The ruminal microbiome, comprising large numbers of bacteria, ciliate protozoa, archaea and fungi, responds to diet and dietary additives in a complex way. The aim of this study was to investigate the benefits of increasing the depth of the community analysis in describing and explaining responses to dietary changes. Quantitative PCR, ssu rRNA amplicon based taxa composition, diversity and co-occurrence network analyses were applied to ruminal digesta samples obtained from four multiparous Nordic Red dairy cows fitted with rumen cannulae. The cows received diets with forage:concentrate ratio either 35:65 (diet H) or 65:35 (L), supplemented or not with sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplied in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and four 35-day periods. Digesta samples were collected on days 22 and 24 and combined. QPCR provided a broad picture in which a large fall in the abundance of fungi was seen with SO in the H but not the L diet. Amplicon sequencing showed higher community diversity indices in L as compared to H diets and revealed diet specific taxa abundance changes, highlighting large differences in protozoal and fungal composition. Methanobrevibacter ruminantium and Mbb. gottschalkii dominated archaeal communities, and their abundance correlated negatively with each other. Co-occurrence network analysis provided evidence that no microbial domain played a more central role in network formation, that some minor-abundance taxa were at nodes of highest centrality, and that microbial interactions were diet specific. Networks added new dimensions to our understanding of the diet effect on rumen microbial community interactions.
Citation: Tapio I, Fischer D, Blasco L, Tapio M, Wallace RJ, Bayat AR, et al. (2017) Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows. PLoS ONE 12(7): e0180260. https://doi.org/10.1371/journal.pone.0180260
Editor: Hauke Smidt, Wageningen University, NETHERLANDS
Received: January 20, 2017; Accepted: June 13, 2017; Published: July 13, 2017
Copyright: © 2017 Tapio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data are available within the Dryad Digital Repository (doi:10.5061/dryad.t6t0q).
Funding: Study was funded by the Finnish Ministry of Agriculture and Forestry as part of the GreenDairy Project (Developing Genetic and Nutritional Tools to Mitigate the Environmental Impact of Milk Production; Project No. 2908234). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The rumen contains a highly complex microbial ecosystem comprising several thousand species of bacteria , of which 30 groups are dominant , 12 genera of ciliate protozoa , 6 genera of anaerobic fungi , and approximately 10 main taxa of methanogenic archaea , as well as variable abundances of mycoplasmas, bacteriophages, archaeophages and viruses that infect the eukaryotic microorganisms. Describing the community and especially its relationship to function is challenging, the latter partly because of the redundancy of function across different taxa [2,5]. Taxon abundances and diversity based on ssu rRNA amplicon analyses are used to evaluate microbial community responses to experimental treatments, time, health or developmental stage of an animal. These data assessed in relation to animal phenotype often lead to retrospective attempts to explain differences in function, such as methanogenesis or feed efficiency [6–8]. However, most of the analytical techniques concentrate on highlighting properties of individual species, leaving the interactions within the microbial community unexplored. Microbial co-occurrence, estimated as positive correlation between taxa, indicates to some extent similarity of function or similarity in response to the same environmental conditions, but to our knowledge few studies have applied such analysis in the rumen [9,10]. Potential interactions between microbial taxa can be investigated further by network analysis of significant taxa co-occurrence patterns, obtained from sequence data. Network analysis approaches have been successfully used in other ecosystems, including marine , soil  or the human gut . It expands upon information generated using standard diversity analysis tools and offers an approach to identify patterns of microbial interactions in ecosystems occupied by uncultured microorganisms.
Rumen microbial community structure is affected by ruminant species , breed , or age of animals . However, diet is the major determinant of ruminal microbial composition . Diet depends on the geographical location and the type of production system. While feedlot cattle are often fed diets based on grain and rich in fermentable carbohydrates, the Nordic dairy sector depends on grass forage based diets, fed mainly in the form of restrictively fermented silage . Enrichment of diets with plant oils or oilseeds has been explored as a way to increase dietary energy supply in dairy cows and alter fatty acid composition in milk and meat . Further, lipid supplements are known to reduce enteric methane production , therefore providing options to lower the environmental impact of animal production.
The aim of the present study was to explore the value of stepwise increases in the depth of community analytical tools in understanding responses to two commercially important dietary changes in dairy cows, namely transition from forage to concentrate diets and the supplementation of diets with vegetable oil. The results indicate that network analysis based on ssu RNA amplicon sequencing adds new dimensions to our understanding of the rumen microbial community.
Materials and methods
Animals, experimental design and diets
The animal study research protocol was approved by the National Ethics Committee (ESAVI/794/04.10.03/2011, Kuopio, Finland) in accordance with the guidelines established by the European Community Council Directives 86/609/EEC . Details on the experimental design have been reported by . In summary, four multiparous Nordic Red dairy cows fitted with rumen cannulae in mid lactation were used in a 4 × 4 Latin Square experiment with a 2 × 2 factorial arrangement of treatments and four 35 day experimental periods. Treatments consisted of iso-nitrogenous total mixed ration based on grass silage containing a low (65:35) or high (35:65) proportion of concentrates supplemented with 0 (L and H, respectively) or 50 g/kg diet DM of sunflower oil (SO) (LSO and HSO, respectively). The forage was restrictively fermented grass silage prepared from mixed timothy meadow fescue swards treated with a formic acid based ensiling additive. Concentrates comprised variable amounts of ground wheat, rolled barley, rapeseed expeller, urea and mineral and vitamin pre-mix . Experimental diets were fed for 26 days followed by 9 days on L diet to minimize treatment carry-over effects.
Rumen sample collection and DNA extraction
Ruminal digesta was sampled on day 22 at 0900 and on day 24 at 1500 of each experimental period. Representative samples were collected from four sites within the reticulorumen (anterior dorsal, anterior ventral, posterior dorsal, and posterior ventral) and composited by manual mixing. Twenty five grams of digesta collected at each time point was preserved in 50 mL of phosphate buffered saline-glycerol (30% v/v), frozen and stored at -80°C until DNA extraction. Total genomic DNA was extracted from combined d 22 and d 24 sub-samples following the protocol of Yu and Morrison .
