Kamke et al. BMC Res Notes (2017) 10:367
DOI 10.1186/s13104-017-2671-0
RESEARCH ARTICLE
BMC Research Notes
Open Access
Gene and transcript abundances
of bacterial type III secretion systems from the
rumen microbiome are correlated with methane
yield in sheep
Janine Kamke1, Priya Soni1, Yang Li1, Siva Ganesh1, William J. Kelly1, Sinead C. Leahy1, Weibing Shi2,3,
Jeff Froula2,3, Edward M. Rubin2,3 and Graeme T. Attwood1*
Abstract
Background: Ruminants are important contributors to global methane emissions via microbial fermentation in their
reticulo-rumens. This study is part of a larger program, characterising the rumen microbiomes of sheep which vary
naturally in methane yield (g CH4/kg DM/day) and aims to define differences in microbial communities, and in gene
and transcript abundances that can explain the animal methane phenotype.
Methods: Rumen microbiome metagenomic and metatranscriptomic data were analysed by Gene Set Enrichment,
sparse partial least squares regression and the Wilcoxon Rank Sum test to estimate correlations between specific
KEGG bacterial pathways/genes and high methane yield in sheep. KEGG genes enriched in high methane yield
sheep were reassembled from raw reads and existing contigs and analysed by MEGAN to predict their phylogenetic
origin. Protein coding sequences from Succinivibrio dextrinosolvens strains were analysed using Effective DB to predict
bacterial type III secreted proteins. The effect of S. dextrinosolvens strain H5 growth on methane formation by rumen
methanogens was explored using co-cultures.
Results: Detailed analysis of the rumen microbiomes of high methane yield sheep shows that gene and transcript
abundances of bacterial type III secretion system genes are positively correlated with methane yield in sheep. Most of
the bacterial type III secretion system genes could not be assigned to a particular bacterial group, but several genes
were affiliated with the genus Succinivibrio, and searches of bacterial genome sequences found that strains of S.
dextrinosolvens were part of a small group of rumen bacteria that encode this type of secretion system. In co-culture
experiments, S. dextrinosolvens strain H5 showed a growth-enhancing effect on a methanogen belonging to the order
Methanomassiliicoccales, and inhibition of a representative of the Methanobrevibacter gottschalkii clade.
Conclusions: This is the first report of bacterial type III secretion system genes being associated with high methane emissions in ruminants, and identifies these secretions systems as potential new targets for methane mitigation
research. The effects of S. dextrinosolvens on the growth of rumen methanogens in co-cultures indicate that bacteriamethanogen interactions are important modulators of methane production in ruminant animals.
Keywords: Rumen, Methane, Bacterial, Type III secretion, Succinivibrio
*Correspondence: graeme.attwood@agresearch.co.nz
1
AgResearch Limited, Grasslands Research Centre, Tennent Drive,
Palmerston North 4442, New Zealand
Full list of author information is available at the end of the article
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kamke et al. BMC Res Notes (2017) 10:367
Background
Methane emissions from enteric fermentation in livestock are important contributors to global warming [1,
2] and most of these emissions are from the action of
methanogenic archaea in the reticulo-rumen of ruminant animals. In the reticulo-rumen, methanogens use
the products of bacterial fermentation, (hydrogen, carbon dioxide, formate and methyl compounds) to produce methane [3, 4]. Considerable amounts of methane
are produced each day, a typical sheep produces ~40 L
of methane per day, while a dairy cow belches around
450 L each day [5–7]. Methane is a particularly strong
greenhouse gas with a global warming potential of 34×
that of CO2 [1], and many researchers around the world
are investigating ways to reduce methane emissions
from ruminants. Animal selection is being investigated
as a means of breeding low methane emitting animals
[8–14], and sheep with consistently high methane yields
(HMY; defined in g methane/kg dry matter intake DMI/
day) or low methane yield (LMY) have been identified.
The methane yield trait is heritable [8], and the animal
characteristics controlling feed particle retention time
[15–18] and rumen volume [10] are likely to contribute
to the trait. Microorganisms in the rumen also contribute
to the methane yield phenotype, and differences between
HMY and LMY sheep in rumen microbiome composition [19] and methanogen gene expression [20] have
been reported. We have recently found that methanogen
changes in LMY animals are paralleled by an enrichment
in Sharpea spp. in the bacterial community and higher
abundance of the genes and transcripts encoding lactate
formation and metabolism pathways that lead to lower
hydrogen production and therefore lower methane formation in the rumen [21]. Here, we report the analysis of
the rumen microbiomes of HMY animals and report the
finding that gene and transcript abundances of bacterial
type III secretion system (T3SS) genes are positively correlated with HMY in sheep.
Methods
Study aim and design
This report is part of a larger study [20], the aim of which
was to characterise the rumen microbiomes of sheep
which differed in their methane yield (g CH4/kg DMI/
day) to investigate whether there were corresponding
differences in microbial communities, and in gene and
transcript abundances that could explain the animal
methane phenotype. Rams (11 HMY and 11 LMY) were
selected based on their methane yields and breeding values [8] and their methane yields were re-measured twice
(measurements were separated by 2 weeks) in respiration
chambers at the New Zealand Ruminant Methane Measurement Centre, AgResearch Grasslands, Palmerston
Page 2 of 14
North, New Zealand, on a pelleted lucerne diet [20].
