary online resources...... 89

CHAPTER 4: EFFECT OF LONG-TERM LAND-USE AND LAND-MANAGEMENT

PRACTICES IN OHIO ON SOIL BACTERIAL COMMUNITY STRUCTURE ...... 90

1. Abstract ...... 90

2. Introduction ...... 92

3. Material and Methods...... 95

4. Results and Discussion ...... 103

5. Conclusion ...... 134

6. Acknowledgments ...... 139

7. References ...... 140

CHAPTER 5: HIGH-THROUGHPUT SEQUENCING TO DETECT DIFFERENCES IN

TYPE I AND TYPE II METHANOTROPHS IN AGRICULTURAL, GRASS, AND

FOREST SOILS IN LONG-TERM EXPERIMENTAL PLOTS IN OHIO ...... 147

1. Abstract ...... 147

2. Introduction ...... 149

3. Material and Methods...... 152

4. Results and Discussion ...... 160

5. Conclusion ...... 176

6. Acknowledgments ...... 179

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7. References ...... 180

Chapter 6: IMPACT OF LOCATION ON METHANOTROPHIC BACTERIAL

DIVERSITY IN SOILS UNDER VARYING LAND-USE AND LAND-

MANAGEMENT PRACTICES AS DETERMINED BY SEQUENCING-BY-

SYNTHESIS OF pmoA GENE ...... 186

1. Abstract ...... 186

2. Introduction ...... 188

3. Material and Methods...... 194

4. Results and Discussion ...... 201

5. Conclusion ...... 215

6. Acknowledgments ...... 219

7. References ...... 220

Chapter 7: FUTURE RESEARCH AND CONCLUSIONS ...... 232

1. Future Research ...... 232

2. Conclusions ...... 233

REFERENCES ...... 238

APPENDIX A: SITE MAPS AND PRIMER DETAILS………..……………………..274

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LIST OF TABLES

Table 2.1 CH4 budget in the years 2000-2009 …………………………………...... 19

Table 2.2 Taxonomy and characteristics of aerobic methanotrophs………………...... 27

Table 3.1 Soil properties and pyrosequencing analysis…………………………...... 75

Table 4.1 Number of sequences after processing, observed OTUs and

Chao1 (richness estimators), and Shannon’s Hʹ (diversity estimator) for

soil samples across two locations…….…………………………………………...... 104

Table 4.2 Identified phylotypes at five taxonomic levels in Hoytville and

Wooster samples……………………………………………………………………...108

Table 4.3 Permutation multivariate analysis of variance using distance matrices

of whole community composition in Hoytville and Wooster samples……….………117

Table 5.1 Total number of sequences after processing, observed OTUs, Chao1

(richness estimator) and Shannon’s Hʹ (diversity estimator) for soil samples

across two locations. Values are specific to Type I methanotrophs obtained from high-

throughput sequence analysis of a partial 16S rRNA gene……………...... 161

Table 5.2 Total number of sequences after processing, observed OTUs, Chao1

(richness estimator) and Shannon’s Hʹ (diversity estimator) for soil samples xiv

across two locations. Values are specific to Type II methanotrophs obtained

from high-throughput sequence analysis of a partial 16S rRNA gene………………163

Table 5.3 Permutation multivariate analysis of variance using distance matrices

of whole community composition of Type I and Type II methanotrophs for

Wooster and Hoytville soils combined…………………………………………...…175

Table 6.1 Total DNA concentration, number of sequences after processing,

observed OTUs, and diversity (Chao1) and evenness (Shannon’s H’) estimators

for 12 soil samples across two locations……..…………………………………...... 202

Table A.1 Field TA-3, Northwest Agricultural Research Station, Hoytville...... 275

Table A.2 Plots 731-732, Triplett-Van Doren Long-term tillage and rotation plots,

Wooster…...... …………………………...………276

Table A.3 Soil Data Analysis...... 277

Table A.4 Nextera codes of i5 and i7 index reads of 16S whole community...... 278

Table A.5 Primer combinations in round 1 PCR of Wooster samples for 16S whole

community analysis……...... ………...... ….278

Table A.6 Illumina primer index combinations of 16S whole community...... 279

Table A.7 Nextera codes of i5 and i7 index reads of Type I and Type II

methanotrophic community analysis...... ………………...... 280

Table A.8 Illumina primer index combination of Type I and Type II methanotrophic

community analysis...... ……………...... 280

Table A.9 Nextera codes of i5 and i7 index reads of pmoA community analysis...... 281

Table A.10 Illumina primer index combinations of pmoA community analysis...... 281 xv

LIST OF FIGURES

Figure 2.1 Schematics of CH4 consumption and emission in soil……………………….20

Figure 2.2 Pathways for oxidation of methane and assimilation of formaldehyde……...28

Figure 2.3 RuMP pathway for formaldehyde fixation by Type I Methanotrophs…...... 29

Figure 2.4 Serine pathway for formaldehyde fixation by Type II Methanotrophs………30

Figure 3.1 Rarefaction curves at 3% sequence dissimilarity………………………….....78

Figure 3.2 Relative abundances of the 10 most abundant phyla in the two soil

samples…………………………………………………………………………...... 80

Figure 3.3 Diversity indices of samples……………………………………………….....82

Figure 4.1 Alpha-diversity estimates of Hoytville and Wooster samples……………...106

Figure 4.2 Relative percent abundance of top ten Phylum in Hoytville samples………110

Figure 4.3 Relative percent abundance of top ten Class in Hoytville samples…………111

Figure 4.4 Relative percent abundance of top ten Order in Hoytville samples………...112

Figure 4.5 Relative percent abundance of top ten Family in Hoytville samples……….113

Figure 4.6 Relative percent abundance of top ten Genus in individual Hoytville

samples………………………………………………………………...... …....114

Figure 4.7 Differential abundance of Genus across Hoytville samples………………..114

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Figure 4.8 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a)

unweighted UniFrac and (b) pairwise Bray-Curtis distance measures of bacterial

community composition of Hoytville soils……………………………………...... 116

Figure 4.9 Phylogenetic tree of methanotrophs in Hoytville soils, subsetted from

original OTU table, with families Methylococcaceae and Methylocystaceae...... 119

Figure 4.10 MEGAN comparison view of subsetted methanotrophs in Hoytville

samples…………………………………………………………...... ………....120

Figure 4.11 Nonmetric Multidimensional Scaling (NMDS) plots derived from

(a) unweighted UniFrac and (b) pairwise Bray-Curtis distances of subsetted

methanotrophic community from Hoytville soils…………………...... …....122

Figure 4.12 Relative percent abundance of top ten Phylum of Wooster samples……...126

Figure 4.13 Relative percent abundance of top ten Class of Wooster samples………...127

Figure 4.14 Relative percent abundance of top ten Order of Wooster samples………..128

Figure 4.15 Relative percent abundance of top ten Family of Wooster samples………129

Figure 4.16 Relative percent abundance of top ten Genus in individual Wooster

samples……………………………………………………………………...... 130

Figure 4.17 Differential abundance of Genus across Wooster samples ………… ...….130

Figure 4.18 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a)

unweighted UniFrac and (b) pairwise Bray-Curtis distances of bacterial community

from Wooster soils……………………………....…………………………………...132

Figure 4.19 Phylogenetic tree of methylotrophs in Wooster soils, subsetted from

original OTU table…………………………………………………………………...135 xvii

Figure 4.20 MEGAN comparison view of subsetted methylotrophs in Wooster

samples………………………………………………………………………………..136

Figure 4.21 Nonmetric Multidimensional Scaling (NMDS) plots derived from

(a) unweighted UniFrac and (b) pairwise Bray-Curtis distances of subsetted

methylotrophic community from Wooster soils……………………….……………..137

Figure 5.1 Phylogenetic tree of Type I methanotrophs at the Genus level……………..166

Figure 5.2 Phylogenetic heatmap of classified Type I methanotrophs……………...….169

Figure 5.3 Phylogenetic tree of Type II methanotrophs at the genus level………..…...170

Figure 5.4 Phyogenetic heatmap of classified Type II methanotrophs…………………171

Figure 5.5 Principle coordinate analysis (PCoA) plot of Type I methanotrophs

using Bray-Curtis measure of (dis)similarity………………..………………………..173

Figure 5.6 Principle coordinate analysis (PCoA) plot of Type II methanotrophs

using Bray-Curtis measure of (dis)similarity…………………..……………………..173

Figure 6.1 Relative abundance of normalized OTUs at Family level……….………….206

Figure 6.2 Nonmetric Multidimensional Scaling (NMDS) plots with distribution of

Family level OTUs based on unweighted UniFrac distance……………………………209

Figure 6.3 Relative distribution of top 5 abundant genera among samples…………….210

Figure 6.4 Differentially abundant genera across samples showing log2fold change

in OTU counts………………………………………………………………………...210

Figure 6.5 Nonmetric Multidimensional Scaling (NMDS) plots derived from

unweighted UniFrac (a) and pairwise Bray-Curtis (b) distances between

methanotrophic bacterial community………………………………….…...... ……..216 xviii

CHAPTER 1: INTRODUCTION

The net flux of methane (CH4), a biogenic greenhouses gas, into the atmosphere is dependent on feedbacks that exist between atmosphere and soil (Six et al., 2004; Trivedi et al., 2013). Soil microbes are both sources and sinks of CH4 (Nazaries et al., 2013).

However, a clear understanding of the role of soil microbes in biogeochemical cycling of

CH4 is lacking. Various land use and land management practices add to the complexity, with a number of recent studies highlighting the effects of land use on the net flux of CH4 from soils to atmosphere (Six et al., 2004; Suwanwaree and Robertson, 2005; Livesley et al., 2008; Nyakatawa et al., 2011; Levine et al., 2011; Bayer et al., 2012; Dorr de

Quadros et al., 2012; Jacinthe et al., 2013).

A particularly small group of soil microbes, aerobic methanotrophs, oxidize CH4 and use it for their growth and survival (Smith et al., 2014). Methane catabolism in our biosphere is dependent on a handful of bacterial species, and the abundance and functionality of these microbes is governed by environmental factors like CH4 concentration, oxygen availability, pH, nutrient availability, temperature, and soil moisture (Henckel et al., 2000; Horz et al., 2005; Knief et al., 2005; Mohanty et al., 2006;

Aronson et al., 2013). 1

While extensive studies have been conducted on soil physical and chemical properties, microbial diversity and abundance studies are becoming more important in order to incorporate the role of soil microbes in ecosystem functioning. Molecular methods, including metagenomic, transcriptomic, proteomic, and high-throughput amplicon sequencing studies, are increasingly being used to answer questions in the field of soil microbial ecology (Tringe et al., 2005; Schloss and Handelsman, 2006; Keiblinger et al., 2012; Langille et al., 2013; Howe et al., 2014). With respect to aerobic methanotrophs, only a few studies have employed a high-throughput sequencing approach to determine and compare methanotrophic bacterial diversity in soils under different land use practices (Shrestha et al., 2012; Deng et al., 2013; Bragina et al., 2013;

Dumont et al., 2014; Lima et al., 2014; Lau et al., 2015).

The goal of this dissertation is to characterize the diversity of methanotrophs in two contrasting soils in Ohio using the sequencing-by-synthesis technique. In addition to soil type, the effect of land use and land management on methanotrophs was also studied.

This study was also the first of its kind to document microbial community composition of two long-term agricultural research plots in Ohio.

1. Rationale and Significance

The rationale for this study was based on knowledge related to environmental and climatic changes impacting CH4 flux of soils. A number of studies have shown that land use and land management practices affect rates of CH4 oxidation (Boeckx et al., Chan and Parkin, Ojima et al., 1993; Adamsen and King, 1993; Willison et al., 1995; Hütsch,

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2001; Mer and Roger, 2010; Jacinthe et al., 2013). The Jacinthe study, in particular, focused on studying CH4 oxidation rates in Alfisols in Ohio. The authors reported significantly higher CH4 oxidation rates in long-term no-till soils as compared to plow-till soils. Additionally, oxidation rates from no-till soils were found to be 36-37% of that at nearby deciduous forests.

A recent review by Tate (2015) highlighted the effect of land use changes on soil properties such as pH, soil moisture, and nitrogen availability which also affects structure and function of methanotrophic communities. The Fifth Assessment Report of IPCC

(Smith et al., 2014) also noted anthropogenic land use activities like management of croplands, forests, and grasslands and changes in land use/cover (e.g. conversion of forest lands to grasslands and cropland, afforestation) can affect soil’s sink strength for atmospheric CH4. Aronson et al. (2013) in their review suggested that different rates of

CH4 oxidation was likely brought about by changes in methanotrophic bacterial structure and abundance.

Since methanotrophs affect biogeochemical cycling of methane, quantitative and qualitative estimates of soil methanotrophic bacterial population stand to serve as important indicators of land use on CH4 flux. Very little is known in terms of their abundance and diversity in upland soils as affected by land use practices. The opportunity to access two long-term plots in Ohio, i.e. the Triplett and Van-Doren Long-term Tillage

Plots in Wooster and Northwest Agricultural Research Station near Hoytville, allowed the study to include an important aspect of microbial community composition of two long-term experimental sites at two contrasting Alfisols in Ohio. Additionally, 3 availability of sequencing and computing facility at Molecular and Cellular Imaging

Center, Ohio Agricultural Research and Development Center, Wooster, Ohio allowed this study to employ a suite of high-throughput sequencing tools.

This study is significant because it will add to our knowledge of land-use and land-management practices that can be employed on a long-term basis to increase biological fixation of CH4 gas. On a broader level, studying methanotrophic diversity in soils has the potential to help develop climate change mitigation strategies with respect to globally shifting soils to become increasingly active as CH4 sinks.

2. Hypothesis

Based on our literature review, we hypothesized that higher methanotrophic bacterial diversity will be observed in forest and grassland soils as compared to soils from arable area. Additionally, within arable area, we expected methanotrophic bacterial diversity to be higher in soils from no-tilled plots as compared to those from plow-tilled plots.

3. Objectives

There were four objectives in this study:

(i) Identifying bacterial community diversity using pyrosequencing.

A pilot study was conducted in two tillage plots, no-till and plow-till, to determine the

feasibility of carrying out sequencing studies of 16S rRNA gene to determine bacterial

community diversity.

(ii) High-throughput sequencing of 16S rRNA gene to determine soil bacterial 4

community structure of agricultural, grass, and forest soils.

(iii) High-Throughput sequencing to detect differences in Type I and Type II

methanotrophs in agricultural, grass, and forest soils.

(iv) High-throughput sequencing of pmoA gene to determine methanotrophic community

composition in agricultural, grass, and forest soils.

Objectives (ii), (iii), and (iv) were completed by employing an amplicon-based sequencing-by-synthesis strategy. The Illumina’s® MiSeq platform was employed to obtain sequences, which were then analyzed using a suite of bioinformatics tools.

4. Data Analysis and Statistics

Sequences obtained in objective (i) were analyzed using CLCommunity 3.31

(ChunLab Inc.). The software was also used to generate taxonomic composition graphs, community comparison tables, and diversity indices tables.

Sequences obtained in objectives (ii), (iii), and (iv) were analyzed in a Linux based system, using command-line tools of Mothur v 1.33.3 (Schloss et al., 2009) and

QIIME v 1.8 (Quantitative Insights Into Microbial Ecology) (Caporaso et al., 2010).

Multiple rarefactions to determine species abundance in relation to number of sequences was performed in QIIME and JMP (Institute, 2013). Taxonomic and phylogenetic graphs, community comparison, and alpha- and beta- diversity analyses were performed using package Phyloseq (Mcmurdie et al., 2014) in R. Significant taxa were identified using package “DESeqβ”(Love et al., 2014) while significant variables which included rotation, location, tillage, and management were determined by performing multivariate analysis

5 of variances using distance matrix (function “adonis”) in package Vegan (Oksanen et al.,

2015). Phyogenetic heatmaps of classified sequences was constructed in

MEtaGenomANalyzer (MEGAN) v 5.0 (Huson et al., 2007).

5. Means of Applying Results

The results of this study will contribute to the ongoing United States Department of

Agriculture-National Institute of Food and Agriculture (USDA-NIFA) sponsored “Cropping

Systems Coordinated Agricultural Project (CAP): Climate Change, Mitigation, and

Adaptation in Corn-based Cropping Systems” project. Being the first of its kind, this study will serve as a reference for researchers aiming to conduct soil microbial diversity studies of amplicon-based sequencing-by-synthesis technique. Research findings will be highlighted through conference posters, presentations, and peer-reviewed journal articles.

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6. References

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Aronson, E.L., S.D. Allison, and B.R. Helliker. 2013. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4(August): 225. Available at http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3743065&tool=pmcentr ez&rendertype=abstract (verified 18 November 2013).

Bayer, C., J. Gomes, F.C.B. Vieira, J.A. Zanatta, M. de Cássia Piccolo, and J. Dieckow. 2012. Methane emission from soil under long-term no-till cropping systems. Soil Tillage Res. 124: 1–7. Available at http://linkinghub.elsevier.com/retrieve/pii/S0167198712000712 (verified 24 September 2013).

Boeckx, P., O. Van Cleemput, and I. Villaralvo. Methane oxidation in soils with different textures and land use. Nutr. Cycl. Agroecosystems 49(1-3): 91–95. Available at http://link.springer.com/article/10.1023/A%3A1009706324386 (verified 10 May 2015).

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Dorr de Quadros, P., K. Zhalnina, A. Davis-Richardson, J.R. Fagen, J. Drew, C. Bayer, F.A.O. Camargo, and E.W. Triplett. 2012. The Effect of Tillage System and Crop Rotation on Soil Microbial Diversity and Composition in a Subtropical Acrisol. Diversity 4(4): 375–395. Available at http://www.mdpi.com/1424-2818/4/4/375/htm (verified 26 April 2015).

Dumont, M.G., C. Lüke, Y. Deng, and P. Frenzel. 2014. Classification of pmoA amplicon pyrosequences using BLAST and the lowest common ancestor method in MEGAN. Front. Microbiol. 5(February): 1–11.

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Horz, H.-P., V. Rich, S. Avrahami, and B.J.M. Bohannan. 2005. Methane-oxidizing bacteria in a California upland grassland soil: diversity and response to simulated global change. Appl. Environ. Microbiol. 71(5): 2642–52. Available at http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1087552&tool=pmcentr ez&rendertype=abstract (verified 13 February 2015).

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CHAPTER 2: LITERATURE REVIEW

1. Soil Microorganisms

Soil microorganisms play a central role in ecosystem functioning and in controlling Earth’s biogeochemical cycles (Brady and Weil, 2008; Falkowski et al.,

2008). Soil organisms represent less than 5% of soil biota which includes invertebrate multicellular animals that live in the soil or in close contact with the soil (Terrestrial

Ecological Evaluation Process:Department of Ecology, University of Washington). The microorganisms constitute cells that are < 0.1mm in size and include bacteria, archaea, fungi, algae, actinomycetes and protozoa (van Elsas et al., 2006). Studies have estimated that one gram of soil can have 50 billion microbes that include as many as 1010–1011 bacteria (Horner-Devine et al., 2004), 6000–50000 bacterial species (Curtis et al., 2002), and up to 200 m of fungal hyphae (Leake et al., 2004). Among the microbial groups in soil, prokaryotes which include bacteria and archeae, are particularly relevant because they are the most abundant and many diverse populations of these groups can be found in a given soil (Alexander, 1977). Although prokaryotes account for less than half of the total microbiological cell mass in soil, soil microbial studies have primarily focused on 13 these groups because of their capacity to grow in various micro-environmental conditions of soil, and because they decompose a variety of substances present in soil (Alexander,

1977). Another important microbial group in soil is fungi (Lauber et al., 2008) and are responsible for the initial decay of cellulosic and lignin material in the soil (Strickland and Rousk, 2010). Studies estimate that fungi make up about one-third of soil microbial biomass although tropical soils report higher numbers (Yang and Insam, 1991) Alexander

(1977) noted that while bacteria and fungi dominate in adequately aerated soils, bacteria is the dominant group responsible for almost all biological and chemical changes in environments containing little or no oxygen. This literature review is focused on soil bacterial diversity since it fits with the aim of this dissertation study.

1.1 Diversity

Within the microbes, it has been argued that soil environments have the highest prokaryotic diversity on earth (van Elsas et al., 2006) and a single gram of soil may contain 103 to 106 unique ‘‘species’’ of bacteria (Torsvik and Øvreås, 2002; Tringe et al.,

2005; Gans et al., 2005). While these numbers are debatable (Gans et al., 2005), they provide a fair idea about soil microbial diversity. Soil microbial diversity studies have traditionally involved culture-dependent techniques (Nannipieri et al., 2003). However, majority of soil bacteria observed under microscope are not culturable using these techniques (Kirk et al., 2004; Stewart, 2012). It has been debated whether the larger proportion of unculturable species are actually in a state of physiological state that makes

14 it impossible to culture. However, it is accepted that many unculturable microorganisms are, in fact unique (Rondon et al., 1999).

The quest to understand the diversity and ecology of soil microorganisms and then to interpret their functional roles, is one that is developing at an ever-increasing pace. The first DNA-based estimate of soil microbial biodiversity, published in 1990, counted about 4,000 different bacterial genomes per gram of soil (Torsvik et al., 1990).

Since then, various studies and models have pushed the number up to as high as 830,000 species per gram (Gans et al., 2005), down to 2,000 (Schloss and Handelsman, 2006), and back up again. Most recently, Roesch and colleagues analyzed 139,000 individual sequences and came up with an estimate of 10,000 to 50,000 species per gram of soil

(Roesch et al., 2007).

1.2 Functional Roles

Soil microorganisms play fundamental role in ecosystem functioning. They exhibit a complex interplay of functions that result in organic matter decomposition, nutrient cycling and energy flow in soil. Neher's review (1999) on ‘Soil community composition and ecosystem processes’ lists the multitude of roles played by soil microorganisms in several ecosystem functions and include (i) cycling of carbon, nitrogen, sulphur, and phosphorus, (ii) decomposition of dead plant and animal organic matter, (iii) breakdown of inorganic compounds (iv) plant health, and (v) soil fertility.

Additionally, microorganisms also secret “glue-like” substances that bind and stabilize soil aggregates. As a result, microbes exert a direct role on soil structure that in turn

15 affects porosity, infiltration rate, water holding capacity, crusting, erodibility, and susceptibility to compaction (Sessitsch et al., 2001). Because of the importance of microorganisms in healthy soil functioning, soil microbial ecology studies have made an effort to better understand the identity, numbers and functions of soil microbes.

1.3 Identification of Soil Microbes

The fundamental factor that defines a community structure is the environment’s richness and factors like geography, productivity, extremeness, climate change can be correlated with patterns of richness (Begon et al., 2006). The same can be extended to studying soil microbial diversity and richness. However, microbial community is affected by soil chemistry, pollution, and land use thereby making their richness and diversity studies challenging (Schloss and Handelsman, 2005).

While soil microbial diversity studies have always faced methodological limitations, the advent of advanced sequencing techniques including tag-based pyrosequencing and high-throughput Illumina sequencing has brought soil microbial diversity studies into the spotlight. Phylogenetic marker genes including the most commonly used 16S rRNA gene, and other protein-encoding genes can be sequenced using advanced sequencing techniques to determine diversity and community composition of bacteria. The ability to generate millions of sequences from less than 1 gm of soil sample allows for fine-scale resolution of community-level bacterial diversity.

With increasing depth of sequencing effort and more efficient sequencing using region specific primers, studies are now able to identify the low abundant taxa in soil, which

16 until a few years back, was not achievable. In this review, we focus on a particularly important group of soil bacteria, aerobic methanotrophs. We will explore the role of these bacteria in biogeochemical cycling of methane, the effect of land-use practices on methane oxidation in soils, and culture-independent techniques employed to study methanotrophic bacteria in soils.

2. Methane Budget

Methane (CH4) is a potent greenhouse gas found at lower concentrations in the atmosphere than carbon dioxide (CO2) but, on a similar mass basis, has a global warming potential 28 times greater than CO2 (Smith et al., 2014). Methane also participates in chemical reactions in the atmosphere which lead to the formation of ozone in the troposphere, and ozone itself is a greenhouse gas. According to the Fifth Assessment

Report of IPCC (Intergovernmental Panel on Climate Change) (Stocker et al., 2013), CH4 is the second most important anthropogenic greenhouse gas and accounts for 16% or

7.8±1.6 GtCO2 eq/yr of the total greenhouse gas radiative forcing in post-industrial era.

Atmospheric CH4 has a lifetime of 12.4 years and has shown a increase of ~150% since pre-industrial times to a current level of 1.8 ppm (Blasing, 2014).

The source of atmospheric CH4 is mainly biological with emissions from biogenic sources accounting for more than 70% of the total (Table 2.1). Wetlands, rice agriculture, livestock, landfills, forests, oceans, and termites (Smith et al., 2014) are sources of biogenic CH4 emission. Biological CH4 production is carried out by methanogens which

17 are anaerobic microorganisms belonging to the domain Archaea (FAO, 1997; Lessner, D.

J., Lhu, L., Wahal, C.S., Ferry, 2010).

Current anthropogenic emissions account for 50-65% of the global CH4 budget of

-1 ~395-427 Tg CH4.y (Miller et al., 2013). These sources include emissions from fossil fuel mining, and burning of biomass.

Predominant CH4 sinks include tropospheric oxidation reactions initiated by hydroxyl radical, stratospheric reactions, and CH4 oxidation in oxic soils brought about by methane oxidizing bacteria or methanotrophs (Holmes et al., 1999; Hütsch, 2001).

Methane has also been reported to be oxidized anaerobically by archaea and sulfate- reducing bacteria (Ettwig et al., 2010). While CH4 consumption in the atmosphere comprises of the bulk of CH4 sink (Table 2.1), microbial mediated oxidation of CH4 is a major terrestrial sink for atmospheric CH4. Methanotrophs consume 43-90 % of the CH4 produced in aerobic soil (Roslev and King, 1996; Hütsch, 2001; Mer and Roger, 2010).

Although soils reportedly consume roughly 7-10% of the net total annual global CH4 emissions, absence of this sink would cause the atmospheric concentration to increase at approximately 1.5 times the current rate (Duxbury, 1994).

Globally, biogeochemical cycling of CH4 and ultimately the net CH4 emission from soil is dependent on two antagonistic but microbially mediated biochemical processes: production by methanogenic archaea and consumption by methanotrophic bacteria and archaea (Figure 2.1).

18

Estimates (Tg CH4 Sources/Sinks yr-1) References Sources 678 [542-852] Natural Wetlands 217 [177–284] (Hodson et al., 2011; Spahni et al., 2011; Ringeval et al., 2011) Geological incl. 54 [33–75] (Etiope et al. 2008; US EPA oceans 2011.; Rhee et al. 2009) Fresh water 40 [8–73] (Walter et al., 2007; Bastviken et al., 2011) Wild animals 15 [15–15] (Denman et al., 2007) Termites 11 [2–22] (Denman et al. 2007; Lassey et al. 2007; Dickens 2003; Rodger 2006) Hydrates 6 [2–9] (Dickens, 2003; Denman et al., 2007; Shakhova et al., 2010) Wildfires 3 [1–5] (Dueck et al., 2007; Denman et al., 2007; US EPA, 2011) Permafrost (excl. 1 [0–1] (US EPA, 2011) lakes and wetlands) Anthropogenic Agriculture (incl. 200 [187–224] (Dentener et al., 2005; EDGAR, ruminants) and waste 2011; US EPA, 2011) (incl. landfills) Biomass burning 35 [32–39] (Dueck et al., 2007) (incl. biofuels) Fossil fuels 96 [85–105] (Dentener et al., 2005; EDGAR, 2011; US EPA, 2011) Sinks 632 [592-785] Tropospheric loss 553 [467-654] (Allan et al., 2007; Voulgarakis et al., 2013; Naik et al., 2013) Stratospheric loss 51 [16–84] (Bousquet et al., 2006; Williams et al., 2012; Voulgarakis et al., 2013) Soils 28 [9–47] (Ito and Penner, 2004; Curry, 2007; Spahni et al., 2011) a 1 Tg = 1012 g; numbers in square brackets represent range

Table 2.1 CH4 budget in the years 2000-2009 (Adapted from Kirschke et al. 2013).

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Figure 2.1 Schematics of CH4 consumption and emission in soil (Conrad, 2009).

Anthropogenic land-use activities (e.g., management of croplands, forests, grasslands, wetlands), and changes in land use/cover (e.g., conversion of forest lands and grasslands to cropland and pasture, afforestation) cause changes superimposed on these natural fluxes (Smith et al., 2014) and thereby impact methane budgets. The crucial role played by methanotrophs thus warrants in depth study of their diversity under different land-use practices.

3.Methanotrophy

Methanotrophy is defined as a microbially mediated process of metabolizing CH4 for carbon and energy. In the presence of oxygen, CH4 is oxidized by aerobic methanotrophic bacteria to produce methanol, formaldehyde, formate and finally CO2.