Quantitative real-time PCR analysis
The total amounts of bacteria, archaea, ciliate protozoa and fungi were measured by qPCR. Primers used for amplification are described in S1 Table. qPCRs were performed in volumes of 12.5 μl using 10 ng of template DNA (50 ng for ciliates), forward and reverse primers (400 nM) and 2x qPCRBIO SyGreen Mix Lo-ROX (PCR Biosystems). Amplification conditions were initial denaturation at 95°C for 5 min, followed by 60 cycles at 95°C for 15 s, annealing temperatures specific for each locus (as indicated in S1 Table) for 30 s, 72°C for 30 s, and 95°C for 15 s. PCRs were carried out in triplicate, including negative controls, on a LightCycler® 480 System (Roche, Mannheim, Germany). The absolute quantity of test samples was estimated against serial dilutions of DNA standards, amplified to generate calibration curves. Methods used to generate standards for bacteria, archaea, ciliate protozoa and fungi are described in detail in [21,22]. The quality of generated calibration curves was R2 = 0.99 for bacteria and fungi and R2 = 0.98 for archaea and ciliate protozoa, respectively. Microbial abundances were calculated from triplicate Ct values using the universal calibration equation. Differences between diets were tested for significance using a paired t-test.
Rumen microbial diversity was assessed by amplicon sequencing of bacteria using a 454 GS Junior (Roche) at Luke (formerly known as MTT Agrifood Research Finland). Archaea, ciliate protozoa and fungi diversity were evaluated using MiSeq (Illumina) platforms. The change in sequencing platform was related to the rapid development of Illumina sequencing capacity at the time when sequencing was done and lower costs in relation to Roche 454. Primer sequences used for amplification are described in S1 Table. For bacteria, five 10 bp barcodes (Roche TCB No. 005–2009) were added at the 5’-end of both forward and reverse primers to make amplicon pooling possible. The PCR amplification, library preparation and sequencing on a 454 GS Junior (Roche) were performed following standard procedures (S1 Text). Sequencing of archaeal libraries was performed at Fasteris SA (Geneva, Switzerland) as described in . Ciliate protozoa and anaerobic fungi libraries were constructed and sequenced at LGC Genomics (Berlin, Germany) following the 250 bp paired-end protocol.
Sequence data processing
Bacterial sequences were demultiplexed based on adaptor and primer barcodes using a custom script written in python. Reads shorter than 200nt in length and with a minimum quality score lower than 25 were discarded. No mismatches in primer sequence or ambiguous bases were allowed. Archaeal sequences were processed as described in . Demultiplexing, adapter removal and merging of paired-end reads of protozoal and fungal sequences were done by sequencing data provider LGC Genomics (Berlin, Germany) following standard in-house procedures. Sequences were clustered into operational taxonomic units (OTU) at 97% similarity using Uclust  and filtered from chimeric reads using ChimeraSlayer or UCHIME (for anaerobic fungi) as implemented in Qiime v1.7.0 . Taxonomy was assigned using BLAST , comparing bacterial 16S rRNA sequences against Greengenes 12_10 , archaeal 16S rRNA sequences to RIM-DB , protozoal 18S rRNA sequences to SILVA 18S  and anaerobic fungi using a curated fungal ITS reference database . Singleton OTUs were removed prior to further analyses resulting in 4,902,197 non-chimeric high quality reads in total (S1 Text).
From taxonomic sequence information, microbial community alpha diversity was estimated using Simpson’s index of diversity (1-D). Species richness and Pielou’s measure of species evenness were calculated using R package vegan . Beta diversity or grouping of samples based on diet was evaluated by using Bray-Curtis as dissimilarity measure and nonmetric multidimensional scaling (NMDS) as ordination method. Distance-based permutational multivariate analysis of variance (adonis) was used to assess significant differences with respect to diet. Significance was defined at P < 0.05 after 999 permutations as implemented in vegan. The impact of diet on individual rumen microbial taxa was analyzed using the Mixed procedure of SAS (v 9.2, SAS institute, Cary, NC) including fixed effects of period, forage to concentrate ratio (FC), SO, and FC by SO interactions, and random effect of cow. Microbial counts were normalized with a log2 transformation and centered so that abundance profiles for all samples exhibited a mean of 0 and a standard deviation of 1. Data normality was checked using the Shapiro-Wilk test. Statistically significant differences were declared at P ≤ 0.05. A post-hoc pairwise comparison of all diets was performed using Tukey’s test.
Microbial networks were created using the merged data for three different combinations of diet changes: (i) from high to low concentrate diets (H-L), (ii) low concentrate diets with and without SO supplement (L-LSO), and (iii) high concentrate diets with and without SO supplement (H-HSO). From the 126 taxa at the genus level, those with less than 2 reads per sample across all diets were first filtered out, resulting in a total of 76 remaining taxa. The absolute counts were log-transformed and for each combination (i)-(iii) the partial correlations were calculated using pairwise complete observations, therefore excluding the fungal population from H-HSO comparison. Partial correlations were calculated by EBICglasso in qgraph -package  based on the nearest positive definite correlation matrix computed using nearPD -function in Matrix package . EBICglasso selects the tuning parameter using the Extended Bayesian Information criterion and computes a sparse Gaussian graphical model with the graphical lasso . The qgraph package was also used for drawing the partial correlation networks presenting the direct biological interactions. Betweennes (shortest distance between all pairs of nodes through the focal node) was calculated using the absolute values of (partial) correlation as the edge weight. Difference in mean betweenness among the microbial groups (bacteria, archaea, ciliate protozoa, fungi) was tested with One Way Anova. Binomial test for vicariance was performed using function binom.test in R . As every edge is connected to only two nodes, by definition, the pattern of edge/node connections has a binomial expectation .
Real-time qPCR targeting bacterial 16S rRNA, archaeal mcrA, ciliate protozoa 18S rRNA genes and for fungi targeting the region between 18S rRNA and ITS1 were used to quantify the abundance changes of ruminal microorganisms across the four diets. SO when supplemented to both H and L diets decreased total amounts of all rumen microbial groups: bacteria, archaea, ciliate protozoa and fungi (Fig 1). However, due to high between-sample variability, differences were significant for fungi only. Fungi were two times more abundant in L as compared to the H treatment (P = 0.03). SO significantly decreased the total abundance of fungi in both HSO and LSO diets but the effect was strongest in HSO (H vs HSO by 10.5-fold, P = 0.03; L vs LSO by 1.9-fold, P = 0.04; Fig 1).
Samples collected from lactating cows fed four diets: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively). Results are expressed as copy number per ng of extracted DNA. Error bars represent standard deviations (SD), n = 4 per diet.
Abundance and diversity derived from amplicon sequencing
Amplicon sequencing showed that rumen bacterial community was dominated by Firmicutes (average per diet from total 55.9–86.8%), Bacteroidetes (8–24.4%) and Proteobacteria (0.9–13.4%). Abundance of minor bacterial phyla (Actinobacteria, Cyanobacteria, Spirochaetes, Synergisteles, TM7 and Tenericutes) was below 0.5% (Fig 2). Lachnospiraceae, Ruminococcaceae, Clostridiaceae, Veillonellaceae and Catabacteriaceae families were the most abundant among Firmicutes, while Bacteroidetes were mainly represented by Prevotellaceae, and Succinivibrionaceae was the most abundant family among Proteobacteria (S2 Table).
