Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

Supplementary Information Shi et al., 2013 For GENOME RESEARCH SUPPLEMENTARY INFORMATION Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome Weibing Shi1,2, Christina D Moon3, Sinead C Leahy3, Dongwan Kang1,2, Jeff Froula1,2, Sandra Kittelmann3, Christina Fan1,2, Samuel Deutsch1,2, Dragana Gagic3, Henning Seedorf3, William J Kelly3, Renee Atua3, Carrie Sang3, Priya Soni3, Dong Li3, Cesar S Pinares-Patiño3, John C McEwan3, Peter H Janssen3, Feng Chen1,2, Axel Visel1,2,4, Zhong Wang1,2,4, Graeme T Attwood3, Edward M Rubin1,2* Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA; 2Genomic Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; 3AgResearch Limited, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand; 4School of Natural Sciences, University of California, Merced, CA 95343 *Corresponding Author: Dr Edward M Rubin, Director, DOE Joint Genome Institute, and Director of Genome Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, MS 84R0171, Berkeley, CA 94720 Tel +1 510-486-6714 (direct) Fax +1 510-486-4229 Email: emrubin@lbl.gov Page -1- Supplementary Information Shi et al., 2013 This PDF file includes: Supplementary Materials and Methods Supplementary Figures Figure S1 Experimental design Figure S2 Methanogen 16S rRNA gene copy numbers in low and high CH4 emission sheep estimated using qPCR Figure S3 Comparison of methanogen abundance between low and high methane emission sheep overall, and by taxonomic class estimated using metagenome sequencing reads Figure S4 Community structure of Methanobacteria in low and high CH4 emission sheep estimated using metagenome sequences Figure S5 Rumen aceticlastic and methylotrophic pathways in low and high CH4 emission sheep Figure S6 Gene abundance profile of low and high CH4 emission sheep Figure S7 mRNA enrichment by applying hybridization-based approach in low and high CH4 emission sheep rumen RNA samples for metatranscriptome studies Figure S8 Gene expression profiles in low and high CH4 yield sheep Figure S9 Validation of assembly accuracy of mcr/mrt operons by PCR using primer pairs designed based on assembled mcr/mrt operons Figure S10 Phylogenetic analyses of the 35 full-length methyl coenzyme M reductase alpha subunit (McrA/MrtA) protein sequences with the unpublished McrA/MrtA sequences from cultured rumen methanogens using the Neighbor-Joining method Figure S11 mcrA gene and transcript abundance in low, intermediate and high CH4 yield sheep Figure S12 Validation of mcrA gene and transcript abundance in high, intermediate and low methane yield sheep using qPCR with gene specific primers to five selected mcrA/mrtA gene loccus Figure S13 Homology based strategies to identify differentially enriched genes and metabolic pathways in community level and mcr/mrt operon reconstruction from metagenome and metatranscriptome data Supplementary Tables Table S1 Summary of sequence data generated from rumen samples from low and high CH4-yield sheep rumen samples Table S2 Gene ontology analysis of the top ten highly expressed KEGG genes in the ‘high’ CH4 yield sheep rumen samples Table S3 Gene and transcript abundance of key methanogenesis pathway enzymes in sheep with low, intermediate and high CH4yields Table S4 Properties of the 35 reconstructed methyl-coenzyme M reductase (mcr/mrt) operons Table S5 Accuracy of assembly assessed by sequencing the PCR products containing mcr/mrt operons using PacBio sequencing technology Table S6 qPCR primers and amplification conditions Table S7 Sheep diet composition Table S8 Oligonucleotide primer sequences for amplification of 35 reconstructed mcr/mrt operons Page -2- Supplementary Information Shi et al., 2013 Supplementary Materials and Methods Sheep CH4 yield measurements and rumen contents sampling Measurement of CH4 yields from 96 rams (born August 2009) from the composite breed rams from the Woodlands Research Station progeny testing flock were originally carried out by Dr Cesar Pinares-Patiño and colleagues in March and April of 2010 and the Pastoral Greenhouse Gas Research Consortium (PGgRC) kindly made these data available to allow selection of rams to be used for this study (Pinares-Patino et al 2013) These CH4 yield data and sheep breeding values from the Central Progeny Testing, were used to select 11 high and 11 low CH 4-yielding rams from the Woodlands Research Station progeny flock These rams were transported to the New Zealand Ruminant Methane Measurement Centre, AgResearch Grasslands, in Palmerston North, and after adaptation to a pelleted lucerne diet (composition given in Supplementary Table 7) for two weeks, their CH4 yields were re-measured twice in respiration chambers over a period of two weeks (Figure 1B) Sheep were fed at 1.9× maintenance (fed twice daily, amounts ranging from 1.1 to 1.4 (average 1.3) kilogram (kg)/day depending on body weight; dry matter intakes ranged from 1.6 to 2.8 kg/day) Rumen contents were collected from all 22 sheep by stomach intubation, hours