Methane oxidation occurs in the aerobic zone of methanogenic soils and in upland soils

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(Adamsen & King 1993; Amaral & Knowles 1997; Bayer et al. 2012; Bourne et al. 2001;

Bull et al. 2000a; Cébron et al. 2007; Chen et al. 2007; Dumont et al. 2014, 2006; Knief et al. 2003; Fjellbirkeland et al. 2001; Henckel et al. 1999; Holmes et al. 1999; Horz et al.

2005; Hütsch 2001; Jang et al. 2011; Jensen et al. 2000; Lau et al. 2007; Lima et al. 2014;

Mckay 1981; Mohanty et al. 2006; Ricke et al. 2005; Siljanen et al. 2012). Oxidation of the gas has also been reported in diverse environments like lake sediments (Bussmann et al. 2004; Costello & Lidstrom 1999; Deutzmann et al. 2011; Lin et al. 2005; Sundh et al.

2005; Murrell et al., 2004; Rudd and Hamilton, 1975; Siljanen et al., 2011; Whalen and

Reeburgh, 1990), acidic peatlands (Dedysh et al., 2001, 2003; Chen et al., 2008; Lau et al., 2013), livestock slurry surface crusts (Duan et al., 2014), submerged rice plants (Horz et al. 2001; Horz et al. 2002), wetlands (Bartlett and Harriss, 1993; Frenzel et al., 1999;

Matthews and Fung, 1987; Svenning et al., 2006; Zhang et al., 2010, Roy Chowdhury et al. 2014), marine systems (Holmes et al., 1995; Tavormina et al., 2010; Boehm et al.,

2013), and gas hydrate vents (Redmond et al., 2010; Boetius et al., 2000; Valentin,

2000).

Anerobic oxidation of CH4 has also been reported (Zehnder and Thomas, 1980;

Caldwell et al., 2008; Colwell et al., 2008). Briefly, there are four known ways in which microorganisms achieve anaerobic oxidation of methane (AOM) as listed out below

(Joye, 2012). One, anaerobic methanotrophic archaea (ANMEs) oxidize CH4 and convert it to carbon dioxide and water (CO2), in cooperation with sulphate-reducing bacteria, which convert sulphate to hydrogen sulphide. Two, microorganisms facilitate the oxidation of methane to CO2 by ANMEs, coupled to the reduction of metal oxides, 21 whereby metals such as manganese (Mn) or iron (Fe) are reduced to the +2 oxidation state. Three, a dismutation reaction is involved in which the bacterium Methoxymirabilis

− oxyfera converts nitrite (NO2 ) to nitric oxide (NO) and then dismutates NO into nitrogen and oxygen as diatomic gases. The bacterium then uses the resulting O2 to support methane oxidation. Four, some ANMEs oxidize methane but also reduce sulphate to

0 − zero-valent sulphur (S ), which they produce in the form of disulphide (HS2 ). The disulphide can be used by associated bacteria, Deltaproteobacteria, to yield sulphide

(HS−) and sulphate.

Aerobic methanotrophs in oxic pockets limit the efflux of methane produced in flooded soils and wetlands, and also consume atmospheric methane directly in aerated upland soils. Consequently aerobic soils consume as much as 90% of the CH4 produced in soils (Roy Chowdhury and Dick, 2013) and are the focus of this study.

3.1 Methanotrophic bacteria

Aerobic methanotrophic bacteria are Gram-negative and have representation in two phyla, three orders, and four families with a total of 22 genera and 56 species being identified up to 2013 (Hanson and Hanson, 1996; McDonald et al., 2008; Nazaries et al.,

2013). Based on carbon assimilation pathways, phylogeny, chemotaxonomy, and internal membrane structure (Hanson and Hanson, 1996; Trotsenko and Khmelenina, 2005;

Nazaries et al., 2013), CH4 oxidizing bacteria have traditionally been divided into two groups: type I and type II. Type I methanotrophs belong to the class

Gammaproteobacteria and include members of the Methylococcaceae family. Type II

22 methanotrophs belong to the class Alphaproteobacteria and include members of

Methylocystaceae and Beijerinckiaceae families. Among the Methylococcaceae, the genera Methylovulum (Iguchi et al., 2011) and Methylomarinum (Hirayama et al., 2013) were recently added. Furthermore, two genera of filamentous methanotrophs have been reported – Clonothrix and Crenothrix (Stoecker et al., 2006; Vigliotta et al., 2007).

Type I methanotrophs are further sub-divided as Type Ia comprising of mesophilic methanotrophs (Methylomonas, Methylobacter, Methylosarcina, and

Methylomicrobium) and Type Ib comprising of thermotolerant/thermophilic members

(Methylococcus, Methylocaldum, and Methylothermus). Type II methanotrophs include

Methylosinus and Methylocystis (Bodrossy et al., 2006) with two new recent additions,

Methyloferula and Methylorosula (Vorobev et al., 2011; Berestovskaya et al., 2012).

Finally, CH4 oxidizing bacteria of the Verrucomicrobia phylum were recently identified with three species that were isolated from geothermal habitats in Italy, New

Zealand and Russia (Dunfield et al., 2007; Pol et al., 2007; Islam et al., 2008) and classified under the Methylacidiphilum genus of the Methylacidiphilales order (Op den

Camp et al., 2009). Verrucomicrobial methanotrophs were capable of growing at very low pH but shared many characteristics with alphaproteobacterial methanotrophs

(Dunfield et al., 2007). A detailed list of methanotrophic taxonomy alongwith characteristics of aerobic methanotrophs, is provided in Table 2.2.

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3.2 Biochemistry of aerobic methanotrophy

The division of methanotrophs into Type I or Type II groups is based upon how they assimilate carbon, although both incorporate methane carbon at the level of formaldehyde. Type I methanotrophs use the ribulose monophosphate pathway and obtain all their carbon from methane. Type II methanotrophs use the serine pathway, which in addition to formaldehyde incorporation further involves fixation of one molecule of CO2 per molecule of formaldehyde assimilated. The former pathway is energetically more favorable, requiring only 1/3 ATP per molecule formaldehyde fixed, instead of 1 ATP and reducing power for the serine pathway (Hanson and Hanson, 1996).

Figure 2.2 presents a schematic of methane oxidation and formaldehyde assimilation by the aerobic methanotrophs, while Type I and Type II assimilation at the formaldehyde step are shown in Figures 2.3 and 2.4. A third pathway for carbon assimilation by

Verrcumicrobial methanotrophs is the fixation of CO2 via the Calvin-Benson-Bassham pathway (Op den Camp et al., 2009). Additionally, genera Methylocaldum,

Methylococcus, Methylogaea, Methyloferula, and Methylacidiphilum also possess

RubisCo activity (Nazaries et al. 2013). Methylococcus, and Methylocaldum (Baani &

Liesack 2008; Nazaries et al. 2013). are also referred to Type X methanotrophs in multiple studies and is classified as a subset of Type I due to minor physiological, biochemical, and phylogenetic characteristics (Hanson and Hanson, 1996; Holmes et al.,

1999; Baani and Liesack, 2008; Nazaries et al., 2013).

The first step of methane oxidation is conversion of CH4 to CH3OH (methanol) by enzyme methane monooxygenase (MMO). There are two kinds of the enzyme, 24 depending on its location in the cell. The pMMO (particulate methane monooxygenase) enzyme is a copper and zinc containing enzyme and consists of three subunits of 27, 47, and 25 kDa encoded by the pmoA, pmoB, and pmoC genes respectively. (Steinkamp et al., 2001; Smith et al., 2011) while the sMMO (soluble methane monooxygenase) contains a di-iron site at its catalytic center and is expressed only when only when the level of copper is very low in the cell (Lee et al., 2006). Thus in the methane oxidation pathway, except for Methylocella and Methyloferula, the first step is mediated by pMMO, even when only small amounts of copper are available. It has been observed that most Type I methanotrophs require higher levels of copper for growth than do Type II methanotrophs and Methylococcus capsulatus (Type X), presumably because they require higher levels of copper for pMMO activity and lack genetic information for sMMO synthesis (Hanson and Hanson, 1996).

The operon encoding pMMO consists of three consecutive open reading frames

(pmoC1, pmoA1, and pmoB1; pmoCAB1) with nearly identical copies found in Type I and Type II methanotrophs. Each of the pmoACAB1 operon is transcribed to form a polycistronic mRNA which is translated to form the α333 component of pMMO (Baani and Liesack, 2008). The pmoA gene encodes the -subunit of pMMO and is widely used as a molecular marker to classify and study methanotrophs (Bürgmann, 2011). Recently, another pmoACAB gene cluster has been identified and has been shown to be widely but not universally distributed among Type II methanotrophs and is not present in representative Type I methanotrophs of the genera Methylobacter, Methylomicrobium,

Methylomonas, Methylococcus, and Methylocaldum (Baani & Liesack 2008; Nazaries et 25 al. 2013). The latter two genera are also referred to Type X methanotrophs in multiple studies and is classified as a subset of Type I due to minor physiological, biochemical, and phylogenetic characteristics (Hanson and Hanson, 1996; Holmes et al., 1999; Baani and Liesack, 2008; Nazaries et al., 2013).

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Phylum Proteobacteria Verrucomicrobia Gammaproteobacteria Alphaproteobacteria Class (Type I) (Type II) Verrucomicrobiae Order Methylococcales Rhizobiales Methylaciiphilales Methylocystaceae Family Methylococcaceae Beijerinckiaceae Methyloacidiphilaceae Genus Methylobacter, Methylosoma, Methylocystis Clonothrix, Methylomicrobium, Methylosinus Methylocaldum, Methylomonas, Methylocapsa Methylococcus, Crenothrix, Methylocella Methylacidiphilum Methylosphaera, Methylogaea, Methyloferula Methylovulum, Methylohalobius, Methylorosula Methylomarinum, Methylothermus, Methylosarcina Formaldehyde RuMP pathway and low levels of Serine pathway in 27 27 assimilation Serine pathway in Methylocystaceae; Variant of Serine Methylocaldum, Methylococcus, Serine & RuMP pathway & Methylogaea pathway in Beijerinckiaceae MMO activity pMMO,sMMOa pMMOb,sMMOc pMMO pmoA genotype USC, Methylococaceae Methylocystaceae, Methyloacidiphilaceae affiliation associated USCα, with oxidation of Beijerinckiaceae atmospheric CH4 a Only in Methylomonas, Methylomicrobium, Methylovulum and Methylococcus. b Not in Methylocella or Methyloferula. c Not in Methylocapsa Table 2.2 Taxonomy and characteristics of aerobic methanotrophs.

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28 28 Figure 2.2 Pathways for oxidation of methane and assimilation of formaldehyde. Abbreviations: CytC – cytochrome c; MDH – methanol dehydrogenase; FADH – formaldehyde dehydrogenase; FDH – formate dehydrogenase.

28

29

Figure 2.3 RuMP pathway for formaldehyde fixation by Type I methanotrophs. Unique enzymes of the pathway, hexulose-6-phosphate synthase and hexulose phosphate isomerase are indicated.

29

30 30

Figure 2.4 Serine pathway for formaldehyde fixation by Type II methanotrophs. Unique reactions catalyzed by serine hydroxymethyl transferase (STHM), hydroxypyruvate reductase (HPR), malate thiokinase (MTK), and malyl coenzyme A lyase (MCL) are indicated.

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3.3 Ecology of aerobic methanotrophs

Aerobic methanotrophs have generally been considered to be obligate, i.e. capable of utilizing only CH4 as their sole source of carbon and energy. However, Theisen &

Murrell (2005), in their review, have documented several cases of facultative aerobic methanotrophy, with special mention of Dedysh et al.'s ( 2005) study on Methylocella species being facultatively methanotrophic by utilizing multicarbon compounds like ethanol and acetate as their carbon and energy source. Methanotrophs have also been found to cometabolize a suite of chlorinated aliphatic compounds (Chang and Alvarez-

Cohen, 1996), and grow on substrates containing carbon-carbon bonds (Anthony, 2015).

Additionally, owing to lack of substrate specificity, the enzyme methane monooxygenases present in aerobic methanotrophic can metabolize a variety of xenobiotic chemicals (Hanson and Hanson, 1996). As a result, apart from oxidizing CH4 in soil, and thereby playing a role in climate change adaptation and mitigation strategies, the ability of methanotrophs to catalyze a large number of bio-transformations makes them potential candidates for development of biological methods for degradation of toxic chemicals (bioremediation), commercial production of bacterial cultures containing methane monooxygenases for the production of chemicals with commercial value

(Hanson and Hanson, 1996), and in the biofuel sector (Fei et al., 2014).

Methanotrophs can also be divided into ‘low capacity-high-affinity’ or ‘high capacity-low affinity’ groups based on their activity. The 'high capacity - low affinity' ones are adapted for growth at high methane concentrations (several 1000 ppm in air),

31 such as those arising from waterlogged soil layers. The 'low capacity - high affinity' methanotrophs, are able to make use of the trace amounts of methane in the atmosphere

(around 1.8 ppm in air) (Bull et al., 2000a; Siljanen et al., 2012). Type I methanotrophs have been grouped into the ‘high capacity’ group and are prevalent in CH4 rich and oxygen poor environments while Type II ‘low capacity’ are predominant in CH4 poor and oxygen rich environments. This suggests that different types of methanotrophs may participate in CH4 oxidation under different moisture conditions (Bull et al. 2000;

Dunfield et al. 1999; Siljanen et al. 2012). Previously, it has been shown that type I methanotrophs have a different evolutionary strategy and adapt faster than type II methanotrophs to changing conditions (Henckel et al., 2000a; Steenbergh et al., 2010). It may be that in poorly drained soils, with high water availability in the pores and the associated oxygen depletion, methanogenesis is active and thus high levels of CH4 are available for Type I methanotrophs. Though many of the 'high capacity' type methanotrophs have been identified and cultured in laboratories, the other 'high affinity' methanotrophs remain poorly understood, with the Upland Soil Cluster-alpha (USCα) being particularly important members of yet-unclassified high affinity methanotrophs

(Ricke et al., 2005).

With respect to nutrient availability, methanotrophs should be considered oligotrophic bacteria (Escoffier et al., 1997). However, studies have isolated different members of the genera Methylocystis, Methylosinus, Methylocaldum and Methylobacter from different upland soils and compared them with type strains for growth and activity under low methane mixing ratios (Knief and Dunfield, 2005). Current studies suggest that 32 soils capable of atmospheric CH4 consumption are the result of the dominance of a few oligotrophic methanotrophic species (for examples Methylocystis strains). Consequently,

Methylococcaceae and Methylocystaceae can be regarded as copiotrophic, whereas

Beijerinckiaceae are oligotrophic (Dörr et al., 2010).

Temperature and pH conditions for growth of aerobic methanotrophs have also reported in the literature. Most are cultured at 20-45°C and neutral pH (Chen et al. 2007;

Holmes et al. 1999). However, methanotrophs from extreme environments have been reported fairly recently. Acidic pH’s of 4.5-5.5 support methanotrophic activity (Dedysh et al., 2001), but pH values as low as below 1, and a temperature of 65 °C have been found to support methanotrophic Verrucomicrobia (Op den Camp et al., 2009). Most pure isolates studied are neutrophilic and mesophilic, although moderately acidophilic, alkaliphilic, psychrophilic and thermophilic species have been described (Dedysh et al.,

2001; Tchawa Yimga et al., 2003; Nercessian et al., 2005; Pol et al., 2007; Op den Camp et al., 2009; Kizilova et al., 2014). Three genera found in warm geothermal springs with growth optima of 45–58°C have been reported and include Methylothermus,

Methylococcus and Methylocaldum (Trotsenko and Khmelenina, 2002; Tsubota et al.,

2005). These grow alongside the mild acidophiles (pH 4.2 to 7.5) in the genera

Methylocystis, Methylocapsa and Methylocella (Dedysh et al., 2003).

Aerobic methanotrophs have been reported from cold ecosystems such as underground waters, Northern taiga and tundra soils, polar lakes and permafrost sediments (Trotsenko & Khmelenina, 2005). On the opposite side of the spectrum are the methanotrophs that consume atmospheric methane and are found in desert soils (Striegl et 33 al., 1992). Another interesting ecological observation related to methanotrophs is the presence of selective grazing of methanotrophs by protozoa, with a study reporting protozoa as a biological factor affecting methanotrophic community in a wetland soil

(Murase and Frenzel, 2008), thereby linking CH4 cycle to the soil microbial food web.

As more experimental evidence is added to the identity, functionality and isolation of this diverse group of bacteria, it will increase our understanding of the roles these microbes play in global biogeochemical cycling of C and especially of CH4.

3.4 Land use practices and methane oxidation

Although, CH4 oxidation in soil is a relatively small sink, it is directly influenced by agriculture, forestry and other land uses (Livesley et al., 2008; Smith et al., 2014).

Environmental and climatic changes can impact CH4 flux profile of soils, likely brought about by changes in soil microbial community structure and function (Aronson et al.,

2013). Various factors, like soil bulk density, water content, temperature, pH (Hanson and Hanson, 1996), and ammonia from fertilizers (Alam and Jia, 2012) affect CH4 oxidation rates in soil. Consequently, methanotroph activity brought about by methanotrophic bacteria in soil is affected by land use changes. The variation in methanotroph activity, as influenced by land use changes serves as a reason to be considered under mitigation strategies which can be employed to reduce atmospheric

CH4 concentrations. Thus, methanotrophs and their diversity in soil as affected by various land uses present themselves as a case for a comprehensive study.

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Regular cultivation disturbs the original soil structure and the metabolic reactions that occur in small pockets of soil aggregates. The individual microbial activities in these aggregates are heterogeneous, depending on the availability of substrate. Conversion of forest or grassland soil into arable land implies disturbing the soil, which destroys soil aggregates and hence the centers of microbial activity (Aronson et al., 2013). This affects physical, chemical and biological parameters that define the ecological niche for microbes like methanotrophic bacteria, which in turn affects the diversity of the microbes. Studies have reported that conversion of a natural subtropical forest to farmland led to a loss of atmospheric CH4 consumption and a shift from Beijerinckiaceae species to Methylococcaceae and Methylocystaceae species (Dörr et al., 2010).

Methanotrophic bacteria utilize CH4 as their sole carbon and energy source due to the presence of a class of enzymes called methane monooxygenases (MMOs) (Amaral and Knowles, 1997). These enzymes exhibit low substrate specificity and can use variety

+ of competing compounds as substrates, including CH4 and ammonium (NH4 ) (King and

Schnell, 1998). Methanotrophic bacteria and chemoautotrophic ammonia oxidizers

+ exhibit similar substrate specificity and can essentially oxidize CH4 or NH4 (Hanson and

+ Hanson, 1996). Essentially, NH4 can fit into the MMO system and inhibit methanotrophs

+ from oxidizing CH4, while CH4 can substitute for NH4 in nitrifiers. Thus addition of ammonium fertilizers can affect the activity and diversity of methanotrophs in soil.

+ The inhibition of CH4 by NH4 is important for the ecology of methanotrophic bacteria in arable, grassland and forest soils. Many nitrogen-based fertilizers contain

+ NH4 , which inhibits CH4 oxidation in soils. Studies suggest that long-term effects of 35 repeated nitrogen fertilizer application can change the microbial ecology of soils and hence create a shift in the microbial population (Adamsen and King, 1993). The

+ interactions between CH4 oxidation and NH4 inhibition can constrain the magnitude of the soil CH4 sink (King & Schnell, 1994). In general, increased CH4 sink strength has been observed in the order of arable

-2 0.28, 0.52, and 1.51 mg CH4 m per day, respectively (Hütsch, 2001).

Tillage also affects the rate of CH4 oxidation in soil, with conservation and conventional tillage practices showing markedly different CH4 oxidation rates (Mer and

Roger, 2010; Jacinthe et al., 2013). Conservation tillage practice leaves crop residue on the soil surface and traditional cultivation techniques like plowing and harrowing are not employed. Conservation practices include like no-till, strip-till, minimum–till and ridge- till, with no-till having minimum disturbance on soil. In contrast, intensive tilling practices includes mixing and/or turning (inverting) the soil, which buries crop residues and breaks up soil aggregates, leaving a bare or nearly bare surface in which to plant seeds (Brady and Weil, 2008). Conventional tillage reduces soil organic matter content, increases soil compaction due to use of heavy machinery and, disrupts pockets of microbial metabolic activity.

Both no-till and crop rotations have been widely adopted, and their wide acceptance has lead to increase microbial biomass and activity (Helgason et al., 2010a).

However, the specific impacts these practices have on microbial community composition are largely unknown. Frey et al. (1999) reported no consistent effects on bacterial abundance or biomass in a 30-year tillage plot. Other studies have indicated that tilled 36 soil may contain greater bacterial diversity than no-tilled soil (Ferreira et al., 2000;

Torsvik and Øvreås, 2002; Upchurch et al., 2008). It is likely that the different tillage practices lead to the development of microenvironments that are partitioned from each other. The partitioned environments possess unique physical, chemical, and biological variations which in turn determine the resources available and consequently the number and type of microbes that thrive in those environments. Ecologically speaking, when multiple niches exist in soil microenvironments, a diverse group of microbes can share the responsibility of breaking down complex compounds, biogeochemical cycling of nutrients, and performing roles in secondary metabolism.

In a recent study by Jacinthe et al. (2013), the effect of no-till duration on methane oxidation capacity of Alfisols was studied. Methane oxidation capacity was found to increase linearly with no-till duration averaging 3.2, 4.2, 11.5, and 1γ.6 μg CH4-

C kg−1 soil day−1 at sites under NT for 9, 13, 36, and 48 years, respectively. While the

CH4 oxidation rate in PT soil was 10–12 % of the level in the forest soils, it was 36–37% at sites under NT for >30 years.

Keeping in mind the ecology of methanotrophs described in the previous section, along with the complexity of soil environments, it is both a challenge and also an opportunity to conduct methanotrophic bacterial identification diversity studies, as affected by management and land uses.

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4. Identification of Methanotrophic Bacteria in Soil from Genes

The unique ability of aerobic methanotrophic bacteria to use CH4 as sole carbon and energy source, and their crucial role in the global CH4 cycle makes this group of bacteria important in terms of studying their ecology, diversity and functionality. Several culture-dependent techniques (Hanson and Hanson, 1996; McDonald et al., 2008b;

Nazaries et al., 2013) have laid the foundation of such studies. However, as aptly noted by Paul (2007), the majority of phylogenetic groups and strains that are abundant and important for soil processes have yet to be isolated and characterized. Therefore, the lack of culturable species has shifted the focus to culture-independent molecular tools to study genes of methanotrophic bacteria. Within the purview of this study, the focus is on culture independent sequence-based identification of methanotrophs.

4.1 Traditional sequence-based approaches

The advent of sequencing techniques have allowed for the use of molecular gene markers to determine phylogenetic relationships between different genera of methanotrophs and also helped determine diversity of these bacteria in soil (Murrell and

Radajewski, 2000). The complete genome sequences of a few methanotrophs being published over the last decade (McDonald et al., 2008), has led to molecular ecology studies that use gene probes specific for methanotrophs. Sequence based approaches have included PCR (polymerase chain reaction) based techniques, DGGE (denaturing gradient gel electrophoresis), and T-RFLP (terminal restriction fragment length polymorphism) to identify methanotrophs in soil. A wide variety of environments including agricultural and

38 forest soils, marine and freshwater sediments, wetlands, and deep sea vents have been studied for methanotrophic diversity using sequencing technologies prior to high- throughput sequencing era by employing analyses of the genes.

Generally, 16S rRNA gene sequences have been used to indicate phylogeny and identity of methanotrophs. Apart from this gene, pmoA (which partial encodes for pMMO), mmoX (coding for sMMO) (McDonald et al., 2008) and a gene sequence, coding the active site of methanol dehydrogenase, mxaF, have been used. Since these gene sequences are highly conserved amongst methanotrophs and show phylogeny similar to those obtained from sequencing 16s rRNA gene, they have been identified as functional genes for studying methanotrophs (McDonald et al., 2008). However, the ubiquitous nature of pmoA and the associated databases available has made it the preferred functional gene of study. Phylogenetic analyses of pmoA and 16S rRNA gene has proved to be effective as a cultivation-independent approach to study aerobic methanotrophs.

Using primers specific for 16S rRNA gene region and/or pmoA, DGGE as well as cloning followed by Sanger sequencing has been used to determine sequence variation of methanotrophs between environmental samples (McDonald et al., 2008). Also, different primer pairs have been tested (Hanson and Hanson, 1996; Cheng et al., 1999; Knief et al.,

2005; Chen et al., 2007, 2008; Lau et al., 2007; McDonald et al., 2008) and multiple studies conducted using DGGE and cloning (Henckel et al., 1999; Steinkamp et al., 2001;

Fjellbirkeland et al., 2001; Horz et al., 2002; Lin et al., 2005; Bodelier et al., 2005; Knief et al., 2005; Lau et al., 2007; Chen et al., 2007; Redmond et al., 2010). However, DGGE 39 has its disadvantages when trying to study community characteristics. Apart from the

PCR-associated biases, DGGE is only semi-quantitative, can give varying results due to different gene copy numbers, and is time consuming. It may not be possible to distinguish multiple bands which are located very close to each other. Also, band position does not provide reliable taxonomic information and is difficult to replicate between gels and runs.

Additionally, secondary artifacts are produced during DGGE-PCR which hinder accurate comparative diversity analysis (Neilson et al., 2013).

Community structure and activity of methanotrophs using the particulate methane monooxygenase gene pmoA by terminal restriction fragment length polymorphism (T-

RFLP), a pmoA-specific diagnostic microarray, and cloning and sequencing have also been used (Reim et al. 2012; Shrestha et al. 2012, Ricke, Kolb, et al. 2005; Deutzmann et al. 2011; Horz et al. 2002; Costello & Lidstrom 1999). However, T-RFLP also produces semi-quantitative data (McDonald et al., 2008a).

4.2 Next-generation sequencing to determine methanotrophic bacterial diversity

Alternate sequencing strategies have become available over the last two decades which are much faster than the traditional Sanger sequencing and are increasingly becoming cost-effective when it comes to sequencing a large number of sequences

(Kircher and Kelso, 2010). Next-Generation Sequencing (NGS) technology allows qualitative (to determine diversity) and quantitative (to determine abundance based on the frequency of sequences detected) assessment of microbial sequences. Under the current sequencing technologies available, several studies have used pyrosequencing to

40 determine methanotrophic bacterial diversity (Deng et al., 2013; Dumont et al., 2014;

Lima et al., 2014; Duan et al., 2014). These studies were successfully able to identify methanotrophic genera including Methylobacter, Methylomicrobium, Methylomonas,

Methylosarcina, Methylocystis (Duan et al., 2014), and Methylacidiphilum (Sharp et al.,

2014). The primer sets being used varied from those targeting the V3 region of the 16S r

RNA gene to that of 16S region specific for Type I and Type II methanotrophs. However, with the 16S rRNA gene, sequence similarity was seen with other groups of bacteria as well. Pyrosequencing of pmoA gene has also been carried out in a handful of studies with most of them using primers A189f/mb661r and A189f/A682r to target methanotrophs

(Lüke and Frenzel, 2011; Deng et al., 2013; Saidi-Mehrabad et al., 2013; Dumont et al.,

2014; Lima et al., 2014). The A189f-A682r primer was found to have broad specificity for pmoA and the corresponding gene of related enzymes (Holmes et al., 1999) whereas the A189f-mb661r primers are generally restricted to pmoA (Deng et al., 2013).

Other studies have combined multiple approaches to study methanotrophic bacterial diversity. qPCR, pyrosequencing, and network analyses were combined to study the functional pmoA gene (Bragina et al. (2013). Changes in methanotroph diversity and abundance were assessed by terminal restriction fragment length polymorphism (T-

RFLP) analysis, cloning and Sanger sequencing, 454 pyrosequencing and quantitative real-time polymerase chain reaction (PCR) of pmoA genes (Shrestha et al., 2012). These multiple approaches indicate that studying diversity and activity of groups like methanotrophs is still a challenge. Very recently, Lau et al. (2015) reported the use of

Illumina® MiSeq multiplex NGS platform to detect differences in Methylococcaceae and 41

Methylocystaceae in surface peat, forest soil, and sphagnum moss by sequencing bacterial the 16S rRNA V3 region. This is the first reported use of the Illumina® MiSeq multiplex NGS platform to study methanotrophs and indicates the application of this current sequencing technique to study methanotrophs.

5. Future Research Needs

Molecular diversity and physiology of methanotrophs is essential to advance our understanding of methane cycling in the environment and to improve the possibilities of exploiting the uniqueness of these microbes. Although advances have been made, much remains to be achieved when it comes to understanding the functions of aerobic methanotrophs in soil. Isolating high-affinity methanotrophs found in upland soils is crucial for understanding their role in the global budget of CH4. The discovery of methanotrophs outside of the phylum hints at the use of emerging metagenomics, transcriptomics, and proteomics tools that can be used to identify novel methanotrophs.