Bubble-charts represent taxonomy at the phylum level for bacteria and genus level for the rest. Diets are as follows: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively).
The archaeal community was dominated by Methanobrevibacter ruminantium (46–74%) and Mbb. gottschalkii (24–46%), with Methanobacterium alkaliphilum, Methanimicrococcus blatticola, Mbb. wolinii, Methanosphaera ISO3-F5 and several groups from the Methanomassiliicoccaceae family observed at lower abundance (Fig 2).
The most numerous protozoa were Entodinium (33.5–96.2%), Isotricha LDK 2011 (0.3–36.3%), Metadinium minorum (1.7–19.7%), Dasytricha (0.1–7.3%) and Entodinium longinucleatum (1.3–4.8%).
The fungal community was represented by six well characterized and six previously identified novel fungal groups (AL1, AL8, KF1, MN3, SK2 and SK3). Neocallimastix 1 (11.8–84.5%) and Caecomyces 1 (10.3–57.5%) were the predominant genera in all diets. FC ratio and SO strongly influenced the abundance of Cyllamyces 1 and 2, Orpinomyces 1a, and Piromyces 6 and 7, and novel groups AL8, KF1, SK3 (Fig 2).
Simpson’s index of diversity was greater in L diets and lowest in HSO (Fig 3). Higher diversity in the L and LSO treatments was related to higher microbiota richness (S1 Fig) as well as higher evenness (S2 Fig).
Diets are as follows: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively).
The structure of overall rumen microbial populations, assessed by two-dimensional NMDS plots based on Bray-Curtis dissimilarities, showed marked differences between diets in bacteria, archaea, ciliate protozoa and fungi, respectively (adonis analysis for diets P < 0.05) (Fig 4). The variance between the animals within the same diet was higher in H-HSO compared to L-LSO treatments. The FC ratio alone significantly affected bacterial, archaeal and fungal (adonis analysis P < 0.05) but not ciliate community structure.
Dietary effects on individual microbial taxa
The effects of diet were evaluated on 37 genus-like taxonomic groups of bacteria, 11 groups of archaea, 14 of ciliate protozoa and 16 of fungi, respectively. FC ratio significantly changed the abundance of 46% of bacterial, 30% of archaeal and 21% of ciliate protozoa groups. The effect of SO supplementation was less pronounced (Table 1).
Diets are as follows: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively). Only taxa significantly affected by diet are presented. The small letter at the beginning of each bacterial taxon name identifies the deepest classification level to which the OTUs were assigned. Diet effect represents significant (P < 0.05) microbial abundance changes due to FC, SO or FC × SO interaction.
The bacteria whose abundance was significantly affected by diet belonged to Firmicutes, Bacteroidetes, Proteobacteria and Fibrobacteres phyla. Clostridium, Anaerostipes and Coprococcus genera were more abundant in L diets, while Shuttleworthia (family Lachnospiraceae) and Succiniclasticum (family Veillonellaceae) as well as two genus-like groups within Succinivibrionaceae family were more frequent when H diets were fed. Among Bacteroidetes, Prevotella was detected at three times higher abundance in association with HSO (24%) compared to L diets (7.0–7.8%). The HSO diet significantly decreased the abundance of Fibrobacter. However, diet did not significantly affect abundance of Mogibacterium, Butyrivibrio, Moryella, Pseudobutyrivibrio, Oscillospira, Ruminococcus or Ruminobacter.
Among archaea, Mbb. ruminantium was more abundant in H, while Mbb. gottschalkii was predominant in L diets. Methanobacterium alkaliphilum was most abundant in LSO (7.6%) but absent from HSO diet, while Mbb. wolinii was more abundant in HSO-H treatments (0.1–0.5%) compared to L diets. Although Methanosphaera ISO3-F5 was observed in all four diets at low abundance, it favored treatments with SO supplement more than without. In contrast, the Methanomassiliicoccaceae Group 10 was present at low abundance only in diets without SO.
The FC ratio and SO had varying effects on rumen ciliate protozoa. Entodinium dominated in all diets but it exhibited lowest abundance in H (33.5%) and highest in HSO (96.2%) diet. L treatments positively affected the abundance of Metadinium minorum and Raabena bella, while Isotricha LDK-2011 was sensitive to SO and was at highest abundance in H (36.3%) and almost absent in HSO (0.3%) treatments (Table 1).
Anaerobic fungal diversity was strongly influenced by diet, however, due to high between-animal variation within the same diet, statistically significant changes were detected only in Caecomyces 1 and KF1 (Table 1). Caecomyces 1 was dominant in L-LSO and Neocallimastix 1 in H-HSO diets. Further, L had higher proportion of KF1 (6.7%), Cyllamyces 2 and 1 (6% and 4%) and Piromyces 6 (2.4%), but Piromyces 2 (19.8%) and Piromyces 7 (2.1%) were more abundant in H (Fig 2).
Many of the metabolic functions of ruminal microorganisms occur across domains and phyla, but often microorganisms share less common properties that link them into groups in which members are species from different high-level groupings. For example, primary degraders provide nutrients for a secondary community, then for tertiary degraders and so on. It might be predicted that both interdependence and competition between taxa should be apparent from community analysis, particularly in their responses to dietary changes. Various depths of network analysis were therefore undertaken to investigate these interdependencies.
Bacteria are the most abundant and diverse group of ruminal microorganisms. Therefore we tested if bacteria play a more central role in network formation compared to protozoa, fungi and archaea. The assessment was based on betweenness centrality values for each microorganism and tested for group effect on centrality with one way ANOVA. Betweenness centrality is an indicator of taxon centrality in the network. It is equal to the number of shortest paths from all vertices to all others that pass through that node (taxon). A node with high betweenness centrality has a large influence on the interaction through the network, under the assumption that interaction follows the shortest paths. In all dietary comparisons (L-H, L-LSO, H-HSO) the absence of significant results (P > 0.3) suggested that bacteria alone do not fulfil a central role in network formation.
We further tested whether bacteria interact more within their own domain as compared to interactions with ciliate protozoa, fungi or archaea. Here we analysed networks at two weight levels, first when including links with partial correlation > 0.05, secondly with only links above 0.25 partial correlation. By limiting to a correlation threshold of > 0.25, few and only the most robust interactions between the taxa were evaluated (Fig 5). Most of ruminal microorganisms create networks fulfilling the first, less stringent criterion (S3–S5 Figs). By analyzing the data at the > 0.05 level, the resulting networks did not show that bacteria would interact more often with other bacteria as compared to other microbial groups in L-LSO and L-H (vicariance test P > 0.05), but did in H-HSO (P = 0.015). When only the most robust interactions were taken into account (>0.25), between-bacteria interactions were significantly more abundant than compared between bacteria and other groups in L-LSO (P = 0.026) and were close to significance in L-H (P = 0.065) and H-HSO (P = 0.077). This suggests that at least strong direct interaction is more common within bacteria domain.
a) L-H, b) L-LSO, and c) H-HSO diet comparisons. Nodes correspond to microbial taxa while green and red edges represent positive and negative partial correlations above 0.25, respectively. Microbial taxa are colored by taxonomy: archaea—light gray, ciliate protozoa—white, fungi—dark grey, bacteria: Bacteroidetes—green, Firmicutes—moss green, Proteobacteria—brown, others—blue.