Samples of rumen contents were collected at the end of
each methane measurement period, by stomach intubation 4 h after the morning feeding. The pH of rumen contents was measured and the samples were immediately
snap-frozen as pellets in liquid N2, and stored at −85 °C
prior to DNA and RNA extractions. Based on the remeasured methane yields, frozen rumen samples were
selected from 4 HMY (group mean 15.85 g CH4/kg DMI/
day) and 4 LMY (group mean 11.44 g CH4/kg DMI/day)
animals (one sample each per methane measurement, 16
samples), and from 2 animals which had intermediate
methane yield (IMY, group mean 13.77 g CH4/kg DMI/
day) (one each at the two methane measurement points,
4 samples) and used for DNA and RNA extractions (20
samples for each extraction). Nucleic acids extractions,
purifications and library construction followed protocols described in the parent study [20]. Briefly, DNA
was extracted using the “Repeated Bead Beating and
Column (RBB + C) purification” method [22]. For large
paired-end insert libraries, high-molecular-weight DNA
was extracted using the method of Rosewarne et al. [23].
RNA extraction was via a hot lysis-acid phenol extraction
method [20]. For cDNA library construction, total RNA
was enriched for mRNA using the Ribo-Zero TM rRNA
Removal Kit (Meta-Bacteria, Epicenter Biotechnologies,
Madison, WI, USA), and fragmented using mRNA Fragmentation Reagents (Ambion, Foster City, CA, USA).
Double-stranded cDNA (ds cDNA) was synthesised
using SuperScript II reverse transcriptase (Invitrogen,
Carlsbad, CA, USA), using random hexamers (MBI Fermentas, NY, USA). The cDNA sequencing libraries were
generated and amplified using the Illumina TruSeqTM
genomic sample prep kit (Illumina, San Diego, CA, USA)
following the manufacturer’s instructions. The amplified
libraries were purified and size-selected and the pooled
library was sequenced using the Illumina HiSeq 2000
platform. The metagenomic sequencing produced, on
average, 216 M reads per sample (51 Gb sequence), while
the metatranscriptome sequencing produced 35 M reads
(5.3 Gb non-rRNA sequence) per sample. All sequencing data used in this study has been previously published
by our group and detailed description of data processing can be found in these publications [20, 21]. Briefly,
quality-checked 16S rRNA amplicon sequencing data
was processed using the QIME pipeline [24] and phylogenetically assigned using the Greengenes database (version gg_13_5) [25]. Metagenomic and metatranscriptome
sequencing datasets were quality and artifact filtered and
paired end reads merged using FLASH [26] and used
for read count based comparative analysis by screening against the KEGG database [27], (version 58.1) using
USEARCH 6.0 [28] at an E-value cutoff of 1 × 10−5 and
Kamke et al. BMC Res Notes (2017) 10:367
read count matrices were constructed and normalised to
reads per million (RPM).
Analysis of fermentation acids from rumen samples was performed on a Shimadzu 5050a GC–MS
(Shimadzu, Kyoto, Japan) equipped with a ZB-5 MS
(30 m × 0.25 mm ID × 0.25 µm film thickness, Phenomenex, USA) capillary column. Acids were extracted from
the acidified samples with ether and derivatised to their
t-butyl-dimethylsilyl esters, which detects both volatile
and non-volatile fatty acids.
Page 3 of 14
ones, were removed using the clustering function in
Vmatch (http://www.vmatch.de). The resulting contigs
were submitted to IMG/Mer [32] for gene-calling and
automatic annotation. All assembled genes with hits to
the relevant KEGG genes were extracted and combined
with the corresponding reads from metagenome and
metatranscriptome data. Contigs were extended using
the Distributed Nucleating Assembler function in Kmernator (https://github.com/JGI-Bioinformatics/Kmernator). Genes on the resulting contigs were predicted using
MetaGeneMark [33].
Data analyses
Statistical analyses were conducted using reads per million (RPM)-normalised read count matrices as previously
described [21]. Gene set enrichment analysis (GSEA,
[29]) was used to estimate differential gene and transcript
abundances between HMY or LMY animals (n = 16) at
the pathway level, based on KEGG pathways. The GSEAP application [29] was used to pre-rank genes based on
signal-to-noise metric scores and to estimate normalised
enrichment scores (NES), nominal P values and false discovery rates (FDR) with 10,000 phenotype permutations.
Sparse partial least squares regression analysis (sPLS) in
R [30] was used to estimate correlations between specific
KEGG genes and methane yield, using the spls package [31]. Genes selected as methane predictors were
assessed using the optimum sparsity tuning parameter
(eta) and number of hidden components (K) predicted in
the mean squared prediction error plot (MSPE). KEGG
gene predictors with confidence intervals (95%) shifting
from positive to negative correlation or from negative to
positive, were excluded from the predictor gene set. The
selected KEGG gene sets of the sPLS regression analysis
of each dataset were manually compared with the results
of categorical statistical analysis using Wilcoxon Rank
Sum (WRS) test (10,000 permutations) [20] and GSEA
pathways enrichment scores. For all statistical analyses a
significance threshold of Benjamini Hochberg (BH) corrected P < 0.05 was used. Subsequent analyses concentrated on KEGG gene sets or gene categories supported
by two or more methods of analysis.
