methane emissions explained by interactions in rumen … · 2018. 9. 30. · candidatus...
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Leading the way in Agriculture and Rural Research, Education and Consulting
METHANE EMISSIONS EXPLAINED BY
INTERACTIONS IN RUMEN MICROBIOME
Auffret MD, Martínez-Álvaro M, Wallace RJ,
Freeman TC, Blasco A, Watson M, Roehe R
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Introduction
Overall aim:
Interactions between rumen microbial
community and microbial genes to explain
methane emissionsMICROBIAL
GENESMICROBIAL
COMMUNITIES
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Data from 63 animals with different breeds and diets
✓ Methane emissions (g CH4/kg DMI)
Rumen fluid
samples after
slaughter
4427 Microbial genes
(KEGG genes
database)
Microbial communities:
1178 metagenome-
assembled genomes
(MAG) Genus level
(Kraken database)
✓ 1557 Microbial
genes
✓1160 MAGs
Relative abundance > 0.001%
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Network analysis with functional genes and microbia
Statistics: Network analysis using MCL algorithm in Miru software (Corr =0.70)
Figure 1. Functional clusters of microbial genera and their functional genes identified using co-abundance network analysis in beef cattle. A.
Distribution of the clusters in the network. B. Distribution of functional genes and microbial genera (bacteria, archaea, fungi and protist) among
the identified clusters. C. Example of the relative abundance profile of one node in cluster 1 (Methanobrevibacter related MAG) in animals
descendently ordered by methane emissions within group and diet. Under the graph, colours red and yellow represent high and low methane
emitters groups respectively and blue and green represent animals fed with concentrate and forage diet, respectively.
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
Cluster 9
Cluster 10
Rel
ativ
e ab
und
ance
(%
) Animals
Microbial genes
Bacteria
Archaea
Fungi
Protist
A B
C
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LIM Aax
FOR 15 17
CONC 14 17
LIM Aax
FOR 7 9
CONC 7 8
LIM Aax
FOR 8 8CONC 7 9
HIGH METHANE EMITTERS (HME)
Grouping within
✓ Breed (Limousin / Aberdeen Angus)
✓ Diet (Forage: 500 forage to 500 concentrate
✓ or Concentrate: 80 forage to 920 g/kg DM concentrate)
Division of animals in high (HME) & low (LME)
methane emitters
LOW METHANE EMITTERS (LME)
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HME - LME
Statistics: GLM in R software. Model y = group + breed + diet + e
In 2717 variables…
P-val<0.05 P-val<0.1
Microbial Genes 22 71
Microbial
Communities34 97
Total 56 168
Methane emissions (g/kg DMI)
MEAN SD CV HME-LME P-val
17.75 5.39 30.53 5.73 3.04E-15
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Where are the variables with different relative abundance
in HME and LME?
Selenomonas
LME > HME
Proteobacteria
LME > HME
Fibrobacter
HME >LME
Methanobrevibacter
HME >LME
Butyrivibrio
HME >LME
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Cluster with main methanogens
Variable Connections
Microbial genes 329
Archaea 15
Bacteria 69
Fungi 13
Protists 1
Total variables 427
Bacteria
Archaea
Fungi Protists
MICROBIAL COMPOSITION IN ABUNDANCES (%)
Cluster description
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Variable Description HME - LME P- val
Candidatus.Azobacteroides Bacteria Bacteroidetes 0.0009 <0.050
Endomicrobium Bacteria Elusimicrobia 0.0015 <0.050
Endozoicomonas Bacteria Proteobacteria 0.0004 <0.050
Pirellula Bacteria Plantomycetes 0.0007 <0.1
Anthracocystis Fungi Basidiomycota 0.0006 <0.1
K00091 dihydroflavonol-4-reductase 0.0015 <0.050
K00639 glycine C-acetyltransferase 0.0045 <0.005
K07072
(4-(4-[2-(gamma-L-glutamylamino)
ethyl]phenoxymethyl)furan-2-
yl)methanamine synthase
0.0009 <0.1
K05884 (R)-2-hydroxyacid dehydrogenase 0.0023 <0.1
Cluster with main methanogens
HME - LME
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Factors %Varability X %Variability of CH4
1 78.1% 57.3%
Variable Information VIPRegression
Coefficient
Candidatus Azobacteroides* Bacteria Bacteroidetes 1.03 0.177
Tremella Fungi Basidiomycota 1.01 0.174
K02585 nitrogen fixation protein NifB 0.99 0.171
K00639* glycine C-acetyltransferase 0.99 0.170
K00091* dihydroflavonol-4-reductase 0.98 0.169
Cluster with main methanogens
Statistics: PLS Regression in R (mixOmics Package)
Final PLS
CH4 = XX = 5 variables
Explaining CH4
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Different interactions in HME and LME
Network analysis within group
HME LME
Statistics: Network analysis using MCL
algorithm in Miru software (Corr = 0.70)
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Conclusions
• Bacteria (n=14) and Fungi (n=3) MAGs and
microbial genes associated with carbohydrate
degradation explain most of the differences
between low and high methane emitters but not
archaea and microbial gene associated with
methane metabolism
• These bacteria and fungi communities are
therefore highly important for prediction of
methane emissions from rumen samples
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Thank you for your attention