Following these observations we investigated microbial taxa that act as main information gateways (hubs) in networks. For this we analysed betweenness centrality data and identified the microorganisms with highest centrality values (>1.5). A node with high betweenness centrality could mean that the microorganism plays a central part in the flow of information from one part of the network to the other. Microorganisms with the most enhanced centrality in the L-H comparison were Clostridiales un2, Clostridium, Shuttleworthia, Succiniclasticum, Eudiplodinium, Metadinium minorum, Neocallimastix1 and Piromyces3. In L-LSO betweenness was highest in Bacteroidales un1, Bacteroidales un2, Anaerostipes, Ruminococcus1, Ruminococcaceae un1, Succinivibrionaceae un2, Orpinomyces1a and Orpinomyces1b. In H-HSO, the highest centrality values were observed for Bacteroidales un2, Succiniclasticum and Methanomassiliicoccaceae Group 8 WGK1.
We further elucidated if microbial taxa significantly affected by dietary changes appear in the networks. For this we screened networks generated with the more stringent filtering parameter (partial correlation > 0.25) as to whether diet affected taxa (Table 1) belong to the most robust correlations or not. In the L-H case, Lachnospiraceae un2, whose abundance decreased significantly from the L to H diet, was detected in strong positive correlation with Ruminococcaceae un1 and Bacteroidetes un (Fig 5A). Other microbes significantly affected by diet, like Succinivibrionaceae un2, Anaerostipes, Clostridium un1 and Raabena bella, were not detected in the most robust partial correlations. In the L-LSO comparison, no bacteria or ciliate protozoa were significantly affected by dietary change. Only the Methanomassiliicoccaceae Group 10 from archaea significantly decreased while fungi AL8 and SK2 significantly increased in LSO (S2 Table). All three were detected in robust associations (Fig 5B). In H-HSO, microbial taxa that were significantly affected by dietary switch were present within the strongest partial correlations as well. The abundance of Ruminococcus1 from the Lachnospiraceae family significantly decreased in HSO (S2 Table) and was observed in a positive correlation within a network of Clostridium un1—Coriobacteriales un—Clostridiales un2 (Fig 5C). Prevotellaceae un1 and Bacteroidales un1 abundance significantly increased in HSO and both were positively correlated, while Ruminococcaceae un1 was negatively associated with Shuttleworthia. Methanomassiliicoccaceae Group 3b, although at low abundance, was negatively associated with Butyrivibrio. Entodinium and Isotricha LDK 2011 were in a negative correlation. It was concluded that taxa significantly affected by diet do not appear in isolation but co-occur in microbial networks with other taxa, which may indicate their metabolic interaction during diet changes.
We further studied if microbial taxa, detected in the most robust between- and within—domain co-occurrences, are the same irrespective of diet. For that we explored the networks of the most robust interactions (> 0.25 partial correlation) for each diet change separately and show that they are diet specific (Fig 5). In the L-H comparison, the density of connections within and between domains indicated that bacteria were slightly more densely connected with other bacteria (1.8%) as compared with density in connections within archaeal, fungal or protozoal groups (1.08%) or the total set (1.09%). Closer examination of inter-domain associations showed the presence of larger microbial clusters. One comprised SK2, Clostridiales un2, Clostridium, Metadinium minorum, Clostridiales un1 and Bacteria1 and the second comprised Clostridium un2, Succiniclasticum, Eudiplodinium, Neocallimastix1 and Clostridiaceae un1. It seems that members of the Clostridiales order were very important in microbial networks while shifting from L to H diet. Examination of within-domain co-occurrences among fungi indicated that Piromyces3 and Piromyces6 as well as Cyllamyces1 and Cyllamices2 were positively correlated. No strong links between ciliate protozoa were observed. Among archaea, Mbb. ruminantium and Mbb. gottschalkii were in negative association, while the Methanomassiliicoccaceae Group 10 and Methanomassiliicoccaceae Group 8 WGK1 were in positive correlation. There were many other robust interactions between microorganisms belonging to all four different domains.
In the L-LSO comparison, no major difference was observed in the density of networks within bacteria (1.35%), as compared to density within archaea-fungi-ciliate protozoa groups (1.48%) or the total set (1.02%). One inter-domain, highly-linked cluster consisted of Orpinomyces1b, Bacteroidales un2, Diplodinium and Prevotellaceae un2, while a second cluster involved Lachnospiraceae un1, Succivibrionaceae un2, Ruminococcus1 and Caecomyces1. Within archaea, a strong positive correlation was observed between Methanomassiliicoccaceae Group 10 and Methanomassiliicoccaceae Group 8 WGK1, between Methanomassiliicoccaceae Group 12 ISO4-H5 and Methanomassiliicoccaceae Group 3b, and negative correlation between Mbb. gottschalkii and Mbb. ruminantium. Among fungi, Cyllamices1 and Cyllamices2 were in positive correlation as well as AL8 and SK2. Fungi were connected mainly with bacteria. In this oil-related dietary shift, strong correlations between ciliate protozoa were also observed. Dasytricha and Raabena bella were positively correlated, while Entodinium was negatively correlated with Isotricha LDK-2011. Among four domain interactions, no robust correlations were observed between bacteria and archaea but active interaction was detected between bacteria-fungi and bacteria-ciliate protozoa.
In the H-HSO comparison, the density of connections within bacteria was higher (2.54%) as compared with the density of connections within archaea and ciliate protozoa (1.73%) or in the total set (1.81%). No larger inter-domain clusters comprising members of all three domains were observed. Looking at bacteria—archaea interactions, Bacteroidales un2, Methanomassiliicoccaceae Group 8 WGK1 and Succiniclasticum, as well as another group of Mbb. gottschalkii, Mbb. ruminantium, Clostridia un, Mmc. Group 10 former linked clusters. Among archaea, only Mbb. gottschalkii and Mbb. ruminantium were negatively associated but there were many robust positive as well as negative correlations with bacteria. In contrast, among ciliate protozoa, positive correlations were observed between Diplodinium, Ophryoscolex and between Isotricha and Metadinium minorum, while a negative correlation occurred between Entodinium and Isotricha LDK 2011. However, no connections were observed between ciliate protozoa and archaea and only one was found between protozoa and bacteria.
