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TRANSCRIPT
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Matthias Hess M.Sc., Ph.D.
Wiley Research Fellow – DOE Environmental Molecular Science Laboratory Assistant Professor – Washington State University
Omics techniques for rumen microbiology
Joint RuminOmics/Rumen Microbial Genomics Network (RO/RMGN) Workshop, Aberdeen, UK June 16th, 2014
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Outline
• Introduction • The Hess Lab • Rumen Systems Microbiology
• Omics Techniques • Challenges & solutions
• Perspectives
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The Hess Lab
Ecosystem-driven enzyme discovery
Cultivation-based techniques
Cultivation-independent techniques
Wet-lab experiments Bioinformatics
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• Impacts health and productivity of host animals • Rumen microbes produce ~21% of anthropogenic methane • A complex system
• 1010 bacteria/mL • 106 protozoa/mL • 105 fungi/mL
The Rumen
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Systems Microbiology toolbox
Warnecke and Hugenholtz 2007; Genome Biol. 8; 231
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• Introduction • The Hess Lab • Rumen Systems Microbiology
• Omics Techniques • Challenges & solutions
• Perspectives
Outline
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Challenge: Dynamic & complex environment
Koropatkin; Nature Reviews Microbiology 10; 323
Spatial dynamics
Carbohydrate utilization along the length of the digestive tract: • variable transit times • different carbohydrates are digested in different gut regions • complex microbe-microbe and microbe-host interactions
Interspecies-Interactions
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High Resolution Imaging
Piao et al 2014; Frontiers in Microbiology
Visual atlas of the rumen microbiome: Request for pure cultures with FISH probe available
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DNA, RNA and protein profiles
Direct sequencing of DNA, RNA and proteins extracted from an environmental sample can provide snapshots of the complete microbiome and subsequently insights into changes of metabolic processes. Ø DNA sequencing (well established) Ø RNA sequencing (moderately established for environmental samples) Ø Protein profiling (little established for environmental samples)
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DNA, RNA and protein profiles
Direct sequencing of DNA, RNA and proteins extracted from an environmental sample can provide snapshots of the complete microbiome and subsequently insights into changes of metabolic processes. Ø DNA sequencing (well established)
• Amplicon sequencing - 16S rRNA gene for prokaryotes (methods, databases and software well
established) - Internal Transcribed Spacer (ITS) region for fungi (less established; methods
databases and software are being established) - Functional genes
• Shotgun sequencing Ø RNA sequencing (moderately established for environmental samples) Ø Protein profiling (little established for environmental samples)
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Cost of nucleotide sequencing
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28S 18S
5.8S
DNA amplicon sequencing
Ø Phylogenetic marker genes
- 16S rRNA (& various hypervariable regions) - Internal Transcribed Spacer (ITS) - 18S rRNA
Hypervariable regions of 16S rRNA gene (Pseudomonas)
Bodilis et al 2012; PLoS ONE 7(4): e35647
Inter Genic Spacer
Transcribed rRNA genes & spacers
ITS1 ITS2
Organization of ribosomal genes in eukaryotes
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DNA amplicon sequencing (16S rRNA gene & ITS)
Fiber colonization during rumen-degradation
Piao et al 2014; Frontiers in Microbiology
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DNA amplicon sequencing
Ø Functional marker genes - detection of genes with relevant function
- mcrA (methanogenesis) - nifH (nitrogen fixation) - dsrAB (sulfate reduction)
Fraction of methanogens in human gut microbiome is age dependent
Mihajjlvski et al 2010; Env. Micro. Rep. 2:272
Compartmentalized microbial nitrogen fixation in the gut of Odontotaenius disjunctus
Ceja-Navarro et al 2014; ISME J 8:6
nifH expression level 1 5 27 2
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Cow rumen metagenome
Library name Insert Size Sequencing Bases (Gb) Reads (M) GAII_200bp 200bp 2x125bp 17.3 138.3 GAII_3kb* 3kb 2x36bp 4.5 124.5 GAII_3kb 2x75bp 27.2 357.6 GAII_5kb* 5kb 2x36bp 4.0 110.8 GAII_5kb 2x75bp 22.8 300.6 HiSeq_300bp 300bp 2x36bp 3.9 108.9 2x100bp 188.2 1,863.6 Total 267.9 3,004.3
* titration data for calibration of full-length run
~3 billion reads
~268 Gb metagenomic data from rumen microbiome
~58,000 Escherichia coli genomes
Hess et al 2011; Science 331; 463
