- Email: [email protected]
Similarity of the ruminal bacteria across individual lactating cows
Anaerobe 18 (2012) 338e343
Contents lists available at SciVerse ScienceDirect
Anaerobe journal homepage: www.elsevier.com/locate/anaerobe
Molecular biology, genetics and biotechnology
Similarity of the ruminal bacteria across individual lactating cows Elie Jami a, b, Itzhak Mizrahi a, * a b
Department of Ruminant Science, Institute of Animal Sciences, Agricultural Research Organization, Volcani Center, PO Box 6, Bet Dagan 50250, Israel Department of Molecular Microbiology and Biotechnology, The George S. Wise Faculty of Life Science, Tel Aviv University, Ramat-Aviv 69978, Israel
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 January 2012 Accepted 14 April 2012 Available online 21 April 2012
Dairy cattle hold enormous significance for man as a source of milk and meat. Their remarkable ability to convert indigestible plant mass into these digestible food products resides in the rumen e an anaerobic chambered compartment e in the bovine digestive system. The rumen houses a complex microbiota which is responsible for the degradation of plant material, consequently enabling the conversion of plant fibers into milk and meat and determining their quality and quantity. Hence, an understanding of this complex ecosystem has major economic implications. One important question that is yet to be addressed is the degree of conservation of rumen microbial composition across individual animals. Here we quantified the degree of similarity between rumen bacterial populations of 16 individual cows. We used real-time PCR to determine the variance of specific ruminal bacterial species with different metabolic functions, revealing that while some bacterial strains vary greatly across animals, others show only very low variability. This variance could not be linked to the metabolic traits of these bacteria. We examined the degree of similarity in the dominant bacterial populations across all animals using automated ribosomal intergenic spacer analysis (ARISA), and identified a bacterial community consisting of 32% operational taxonomic units (OTUs) shared by at least 90% of the animals and 19% OTUs shared by 100% of the animals. Looking only at the presence or absence of each OTU gave an average similarity of 75% between each cow pair. When abundance of each OTU was added to the analysis, this similarity decreased to an average of less than 60%. Thus, as suggested in similar recent studies of the human gut, a bovine rumen core microbiome does exist, but taxa abundance may vary greatly across animals. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Microbial ecology Rumen bacteria Anaerobic bacteria
1. Introduction A significant proportion of domesticated animal species worldwide e the source of most meat and dairy products e are ruminants. Chief among these are dairy cattle. Ruminants are herbivores, and their digestive system allows them to absorb and digest large amounts of plant material. This capacity is of enormous significance to man, as ruminants essentially convert the energy stored in plant mass, composed mainly of cellulose and hemicellulose sugar polymers which are indigestible by humans, to digestible food products. The ability to absorb and digest these polymers resides in the ruminants’ foregut, the rumen, which is essentially an anaerobic chambered compartment. The rumen is inhabited by a high density of resident microbiota, consisting of bacteria, protozoa, archaea and fungi, which degrade the consumed plant materials [1]. The rumen microorganisms, of which bacteria are the most abundant and diverse (w95% of the total microbiota),
* Corresponding author. E-mail address: [email protected] (I. Mizrahi). 1075-9964/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.anaerobe.2012.04.003
utilize specialized enzymes and enzyme complexes to degrade these polysaccharides into monomeric or dimeric sugars and subsequently ferment them [2]. The degradation and fermentation take place in a coordinated and complex manner involving functions and metabolic activities such as volatile fatty acid synthesis, ammonia utilization, and cellulolytic and amylolytic activities, with one microorganism’s product being the substrate of another. These complex metabolic interactions result in the conversion of plant materials into digestible compounds, such as volatile fatty acids and bacterial proteins, which in turn define the quality and composition of milk and meat and their production yields [3e5]. Because the functions of the rumen microbiota are essential to the animal’s well being and productivity, understanding these complex microbial populations and their interaction is of great significance. A recent study revealed that there is a high similarity (93e95%) in rumen bacterial community within an individual cow across different sampling times and anatomical location and lower similarity of 85% between the different cows sampled on a controlled diet [6]. In another study examining the changes in ruminal bacterial communities during the feeding cycle it was noticeable that the ruminal bacterial communities were more similar within
E. Jami, I. Mizrahi / Anaerobe 18 (2012) 338e343