Read extraction and assembly
To gain an overview of the taxonomic origin of bacterial
T3SS genes we reassembled these genes based on raw
reads and contigs from existing assemblies, with hits to
any of the KEGG genes encoding for the relevant subunits (K03219, K03221–K03230, K04056–K04059) from
both metagenome and metatranscriptome data. Individual assemblies based on the 20 metagenome and 20
metatranscriptome datasets were constructed as previously described [20]. Assemblies were combined, and
duplicated contigs, or smaller contigs covered by larger
Phylogenetic assignments of bacterial T3SS genes
and prediction of effector proteins
Re-assembled contigs that contained partial and full
length T3SS genes were imported into Geneious v.7.0.5
(Biomatters Ltd, Auckland, New Zealand) and de novo
assembled into larger contigs where possible. The resulting 73 contigs were BLAST searched against the NCBI
non-redundant database using BLASTX [34]. The output was analysed using MEGAN5 [35] for phylogenetic
assignment using default settings. Matches were confirmed by homology searches of relevant genes in the
IMG/M database [32]. Protein coding sequences from
the draft genomes of S. dextrinosolvens strains H5, 22B
and ACV-10 were analysed using EffectiveDB [36], which
uses various software tools to predict bacterial secreted
proteins based on their amino acid sequences. These
tools include EffectiveT3 for prediction of Type III secretion signals, EffectiveCCBD for detection of conserved
binding domains of Type III chaperones, EffectiveELD
for secretion system independent prediction of secreted
proteins based on eukaryotic-like domains, T4SEpre for
recognition of Type IV secreted proteins and Predotar to
screen N-terminal targeting sequences to predict their
subcellular localization in eukaryotic host cells.
Co‑culture experiments
Cultures of rumen methanogens were inoculated with
Succinivibrio dextrinosolvens strain H5 to explore its
effects on methane production in vitro. Two Methanobrevibacter species were used, representing hydrogenotrophic methanogens within the Mbb. ruminantium
clade (Mbb. ruminantium M1) or Mbb. gottschalkii clade
(Mbb. millerae SM9), along with two methylotrophs representing the Methanosphaera clade (Methanosphaera
sp. ISO3-F5) and the order Methanomassiliicoccales,
(methanogenic archaeon ISO4-H5). Each methanogen
was grown with S. dextrinosolvens strain H5 in either
RM02 medium [37] with hydrogen + carbon dioxide (for
the hydrogenotrophic methanogens, 1 atm over-pressure of a 80:20, hydrogen:carbon dioxide) or BY medium
[38] with methanol + hydrogen (1 atm over pressure of
Kamke et al. BMC Res Notes (2017) 10:367
80:20, hydrogen:carbon dioxide + 20 mM methanol).
Both media contained acetate (20 mM) and coenzyme
M (1 mM). The hydrogenotrophic and methylotrophic
methanogen cultures were grown in their respective
media until methane was first detected in the culture
headspace, then were inoculated with 10% inoculum
S. dextrinosolvens strain H5 with glucose (10 mM final
concentration) or pectin (1% pectin final concentration)
added as a substrate for the S. dextrinosolvens, respectively. Incubation was continued until maximal methane
production was detected in the control tubes. Control
tubes contained methanogen cultures which received
only the growth substrate without the S. dextrinosolvens
inoculum. The pHs of the cultures were measured after
inoculation and after completion of growth using pH
indicator strips (Merck KGaA, Darmstadt, Germany).
Methane and hydrogen concentrations in the culture
headspaces were determined by removing a sample of
the gases and analysing by gas chromatography (Aerograph 660, Wilkins Instruments & Research Inc., Walnut
Creek, CA, USA) against methane and hydrogen standards. Samples of cultures were taken before and after
incubation for analysis by gas–liquid chromatography
to determine production or use of volatile fatty acids.
Samples were centrifuged (21,000×g at 4 °C for 10 min)
and 0.9 mL of the supernatant was added to 0.1 mL of
internal standard solution (19 mM ethyl butyrate in 20%
(v/v) phosphoric acid). Samples were kept at −20 °C
until analysis, when they were thawed, clarified by centrifugation (21,000×g at 4 °C for 10 min) and 0.8 mL of
the supernatant was transferred into a 2-mL crimp cap
vial for analysis by gas–liquid chromatography. Supernatant samples were analysed on a nitroterephthalic
acid-modified polyethylene glycol column (DB-FFAP;
30 m × 0.53 mm × 1.0 µm film thickness; J & W Scientific, Folsom, CA, USA) attached to a Hewlett-Packard
6890 series gas chromatography system, using helium
as the carrier gas (5 mL/min). The oven temperature
started at 85 °C, ramped to 200 °C at 10 °C/min, was
held at 200 °C for 10 min, and then decreased to 50 °C
and held for 5 min before the next sample was injected.
Peaks were detected with a flame ionization detector,
identified by comparison with standards, and integrated
with Hewlett-Packard ChemStation software (version
4.02).
Animal ethics approval
The collections of rumen contents from sheep were carried out under the approval of the AgResearch Ltd Grasslands Animal Ethics Committee (Approval 13606).
Consent to participate and publish
Not applicable.
Page 4 of 14
Results
T3SS genes and transcripts are enriched in HMY animals
The metagenomic and metatranscriptomic data generated
from rumen samples collected from LMY and HMY sheep
were analysed by sPLS and the WRS test, and 60 genes and
36 transcripts were significantly (BH corrected P < 0.05)
more abundant in the HMY animals in both analyses (Additional file 1). Those genes and transcripts that were twofold or greater more abundant in the HMY animals were
enriched for KEGG genes related to bacterial T3SS subunits
and are listed in Table 1. In the WRS test, 12 out of a total of
15 subunits known to be involved in bacterial T3SSs had significantly more reads in the HMY animals. Ten T3SS subunits were also identified as correlation predictors in the sPLS
analysis (Table 2) with an adjusted multiple regression coefficient of R2 = 0.76, (P = 3.48 × 10−7, Fig. 1). At the transcriptome level, a similar but weaker trend was observed,
with two subunits showing significantly higher transcript
abundance in HMY animals and one subunit being selected
as a predictor gene for methane yield in the sPLS regression
analysis (Table 2). The GSEA identified Bacterial secretion
systems (ko03070) as the top ranked pathway represented
in the metagenome dataset associated with HMY animals
(Table 3) along with Drug metabolism and other enzymes
(ko00983) and Alanine, aspartate and glutamate metabolism (ko00250) which showed a similar trend. Hierarchical
clustering analysis based on Z-scores for both metagenome
and metatranscriptome data, provided further support for
differential T3SS gene abundance and expression between
HMY and LMY animals (Fig. 2).