after the morning feeding Rumen contents were collected on two occasions (June 13 and June 28, 2011) immediately after the end of the CH4 measurement periods Immediately after collection, the pH of the rumen contents was measured and the samples were snap-frozen in liquid N2, and stored at -85°C for DNA and RNA extraction DNA extraction The extracted DNA was quantified using a Qubit dsDNA Broad Range Assay kit (Qubit 2.0, Invitrogen Inc., Carlsbad, CA, USA), for quality using a NanoDrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA), and for molecular weight via agarose gel electrophoresis with appropriate DNA size markers RNA extraction Page -3- Supplementary Information Shi et al., 2013 The RNA extraction method was based on hot lysis-acid phenol extraction Briefly, hot lysis buffer (3 mM sodium acetate, 30 mM EDTA and 1.5% SDS (w/v)) was mixed with frozen rumen contents and incubated at 95 °C for An equal volume of acid-phenol:chloroform, pH 4.5, with isoamyl alcohol (125:24:1; Ambion) was added to the mixture, and shaken vigorously, then cooled on ice The aqueous phase was recovered after brief, low-speed centrifugation and the acid-phenol isoamyl alcohol extraction was repeated The RNA extracted into the aqueous phase was finally precipitated with isopropanol RNA concentration was determined using Qubit analysis, and RNA quality was checked via Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA) SSU rRNA gene sequencing and analysis The amplicons of archaeal ssrRNA genes were generated according to Kittelmann et al (2013); and were quantified, and pooled at equimolar concentrations Subsequently, the amplicon pool was gel-purified, re-quantified and diluted to obtain a total of × 105 copies per μl according to the 454 pyrosequencing protocol for library preparation (454 Life Sciences, Branford, CT, USA) Ribosomal RNA depletion and cDNA library generation and sequencing for metatranscriptomic analysis For cDNA library construction, ~2.0 µg of total RNA per rumen sample was used as starting material for each sheep rumen sample Ribosomal RNA was removed from the total RNA samples using Ribo-Zero TM rRNA Removal Kit (Meta-Bacteria, Epicenter Biotechnologies, Madison, WI, USA) following the maunfacturer’s instructions The mRNA-enriched RNA resulting from this treatment, and one sample of untreated total RNA, were chemically Page -4- Supplementary Information Shi et al., 2013 fragmented to ~150 – 250 bp using mRNA Fragmentation Reagents (Ambion, Foster City, CA, USA) Double-stranded cDNA was synthesised using SuperScript II reverse transcriptase (Invitrogen, Carlsbad, CA, USA), with priming of the first strand using random hexamer primers (MBI Fermentas, NY, USA), and synthesis of the second strand by nick translation The cDNA sequencing libraries were generated using the Illumina TruSeqTM genomic sample prep kit (Illumina, San Diego, CA, USA) following the manufacturer’s instructions The synthesized ds cDNA was end-repaired and phosphorylated to generate blunt ends The ds cDNA was A-tailed and ligated to the sequencing adapters before library amplification by PCR The sequencing adapters contained unique index sequences which allowed samples to be pooled for sequencing and identified during subsequent sequence analysis A 10-cycle PCR with adaptor primers was applied using Illumina PCR master mix which was supplied with the Illumina TruSeq genomic prep kit The amplified libraries were purified and size-selected using 1.15× AMPure SPRI beads (Beckman Coulter, Brea, CA, USA) The cDNA libraries of the high and low CH4 yield sheep rumen samples were quantified, pooled equally and the pooled library was sequenced using the Illumina Hi-Seq 2000 platform to generate 2×150 bp paired-end reads Four Hi-Seq lanes were sequenced and generated a total of 135 Gb transcriptomic sequence data (Supplementary Table 1) The raw Illumina reads from transcriptomic sequencing were passed through the JGI-developed filtering program In addition, we also aligned the raw reads to the SILVA database (Pruesse et al 2007) to remove residual ribosomal RNA (rRNA) reads The artefact-filtered metatranscriptomic sequence data were used for further functional analyses Annotation of metagenome and metatranscriptome whole genome shotgun (WGS) reads Page -5- Supplementary Information Shi et al., 2013 Artifact-filtered metagenome and metatranscriptome WGS reads were annotated by comparison with the KEGG database (Release 58.1, June 1, 2011) (Kanehisa and Goto 2000) using USEARCH 6.0 (Edgar 2010) at an E-value cutoff of 1×10-5 (Mackelprang et al 2011) The relative abundance of each KEGG gene equals the number of hits to that gene in the specific sample normalized to the number of reads per million (RPM) reads used for USEARCH To quantify the abundance of ssr RNA genes in the low and high CH4 yield sheep rumen metagenome data, we aligned the jointed WGS reads to SILVA database (Pruesse et al 2007) and Greengenes database (DeSantis et al 2006) through