the same cows across the feeding cycle than between the different cows sampled [7]. This study also implied that cows fed the same diets can have substantial differences in bacterial community composition. These observations highlight important and fundamental questions regarding the ruminal bacterial populations: How similar are the ruminal bacterial populations across individuals in terms of abundance and occurrence? Are there specific populations which are present across all individual rumens? What is their extent? Recent studies examining these questions in human and mouse gut microbial populations have revealed that microbial communities vary considerably across individuals and that only some of the taxa are present in most individuals. These taxa are defined as the core microbiome, even though their abundance across individuals also varies remarkably [8,9]. Two studies examining different aspects of the rumen microbiota have provided some data regarding these questions. The first investigated the linkage of particular bacterial PCR-DGGE patterns to bovine ruminal fermentation parameters and feed efficiency traits [10]. Using PCR-DGGE, the authors reported a similarity of 75.5% between ruminal bacteria from 58 steers. This similarity was determined using Dice similarity index which is a binary index that represents the presence or absence of specific taxa without considering their abundance. The second study investigated the metagenome of the fiber-adherent population of the bovine rumen [11]. In that study, Sanger sequencing and deep sequencing methods were employed to characterize the rumen microbiome and metagenome of three steers. The obtained data revealed that from the three animals studied, one had a remarkably different microbiome and metagenome with a much larger fraction of Gammaproteobacteria relative to Firmicutes and Bacteroidetes. The somewhat conflicting results of those studies further emphasize the need to address the afore-listed questions. Therefore, the purpose of our study was to characterize the similarity across rumens of individual cows and to define the bacterial populations which are shared by all animals. Using automated ribosomal intergenic spacer analysis (ARISA), and quantitative real-time PCR analysis of specific bacterial 16S RNA genes, we analyzed the similarity of bacterial populations from 16 animals’ rumens. Here we present a comparative study of occurrence and abundance of the general ruminal bacterial population, as well as of specific bacteria with important metabolic traits occupying the rumen. 2. Methods 2.1. Animal handling and sampling The experimental procedures used in this study were approved by the Faculty Animal Policy and Welfare Committee at the ARO, Volcani Research Center and were in accordance with the guidelines of the Israel Council of Animal Care. Israeli Holstein cows (n ¼ 16) were housed at the Agricultural Research Organization’s dairy farm in one shaded corral with free access to water. One month prior to sample collection, the cows were fed a diet ad libitum consisting of 70% concentrated food, mineral and vitamin mix and 30% roughage, 17% of which was dietary natural detergent fiber (NDF) roughage. One hour after the morning feeding, 500 ml of ruminal contents were collected via the cow’s mouth using a stainless-steel stomach tube with a rumen vacuum sampler. The pH was determined immediately and ranged across all samples at 6.5 0.37 Samples were transferred to CO2-containing centrifuge bottles to maintain anaerobic conditions, kept on ice. Immediately after collection, the ruminal samples were processed in the lab, located 100 m away, as previously described [12] with some minor modifications. Briefly, following 2 min of homogenization using a blender, the homogenate was centrifuged at 10,000g and the
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pellet was dissolved in extraction buffer (100 mM TriseHCl, 10 mM ethylenediaminetetraacetic acid [EDTA], 0.15 M NaCl, pH 8.0): 1 g of pellet was dissolved in 4 ml of buffer and incubated at 4 C for 1 h, as chilling has been shown to maximize the release of particleassociated bacteria from ruminal contents [13]. The suspension was then centrifuged gently at 500g for 15 min at 4 C to remove ruptured plant particles while keeping the bacterial cells in suspension. The supernatant was then passed through four layers of cheesecloth, centrifuged (10,000g, 25 min, 4 C), and the pellets were kept at 20 C until DNA extraction. 2.2. DNA extraction The DNA extraction was performed as described by [12]. In brief, cell lysis was achieved by bead disruption with phenol followed by phenol/chloroform DNA extraction. The final supernatant was precipitated with 0.6 vol of isopropanol and resuspended overnight in 50e100 ml TE, then stored at 4 C for short-term use, or archived at 80 C. 2.2.1. ARISA DNA from all rumen samples was subjected to PCR amplification for ARISA [14]. The oligonucleotide primers ITSF (50 -GTCGTAACAAGGTAGCCGTA-30 ) and ITSRtet (50 -GCCAAGGCATCCAAC-30 ) were as described recently for ARISA of rumen bacteria, with the fluorescent molecule being tet. ARISA PCRs were carried out in 15ml volumes containing Fermentas Dreamtaq (Madison, WI) master mix, 0.5 ml of 10 mM stock solution for each primer, 20 ng of template DNA and 4.5 ml of nuclease-free water. PCRs were carried out using a Sensiquest thermocycler (Gottingen, Germany) under the following conditions: 94 C for 2 min (1 cycle), followed by 30 cycles of 94 C for 1 min, 55 C for 60 s and 72 C for 120 s, and finally 1 cycle of 72 C for 5 min. 2.2.2. ARISA resolution and analysis For each DNA sample, two technical replicates of PCR products were analyzed using an ABI PRISM 3100 Genetic Analyzer. The labeled fragments were separated