Bacterial T3SS genes in the rumen are associated
with unidentified Proteobacteria and the genus
Succinivibrio
To identity the origin of the T3SS genes found in the
rumen metagenome and metatranscriptome sequences,
we screened for contigs containing these genes, and a total
of 101 genes were found on the 73 contigs retrieved. Using
the lowest common ancestor algorithm in MEGAN, 82%
of contigs were assigned to Bacteria, while the remaining
18% could not be assigned to any phylogenetic grouping. Some Bacteria-associated contigs could be further
assigned (Fig. 3), with the largest group falling into the
Proteobacteria (40%, 24 contigs). The largest genus-level
group within the Proteobacteria was Succinivibrio (9 contigs, 12%). The phylogenetic affiliations of the Succinivibrio contigs were explored using manual BLAST searches
against the IMG database. T3SS genes were identified in
draft genomes of Succinivibrio dextrinosolvens strains H5,
22B, ACV-10, and DSM 3072 (Additional file 2). Further
searches for T3SS KOs in bacteria isolated from the rumen
showed that T3SS genes were also present in Succinimonas
amylolytica DSM 2873 (type III secretion genes yscC, J, L,
Kamke et al. BMC Res Notes (2017) 10:367
Page 5 of 14
Table 1 T3SS genes significantly enriched in metagenome and metatranscriptome datasets from HMY animals
Dataset
Metagenome
Metatranscriptome
KEGG
gene
Definition/gene name
Mean RPM
LMY ± SD
WRS
HMY ± SD
Fold
change
P adj. BH
sPLS
coefficient
K03224
ATP synthase type III secretion protein sctN 2.47 ± 1.22
8.32 ± 2.45
3.37
<0.01
0.02
K03227
Type III secretion protein sctS
1.54 ± 0.44
3.33
<0.01
0.02
0.46 ± 0.23
K03230
Type III secretion protein sctV
3.68 ± 1.64
12.2 ± 3.52
3.32
<0.01
0.02
K03228
Type III secretion protein sctT
1.11 ± 0.56
3.57 ± 1.10
3.21
<0.01
0.02
K03229
Type III secretion protein sctU
1.60 ± 0.72
5.05 ± 1.47
3.16
<0.01
0.02
K03226
Type III secretion protein sctR
1.23 ± 0.52
3.78 ± 1.21
3.06
<0.01
0.01
K03223
Type III secretion protein sctL
0.46 ± 0.23
1.31 ± 0.38
2.83
<0.01
0.02
K03222
Type III secretion protein sctJ
0.81 ± 0.44
2.52 ± 0.74
3.13
0.02
0.02
K03219
Type III secretion protein sctC
1.22 ± 0.60
3.71 ± 1.03
3.03
0.02
0.02
K13853
3-deoxy-7-phosphoheptulonate synthase/ 0.14 ± 0.04
chorismate mutase aroG, aroA
0.38 ± 0.39
2.82
0.03
NA
K04058
Type III secretion protein sctW
0.17 ± 0.10
0.40 ± 0.07
2.33
0.05
0.02
K04056
Type III secretion protein sctO
0.11 ± 0.07
0.30 ± 0.08
2.72
0.05
0.02
K03230
Type III secretion protein sctV
0.98 ± 0.33
2.42 ± 0.97
2.47
0.03
NA
K00814
Alanine transaminase GPT
2.15 ± 0.77
4.77 ± 1.25
2.21
0.03
0.03
K00772
5′-methylthioadenosine phosphorylase
mtaP
3.07 ± 0.84
7.64 ± 5.98
2.49
0.04
NA
K03226
Type III secretion protein sctR
0.18 ± 0.15
0.62 ± 0.30
3.41
0.04
0.03
N, R, S, T, U, and V) and Desulfotomaculum ruminis DSM
2154 (yscN, ATP synthase type III secretion protein N
[EC:3.6.3.14]) but in no other rumen bacteria.
Analysis of the scaffold sequences of the draft genomes of
S. dextrinosolvens strains H5, 22B, ACV-10, and DSM 3072,
showed that the genes encoding the T3SS structural subunits are arranged in conserved structures (Fig. 4) with several other genes known to be associated with T3SSs. These
include genes encoding low calcium response chaperones
(LcrH/SycD), several Tir chaperone proteins (CesT), secretion system effector C-like family protein (SseC; involved
in translocon formation) and TyeA proteins (involved in
translocation of Yersinia outer proteins into eukaryotic
cells). Analysis of the amino acid sequences of the predicted
protein coding sequences of S. dextrinosolvens strains H5,
22B and ACV-10 using the EffectiveDB suite of software,
identified proteins predicted to be secreted by T3SSs, proteins containing conserved chaperone binding domains in
their N-terminal regions, eukaryotic-like N-terminal signal
sequences, and also uncovered several T4SS effector proteins in each of the strains (summarised in Table 4, individual proteins are shown in Additional file 3).