Burrows-Wheeler Aligner (BWA)-based JGI in-house developed gene counting software at a cutoff of 97% identity A RDP Classifierbased JGI in-house developed pipeline was used to confirm these alignments (Supplementary Figs 3C & D) Additionally, 16S rRNA genes >200 bp in length from the jointed metagenome WGS were clustered into OTUs (>97% similarity) and a representative for each cluster was BLAST searched against an AgResearch in-house rumen archaea reference database (Supplementary Materials and Methods) to classify the taxonomy of each of the 16S rRNA gene reads based on the Green genes database (DeSantis et al 2006) Similar results were observed (Supplementary Figs 3C&D) In addition, the jointed metagenome WGS reads containing potential 16S rRNA genes identified by the RDP Classifier-based pipeline described above were filtered so that only the reads ≥ 200 bp were retained Subsequently, the sequencing reads were clustered into OTUs at 97% sequence similarity using the uclust algorithm A representative sequence from each cluster was blasted against the Greengenes database (McDonald et al 2012), in which all archaeal references were replaced with the AgResearch in-house rumen archaea reference database (Janssen and Kirs 2008) All OTUs that were assigned to bacterial taxa or remained unassigned (e.g low Page -6- Supplementary Information Shi et al., 2013 complexity reads) were deleted from the OTU table, and remaining archaeal OTUs were summarized at the clade level Reconstruction of mcr/mrt-containing operons of methanogens To reconstruct the mcr-containing operons (mcr/mrt operons) from metagenome sequence data, the jointed metagenome WGS reads were trimmed using a k-mer-based filtering approach For each sheep rumen sample, the reads with greater than two depths of k-mer were de novo assembled by Velvet (Zerbino and Birney 2008) using a k-mer length of 151, and insertion length of 250 bp All the contigs from low and high CH4 yield sheep rumen samples were combined The duplicated or small contigs covered by larger contigs were removed using the clustering function of Vmatch (http://www.vmatch.de) A total of 66,970 filtered contigs were subject to gene prediction using getorf software (Rice et al 2000) and resulted in 89,834 predicted open reading frames (ORFs) Then, the predicted ORFs were functionally annotated by blastx against the KEGG database, which indicated that 318 assembled contigs contained partial mcr/mrt operons, which were selected for further analysis Mcr-containing operons usually have four or five subunit genes (alpha, beta, delta, gamma, and epsilon) (Pihl et al., 1994) To obtain the complete or near complete mcr/mrt operons, we extracted the reads hitting mcr/mrt operon components from the entire metagenomic and metranscriptomic data sets, based on the Usearch results described above The pooled reads were used to extend the mcr/mrt operon containing contigs using an in-house program that finds reads that overlap the contigs by 51 bases and merges them onto the end using Cap3 (Huang and Madan 1999) Multiple iterations of alignment and merging will extend the contigs indefinitely as long as there is read coverage and 98% identity overlap of the reads to contigs From Page -7- Supplementary Information Shi et al., 2013 metagenome sequence data, we finally reconstructed a total of 170 complete or near complete mcr/mrt operons To capture the methanogens which have enriched transcripts but very low abundance, which did not allow them to be assembled from metagenome data, the metatranscriptome WGS reads were de novo assembled using Rnnotator (Martin et al 2010), a software pipeline for reference independent transcriptome assembly The same pipeline was applied (Supplementary Figure 13) to reconstruct the mcr/mrt operons from metatranscriptome data which resulted in 84 compete or near complete mcr/mrt operons The reconstructed operons from both metagenome and metatranscriptome sequences were combined, duplicates removed and small contigs covered by larger contigs using Vmatch as described above Finally, we obtained a total of 35 different mcr/mrt operons with full-length methyl coenzyme M reductase alpha subunit (mcrA/mrtA gene) from the sheep rumen samples Validation of gene assembly To validate the accuracy of gene assembly, we designed oligonucleotide primers (Supplementary Table 8) for each reconstructed mcr/mrt operon, and performed direct PCR amplification with metagenomic DNA extracted from the sheep rumen content samples as template Amplification used the Advantage Genomic LA Polymerase (Clontech, Bella Avenue Mountain View, California) with standard PCR conditions for specific amplification as follows: initial denaturation at 94°C for min; 30 cycles of denaturation at 98°C 10 s and extension at 68°C for per kb The final step of the PCR was an extension step at 72°C for 10 min, followed by cooling at 4°C The PCR products were analyzed by gel electrophoresis, and further confirmed Page -8- Supplementary Information