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

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

For GENOME RESEARCH

Attwood3, Edward M Rubin1,2*

1 Department of Energy, Joint Genome Institute, Walnut Creek, CA 94598, USA; 2 Genomic Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; 3 AgResearch Limited, Grasslands Research Centre, Tennent Drive, Palmerston North 4442, New Zealand; 4 School 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

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

estimated using metagenome sequences

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alpha subunit (McrA/MrtA) protein sequences with the unpublished McrA/MrtA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

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 8 low and 8

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

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

Supplementary Materials and Methods

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 CH4-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, 4 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

RNA extraction

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 5 min 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 2 × 105 copies per μl according to l according to the 454 pyrosequencingprotocol 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,

resulting from this treatment, and one sample of untreated total RNA, were chemically

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 cDNAwas 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 8 high and

8 low CH4 yield sheep rumen samples were quantified, pooled equally and the pooled library wassequenced 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

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 Classifier-based 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 jointedmetagenome 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% sequencesimilarity 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

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 8 low and 8 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 byblastx 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 wereused 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

as long as there is read coverage and 98% identity overlap of the reads to contigs From

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 1 min; 30 cycles of denaturation at 98C 10 s and extension at 68C for 1min 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

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)

in parallel to the rumen metagenomic DNA to enable 16S rRNA gene copy numbers

determination using Rotor-Gene 6000 Series Software (version 1.7)

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 6 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 5 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 5 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 used to represent these values If the sample N0 value fell below the linear region of the standardcurve, these values were conservatively estimated by the value of the lowest linear standard on the standard curve Gene and transcript concentrations were determined by normalizing the qPCR measurements to the standard curve, and statistical analyses were performed as described

RT-qPCR of mcrA genes was performed by cleaning total RNA samples using Ambion

MEGAclear Purification kits (Life Technologies NZ Ltd, New Zealand) and removing DNA with a TURBO DNA-free (Applied Biosystems, Foster City CA, USA) DNase treatment

according to the manufacturer’s instructions The efficacy of the DNase treatment was confirmed

by the absence of amplified DNA products after PCR amplification of DNase-treated samples using universal bacterial 16S primers DNA-free RNA was quantified using a Qubit RNA HS assay kit, and five µg of cleaned RNA was reverse transcribed using a SuperScript

VILO_cDNASynthesis Kit (Invitrogen, Life Technologies NZ Ltd, New Zealand) The cDNA was further purified and concentrated using a DNA Clean and Concentrator kit (Zymo Research,

Irvine CA, USA), and used neat as a template in triplicate mcrA gene qPCR assays as described

above

DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL

2006 Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB

Appl Environ Microb 72(7): 5069-5072.

Edgar RC 2004 MUSCLE: multiple sequence alignment with high accuracy and high throughput Nucleic acids

research 32(5): 1792-1797.

Huang XQ, Madan A 1999 CAP3: A DNA sequence assembly program Genome Res 9(9): 868-877.

Janssen PH, Kirs M 2008 Structure of the archaeal community of the rumen Appl Environ Microb 74(12):

3619-3625

Jeyanathan J, Kirs M, Ronimus RS, Hoskin SO, Janssen PH 2011 Methanogen community structure in the rumens

of farmed sheep, cattle and red deer fed different diets Fems Microbiol Ecol 76(2): 311-326.

Kanehisa M, Goto S 2000 KEGG: kyoto encyclopedia of genes and genomes Nucleic acids research 28(1): 27-30.

Kittelmann S, Seedorf H, Walters WA, Clemente JC, Knight R, Gordon JI, Janssen PH 2013 Simultaneous

amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic

microorganisms in rumen microbial communities PloS one 8(2 ): e47879.

Mackelprang R, Waldrop MP, DeAngelis KM, David MM, Chavarria KL, Blazewicz SJ, Rubin EM, Jansson JK

2011 Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw Nature

480(7377): 368-371.

Magoc T, Salzberg SL 2011 FLASH: fast length adjustment of short reads to improve genome assemblies

Bioinformatics 27(21): 2957-2963.

Martin J, Bruno VM, Fang ZD, Meng XD, Blow M, Zhang T, Sherlock G, Snyder M, Wang Z 2010 Rnnotator: an

automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads BMC Genomics 11:

663.

McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P

2012 An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of

bacteria and archaea ISME J 6(3): 610-618.

Pihl TD, Sharma S, Reeve JN 1994 Growth phase-dependent transcription of the genes that encode the two methyl

coenzyme M reductase isoenzymes and N5- methyltetrahydromethanopterin:coenzyme M

methyltransferase in Methanobacterium thermoautotrophicum ΔH H Journal of Bacteriology 176:6384-6391

Pinares-Patino C, Hickey, SM, Young, EA, Dodds, KG, MacLean, S, Molano, G, Sandoval, E, Kjestrup, H,

Harland, R, Hunt, C, Pickering, NK, McEwan, JC 2013 Heritability estimates of methane emissions in

sheep Animal 7: 316-321

Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig WG, Peplies J, Glockner FO 2007 SILVA: a comprehensive

online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB

Nucleic acids research 35(21): 7188-7196.

Rice P, Longden I, Bleasby A 2000 EMBOSS: The European molecular biology open software suite Trends Genet

16(6): 276-277.

Saitou N, Nei M 1987 The Neighbor-Joining method - a new method for reconstructing phylogenetic trees Mol

Biol Evol 4(4): 406-425.

Srivastava GP, Guo J, Shi H, Xu D 2008 PRIMEGENS-v2: genome-wide primer design for analyzing DNA

methylation patterns of CpG islands Bioinformatics 24(17): 1837-1842.

Zerbino DR, Birney E 2008 Velvet: algorithms for de novo short read assembly using de Bruijn graphs Genome

Res 18(5): 821-829.

Supplementary Figure

Figure S1 Experimental design

Figure S2 Methanogen 16S rRNA gene copy numbers in low and high CH 4 emission sheep estimated using qPCR

Figure S3 Comparison of methanogen abundance between low and high CH4 emission sheep overall, and by taxonomic class estimated using metagenome sequencing reads (a)

Overall abundance of methanogens in low and high CH4 emission sheep estimated based on Greengenes 16S rRNA database; (b) Methanogen community structure based on Greengenes 16SrRNA database; (c) Overall abundance of methanogens quantified using RDP classifier based on Greengenes 16S rRNA database; (d) Methanogen community structure based on RDP classifier results Error bars denote standard deviations

Figure S4 Community structure of Methanobacteria in low and high CH 4 emission sheep estimated using metagenome sequences (a) and PCR-based pyrotag sequences (b) based on

assignment using a defined taxonomy database *, P<0.05 in Wilcoxon rank-sum test; **,

P<0.01.

Figure S5 Rumen aceticlastic and methylotrophic pathways in low and high CH 4 emission sheep (a) Diagram of aceticlastic and methylotrophic pathways (b) Comparison of abundance

of gene encoding ECs of aceticlastic and methylotrophic pathways between low and high CH4emission sheep (c) Comparison of expression of gene encoding ECs of aceticlastic and

methylotrophic pathway between low and high CH4 emission sheep Error bars denote standard

deviation (n = 8) NS, no significant difference in Wilcoxon rank-sum test,* P<0.01; **, P <

0.05