Succinivibrio dextrinosolvens stimulates methane formation
in the Methanomassiliicoccales affiliated methanogenic
archaeon ISO4‑H5
The preceding analyses indicated that T3SSs, some of
which were similar to those found in ruminal strains
of Succinivibrio spp., were more abundant within the
microbiomes of HMY versus LMY animals, and indicate
a possible association of Succinivibrio with the HMY
phenotype. Therefore experiments were performed to
explore the effects of S. dextrinosolvens strain H5 on
methane formation by common hydrogenotrophic and
methylotrophic methanogens from the rumen. Inoculation of S. dextrinosolvens H5 into methanogen cultures
stimulated methane formation in the methanogenic
archaeon sp. ISO4-H5 (Fig. 5). The stimulation of methane formation was observed 24 h after S. dextrinosolvens
inoculation and became significant after 48 h of co-culture. S. dextrinosolvens inoculation had a slight inhibitory
effect on Mbb. millerae SM9, reducing methane by 21%
at 96 h relative to the control (P < 0.05). S. dextrinosolvens inoculation had no effect on methane formation in
either Mbb. ruminantium M1 or Methanosphaera sp.
ISO3-F5 (Fig. 5). Formate was the main VFA produced in
the co-cultures (Table 5).
Discussion
The reduction of methane emissions from ruminants is
being addressed via several lines of research, including
the selection of animals based on their methane yields
[8]. As the methane emission trait is heritable in sheep,
there is scope to select for flocks with low methane emissions, and such breeding programmes need to understand the mechanisms underlying the trait to enhance the
Kamke et al. BMC Res Notes (2017) 10:367
Page 6 of 14
Table 2 T3SS subunits identified as correlation predictors in sPLS analysis
T3SS protein
KEGG
LMY
HMY
P
P adj. BH
Fold
change
sPLS
coefficient
Sample
type
SctC
K03219
1.22
3.71
0.00
0.02
3.03
0.02
DNA
SctF
K03221
0.06
0.05
0.92
1.00
1.22
na
DNA
SctJ
K03222
0.80
2.52
0.00
0.02
3.13
0.02
DNA
SctL
K03223
0.46
1.31
0.00
0.01
2.83
0.02
DNA
SctN ATP synthase
K03224
2.47
8.32
0.00
0.01
3.37
0.02
DNA
SctQ
K03225
0.04
0.07
0.00
0.05
1.95
0.02
DNA
SctR
K03226
1.23
3.78
0.00
0.01
3.06
0.01
DNA
SctS
K03227
0.46
1.54
0.00
0.01
3.33
0.02
DNA
SctT
K03228
1.11
3.57
0.00
0.01
3.21
0.01
DNA
SctU
K03229
1.60
5.05
0.00
0.01
3.16
0.02
DNA
SctV
K03230
3.68
12.22
0.00
0.01
3.32
0.02
DNA
SctO
K04056
0.11
0.30
0.00
0.05
2.72
0.02
DNA
SctP
K04057
0.01
0.00
0.65
1.00
1.23
na
DNA
SctW
K04058
0.17
0.40
0.00
0.05
2.33
0.02
DNA
SctX
K04059
0.02
0.04
0.14
0.63
1.91
na
DNA
SctC
K03219
0.21
0.34
0.02
0.14
1.62
na
RNA
SctF
K03221
0.04
0.03
0.71
0.84
1.30
na
RNA
SctJ
K03222
0.28
0.79
0.01
0.09
2.78
na
RNA
SctL
K03223
0.04
0.07
0.37
0.56
1.91
na
RNA
SctN ATP synthase
K03224
0.73
1.99
0.00
0.07
2.71
na
RNA
SctR
K03226
0.18
0.62
0.00
0.04
3.41
0.03
RNA
SctS
K03227
0.08
0.24
0.01
0.07
3.03
na
RNA
SctT
K03228
0.04
0.15
0.01
0.12
3.38
na
RNA
SctU
K03229
0.23
0.38
0.07
0.23
1.63
na
RNA
SctV
K03230
0.98
2.42
0.00
0.03
2.47
na
RNA
SctW
K04058
0.03
0.04
0.56
0.73
1.24
na
RNA
na not applicable
Fig. 1 Multiple regression plot of 10 bacterial T3SS subunit genes.
The multiple regression plot is based on partial least squares prediction of correlation coefficient over all 20 samples. Low methane yield
animals are shown in green, intermediate methane yield animals in
blue and high methane yield animals in red. Adjusted multiple regression coefficient R2 = 0.76, P = 3.48 × 10−7
selection process and to avoid negative impacts on digestive processes and animal productivity. We have shown
previously that the microbiome of LMY sheep has small
differences in methanogen communities [19] but large
differences in the expression of genes encoding the methanogenesis pathway [20]. We have also recently shown
that the bacterial community in LMY animals is enriched
for Sharpea spp. and expresses metabolic pathways that
lead to lower hydrogen production and therefore lower
methane formation. Here, the microbiomes of HMY
animals have been analysed and we report the unusual
finding that gene and transcript abundances of bacterial
T3SS genes are positively correlated with HMY in sheep.