on the capillary sequencer along with a custom-made ROX-labeled 250- to 1150-bp standard (Bioventures). Raw data generated by the genetic analyzer were initially analyzed using GeneMarkerÔ (SoftGenetics) according to Kovacs et al. (2010) [15]. After performing accurate size calling using the program, all data were exported to Microsoft Excel for further analysis. All OTUs with fluorescence intensity of 10 RFU (relative fluorescence units) or lower were excluded. The remaining OTUs were binned as described by Brown et al. [16] with the following parameters: bins of 3 bp (1bp) for fragments up to 700 bp in length, bins of 5 bp for fragments between 700 and 1000 bp in length, and bins of 10 bp for fragments above 1000 bp. Intensities were then summed for each bin. Next, relative intensities for each binned OTU in a given sample were calculated and OTUs which constituted less than 0.1% of the total intensity of the sample were excluded. Technical duplicates were compared as if they were individual samples and served as an internal control for the quality of the analysis. Duplicates that were not similar to each other were further checked for technical run problems and were either discarded or run again. 2.2.3. Statistical analysis All statistical analyses were performed using PAleontological Statistics (PAST) software, available on: http://www.nhm.uio.no/ norges/past/download.html. The Dice and BrayeCurtis distance indexes, as well as the rarefaction test, were calculated using this program. The data was further processed using Microsoft Excel program.
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2.3. Real-time PCR Quantitative real-time PCR analysis was performed to investigate the relative abundance of 13 bacterial species through amplification of their copy of the 16S rRNA gene using the primers shown in Table S1 [12,17]. A standard curve was generated for each bacterial strain selected. By amplifying a serial twofold dilution of gel-extracted PCR products obtained by the amplification of each amplicon, we generated individual standard curves suitable for the quantification of each bacterial strain individually. A standard curve was also generated for the total bacterial 16S rRNA gene in the samples by amplifying 10-fold dilutions of the gel-purified PCR product of one rumen sample. The standard curves were obtained using four dilution points, and were calculated using Rotorgene 6000 series software (Qiagen, Germany). Subsequent quantifications were calculated with the same program using the standard curve generated in each run (equating to one bacterial species), and at least one known purified product dilution used for the standard curves was added to each quantification reaction in order to assess the reproducibility of the reactions. All obtained standard curves met the required standards of efficiency (R2 > 0.99, 90% > E > 15%). Real-time PCR was performed in a 10-ml reaction mixture containing 5 ml Absolute Blue SYBR Green Master Mix (Thermo Scientific), 0.5 ml of each primer (10 mM working concentration), 3 ml of nuclease-free water and 2 ml of 10 ng DNA templates. Amplification involved one hold cycle at 95 C for 15 min for initial denaturation and activation of the hot-start polymerase system, and then 40 cycles at 95 C for 10 s followed by annealing for 15 s at 60 C and extension at 72 C for 20 s. To determine the specificity of amplification, a melting curve of PCR products was monitored by slow heating with an increment of 1 C and holding for 10 s from 60 to 99 C, with fluorescence collection at 1 C intervals. Quantification of the selected bacteria was performed by dividing the number of the specific bacterial count obtained for each bacterium, using the appropriate set of primers, to the total bacterial count obtained by the amplification of the universal bacterial primer.
Fig. 2. Occurrence of OTUs across samples. Different OTUs were summed into categories according to their frequency of occurrence across cows. Each category’s percentage of the total OTU population was calculated. The X-axis represents the percentage of cows sharing a specific OTU. The Y-axis represents the percentage of OTUs in each category.
experimental conditions (see Section 2). Microbial DNA was extracted and subjected to ARISA, which is a high-resolution method capable of assessing bacterial diversity in environmental samples [14]. We counted a total of 250 OTUs across all 16 cows’ rumens and an average 149 OTUs per rumen sample. The number of OTUs was similar to the average in all samples except one (196) that had a higher OTU richness. We first performed a rarefaction analysis to assess whether our coverage depth of the OTU population was sufficient in our samples [18]. An asymptotic curve is obtained when a sufficient sampling size has been reached which represents most of the possible OTUs. As can be seen in Fig. 1, 90% of all OTUs were covered when the
3. Results 3.1. OTU diversity across individual cows Microbial cells were separated from the ruminal contents of 16 Holstein Friesian cows fed the same diet and held under the same
Fig. 1. Sample based rarefaction. This curve plots the number of OTUs as a function of the number of individuals sampled. Each samples added, adds to the plot the OTUs not yet seen in the previous samples. The curve becomes asymptotic as the OTU number saturates, and each sample adds an increasingly smaller number of new OTUs meaning that an adequate coverage was reached for the environment tested.