Studying the niche adaption of aerobic methanotrophs in soil can help us better understand the different life strategies adopted by methanotrophs based on their functional and ecological characteristics. Additionally, using advanced bioinformatics tools like PICRUSt, one predict metagenomic functions of bacterial communities using sequence information obtained from high-throughput sequencing analyses.

A study by Ho et al.'s (2013) focused on three-dimensional functional classification network (Competitor-Stress tolerator-Ruderal). They suggested that

Gammaproteobacterial methanotrophs were composed of competitor and ruderal (word

42 originating from definition of plant species able to colonize disturbed lands) species since they responded rapidly to disturbances and were active in many environments while the alphaproteobacterial methanotrophs were stress tolerant species due to their survival in fluctuating environmental conditions. Levine et al's. study (2011) noted that the complimentary theory explains CH4 flux and that increased biodiversity increases function. This was contradicted by Singh et al. 2010, Nazaries et al. 2013, and Kolb et al.

2005 who suggested that selection theory (i.e. that only a selected number of species performs the main function) explained methanotrophy. It is possible that both theories explain CH4 flux under different environmental conditions but distinguishing which theory explains microbial regulation of CH4 flux at the global scale is important in order to incorporate microbial data in predictive models of climate change scenarios.

Additionally, with different environment and ecological factors driving methanotrophic activity, the study of aerobic methanotrophs diversity in soil as affected by land-use and land-management practices is crucial in understanding the role that land use dynamics may play in net CH4 flux. Results from such studies can then be used to recommend approaches that can be undertaken to reduce concentration of CH4 emission in the atmosphere.

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Published in: Microbial Ecology

Type of submission: Short commentary

Title: Community Diversity in Soil Under Two Tillage Practices as Determined by

Pyrosequencing

Authors: Aditi Sengupta and Warren A. Dick*

School of Environment and Natural Resources, The Ohio State University

Wooster, OH 44691, USA

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*Corresponding Author: Dr. Warren A. Dick

Email: [email protected]

Phone: 330-263-3877

Fax: 330-263-3788

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CHAPTER 3: BACTERIAL COMMUNITY DIVERSITY IN SOIL UNDER

TWO TILLAGE PRACTICES AS DETERMINED BY PYROSEQUENCING

1. Abstract

The ability of soil to provide ecosystem services is dependent on microbial diversity, with 80-90% of the processes in soil being mediated by microbes. There still exists a knowledge gap in the types of microorganisms present in soil and how soil management affects them. However, identification of microorganisms is severely limited by classical culturing techniques that have been traditionally used in laboratories.

Metagenomic approaches are increasingly becoming common, with current high- throughput sequencing approaches allowing for more in-depth analysis. We conducted a preliminary analysis of bacterial diversity in soils from the longest continuously maintained no-till (NT) plots in the world (52 years) and in adjacent plow-till (PT) plots in Ohio, USA managed similarly except for tillage. Bacterial diversity was determined using a culture-independent approach of high-throughput pyrosequencing of the 16S rRNA gene. Proteobacteria and Acidobacteria were predominant in both samples but the

NT soil had a higher number of reads, bacterial richness, and five unique phyla. Four

67 unique phyla were observed in PT and 99% of the community had relative abundance of

<1%. Plowing and secondary tillage tend to homogenize the soil and reduces the unique

(i.e. diverse) microenvironments where microbial populations can reside. We conclude that tillage leads to fewer dominant species being present in soil but that these species contribute to a higher percentage of the total community.

Keywords: pyrosequencing, soil community analysis, 16S rRNA gene, long-term tillage, plow tillage, no-tillage

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2. Introduction

Soil is a complex ecosystem that is part of our biosphere. Soil maintains biogeochemical nutrient cycles and ensures proper functioning of our dynamic ecosystems. The ability of soil to provide ecosystem services is dependent on microbial diversity. Approximately 80-90% of the processes in soil are mediated by microorganisms including soil structure maintenance, organic matter decomposition, nitrogen fixation, breakdown of toxic compounds, and inorganic compound transformations (Nannipieri et al., 2003).

The microbial population of soil is diverse. Although the number of microbial species in soil is still being debated, studies have reported ranges that span from 10,000

(Torsvik et al., 1990) to a predicted 107 per gram of soil, with the upper limit being based on DNA sequencing technologies (Gans et al., 2005). A metagenomic approach to estimate microbial diversity predicted about 2000-18000 bacterial genomes in one gram of soil (Daniel, 2005).

Previous studies have indicated soil properties like structure, pH, water, air and nutrient availability, oxidation-reduction potentials, organic matter availability, etc. all affect bacterial community structure (Wakelin et al., 2008). Because physical disturbances in soil are known to impact soil properties greatly (Dick, 1992), they also impact the abundance, community structure and activity of soil microorganisms

(Lienhard et al., 2012). Thus, land-use management practices, like application of different tillage systems, present themselves as case studies for soil microbial diversity studies.

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Tillage practices (i.e. conventional tillage, conservation tillage) are defined by the amount of crop residue (including straw, stubble, leaves, stalk, etc.) left on the ground after a crop has been harvested. Plow tillage involves soil inversion that buries most of the residues so that the surface is left without cover protection (Cowan et al., 2008).

Conservation tillage retains a minimum of 30% crop residue on the surface at crop planting time and involves minimal disturbance to soil (Mathew et al., 2012) due to absence of traditional cultivation techniques like plowing and harrowing. Conservation tillage includes processes like no-till, strip-till, minimum-till and ridge-till. No-till causes the least disturbance of soil due to the avoidance of any mechanical mixing (Cowan et al.,

2008). Long term tillage practices affect soil characteristics like water content, temperature, aeration, aggregation, organic matter stratification, and nutrient distribution

(Wakelin et al., 2008, Mathew et al., 2012) which, in turn, impact microbial diversity.

Long-term conservation tillage practices, like no-till, are generally considered beneficial because it results in reduced soil erosion, improved soil structure, increased soil organic matter concentrations, and increased pore-space and water infiltration. All of these factors create an abundance of diverse microhabitats for microorganisms. It has been argued that microbial community richness and diversity are indicators of soil quality

(Mathew et al., 2012), with studies proposing the development of high species diversity as one goal of remediating degraded soil (García-Orenes et al., 2013).

The dynamic microhabitats that exist in soil pose a challenge when it comes to isolating soil microbes by standard culturing techniques. Recreating appropriate soil environmental conditions in the laboratory is limited in its scope and extent. Hence, with 70 the advent of sequencing techniques, soil microbes are being identified using the information stored in their DNA. High-throughput sequencing technologies present a culture-independent and rapid method to determine microbial diversity in different environments.

While the 454 pyrosequencing platform is being slowly phased out, this less-labor intensive and fast method has been used to evaluate microbial diversity in a number of studies (Zhang et al., 2012). For example, soil bacterial diversity was determined by pyrosquencing in forest and grassland soils in Germany (Nacke et al., 2011), pasture and agriculture systems in Texas (Acosta-Martínez et al., 2008), agricultural soils from

Brazil, Florida, and the Morrow plots in Illinois and a boreal forest in Canada (Roesch et al., 2007), and forest soils from Korea (Jung and Kang, 2014).

This study focuses on conducting preliminary analysis of bacterial diversity, using pyrosequencing, in soils from the Triplett-Van Doren long-term tillage plots in Wooster,

Ohio. The plots are unique in terms of being part of a long-term agricultural research experiment that was established in 1962 (Dick and Doren, 1985). Thus the no-till plots have never been disturbed, except for placing of seed into the ground, for more than 50 years. The most dramatic changes in the soil occur near the surface (Dick, 1983) and so we hypothesize that long-term application of no-till practices will result in a very different microbial community composition at the soil surface compared to plow till (i.e. inversion tillage) when a continuous corn crop is grown. To determine bacterial diversity and community composition (structure), we employed a culture-independent approach of high-throughput pyrosequencing of the 16S rRNA gene. 71

3. Materials and Methods

3.1 Plots, experimental design, and soil Sampling

The Triplett-Van Doren Long-Term Tillage Plots are located in Wooster, Ohio,

USA. The soil at this site is a Wooster silt loam and is classified as a fine-loamy Typic

Fragiudalf. The plots are arranged in a randomized manner consisting of three treatments, three rotations and three replicates. Every crop in the rotation is grown each year so that the total number of plots is 54. The treatment variables include no-till (NT), plow till

(PT) and chisel (minimum) till (MT). The rotation variable consists of (i) continuous corn

(CC), (ii) corn and soybean in a 2-year rotation (CS), and (iii) corn, oats, and alfalfa or mixed grass meadow in a 3-year rotation (COM) (Dick and Doren, 1985). Three subsamples (0-10 cm) were collected on June 9, 2014 from each of the three replicates of the CC/NT and CC/PT plots before liming (total number of subsamples = 18).

Subsamples from the NT and PT replicates were pooled together treatment wise to make two composite samples, one representing NT and the other PT. These pooled samples were transported to the laboratory and divided into two fractions. One fraction was used for measurement of soil properties. The soil was air-dried at room temperature and sieved through a 2-mm sieve. Soil pH was determined with a soil:water ratio of 1:1.

Standard soil chemical properties that included lime test index (LTI), available P, exchangeable Ca, exchangeable Mg, exchangeable K, cation exchange capacity (CEC), and organic matter content were determined by STAR Laboratory, Wooster, OH

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(http://oardc.osu.edu/starlab). Methods used are reported on the STAR Laboratory web page.

3.2 DNA extraction, PCR, and pyrosequencing

Genomic DNA was extracted from approximately 1 gm of field-moist soil immediately after sampling by using the UltraClean® Soil DNA Isolation Kit (MO BIO

Laboratories, Inc., Carlsbad, CA) following the manufacturer’s instructions. The extracted DNA was quantified using the Nanodrop ND-1000 spectrophotometer

(Nanodrop Technologies, Wilmington, DE). The quality of the extracted DNA was confirmed by running the extracts on 1% agarose gel with 1x TAE buffer (40 mM Tris,

20 mM acetic acid, 1 mM EDTA, pH 8.0).

Purified DNA samples were amplified using phylogenetic markers targeting the

V1-V3 regions of the bacterial 16S rRNA gene (~ 600 bp). The primer set included the forward primer B16S-F (5´-CCTATCCCCTGTGTGCCTTGGCAGTC-TCAG-AC-

GAGTTTGATCMTGGCTCAG-3´) and the reverse primer B16S-R (5´-

CCATCTCATCCCTGCGTGTCTCCGAC-TCAG-X-AC-WTTACCGCGGCTGCTGG-

3´). The first two primer sections are the adaptor and key, AC is a linker, and underlined sequences are gene specific primers. The barcode primer in B16S-R is marked as X and depends on the sequencing platform or the number of pooling samples. The extracted

DNA was amplified in a 50 μl reaction mixture containing 5U Taq DNA Polymerase, 5

μl of 10X ExTaq Buffer (β0 mM Mg2+), 2.5 mM dNTP mix, 20 pmol/μl of each primer and 1 μl of template DNA. The thermocycler conditions were: initial denaturation at

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95°C for 5 min; 30 cycles of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, and extension at 72°C for 60 sec; and a final extension at 72°C for 7 min in a Bio-

Rad C1000 Touch (Hercules, CA).

The quality of the PCR products was confirmed by gel electrophoresis and the amplified products were purified using a QIAquick PCR Purification kit (QIAGEN, Cat.

No. 28106). Equimolar amounts of samples were pooled together in a tube and the shorter fragments or dimers (under 300 bp) were removed using a QIAquick Gel

Extraction Kit (QIAGEN, Cat. No. 28706) in a subsequent gel electrophoresis step.

Pyrosequencing was performed with 454 GS FLX Titanium (454 Life Science, Rosche) in Chunlab, Inc. (Korea) according to the manufacturer’s instructions.

3.3 Pyrosequencing data analysis

Unique barcodes on the reverse primers were used to separate DNA sequences that originated from either the NT or PT sample. The gene-specific primers, sequencing primers, barcodes and linkers were removed from the original sequencing reads. The resulting sequences were further narrowed down to select for sequences >300 bp. Non- specific sequences with an expectation value of e>-5 in BLASTN search and chimeric sequences were removed. The sequences were then assembled as a way of de-noising.

The taxonomy of the resulting contig sequences were identified using the

EZTaxon-e database (http://www.ezbiocloud.net/eztaxon) (Kim et al., 2012). In brief, the top five sequences with the highest similarity were extracted from the database using

BLASTN. Next, the similarity value of each sequence was obtained using global pairwise

74 sequence alignment. The species were then identified according to their similarity values.

The algorithm used was a simple identification scheme where x = sequence similarity to type strain species (x > 97%), genus (97> x > 94%), family (94> x > 90%), order (90> x >

85%), class (85> x > 80%), and phylum (80> x > 75%). The operational taxonomic unit

(OTU) was used to classify species at 97% sequence similarity. Clustering of sequence data was done using CD-HIT (Fu et al., 2012). Species richness and diversity were estimated by the abundance-based coverage estimator (Ace), Chao 1 estimator (Chao1),

JackKnife richness estimator (JackKnife), non-parametric Shannon diversity index

(NpShannon), Shannon diversity index (Shannon), and Good’s library coverage using

Mothur v.1.33.3 (Schloss, 2009). A interactive visualization tool, Krona, was used to visualize quantitative community composition and hierarchical relationships (Ondov et al., 2011). CLCommunity 3.31 (Chunlab Inc.) was used to generate taxonomic composition graphs, community comparison tables and diversity indices tables.

4. Results and Discussion

4.1 Soil characteristics

The soil samples were below the pH optimum for good crop growth with a lower pH recorded in the NT sample as compared to the PT sample (Table 3.1). Available P concentration in the NT soil was almost double that of the PT soil. pH and available P are often stratified in no-till soils with lower pH values, but higher available P concentrations, at the surface compared to deeper soil layers or compared to surface PT soils. The organic matter content of the NT soil was slightly higher than of the PT soil 75

Measurements No-Till Plow-Till

Soil Properties pH 4.84 5.96 Lime Test Index 65.9 70.0 Organic Matter (%) 3.57 3.15 Cation Exchange Capacity (cmol/kg) 8.80 11.5 P (mg/kg) 44.6 24.8 K (mg/kg) 155 188 Ca (mg/kg) 563 1899 Mg (mg/kg) 85.2 186

Pyrosequencing Analysis Total number of raw sequence reads 2740 1894 OTUs using CD-HIT 1386 1177 OTUs unique to each soil sample, 1079 873 using CD-HIT Percentage of population in a given rank with relative abundance less than 1%: 5 5  Phylum (35)a 15 16  Class (96) 25 28  Order (186) 39 42  Family (360) 56 63  Genus (739) 91 99  Species (1680) a Indicates total number of taxa observed in the samples for the respective rank.

Table 3.1 Soil properties and pyrosequencing analysis. due to deposition of crop residues on the soil surface. The exchangeable cation concentrations, however, were all less in the NT soil due to the lower soil pH in this sample compared to the PT sample.

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4.2 Pyrosequencing analysis and community composition

The number of sequence reads obtained for NT (2740) was higher than PT (1894).

The maximum number of OTUs for NT soil predicted at 3% dissimilarity was 1386 while for PT, it was 1177. Each soil sample had OTUs unique to the sample, with 1079 OTUs unique to NT and 873 OTUs unique to PT (Table 3.1).

Rarefaction curves (Figure 3.1), depicting the effect of dissimilarity on the number of OTUs, showed distinct patterns for NT and PT. The curve at 3% dissimilarity, corresponding to species level diversity, was starting to plateau at around 1400 OTU for

NT. This suggests that a reasonable number of individual species were sequenced and that more intensive sampling is likely to yield only a few additional species for NT. The curve for PT did not reach a plateau and its steeper slope indicates that a large fraction of the species diversity remains to be discovered. It is however, not uncommon for rarefaction curves to not reach a plateau in soil bacterial communities (Nacke et al.,

2011), with both depth of the sequence and number of valid reads determining the OTUs obtained.

The total number of taxa observed in each rank is listed in Table 3.1. Rank-wise, the relative abundance of each taxon was calculated. The sum of the relative abundances of taxa whose abundance was <1%, was also calculated for each rank. For phylum, 5% of the total population was made up of phyla with relative abundance <1% for both NT and

PT soils. The differences in species composition became clearer when the relative abundances for species level was studied (Table 3.1).

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Figure 3.1 Rarefaction curves at 3% sequence dissimilarity.

Briefly, for NT, 91% of the total population comprised of species with relative abundance <1% while for PT, 99% of the total population belonged to species with relative abundance <1%. While the predominance of a few taxa may indicate high species richness, the presence of low abundance taxa also contributes to community diversity.

The 10 most abundant phyla in each sample and their relative abundances are shown in Figure 3.2. Acidobacteria and Proteobacteria were the dominant phyla across both soil samples. The relative abundance of Acidobacteria in NT (36%) vs. PT(26%) can be attributed to the pH difference in the soil, with soil pH being identified as a major

78 contributor of microbial diversity in a number of studies (Fierer and Jackson, 2006;

Rousk et al., 2010; Zhalnina et al., 2015). Among the top 20 predominant bacteria in the soil samples, the relative abundances of Proteobacteria, Bacteroidetes, Actinobacteria,

Chloroflexi, Gemmatimonadetes, Planctomycetes, Nitrospirae, Verrumicrobia,

Armatimonadetes and Cyanobacteria were lower in NT as compared to PT. Plowing and secondary tillage tend to homogenize the soil and reduces the unique (i.e. diverse) microenvironments where microbial populations can reside. Also, previous studies have suggested that the presence of crop residue and thus higher organic matter content on the surface, favors fungi as primary decomposers (Sipilä et al., 2012; García-Orenes et al.,

2013). Higher organic matter content in NT suggests the likelihood of a predominantly fungal community favoring organic matter deposition, while bacterial community would be predominant in a tillage management practice like PT. Additionally, acidic soils have been reported to exhibit lower bacterial diversity (Fierer and Jackson, 2006), which is in agreement with lower relative abundance of species in NT as compared to PT.

Five taxa unique to the NT soil included HM187141_p, Bacteria_uc, DQ404828_p,

TM6 and Lentisphaerae. The PT sample included four unique taxa, namely,

Spirochaetes, Tenericutes, 10BAV and DQ833500_p. For a clearer hierarchical view of the relative abundance of taxa, the species were viewed under an interactive web browser

(Supplementary Online Figure 3.1 and 3.2). It should be noted that a number of sequence reads did not exhibit similarity to any of the known and sequenced microbial

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Figure 3.2 Relative abundances of the 10 most abundant phyla in the two soil samples. The relative percentages of phyla Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Chloroflexi, Gemmatimonadetes, Planctomycetes, Nitrospirae, Verrumicrobia, and OD1 are marked. species, thereby implying the presence of large number of unidentified microbes in soil.

Sequences from the NT and PT samples were submitted to Sequence Read Archive

(SRA) under accession numbers SRR1610992 and SRR1610991 respectively.

4.3 Link between diversity, richness, and evenness

The diversity indices for the NT and PT samples (Figure 3.3) are dependent both on richness and evenness of a community. These indices provide a link between 80 diversity, richness and evenness of the communities. Although the rarefaction curves indicated that for a fixed number of reads; bacterial richness seemed to be higher in PT as compared to NT, comparison of species richness indices (Figure 3.3a) Chao 1, Ace and

JackKnife showed higher values for NT over PT. However, when diversity indices like

Shannon and NPShannon were compared (Figure 3.3b), PT showed higher values. The real bacterial community in NT can thus be said to be less diverse in terms that it is less even but not less rich.

The lower library coverage of PT as compared to NT from Good’s Library

Coverage (Figure 3.3b) values suggests that increasing the sequencing depth of the amplicons might have shown a clearer relation between species diversity in terms of abundance and evenness. Although our samples clearly reflect the tillage differences imposed at our long-term research site, we also recognize Amend et al.’s work about problems associated with read abundance, biological abundance and inherent biases that the lack of replicates may create (Amend et al., 2010). However, we provide preliminary information about the differences that exist in bacterial community diversity under contrasting tillage practices that are continuously maintained on a site for more than 50 years.

4.4 Soil properties and bacterial ecology

Soil properties affect the ecology of microbes. While the acidic pH of the soils resulted in dominant Acidobacterial population, there is always the possibility of a few abundant taxa skewing the community composition study. Fungal diversity studies by Adams et al.

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Figure 3.3 Diversity indices of samples: (a) Graphical representation of valid reads and OTUs obtained, along with the species richness indices Ace, Chao1 and JackKnife. (b) Graphical representation of diversity indices (i.e. NP Shannon and Shannon) in samples along with read coverage.

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(Adams et al., 2013) showed that a handful of dominant taxa skews species richness and composition when the sequencing depth is even.While there is a greater chance of the abundant taxa being sequenced and identified, the real challenge of studying soil microbial diversity is in sequencing and identifying the low abundant taxa. Another crucial challenge lies in how one approaches these low abundant taxa. Are the low abundant taxa common soil species or different, but rare species? Indeed, Huse (Huse et al., 2010) discusses the legitimacy of the occurrence of such rare taxa and proposed the sequences observed could be common biological occurrences or the end-result of sequencing techniques.

An approach to study the low abundant or rare taxa is by using taxa specific primers. In one of our parallel studies using high-throughput sequencing, targeting methanotrophic bacteria (data not shown), we have used primers and successfully sequenced methanotrophs from the same soil samples. Methanotrophs belong to

Proteobacteria and aerobically oxidize methane, thereby serving as the sole biological sink of methane in our biosphere. In spite of our samples recording high abundance of

Proteobacteria in this study, we were able to identify only a single species of methanotroph, Methylocystaceae_uc in NT. This strengthens the need to carry in-depth analysis of soil microbial community composition by targeting taxa-specific microbes that might be beneficial to the environment.

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5. Conclusion

Sustenance of life on earth is dependent on the ecosystem services provided by soil and soil microorganisms. The microbial component of soil plays a crucial role in cycling of nutrients in the biosphere. Our study was a preliminary attempt at observing bacterial diversity in soils under two common but contrasting tillage practices that have been maintained for 52 years at a site in Ohio, USA. Our sequencing study reveals a number of uncultured species, many of which are undoubtedly still impossible to study using standard culture techniques. In this study, the results were mixed with some diversity indices favoring NT and some favoring PT, but in general the PT treatments lead to higher relative abundance of a few species. We conclude that plowing and secondary tillage homogenizes the soil, leading to a reduction in the unique (i.e. diverse) microenvironments where microbial populations can reside.

6. Acknowledgments

The authors thank Victor M. Valentin for his help in sample collection, STAR Lab

(Wooster) for analysis of soil properties, and Yoon-Seong (ChunLab, Inc.) for his assistance in bionformatics. Funding for this study was provided by the USDA-NIFA,

Award No. 2011-68002-γ0190 “Cropping Systems Coordinated Agricultural Project

(CAP): Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems”

7. Conflict of Interest

The authors declare that there are no conflicts of interest.

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9. Supplementary online resources

Supplementary Figure 3.1 Interactive species composition for the no-till soil sample http://goo.gl/p11ne3. The reader may wish to open the link in a web-browser. We suggest double-clicking specific areas on the pie chart to study species details in NT.

Supplementary Figure 3.2. Interactive species composition for the plow-till soil sample http://goo.gl/7gPt7L. The reader may wish to open the link in a web-browser. We suggest double-clicking specific areas on the pie chart to study species details in PT.

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CHAPTER 4: EFFECT OF LONG-TERM LAND-USE AND LAND-

MANAGEMENT PRACTICES IN OHIO ON SOIL BACTERIAL

COMMUNITY STRUCTURE

1. Abstract

Land-use practices impact soil microbial functionality and biodiversity, with reports suggesting that anthropogenic activities potentially result in reduced microbial functions and loss of species. The main objective of this study was to assess the effect of long-term natural (forest and grassland) and managed (agricultural land) ecosystems on soil bacterial community structure. Additionally, the study included investigating community structure of a rare group of bacteria in upland soils, i.e. aerobic methanotrophs, in order to better incorporate climate-change adaptation and mitigation strategies. A high- throughput sequencing-by-synthesis approach of the 16S rRNA gene was used to study bacterial community profile and aerobic methanotrophic community composition of

Alfisols, as affected by variables like rotation, location, tillage, and management in two long-term experimental plots in Ohio. While about 40% of sequences remained unclassified, the study was able to identify small-scale resolution of microbial diversity.

The distribution of the abundant phyla was different across samples with the agricultural

90 soils representing a similar relative abundance distribution. No-till soils generally showed higher diversity indices. Ordinations across locations suggested that no-till soils had similar and distinct community structure as compared to plow-till soils, while grassland and forest soils were individually distinct. Of the variables tested, land-management

(P<0.05), followed by tillage (P<0.01) were found to be significant when determining bacterial community structure. Results also indicated the presence of relatively large percentages of rare groups in the communities. The study of methanotrophs at one study site showed higher relative abundance of the bacteria while the second study site recorded the presence of a broader group of bacteria, the methylotrophs, thereby indicating the possibility of resource sharing in the niche environments and co- dependence of ecologically important groups of bacteria. keywords: 16S rRNA gene, high-throughput sequencing, microbial ecology, no-tillage, ordination, methanotrophic bacteria.

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2. Introduction

Microorganisms play a crucial role in the maintenance of soil health and function

(Doran and Zeiss, 2000; Garbeva et al., 2004). Both in natural and agricultural lands, microorganisms contribute to processes like soil structure formation, decomposition of organic matter, breakdown of xenobiotic compounds, biogeochemical cycling of nutrients, plant disease suppression, and plant growth (Nannipieri et al., 2003; Garbeva et al., 2004). Land-use practices impact soil microbial functionality and biodiversity, with reports suggesting that anthropogenic activities potentially result in reduced microbial functions and loss of species (Brown et al., 2002). Globally, implementation of soil conservation practices in agriculture has become crucial, with a number of studies indicating that conventional and conservation practices have different microbial community structure (Mathew et al., 2012; García-Orenes et al., 2013; Reilly et al., 2013;

Milner, 2014; Hartmann et al., 2014).

Conventional agriculture practices include plowing and sowing while conservation practices, such as no-tillage, are characterized by sowing directly into the soil, with 30% crop residue present on the surface (USDA, 1999). Worldwide, no-tillage systems account for 117 million hectares with about 27 million hectare of no-tillage farming in the United States in 2007 (Huggins and Reganold, 2008). Based on the last official estimates in 2004, about 10% of the total cropland in Ohio is in continuous no- tillage (Randal Reeder, personal communication). In spite of long-term no-tillage proving to be beneficial to soil health (Triplett and Dick, 2008), the trend of implementing it on field says otherwise. 92

Studies underlying effects of no-till on soil physical and chemical properties have been extensively conducted (Dick, 1983, 1992; Kumar et al., 2012; Jacinthe et al., 2013;

Campbell et al., 2014). A few studies have noted that the choice of tillage and rotation for an area depends on the climate, type of soil, and crop (Kumar et al., 2012). In some areas of the world, benefits of long-term no-till practices included increased soil organic matter content, improved soil physical properties, better soil moisture levels, and stabilizing against adverse temperature (Campbell et al., 2014, Triplett and Dick, 2008), In contrast, long-term tilling of soil disrupts soil structure, depletes soil organic carbon stocks, and increases soil erosion (Triplett and Dick, 2008). The complexity of results obtained with various agricultural activities like crop rotation, tillage, and fertilizer application indicate a need for more research to understand their effect on microbial community dynamics

(Balota et al., 2004; Silva et al., 2013).

Most microbial activity in soil occurs within a few centimeters of the surface

(Babujia et al., 2014) and this is especially the case for no-tillage. Therefore, activities in this soil layer like disruption by tillage, crop residues from no-tillage, leaf litter from forests, and biomass accumulated on surface of grasslands affect microbial biomass, activity, and diversity (McCaig et al., 2001; Potthoff et al., 2006; Garbeva et al., 2006;

Singh et al., 2007; Ceja-Navarro et al., 2010; Mathew et al., 2012; Lienhard et al., 2012;

Chapman et al., 2013; Babujia et al., 2014; Purahong et al., 2014).

Land-use and land-management practices account for 20-24% of total anthropogenic greenhouse gas emissions (Smith et al., 2014). Since soil microbes play a crucial role in biogeochemical cycling of nutrients and controlling greenhouse gas fluxes, 93 there is a need to link microbial diversity, land use practices, and climate change adaptation and mitigation strategies. Soil quality, which is defined as the “capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation” (Karlen et al.'s , 1997) assumes that microbial diversity of both natural and managed ecosystems is important.

To the best of our knowledge, only a handful of studies have attempted to study the effect of agricultural practices and land-use change on soil microbiomes using high- throughput sequencing strategies. These include investigations of soil microbiome in

Argentinean pampas (Figuerola et al., 2012; Carbonetto et al., 2014), soil microbial diversity and composition studies of a long-term agricultural experiment field in south

Brazil (Dorr de Quadros et al., 2012), and a pilot study of soil bacterial community in long-term till and no-till plots in Ohio (Sengupta and Dick, 2015).

