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An Integrated Investigation of Ruminal Microbial Communities

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AN INTEGRATED INVESTIGATION OF RUMINAL MICROBIAL COMMUNITIES USING 16S rRNA GENE-BASED TECHNIQUES DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Min Seok Kim Graduate Program in Animal Sciences The Ohio State University 2011 Dissertation Committee: Dr. Mark Morrison, Advisor Dr. Zhongtang Yu, Co-Advisor Dr. Jeffrey L. Firkins Dr. Michael A. Cotta ABSTRACT Ruminant animals obtain most of their nutrients from fermentation products produced by ruminal microbiome consisting of bacteria, archaea, protozoa and fungi. In the ruminal microbiome, bacteria are the most abundant domain and greatly contribute to production of the fermentation products. Some studies showed that ruminal microbial populations between the liquid and adherent fraction are considerably different. Many cultivation-based studies have been conducted to investigate the ruminal microbiome, but culturable species only accounted for a small portion of the ruminal microbiome. Since the 16S rRNA gene (rrs) was used as a phylogenetic marker in studies of the ruminal microbiome, the ruminal microbiome that is not culturable has been identified. Most of previous studies were dependent on sequences recovered using DGGE and construction of rrs clone libraries, but these two techniques could recover only small number of rrs sequences. Recently microarray or pyrosequencing analysis have been used to examine microbial communities in various environmental samples and greatly contributed to identifying numerous rrs sequences at the same time. However, few studies have used the microarray or pyrosequencing analysis to investigate the ruminal microbiome. The overall objective of my study was to examine ruminal microbial diversity as affected by dietary modification and to compare microbial diversity between the liquid and adherent fractions using the microarray and pyrosequencing analysis. In the first study (Chapter 3), a meta-analysis of all the rrs sequences of rumen origin deposited in the RDP database was performed. Collectively, 5,271 and 943 OTUs of bacteria and archaea, respectively, were identified at 0.03 phylogenetic distance. The ii predominant bacterial phyla were Firmicutes and Bacteroidetes, while the largest archaeal phylum was Euryarchaeota. More than 50% of all the bacterial sequences could not be classified into any known genus. The bacterial OTUs identified in this study were used to develop a phylogenetic microarray as demonstrated in Chapter 6. In the second study (Chapter 4), select cultured bacteria and uncultured bacteria were quantified using specific real-time PCR assays in order to compare the abundance between the cultured bacteria and uncultured bacteria. The populations of some uncultured bacteria were as abundant as those of major cellulolytic cultured bacteria such as Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens. In the third study (Chapter 5), the diversity of ruminal microbiome in cattle was examined using rrs clone libraries. Six known rrs clones were used to validate the phylogenetic microarray (Chapter 6). The phylogenetic data of the cloned sequences supported the predominance of Firmicutes and Bacteroidetes and the abundance of unclassified groups as described in the meta-analysis (Chapter 3). In the fourth study (Chapter 6), a phylogenetic microarray that detects 1,600 OTUs of ruminal bacteria was developed in a 6×5K format based on the OTUs identified in Chapter 3. The utility of the phylogenetic microarray (referred to as RumenArray) was tested in comparative analysis of fractionated bacterial microbiomes obtained from sheep fed two different diets. Species-level OTUs are commonly defined at 0.03 phylogenetic distance based on full-length rrs sequences. However, the current 454 pyrosequencing method is not able to produce full-length rrs sequences. Because sequence divergence is not distributed evenly along the rrs, pyrosequencing analysis of different rrs regions can lead to overestimated or underestimated species richness. To identify a region or phylogenetic distance that can iii support species richness estimate as reliably as full-length rrs sequences, in the fifth study I compared datasets of partial rrs sequences corresponding to different variable regions with a dataset of nearly full-length rrs sequences (Chapter 7). The results indicated that the V1-V3 and the V1-V4 regions at 0.04 distance provide more accurate estimates than other partial regions. Based on the results obtained in Chapter 7, pyrosequencing analysis was performed to investigate bacterial diversity in the rumen of cattle as affected by supplementation of monensin, or 4% fat from distillers