Fig. 3. Pairwise similarity measurements. Using the PAST program package, we calculated two similarity indexes for all samples. We calculated the average similarity and standard deviation of each sample relative to all of the others in each of the distance matrixes. The X-axis represents the cows’ serial numbers. The Y-axis represents the degree of similarity, with ‘0’ indicating no similarity between samples and 1 indicating that the samples are the same. The filled circles represent the degree of similarity using the Dice similarity index which only relates to the presence or absence of a given OTU. The empty circles represent the similarity using the BrayeCurtis similarity index, which also takes into account the abundance of each OTU.
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number of cows reached eight e a lower value than that obtained from our sampling effort. To investigate the distribution of the different OTUs across individual cows, and to determine whether there are any OTUs that are present in all samples (core microbiome), we evaluated the occurrence of each OTU in the different ruminal samples: the OTUs were binned according to their percentage of occurrence across cows (Fig. 1). We found 32% of the total OTUs (78 OTUs) present in over 90% of all samples (n > 15). This ensemble of OTUs had the largest number of OTUs of all conservation categories. A subset of OTUs in that group was present in 100% of the cows, constituting 19% of the total OTU population, hereafter referred to as the core microbiome group. 3.2. OTU similarity across individual cows We then investigated the degree of similarity of the bacterial communities between two individual cows. We performed pairwise analysis in which the similarity distance of each sample was measured with respect to all others. To this end, we compared two different similarity indices e Dice and BrayeCurtis. The Dice similarity coefficient only investigates the presence or absence of each OTU, while BrayeCurtis takes into account the abundance of each OTU. When the Dice coefficient was used, the similarity values
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ranged between 70% and 90% (Fig. 3). A significantly lower similarity (P < 0.001) was observed when the BrayeCurtis index was, with pairwise similarity values ranging between 50% and 75% (Fig. 3). This reduction in similarity values indicates differences in abundance of OTUs across the samples. 3.3. Quantification of specific selected bacteria across individual cows The fluctuation in the general bacterial populations observed by the ARISA was further examined by quantitatively monitoring specific bacterial species that have been previously studied and demonstrate specific enzymatic traits important for proper ruminal function. To estimate the abundance, degree of similarity and fluctuation that might occur for these specific species across a wide array of rumen samples, we performed quantitative real-time PCR analysis. The sum of the 13 bacterial strains quantified across the 16 cows’ rumens accounted for, on average, w3.4% of the total bacterial count although these species represent a small fraction of the total ruminal bacterial population their metabolic function are essential for proper plant mass breakdown and digestion (Fig. 4). This could be exemplified by the three cellulolytic species e Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens which are considered to be the major cellulolytic rumen
Fig. 4. Relative abundance of 13 selected functional bacterial species. Quantitative real-time PCR analysis was used to calculate the abundance of each species compared to the total bacterial population, represented as percentage on the X-axis. The boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively) and the vertical line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Samples with a relative abundance of a given bacteria exceeding those values are represented as points beside the boxes.
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bacteria. This further emphasizes the vastness of the still mostly unknown rumen bacterial microbiota. The quantification analysis revealed that while some strains are relatively stable in their concentration across all rumen samples, such as Prevotella ruminicola and Selenomonas ruminantium, some have a high degree of variance in their relative abundance between different samples (Fig. 4). Although the high variations were observed mostly within the bacterial strains with low abundance, some species with low abundance, such as R. albus and Eubacterium ruminantium showed remarkable stability in abundance across all sampled cows.
bacterial community which varies considerably in abundance across animals. We also found that some specific bacteria with known metabolic functions show a high degree of variation between cows while others appear to be very stable. Understanding the reasons for these variations is important as they may be linked to the animal’s productivity. Furthermore, the identity of the core bacterial community and its existence and variance under other diets and at other time points are of great interest to the understanding of this important ecosystem and the way in which we study it.