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DNA shotgun sequencing
0.01
0.1
1
10
100
0.01 0.1 1 10 100 1000
~80 CPU years (completed in 2090)
~1 day on NERSC’s 30,000-‐CPU-‐cluster
Cow Rumen I (20 Gb)
Cow Rumen HiSeq; 2011 (240 Gb)
Termite Hindgut; 2007 (0.04 Gb)
Sargasso Sea; 2004 (1 Gb)
Global Ocean Survey; 2007 (6 Gb)
Data [Gbp]
Run
ning
tim
e [C
PU-y
ears
]
BLAST time
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DNA shotgun sequencing
Ø Targeted gene discovery (e.g. carbohydrate active enzymes)
Hess et al 2011; Science 331; 463
CAZy database increased by ~30%
K-mer Filter (read selection /
trimming)
Velvet Assembly
Gene prediction
HMMSearch (GH & CBM
domains)
111 Gb
179,092 scaffolds >1kb (1.9 Gb)
2.5 M genes
27,755 CAZymes genes
Gene prediction 268 Gb
85,740 CAZymes in CAZy DB
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DNA shotgun sequencing
Ø Assembly of individual genomes from metagenome data
Hess et al 2011; Science 331; 463
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Ø DNA sequencing (well established)
• Amplicon sequencing • Shotgun sequencing
• Provides overview of metabolic repertoire of microbial community • Can be used to assemble genomes from individual community
members • Targeted gene discovery
Ø RNA sequencing (moderately established for environmental samples) Ø Protein profiling (little established for environmental samples)
Community Profiling
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RNA sequencing
He et al 2010; Nature Methods 7; 807
Ø RNA sequencing (moderately established for environmental samples) • identification of genes that are expressed • rRNA needs to be removed for functional gene information • t1/2 of mRNA is short and varies between different transcripts
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Community Profiling
Ø DNA sequencing (well established)
• Amplicon sequencing • Shotgun sequencing
• Provides overview of metabolic repertoire of microbial community • Can be used to assemble genomes from individual community
members • Targeted gene discovery
Ø RNA sequencing (moderately established for environmental samples) Ø Protein profiling (little established for environmental samples)
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Database search
Protein Profiling
Hettich et al 2013; Anal. Chem. 2013, 85, 4203
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Challenge: Data Integration & verification
Warnecke and Hugenholtz 2007; Genome Biol. 8; 231
Microbial Ecology omics toolbox
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Challenge: Data Integration Ø Omics platforms generate non-standardized output Ø No general SOP for sample prep and data analysis (even with same platform) Ø Different scientific questions Ø Different background of “users” Ø Developing in-house solutions is currently not economical (even at the institution-
level) Some Integrative Data Analysis Tools
16S rRNA gene sequencing ITS sequencing
Metagenomics Metatranscriptomics
Metaproteomics Metametabolomics
Imaging
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Hess et al 2011; Science 331; 463
Identification of cellulolytic proteins using high-throughput screening assays
Large scale in-vivo production of cellulolytic proteins
Expression of target sequences
In-vitro In-vivo
Synthesis of target nucleotide sequence
Direct amplification from metagenomic DNA Codon optimized sequence
Chemical synthesis
Challenge: Verification of assembly at the gene level
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Warnecke & Hugenholtz, 2007
Beyond gene & genome inventories
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Metagenome
Metatranscriptome
Metaproteome
Hess et al; unpublished
Reconstruction of metabolic pathways Glycolysis/Gluconeogenesis
For microbial dark matter: Piao et al (2014) Biotechnology & Bioengineering
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Perspectives
• BUILD REFERENCE GENE/GENOME DATABASE • Define Standard Operational Procedures • Take advantage of JGI & EMSL resources and
data generated through existing CSPs (Hungate1000; Fungi1000; Rumen MT, MP & MB)
• Research Topic in Frontiers in Systems Microbiology
• Visual atlas of rumen microbiome
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Hailan Piao; WSU – Postdoc Erik Hawley; WSU – Graduate Student
UI Urbana-Champaign: Roderick Mackie
LBNL/DOE JGI: Eddy Rubin, Nikos Kyrpides, Igor Grigoriev, Susannah Green Tringe, Stefanie Malfatti, Erika Lindquist, Natalia Ivanova, Jeff Froula, Chongbin Du, Amrita Pati and Kristin Tennessen
PNNL/EMSL: Scott Baker, Jon Magnuson, Lili Pasa-Tolic, Dave Culley, Kenneth Bruno, Heather Brewer, Angela Norbeck and Nancy Isern
UC Berkeley: Doug Clark, Tae-Wan Kim, Chris Sommerville and Stefan Bauer
University of Bielefeld, GER: Alex Sczyrba
Acknowledgements
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Q & A