The last decade has seen an enormous growth in molecular biology tools to study microbial diversity. However, studies that present overarching bacterial community diversity with respect to land-use practices, and simultaneously focus on a particular group of bacteria, are still rare. We are interested in investigating the overall community structure and that of a rare group of bacteria in upland soils, i.e. aerobic methanotrophs.

These bacteria are important in controlling the cycling of a major greenhouse gas, methane (CH4). Consequently, studying microbes such as methanotrophs, which have specific roles in controlling greenhouse gas fluxes such as CH4, is relevant.

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In this study, we hypothesized that different land-use and land-management practices would affect soil bacterial diversity. These practices include crop rotation, tillage, and land use management as well as location. A high-throughput sequencing-by- synthesis approach of 16S rRNA gene was conducted to study bacterial community profile and aerobic methanotrophic community composition of Alfisols was conducted in soil obtained from two long-term (52 years) land-use sites in Ohio. These two sites include tillage and crop rotation as well as forest and grass areas.

3. Material and Methods

3.1 Description of the field sites, and soil sampling and processing

The study was performed by collecting soil samples from two long-term experimental field sites in Ohio. These sites are the Triplett-Van Doren Experimental

Plots at the Ohio Agricultural Research and Development Center/The Ohio State

University (OARDC/OSU) located near Wooster and the Northwest Agricultural

Research Station of OARDC/OSU located at Hoytville, near Hoytville, Ohio, USA. In this study, the plots will henceforth be referred to as W and H, based on their location.

Tillage and rotation plots were established at the Wooster site in 1962 and at the

Northwest Agricultural Research Station site in 1963. These sites are unique because they contain the longest continuously maintained no-till research plots in the world.

Field maps of each location, plot design, and treatments are provided in supplementary materials (Tables A1 and A2). Briefly, the plots are arranged in a randomized manner consisting of three treatments, three rotations and three replicates at 95 both locations. The treatment variables include no-till (NT), plow till (PT) and chisel

(minimum) till (MT). The rotation variable consists of (i) continuous corn (CC), (ii) corn and soybean in a 2-year rotation (CS), and (iii) corn, oats, and alfalfa or mixed grass meadow in a 3-year rotation (COM) (Dick and Doren, 1985). Adjacent grass and forest sites accompany the agricultural plots and were considered in this study. The grass area consisted of hay while the forest areas had oak, ash, and maple trees. The forest sites have never been tilled while the only disturbance in the grass areas were the occasional use of farm equipment to manage adjacent agricultural plots.

The following treatments were studied: (1) no-till continuous corn, NT-CC, (2) no-till corn-soybean, NT-CS, (3) plow-till continuous corn, PT-CC, (4) plow-till corn- soybean, PT-CS, (5) grassland, G and (6) forest, F. Samples were collected at both sites within a week of each other in spring 2013, before the planting season. It has been reported that microbial diversity is highest during spring ( Lauber et al. 2013; Pereira e

Silva et al. 2012). The plots of the corn-soybean rotation had corn growing in the previous season. Three sub-samples (0-10 cm) were collected from each replicated treatment. A composite soil sample was prepared for each replicate plot by pooling in the sub-samples and then passing the soil through a 2mm sieve prior to DNA extraction.

Genomic DNA of 18 samples was extracted from approximately 0.25 gm of field- moist soil immediately after sampling by using an UltraClean® Soil DNA Isolation Kit

(MO BIO Laboratories, Inc., Carlsbad, CA) following the manufacturer’s instructions.

The extracted DNA was quantified using a Nanodrop ND-1000 spectrophotometer

(Nanodrop Technologies, Wilmington, DE). The quality of the extracted DNA was 96 confirmed by running the extracts on 1% agarose gel with 1x TAE buffer (40 mM Tris,

20 mM acetic acid, 1 mM EDTA, pH 8.0). The DNA extracted from the replicates were pooled together to obtain 12 samples representing six treatments and two locations.

3. 2 Illumina library generation

Sample preparation was performed according to an in-house two-step PCR

(polymerase chain reaction) amplification protocol targeting partial region of the 16S rRNA gene. The Hoytville and Wooster samples were sequenced separately, with

Hoytville sample depth coverage being ~180 bp (V3 region) and Wooster sample depth being ~490 bp (V1-V3 region). The protocol for the library generation of Hoytville samples is outlined below, followed by the protocol for the Wooster samples denoting procedures that were different for this set of samples. The reason for different library preparation strategies was because we failed to obtain good quality reads for Wooster samples when targeting the 180 base-pair region. Consequently, the Hoytville and

Wooster datasets have been treated as standalone datasets in our study and have not been compared to each other.

For the Hoytville samples, libraries were prepared using Illumina compatible

Nextera™ Technology (Illumina, Inc., San Diego, CA, USA). Each amplicon was generated using locus specific PCR primers carrying Illumina compatible adapter sequences. The first round of PCR reaction was conducted using modified primers 341F

(5ʹ-TCGTCGGCAGCGTC AGATGTGTATAAGAGACAG-

CCTACGGGAGGCAGCAG-γʹ) and 518R (5ʹ-GTCTCGTGGGCTCGG

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AGATGTGTATAAGAGACAG-ATTACCGCGGCTGCTGG -γʹ). The first section of the primers includes partial Illumina adapters while the underlined region denotes the locus specific primer. The PCR reaction was carried out using 25-μl reaction mixtures that included soil genomic DNA from six samples, and nuclease-free water as the negative control. Each reaction mixture contained 1X GoTaq® Colorless Mastermix , 10

μmol forward and reverse primers, 1X BSA, and 0.6 μl of template. The PCR conditions involved an initial denaturation step at 94°C for 1 min followed by 20 cycles of denaturation at 94°C for 30 sec, annealing at 49°C for 1 min, and extension at 72°C for 1 min, with a final extension step at 72°C for 5 min in a Bio-Rad C1000 Touch

Thermocycler (Hercules, CA). Following separation of products from primers and primer dimers by electrophoresis on a 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)], PCR products of the correct size were recovered using a QIAquick gel extraction kit (Qiagen, Mississauga, Ontario, CA) and quantified using Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington,

DE).

The second PCR involved attaching complementary primers to the Illumina forward, reverse, and multiplex sequencing primers with the forward and reverse primer also containing a unique 8-bp read index allowing for multiplexing. The Nextera® Index

Kit PCR primers were used with an i5 index (5’-

AATGATACGGCGACCACCGAGATCTACAC-i5-TCGTCGGCAGCGTC-3’) and an i7 index (5’-CAAGCAGAAGACGGCATACGAGAT-i7-GTCTCGTGGGCTCGG-3’).

Detailed information of the primer index combinations used for each sample is provided 98

(Appendix A4.1 & A4.3). The PCR reaction was carried out using a β5 μl sample reaction mixture containing 1X GoTaq® Colorless Mastermix, β μmol forward and reverse primers, and 6 μl of template. The PCR conditions involved an initial denaturation step at 98°C for 1 min followed by 10 cycles of denaturation at 98°C for 30 sec, annealing at 63°C for 1 min, and extension at 72°C for 30 sec, with a final extension step at 72°C for 1 min in a Bio-Rad C1000 Touch Thermocycler. All PCR reactions were performed in triplicates.

For the Wooster samples, a ~490 base-pair region was amplified. The first round of PCR reaction was conducted using a combination of modified primers 27F and 518R with variable regions. Supplementary Table S4.2 provides primer information. The PCR conditions involved an initial denaturation step at 96°C for 3 min followed by 25 cycles of denaturation at 96°C for 30 sec, annealing at 55°C for 30 sec, and extension at 72°C for 30 sec, with a final extension step at 72°C for 5 min in a Bio-Rad C1000 Touch

Thermocycler (Hercules, CA). The second PCR involved attaching complementary primers to Illumina forward, reverse, and multiplex sequencing primers with the forward and reverse primer also containing unique a unique 8-bp read index allowing for multiplexing. The Nextera® Index Kit PCR primers were used with an i5 index (5’-

AATGATACGGCGACCACCGAGATCTACAC-i5-TCGTCGGCAGCGTC-3’) and an i7 index (5’-CAAGCAGAAGACGGCATACGAGAT-i7-GTCTCGTGGGCTCGG-γ’).

Detailed information of the primer index combinations used for each sample is provided

(Table S4.1 and S4.3). The PCR reaction conditions were the same as outlined for the

Hoytville samples. 99

Following the second round of PCR amplification, amplicons were run on 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)] to ensure amplification was correct. Additionally, amplicons were quantified using a

Nanodrop ND-1000 spectrophotometer. Amplicons were then purified using Performa®

V3 96-Well Short Plate (EdgeBio, Gaithersburg, MD, USA) and AMPure® XP beads

(Beckman Coulter Inc., Beverly, MA, USA) following the manufacturer’s instructions.

The quantity of the purified products of each sample was checked using Qubit double- stranded DNA high-sensitivity assay (Thermo Fisher Scientific, Grand Island,NY) using the manufacturer’s instructions and then pooled in equimolar ratios. The Hoytville samples were pooled together and the Wooster samples were pooled together. The two separate pools were loaded onto 1.5% agarose Pippin PrepTM (Sage Science, Beverly,

MA) instrument for targeted size selection of the pooled fragment, i.e. ~180 bp for

Hoytville and ~500 bp for Wooster. The pooled samples were extracted and quantified again using Qubit® Fluorometer. The samples were subsequently submitted to Molecular and Cellular Imaging Center (MCIC) housed at Ohio Agricultural Research and

Development Center (OARDC) for sequencing. Sequencing was performed using the

Illumina MiSeq instrument with MiSeq Reagent Kit v3 and MiSeq Control Software and

Reporter v2.4.1 (Illumina, Inc., San Diego, CA). Samples were sequenced as 2x150 (for

Hoytville) and 2x300 (for Wooster) paired-end reads and two 8 bp index reads. Data obtained from sequencing were processed with an in-house data analysis pipeline.

3.3 Initial quality filtering

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Demultiplexed sequences were downloaded from Illumina’s Basespace® as individual read 1 (R1) and read 2 (R2) files for each of the twelve samples. All steps were performed on a Linux system. FastQC was used to perform quality control checks on the raw sequence reads followed by trimming the sequences using TrimGalore, with the following parameters: min length=200, quality score=25, minimum required adapter overlap=6. The rest of the steps outlined below were performed on the Linux terminal using Mothur v.1.33.3 (Schloss et al., 2009). The quality trimmed reads were joined using default parameters of ‘make.contigs’ into a single fasta file containing sample-wise merged sequences. The sequences were screened to ensure stringent quality using the following parameters: Hoytvile (minlength=150, maxlength =180, maxambig=0, maxhomop=10) and Wooster (minlength=470, maxlength =500, maxambig=0, maxhomop=10). The sequences were then split sample-wise to obtain individual sample fasta files for further downstream analysis.

3.4 Sequence classification and OTU picking

Scripts provided in the Quantitative Insights Into Microbial Ecology (QIIME) software suits (Caporaso et al., 2010b) was used for sequence processing. Briefly, the

“pick_open_reference.py” operational taxonomic unit (OTU) picking protocol in QIIME was used with modification. Taxonomy was assigned to all OTUs using the default

Uclust classifier within QIIME using the Greengenes database (DeSantis et al., 2006).

Chimeric sequences were checked and filtered out using

“parallel_identify_chimeric_seqs.py” and “filter_fasta.py”. The sequences were then

101 aligned, followed by generation of phylogenetic tree using “make_phylogeny.py”. The sequences that failed to align were filtered out, followed by generation of a new OTU table used for downstream analysis.

3.5 Data analysis

Multiple rarefactions were performed (“multiple_rarefy.py”) to determine alpha- diversity at different depths followed by collating the multiple rarefactions using

“collate-alpha.py”. The results were then imported into JMP® (SAS Institute Inc.

2013)and were subjected to analyses of variance using the general linear model in JMP.

All subsequent analyses were performed in R (R Core Team, 2014) using output files generated in QIIME. OTU table containing read counts for each OTU in each sample, taxonomy information for each OTU, sample metadata, representative sequences, and representative tree were exported from QIIME, and imported into R using Phyloseq

(McMurdie and Holmes, 2013). Sequences observed with very low frequency i.e. OTUs representing less than 0.001% of the total number of sequences, were removed. Alpha- diversity indices including number of observed phylotypes, Chao1, and Shannon’s H’ index were studied. Variances in OTU abundance were accounted for by transforming abundances, sample-wise. After the normalization step, relative abundances of the OTUs at each taxonomic rank and in each sample were studied to determine community composition of the samples.

To explore whether bacterial community composition clustered according to land-use, the results of non-metric multidimensional scaling (NMDS) using the Bray-

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Curtis and unweighted UniFrac dissimilarities were plotted. These results were further evaluated with adonis (Permutation Multivariate Analysis of Variance using Distance

Matrices) using Vegan package (Oksanen et al., 2015) in R. Relationship between community composition and environmental variables (Rotation, Tillage, Management) were analyzed. Raw OTU tables were subsetted based on abundant rank orders and formatted for the DESeq2 package in R (Love et al., 2014). Differential abundance of

OTUs by sample type was determined using DESeq2.

In order to study community profile of methanotrophs, OTUs representing the bacteria were subsetted in Phyloseq package. The subsetted methanotrophic group was studied for relative abundances of representative methanotrophs, followed by nonmetric multidimensional scaling studies, adonis studies, and identification of differentially abundant genera. The subsetted group was also imported into MEGAN5 (MEtaGenome

ANalyzer v 5.0) (Huson et al., 2007) to compare abundance of representative group in the form of a phylogenetic heatmap.

4. Results and Discussion

4.1 Whole community profile of Hoytville samples

Pre-processing of sequences resulted in about 1.7 million sequences from about

2.1 million combined reads. A total of 30517 OTUs were obtained with OTUs ranging from 11978-18145 OTUs in each sample (Table 4.1). While rarefaction was not performed for downstream analyses, results from collated multiple rarefactions for each sample, at 3% dissimilarity level, showed that for all samples but H-PTCC, rarefaction 103

Sequences after OTU Observed Shannon’s Samples picking* OTUs Chao1# Hʹ Hoytville

H-NT-CC 233706 13788 20049 (+255) 7.5 H-NT-CS 367053 18145 25132(+222) 7.5 H-PT-CC 240512 14169 21447(+255) 7.3 H-PT-CS 180487 11978 18728(+256) 7.2 H-GRA 364930 15326 21211(+202) 7.0 H-FOR 275705 13764 18508(+177) 7.3 Wooster W-NT-CC 69095 14563 15968(+61) 8.5 W-NT-CS 112449 19936 20863(+44) 8.7 W-PT-CC 45729 8899 8899(+0.45) 8.4 W-GRA 162974 2461 24616(+0.15) 8.9 a NT=No-Till; PT=Plow Till; CC=Continuous-Corn; and CS=Corn-Soybean. # Chao1 provides an estimation of observed species. Confidence intervals are marked in parentheses.

Table 4.1 Number of sequences after processing, observed OTUs and Chao1 (richness estimators), and Shannon’s Hʹ (diversity estimator) for soil samples across two locations.

curves were saturated at about 20000 sequences (data not shown). This implied that for

most samples, intensive sampling depth was less likely to yield additional species in all

the samples and that the sequencing effort covered all the taxonomic diversity of the 16S

rRNA gene in the soils.

The original library size of the samples was preserved by not performing

rarefactions, as has been recommended in recent studies (McMurdie and Holmes, 2014;

Debenport et al., 2015). Richness and diversity estimates are graphically represented in

Figure 4.1, with observed species and Chao1 representing richness and Shannon’s Hʹ

representing diversity. While H-NTCC exhibited highest number of observed species,

richness estimate (Chao1), richness and diversity differences in H-NT-CC, H-PT-CS, H- 104

GRA,and H-FOR were notable. While H-NTCC and H-PTCS had higher diversity but low richness in terms of observed species and Chao1 metrics (lower in H-PTCS), sample

H-GRA had greater number of species observed and yet exhibited low diversity metric.

Sample H-FOR on the other hand had higher diversity but showed a low Chao1estimate.

Sample H-PT- also exhibited higher diversity estimate but lower richness estimate.

The patterns observed here are interesting because diversity measures account for both richness and evenness of a community(Gosselin, 2006; Wilsey and Stirling, 2007).

In other words, the diversity of a community depends upon the number of members

(richness) and how evenly those members are distributed in that community (evenness).

Thus, even though a community might have a higher number of species, the diversity may be low if those species are distributed less evenly. For H-NTCS, both evenness and richness estimates were high, resulting in high diversity while for H-GRA, there was comparably higher number of species although the low diversity metric indicates that the species present were less evenly distributed. No-till soils, irrespective of rotation, showed higher diversity as compared to plow-till soils. This can be attributed to the presence of higher organic matter in the no-till soils. However, H-NTCC showed lower richness estimates as compared to H-PTCS. With respect to rotation however, H-NTCS had the highest richness and diversity while H-PTCS was less rich.

Plowing results in breaking up of soil structure while rotating crops increases the diversity of residues types and also, often fertilizer inputs into the soil. Both these activities affect belowground microbial communities in terms of availability of nutrients and edaphic factors like oxygen availability and water content. 105

(a) Hoytville Samples

1 No-Till Continuous Corn 2 No-Till Corn-Soybean 3 Plow-Till Continuous Corn 4 Plow-Till Corn-Soybean 5 Grass 6 Forest

(b) Wooster Samples

1 No-Till Continuous Corn 2 No-Till Corn-Soybean 3 Plow-Till Continuous Corn 4 Grass

Figure 4.1 Alpha-diversity estimates of (a) Hoytville samples: NTCS showed highest richness and evenness. NTCC showed high diversity but low number of observed species, while GRA exhibited higher richness index but lower diversity index and (b) Wooster samples: GRA showed highest richness and diversity while PTCC was the least rich and diverse amongst four samples. The standard error values of Chao1 estimates in Wooster samples were not large enough to be visible on the scale.

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With respect to H-FOR and H-GRA, no significant correlations could be made when compared to field soils although it was inferred that forest soil was more diverse and less rich compared to soil under grass. A cause for this observation may be due to the the fact that above-ground matter of grasses is comprised of a monoculture of hay, which when incorporated into the soil provides ample organic matter for microbes but is limited in the range of organic matter and degradation compounds. Forest soil on the other hand comprised of leaf litter layer, which when degraded at varying rates, allows for a smaller but diverse group of bacteria to colonize the forest floor.

4.1.1 Bacterial community composition and ecological significance

While a total of 30517 OTUs for the six Hoytville samples, in order to exclude sequences observed with very low frequency, OTUs with less than 17 sequences

(representing less than 0.001% of the total number of sequences) were removed. This resulted in 30214 OTUs that were further normalized and analyzed. Overall, 80% of all reads could be assigned to phylotypes, i.e. 20-25% of reads remained unassigned.

Focusing on the identifiable phylotypes (Table 4.2), a total of 44 bacterial phyla were found across all samples. Overall, the dominant phylum was Proteobacteria (~25% in

NTCC, NTCS, PTCC, PTCS, and FOR) and Actinobacteria (~30% in GRA). The 10 abundant phyla occupied about 95% of the total distribution in each sample (Figure 4.2).

In addition to the two noted above, abundant phyla included Acidobacteria,

Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Nitrospirae,

Planctomycetes, Verrucomicrobia and candidate WS3.

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Rank Hoytville Wooster Phylum 44 35 Class 148 115 Order 307 222 Family 477 331 Genus 758 495

Table 4.2 Identified phylotypes at five taxonomic levels in Hoytville and Wooster samples.

The distribution of the abundant phyla was different across samples with the agricultural soils representing a similar relative abundance distribution. Additionally, abundance of

Acidobacteria showed that while similar relative abundances were noted for agricultural soils, smaller percentages were noted in grassland and forest soil. It has been reported that members of Actinobacteria are among the most important litter decomposers in soil

(Kopecky et al., 2011) which explains the relatively high percentage in GRA, FOR and

NTCS soils.

In the next taxonomic level, 144 Classes were identified, with an average of

~60% phylotypes represented by the 10 abundant Classes (Figure 4.3). Among

Proteobacteria, alpha-, beta-, gamma-, and deltaproteobacteria were found in all samples but with varied relative abundances. Figure 4.4 displays the relative percentage abundance of the top 10 Orders out of 307 identified ones, with NTCC, NTCS, PTCC, and PTCS averaging about 40% of reads represented by the 10 abundant groups while

GRA had almost 60 % of reads and FOR had about 50% of reads represented by the 10

Orders. It is interesting to note that for the agricultural soils; more than 50% of reads 108 were represented by Orders that were less abundant than the top 10 groups. Another observation was the presence of Actinomycetales in GRA soil.

The effect of unclassified sequences in the lower ranks of classification was observed at the Family level (Figure 4.5) when the 10 abundant Families out of 477 were plotted. This effort showed consistent presence of OTUs that were not classified below the Family level. When the relative percent abundances of top10 Genera were facetted and plotted according to samples (Figure 4.6), the highest percent of sequences was observed to be unclassified. This indicates the challenges that still need to be overcome when studying microbial diversity of complex environments like soil.

Interestingly, when genera that were differentially abundant across samples

(alpha=0.05) was studied (Figure 4.7), seven out of the nine identified genera were found to be significant. These included Steroidobacter, Sphingomonas, Skermanella,

Rhodoplanes, Pseudonocardia, Pedomicrobium, and DA 101. Out of the twenty-nine that were differentially abundant, more than 20 did not feature in the 10 abundant genera, and yet recorded significantly different log2fold changes in their count abundance. This suggests that microbial diversity studies of an environment as diverse as soil should also include studying the less-abundant genera. It is quite likely that even with low abundances, there are groups that significantly differ from one another and affect

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110

NTCC NTCS PTCC PTCS GRA FOR Samples

Figure 4.2 Relative percent abundance of top ten Phylum in Hoytville samples.

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111

NTCC NTCS PTCC PTCS GRA FOR Samples

Figure 4.3 Relative percent abundance of top ten Class in Hoytville samples.

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112

NTCC NTCS PTCC PTCS GRA FOR Samples

Figure 4.4 Relative percent abundance of top ten Order in Hoytville samples.

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113

NTCC NTCS PTCC PTCS GRA FOR Samples

Figure 4.5 Relative percent abundance of top ten Family in Hoytville samples

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GENUS Unclassified Bradyrhizobium DA101 Kribbella Pedomicrobium Pseudonocardia Rhodoplanes Skermanella Sphingomonas Steroidobacter Figure 4.6 Relative percent abundance of top ten Genus in individual Hoytville samples.

DA101 Devosia Opitutus Gepbacter Williamsia Lysobacter Luteibacter Candidatus Kouleothrix Anaerolinea Skermanella Unclassified Segetibacter Methylibium Kaistobacter Rhodoplanes Amycolatopsis Hymenobacter Sphingomonas Steroidobacter Catellatospora Janthinobacter Flavisolibacter Flavobacterium Pedomicrobium Pseudonocardia Dactylosporangiu, Xiphinematobacter Candidatus solibacter Candidatus koribacter

Figure 4.7 Differential abundance of Genus across Hoytville samples (alpha=0.05); Agricultural lands were compared to soils from grass and forest areas which were treated as control; log2foldChange indicates log2fold abundance changes of genera; boxes represent genera that are part of top 10 abundant classified genera.

114 microbial community ecology of the environment being studied.

4.1.2 Similarity and differences in community structure between samples

Overall similarities and differences in community structure between soil samples was visualized by calculating pairwise Bray-Curtis and unweighted UniFrac dissimilarities, and ordinating them in two dimensional nonmetric multidimensional scaling (NMDS) plots (Figures 4.8a and 4.8b). The NMDS plot ordinations were derived from both metrics, taxonomic (Bray-Curtis) and phylogenetic (unweighted UniFrac).

Predominantly, samples were grouped according to the land-use and land-management.

Both plots showed similar patterns with respect to PT management. Also, GRA and FOR were distinctly separated from agricultural soils. The NT soils while falling in the same quadrant in UniFrac, were found to be differently placed on Bray-Curtis dissimilarity measure. This measure indicates species that are shared between sites. Plot 4.8(b) therefore indicated that while being close to each other, the No-till plots did not form a clear defined group as far as shared species is concerned. The NMDS plots provided a good visual of community diversity with respect to sample variables like rotation, tillage, and management.

4.1.3 Distance measure of OTUs with respect to variables

Ordinations in Figure 5 suggest that bacterial community structure differed with respect to land-use and land-management. However, in order to statistically determine the quantitative differences in their distribution, distance measures were analyzed using

Permutation Multivariate Analysis of Variance using Distance Matrices (also called as 115

(a) GRA

PTCC

NTCS

FOR PTCS

NTCC

GRA (b)

PTCC

PTCS FOR NTCS

NTCC

NTCC=No-Till Continuous-Corn, NTCS=No-Till Corn-Soybean, PTCS=Plow-Till Continuous-Corn, PTCS=Plow-Till Corn-Soybean, G=Grass, F=Forest

Figure 4.8 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a) unweighted UniFrac and (b) pairwise Bray-Curtis distance measures of bacterial community composition of Hoytville soils.

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“adonis” in Vegan package in R). When the effect of rotation, tillage, and management was determined using Bray-Curtis measure of distance, management individually had a significant effect (P<0.05) (Table 4.3).

A combination of variables was tested, including rotation and tillage, tillage and management, and rotation and management. When tested in combination, while tillage and rotation had no significant effect on the distances of the samples, both tillage and rotation had significant effects when tested in pairs with management. Multivariate analyses of the treatment variables, along with NMDS plots in Figure 4.8 indicate two things. Firstly, overall, land management has a significant role to play in determining community pattern composition followed by land-use in terms of tillage practices employed. Secondly, crop rotations did not appear to have significant effect on the distribution of bacterial community.

Hoytville Wooster Factors tested Variables P valuea Variables P valuea Rotation 0.26 0.66 Tillage 0.06* 0.04** Management 0.02** 1.0 Rotation +Tillage Rotation 0.30 Rotation 1.0 Tillage 0.40 Tillage 1.0 Tillage +Management Tillage 0.001*** Tillage 1.0 Management 0.04* Management 1.0 Rotation +Management Rotation 0.06* Rotation 1.0 Management 0.02** Management 1.0 a Significance codes: ‘***’ =0.01, ‘**’= 0.05, ‘*’=0.1 level of significance

Table 4.3 Permutation multivariate analysis of variance using distance matrices of whole community composition in Hoytville and Wooster samples.

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4.2 Aerobic methanotrophic community composition in Hoytville soils

The original OTU table was subsetted in R to create a smaller OTU table comprising of identifiable methanotrophs, family level and down. This subsetted OTU table had 48 taxa with identifiable genuses including Methylopila, Methylosinus,

Methylosarcina, and Methylocaldum. A phylogenetic tree, representing the subsetted group (Figure 4.9) revealed a number of unclassified genera across samples that were phylogenetically similar to identifiable clades of methanotrophs. The genera

Pleomorphomonas is known for its role in nitrogen fixation but belonged to the order

Rhizobiales that also includes the family Methylocystaceae.

A MEGAN comparison of a phylogenetic heatmap of classified methanotrophs

(Figure 4.10) was created by plotting the relative abundance on a logarithmic scale to better represent composition of the bacteria in the samples. For the Methylocystaceae family, NT soils and FOR soils had higher percentages while Methylococacceae family showed higher abundances in GRA and FOR soils. PT soils had relatively lower abundance of both families. Genus Methylopila, inspite of showing up in Figure 4.9, was not visible on the graphic (Figure 4.10) due to very low numbers. The relative abundance of methanotrophic bacteria among soil samples supports the a recently conducted study by Jacinthe et al. (2013) that noted higher oxidation rates in long term no-till soils as compared to plow-till soils, with oxidation rates in no-till soils closer in range to that of forest soils.

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Methylocaldum

Unclassified

Methylosarcin 119 Pleomorphomonas

Unclassified

Methylosinus Methylosinus

Methylopila

Figure 4.9 Phylogenetic tree of methanotrophs in Hoytville soils, subsetted from original OTU table, with families Methylococcaceae and Methylocsytaceae. Sizes of the data points indicate relative abundance.

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120

Figure 4.10 MEGAN comparison view of subsetted methanotrophs in Hoytville samples. MEGAN only shows taxa that are classified. Height of the bars were plotted to depict logarithmic values for better resolution.

120

Statistically analyzing distance measures of such small dataset is not feasible and hence was not attempted for this subsetted group. However, similarity and differences in community structure was studied using NMDS plots of UniFrac and Bray-Curtis distances (Figure 4.11a & 4.11b). An observation worth noting was the distribution of the community with respect to tillage. In both the ordinations, NT samples were grouped together distinctively from the PT samples, and both the tillage practices differed from

GRA and FOR.

Although, this is a small subsetted dataset and must be treated with caution, the ordination results suggest that when it comes to a specific group of bacteria, in our case aerobic methanotrophs, community composition is likely to be closely dependent on management practices.