Previous studies across a wide range of habitats have demonstrated that network based approaches can help in predicting the dynamics and structure of oceanic plankton ecosystems , reveal relations within microbial communities in soil  or highlight differences in taxa interactions within the human gut in health and disease . Despite this development in other ecosystems, the rumen microbial community has not yet been widely explored by these methodologies. In this study we investigated advantages of stepwise increases in the depth of rumen microbial community analysis by evaluating microbial responses in a dietary intervention experiment. We applied standard alpha and beta diversity measurements and went a step further by introducing inter-domain co-occurrence network analysis.
Rumen microbial community is shaped by complex relationships between bacteria, ciliate protozoa, fungi and archaea. Different diets provide different primary substrates for fermentation; animal physiological features affect temperature, pH or feed retention time–factors that may cause the same microbial taxa to establish different symbiotic or antagonistic microbial interactions. Studying these interactions is difficult, as the ecological role of most of the ruminal taxa remains unknown and is impossible to replicate in vitro. Due to this complexity most culture based or molecular genetic studies have focused on one or two rumen microbial groups, without the ability to explore the ruminal microbiome as a whole. Recent transformations in the speed and cost of sequencing open new opportunities. Kittelmann et al.  for the first time presented simultaneously the whole rumen microbial community structure in three ruminant species and demonstrated non-random associations between some microbes at within- and inter-domain level. Kumar et al.  demonstrated diet and age effects on fungal, bacterial and archaeal taxa co-occurrence in dairy cows, suggesting that biotic and abiotic factors affecting rumen microbial community function still need to be better understood.
In this study we elucidated the effects of FC ratio and SO supplement on the ruminal bacteria, archaea, ciliate protozoa and fungi communities in dairy cows simultaneously. Plant oils are an attractive possibility to be used as feed additives to mitigate methane emissions in livestock. Both FC ratio and oil supplements have been demonstrated to alter protozoa [37,38], change methanogen diversity  and affect bacterial [1,40–43], or fungal  community structure, but the influence of lipid on the entire rumen microbial community is still largely unknown. Changes in one or several microbial species may be balanced by adjustments within the remaining rumen consortium, affecting not only diversity but also the function of the community. Understanding how lipid supplements modify the whole rumen microbial consortium is therefore crucial to understand better its effect on rumen fermentation and host metabolism in general.
Rumen microbiome is shaped by diet composition
Inclusion of SO in the diet had no significant effect on total numbers of rumen bacterial, archaeal or protozoal communities but strongly affected fungi, as indicated by qPCR analysis. Although significant decreases in fungi were detected in both SO containing diets, the effect was more pronounced in HSO. Why forage rich diets supplemented with SO have less negative effect on fungi is not clear but it may be related to higher fungal diversity and particular fungal species being less sensitive to oil inhibition or indirectly to changes in general microbial metabolism within the rumen. In this study protozoa were not significantly decreased with the supplement of SO, even though toxicity of polyunsaturated FA to protozoa has been observed in sheep . No significant decrease in the amount of protozoa was detected in response to various oil supplements in Nordic Red cattle diet [23,45], indicating that the composition of the basal diet and dosage of oil supplement may be among factors influencing oil effects on rumen protozoa.
The composition and diversity of ruminal bacteria were significantly influenced by diet, with higher proportions of Firmicutes detected in L diets and increased abundance of Bacteroidetes and Proteobacteria in H diets, as observed previously [22,46]. The abundance of known cellulolytic bacteria  was low in all diets. Fibrobacter was observed below 1%, while the Ruminococcus genus was not affected by dietary changes and was detected at 4% on average abundance in all diets. Although results are consistent with published studies [48–50], low abundance of the key cellulolytic bacteria may mean that other novel cellulolytic species may reside in rumen. Increases in abundance in the L diet of as yet uncharacterized groups of bacteria within Clostridiaceae and Lachnospiraceae families could have cellulolytic properties, supporting similar observations in gut of humans on higher fiber diets  or in global rumen census data on forage-rich diets . In general, out of 23 diet-affected bacterial groups, 15 are still not identified at genus level. Among them were bacterial groups within Prevotellaceae, Clostridiaceae, Lachnospiraceae, Ruminococcaceae and Succinivibrionaceae families. These groups are relatively abundant, suggesting that their (unknown) metabolic role in the rumen ecosystem may be important. Further research in identifying unknown strains of microorganisms and clarifying their functional role is needed.
Methanogenic archaea indirectly stabilize the fermentation process in the rumen by utilizing H2 and reducing H2 partial pressure. In this study, the ruminal methanogenic community was dominated by Mbb. ruminantium and Mbb. gottschalkii. Both are H2 utilizers but their abundance correlated negatively with each other. Animals receiving forage diets often produce more methane, suggesting higher availability of H2 in the rumen for archaeal utilization. The higher proportion of Mbb. gottschalkii observed in the L treatment could indicate that it thrives better with other H2-producing microorganisms. Methanosphaera, usually a minor group of archaea in rumen , was found with all four dietary treatments at low abundance and was not susceptible to SO.
Ruminal protozoa contribute to fiber digestion, help control the rate of carbohydrate fermentation and may impact negatively on protein metabolism . Higher protozoal diversity in L treatment may be beneficial for fiber utilization as up to 40% of fiber degradation in rumen may be associated with protozoal activity . Addition of SO to the diet did not significantly decrease protozoal counts but negatively affected protozoal diversity, with a stronger effect in H compared to L diets. Our results confirm previous observations that Entodinium spp are less sensitive to SO or other lipids [37,54] as they dominated in all, but in particular in SO-containing diets.
Fungi initiate mechanical and enzymatic plant fiber degradation and enable access for secondary metabolizers. The higher diversity of anaerobic fungi in L diets observed in this as well as other studies [44,55] suggest that fungi are important for feed utilization efficiency and play an active role in shaping rumen conditions for bacteria, archaea and ciliates. Among well characterized genera, the predominant fungus in the L treatment was Caecomyces1. Its higher abundance may be related to complementary fibrolytic activity with Fibrobacter, as shown under in vitro conditions . Addition of SO to the H diet suppressed overall fungal diversity but oil in the L diet increased the abundance of novel groups AL8, SK3 and SK2, while it decreased KF1 and MN3, and Cyllamices. Information on the functional properties of novel fungal groups is still missing. The fact that many of them were detected in our dataset indicates that novel groups actively share a specific niche within the microbial community and react strongly to changing physico-chemical conditions within the rumen.
Rumen microbial taxa form diet-specific networks.