Shi et al., 2013 by PacBio sequencing (Pacific Bioscience, CA, USA) Phylogenetic analysis of conserved mcrA/mrtA genes The protein sequences with homology to conserved mcrA/mrtA gene families were retrieved from the 35 assembled mcr/mrt operons First, the ORFs in assembled operons were predicted using getorf software (Rice et al 2000) Predicted ORFs were searched for the MCR_alpha domains using HMMER hmmsearch with Pfam HMMs with e-values smaller than 1×10 -4 To indicate the relationship of assembled sheep rumen mcrA/mrtA genes with known mcrA/mrtA genes in the NCBI protein database, we downloaded a total of 146 full-length mcrA/mrtA genes from various methanogens from a variety of environments Multiple sequence alignments of the predicted protein sequences of 35 mcrA/mrtA genes from sheep rumen and 146 mcrA/mrtA genes from NCBI protein database were created using Muscle (Edgar, 2004) and a phylogenetic tree was reconstructed using the Neighbor-Joining method (Saitou and Nei 1987) Quantitative PCR Methanogen abundance within rumen samples was validated by enumerating 16S rRNA gene copies by qPCR as described previously (Jeyanathan et al 2011) RBB+C extracted metagenomic DNA, diluted 100- and 1000-fold, was used as template for qPCR, and each dilution was amplified in duplicate External DNA standards, comprised of purified cloned methanogen 16S rRNA gene copies(Jeyanathan et al 2011), were serially diluted and amplified in parallel to the rumen metagenomic DNA to enable 16S rRNA gene copy numbers determination using Rotor-Gene 6000 Series Software (version 1.7) Page -9- Supplementary Information Shi et al., 2013 qPCR and reverse transcription qPCR (RT-qPCR) were used to quantify the abundance and expression of mcrA/mrtA genes from four randomly selected mcr/mrt operons that had higher levels of transcripts in the high CH4 yield sheep, and one mcr/mrt operon that was not differentially expressed between the high and low CH4 yield sheep Locus-specific PCR primers, designed in PRIMEGENS-v2 (Srivastava et al 2008), are listed in Supplementary Table RBB+C extracted metagenomic DNA was diluted 5-fold and 10-fold and used as template in qPCR reactions with specific primers at a final concentration of 0.5 µM each, and LightCycler 480 SYBR Green I Master kit (Roche Applied Science) amplification reagents Four replicates were performed for each sample DNA standards for quantification of mcr loci copy numbers were constructed by PCR amplification of each of the mcr loci using the locus-specific PCR primers, and ligating each amplicon separately into the pCR2.1 vector and transforming into TOP10 Chemically Competent E coli using a TA Cloning Kit (Life Technologies NZ Ltd, New Zealand) Plasmid DNAs from each cloned mcr locus, was purified using a Plasmid midi kit (Qiagen, Hilden, Germany), quantified by Qubit analysis using a Qubit ds DNA HS assay kit (Invitrogen Inc., Carlsbad, CA, USA) and diluted to generate a range of standards between 102 – 107 copies per µl for use in parallel PCR reactions to quantify the gene copy number of each locus Amplifications were performed in a Rotor-Gene 6000 using the following programme: initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 10 sec, annealing at 55°C for sec and elongation at 72°C for 10 sec Data were analyzed using LinRegPCR V12 (Magoc and Salzberg 2011) Samples with individual PCR efficiencies outside ±10% of the mean PCR efficiency were omitted from further analysis If the sample qPCR measurement (N0) fell below that of the limit of detection of the assay (estimated by the mean plus three standard deviations for the no template control reactions), the limit of detection was Page -10- Supplementary Information Shi et al., 2013 Figure S9 Validation of assembly accuracy of mcr/mrt operons by PCR using primer pairs designed based on assembled mcr/mrt operons Page -21- Supplementary Information Shi et al., 2013 Figure S10 Phylogenetic analyses of the 35 full-length methyl coenzyme M reductase alpha subunit (McrA/MrtA) protein sequences with the unpublished McrA/MrtA sequences from cultured rumen methanogens using the Neighbor-Joining method The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to each of the nodes The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site Page -22- Supplementary Information Shi et al., 2013 Figure S11 mcrA gene and transcript abundance in low, intermediate and high CH4 yield sheep (a) Gene abundance of 35 assembled mcrA/mrtA genes in low, intermediate and high CH4 yield sheep estimated by metagenome sequence; (b) Average gene abundance for three groups of identified sheep rumen methanogens; (c) Transcript abundance of 35 assembled mcrA/mrtA genes in low, intermediate and high CH4 emission sheep estimated by metatranscriptome sequence; (d) Average transcript abundance for three groups of identified sheep rumen methanogens RPM, mean reads per million NS, no significant difference in Wilcoxon rank-sum test between low and high methane emission sheep; *, P