4. Discussion
Acknowledgments
The aim of the current study was to explore the similarity in ruminal bacterial communities using a larger number of animals. This aspect is fundamental for an understanding of this complex ecosystem as well as for the design of future experimental procedures involving rumen bacterial communities. Sample based rarefaction analysis indicated that our sampling effort was sufficient and included both the most common and rarest OTUs existing in the ruminal bacterial community, as represented by the asymptotic shape of the generated curve (Fig. 1). We observed that 19% of the OTUs were found in all rumen samples and 32% were found in at least 90% (n > 15), implying a shared core community between different individual cows (Fig. 2), i.e. a core ruminal microbiome. This number might be an underestimate, due to the fact that when processing the retrieved ARISA data, we considered a signal lower than 0.1% as background noise, as previously described [15], and we may therefore have discarded some of the less abundant species that are potentially shared by all samples. Using pairwise comparison between samples with the Dice similarity index, we found a similarity between two individual cows of w75% (Fig. 3), in agreement with the value reported by Hernandez [19] who used the same similarity index with DGGE. This suggests comparable sensitivity of the ARISA and DGGE methods [19]. The Dice similarity index is a binary index reflecting only the presence or absence of an OTU in each sample. One advantage provided by ARISA is that each OTU has an abundance value represented by signal strength. Therefore, we used the BrayeCurtis similarity index which factors in the abundance of each OTU. When using this index, the average similarity decreased to less than 60% between individual samples which suggests a high degree of variation in abundance of some OTUs between samples (Fig. 3) We then used real-time PCR analysis to examine 13 bacterial species holding metabolic traits important for the rumen ecosystem, as described by Stevenson [12]. These metabolic traits include cellulose degradation in the case of R. albus, R. flavefaciens and F. succinogenes, and starch degradation in the case of Streptococcus bovis and Ruminobacter amylophilus [20,21]. We studied the variations of these specific bacterial strains in different rumen samples as percentage of the total bacteria present in each sample. All tested strains represented 2.7e4.3% of the total bacterial count. Our results indicated that certain bacterial strains are relatively stable in abundance across all tested rumen samples as demonstrated by E. ruminantium, a classic biohydrogenating species with hemicellulolytic activities, while others, such as Megasphaera elsdenii, a lactic-acid-fermenting bacteria, seem to exhibit a high degree of variation between samples, with values spreading across three orders of magnitude (Fig. 4).
We thank Amir Kovacs for His help and useful suggestions with the ARISA.
5. Conclusion Under the conditions of this study e 16 animals on a specific diet at a given time point e we established the existence of a core
Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.anaerobe.2012.04.003 References [1] Flint HJ. The rumen microbial ecosystem e some recent developments. Trends Microbiol 1997;5:483e8. [2] Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol 2008;6:121e31. [3] Ogino A, Kaku K, Osada T, Shimada K. Environmental impacts of the Japanese beef-fattening system with different feeding lengths as evaluated by a lifecycle assessment method. J Anim Sci 2004;82:2115e22. [4] Tedeschi LO, Fox DG, Tylutki TP. Potential environmental benefits of ionophores in ruminant diets. J Environ Qual 2003;32:1591e602. [5] Duffield TF, Bagg RN. Use of ionophores in lactating dairy cattle: a review. Can Vet J 2000;41:388e94. [6] Li M, Penner GB, Hernandez-Sanabria E, Oba M, Guan LL. Effects of sampling location and time, and host animal on assessment of bacterial diversity and fermentation parameters in the bovine rumen. J Appl Microbiol 2009;107: 1924e34. [7] Welkie DG, Stevenson DM, Weimer PJ. ARISA analysis of ruminal bacterial community dynamics in lactating dairy cows during the feeding cycle. Anaerobe 2009;16:94e100. [8] Benson AK, Kelly SA, Legge R, Ma F, Low SJ, Kim J, et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc Natl Acad Sci U S A; 2010. [9] Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 2010;464:59e65. [10] Hernandez-Sanabria E, Guan LL, Goonewardene LA, Li M, Mujibi DF, Stothard P, et al. Correlation of particular bacterial PCR-denaturing gradient gel electrophoresis patterns with bovine ruminal fermentation parameters and feed efficiency traits. Appl Environ Microbiol;76:6338e6350. [11] Brulc JM, Antonopoulos DA, Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci U S A 2009;106:1948e53. [12] Stevenson DM, Weimer PJ. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol 2007;75:165e74. [13] Dehority BA, Grubb JA. Effect of short-term chilling of rumen contents on viable bacterial numbers. Appl Environ Microbiol 1980;39:376e81. [14] Fisher MM, Triplett EW. Automated approach for ribosomal intergenic spacer analysis of microbial diversity and its application to freshwater bacterial communities. Appl Environ Microbiol 1999;65:4630e6. [15] Kovacs A, Yacoby K, Gophna U. A systematic assessment of automated ribosomal intergenic spacer analysis (ARISA) as a tool for estimating bacterial richness. Res Microbiol 2010;161:192e7. [16] Brown MV, Schwalbach MS, Hewson I, Fuhrman JA. Coupling 16S-ITS rDNA clone libraries and automated ribosomal intergenic spacer analysis to show marine microbial diversity: development and application to a time series. Environ Microbiol 2005;7:1466e79. [17] Walter J, Tannock GW, Tilsala-Timisjarvi A, Rodtong S, Loach DM, Munro K, et al. Detection and identification of gastrointestinal Lactobacillus species by using denaturing gradient gel electrophoresis and species-specific PCR primers. Appl Environ Microbiol 2000;66:297e303. [18] Mao CX, Colwell RK, Chang J. Estimating the species accumulation curve using mixtures. Biometrics 2005;61:433e41.