Microbial interaction patterns are best understood when samples are collected from environments undergoing shifts in specific characteristics that cause sufficient changes in microbial abundance. Here we evaluated networks of rumen microbial taxa responding to the changing proportions of fiber and concentrates (L-H); and microbial responses to SO in diets containing high (H-HSO) or low (L-LSO) proportion of concentrates. We created undirected networks by calculating pairwise partial correlations instead of correlations for each pair of taxa. While a correlation network would imply interactions between taxa even when the connection is indirect, for example when both are controlled by a third taxon, partial correlation network does not include such indirect interactions in the network. The second challenge in constructing robust associations between microbial taxa is the small sample size normal in animal experimental trials. Several approaches and statistical models have been created and tested , showing that specific benefits related to a particular method are visible in big data sets (n > 1000), while their performance in small sample sets is similar. The ‘graphical lasso’ performs reasonably well in small data sets and therefore was used in this analysis.
We explored within- and between-domain microbial associations and showed that they are diet-specific. This reflects that the diets chosen for comparison contained different types of fermentable substrates, thereby defining biochemical processes leading to specific microbial interactions. We explored the role of bacteria, archaea, ciliate protozoa and fungi in the organization of association networks. Although bacteria is the most diverse group of microorganisms in the rumen, we did not find evidence for bacteria to play a more central or peripheral role in the rumen biological networks as compared to microorganisms from other domains. However, bacteria formed stronger associations with each other as compared to interactions with ciliate protozoa, fungi or archaea when only the most robust correlations were evaluated. The graphical model of interaction between taxa nodes does not differentiate between direct nutritional and indirect specific niche sharing associations. Nevertheless, it indicates that there is substantial interaction between certain sets of bacteria in response to dietary changes.
Another novel general observation was that no strong associations were detected between archaea and ciliate protozoa in all three diet-change cases. Associations between protozoa and archaea are of high relevance because of the H2 transfer between them and their role in methane production. The lack of strong archaea-protozoa associations in this study supports similar observations reported by , suggesting less specific and more random associations between archaea and protozoa in their interactions in in vivo environments.
Our results suggest that bacteria-archaea interactions respond to SO but may be extremely diet sensitive. Addition of SO to concentrate rich diet (H-HSO) stimulated the strong positive and negative associations between bacteria and Methanobrevibacter, as well as Methanomassiliicoccaceae. More specifically, positive co-occurrences were observed between Mbb. RT and Fibrobacter, Mbb. wolinii and Clostridiales un1, Mmc. Group 10 and Clostriadia un2, Mmc. Group8 WGK1 and Succiniclasticum. Negative co-occurrence was observed between Prevotella and Methanimicrococcus blatticola, Mbb. ruminantium and Clostridia un2, Mmc. Group8 WGK1 and Bacteroidales un2. In contrast, when SO was added to the forage-rich diet (L-LSO), no strong correlation between bacteria and archaea was detected. In the global rumen census , positive associations were detected between less abundant rather than most dominant bacteria and archaea. Similarly, in this study many bacteria associated with archaea were low in abundance and not yet identified at genus level, therefore the nature of these inter-domain interactions remains to be explored.
Similarly, diet specificity was observed also in bacteria-ciliate protozoa associations. Most of the strongest positive as well as negative co-occurrences were detected either when diet composition shifted from high forage to concentrate (L-H) or when SO was added to the L but not the H diet. In the first case, Metadinium minorum, Eudiplodinium and Isotricha were co-occurring with some bacterial taxa, while in the second case a similar co-occurrence was found with Metadinium minorum and Diplodinium. Some positive associations between bacteria and ciliate protozoa, especially between Isotricha and Dasytricha with Fibrobacter were detected in the global rumen census . Although no such association was observed here, differences between the studies may reflect variations in dietary background.
Rumen anaerobic fungi formed positive associations with each other and with microorganisms from other domains. In particular, many positive associations were observed between bacteria, mainly from the Firmicutes phylum and fungi in L-LSO comparison: Caecomyces1—Ruminococcus1, Piromyces3—Succiniclasticum, Piromyces7—Lachnospiraceae un2, MN3—Catabacteriaceae un. Considering that diets high in forage stimulate proliferation of fungi and Firmicutes, strong associations detected between some of them may represent more specific interactions or could be due to the availability of common metabolites.
We explored the organization of microbial co-occurrence networks by looking for the existence of main hub(s), as strong regulation of bacterial interactions was suggested in the human gut microbiome . In this study, most of the microorganisms serving as main hubs in microbial networks were low abundance taxa. These observations are consistent with the human gut data, where very low abundance Christensenellaceae (Firmicutes order Clostridiales) was identified as highly heritable bacteria serving as a hub in gut bacterial networks . In the rumen, not only bacteria but also some ciliate protozoa, fungi and archaea were identified as hubs, suggesting a complex involvement of all four domains in microbial interaction regulation. Interestingly, each specific diet change analysed had own set of taxa serving as central hubs, suggesting that, although a core microbiome can be identified across different diets and hosts , diet is the regulating factor defining which of the taxa act as hubs and govern microbial interactions.
The diet induced changes in rumen microbial community structure are related to changes in ruminal VFA synthesis or methane production. A common approach to explore which diet affected taxa could be related to changes in particular phenotype is by performing taxa abundance—phenotype regression analysis. Using standard diversity analyses it is however not possible to deduce whether microbial taxa significantly associated with animal phenotype work in isolation or are members of more complex networks. Therefore, we explored the behavior of the microbial taxa significantly affected by diet in co-occurrence networks and found that many of them co-occurred together in the more stringent correlation networks. These microbial clusters would be the first candidates to study in vitro and/or via metagenome and metatranscriptome approaches to understand biological interaction mechanisms leading to specific phenotypes.
The various layers of community structure analysis currently available provide a wealth of information about the abundance of different microbial taxa and their response to dietary changes. Here, network analysis performed using ssu rRNA amplicon sequence data expanded on the information obtained from standard diversity methods. It predicted taxa at hubs of interaction, and interactions between taxa, that mostly as yet have no mechanistic explanation. Validation of these interactions will require basic investigation of the physiology of the microorganisms and should be complemented by metagenomics as well as metatranscriptomics analyses of microbiome genes and gene networks.
S1 Text. Supporting information, containing supporting methods, supporting data, and supporting references.
S1 Table. Sequences of primers used for qPCR and metabarcoding amplicon sequencing.
S2 Table. Rumen bacterial, archaeal, ciliate protozoa and fungal taxa average abundances per diet and Tukey test for pairwise comparisons (P values).
S1 Fig. Bacterial, archaeal, protozoal and fungal richness as measured from amplicon sequence data, in four diets.
Diets are as follows: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively).
S2 Fig. Bacterial, archaeal, protozoal and fungal evenness as measured from amplicon sequence data, in four diets.