4.3. Whole community profile of Wooster samples

Bacterial community analysis of Wooster samples was restricted to four samples:

W-NTCC (No-Till Continuous-Corn), W-NTCS (No-Till Corn-Soybean), W-PTCC

(Plow-Till Continuous-Corn), and W-GRA (Grass). W-PTCS (Plow-till Corn-Soybean) and W-FOR (Forest) gave very low reads (a few hundred) even though the sequencing run was repeated twice. Polymerase chain reactions showed good amplification of all samples, with distinct bands (data not shown), prior to loading of the samples into the sequencer. Therefore, low number of reads obtained for these two samples could have possibly arisen from samples not binding to the flow-cell. These two samples were consequently discarded from further downstream analysis. Although this compromised

121

(a) PTCS

NTCC PTCC FOR

NTCS

GRA

(b) FOR PTCS GRA PTCC

NTCS

NTCC

NTCC=No-Till Continuous-Corn, NTCS=No-Till Corn-Soybean, PTCC=Plow-Till Continuous-Corn, PTCS=Plow-Till Corn-Soybean, G=Grass, F=Forest

Figure 4.11 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a) unweighted UniFrac and (b) pairwise Bray-Curtis distances of subsetted methanotrophic community from Hoytville soils.

122 the objectives of the study, it was still possible to discern community profile dynamics of the other samples and also focus on rare-abundant groups. The comparison of land use however was narrowed, with variables being tested restricted to management: no-till

(both rotations), plow-till, and grass.

Pre-processing of sequences resulted in 390,247 sequences from about 2.5 million combined raw reads. A total of 40103 OTUs were obtained with OTUs ranging from

8899-24616 (Table 4.1) in each sample. On average, 40% of reads in each sample remained unassigned. Or in other words, these sequences did not get mapped to any reference sequence in the database. Such a high percentage of unclassified sequences indicate the complexity of soil bacterial community and the inability to fully capture diversity in environments which have a high proportion of uncultured species.

While rarefaction was not performed for downstream analyses, results from collated multiple rarefactions for each sample at the 3% dissimilarity level, showed that only for sample W-GRA, the rarefaction curve was saturated at 30000 (data not shown).

This implied that for most samples, the sampling depth was not enough to fully capture community diversity. Deeper sampling is necessary to cover maximum taxonomic diversity of bacteria in the Wooster samples. However, Illumina’s MiSeq technology only allows 2x300 or roughly 600 bp sampling depth. This study therefore presents a case where an effort needs to be made to develop deeper sequencing strategies while keeping error rates low.

Richness and diversity estimates are graphically represented in Figure 4.1, with observed species and Chao1 representing richness and Shannon’s Hʹ representing 123 diversity. W-GRA exhibited highest levels of richness and diversity, followed by W-

NTCC and W-NTCS. The W-PTCS was low in richness and diversity. As opposed to the

Hoytville samples, which exhibited varying levels of diversity and richness metrics, the indices in Wooster samples followed a consistent trend. While it can be concluded that the samples were uniformly rich and even, the occurrence of unsaturated rarefaction curves may hint at a lack of full-coverage of species diversity, richness, and evenness.

With respect to tillage, both the NT soils had higher diversity estimates than PT soil.

Higher-organic matter content and better soil structure associated with NT has lead to these soils harboring a greater diversity of microbes. Within NT soils, the plot with the corn-soybean rotation showed higher diversity and evenness and this was attributed to greater residue diversity due to crop rotation with a leguminous plant.

4.3.1 Bacterial community composition and ecological significance

Sequences that were observed with very low frequency, i.e. OTUs with less than 4 sequences (representing less than 0.001% of the total sequences) were discarded. This resulted in 38768 OTUs out of a total of 40103original OTUs. The resulting OTUs were further normalized and analyzed. Overall, 60% of reads were classified, whereas approximately, 38-40% of reads remained unclassified across all samples. The identifiable phylotypes at different taxonomic ranks are shown in Table 4.2. Overall, 35 bacterial phyla were identified, with Acidobacteria (~25% in NTCC, NTCS, and PT-CC) and Proteobacteria (~30% in GRA) being the dominant groups.

The distribution of abundant phyla was different across samples and the 10

124 abundant phyla occupied about 95% of the total distribution in each sample (Figure 4.12).

Other abundant phyla included Actinobacteria (dominant population noted in PTCS),

Bacteroidetes, Chloroflexi, Firmicutes, Gemmatimonadetes, Nitrospirae (dominant population noted in NTCS), Planctomycetes, and Verrucomicrobia (dominant population noted in no-till soils). A distinct population of Chloroflexi was noted in grassland soil while Verrucomicrobia was observed across all samples, most notably in the NT soils.

In the next taxonomic level, 115 Classes (Figure 4.13) were identified with an average of 60% reads in no-till soils occupied by top 10 abundant groups. Among

Proteobacteria, alpha-, beta-, and gammaproteobacteria were found in all samples, with

Betaproteobacteria being the most abundant across samples. Figure 4.14 displays relative percentage abundance of top 10 Orders out of 222 identified with 60% of W3 community occupied by the top 10 orders. W-GRA had a pronounced abundance of Order RB41 and represent uncultured clone of Acidobacterium.

Across all samples, on average, 35-50% of reads were represented by Orders that were less abundant than the top 10 groups. The effect of unclassified sequences in the lower ranks of classification was noted at the Family level (Figure 4.15) when the top 10 abundant Families out of 495 were plotted. Samples NTCS, PTCC, and GRA showed distinct abundances of Family that were not classified beyond this level. When the relative percent abundance of top 10 genera were facetted and plotted (Figure 4.16), the highest percentages of OTUs that remained unclassified were at the genus level.

The stark difference in the relative abundance of groups that were identified as compared to those that remained unclassified represents the challenges and opportunities 125

126

NTCC NTCS PTCC GRA

Samples

Figure 4.12 Relative percent abundance of top ten Phylum of Wooster samples.

126

127

NTCC NTCS PTCC GRA

Samples

Figure 4.13 Relative percent abundance of top ten Class of Wooster samples.

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128

NTCC NTCS PTCC GRA

Samples

Figure 4.14 Relative percent abundance of top ten Order of Wooster samples.

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129

NTCC NTCS PTCC GRA

Samples

Figure 4.15 Relative percent abundance of top ten Family of Wooster samples.

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GENUS Unclassified Bacillus DA101 Janthinobacter Nitrospira Rhodoplanes

Figure 4.16 Relative percent abundance of top ten Genus in individual Wooster samples.

DA1010 DA1010

Nitrospira Unclassified

Rhodoplanes Janthinobacter

Figure 4.17 Differential abundance of Genus across Wooster samples (alpha=0.05); boxes represent genera that are part of top 10 abundant classified genera. Agricultural lands were compared to soils from grass which was treated as control; log2foldChange indicates log2fold abundance changes of genera; boxes represent genera that are part of top 10 abundant classified genera.

130 brought associated with studying soil microbial diversity. When differential abundance of genera across samples (alpha=0.05) was studied (Figure 4.17), four identified genera that were found to be differentially abundant were also found to be abundant across samples.

It is also worth noting that. Also, while the identity of unclassified genera was unknown, the OTU abundance counts recorded substantial log2FoldChange, thereby indicating the role that unclassified genera have on community dynamics.

4.3.2 Similarity and differences in community structure between samples

Overall similarity and difference in community structure between samples was visualized by calculating pairwise Bray-Curtis and unweighted Unifrac dissimilarities, and ordinating them in two-dimensional NMDS plots (Figures 4.18a and 4.18b). The

UniFrac plot showed NT soils grouped together while PT and GRA were two distinct groups. The Bray-Curtis measure however, placed each of the four samples in separate quadrants. While UniFrac measures phylogeny, Bray-Curtis dissimilarity measures taxonomy. It can thus be inferred that phylogenetically, the no-till plots were similar but with respect to taxonomic assignments, they differed from one another.

4.3.3 Distance measure of OTUs with respect to variables

Ordinations (Figure 18) suggest that bacterial community structure differed with respect to management in the Wooster samples. However, in order to statistically determine the quantitative differences in community distribution, with respect to land use, “adonis” measures in Vegan were performed. Unlike Hoytville soils which showed

131 pronounced effects of tillage and management, Wooster soils exhibited significant effect of tillage on community distribution (P<0.05) (Table 4.3).

(a) GRA

NTCS

NTCC

PTCC

NTCC (b)

GRA

PTCC NTCS

NTCC=No-Till Continuous-Corn, NTCS=No-Till Corn-Soybean, PTCC=Plow-Till Continuous-Corn, G=Grass Figure 4.18 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a) unweighted UniFrac and (b) pairwise Bray-Curtis distances of bacterial community from Wooster soils.

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4.4 Methanotrophic bacterial diversity in Wooster soils

Global carbon cycle is affected by metabolism of organic C1 compounds and while CH4 is a major contributor to the greenhouse effect, global emissions of other C1 compounds (methanol, methylated amines) have been recently compared to that of CH4

(JGI, 2009). From the original OTU table, it was observed that apart from identifiable methanotrophic bacteria, which included members Methylococcales, Methylocystaceae,

Crenothrix, and Methyloacidiphila, other abundant groups of methylotrophs including

Methylibium, Methylotenera, and Methylobacterium were also observed. Methylotrophs are defined as a group of microorganisms that can reduce one-carbon compounds such as methane, methanol, and methylated amines with methanotrophs being a sub-class of methylotrophs (Kalyuzhnaya et al., 2010). Additionally, the Beijerinckiaceae family

(comprises of diverse groups of Type II methanotrophs) was also identified although the sequences failed to be classified below the family level.

Since methylotrophs and members of Beijerinckiaceae family were identified in comparable numbers, it was decided to subset classified methanotrophs, methylotrophs, and Beijerinckiaceae family from the original OTU table for further analyses. The subsetted OTU included 94 taxa and was comprised of families of Methyloacidphilaceae,

Beijerinckiaceae, Methylobactericeae, Methylocystaceae, Comamonadaceae,

Methylophilaceae, and Crenotrichaceae. While members of Methylibium and

Methylobacterium have been noted to facultatively oxidize CH4 (Patel et al., 1982;

Nakatsu et al., 2006), the presence of Methylotenera in our samples indicated availability

133

of methylated amines, especially in W-GRA and W-PT soils.

A phylogenetic tree, representing Families of the subsetted group is shown

(Figure 4.19) and a MEGAN comparison of a phylogenetic heatmap of classified methylotrophs (Figure 4.20) are reported. The relative abundance was plotted on a logarithmic scale to better represent composition of the bacteria in the samples. The relative abundances of obligate methanotrophs, facultative methanotrophs and non- methane-oxidizing methylotrophs may indicate cooperative behavior being exhibited by these species and fulfilled in the niche environments of soil. Overall, no-till soilsW-NT and W-GRA showed distinctly high abundance of methylotrophs. Statistically analyzing distance measures of such small dataset is not feasible and hence was not attempted for this subsetted group. However, similarity and differences in community structure was studied using NMDS plots of Unifrac and Bray-Curtis distances (Figure 4.21). An observation worth noting was the distribution of the community with respect to tillage. In both the ordinations, No-till NT samples were grouped together distinctively , and both the tillage practices differed from GRA soil.

5. Conclusion

Here, we reported bacterial community diversity with respect to land-use and land-management practices at two locations in Ohio. The two sites were analyzed separately with the goal of identifying community diversity patterns with respect to variables like tillage, rotation. The results of this study show that bacterial communities in agricultural soils, forest, and grass areas are diverse and affected by land use and land management practices.

134

Unclassified Methylacidiphilaceae Methylocystaceae

Beijerinckiaceae Methylobacteriaceae

135 Crenotrichiaceae

Methylophilaceae

Methylophilaceae

Comamonadaceae

Figure 4.19 Phylogenetic tree of methylotrophs in Wooster soils, subsetted from original OTU table. Sizes of the data points indicate relative abundance.

135

136

Figure 4.20 MEGAN comparison view of subsetted methylotrophs in Wooster samples. MEGAN only shows taxa that are classified. Height of the bars were plotted to depict logarithmic values for better resolution.

136

NTCC (a) NTCS

PTCC

GRA

(b) GRA

NTCC

NTCS

PTCC

NTCC=No-Till Continuous-Corn, NTCS=No-Till Corn-Soybean, PTCC=Plow-Till Continuous-Corn, G=Grass

Figure 4.21 Nonmetric Multidimensional Scaling (NMDS) plots derived from (a) unweighted UniFrac and (b) pairwise Bray-Curtis distances of subsetted methylotrophic community from Wooster soils.

137

Modern agriculture practices impact soil microbial communities. While the specific impacts are largely unknown (Frey et al., 1999), there is no doubt that tilling destroys the soil structure and when practiced long-term, deteriorates soil health.

Differences in community composition in the Hoytville soils were attributed to land management, followed by tillage practices while the limited sample size in Wooster hinted at tillage practices being the governing factor. However, across both locations, when agricultural soils were studied, no-till soils showed higher diversity metrics as compared to plow-till soils. Analysis of beta diversity metrics showed distinct groups of bacterial community composition as determined by land-management.

Sequencing efforts carried out in this study suggest that while 16S rRNA gene studies are beneficial in studying bacterial community structure, the diversity present in soils, along with limitations in current sequencing techniques, may not be able to characterize the entire community composition. Although a clear pattern of dominant groups was missing specific to location or tillage, the general trend of abundant lower- level OTUs representing only about 40-50% of taxa was observed overall. The trends obtained in the study repeatedly hint at the importance of rare taxa and their combined relatively high abundance in soil microbial communities.

This study also focused on methanotrophs, a rare but ecologically important group of bacteria in upland soils. This specific group, while present in relatively low abundance, contributes significantly to the biogeochemical cycling of carbon and the important greenhouse gas, methane. Additionally, the current study is part of a broader research effort to characterize methanotrophic diversity in soils under different land use and land 138 management practices. Across both locations, methanotrophic population was relatively higher in the no-till soils. Additionally, forests soil in Hoytville also had higher percentage of methanotrophs relative to the other samples.

Given that high percentage of OTUs belonging to methanotrophic groups remained unclassified at the Family and Genus level at both sites, it is difficult to accurately estimate community composition of such groups. Quite likely, a combination of culture-dependent techniques and marker-assisted recovery of species using specific probes, present themselves as important tools that need to be employed along with high- throughput sequencing techniques. We may be able to discern the effects of land-use and land-management practices on functionality and ecological roles of microbes based on community composition of rare and abundant groups by employing multiple approaches to study community dynamics.

6. Acknowledgments

Funding for this study was provided by the USDA-NIFA, Award No. 2011-68002-30190

“Cropping Systems Coordinated Agricultural Project (CAP): Climate Change,

Mitigation, and Adaptation in Corn-based Cropping Systems”. Sequencing was done at

Molecular and Imaging Center at Ohio Agricultural Research and Development Center,

Wooster.

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CHAPTER 5: HIGH-THROUGHPUT SEQUENCING TO DETECT

DIFFERENCES IN TYPE I AND TYPE II METHANOTROPHS IN

AGRICULTURAL, GRASS, AND FOREST SOILS IN LONG-TERM

EXPERIMENTAL PLOTS IN OHIO

1. Abstract

Methanotrophic bacterial diversity and abundance was contrasted in (i) no-till, (ii) plow-till, (iii) grass, and (iv)forest soils across two long-term experimental field sites in

Ohio, using multiplex sequencing of the 16S rRNA gene that specifically targets Type I and Type II methanotrophs which represented about 2% of the microbiota. From approximately 2 million reads each for Type 1 and Type II methanotrophs, 39 unique

Type I and 213 unique Type II OTUs (operational taxonomic units) were identified.

Methanotrophic OTUs belonging to genera Methylobacter, Methylocaldum,

Methylomicrobium, Methylomonas, Methylosarcina, Crenothrix, and Methylosinus were identified in varying abundances in the soil samples. Potential methanotrophs in

Methylacididiphilaceae and Beijerinckiaceae, along with non-methanotrophic methylotrophs including Methylobacterium and Methylopila were also identified.

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Methanotrophic diversity was greater in forest soils as compared to other managements.

Within tillage, higher diversity was observed in plow-till soils for Type I methanotrophs and in no-till soils for Type II methanotrophs. Principle coordinate analyses (PCoA) indicated the majority of the methanotrophic community is grouped primarily by location, while multivariate analyses suggested that location (for both Type I and Type

II), tillage (for Type I), and rotation (Type II) were significant variables affecting community dynamics. This study suggested that methanotrophic bacterial population in soils was governed by the type of soil under consideration, with communities of one soil type similar to each other as opposed to communities of another soil type, followed by practices like rotation and tillage as variables affecting the community composition.

Keywords: Type I and Type II methanotrophic bacteria, high-throughput sequencing,

Illumina® MiSeq, multiplex sequencing, microbial ecology

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2. Introduction

Methane (CH4) is the second most important anthropogenic greenhouse gas and accounts for ~ 16% of current greenhouse gas radiative forcing (Smith et al., 2014).

While soils serve both as a source and sink for CH4, the net emission of CH4 from soils is dependent on two microbial mediated processes: production by methanogenic archaea, and consumption by methanotrophic bacteria and archaea (Aronson et al., 2013).

Although soils reportedly consume only about 7-10% of the net total annual global CH4 emissions, absence of this sink would cause the atmospheric concentration to increase at approximately 1.5 times the current rate (Duxbury, 1994).

In aerated soils, the microbially mediated process of metabolizing CH4 for carbon and energy, in the presence of oxygen, is carried out by aerobic methanotrophic bacteria to produce methanol, formaldehyde, formate and finally CO2. Methanotrophs act as the only known biological sink of atmospheric CH4 and consume 43 % to 90 % of the CH4 produced in aerobic soil (Hütsch, 2001, Le Mer and Roger, 2001, Roslev and King,

1996).

Upland soils including agricultural fields, grass, and forests have been studied for occurrence of methanotrophic bacteria, with a number of studies determining diversity and/or abundance of methanotrophs using PCR (polymerase chain reaction)-based approaches like DGGE (denaturing gradient gel electrophoresis) and cloning followed by

Sanger sequencing, PLFA (phospholipid fatty acid) synthesis, T-RFLP (terminal

149 restriction fragment length polymorphism), and microarrays (Dunfield et al., 1999;

Henckel et al., 2000; Bull et al., 2000; Bourne et al., 2001; Steinkamp et al., 2001;

Fjellbirkeland et al., 2001; Knief et al., 2005; Horz et al., 2005; Lau et al., 2007; Lima et al., 2014; Werling et al., 2014).

Methane oxidation rates in soil are affected by land-use practices. A review of

CH4 oxidation rates in upland soils worldwide suggested an increase in soil CH4 sink strength was observed in the order of arable soil < grassy soil < forest soil (Hütsch,

2001). Within arable soils, CH4 oxidation in soil is affected by nitrogen fertilizer addition, crop residues, and liming (Hütsch, 2001). Very recently, a study by Jacinthe et al. (2013) highlighted the effect of long-term tillage and no-tillage practices on CH4 oxidation rates as compared to forest soils. They found significantly higher CH4 oxidation rates in long- term no-till soils as compared to plow-till soils. Additionally, oxidation rates from no-till soils were found to be 36-37% of that at nearby deciduous forests. Methane flux in soils is also affected by the ecology of aerobic methanotrophs which in turn is governed by factors like diffusivity of CH4 from the atmosphere, soil type, soil moisture, oxygen availability, C:N (carbon: nitrogen) ratio, soil temperature, landscape, and soil pH

(Hütsch, 2001; Aronson et al., 2013).

Aerobic methanotrophs are found among relatively limited microbial taxa.

Broadly, methanotrophs are classified as Type I and Type II based on their carbon assimilation pathways and morphological traits (Hanson and Hanson, 1996). Type I methanotrophs belong to the class Gammaproteobacteria and include members of the

Methylococcaceae and Methylothermaceae family. These families include the genera 150

Methylococcus, Methylocaldum, Methylobacter, Methylomicrobium, Methylomonas,

Methylosarcina, Methylosoma, Methylovulum, Crenothrix, Clonothrix, Methylosphaera,

Methylohalobius, Methylothermus, and Methylomarinovum. Type II methanotrophs belong to the class Alphaproteobacteria and include members of Methylocystaceae and

Beijerinckiaceae families. Type II groups include the genera Methylosinus,

Methylocystis, Methylocapsa, Methylocella, and Methyloferula. Aerobic methanotrophs have also been reported from extreme environments and belong to phylum

Verrucomicrobia and placed in the family Methylacidphilaceae (Hanson and Hanson,

1996; Dedysh et al., 2005; Op den Camp et al., 2009; Vorobev et al., 2011).

Taking advantage of 16S rRNA gene phylogeny, a number of studies have identified Type I and Type II methanotrophs by the traditional Sanger sequencing technique to sequence fragments amplified by using primers custom-designed for this group of bacteria (Costello and Lidstrom, 1999a; Henckel et al., 1999; Wise et al., 1999;

Fjellbirkeland et al., 2001; Dedysh et al., 2003; Lau et al., 2007; Chen et al., 2007;

McDonald et al., 2008b; Duan et al., 2014). While high-throughput sequencing of a ubiquitous gene like 16S rRNA stands to identify non-methanotrophic groups even when using custom primers, high depth coverage of Illumina’s® MiSeq platform is able to identify diversity of this rare group of bacteria in upland soils. Recently, the bacterial 16S rRNA gene was sequenced using Illumina’s® MiSeq high throughput multiplex sequencing technique to specifically study differences in Methylococcaceae and

Methylocystaceae abundance in surface peat, forest soil, and sphagnum moss in West

Virginia (Lau et al., 2015). 151

To the best of our knowledge, studies comparing methanotrophic diversity in soils managed for agricultural crops, grass hay, and forests under long-term experimental conditions, using Illumina's sequencing-by-synthesis technique, are lacking. In this study, group-specific primers based on Chen et al.'s (2007), were used to amplify the 16S r RNA gene region of Type I and Type II methanotrophs. For Type I,modified primers

TypeIF and TypeIR amplifying a 670 bp region were used. For Type II, modified

TypeIIF and TypeIIR amplifying a 525 bp region were used. Both primer sets were modified to be compatible with Illumina® MiSeq sequencing. Six management practices, spread over two long-term experimental sites were studied for the abundance and diversity of Type I and Type II methanotrophs.

3. Material and Methods

3.1 Description of the field sites, and soil sampling and processing

The study was performed by collecting soil samples from two long-term experimental field sites in Ohio. These sites are the Triplett-Van Doren Experimental

Plots at the Ohio Agricultural Research and Development Center/The Ohio State

University (OARDC/OSU) located near Wooster and the Northwest Agricultural

Research Station of OARDC/OSU located near Hoytville, Ohio, USA. In this study, the plots will henceforth be referred to as W and H, based on their location. The tillage and rotation plots at Wooster were established in 1962 and at Northwest Agricultural

Research Station in 1963. These sites are unique because they contain the longest continuously maintained no-till research plots in the world. 152

Field maps of each experimental location, plot design, and treatments are provided in supplementary materials (Tables A1 and A2). Briefly, the plots are arranged in a randomized block design that consists of three tillage treatments, three crop rotations and three replicates at both locations. The tillage variable includes no-till (NT), plow till (PT) and chisel (minimum) till (MT). The rotation variable consists of (i) continuous corn (CC), (ii) corn and soybean in a 2-year rotation (CS), and (iii) corn, oats, and alfalfa or mixed grass meadow in a 3-year rotation (COM) (Dick and Doren, 1985).

Sufficient plots were created so that every crop in the rotation is planted every year.

Adjacent grass and forest sites accompany the agricultural plots and were considered in this study. More detailed description of the plots can be found (Dick et al., 1991).

Adjacent grass and forest sites accompany the agricultural plots and were considered in this study. The grass area consisted of hay while the forest areas had oak, ash, and maple trees. The forest sites have never been tilled while the only disturbance in the grass areas were the occasional use of farm equipment to manage adjacent agricultural plots.

Soil samples (0-10 cm depth) were obtained from both the Wooster and Hoytville sites within a week of each other in the spring of 2013. It has been reported that microbial diversity is highest during spring ( Lauber et al. 2013; Pereira e Silva et al. 2012). The specific treatments sampled were (1) no-till continuous corn, NT-CC, (2) no-till corn- soybean rotation, NT-CS, (3) plow-till continuous corn, PT-CC, (4) plow-till corn- soybean rotation PT-CS, (5) grass, G and (6) forest, F. The plots of the corn-soybean rotation had corn growing in the previous season. Three sub-samples were collected from each replicate plot as well as three samples each from the soil under grass and forest to 153 provide a total of 18 samples from each site. These subsamples were composited composite soil sample was prepared by pooling the sub-samples and thoroughly mixing.

The samples were then passed through a 2-mm sieve prior to DNA extraction.

Genomic DNA of the 18 samples from each site was extracted from approximately 0.25 gm of field-moist soil immediately after sampling by using an

UltraClean® Soil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA following the manufacturer’s instructions. The extracted DNA was quantified using

Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE). The quality of the extracted DNA was confirmed by running the extracts on 1% agarose gel with 1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0). The DNA extracted from the replicates was then pooled together to obtain 12 samples representing six treatments and two locations.

3. 2 Illumina library generation

Sample preparation was performed according to an in-house two-step PCR

(polymerase chain reaction) amplification protocol targeting a partial region of the 16S rRNA gene. Amplicon-specific primers were used with Type I sample depth coverage being ~670 bp and Type II sample depth being ~525bp.

Libraries were prepared using Illumina compatible Nextera™ Technology

(Illumina, Inc., San Diego, CA, USA). Each amplicon was generated using locus specific

PCR primers carrying Illumina compatible adapter sequences. The first round of PCR reaction was conducted using modified primers Type IF (5ʹ-TCGTCGGCAGCGTC

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AGATGTGTATAAGAGACAG ATGCTTAACACATGCAAGTCGAACG-3ʹ) and

Type IR (5ʹ-GTCTCGTGGGCTCGG AGATGTGTATAAGAGACAG

CCACTGGTGTTCCTTCMGAT-3ʹ), and Type IIF (5ʹ-TCGTCGGCAGCGTC

AGATGTGTATAAGAGACAG GGGAMGATAATGACGGTACCWGGA-3ʹ) and

Type II R (5ʹ-GTCTCGTGGGCTCGG AGATGTGTATAAGAGACAG

GTCAARAGCTGGTAAGGTTC-3ʹ). The first section of the primers includes partial

Illumina adapters while the underlined region denotes the locus specific primer. For each primer set, PCR reactions were carried out using 25 μl reaction mixtures using nuclease- free water as the negative control. Each reaction mixture contained 1X GoTaq®

Colorless Mastermix , 10 μmol forward and reverse primers, 1X BSA, and 0.6 μl of template. The PCR conditions involved an initial denaturation step at 94°C for 1 min followed by 20 cycles of denaturation at 94°C for 30 sec, annealing at 55°C (Type I) and

49°C (Type II) for 1 min, and extension at 72°C for 1 min, with a final extension step at

72°C for 5 min in a Bio-Rad C1000 Touch Thermocycler (Hercules, CA). Following separation of products from primers and primer dimers by electrophoresis on a 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)],

PCR products of the correct size were recovered using a QIAquick gel extraction kit

(Qiagen, Mississauga, Ontario, CA) and quantified using Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE).

The second PCR involved attaching complementary primers to the Illumina forward, reverse, and multiplex sequencing primers with the forward and reverse primer also containing a unique 8-bp read index allowing for multiplexing. The Nextera® Index 155

Kit PCR primers were used with an i5 index (5ʹ-

AATGATACGGCGACCACCGAGATCTACAC-i5-TCGTCGGCAGCGTC-3ʹ) and an i7 index (5ʹ-CAAGCAGAAGACGGCATACGAGAT-i7-GTCTCGTGGGCTCGG-3ʹ).

Detailed information of the primer index combinations used for each sample is provided

(Appendix A5.1 & A5.2). The PCR reaction was carried out using a β5 μl sample reaction mixture containing 1X GoTaq® Colorless Mastermix, β μmol forward and reverse primers, and 6 μl of template. The PCR conditions involved an initial denaturation step at 98°C for 1 min followed by 10 cycles of denaturation at 98°C for 30 sec, annealing at 63°C for 1 min, and extension at 72°C for 30 sec, with a final extension step at 72°C for 1 min in a Bio-Rad C1000 Touch Thermocycler. All PCR reactions were performed in triplicates.

Following the second round of PCR amplification, amplicons were run on 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)] to ensure amplification was correct. Additionally, amplicons were quantified using a

Nanodrop ND-1000 spectrophotometer. Amplicons were then purified using Performa®

V3 96-Well Short Plate (EdgeBio, Gaithersburg, MD, USA) and AMPure® XP beads

(Beckman Coulter Inc., Beverly, MA, USA) following the manufacturer’s instructions.

The quantity of the purified products of each sample was checked using Qubit double- stranded DNA high-sensitivity assay (Thermo Fisher Scientific, Grand Island,NY) using the manufacturer’s instructions and then pooled in equimolar ratios.