E. Jami, I. Mizrahi / Anaerobe 18 (2012) 338e343 [19] Hernandez-Sanabria E, Guan LL, Goonewardene LA, Li M, Mujibi DF, Stothard P, et al. Correlation of particular bacterial PCR-denaturing gradient gel electrophoresis patterns with bovine ruminal fermentation parameters and feed efficiency traits. Appl Environ Microbiol 2010;76: 6338e50.
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[20] Russell JB, Rychlik JL. Factors that alter rumen microbial ecology. Science 2001;292:1119e22. [21] Krause DO, Denman SE, Mackie RI, Morrison M, Rae AL, Attwood GT, et al. Opportunities to improve fiber degradation in the rumen: microbiology, ecology, and genomics. FEMS Microbiol Rev 2003;27:663e93.
Contents lists available at SciVerse ScienceDirect
Anaerobe journal homepage: www.elsevier.com/locate/anaerobe
Molecular biology, genetics and biotechnology
Similarity of the ruminal bacteria across individual lactating cows Elie Jami a, b, Itzhak Mizrahi a, * a b
Department of Ruminant Science, Institute of Animal Sciences, Agricultural Research Organization, Volcani Center, PO Box 6, Bet Dagan 50250, Israel Department of Molecular Microbiology and Biotechnology, The George S. Wise Faculty of Life Science, Tel Aviv University, Ramat-Aviv 69978, Israel
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 January 2012 Accepted 14 April 2012 Available online 21 April 2012
Dairy cattle hold enormous significance for man as a source of milk and meat. Their remarkable ability to convert indigestible plant mass into these digestible food products resides in the rumen e an anaerobic chambered compartment e in the bovine digestive system. The rumen houses a complex microbiota which is responsible for the degradation of plant material, consequently enabling the conversion of plant fibers into milk and meat and determining their quality and quantity. Hence, an understanding of this complex ecosystem has major economic implications. One important question that is yet to be addressed is the degree of conservation of rumen microbial composition across individual animals. Here we quantified the degree of similarity between rumen bacterial populations of 16 individual cows. We used real-time PCR to determine the variance of specific ruminal bacterial species with different metabolic functions, revealing that while some bacterial strains vary greatly across animals, others show only very low variability. This variance could not be linked to the metabolic traits of these bacteria. We examined the degree of similarity in the dominant bacterial populations across all animals using automated ribosomal intergenic spacer analysis (ARISA), and identified a bacterial community consisting of 32% operational taxonomic units (OTUs) shared by at least 90% of the animals and 19% OTUs shared by 100% of the animals. Looking only at the presence or absence of each OTU gave an average similarity of 75% between each cow pair. When abundance of each OTU was added to the analysis, this similarity decreased to an average of less than 60%. Thus, as suggested in similar recent studies of the human gut, a bovine rumen core microbiome does exist, but taxa abundance may vary greatly across animals. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Microbial ecology Rumen bacteria Anaerobic bacteria
1. Introduction A significant proportion of domesticated animal species worldwide e the source of most meat and dairy products e are ruminants. Chief among these are dairy cattle. Ruminants are herbivores, and their digestive system allows them to absorb and digest large amounts of plant material. This capacity is of enormous significance to man, as ruminants essentially convert the energy stored in plant mass, composed mainly of cellulose and hemicellulose sugar polymers which are indigestible by humans, to digestible food products. The ability to absorb and digest these polymers resides in the ruminants’ foregut, the rumen, which is essentially an anaerobic chambered compartment. The rumen is inhabited by a high density of resident microbiota, consisting of bacteria, protozoa, archaea and fungi, which degrade the consumed plant materials [1]. The rumen microorganisms, of which bacteria are the most abundant and diverse (w95% of the total microbiota),
* Corresponding author. E-mail address: [email protected] (I. Mizrahi). 1075-9964/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.anaerobe.2012.04.003