The enrichment of T3SS genes in rumen metagenome
sequences from HMY animals is surprising as T3SSs are
uncommon in the rumen microorganisms that have been
characterised to date. T3SS are most commonly found
in pathogenic, Gram-negative bacteria where they are
known as “injectisomes”, because they encode a protein
complex that forms a needle-like appendage [39] which is
Kamke et al. BMC Res Notes (2017) 10:367
Page 7 of 14
Table 3 Gene set enrichment analysis of the metagenome dataset
KEGG pathway
NES
NOM P‑val
FDR q‑val
FWER P‑val
ko03070-bacterial secretion system
1.70
0.019
0.470
0.386
ko00983-drug metabolism and other enzymes
1.55
0.039
0.688
0.773
ko00250-alanine, aspartate and glutamate metabolism
1.55
0.036
0.538
0.782
Pathways with genes differentially present or expressed based on nominal P value (NOM P ≤ 0.05) are shown, ranked by normalised enrichment score (NES). Corrected
P values for false discovery rate (FDR q-val) and familywise-error rate (FWER P val) are shown.
used during infection to translocate effector proteins into
the host to promote pathogen survival and resistance to
the host immune system. The protein subunits which
are typically found in T3SSs are the cytoplasmic subunits (SctQ, L, O, and N which make up the cytoplasmic
ring protein, stator, stalk, accessory protein, and ATPase
respectively); the export apparatus (SctV, U, R, S, and T
which are the major export protein, switch protein, three
minor export proteins, respectively) and the base, and
needle proteins (SctJ, D, I, C, P, X and F which are the
inner Membrane Supramembrane (MS) ring, the outer
MS ring, the inner rod, the secretin, the needle length
regulator, a secretion protein and the needle filament,
respectively). Representatives of nearly all the genes
encoding subunits required for T3SS assembly were
identified in the metagenome of HMY animals, including the cytoplasmic components (SctQLON), the export
proteins (SctVURST) and the base and needle proteins
(SctJDICF) and their occurrence and level of statistical
support are shown in Fig. 6. Interestingly, genes encoding the needle protein (SctF), the needle length regulator
(SctP) and the putative animal-specific secretion protein (SctX) were present at very low levels and were not
enriched in HMY animals. T3SS in human pathogenic
bacteria are also known to allow invasion of other hosts,
including free-living amoebae and protozoa, which forms
important environmental reservoirs for these pathogens
and may protect the internalized bacteria from detection and treatment with biocides [40]. Although most
common in pathogens, T3SS are also found in bacteria
that engage in symbiotic relationships with their animal
or plant hosts, and in this context the role of T3SS is
thought to be in determining the specificity and maintenance of the symbiotic interaction [41].
The majority of T3SS genes found in the metagenome dataset could not be assigned a particular phylogenetic origin, and appear to belong to unidentified and/or
uncultured members of the rumen microbiome. Of those
sequences that could be assigned, most were affiliated
with the phylum Proteobacteria, and at the genus level
to Succinivibrio, which includes as its only species the
succinate-producing S. dextrinosolvens [42]. Overall, the
occurrence of genes encoding T3SSs in rumen bacteria
was low; using available genome sequences, T3SS genes
were only found in four strains of Succinivibrio dextrinosolvens, in another succinate-forming bacterium Succinimonas amylolytica DSM 2873 [43] and one gene (yscN)
was found in the sulphate-reducing bacterium Desulfotomaculum ruminis DSM 2154 [44]. None of the ruminal
strains of Sharpea, or the human gut organism Akkermansia mucinophila (the only genome available from a
member of the phylum Verrucomicrobia isolated from
a gut environment), contained T3SS gene homologues,
therefore it is likely that the T3SS assigned to these genera by MEGAN belong to unidentified ruminal species.
From examination of their draft genome sequences,
strains of the rumen bacterial species S. dextrinosolvens
appear to encode complete T3SS, and along with Succinimonas amylolytica are the only two characterised
rumen bacteria that have this type of secretory system.
S. dextrinosolvens is a Gram negative, curved rod-shaped
bacterium that degrades starch, and produces succinate,
acetate, formate and sometimes lactate as its main end
products of fermentation [42]. It is known to be part of
the epimural (adherent to the rumen wall) community
in the bovine [45, 46] and is typically enriched when the
diet contains high levels of starch [47, 48]. Succinivibrio
are considered to be a small, but consistent part of the
normal rumen microbiome [49] and is not known to be
pathogenic to ruminants, although it has been reported
as the infective agent in 2 cases of human disease [50,
51]. It seems unlikely that Succinivibrio spp. use T3SS
for mounting infections, and it is more plausible that
it mediates non-lethal relationships, either with other
rumen organisms, or possibly the animal host itself. In
pathogenic bacteria, effector proteins secreted by the
T3SS act to modulate host cell functions to help avoid
immune detection and disable protective functions such
as macrophages [52]. An amino acid sequence-based
analysis of the protein-coding genes of S. dextrinosolvens strains H5, 22B and ACV-10 identified a large number of proteins containing N-terminal motifs indicative
of involvement in T3SSs (Table 4), including chaperone
binding domains and eukaryotic-like signal sequences
specific for the endoplasmic reticulum or mitochondria
(Additional file 2). The annotated functions of these proteins were quite diverse but many of them have putative
roles in transcriptional regulation or transport functions,
Kamke et al. BMC Res Notes (2017) 10:367
Page 8 of 14
Fig. 2 Z-score based hierarchical clustering of bacterial T3SS genes. Hierarchical clustering based on metagenome (a) and metatranscriptome (b)
data
Kamke et al. BMC Res Notes (2017) 10:367
Page 9 of 14
Fig. 3 Phylogenetic affiliations of T3SS genes. Sunburst plot showing phylogenetic affiliations of contigs containing T3SS genes within the domain
bacteria
indicating they may mediate changes in gene expression
and transport of molecules in the target organisms.