The Type I samples were pooled together separately from the Type II samples. The two separate pools were loaded onto 1.5% agarose Pippin PrepTM (Sage Science, Beverly, 156

MA) instrument for targeted size selection of the desired fragment, i.e. ~670 bp for Type

I and ~525 bp for Type II. The pooled samples were extracted and quantified again using

Qubit® Fluorometer. The samples were subsequently submitted to Molecular and

Cellular Imaging Center (MCIC) housed at Ohio Agricultural Research and Development

Center (OARDC) for sequencing. Sequencing was performed using the Illumina MiSeq instrument with MiSeq Reagent Kit v3 and MiSeq Control Software and Reporter v2.4.1

(Illumina, Inc., San Diego, CA). Samples were sequenced as 2x150 (for Hoytville) and

2x300 (for Wooster) paired-end reads and two 8 bp index reads. Data obtained from sequencing were processed with an in-house data analysis pipeline.

3.3 Initial quality filtering

Demultiplexed sequences were downloaded from Illumina’s Basespace® as individual forward Read 1 (R1) and reverse Read 2 (R2) files for each of the twelve samples. All steps were performed on a Linux system. FastQC was used to perform quality control checks on the raw sequence reads followed by trimming the sequences using TrimGalore. Since R1 and R2 of Type I did not have overlaps, the reads were processed separately using the following parameters: min length=250, quality score=25.

The rest of the steps outlined below were performed on the Linux terminal using Mothur v.1.33.3 (Schloss et al., 2009). For Type II, the reads were quality trimmed using Trim

Galore (min length=490, quality score=25, minimum overlap=6). The quality trimmed reads were joined using default parameters of “make.contigs” into fasta files containing sample-wise merged sequences.

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The sequences were screened to ensure stringent quality using the following parameters: Type I (minlength=250, maxlength =300, maxambig=0, maxhomop=10) and

Type II (minlength=500, maxlength =530, maxambig=0, maxhomop=10). The sequences were then combined into a single fasta file, each for Type I and Type II, using

“add_qiime_labels.py” to add metadata and incorporate labels compatible with QIIME

(Quantitative Insights Into Microbial Ecology) for further downstream analysis.

3.4 Sequence classification and OTU picking

Scripts provided in the QIIME software suits (Caporaso et al., 2010b) were used for sequence processing. Briefly, the “pick_open_reference_otus.py” operational taxonomic unit (OTU) picking protocol in QIIME was used with modification.

Taxonomy was assigned to all OTUs using default Uclust within QIIME using the

Greengenes database (DeSantis et al., 2006). Chimeric sequences were checked and filtered out using “parallel_identify_chimeric_seqs.py” and “filter_fasta.py”. The sequences were then aligned. This was followed by generation of a phylogenetic tree using “make_phylogeny.py”. The sequences that failed to align were filtered out, followed by generation of a new OTU table used for downstream analysis.

3.5 Data analysis

Multiple rarefactions were performed (“multiple_rarefy.py”) to determine alpha- diversity at different depths followed by collating the multiple rarefactions using

“collate-alpha.py”. The results were then imported into JMP® (SAS Institute Inc.

2013)and were subjected to analyses of variance using the general linear model in JMP. 158

All subsequent analyses were performed in R (R Core Team, 2014) using output files generated in QIIME.

An OTU table containing read counts for each OTU in each sample, taxonomy information for each OTU, sample metadata, representative sequences, and representative tree were exported from QIIME, and imported into R using Phyloseq package

(McMurdie and Holmes, 2013). Sequences observed with very low frequency i.e. OTUs representing less than 0.001% of the total number of sequences, were removed. Alpha- diversity indices including number of observed phylotypes, Chao1, and Shannon’s Hʹ index were studied. Variances in OTU abundance were accounted for by transforming abundances, sample-wise. After the normalization step, relative abundances of the OTUs at each taxonomic rank and in each sample were studied to determine community composition of the samples. Additionally, phylogenetic trees were constructed to determine the distribution of unclassified sequences with respect to classified methanotrophs in the OTU table.

To explore whether bacterial community composition clustered according to the management practices, the results of principal coordinate analysis (PCoA) using the

Bray-Curtis dissimilarities were plotted. These results were further evaluated with adonis

(Permutation Multivariate Analysis of Variance using Distance Matrices) using Vegan package (Oksanen et al., 2015) in R. Relationship between community composition and environmental variables (Rotation, Tillage, Management, and Location) were analyzed.

The OTU table with normalized counts was also imported into MEGAN5 (MEtaGenome

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ANalyzer v 5.0) (Huson et al., 2007) to compare abundance of representative group in the form of a phylogenetic heatmap.

4. Results and Discussion

4.1 Bacterial taxon richness and diversity

Sequencing of a ubiquitous gene like the 16S rRNA gene, and mapping the sequences to the 16S rRNA gene database included sequences that matched non- methanotrophic bacteria. The non-methanotrophic OTUs were filtered out leaving OTUs having taxonomic association with methanotrophs.

4.1.1. Type 1

Initial processing of Type I methanotrophs included analyzing about 5 million sequences of Read 1. Read 2 sequences were of low quality and were discarded. A total of 15000 OTUs were identified while 20% of the sequences failed to be assigned to any

OTU. The rarefaction curve of Type I samples started plateauing at about 25,000 sequences across samples. Within Proteobacteria, the Gammaproteobacteria class was found in 99% of reads and this is expected since the primers used for Type I were specific for Gammaproteobacteria. Roughly, 2% of Gammaproteobacterial reads consisted of methanotrophs. The Verrucomicrobial group accounted for reads that mapped to Methyloacidiphilales. A total 10980 sequences belonging to methanotrophic bacteeria were subsetted and analyzed. A total of 80 OTUs belonging to

160

Methylococcaceae and Methyloacidiphilaceae were obtained with about 18% of

methanotroph reads unclassified at the family level.

Methylococcaceae and Methyloacidiphilales Observed Shannon’s Samplesa sequences OTUs Chao1# Hʹ Hoytville H-NT-CC 443 8 8.0 1.8 H-NT-CS 203 7 7 .0(+0.46) 1.6 H-PT-CC 2117 12 13 (+2.2) 2.2 H-PT-CS 837 9 9 .0(+0.47) 1.7 H-GRA 380 6 6.0 0.9 H-FOR 2126 12 12 0.7 Wooster W-NT-CC 440 6 6.0 1.6 W-NT-CS 577 7 7.0 1.8 W-PT-CC 553 7 7.0 1.7 W-PT-CS 847 10 10 2.1 W-GRA 972 7 7.0 1.8 W-FOR 1485 8 8.0 1.9 # Chao1 provides an estimation of observed species. Confidence intervals are marked in parentheses.

a NT=No-Till; PT=Plow Till; CC=Continuous-Corn; and CS=Corn-Soybean.

Table 5.1 Total number of sequences after processing, observed OTUs, Chao1 (richness estimator) and Shannon’s Hʹ (diversity estimator) for soil samples across two locations. Values are specific to Type I methanotrophs obtained from high-throughput sequence analysis of a partial 16S rRNA gene.

Overall, for Type I, Hoytville soils had higher diversity than Wooster soils, with

highest diversity, species richness, and expected richness (Chao1) among all samples

observed in Hoytville (Table 5.1). Type I methanotrophs are classified as ‘low-affinity

high-capacity’ methanotrophs (Siljanen et al., 2012), i.e. they are active in high CH4

environments. Given that Hoytville soils are more clayey than Wooster soils, it is likely

161 that the presence of anaerobic pockets in poorly drained Hoytville soil allow methanogens to thrive. This may create micro-environments that are intermittently rich in the gas and subsequently select for methanotrophs.

Although H-FOR showed low Shannon’s diversity measure, it had the highest number of sequences assigned to methanotrophs. The W-FOR sample had both high number of sequences and high diversity. This suggests that forest soils may have higher abundances, but fewer species of methanotrophs, as compared to agricultural soils. While distinct diversity metric were not observed with respect to tillage and rotation, overall, plow-till soils had higher OTU numbers than no-till soils at both locations. It is likely that high-capacity methanotrophs were stimulated by oxygen tintroduced by the tillage (i.e. plowing) treatment.

4.1.2 Type II

Initial processing of Type II methanotrophs included inputting about 2 million sequences of contiguous reads of which 30% were not assigned any OTU while for the assigned sequences, a total of 44990 OTUs were obtained, 98% of which belonged to class Alphaproteobacteria while 2% of reads were classified as Actinobacteria.

Rarefaction curves for Hoytville samples (data not shown) appeared to be plateauing after

200 sequences, while for Wooster samples, the curves did not plateau. This implies, that while for Hoytville maximum OTUs were captured, sequencing did not encompass the entire community dynamics of Wooster samples. A total 34947 sequences belonging to

162

the methanotrophic family were subsetted and analyzed. A total of 764 OTUs belonging

to Methylocystaceae, Methylobacteriaceae and Beijerinkiaceae were obtained.

Methylocystaceae and Beijerinckiceae Observed Shannon’s Samples a sequences OTUs Chao1# Hʹ Hoytville H-NT-CC 3181 84 158(+36.5) 3.0 H-NT-CS 381 64 87.1(+13.1) 2.1 H-PT-CC 1 1 1.0(+NA) NA H-PT-CS 223 42 80.2(+25.6) 2.6 H-GRA 246 49 77.1(+15.8) 3.1 H-FOR 2750 79 93.8(+8.21) 2.2 Wooster W-NT-CC 1679 83 110(+13.3) 3.3 W-NT-CS 879 76 109(+16.9) 3.2 W-PT-CC 3440 105 134(+14.0) 2.7 W-PT-CS 1840 76 120(+21.9) 2.9 W-GRA 4915 89 128(+17.5) 2.7 W-FOR 15632 123 143(+9.60) 3.7 a NT=No-Till; PT=Plow Till; CC=Continuous-Corn; and CS=Corn-Soybean. # Chao1 provides an estimation of observed species. Confidence intervals are marked in parentheses.

Table 5.2 T Total number of sequences after processing, observed OTUs, Chao1 (richness estimator) and Shannon’s Hʹ (diversity estimator) for soil samples across two locations. Values are specific to Type II methanotrophs obtained from high-throughput sequence analysis of a partial 16S rRNA gene.

Overall, for Type II, Wooster soils had higher diversity as compared to Hoytville

soils, with forest soil in Wooster showing highest diversity, species richness, and

expected richness (Chao1) among all samples (Table 5.2). Type II methanotrophs are

classified as ‘high-affinity low-capacity’ methanotrophs active under atmospheric CH4

concentrations (Bull et al., 2000a). Wooster soils are well drained and aerated as

163 compared to Hoytville soils. Since these soils lack CH4 rich conditions as opposed to

Hoytville soils, our results fit well with the community profile expected due to differences in CH4 and oxygen availability.

H-PT-CC had 200 reads in total and only 1 read when sequences were narrowed down to only methanotrophs. Consequently, this sample was rejected for further downstream analysis. The no-till soils across both locations showed higher diversity as compared to plow-till soils. In Hoytville samples, no-till soils had higher species richness as well when compared to plow-till corn-soybean sample. However, in Wooster samples, both plow-till soils had higher species richness as compared to the no-till soils.

4.2 Phylogeny of 16S rRNA sequences related to methanotrophs

4.2.1 Type I

A total of 17 OTUs related to Methylococcaceae, 2 OTUs related to

Crenotrichaceae, 20 OTUs related to Methylacidiphilaceae, and 4 OTUs belonging to order Methylococcales but unclassified family were identified. Within the

Methylococcaceae family, 4 OTUs each were identified as Methylosarcina and

Methylomicrobium, while 3 OTUs were identified as Methylocaldum, 2 OTUs as

Methylobacter, and 1 as Methylomonas. Additionally, 2 OTUs were identified as

Crenothrix, while 6 OTUs were unclassified at this level. Figure 5.1 represents phylogenetic association and abundance of methanotrophic OTUs, with size of the sample points depicting abundance between samples and node ends depicting both

164 classified and unclassified sequences at genus level. Log-normal abundance distribution of the classified sequences among the samples is presented in Figure 5.2.

An interesting observation is the presence of Verrucomicrobial methanotrophs

(Methylacidiphilales) and more so in the Wooster soils than in the Hoytville soils. This group of methanotrophs are unique in their capacity to function at very low pH but are similar to alphaproteobacterial methanotrophs (Nazaries et al., 2013). Given that primers specific for Type I were able to detect the otherwise Type II-like Methylacidiphilales representatives of Verrucomicrobia is intriguing. Studies so far, have not been able to identify CH4 metabolism beyond the oxidation step. Since most Verrucomicrobia remain uncultivated, it is hard to understand the biochemical pathway of CH4 assimilation in these microbes (Chistoserdova et al., 2009). Our results suggest that marker assisted

165

Relative Abundance

Crenothrix

Methylobacter

Methylomonas 166 Methylocaldum

Unclassified

Methylosarcina

Figure 5.1 Phylogenetic tree of Type I methanotrophs at the Genus level. Sizes of the data points indicate relative abundance.

166

167 167

Figure 5.2 Phyogenetic heatmap of classified Type I methanotrophs. The height of the bars represent relative abundance of the members, transformed to the logarithmic scale for better representation

167 recovery of Verrucomicrobial species, targeting a region similar to both Type I and Type

II may prove to be successful for isolation and culturing of this group. It is also likely that

Verrucomicrobial methanotrophs may be an entirely new type of group, distinct in their biochemistry and phylogeny.

4.2.2 Type II

A total of 143 unique OTUs related to Methylocystaceae and 70 unique OTUs related to Beijerinckiaceae were identified. Associated methylotrophs accounting for 16 unique OTUS were identified as Methylobacteriaceae. Within Methylocystaceae family,

47 unique OTUs belonged to Methylosinus, 6 unique OTUs belonged to Methylopila

(facultative methylotroph), and 7 unique OTUs belonged to Pleomorphomonas which is a nitrogen-fixing bacterial group. Within Beijerinckiaceae, 3 OTUs were identified as belong to Beijerinckia (non-methanotroph) genus, while within Methylobacteriaceae family, 11 OTUs were identified as Methylobacterium (facultative methanotroph). A total of 116 genera were unclassified at the genus level. Figure 5.3 represents phylogenetic association and abundance of methanotrophic families, with size of the sample points depicting abundance between samples and node ends depicting genera including both classified and unclassified ones. A MEGAN view of classified sequences and their log- normal abundance is presented in Figure 5.4.

While our analysis was not able to detect the more commonly observable

Methylocystis genera as has been reported in Lau et al's study (2015), we chose to adhere to strict confidence threshold of 80% for classification of sequences in order to ensure

168 that we were not inherently biasing our study by searching for potential sequences to be classified as methanotrophs. However, the decision to include methylotrophic OTUs by subsetting Order Rhizobiales was made in order to include a broader group of non- methane methylotrophs in addition to methanotrophic ones. The reason we chose this was to acknowledge the presence of niches that may be inhabited by organisms that are dependent on each other for growth and survival. The presence of methylotrophic bacterial OTUs is indicative of the fact that methanol is presented in the niche space.

Given that the first step of CH4 assimilation is conversion of the gas into methanol, it is therefore not surprising to detect methylotrophic species from the sequences. Therefore, one can argue that while a more common group of methanotrophs was not detected, there is evidence of resource sharing among similar group of microbes (in this case methantrophs and non-methane methylotrophs) and therefore, presence of methanol- utilizing methylotrophs.

The presence of Pleomorphomonas, a genus grouped under Methylocystaceae is not surprising since alphaproteobacterial methanotrophs and nitrogen-fixing bacteria belong to the same order. Beijerinckiaceae OTUs were included in the study to account for methanotrophic bacteria that belong to this group, namely Methylocapsa,

Methylocella, Methylorosula, and Methyloferula. Since 67 OTUs in the Beijerinckiceae family were not classified at the genus level, it is a possibility that the above mentioned methanotrophic bacteria were present but not identifiable due to our choice of primer set and region chosen for amplification. Additional primer sets that amplify Beijerinkiaceae in particular can be chosen to identify methanotrophs that may be present in these soils. 169

Relative Abundance

Methylopila

Beijerinckia

Methylosinus

Unclassified

170 Methylobacter

Unclassified

Unclassified

Pleomorphomonas

Methylibium

Figure 5.3 Phylogenetic tree of Type II methanotrophs at the genus level. Sizes of the data points indicate relative abundance

170

171

Figure 5.4 Phyogenetic heatmap of classified Type II methanotrophs. The height of the bars represent relative abundance of the members, transformed to the logarithmic scale for better representation

171

4.3 Principle Coordinate Analysis of Type I and Type II communities

Principal coordinate plots depict (dis)similarity of OTUs based on their proximity.

While there are different measures of dis(similarity) one can use, the Bray-Curtis measure is used to compare distance measures based on taxonomic differences. Given that our dataset focused on a specific group of bacteria, i.e. alpha- and gammaproteobacteria, the decision to use only the Bray-Curtis measure was justified.

The PCoA plot for Type I (Figure 5.5) showed that firstly, samples were grouped according to location. Within a location, rotation was the second variable that appeared to influencing community structure. Forest and grass soil samples were distinct groups at each location. At Hoytville, the effect of rotation was more pronounced, but the communities of W-NTCC and W-NTCS almost overlapped.

The PCoA plot for Type II (Figure 5.6) showed that broadly, location was a contributor to community (dis)similarity. Forest communities at both locations were distinct from the agricultural soils at both locations, while grass soils at both locations appeared to cluster together with agricultural soils. Within Wooster soils, no-till continuous corn (NT-CC) was clustered with plow-till soils (PT) and grass (GRA) while no-till corn-soybean (NT-CS) appeared to be clustered in closer proximity to the same

NT-CC at Hoytville. For Hoytville, no-till continuous corn (NT-CC) was closely clustered with grass (GRA) and plow-till corn-soybean (PT-CS). Multivariance analysis of variance in Vegan showed location and rotation to be significant variables affecting community structure for both Type I and Type II samples

172

H-NTCC

H-PTCC W-FOR

H-PTCS W-PTCS W-NTCS

H-NTCS W-NTCC W-GRA

H-GRA W-PTCC H-FOR

Figure 5.5 Principle coordinate analysis (PCoA) plot of Type I methanotrophs using Bray-Curtis measure of (dis)similarity. Samples show groupings according to location.

H-FOR

H-NTCS

H-PTCS W-NTCS

H-NTCC

W-NTCC W-FOR H-GRA W-PTCS

W-GRA

W-PTCC

Figure 5.6 Principle coordinate analysis (PCoA) plot of Type II methanotrophs using Bray-Curtis measure of (dis)similarity. Samples show groupings according to location. H-F did not group with other samples from Hoytville. Also, sample H-NTCC was removed from the main OTU table due to low reads.

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4.4 Testing for significant variables affecting the community composition of methantorophs

Multivariate analysis of variance in Vegan showed location and tillage to be significant factors affecting community dynamics (Table 5.3). Location appeared to be significant for both Type I and Type II communities. This suggested that, more than any other variable, geographic location, which in turn governs the parent material found at any site, affected microbial community composition of methanotrophic bacteria more than land-management, crop-rotation or tillage practice.

For Type I methanotrophs, tillage was the second significant variable that appeared to affect (dis)similarity of the methanotrophic communities. An important factor to consider in this case is the presence of higher organic matter in the no-till soils as compared to plow-till soils. While this study did not focus on organic matter content in the soils, field data record of the plots, made available through the Cropping Systems

Coordinated Agricultural Project (CAP): Climate Change, Mitigation, and Adaptation in

Corn-based Cropping Systems (USDA and NIFA, 2013) of the same plots have shown higher organic matter content in the no-till soils as compared to the plow till soils.

Organic matter content will in turn determine the carbon to nitrogen ratio which in turn also affects oxygen availability since oxygen is consumed by microbes during mineralization. Also, while no-till soils are known to hold more water and are considered to have better aggregate structure, plowing breaks up the soil, thereby providing intermittent oxygen and aerobic environments. Thus, it is possible that under no-till and

174 plow-till, varying environmental conditions like organic matter content, oxygen availability, and anaerobic environments conducive for methanogenesis at deeper depths of the soil, were exerting greater effects on “low-affinity high-capacity” Type I methanotrophs.

Type I Type II Factors tested Variables P valuea Variables P valuea Rotation 0.71 0.04**

Tillage 0.40 0.50

Management 0.40 0.68

Location 0.005*** 0.05**

Rotation + Tillage Rotation 0.77 Rotation 0.06* Tillage 0.74 Tillage 0.80

Rotation + Location Rotation 0.23 Rotation 0.02** Location 0.006*** Location 0.02**

Tillage + Location Tillage 0.09* Tillage 0.35 Location Location 0.05** 0.001*** Management + Location Management 0.52 Management 0.51 Location 0.001*** Location 0.05** a Significance codes: ‘***’ =0.01, ‘**’= 0.05, ‘*’=0.1 level of significance

Table 5.3 Permutation multivariate analysis of variance using distance matrices of whole community composition of Type I and Type II methanotrophs for Wooster and Hoytville soils combined.

For Type II methanotrophs, rotation appeared to be the second significant variable that was affecting the dis(similarity) of methanotrophic communities. It is likely that the presence of crop rotation with a leguminous plant like soybean was affecting the community composition of Type II methanotrophs. Previous studies have shown that C/N

175 ratio of soil organic matter can significantly affect methanotrophic community structures

(Lin et al., 2009; Pawlowska, 2014). The presence of biologically fixed nitrogen in corn- soybean soils and absence in continuous-corn soils was potentially impacting thmethanotrophy of the “high-affinity low-capacity” Type II methanotrophs that are adapted to survive in low methane concentrations.

It is however unclear as to why tillage was seemingly impacting Type I and not

Type II methanotrophs while rotation was potentially having an impact on Type II and not on Type I methanotrophs. With the current knowledge of methanotrophic ecology in soils, it may be hard to accurately identify the mechanisms that govern dominance of one group of bacteria over another or ways in which soil environmental variables affect one group but not another. However, this study was able to identify a few determinants that were plausibly influencing community composition.

5. Conclusion

In this study, high-throughput sequencing analysis of partial 16S rRNA gene region of Type I and Type II methanotrophic communities was carried out to identify methanotrophs in soil and quantify their relative abundances, alongwith representation of community (dis)similarity. Multiple variables like rotation, tillage, location, and management were also tested to determine factors governing methanotrophic community composition.

We found that for Type I, Hoytville soils were more diverse than Wooster soils, while in Type II, Wooster soils showed higher diversity than Hoytville soils. A

176 combination of variables and methanotroph ecology seems to be governing the abundance of this group of bacteria. Additionally, it was also found that forest soils had the maximum number of sequences mapping to known methanotrophic OTUs, thereby suggesting forest soils are rich in methanotrophic bacteria as compared to soils under other land-management practices.

Species richness and diversity indices did not follow the same trend for most samples. It seems that while communities may be diverse, they may not necessarily be rich, i.e. having higher numbers of more even taxa but less number of diverse species.

Plow-till soils had a higher diversity metric than no-till soils for Type I methanotrophs, quite likely due to their high-oxygen requirements. The reverse was true for Type II methanotrophs and possibly can be attributed to low-oxygen requirements by these microbes on account of being able to function in low CH4 environments. This suggests that both CH4 and oxygen availability determine community composition dynamics of methanotrophs.

While total number of methanotroph OTUs in Type I were less as compared to

Type II, higher reads were mapped to methanotroph specific genera in Type I. The low number of OTUs in Type I can be attributed to the fact that only Read 1 was used for analyses and that amplicon overlap must be ensured before conducting paired-end high- throughput sequencing. Type II had higher reads mapped to methylotrophic and facultatively methanotrophic bacteria. This suggests niche-specific functionalities being fulfilled by related group of microbes. It is likely that a community of methanotrophic, facultatively methanotrophic, and non-methanotrophic methylotrophs co-exist in which 177

Type I methanotrophs are involved in oxidation of CH4, while Type II related methanotrophic families utilize products of secondary metabolism (methanol) to grow and survive. This suggests synergistic relationships between members of the community, thereby contributing to overall methane utilization by the microbes.

Verrcumicrobial methanotrophs, while being similar to Type II methanotroph, were identified in Type I sequence analysis. It is possible that in spite of choosing primers specific to Type I, our region of amplification accounted for Verrucumicrobial species being identified from the dataset.

Even though there were differences in the diversity metrics of Type I and Type II communities across locations, multivariate analyses revealed location (soil properties) to be the common significant factor affecting community composition. For Type I, tillage was found to be the second most significant variable while for Type II, crop rotation was the second most significant variable.

The presence of non-methanotroph OTUs in our sequence analysis indicates two ecologically relevant observations: (i) presence of methylotrophic OTUs and nitrogen fixing bacteria in the niche space and (ii) the need to develop and use primers that are even more specific as opposed to those targeting partial 16S rRNA gene sequences. Our studies reveal that primers identified in the literature may be beneficial when conducting sequencing studies using techniques like DGGE and cloning followed by Sanger sequencing. However, fine-scale resolution of methanotrophic communities using high- throughput sequencing calls for the development and testing of specific primers, or even perhaps targeting functional genes, of methanotrophs to identify community level 178 differences in this rare group of soil bacteria. Additionally, it is important to create databases specific to the community of bacteria being studied if one wants to study these rare but biogeochemically important groups of bacteria.

This study, by profiling both Type I and Type II methanotrophs, outlines the community structure, as shaped by different management practices and over two geographical locations. It provides insights into the roles played by parent material and land-management practices in making soils serve as CH4 sinks.

6. Acknowledgments

Funding for this study was provided by the USDA-NIFA, Award No. 2011-68002-30190

“Cropping Systems Coordinated Agricultural Project (CAP): Climate Change,

Mitigation, and Adaptation in Corn-based Cropping Systems”. Sequencing was done at

Molecular and Imaging Center at Ohio Agricultural Research and Development Center,

Wooster.

179

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Vorobev, A. V, M. Baani, N. V Doronina, A.L. Brady, W. Liesack, P.F. Dunfield, and S.N. Dedysh. 2011. Methyloferula stellata gen. nov., sp. nov., an acidophilic, obligately methanotrophic bacterium that possesses only a soluble methane monooxygenase. Int. J. Syst. Evol. Microbiol. 61(10): 2456–2463.Available at http://www.ncbi.nlm.nih.gov/pubmed/21097638 (verified 27 March 2015).

Werling, B.P., T.L. Dickson, R. Isaacs, H. Gaines, C. Gratton, K.L. Gross, H. Liere, C.M. Malmstrom, T.D. Meehan, L. Ruan, B.A. Robertson, G.P. Robertson, T.M. Schmidt, A.C. Schrotenboer, T.K. Teal, J.K. Wilson, and D.A. Landis. 2014. Perennial grasslands enhance biodiversity and multiple ecosystem services in bioenergy landscapes. Proc. Natl. Acad. Sci. U. S. A. 111(4): 1652–1657. Available at http://www.pnas.org/content/111/4/1652.full (verified 10 December 2014).

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CHAPTER 6: IMPACT OF LOCATION ON METHANOTROPHIC

BACTERIAL DIVERSITY IN SOILS UNDER VARYING LAND-USE AND

LAND-MANAGEMENT PRACTICES AS DETERMINED BY SEQUENCING-

BY-SYNTHESIS OF PMOA GENE

1. Abstract

Aerobic soils, owing to presence of methanotrophic bacteria, serve as the only known biological sink of atmospheric methane. Methanotrophs thus play an important role in biogeochemical cycling of methane gas. Additionally, methane oxidation in soil is directly influenced by land-use practices. Consequently, diversity studies of these bacteria in upland soils hold promising clues in determining the effect of land-use and land-management on greenhouse gas emissions or removal. In this study, high- throughput sequencing of the pmoA gene was used to determine methanotrophic bacterial diversity in soils under varying land-use and land-management practices. Two locations were selected in Ohio, each comprising of long-term no-tillage plots, plow-tillage plots, grassland, and forest. The agricultural plots also included continuous-corn and corn- soybean crop rotations. Referencing a curated database of pmoA gene, we were able to identify eight genera of methanotrophs with varying relative abundances in the samples.

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Additionally, four genera were differentially abundant across samples (alpha=0.005) and included Methylocystis, Methylocococcus, Methylosoma, and USCa. About 30% of processed sequences were unclassified. Additionally, non metric multidimensional scaling (NMDS) of unweighted UniFrac and Bray-Curtis metrics showed clear community distinction based on sites. Among the variables analyzed statistically

(including rotation, location, tillage, and managment), location had a significant effect

(P<0.005) on community composition. Also, community composition of samples from agricultural fields appeared to cluster closer to each other as compared to untouched forests and grass areas. Our study was the first of its nature to employ high-throughput paired-end sequencing of a functional gene to understand microbial community dynamics. The findings of this study suggest that, in addition to using the more common approach of sequencing 16S rRNA gene, functional gene sequencing on advanced sequencing platforms has the potential to not only identify microbial community but also emphasize the functionality of the community. This study also highlights the need to develop reference databases of functional genes in order to allow for robust analyses of microbial community composition with respect to their functionalities.