utilize specialized enzymes and enzyme complexes to degrade these polysaccharides into monomeric or dimeric sugars and subsequently ferment them [2]. The degradation and fermentation take place in a coordinated and complex manner involving functions and metabolic activities such as volatile fatty acid synthesis, ammonia utilization, and cellulolytic and amylolytic activities, with one microorganism’s product being the substrate of another. These complex metabolic interactions result in the conversion of plant materials into digestible compounds, such as volatile fatty acids and bacterial proteins, which in turn define the quality and composition of milk and meat and their production yields [3e5]. Because the functions of the rumen microbiota are essential to the animal’s well being and productivity, understanding these complex microbial populations and their interaction is of great significance. A recent study revealed that there is a high similarity (93e95%) in rumen bacterial community within an individual cow across different sampling times and anatomical location and lower similarity of 85% between the different cows sampled on a controlled diet [6]. In another study examining the changes in ruminal bacterial communities during the feeding cycle it was noticeable that the ruminal bacterial communities were more similar within
E. Jami, I. Mizrahi / Anaerobe 18 (2012) 338e343
the same cows across the feeding cycle than between the different cows sampled [7]. This study also implied that cows fed the same diets can have substantial differences in bacterial community composition. These observations highlight important and fundamental questions regarding the ruminal bacterial populations: How similar are the ruminal bacterial populations across individuals in terms of abundance and occurrence? Are there specific populations which are present across all individual rumens? What is their extent? Recent studies examining these questions in human and mouse gut microbial populations have revealed that microbial communities vary considerably across individuals and that only some of the taxa are present in most individuals. These taxa are defined as the core microbiome, even though their abundance across individuals also varies remarkably [8,9]. Two studies examining different aspects of the rumen microbiota have provided some data regarding these questions. The first investigated the linkage of particular bacterial PCR-DGGE patterns to bovine ruminal fermentation parameters and feed efficiency traits [10]. Using PCR-DGGE, the authors reported a similarity of 75.5% between ruminal bacteria from 58 steers. This similarity was determined using Dice similarity index which is a binary index that represents the presence or absence of specific taxa without considering their abundance. The second study investigated the metagenome of the fiber-adherent population of the bovine rumen [11]. In that study, Sanger sequencing and deep sequencing methods were employed to characterize the rumen microbiome and metagenome of three steers. The obtained data revealed that from the three animals studied, one had a remarkably different microbiome and metagenome with a much larger fraction of Gammaproteobacteria relative to Firmicutes and Bacteroidetes. The somewhat conflicting results of those studies further emphasize the need to address the afore-listed questions. Therefore, the purpose of our study was to characterize the similarity across rumens of individual cows and to define the bacterial populations which are shared by all animals. Using automated ribosomal intergenic spacer analysis (ARISA), and quantitative real-time PCR analysis of specific bacterial 16S RNA genes, we analyzed the similarity of bacterial populations from 16 animals’ rumens. Here we present a comparative study of occurrence and abundance of the general ruminal bacterial population, as well as of specific bacteria with important metabolic traits occupying the rumen. 2. Methods 2.1. Animal handling and sampling The experimental procedures used in this study were approved by the Faculty Animal Policy and Welfare Committee at the ARO, Volcani Research Center and were in accordance with the guidelines of the Israel Council of Animal Care. Israeli Holstein cows (n ¼ 16) were housed at the Agricultural Research Organization’s dairy farm in one shaded corral with free access to water. One month prior to sample collection, the cows were fed a diet ad libitum consisting of 70% concentrated food, mineral and vitamin mix and 30% roughage, 17% of which was dietary natural detergent fiber (NDF) roughage. One hour after the morning feeding, 500 ml of ruminal contents were collected via the cow’s mouth using a stainless-steel stomach tube with a rumen vacuum sampler. The pH was determined immediately and ranged across all samples at 6.5 0.37 Samples were transferred to CO2-containing centrifuge bottles to maintain anaerobic conditions, kept on ice. Immediately after collection, the ruminal samples were processed in the lab, located 100 m away, as previously described [12] with some minor modifications. Briefly, following 2 min of homogenization using a blender, the homogenate was centrifuged at 10,000g and the