grains, roasted soybeans and an animal vegetable blend in my sixth study (Chapter 8). Supplementary fat resulted in significant shift of bacterial populations when compared to the control diet, but supplementary monensin did not. As shown in the previous Chapters, the pyrosequencing analysis showed that numerous rrs sequences that cannot be assigned to any characterized genus were predominant in the rumen. The overall results of the above studies provided further insights into the ruminal microbiome as affected by different diets and different fractions. Integration of RumenArray and pyrosequencing techniques will improve our understanding of the ruminal microbial microbiome and its integration with nutritional studies. iv ACKNOWLEDGMENTS I first would like to thank my advisors, Drs. Mark Morrison and Zhongtang Yu, for their support and guidance during my graduate study. I would like to express special thanks to Dr. Zhongtang Yu for his support and thoughtful discussions during individual meetings. I would like to thank Drs. Jeff Firkins and Mike Cotta for their service on my dissertation committee. I wish to thank Dr. Kichoon Lee for his service on my candidacy committee. I wish to acknowledge all former and present colleagues for their friendship in the Morrison/Yu lab: Seungha Kang, Jill Stiverson, Mike Nelson, Mike Cressman, Lingling Wang, Shan Wei, Wen Lv, Katie Shaw, Yueh-Fen Li, Amanda Gutek, Amlan Patra, Phongthorn Kongmun, Gunilla Bech-Nielsen, Sally Adams, Yan Zhang, Zhenming, Mohd Saufi Bastami, Jing Chen, Premaraj, Bethany and anyone else I missed. I am especially grateful to Jill Stiverson for her help and technical support when I first joined the lab. I especially thank Mike Nelson for his help in bioinformatics analysis when I first started pyrosequencing analysis. I would like to thank all my fellow graduate students working in the Department of Animal Sciences for their friendship. I would like to thank Sangsu Shin for his friendship since 1995. I would like to thank Sunghee Park and Changsoo Lee working in the Department of Food Science & Technology for their friendship. I wish to acknowledge all my family members for their support, love and encouragement. v VITA April 1977 ......................................................Born-Kwangju, Republic of Korea 2002 ...............................................................B.A., Department of Animal Sciences, Seoul National University, Republic of Korea 2002 to February 2004 ..................................M.S., Department of Animal Sciences, Seoul National University, Republic of Korea March 2004 to August 2006 .........................Researcher, Department of Agricultural Sciences, Korea National Open University September 2006 to present ............................Graduate Research Associate, Department of Animal Sciences, The Ohio State University Publications Kim, M., Morrison, M., Yu, Z., 2011. Phylogenetic diversity of bacterial communities in bovine rumen as affected by diets and microenvironments. Folia Microbiologica, DOI 10.1007/s12223-011-0066-5 Kim, M., Morrison, M., Yu, Z., 2011. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiology Ecology, 76, 49-63 Kim, M., Morrison, M., Yu, Z., 2011. Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes. Journal of Microbiological Methods, 84, 81-87 vi Nam, E.S., Kim, M.S., Lee, H.B., Ahn, J.K., 2010. β-Glycosidase of Thermus thermophilus KNOUC202: Gene and biochemical properties of the enzyme expressed in Escherichia coli. Applied Biochemistry Microbiology, 46, 515-524 Kim, M.S., Sung, H.G., Kim, H.J., Lee, S.S., Chang, J.S., Ha, J.K., 2005. Study on rumen cellulolytic bacterial attachment and fermentation dependent on initial pH by cPCR. Journal of Animal Science and Technology (in Korean). 47, 615-624 Fields of Study Major Field: Animal Science Focus: Rumen Microbial Ecology vii LIST OF TABLES Table 3.1 The number of OTUs for total bacteria, total archaea and major groups of bacteria, and their percentage coverage at three phylogenetic distances .......................... 49 Table 4.1 Primers and a TaqMan probe used in the real-time PCR assays for total bacteria, total archaea or cultured bacteria ....................................................................... 72 Table 4.2 Primers used in the real-time PCR assays for uncultured bacteria .................. 73 Table 7.1 Estimates of species-level OTUs calculated from partial and full-length archaeal 16S rRNA gene sequences .............................................................................. 132 Table 7.2 Estimates of genus- and family-level OTUs calculated from partial and full- length archaeal 16S rRNA gene sequences ..................................................................
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