Keywords methanotrophs, tillage, no-tillage, greenhouse gas, high-throughput sequencing, pmoA, bacterial diversity

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2. Introduction

Methane (CH4) is a potent greenhouse gas found at lower concentrations in the atmosphere than carbon dioxide (CO2) but, on a similar mass basis, has a global warming potential 28 times greater than CO2 (Smith et al., 2014). While there are many biological sources of atmospheric CH4 emission, oxidation of CH4 in aerobic soils serve as the only known biological sink for atmospheric CH4 (Nazaries et al., 2013).

Aerobic CH4 oxidation in soil is brought about by methane oxidizing bacteria

(MOB) or methanotrophs. Methanotrophs have a unique ability to use CH4 as their sole carbon and energy source and are thus able to consume 43 % to 90 % of the CH4 produced in aerobic soil (Roslev and King, 1996; Hütsch, 2001; Mer and Roger, 2010).

Soils reportedly consume roughly 7-10% of the net total annual global CH4 emissions, and absence of this sink would cause the atmospheric concentration to increase at approximately 1.5 times the current rate (Duxbury, 1994).

Although, CH4 oxidation in soil is relatively a small sink, it is directly influenced by agriculture, forestry and other land use (AFOLU) practices (Smith et al., 2014).

Various factors, like soil bulk density, water content, temperature, pH (Hanson and

Hanson, 1996), and ammonium from fertilizers (Bodelier et al., 2000; Alam and Jia,

2012) affect CH4 oxidation rates in soil. Studies have documented varying CH4 rates in upland soils with maximum rates being reported in forest soils (Suwanwaree and

Robertson, 2005) and long-term no-till agricultural plots (Jacinthe et al., 2013). It is likely that factors which affect physical and chemical properties of soils, influence the microbial community and functionality. Consequently, results of methanotrophic 188 bacterial diversity in soil, as influenced by land use changes, can be considered when formulating mitigation strategies to reduce anthropogenic CH4 emissions from upland soils.

Methanotrophic bacteria are classified into two broad taxonomic groups, Type I and Type II, based on metabolic pathways, cell morphology, phylogeny, and ultrastructure (McDonald et al., 2008b). Type I methanotrophs belong to the subdivision of Proteobacteria and include genera Methylococcus, Methylobacter,

Methylomicrobium, Methylomonas, Methylocaldum, Methylosphaera, Methylothermus,

Methylosarcina, Methylohalobius, Methylosoma, Methylomarinovum, Crenothrix, and

Clonothrix (Hanson et al., 1996; Stoecker et al., 2006; Vigliotta et al., 2007). Type II methanotrophs belong to the α subdivision of Proteobacteria and include genera

Methylocystis, Methylosinus, Methylocella, Methylocapsa, Methylorosula, and

Methyloferula (Hanson and Hanson, 1996; McDonald et al., 2008b). A few members of methanotrophic bacteria have also been reported from extreme environments and belong to the phylum Verrucomicrobia (Pol et al., 2007; Lau et al., 2013).

The first step in CH4 oxidation by proteobacterial methanotrophs is the conversion of CH4 to methanol by the enzyme methane monooxygenase (MMO). The enzyme has two forms: a membrane bound particulate form (pMMO) and a cytosolic soluble form

(sMMO). The pMMO is found in all methanotrophs except Methylocella and

Methyloferula while sMMO is present only in certain methanotroph strains. With this limitation, studies have focused more on strains containing pMMO.

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Identification of methanotrophic bacteria in soils is often performed by the cultivation-independent detection of a fragment of pmoA, a gene encoding the active-site subunit of pMMO (Dumont et al. 2014; McDonald et al. 2008; Strapoc et al. 2008; Chen et al. 2007; Sc et al. 2004; Lin et al. 2005; Erwin et al. 2005; Knief et al. 2005; Bourne et al. 2001; Costello & Lidstrom 1999; Henckel et al. 1999). The advantage of studying sequences of a protein-encoding functional gene, pmoA, is that the gene is unique to the physiology and metabolism of methanotrophs as opposed to studies targeting non-protein coding genes like 16S rRNA which have the possibility of the sequences being identified as closely related to methanotrophs but not true methanotrophs (Lau et al., 2013).

Sequence-based pmoA phylogeny correlates well with 16S rRNA-based phylogeny. Therefore, pmoA sequences can be assigned to specific genera or even species of methanotrophic bacteria. The only exception to the close correspondence of 16S rRNA and pmoA phylogeny is found in the gammaproteobacterium Crenothrix polyspora

(Stoecker et al., 2006). Additionally, the pmoA gene is highly conserved. The pmoA gene has been widely used to characterize methanotrophic communities in upland soils that consume atmospheric methane (Holmes et al., 1999; Henckel et al., 2000a; Jensen et al.,

2000; Bourne et al., 2001; Steinkamp et al., 2001; Fjellbirkeland et al., 2001; Reay et al.,

2001). In some of these soils, a novel sequence cluster usually called the “forest sequence cluster” (here USCα) has been detected (Holmes et al., 1999; Henckel et al., 2000a;

Jensen et al., 2000; Bourne et al., 2001). Besides this novel sequence cluster, pmoA sequences related to the genera Methylocystis, Methylosinus, Methylomonas,

Methylobacter, Methylomicrobium, Methylococcus, and Methylocaldum have also been 190 detected (Holmes et al., 1999; Jensen et al., 2000; Bourne et al., 2001; Steinkamp et al.,

2001; Reay et al., 2001)

Most studies have documented methanotrophic activity by measuring CH4 oxidation rates and/or methanotrophic diversity in various upland soils, wetlands, and soil sediments in aquatic bodies (Amaral and Knowles, 1997; Hütsch, 2001; Suwanwaree and

Robertson, 2005; Mer and Roger, 2010; Bayer et al., 2012; Jacinthe et al., 2013).

Methanotrophic bacterial diversity has been studied using techniques like denaturing gradient gel electrophoresis (DGGE) and cloning (Henckel et al., 1999; Steinkamp et al.,

2001; Fjellbirkeland et al., 2001; Horz et al., 2002; Lin et al., 2005; Bodelier et al., 2005;

Knief et al., 2005; Lau et al., 2007; Chen et al., 2007; Redmond et al., 2010), stable- isotope probing (SIP) (Morris et al., 2002; Sundh et al., 2005; Kolb et al., 2005; Cébron et al., 2007; Redmond et al., 2010; Roy Chowdhury et al., 2014), terminal restriction fragment length polymorphism (T-RFLP) (Costello and Lidstrom, 1999b; Horz et al.,

2002; Ricke et al., 2005a; Deutzmann et al., 2011), microarrays (Bodrossy et al., 2003;

Stralis-Pavese et al., 2011; Siljanen et al., 2012), and fluorescent in-situ hybridization experiments (Eller et al., 2001; Dedysh et al., 2001, 2003; Kalyuzhnaya et al., 2006). The types of environments for which methanotrophic diversity has been studied include rice field soil (Henckel et al., 2000a), freshwater lake sediment (Lin et al., 2005; Deutzmann et al., 2011), acidic peatlands (Dedysh et al., 2003), forest soil (Bourne et al., 2001;

Knief et al., 2005), and agricultural fields (Knief et al., 2005).

It is becoming increasingly common to characterize methanotrophic communities

(i.e. abundance and diversity) using high-throughput sequencing. Pyrosequencing of 191 pmoA gene has been used to identify methanotroph species present in soils (Levine et al.,

2011; Saidi-Mehrabad et al., 2013; Dumont et al., 2014; Lima et al., 2014; Sengupta and

Dick, 2015). However, employing advanced sequencing tools to study the pmoA gene to determine methanotrophic activity as affected by land use is limited. Additionally, to our knowledge, no studies have used Illumina’s sequencing-by-synthesis technique of pmoA gene to determine methanotrophic bacterial population in soils under various land use practices. In this study, Illumina’s MiSeq high-throughput multiplex sequencing of pmoA gene was performed to determine methanotrophic bacterial diversity in soils from agricultural plots, grasslands and forests.

A challenge when conducting high-throughput sequence analyses of functional genes (in this case, pmoA) is the lack of appropriate and in-depth reference databases that are compatible with current sequence analyses software. In this study, we heavily relied on Dumont et al.'s (2014) reference database to assign, cluster and classify the sequences.

Briefly, this reference dataset consists of sequences corresponding to 53 low-level taxa.

The enzyme pMMO belongs to the class of copper-containing membrane-bound monooxygenase enzymes. This class of enzymes, in addition to pMMO, also contains bacterial ammonia monooxygenase, thaumarchealmonooxygenase, alkane monooxygenases, and various uncharacterized enzymes. Consequently, attempts to sequences methanotrophs using common pmoA primers often co-amplifies sequences of the above mentioned related enzyme family, such as the bacterial amoA. The curated database included 6628 reference sequences from multiple environments like upland soil, landfill soil, rice field soil, organic soil, freshwater lakes, geothermal soil, freshwater soil, 192 and marine sediment, to name a few, and that of cultivated methanotrophs. At the class level, the sequences were categorized as MOB (methane oxidizing bacteria)-like or AOB

(ammonia oxidizing bacteria)-like while at the order level, they were classified according to the broad groups of Type I and Type II. Taxa comprising cultivated methanotrophs were referred to as the respective genera or species (e.g., Methylobacter) while taxa representing isolates were names according to representative clones or to the environment in which they were predominantly found or first found (e.g., Aquifer_cluster or upland soil cluster-USC).

The MOB_like sequences were assigned to either Type I, Type II or pXMO_like.

The Type I sequences were further divided into Type Ia, Ib,or Ic. TypeIa are pmoA sequences affiliated to the classic Type I methanotrophs (i.e., not Type X). Type Ib (also referred to elsewhere as Type X) are those of Methylococcus and closely related genera.

Type Ic are all other Type I-related sequences with a more ambiguous affiliation. Type II sequences were divided into Type IIa and IIb. Type IIa was used for the primary pmoA sequences of Methylocystaceae. Type IIb was used to group all other Type II- related (i.e.

Alphaproteobacteria) sequences, including those from the Beijerinckiaceae (Theisen and

Murrell, 2005; Dunfield et al., 2007; Vorobev et al., 2011) and the alternate pMMO2 identified in some Methylocystis species (Dunfield et al., 2007; Baani and Liesack,

2008). It is important to have an understanding of the reference database since the taxonomy assignment further downstream is different from the common rank specific designation that is observed in microbial diversity studies.

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3. Material and Methods

3.1 Description of the field sites, and soil sampling and processing

This study was performed by collecting soil samples from two long-term experimental field sites in Ohio. These two field sites are called the Triplett-Van Doren

Experimental Plots and were established in 1962 near Wooster, OH and in 1963 at the

Northwest Agricultural Research Station located near Hoytville, OH. These field sitesrepresent the longest continuously maintained no-till research plots in the world. In this study, the plots will henceforth be referred to as W (for Wooster) and H (for

Hoytville), based on their location. Field maps of each experimental site, plot design, and treatments are provided in supplementary materials (Tables A1 and A2).

Briefly, the plots are arranged in a randomized block design consisting of three treatments, three rotations and three replicates (i.e. blocks) at both locations. The treatment variables include no-till (NT), plow till (PT) and chisel (minimum) till (MT).

The rotation variable consists of (i) continuous corn (CC), (ii) corn and soybean in a 2- year rotation (CS), and (iii) corn, oats, and alfalfa or mixed grass meadow in a 3-year rotation (COM) (Dick and Doren, 1985). Sufficient plots were established so that every crop for each rotation appears every year. Adjacent grassland and forest sites accompany the agricultural plots and were considered in this study. The grass area consisted of hay while the forest areas had oak, ash, and maple trees. The forest sites have never been tilled while the only disturbance in the grass areas were the occasional use of farm equipment to manage adjacent agricultural plots.

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The following treatments were studied: (1) no-till continuous corn (NTCC), (2) no-till corn-soybean (NTCS), (3) plow-till continuous corn (PTCC), (4) plow-till corn- soybean (PTCS), (5) grass (GRA), and (6) forest (FOR). Samples were collected at both sites within a week of each other in spring 2013, before the planting season. It has been reported that microbial diversity is highest during spring ( Lauber et al. 2013; Pereira e

Silva et al. 2012). The plots with the corn-soybean rotation had corn growing in the previous season. Three sub-samples (0-10 cm) were collected from each replicated treatment. A composite soil sample was prepared for each replicate plot by pooling in the sub-samples and then passing the soil through a 2-mm sieve prior to DNA extraction.

This created a total of 18 samples from each location or 36 total samples.

Total genomic DNA of 36 samples was extracted from approximately 0.25 gm of field-moist soil immediately after sampling by using UltraClean® Soil DNA Isolation Kit

(MO BIO Laboratories, Inc., Carlsbad, CA) following the manufacturer’s instructions.

The extracted DNA was quantified using Nanodrop ND-1000 spectrophotometer

(Nanodrop Technologies, Wilmington, DE). The quality of the extracted DNA was confirmed by running the extracts on 1% agarose gel with 1x TAE buffer (40 mM Tris,

20 mM acetic acid, 1 mM EDTA, pH 8.0). The DNA extracted from the replicates were pooled together to obtain 12 samples representing six treatments and two locations.

3. 2 Illumina library generation

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Sample preparation was performed according to an in-house two-step PCR

(polymerase chain reaction) amplification protocol targeting partial the 470 bp region of the pmoA gene. Libraries were prepared using Illumina compatible Nextera™

Technology (Illumina, Inc., San Diego, CA, USA). Each amplicon was generated using locus-specific PCR primers carrying Illumina compatible adapter sequences.

The first round of PCR reaction was conducted using modified primers A189F

(5ʹ-TCGTCGGCAGCGTC AGATGTGTATAAGAGACAG-

GGNGACTGGGACTTCTGG-3ʹ) and 650R (5ʹ-GTCTCGTGGGCTCGG

AGATGTGTATAAGAGACAG-ACGTCCTTACCGAAGGT-3ʹ). The locus specific primers A189F and 650R were chosen after in-depth literature survey and preliminary analysis of multiple pmoA specific primer sets ( Dumont et al. 2014; Lau et al. 2013;

McDonald et al. 2008; Lin et al. 2005; Bussmann et al. 2004; Bodrossy et al. 2003). The primers were custom designed (Sigma Aldrich, St. Louis, MO) to have part of Illumina adapters as outlined in Lundberg et al. (2013). The first section of the primers included partial Illumina adapters while the underlined region denoted locus specific primer. The

PCR reaction was carried out using a β5 μl reaction mixture for 14 samples which included soil genomic DNA from 12 samples, genomic DNA of Methylococcus capsulatus (ATCC 33009) as a positive control and nuclease-free water as negative control. Each reaction mixture contained 1X GoTaq® Colorless Mastermix , 10 μmol forward and reverse primers, 1X BSA, and 0.6 μl of template. The PCR conditions involved an initial denaturation step at 94°C for 1 min followed by 20 cycles of denaturation at 94°C for 30 sec, annealing at 50°C for 1 min, and extension at 72°C for 1 196 min, with a final extension step at 72°C for 5 min in a Bio-Rad C1000 Touch

Thermocycler (Hercules, CA). Following separation of products from primers and primer dimers by electrophoresis on a 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)], PCR products of the correct size were recovered using a QIAquick gel extraction kit (Qiagen, Mississauga, Ontario, CA) and quantified using Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington,

DE).

The second PCR involved attaching complementary primers to Illumina forward, reverse, and multiplex sequencing primers with the forward and reverse primer also containing unique 8-bp read indices allowing for multiplexing. The Nextera® Index Kit

PCR primers were used with an i5 index (5ʹ-

AATGATACGGCGACCACCGAGATCTACAC-i5-TCGTCGGCAGCGTC-3ʹ) and an i7 index (5ʹ-CAAGCAGAAGACGGCATACGAGAT-i7-GTCTCGTGGGCTCGG-3ʹ).

Appendix A6.1 and A6.2 provide detailed information of the primer index combinations used for each sample. The PCR reaction was carried out using β5 μl sample reaction mixture containing 1X GoTaq® Colorless Mastermix, β μmol forward and reverse primers, and 6 μl of template. The PCR conditions involved an initial denaturation step at

98°C for 1 min followed by 10 cycles of denaturation at 98°C for 30 sec, annealing at

63°C for 1 min, and extension at 72°C for 30 sec, with a final extension step at 72°C for 1 min in a Bio-Rad C1000 Touch Thermocycler. All PCR reactions were performed in triplicates.

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Following the second round of PCR amplification, amplicons were run on 1% agarose gel [1x TAE buffer (40 mM Tris, 20 mM acetic acid, 1 mM EDTA, pH 8.0)] to ensure amplification was correct. Additionally, amplicons were quantified using a

Nanodrop ND-1000 spectrophotometer, followed by pooling together of replicate amplicons. Amplicons were then purified using Performa® V3 96-Well Short Plate

(EdgeBio, Gaithersburg, MD, USA) and AMPure® XP beads (Beckman Coulter Inc.,

Beverly, MA, USA) following the manufacturer’s instructions. The quantity of the purified products of each sample was checked using Qubit double-stranded DNA high- sensitivity assay (Thermo Fisher Scientific, Grand Island,NY) according to the manufacturer’s instructions and then pooled in equimolar ratios. The pooled product was loaded onto a 1.5% agarose Pippin PrepTM Instrument (Sage Science, Beverly, MA) for targeted size selection of the pooled fragment. The pooled sample was extracted and quantified again using Qubit® Fluorometer.

The sample was then submitted to the Molecular and Cellular Imaging Center

(MCIC) housed at Ohio Agricultural Research and Development Center (OARDC) for sequencing. Sequencing was performed using the Illumina MiSeq instrument with MiSeq

Reagent Kit v3 and MiSeq Control Software and Reporter v2.4.1 (Illumina, Inc., San

Diego, CA). Samples were sequenced as 2x300 paired-end reads and two 8 bp index reads. The run was spiked by adding sheared genomic DNA from virus phiX174 to account for lack of diversity at specific positions that may exist in the amplicon library owing to sequence conservation. Data obtained from sequencing were processed with an in-house data analysis pipeline. 198

3.3 Initial quality filtering

Demultiplexed sequences were downloaded from Illumina’s Basespace® as individual forward read 1 (R1) and reverse read 2 (R2) files for each of the twelve samples plus one positive control. All steps were performed on a Linux system. FastQC was used to perform quality control checks on the raw sequence reads followed by trimming the sequences using TrimGalore, with the following parameters: min length=200, quality score=25, minimum required adapter overlap=6. The rest of the steps outlined below were performed on the Linux terminal using Mothur v.1.33.3 (Schloss et al., 2009). The quality trimmed reads were joined using default parameters of

“make.contigs” into a single fasta file containing sample wise merged sequences. The sequences were screened to ensure stringent quality using the following parameters: minlength=400, maxlength =510, maxambig=0, maxhomop=10. Sequences were reduced to only unique sequences using “unique.seqs” and aligned using “align.seqs” to 518 randomely chosen pmoA representative sequences provided by Dumont et al. (2014).

Aligned sequences were reduced to only the overlapping region using “screen.seq”

(start=305, optimize=920, minlength=490) and “filter.seqs” ( trump=.). The remaining sequences were sorted according to their abundance using “split.abund” (cutoff=10) to create a reference of abundance sequences to be used in the next step. The

“chimera.uchime” command was used to account for chimeric sequences

(reference=abund.fasta) with the abundant sequences being used as a reference. This allowed for parallelization of the step. Chimeric sequences were removed using

“remove.seqs”. Next, “deunique.seqs” was used to add the replicate sequences. The 199 sequences were then split sample wise to obtain individual sample fasta files for further downstream analysis.

3.4 Sequence classification and OTU picking

Scripts provided in the Quantitative Insights Into Microbial Ecology (QIIME) software suits (Caporaso et al., 2010b) were used for sequence processing. Briefly, the de novo operational taxonomic unit (OTU) picking protocol in QIIME was used with modification. The run was parallelized by adding a reference database of 6628 reference sequences from Dumont et al.’s (β014) study. Sequences were clustered against the database at 97% sequence similarity with UCLUST. Taxonomy was assigned to all OTUs using the RDP classifier within QIIME using the reference dataset.

3.5 Data analysis

Alpha-diversity indices were studied following QIIME’s “core_diversity_py” script. Multiple rarefactions were performed (“multiple_rarefy.py”) to determine alpha- diversity at different depths followed by collating the multiple rarefactions using ‘collate- alpha.py’. The results were then imported into JMP® (SAS Institute Inc. 2013)and diversity metrics including number of observed phylotypes, Chao1, and Shannon’s Hʹ index were subjected to analyses of variance using the general linear model in JMP. All subsequent analyses were performed in R (R Core Team, 2014) using output files generated in QIIME at the Sequence classification and OTU picking step.

OTU table containing read counts for each OTU in each sample, taxonomy information for each OTU, sample metadata, representative sequences, and representative 200 tree were exported from QIIME, and imported into R using “Phyloseq” (McMurdie and

Holmes, 2013). Sequences observed with very low frequency i.e. OTUs representing less than 0.001% of the total number of sequences, were removed. Variances in OTU abundance was accounted for by transforming abundances, sample-wise. After the normalization step, relative abundances of the OTUs at each taxonomic rank and in each sample were studied to determine community composition of the samples.

To explore whether bacterial community composition clustered according to land use, the results of non-metric multidimensional scaling (NMDS) using the Bray-Curtis and Unweighted UniFrac dissimilarities were plotted. These results were further evaluated with adonis (permutation multivariate analysis of variance using distance matrices) using “Vegan” package (Oksanen et al., 2015) in R. Relationship between community composition and environmental variables (rotation, location, tillage, and management) were analyzed. OTU tables were subsetted based on abundant rank orders and formatted for the “DESeqβ” package in R (Love et al., 2014). Differential abundance of OTUs by sample type was determined using DESeq2.

4. Results and Discussion

4.1 Composition of methanotrophic bacterial communities

Soil microbial DNA (deoxyribonucleic acid) recovered from the samples (Table

6.1) were higher in the agricultural plots as compared to the grass and forest sites at both locations. The amount of DNA recovered from the forest and grass soils at both locations was greater than from the plow-till, but slightly less than from the no-till plots. A 201 dramatic contrast was observed for DNA recovered from plow-till soils. At both the locations, the values observed were low in comparison to no-till, grass, and forest soils.

4.2 Richness and diversity estimates

Pre-processing of sequences resulted in about 972,115 sequences from about 2.5 million combined reads. A total of 1209 OTUs were obtained with OTUs ranging from

884 to 980 in the samples (Table 6.1). While rarefaction was not performed for further

DNA Sequences after Observed Shannon’s Samplea ug/g OTU picking OTUs Chao1 Hʹ Hoytville NTCC 11.3 87093 955 976.7 (+9.6) 5.6 NTCS 10.3 92442 950 974.8 (+11.0) 5.4 PTCC 5.92 67679 930 949.2 (+10.9) 5.7 PTCS 4.12 34658 906 935.3 (+12.0) 5.5 GRA 6.62 48880 884 919.4 (+14.8) 5.2 FOR 8.82 96553 780 832.6 (+21.3) 5.1 Wooster NTCC 11.5 104432 959 971.4 (+8.0) 5.5 NTCS 11.0 73216 967 1002 (+15.2) 5.4 PTCC 5.24 104723 980 1001 (+12.2) 5.6 PTCS 5.68 56562 971 978.0 (+3.7) 5.5 GRA 8.48 66849 974 1003 (+13.6) 5.4 FOR 8.52 139028 975 992.0 (+7.7) 4.9 a CC=Continuous Corn; CS=Corn-Soybean; NT=No-Till; and PT=Plow Till. b Standard errors are in parentheses.

Table 6.1 Total DNA concentration, number of sequences after processing, observed OTUs, and diversity (Chao1) and evenness (Shannon’s H’) estimators for 1β soil samples across two locations.

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downstream analyses, results from collated multiple rarefactions for each sample, at the 3% dissimilarity level, showed that for each sample, rarefaction curves were saturated at about 35000 sequences (data not shown). This implied that more intensive sampling depth was less likely to yield additional species in all the samples and that the sequencing effort covered all the taxonomic diversity of the pmoA gene in the soils. The original library size of the analyses was preserved by not performing rarefactions, as has been recommended in recent studies (McMurdie and Holmes, 2014; Debenport et al., 2015).

Additionally, since this study was focused on a narrow group of bacteria, which make up rare abundant groups in upland soils (Sengupta and Dick, 2015), the decision to not rarefy was made in order to avoid discarding valid sequences at random. Corresponding sequences assigned to OTUs ranged from 34,658 in sample H-PTCS to a maximum of

139,028 sequences in sample W-FOR.

The analyses of richness (Observed OTUs and Chao1) and diversity (Shannon’s Hʹ) estimators suggested a slight increase in richness of the methanotrophic communities from the Wooster location (Table 6.1). Higher Shannon’s Hʹ was observed in samples H-

NTCC, H-PTCC, and W-PTCC while highest richness was observed in samples W-

NTCS, W-PTCC, and W-GRA. Since diversity is dependent both on richness and evenness, even though the Hoytville soils showed lower richness, higher diversity numbers can be attributed to a community that is more evenly distributed. Consequently, though a microbial community may have low number of species present, the evenness of 203 those species can potentially increase the diversity of the community as a whole. This observation was further corroborated by the fact that samples W-NTCS and W-GRA, inspite of having high richness indicators, had lower diversity index.

The diversity index was the lowest among all samples for W-FOR, in spite having a higher richness estimate compared to other Wooster samples. In contrast, the H-FOR had a higher Shannon’s Hʹ but showed a lower richness estimates. This suggests that while W-FOR may have higher numbers of species, the distribution of these species was less even in the community. However, for H-FOR sample, few species were evenly distributed in the community.

Based on organic carbon data of our samples, obtained from the Cropping

Systems Coordinated Agricultural Project (CAP): Climate Change, Mitigation, and

Adaptation in Corn-based Cropping Systems project (USDA and NIFA, 2013), we hypothesized that while no-till soils contain higher organic matter and, consequently, higher C availability for microbial growth, the plow-till soils can provide more unique niche environments for multiple communities of bacteria. Additionally, the no-till soils have been known to hold more water (Kumar et al., 2012), thereby reducing the aerobic pockets that might potentially be available for aerobic bacteria like methanotrophs. Both rotation and tillage also affect how nutrients are distributed in a soil profile. Nitrogen- based fertilizer application has also been found to lower the activity of methanotrophs

(Conrad, 1996; King and Schnell, 1998) and for no-tillage, this fertilizer is restricted in its mixing with soil and is generally and mostly surface applied while for plowed soil, the fertilizer is incorporated in the deeper layers. Consequently, community diversity defined 204 by what species are present (richness), how they are distributed (evenness), and what functions they perform is dependent on a number of physico-chemical factors present in the micro-environment of the soil. While one environment can select for a narrow group of microbes and allow them to thrive, another environment plausibly can provide broader groups of microbes to thrive in a more even manner.

4.3 Community structure between soil samples

A total 1209 OTUs were obtained. To exclude sequences observed with very low frequency, OTUs with less than 10 sequences (representing less than 0.001% of the total number of sequences) were removed. This resulted in a total of 1070 OTUs that were further normalized and analyzed.

The Hotyville data set had the maximum unassigned sequences. Briefly, about

30% of OTUs for NTCC and FOR samples at Hoytville were unassigned. The rest of the sites had 5-15% of unassigned sequences (Figure 6.1). An unassigned sequence means that a sequence was classified at the Kingdom and Phylum level but failed to be classified for the ranks below that of phylum.

The relative abundance of normalized OTUs at the Family level is shown in

Figure 6.1. There was a clear dominance of two major groups between the two locations.

Hoytville was dominated by the Type Ib group of methanotrophs while Wooster had a mix of Type Ib and Type IIb methanotrophs. A small abundance (<5%) of Type Ia in H-

PTCS, and a TUSC-like (Tropical Upland Soil Cluster) group in NTCC, NTCS, PTCC,

GRA and FOR at Wooster were observed.

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Family Unclassified TUSC-like Type Ia Type Ib Type IIa Type IIb

NTCC NTCS PTCC PTCS GRA FOR NTCC NTCS PTCC PTCS GRA FOR

Hoytville Wooster Samples Figure 6.1 Relative abundance of normalized OTUs at Family level. The nomenclature is explained in detail in the text.

Comparing the reference data set revealed sequences grouped into Type Ia were pmoA sequences affiliated with the traditional Type I methanotrophs while Type Ib sequences refer to Methylococcus and closely related genera. Type IIa methanotrophs were grouped as primary pmoA sequences from the family Methylocystaceae while Type

IIb methanotrophs were grouped with other Type II-related sequences and

Beijerinckiaceae. The TUSC-like group was part of a third group of divergent pmoA- related sequences classified as pxma-like and included the Methylomonas spp.

In order to visualize the overall distribution of OTUs at the family level, nonmetric multidimensional scaling (NMDS) of Unweighted Unifrac distances was plotted (Figure

6.2). There was a clear abundance of Type Ib groups followed by Type IIb groups. The 206 overall distribution of taxa observed from Figure 6.2 can also be superimposed with

Figure 6.5 (described later in the text) to understand the distribution across samples. In spite of using a database which incorporated pmoA sequences from diverse environments that included agriculture soil, aquatic sediments, wetlands, geothermal soil, marine deep sea, and AOB-like sequences, we did not obtain any hits with environments other than soil and pure culture sequences. This suggests that the parameters that were chosen for

OTU picking followed by taxonomic assignment were stringent and appropriate.