The specific contribution of the T3SS genes detected
in the microbiomes of the HMY animals to the HMY
phenotype remains unclear. The HMY phenotype in
sheep is thought to be related to rumen size, particle
retention time and turnover rate, such that HMY animals have a larger rumen which retains feed particles
for longer and therefore have a slower turnover rate.
These conditions are thought to lead to low hydrogen
partial pressure, but rapid hydrogen production, which
results in elevated expression of genes encoding the
hydrogenotrophic methanogenesis pathway [20]. The
assignment of several of the T3SS genes to Succinivibrio suggests that this bacterial genus may be important in the HMY microbiome. However, this does not
appear to be the case as the 16S rRNA genes retrieved
from the metagenome sequences or from amplicon
sequencing did not show Succinivibrio spp. as being significantly enriched in either the LMY (0.065% relative
abundance) or HMY animals (0.033%). Investigations of
the microbiomes of cattle with differing methane yield,
have shown that OTUs assigned at a higher family level
to the Succinivibrionaceae were more abundant in the
microbiomes of LMY animals [53], while another study
of cattle has reported OTUs corresponding to Succinivibrionaceae were reduced in feed restricted animals
compared to ad libitum fed animals [54]. The majority of
T3SS genes which make up the main component of the
correlation with methane yield to appear to come from
unidentified rumen bacteria, but without knowledge of
their metabolisms or physiologies, it is not possible to
predict how their T3SS may influence the methane yield
phenotype.
Kamke et al. BMC Res Notes (2017) 10:367
Page 10 of 14
Fig. 4 T3SS gene arrangement in genomes of Succinivibrio dextrinosolvens strains. Sct genes are shown in yellow with their associated gene subunit
letter. Scaffold boundaries are shown by square brackets. The remaining genes are identified by COG categories
Table 4 Effective T3 predictions of chaperone binding, secreted, N-terminal signal and T4SS proteins with protein coding
sequences of Succinivibrio dextrinosolvens
Strain
Conserved chaperone binding
(within/outside expected region)
Secreted
T3SS
N‑terminal signal
(ER/mitochondrial)
T4SS
effector
H5
111 (40/71)
254
447 (412/35)
126
22B
115 (36/79)
288
500 (452/48)
164
ACV-10
111 (35/76)
282
534 (493/41)
148
The co-culture experiments showed that S. dextrinosolvens had either stimulatory, inhibitory or neutral
effects, depending on the type of methanogens in the
co-culture. Succinivibrio spp. have been observed on
several occasions in enrichment cultures with methanogens of the order Methanomassiliicoccales, and have
proven difficult to remove from these enrichments to
allow purification of the methanogens [55]. This suggests that Succinivibrio spp. form close relationships
with Methanomassiliicoccales, and helps explain the
stimulation of ISO4-H5 by S. dextrinosolvens H5 in
co-cultures. In a global census of microbes from ruminant animals, Henderson et al. [49] reported a positive
association between succinate-producing Succinivibrionaceae, and methanogens belonging to the family
Methanomassiliicoccaceae, particularly the subgroups,
Candidatus Methanomethylophilus alvus and Methanomassiliicoccales group 11 sp. The mechanism of
stimulation is currently not known, but it is possible
that Succinivibrio spp. provide substrates or growth
factors that stimulate the growth and methane formation in the methanogenic archaeon ISO4-H5. Some
Succinivibrio spp. are able to degrade pectin [42] and
release methanol [56] which is a known substrate for
methane formation by the Methanomassiliicoccaceae
[57].
The inhibition of methane formation during S. dextrinosolvens co-culture with Mbb. millerae SM9 is interesting as the Succinivibrionaceae bacterium WG-1 isolate
from the Wallaby gut has been implicated in lower methane emissions from starch-containing diets, presumably via close coupling of redox reactions which led to
less methane being formed [58]. The global census of
rumen microbes also reported a negative association
between Succinivibrionaceae and the Methanobrevibacter gottschalkii clade [49]. In our co-cultures, hydrogen
was provided in excess, so the weak inhibition of Methanobrevibacter sp. SM9 by S. dextrinosolvens may be via
Kamke et al. BMC Res Notes (2017) 10:367
Page 11 of 14
Fig. 5 Co-cultures of rumen methanogens with Succinivibrio dextrinosolvens H5. Methane production and hydrogen consumption during coculture experiments of Succinivibrio dextrinosolvens H5 with the rumen methanogens Methanobrevibacter ruminantium M1 (a), Methanobrevibacter
sp. SM9 (b), Methanosphaera sp. ISO3-F5(c) and methanogenic archaeon ISO4-H5 (d). Red arrows indicate the time point of inoculation with S.
dextrinosolvens H5
a direct effect, rather than via competition for hydrogen. This is possibly via the production of formate by S.
dextrinosolvens in the co-culture, which cannot be used
for methane formation by Mbb. millerae [59] and which
can slow growth when added to Mbb. millerae SM9 cultures growing on H2 + CO2 (Peter Janssen, pers. comm.).
Investigating the exact mechanisms by which Succinivibrio spp. influences methanogen growth and methane
formation, and the potential involvement of their T3SSs,
are the subject of on-going research.
Conclusions
Bacterial T3SS genes and transcripts, were found to be
positively correlated with methane yield in sheep. Most
of these genes could not be assigned phylogenetically,
but several were affiliated with the genus Succinivibrio,
and genes encoding complete T3SSs were found in the
genome sequences of S. dextrinosolvens strains H5,
22B and ACV-10, and another rumen succinate-producing rumen bacterium, Succinimonas amylolytica.