Diets are as follows: high (H) or low (L) proportion of concentrates without oil, or supplemented with SO (HSO and LSO, respectively).
S3 Fig. Graphical network representing the interactions between rumen microorganisms within L-H diet comparison.
Nodes correspond to microbial taxa while green and red edges represent positive and negative partial correlations above 0.05, respectively. Microbial taxa are colored by taxonomy: archaea—gray, ciliate protozoa—blue, fungi—dark yellow, bacteria—white.
S4 Fig. Graphical network representing the interactions between rumen microorganisms within L-LSO diet comparison.
Nodes correspond to microbial taxa while green and red edges represent positive and negative partial correlations above 0.05, respectively. Microbial taxa are colored by taxonomy: archaea—gray, ciliate protozoa—blue, fungi—dark yellow, bacteria—white.
S5 Fig. Graphical network representing the interactions between rumen microorganisms within H-HSO diet comparison.
Nodes correspond to microbial taxa while green and red edges represent positive and negative partial correlations above 0.05, respectively. Microbial taxa are colored by taxonomy: archaea—gray, ciliate protozoa—blue, bacteria—white.
We thank Mari Talvisilta and the staff in the metabolism unit at Natural Resources Institute Finland for technical support, care of experimental animals and assistance in sample collection. We thank Paula Lidauer for ruminal cannulation surgeries, Richard Hill from Aberystwyth University, UK for performing qPCR and Aurélie Bonin from Laboratoire d'Ecologie Alpine, CNRS, France for preparing archaea amplicon libraries for sequencing. Kevin J. Shingfield passed away before the submission of the final version of this manuscript. Ilma Tapio accepts responsibility for the integrity and validity of the data collected and analyzed.
- Conceptualization: EN KJS JV.
- Formal analysis: IT DF LB MT.
- Funding acquisition: EN KJS JV.
- Investigation: IT ARB LV.
- Writing – original draft: IT RJW MT DF JV.
- Writing – review & editing: DF LB MT RJW ARB LV EN JV MK.
- 1. Kim M, Morrison M, Yu Z. Phylogenetic diversity of bacterial communities in bovine rumen as affected by diets and microenvironments. Folia microbiologica. 2011; 56(5):453–8. pmid:21901294
- 2. Henderson G, Cox F, Ganesh S, Jonker A, Young W, Global Rumen Census Collaborators, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Scientific Reports 2015; 5:14567. pmid:26449758
- 3. Gruninger RJ, Puniya AK, Callaghan TM, Edwards JE, Youssef N, Dagar SS, et al. Anaerobic fungi (phylum Neocallimastigomycota): advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS microbiology ecology. 2014; 90(1):1–7. pmid:25046344
- 4. Janssen PH, Kirs M. Structure of the archaeal community of the rumen. Applied and Environmental Microbiology. 2008; 74(12):3619–25. pmid:18424540
- 5. Taxis TM, Wolff S, Gregg SJ, Minton NO, Zhang C, Dai J, et al. The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity. Nucleic acids research. 2015; pmid:26420832
- 6. Zhou MI, Hernandez-Sanabria E, Guan LL. Assessment of the microbial ecology of ruminal methanogens in cattle with different feed efficiencies. Applied and Environmental Microbiology. 2009; 75(20):6524–33. pmid:19717632
- 7. Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS ONE. 2014; 9(1):e85423. pmid:24465556
- 8. Shabat SK, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. The ISME journal. 2016; 10:2958–2972. pmid:27152936
- 9. Kittelmann S, Seedorf H, Walters WA, Clemente JC, Knight R, Gordon JI, et al. Simultaneous amplicon sequencing to explore co-occurrence patterns of bacterial, archaeal and eukaryotic microorganisms in rumen microbial communities. PLoS ONE. 2013; 8(2):e47879. pmid:23408926
- 10. Kumar S, Indugu N, Vecchiarelli B, Pitta DW. Associative patterns among anaerobic fungi, methanogenic archaea, and bacterial communities in response to changes in diet and age in the rumen of dairy cows. Frontiers in microbiology. 2015; 6:781. pmid:26284058
- 11. Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Determinants of community structure in the global plankton interactome. Science. 2015; 348(6237):1262073. pmid:25999517
- 12. Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. The ISME journal. 2012; 6(2):343–51. pmid:21900968
- 13. Baldassano SN, Bassett DS. Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease. Scientific reports. 2016; 6: 26087. pmid:27188829
- 14. King EE, Smith RP, St-Pierre B, Wright AD. Differences in the rumen methanogen populations of lactating Jersey and Holstein dairy cows under the same diet regimen. Applied and environmental microbiology. 2011; 77(16):5682–7. pmid:21705541
- 15. Huhtanen P, Jaakkola S, Nousiainen J. An overview of silage research in Finland: from ensiling innovation to advances in dairy cow feeding. Agricultural and Food Science. 2013; 22(1):35–56.
- 16. Shingfield KJ, Bonnet M, Scollan ND. Recent developments in altering the fatty acid composition of ruminant-derived foods. Animal. 2013; 7(Suppl 1):132–62.
- 17. Martin C, Morgavi DP, Doreau M. Methane mitigation in ruminants: from microbe to the farm scale. Animal. 2010; 4(03):351–65.
- 18. Council European (1986) Council Directive of 24 November 1986 on the approximation of laws, regulations and administrative provisions of the Member States regarding the protection of animals used for experimental and other scientific purposes (86/609/EEC). Off J Eur Comm L358, 1–28.
- 19. Ventto L, Leskinen H, Kairenius P, Stefański T, Bayat AR, Vilkki J, et al. Diet-induced milk fat depression is associated with alterations in ruminal biohydrogenation pathways and formation of novel fatty acid intermediates in lactating cows. Br. J. Nutr. 2017; 117(3):364–376. pmid:28236814
- 20. Yu Z, Morrison M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques. 2004; 36(5):808–13. pmid:15152600
- 21. Belanche A, De la Fuente G, Moorby JM, Newbold CJ. Bacterial protein degradation by different rumen protozoal groups. Journal of animal science. 2012; 90(12):4495–504. pmid:22829613
- 22. Belanche A, Doreau M, Edwards JE, Moorby JM, Pinloche E, Newbold CJ. Shifts in the rumen microbiota due to the type of carbohydrate and level of protein ingested by dairy cattle are associated with changes in rumen fermentation. The Journal of nutrition. 2012; 142(9):1684–92. pmid:22833657
- 23. Tapio I, Shingfield KJ, McKain N, Bonin A, Fischer D, Bayat AR, et al. Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community. PloS ONE. 2016; 11(3):e0151220. pmid:26986467
- 24. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010; 26(19):2460–1. pmid:20709691
- 25. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature methods. 2010; 7(5):335–6. pmid:20383131
- 26. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of molecular biology. 1990; 215(3):403–10. pmid:2231712
- 27. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME journal. 2012; 6(3):610–8. pmid:22134646
- 28. Seedorf H, Kittelmann S, Henderson G, Janssen PH. RIM-DB: a taxonomic framework for community structure analysis of methanogenic archaea from the rumen and other intestinal environments. PeerJ. 2014; 2:e494. pmid:25165621
- 29. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research. 2013; 41(D1):D590–6.