A closer look at genus level OTU distribution by land use (Figure 6.3) revealed the presence of 8 genera, with the top 5 genera represented by Mcystis, Msarcina, USCα,

Mcoccus, and sequences unclassified at the genus level. The USCa cluster and Mcoccus had the highest relative abundances. Prior to normalizing, comparisons of the abundance of OTUs classified to genera were made. Populations differing by at least 2-fold (i.e. log2=1) between soil samples is shown in Figure 6.4. Four genera were differentially abundant across samples (alpha=0.005) and included Mcystis, Mcoccus, Msoma, and

USCα. Drawing parallels from RNA-expression studies and differentially expressed genes (McMurdie and Holmes, 2014), a species/OTU was considered differentially abundant if its mean proportion was significantly different between two or more sample classes in the experimental design.

The USCα genus was seen consistently in the Wooster soils, with W-FOR recording the highest relative abundance. This genus is a member of the upland soil cluster alpha, assumed to be methanotrophic bacteria adapted to trace levels of methane

(Ricke et al., 2005a). The 16S rRNA phylogeny of this group is not known since the 207 group has not been cultivated to date. However, phylogenies constructed for the active- site polypeptide (encoded by pmoA) of the enzyme pMMO placed USCα next to the alphaproteobacterial Methylocapsa acidiphila B2, a Type II methanotroph (Tchawa

Yimga et al., 2003).

Both Wooster and Hoytville soils had relatively high proportions of Mcoccus genus with a consistently higher proportion in the Hoytville soils. The Mcoccus genera belongs to the Type Ib group of methanotrophs. Studies have reported two copies of pmoA gene in Mcoccus species (Stolyar et al., 2001). Although this study did not focus on normalizing the copy numbers of the gene, the expression levels of this genera could be vastly different from the others which have only one copy of the gene.

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209

Figure 6.2 Nonmetric Multidimensional Scaling (NMDS) plots with distribution of Family level OTUs based on unweighted UniFrac distance. The nomenclature of the families are explained in detail in the text. Sequences that failed to classify below the Family level were labeled as f_.

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Genus Unclassified Mcoccus Mcystis Msarcina USCα

NTCC NTCS PTCC PTCS GRA FOR NTCC NTCS PTCC PTCS GRA FOR

Hoytville Wooster

Samples

Figure 6.3 Relative distribution of top 5 abundant genera among samples. log2foldchange log2foldchange

Mcystis Mcoccus Unclassified Msarcina Microbium Msoma USCα Genera

Figure 6.4 Differentially abundant genera (alpha-0.005) across samples showing log2fold change in OTU counts.

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A particularly important trend to note here was the partition of Type I and Type II methanotrophs between the two locations. Type I methanotrophs are classified as ‘high capacity – low affinity’ methanotrophs and are adapted for growth at high CH4 concentration (e.g. in wetland soils) (Siljanen et al., 2012). In contrast, Type II methanotrophs are classified as ‘low capacity – high affinity’ and are adapted for growth on trace CH4 concentration (Dunfield et al., 1999; Bull et al., 2000a). Given that

Hoytville soil has a higher clay content and is poorly drained, the presence of water and the associated depletion of oxygen presents an environment for methanogenesis and thus more CH4 availability for methanotrophs. Conversely, the well-drained Wooster soils have more oxygen which is not an environment conducive for methanogenesis, thereby favoring low amounts of CH4 and the presence of Type II-USCα -high affinity group.

This suggests that methanotrophic activity in soil is possibly affected by CH4 concentration as impacted by concentration of the gas absorbed from the soil atmosphere that is influenced by the microbial community.

The NTCS and FOR samples from Hoytville also had a notable proportion of unassigned genera. Notably, there were less abundant genera observed in some of the soils including: (i) Mcystis in H-PTCC and H-FOR and W-PTCS; (ii) MO3 in H-NTCC and W-PTCC; Msarcina in W-PTCC; Msinus in W-PTCS; RPC in W-PTCS; TUSC in

W-NTCS and W-GRA. It is evident from the distribution of the genera that while definite patterns could not be drawn regarding diversity and soil sample, more rare abundant

211 groups were observed in plow-till soils, irrespective of the location or rotation variable.

This leads one to consider the role played by the rare abundant taxa.

Plowing brings about disturbances in the soil profile, thereby giving rise to small microenvironments, partitioned from each other. The partitioned environments possess physical, chemical, and biological variations which in turn determine the resources available and consequently the number and type of microbes that thrive in those environments. It is possible that in soils with relatively less disturbance and high organic matter, e.g. no-till soils, grasslands and forests, microenvironments may be present that are less limited in the kinds of microbes but have higher numbers of those as opposed to soils which undergo disturbance (e.g. plowing) and allow for greater types of different species of microbes which may be more evenly distributed in their numbers.

Understanding the effects of modern agricultural practices on soil microbial communities is important for the effective and sustainable management of agricultural ecosystems. Both no-till and crop rotations have been widely adopted in many agricultural settings. Wide acceptance of these practices to potentially increase microbial biomass and activity is well documented (Helgason et al., 2010). Other studies have indicated that tilled soil may or may not contain greater bacterial diversity than no-tilled soil (Ferreira et al., 2000; Torsvik and Øvreås, 2002; Upchurch et al., 2008) while Frey et al.'s (1999) study reported no consistent effects on bacterial abundance or biomass in a

30-year tillage practice. There are conflicting studies about microbial species richness comparisons in soils under different management practices. In Steenwerth et al. (2014), comparisons between agricultural and grassland soils reported decreased microbial 212 species richness in agricultural soils while Rodrigues et al. (2013) found that conversion of the Amazon rainforest in South America to cultivation results in an increase of microbial diversity

Several studies have found that increase in microbial richness in no-till soils may be closely related to increase in the diversity of fungal communities (Frey et al. 1999;

Helgason et al. 2010; Jones et al. 2009). Given that fungi are in the forefront of cellular degradation of organic compounds it will be interesting to study the effects of fungal population in this long-term no-till and plot-till plots. It can very well be theorized that in a given soil environment, the functions performed by the microbes is a cumulative effect of their association with their environment and with each other. Ecologically speaking, when multiple niches exist in soil microenvironments, diverse group of microbes share the responsibility of breaking down complex compounds, biogeochemical cycling of nutrients, and performing roles in secondary metabolism. However, when the niches are few, it harbors fewer kinds of microbes in greater numbers in order for similar microbial functions to be performed in soil.

4.4 Similarity and differences in community structure between samples

Overall similarities and differences in community structure between soil samples was visualized by calculating pairwise Bray-Curtis and unweighted UniFrac dissimilarities and ordinating in two dimensional NMDS plots (Figures 6.5a and 6.5b).

The NMDS plot ordinations of soils from Hoytville and Wooster were derived from both metrics, taxonomic (Bray-Curtis) and phylogenetic (Unweighted Unifrac) and both

213 showed similar patterns. Predominantly, samples were grouped according to their location. In Figure 6.5a, management was the second grouping with tillage playing a visible role in distribution. W-GRA and W-FOR were clustered together in Wooster while H-GRA and H-FOR were placed near each other. In Figure 6.5b, an interesting pattern was observed with W-PTCC and W-NTCS grouped together while W-GRA and

W-NTCC were grouped together in Wooster. For Hoytville, PTCS, NTCC, and GRA were grouped together. Overall, composition of bacterial communities within each location was more similar to each other than to the other location. In fact, recent studies have underlined that geography plays an important role in microbial community composition (Yannarell and Triplett, 2005; Franklin and Mills, 2007; Finkel et al., 2011)

4.5 Distance measure of OTUs

The ordinations in Figures 6.5a and 6.5b suggested that the structure of the methanotrophic bacterial community differed with location. However, in order to statistically determine the quantitative differences in their distribution, distance measures were analyzed using permutation multivariate analysis of variance using distance matrices (also called as “adonis” in Vegan package in R). When the effect of location, rotation, tillage, and management was determined using Bray-Curtis measure of distance, location had highly significant effect (P<0.005). Our study indicated that when considering a scale where two different soil types were being studied, similar weather pattern, same land-use practices, and a functional gene sequence of a very narrow group of bacteria were studied, location appeared to be the determining factor in influencing the community composition. Even though we observed that the number and counts of OTUs 214 varied in each site, and the relative abundance specific OTUs varied with different management practices, the separation of our samples based on location indicates that geography plays an important role in affecting microbial community diversity. The poorly drained silty clay loam soil (fine, illitic, mesic Mollic Epiaqualf) at the Hoytville site, and a well drained Wooster silt loam soil (mixed, mesic, Typic Fragiudalf) at the

Wooster site (Campbell et al., 2014) determined the overall distribution of methanotrophic community in our samples.

Previous chapters (Chapter 4 and Chapter 5) of this dissertation on 16S rRNA gene studies noted that firstly location was highly significant in determining community composition dynamics, followed by tillage and rotation. It was interesting to observe that neither tillage nor rotation had any significant effect on the pmoA community composition. This presents an opportunity to reflect on where our focus lies when studying how microbial communities in soil are distributed. It is possible that when studying an ubiquitous gene like 16S rRNA, the effect of dominant groups of bacteria are noticed. The diversity of the dominant groups in all likelihood is observed which in turn also influences the microbial community composition. The same is then affected significantly by land-use and management practices like tillage and rotation. However, when we shift our focus in an attempt to study community composition based on functional gene sequences (like pmoA), it is likely that the community composition of the specific group of microbe being studied is not affected significantly by land-use and management practices. Instead, the functionality of the community may be more dependent on the type of soil the community is found in. 215

W-NTCS H-GRA (a) W-NTCC H-FOR

H-NTCC

W-PTCS H-NTCS W-GRA

W-PTCC W-FOR H-PTCC H-PTCS

H-PTCS H-GRA (b) W-PTCS W-PTCC H-NTCC

W-NTCS

W-GRA H-PTCC

W-NTCC

W-FOR H-FOR

H-NTCS

Figure 6.5 Nonmetric Multidimensional Scaling (NMDS) plots derived from unweighted UniFrac (a) and pairwise Bray-Curtis (b) distances between methanotrophic bacterial community.

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5. Conclusion

In the current study, we used the sequencing-by-synthesis technique to study pmoA sequences in order to determine methanotrophic bacterial diversity in soils under different land-use and land-management practices. Next-generation sequencing techniques, like the one we used in our study, allows the estimation of microbial population both qualitatively (to determine diversity) and quantitatively (to determine abundance based on the frequency of sequences detected) at a fine scale resolution.

Although next-generation sequencing studies to determine microbial diversity has weaknesses and limitations, including PCR biases, sequencing error, Taq polymerase fidelity, and gene copy number variations, the data available from this technology provides us with unprecedented knowledge about microbial species.

Here, we report methanotrophic community diversity with respect to land-use and land-management practices at two locations in Ohio. The two sites allowed us to compare the diversity of aerobic methanotrophs and identify community diversity patterns with respect to variables like tillage, rotation, and location. While we could not detect consistent richness and evenness patterns in our samples, community diversity was overall higher in the Wooster soils as compared to the Hoytville soils.

Our sequence based approach allowed us to detect and quantify unique members of individual genera. We were also able to note preferential distribution of genera across locations. By identifying certain groups of microorganisms with particular function in a given ecosystem, it is possible to target those microorganisms through marker assisted 217 selection and single cell genomic studies (Benítez and Gardener, 2009). It is also important to note that around 30% of our sequences remained unclassified. This observation ascertains the fact that continuous strides need to be made in culturing soil microbes in order for us to be able to fully understand the functional role played by this diverse and dynamic group of organisms.

Using computational ecology tools such as the Bray-Curtis and Unifrac metrics, we were able to plot, compute, and compare differences in microbial communities between ecosystems and along environmental gradients using the R packages Phyloseq and Vegan. Additionally, we were also able to use the DESeq2 package, originally written for differential analysis of RNAseq data, to analyze differential abundance of

OTUs between samples.

We also noted the need for robust databases when performing high-throughput sequence analyses of functional genes like pmoA. Relationships between microbial species diversity and their ecological functions underline the important role played by functional genes. If functionalities of these microbes, in relation to global ecological roles are to be studied, one must focus on studying the functional aspects of the communities.

This can become increasingly robust if we can tap into studying functional genes like pmoA in addition to 16S rRNA gene studies.

Well-aerated soils serve as a significant global sink for atmospheric CH4.

Methane-oxidizing bacteria contribute to net emission of CH4 from soil and therefore regulate net CH4 flux. Incorporating land-use and land-management practices that reduce the overall emission load of greenhouse gases from soils can add to sustainable practices. 218

Understanding the microbial community composition of methane oxidizing bacteria under different agriculture, forest, and other land uses can thus help discern the sensitivity and responsiveness of microbial communities to land-use practices and contribute to ‘climate-smart’ agricultural research.

6. Acknowledgments

Funding for this study was provided by the USDA-NIFA, Award No. 2011-68002-30190

“Cropping Systems Coordinated Agricultural Project (CAP): Climate Change,

Mitigation, and Adaptation in Corn-based Cropping Systems”. Sequencing was done at

Molecular and Imaging Center at Ohio Agricultural Research and Development Center,

Wooster.

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CHAPTER 7: FUTURE RESEARCH AND CONCLUSIONS

1. Future Research

A number of research ideas can be built upon the results of this dissertation.

Research questions can be applied and extended to study community dynamics of methanotrophic bacteria with respect to seasonal variation, temperature variation, and pulsing environments which have alternate wetting and drying cycles. Studies have reported that compared to Type I methanotrophs, Type II are more resilient and able to respond faster to specific environmental signals such as CH4 concentrations (Tate, 2015).

Therefore, studies could be conducted on disturbed sites to document how methanotrophic communities recover after severe disturbances, e.g. long periods of drought followed by rain due to climate change scenarios. Mesocosm experiments which control multiple variables including CH4 concentration, temperature, precipitation, vegetation, and fertilizer application can be set up to monitor community composition as affected by these variables. For research that is focused on single-cell genomic studies, nucleotide markers specific for methanotrophs can be developed. Sequence analysis such as this current study can be used to create transcriptomic arrays for global gene

232 expression studies to identify targets for molecular genetic analysis of key pathways of methylotrophy, central C and N metabolism, and metal ion sequestration and uptake.

Predictor functions of soil microbial communities can also be studied using tools like

PICRUSt (Langille et al., 2013) with the resulting information being fed into ecosystem modeling focused on studying the functional roles of microbes in global ecosystem functioning. The results of this dissertation can also be fed into global environmental sample databases like The Earth Microbiome Project and Critical Zone Observatory

Network to compare and contrast methanotrophic diversity across large spatial scales.

2. Conclusions

Aerobic methanotrophs in upland soils play a crucial link in the soil’s ability to act as a CH4 source or sink. The ability to utilize CH4 for C and energy makes this small group of bacteria functionally unique and vital in the global C cycle. Land use changes such as conversion of forest lands to agricultural soils, and land management practices such as no-till and plow-till change soil characteristics. They are the drivers that affect soil texture, porosity, CH4 diffusivity, bulk density, clay content, soil moisture, pH, and temperature. In turn, these soil properties impact the microbial community dynamics and, specifically, the net soil CH4 flux into the atmosphere. Apart from playing a major role in

CH4 cycling in the natural environment, methanotrophs have bioremediation, biotransformation, and biofuel production potential.

A number of research facilities and tools were used in this study. Similarly created and maintained long-term experimental plots at two contrasting soils in Ohio

233 allowed location to be studied as a variable affecting methanotrophic community.

Additionally, agricultural plots with different tillage and rotation practices, and grass and forest areas allowed tillage, rotation, and management to be studied as variables affecting methanotrophic community. Access to high-throughput sequencing tools and super- computing facility were made available to generate and analyze large amounts of sequence data.

The pilot study conducted using pyrosequencing revealed differences in the bacterial community profile of a no-till versus plow-till soil. The results indicated soil microbial communities are made up of large proportions of rare groups of bacteria. The challenge facing soil microbial diversity studies is that while more-abundant groups are easy to study, the less-abundant groups, which may make up the greatest amount of the total microbial population, go unrecognized. For example, while dominant phyla including Proteobacteria, Acidobacteria, and Actinobacteria were readily identified, only one species of methanotrophs, Methylocystaceae was identified even though methanotrophs play important roles in controlling fluxes of CH4, a major greenhouse gas, through natural environments.

A similar trend was observed in the Illumina sequencing of 16S rRNA gene for study of the entire bacterial community, and of Type I and Type II methanotrophic communities. The soil for this study came from the same long-term tillage and rotation sites sampled for the pyrosequencing study. In this study, in addition to tillage we also evaluated the effect of crop rotation, forest and grassed areas on microbial communities.

The richness estimates did not always correlate with diversity metrics. The ability to 234 successfully identify soil microbial community diversity not only rests on richness estimates (which account for number of species identified) but also on evenness of a community (how evenly the species present are distributed). We observed that while one environment can be rich in terms of having high proportions of a handful of dominant groups, another environment can be diverse in terms of having smaller proportions of multiple rare groups. The 16S rRNA dataset showed that majority of the significant groups did not contribute to the abundant genera.

About 25-40% of 16S rRNA sequences remained unclassified in each of our datasets. This limitation continues to present itself as a challenge and one that needs to be resolved by employing multiple techniques that include metagenomic and phylogenetic approaches. A greater challenge lies in specifically identifying rare but functionally important groups of bacteria, like aerobic methanotrophs. In our sequencing-by-synthesis efforts of whole community 16S rRNA and partial 16S rRNA region of Type I and Type

II methanotrophs, about 2% of the sequences obtained were classified as methantrophs and associated non-methanotrophic methylotrophs. A combination of culturing techniques, high-throughput sequencing, and marker assisted recovery of microbes including development of fluorescent in-situ hybridization (FISH) probes to target and sort single cells need to be employed simultaneously in order to be able to study the identity, diversity, and functionality of rare group of microbes like aerobic methanotrophs.

For most of the datasets, rarefaction curves depicting number of species observed with respect to the number of sequences analyzed showed that the curves started to 235 plateau around the range of 20,000-30,000 sequences. This suggests that deeper sequencing efforts combined with current reference databases are less likely to yield more species information. Instead, the focus must shift towards creating additional databases, including that of functional genes, to enable soil microbial diversity studies of biogeochemically important microbes.

With respect to the bioinformatics tools available to analyze large amounts of

DNA sequencing data, this study used both Mothur and QIIME. Certain functions were individually suited to perform better in one than the other. Depending on the requirements of the study, parameters can be chosen that are study-specific. For example, since this study was focused on studying a rare group of soil bacteria, sequence similarity thresholds were kept at 97% to identify genus level matches for all datasets instead of attempting to be ultra-narrow and identifying specific species. The availability of R packages like “Phyloseq”, “DESeqβ”, and “Vegan” allowed for statistical analyses of large sequence analyses datasets. While “Phyloseq” commands and ordination methods proved particularly helpful to graphically examine information-dense phylogenetic sequencing data, “DESeqβ” was able to identify differentially abundant OTUs and

“Vegan” allowed for multivariate analyses of factors affecting community composition.

The decision to include non-methanotrophic methylotrophs in the analyses was recognized as leading to co-occurrence of functionally dependent microbes in a given niche. However, the non-methanotrophic methylotrophs also contribute to the global carbon cycle and indicate the possibility of resource sharing in the niche environments of methanotrophs. The presence of Verrucomicrobial Methylacidiphilales in the Type I 236 dataset indicated the likelihood of these unique group of methanotrophs to share characteristics of both Type I and Type II methanotrophs. The presence of “high-affinity”

Upland Soil Cluster-alpha (USCα) groups in the pmoA dataset presented the opportunity to develop probes to target and isolate these yet uncultured group of methanotrophs.

For most datasets, no-till soils had higher diversity than plow-till soils and community composition of both agricultural practices were distinctly different from forest and grass areas. Due to the fact that forest soils were undisturbed, highest number of different species was generally recovered from these soils. These observations appeared to be closely linked with results by Jacinthe et al.'s (2013) which formed the basis of this current study. This study noted CH4 oxidation rates to be the highest in forest soils followed by no-till soil. Continuous of these systems is needed in order for them to have soils that serve as CH4 sinks.

Among the variables analyzed, soil type that is a function of location was dominant, followed by tillage and rotation. The effect of location became clear when the pmoA dataset showed that even over a long period of time (>50 years), soil methanotrophy function was governed by soil type. It can be concluded that even with soil disturbance, the inherent functioning of microbes in these soils is possibly more impacted by soil type (i.e. location), followed by land-use.

Underlying exact mechanisms of microbial community structure and diversity patterns in soil is a vast area of research, this dissertation was able to identify a soil type impacted by location, followed by land use that were plausibly influencing community composition of aerobic methanotrophic bacteria in soil. 237

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APPENDIX A: SITE MAPS AND PRIMER DETAILS

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2013 Sample Plots

Plot Tillage Rep/Rot/ Plot Tillage Rep/Rot/ Plot Tillage Rep/Rot Plot size: 30.5m No. Till No. Till No. /Till

101 MT 132 201 PT 221 301 NT 313 x 6.4 m 102 MT 132 202 PT 221 302 PT 311

103 MT 132 203 MT 212 303 NT 323 104 NT 123 204 NT 233 304 NT 323 Grass and 105 NT 123 205 NT 233 305 MT 332 Forested area 106 PT 131 206 NT 233 306 MT 332 not shown in the 107 PT 131 207 PT 211 307 MT 332 108 PT 131 208 MT 222 308 PT 321 map; they are 109 PT 111 209 MT 222 309 PT 321 located

northwest of the 110 MT 122 210 MT 232 310 MT 312 plots. 27

5 111 MT 122 211 MT 232 311 PT 331 112 NT 113 212 MT 232 312 PT 331 113 NT 133 213 NT 213 313 PT 331 114 NT 133 214 NT 223 314 MT 322 N 115 NT 133 215 NT 223 315 MT 322 116 PT 121 216 PT 231 316 NT 333 117 PT 121 217 PT 231 317 NT 333

118 MT 112 218 PT 231 318 NT 333 Rep 1 Rep 2 Rep 3

Rotation 1 = Continuous Corn Till 1 = Moldboard Plow Rotation 2 = Corn-Soybean Till 2 = Medium (Conservation) Rotation 3 = Corn-Oat-Hays Till 3 = No-Till Table A.1 Field TA-3, Northwest Agricultural Research Station, Hoytville.

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2013 Sample Plots

Plot No. Tillage Rep/Rot/Till Plot No. Tillage Rep/Rot/Till 1 MT 132 2 PT 111 3 MT 132 4 PT 131 5 MT 132 6 PT 131 7 MT 122 8 PT 131 9 MT 122 10 PT 121 11 MT 112 12 PT 121 13 NT 133 14 NT 123 15 NT 133 16 NT 123 17 NT 133 18 NT 113 19 NT 223 20 MT 222 21 NT 223 22 MT 222 23 NT 233 24 MT 212 25 NT 233 26 MT 232 27 NT 233 28 MT 232 29 NT 213 30 MT 232 31 PT 231 32 PT 211 33 PT 231 34 PT 221 35 PT 231 36 PT 221 37 PT 321 38 NT 313 39 PT 321 40 NT 333 41 PT 311 42 NT 333 43 PT 331 44 NT 333 45 PT 331 46 NT 323 47 PT 331 48 NT 323 49 MT 312 50 MT 332 51 MT 322 52 MT 332 53 MT 322 54 MT 332 Rotation 1 = Continuous Corn Till 1 = Moldboard Plow N Rotation 2 = Corn-Soybean Till 2 = Medium (Conservation) Rotation 3 = Corn-Oat-Hays Till 3 = No-Till Plot Size: 22.3 m x 4.3 m

 Grass and Forested area not shown in the map; they are located south and southwest of the plots, respectively.

Table A.2 Plots 731-732, Triplett-Van Doren Long-term tillage and rotation plots, Wooster. 276

% Organic DNA conc. Samples pH* Matter* (ug/g)* H-NTCC 5.59 5.48 11.3 H-NTCS 6.61 5.03 10.3 H-PTCC 6.70 4.87 5.92 H-PTCS 6.46 3.87 4.12 H-GRA 6.43 7.77 6.62 H-FOR 5.48 11.8 8.82 W-NTCC 5.37 4.06 11.5 W-NTCS 6.19 3.66 11.0 W-PTCC 5.92 3.37 5.24 W-PTCS 6.14 2.96 5.68 W-GRA 5.90 3.05 8.48 W-FOR 5.18 5.57 8.52 * Avg. of 3 replicates, values were significant for each dataset at p<0.005

Table A.3 Soil Data Analysis of Hoytville and Wooster samples.

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Nextera i5 bases in Nextera i7 bases in DNA i5 adaptor DNA i7 adaptor index index name name N517 GCGTAAGA N709 GCTACGCT N502 CTCTCTAT N712 GTAGAGGA N503 TATCCTCT n.a. n.a. N504 AGAGTAGA n.a. n.a. N505 GTAAGGAG n.a. n.a. N506 ACTGCATA n.a. n.a. N507 AAGGAGTA n.a. n.a.

Table A.4 Nextera codes of i5 and i7 index reads of 16S whole community.

27F_A TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG Combinati TCGATCG ons used GAAKRGTTYGATYNTGGCTCAG in samples 27F_B TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG ATCTGTCATG GAAKRGTTYGATYNTGGCTCAG W1 & 27F_C TCGTCGGCAGCGTCAGATGTGTATAAGAG W2: 27F- ACAG CGAGCAATCCACTC 519R (A) GAAKRGTTYGATYNTGGCTCAG W3 & 519R_A GTCTCGTGGGCTCGGAGATGTGTATAAGAGACA W4: 27F- G CGGACTTGATGTACGA 519R (B) ACGTNTBACCGCDGCTGCTG 519R_B GTCTCGTGGGCTCGGAGATGTGTATAAGAGACA W5 & G TCAGTAGCTACGC W6: 27F- ACGTNTBACCGCDGCTGCTG 519R (C) 519R_C GTCTCGTGGGCTCGGAGATGTGTATAAGAGACA G GATTAGCTGC ACGTNTBACCGCDGCTGCTG

Table A.5 Primer combinations in round 1 PCR of Wooster samples for 16S whole community analysis. In black: overhang for appending Illumina Nextera adapter; In bold: variable spacers; Underlined: Locus specific primer.

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Sample Indices H1 N709 & N517 H2 N709 & N502 H3 N709 & N503 H4 N709 & N504 H5 N709 & N505

H6 N709 & N506 W1 N712 & N517 W2 N712 & N502 W3 N712 & N503 W4 N712 & N504 W5 N712 & N505 W6 N712 & N506

Table A.6 Illumina primer index combinations of 16S whole community.

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Nextera i5 bases in Nextera i7 bases in DNA i5 adaptor DNA i7 adaptor index index name name N517 GCGTAAGA N701 TAAGGCGA N502 CTCTCTAT N705 GGACTCCT N503 TATCCTCT N708 CAGAGAGG N504 AGAGTAGA N711 AAGAGGCA N505 GTAAGGAG n.a. n.a. N506 ACTGCATA n.a. n.a. N507 AAGGAGTA n.a. n.a. Table A.7 Nextera codes of i5 and i7 index reads of Type I and Type II methanotrophic community analysis.

Sample Indices Type I Type II H1 N705 & N517 N711 & N517 H2 N705 & N502 N711 & N502

H3 N705 & N503 N711 & N503 H4 N705 & N504 N711 & N504 H5 N705 & N505 N711 & N505 H6 N705 & N506 N711 & N506 W1 N701 & N517 N708 & N517 W2 N701 & N502 N708 & N502 W3 N701 & N503 N708 & N503 W4 N701 & N504 N708 & N504 W5 N701 & N505 N708 & N505

W6 N701 & N506 N708 & N506 Table A.8 Illumina primer index combinations of Type I and Type II methanotrophic community analysis.

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Nextera i5 bases in Nextera i7 bases in DNA i5 adaptor DNA i7 adaptor index index name name N517 GCGTAAGA N701 TAAGGCGA N502 CTCTCTAT N705 GGACTCCT N503 TATCCTCT n.a. n.a. N504 AGAGTAGA n.a. n.a. N505 GTAAGGAG n.a. n.a. N506 ACTGCATA n.a. n.a. N507 AAGGAGTA n.a. n.a.

Table A.9 Nextera codes of i5 and i7 index reads of pmoA community analysis.

Sample Indices H1 N705 & N517 H2 N705 & N502 H3 N705 & N503 H4 N705 & N504

H5 N705 & N505 H6 N705 & N506 W1 N701 & N517 W2 N701 & N502 W3 N701 & N503 W4 N701 & N504 W5 N701 & N505 W6 N701 & N506 Positive Control N701 & N507

Table A.10 Illumina primer index combinations of pmoA community analysis.

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