This is the first report of T3SS genes being associated
Kamke et al. BMC Res Notes (2017) 10:367
Page 12 of 14
Table 5 Concentrations (mM) of volatile fatty acids in co-cultures
Culture
VFAsa
Formic
Acetic
Before/after
Net
ISO3-F5
0.31/0.00
ISO3-F5 + H5
0.06/1.13
Butryric
Before/after
Net
−0.31
31.09/30.36
1.07
31.92/32.48
Propionic
Before/after
Net
Before/after
Net
−0.73
0.84/0.83
−0.02
2.84/2.24
−0.59
0.56
0.82/0.86
0.04
2.41/2.30
−0.11
−0.91
ISO4-H5
0.00/0.00
0.00
31.86/32.79
0.93
0.88/0.89
0.01
3.15/2.25
ISO4-H5 + H5
0.00/1.88
1.88
32.87/34.62
1.75
0.87/0.87
0.01
2.51/2.70
0.19
M1
0.00/0.00
0.00
40.00/38.06
−1.95
0.78/0.68
−0.11
2.50/2.50
−0.01
M1 + H5
0.00/0.00
0.00
37.33/36.37
−0.97
0.84/0.82
−0.02
2.68/2.68
0.01
SM9
0.00/0.00
0.00
30.83/33.00
2.17
0.74/0.81
0.06
1.55/2.41
0.86
SM9 + H5
0.00/0.84
0.84
31.78/34.07
2.29
0.83/0.81
−0.01
2.52/2.30
−0.22
a
No lactic acid was detected in any of the cultures. Negative numbers in the Net VFA columns indicates disappearance of that VFA during co-culture growth
Fig. 6 Schematic overview of T3SS subunits based on KEGG genes. The T3SS subunits are labelled using the Yersinia gene annotation scheme.
The colour shading of each subunit shows the level of support for the related genes (DNA) or transcripts (RNA) being significantly (P ≤ 0.05) more
abundant in HMY animals based on the WRS test and sPLS
with methane emissions in ruminants, and identifies
these secretions systems as potential new targets for
methane mitigation research. S. dextrinosolvens H5
was shown to have direct growth-enhancing effects
on a member of the Methanomassiliicoccales, and an
inhibitory effect on a member of the Mbb. gottschalkii
clade in co-culture experiments, which point towards
bacteria-methanogen interactions being important modulators of methane production in ruminant
animals.
Kamke et al. BMC Res Notes (2017) 10:367
Additional files
Additional file 1. KEGG genes significantly more abundant in HMY
animals in the metagenome and metatranscriptome datasets by WRS and
sPLS analyses.
Additional file 2. Type III secretion system Kos in foregut bacteria in the
Integrated Microbial Genome.
Additional file 3. Effective analysis of protein coding sequences in S.
dextrinosolvens strains H5, 22B and ACV-10.
Abbreviations
HMY: high methane yield; LMY: low methane yield; DMI: dry matter intake;
IMY: intermediate methane yield; GSE: gene set enrichment analysis; sPLS:
sparse partial least squares; WRS: Wilcoxon Rank Sum; T3SS: type III secretion system; KEGG: Kyoto encyclopedia of genes and genomes; MSPE: mean
squared prediction error; NES: normalised enrichment score; IMG/Mer:
integrated microbial genomes and microbiomes (expert review); FDR: false
discovery rates; BH: Benjamini–Hochberg procedure; MEGAN: metagenome
analyser; MS: membrane supramembrane; RPM: reads per million; NOM P-val:
nominal P value; FWER: familywise-error rate.
Authors’ contributions
The following authors contributed to the study design: GA, ER. Data collection
and experimental procedures were conducted by: JK, GA, BK, SL, PS, and YL.
Data analysis and interpretation: JK, GA, BK, SL, SG, WS, JF. The manuscript was
prepared by: JK and GA. All authors read and approved the final manuscript.
Author details
1
AgResearch Limited, Grasslands Research Centre, Tennent Drive, Palmerston
North 4442, New Zealand. 2 Department of Energy, Joint Genome Institute,
Walnut Creek, CA 94598, USA. 3 Genomic Division, Lawrence Berkeley National
Laboratory, Berkeley, CA 94720, USA.
Acknowledgements
We thank Christina Moon for critiquing the manuscript.
Competing interests
The authors declare that they have no competing interests.
Animal ethics approval
The collections of rumen contents from sheep were carried out under
the approval of the AgResearch Ltd Grasslands Animal Ethics Committee
(Approval 13606).
Availability of data and materials
The metagenome and metatranscriptome datasets used in this study are
accessible at the National Centre for Biotechnology Information Sequence
Read Archive (SRA; http://www.ncbi.nlm.nih.gov/sra) accession number
SRA075938, bioproject number PRJNA202380, and additional 16S rRNA gene
amplicon sequence data was submitted under the same bioproject number
under SRA experiment Accession Numbers: SRX1079958–SRX1079985.
Consent to participate
Not applicable.
Funding
This work was supported by the New Zealand Fund for Global Partnerships
in Livestock Emissions Research, supporting the objectives of the Livestock
Research Group of the Global Research Alliance on Agricultural Greenhouse
Gases. The US Department of Energy Joint Genome Institute contribution was
supported by the Office of Science of the US Department of Energy under
Contract No. DE-AC02-05CH11231.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Page 13 of 14
Received: 4 August 2016 Accepted: 22 July 2017
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