- 30. Koetschan C, Kittelmann S, Lu J, Al-Halbouni D, Jarvis GN, Müller T, et al. Internal transcribed spacer 1 secondary structure analysis reveals a common core throughout the anaerobic fungi (Neocallimastigomycota). PLoS ONE. 2014; 9:e91928. pmid:24663345
- 31. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, et al. vegan: community ecology package. R package ver. 2.0–10.
- 32. Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software. 2012; 48(4):1–8.
- 33. Bates D, Maechler M. Matrix: sparse and dense matrix classes and methods. R package version 1.2–7.1, URL http://cran.r-project.org/package=Matrix. 2016.
- 34. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008; 9(3):432–41 pmid:18079126
- 35. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 2016.
- 36. Bollobás B. Random Graphs. 2001. Cambridge Stud. Adv. Math. 2001.
- 37. Ivan M, Mir PS, Koenig KM, Rode LM, Neill L, Entz T, et al. Effects of dietary sunflower seed oil on rumen protozoa population and tissue concentration of conjugated linoleic acid in sheep. Small Ruminant Research. 2001; 41(3):215–27.
- 38. Hook SE, Steele MA, Northwood KS, Wright AD, McBride BW. Impact of high-concentrate feeding and low ruminal pH on methanogens and protozoa in the rumen of dairy cows. Microbial ecology. 2011; 62(1):94–105. pmid:21625972
- 39. Popova M, Martin C, Eugène M, Mialon MM, Doreau M, Morgavi DP. Effect of fibre-and starch-rich finishing diets on methanogenic Archaea diversity and activity in the rumen of feedlot bulls. Animal Feed Science and Technology. 2011; 166:113–21.
- 40. Belenguer A, Toral PG, Frutos P, Hervás G. Changes in the rumen bacterial community in response to sunflower oil and fish oil supplements in the diet of dairy sheep. Journal of dairy science. 2010; 93(7):3275–86. pmid:20630243
- 41. Zened A, Combes S, Cauquil L, Mariette J, Klopp C, Bouchez O, et al. Microbial ecology of the rumen evaluated by 454 GS FLX pyrosequencing is affected by starch and oil supplementation of diets. FEMS microbiology ecology. 2013; 83(2):504–14. pmid:22974422
- 42. Petri RM, Schwaiger T, Penner GB, Beauchemin KA, Forster RJ, McKinnon JJ, et al. Characterization of the core rumen microbiome in cattle during transition from forage to concentrate as well as during and after an acidotic challenge. PLoS ONE. 2013; 8(12):e83424. pmid:24391765
- 43. Huws SA, Kim EJ, Cameron SJ, Girdwood SE, Davies L, Tweed J, et al. Characterization of the rumen lipidome and microbiome of steers fed a diet supplemented with flax and echium oil. Microbial biotechnology. 2015; 8(2):331–41. pmid:25223749
- 44. Boots B, Lillis L, Clipson N, Petrie K, Kenny DA, Boland TM, et al. Responses of anaerobic rumen fungal diversity (phylum Neocallimastigomycota) to changes in bovine diet. Journal of applied microbiology. 2013; 114(3):626–35. pmid:23163953
- 45. Bayat AR, Kairenius P, Stefański T, Leskinen H, Comtet-Marre S, Forano E, et al. Effect of camelina oil or live yeasts (Saccharomyces cerevisiae) on ruminal methane production, rumen fermentation, and milk fatty acid composition in lactating cows fed grass silage diets. Journal of dairy science. 2015; 98(5):3166–81. pmid:25726099
- 46. Fernando SC, Purvis HT, Najar FZ, Sukharnikov LO, Krehbiel CR, Nagaraja TG, et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Applied and Environmental Microbiology. 2010; 76(22):7482–90. pmid:20851965
- 47. Forsberg CW, Cheng KJ, White BA. Polysaccharide degradation in the rumen and large intestine. In: Mackie RI, White BA, editors. Gastrointestinal microbiology. Springer US; 1997. pp. 319–379.
- 48. Weimer PJ, Waghorn GC, Odt CL, Mertens DR. Effect of diet on populations of three species of ruminal cellulolytic bacteria in lactating dairy cows. Journal of Dairy Science. 1999; 82(1):122–34. pmid:10022014
- 49. Koike S, Kobayashi Y. Development and use of competitive PCR assays for the rumen cellulolytic bacteria: Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens. FEMS microbiology letters. 2001; 204(2):361–6. pmid:11731149
- 50. Pitta DW, Pinchak WE, Dowd SE, Osterstock J, Gontcharova V, Youn E, et al. Rumen bacterial diversity dynamics associated with changing from bermudagrass hay to grazed winter wheat diets. Microbial ecology. 2010; 59(3):511–22. pmid:20037795
- 51. Salonen A, Lahti L, Salojärvi J, Holtrop G, Korpela K, Duncan SH, et al. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. The ISME journal. 2014; 8(11):2218–30. pmid:24763370
- 52. Williams AG, Coleman GS. The rumen protozoa. In: Hobson PN, Stewart CS, editors. The rumen microbial ecosystem. Springer Science & Business Media; 1997. pp. 73–139.
- 53. Demeyer DI. Rumen microbes and digestion of plant cell walls. Agriculture and Environment. 1981; 6(2):295–337.
- 54. Hristov AN, Callaway TR, Lee C, Dowd SE. Rumen bacterial, archaeal, and fungal diversity of dairy cows in response to ingestion of lauric or myristic acid. Journal of animal science. 2012; 90(12):4449–57. pmid:22952367
- 55. Denman SE, Nicholson MJ, Brookman JL, Theodorou MK, McSweeney CS. Detection and monitoring of anaerobic rumen fungi using an ARISA method. Letters in applied microbiology. 2008; 47(6):492–9. pmid:19120916
- 56. Joblin KN, Matsui H, Naylor GE, Ushida K. Degradation of fresh ryegrass by methanogenic co-cultures of ruminal fungi grown in the presence or absence of Fibrobacter succinogenes. Current microbiology. 2002; 45(1):46–53. pmid:12029527
- 57. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015; 11(5):e1004226. pmid:25950956
- 58. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, et al. Human genetics shape the gut microbiome. Cell. 2014; 159(4):789–99. pmid:25417156