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pellet was dissolved in extraction buffer (100 mM TriseHCl, 10 mM ethylenediaminetetraacetic acid [EDTA], 0.15 M NaCl, pH 8.0): 1 g of pellet was dissolved in 4 ml of buffer and incubated at 4 C for 1 h, as chilling has been shown to maximize the release of particleassociated bacteria from ruminal contents [13]. The suspension was then centrifuged gently at 500g for 15 min at 4 C to remove ruptured plant particles while keeping the bacterial cells in suspension. The supernatant was then passed through four layers of cheesecloth, centrifuged (10,000g, 25 min, 4 C), and the pellets were kept at 20 C until DNA extraction. 2.2. DNA extraction The DNA extraction was performed as described by [12]. In brief, cell lysis was achieved by bead disruption with phenol followed by phenol/chloroform DNA extraction. The final supernatant was precipitated with 0.6 vol of isopropanol and resuspended overnight in 50e100 ml TE, then stored at 4 C for short-term use, or archived at 80 C. 2.2.1. ARISA DNA from all rumen samples was subjected to PCR amplification for ARISA [14]. The oligonucleotide primers ITSF (50 -GTCGTAACAAGGTAGCCGTA-30 ) and ITSRtet (50 -GCCAAGGCATCCAAC-30 ) were as described recently for ARISA of rumen bacteria, with the fluorescent molecule being tet. ARISA PCRs were carried out in 15ml volumes containing Fermentas Dreamtaq (Madison, WI) master mix, 0.5 ml of 10 mM stock solution for each primer, 20 ng of template DNA and 4.5 ml of nuclease-free water. PCRs were carried out using a Sensiquest thermocycler (Gottingen, Germany) under the following conditions: 94 C for 2 min (1 cycle), followed by 30 cycles of 94 C for 1 min, 55 C for 60 s and 72 C for 120 s, and finally 1 cycle of 72 C for 5 min. 2.2.2. ARISA resolution and analysis For each DNA sample, two technical replicates of PCR products were analyzed using an ABI PRISM 3100 Genetic Analyzer. The labeled fragments were separated on the capillary sequencer along with a custom-made ROX-labeled 250- to 1150-bp standard (Bioventures). Raw data generated by the genetic analyzer were initially analyzed using GeneMarkerÔ (SoftGenetics) according to Kovacs et al. (2010) [15]. After performing accurate size calling using the program, all data were exported to Microsoft Excel for further analysis. All OTUs with fluorescence intensity of 10 RFU (relative fluorescence units) or lower were excluded. The remaining OTUs were binned as described by Brown et al. [16] with the following parameters: bins of 3 bp (1bp) for fragments up to 700 bp in length, bins of 5 bp for fragments between 700 and 1000 bp in length, and bins of 10 bp for fragments above 1000 bp. Intensities were then summed for each bin. Next, relative intensities for each binned OTU in a given sample were calculated and OTUs which constituted less than 0.1% of the total intensity of the sample were excluded. Technical duplicates were compared as if they were individual samples and served as an internal control for the quality of the analysis. Duplicates that were not similar to each other were further checked for technical run problems and were either discarded or run again. 2.2.3. Statistical analysis All statistical analyses were performed using PAleontological Statistics (PAST) software, available on: http://www.nhm.uio.no/ norges/past/download.html. The Dice and BrayeCurtis distance indexes, as well as the rarefaction test, were calculated using this program. The data was further processed using Microsoft Excel program.
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2.3. Real-time PCR Quantitative real-time PCR analysis was performed to investigate the relative abundance of 13 bacterial species through amplification of their copy of the 16S rRNA gene using the primers shown in Table S1 [12,17]. A standard curve was generated for each bacterial strain selected. By amplifying a serial twofold dilution of gel-extracted PCR products obtained by the amplification of each amplicon, we generated individual standard curves suitable for the quantification of each bacterial strain individually. A standard curve was also generated for the total bacterial 16S rRNA gene in the samples by amplifying 10-fold dilutions of the gel-purified PCR product of one rumen sample. The standard curves were obtained using four dilution points, and were calculated using Rotorgene 6000 series software (Qiagen, Germany). Subsequent quantifications were calculated with the same program using the standard curve generated in each run (equating to one bacterial species), and at least one known purified product dilution used for the standard curves was added to each quantification reaction in order to assess the reproducibility of the reactions. All obtained standard curves met the required standards of efficiency (R2 > 0.99, 90% > E > 15%). Real-time PCR was performed in a 10-ml reaction mixture containing 5 ml Absolute Blue SYBR Green Master Mix (Thermo Scientific), 0.5 ml of each primer (10 mM working concentration), 3 ml of nuclease-free water and 2 ml of 10 ng DNA templates. Amplification involved one hold cycle at 95 C for 15 min for initial denaturation and activation of the hot-start polymerase system, and then 40 cycles at 95 C for 10 s followed by annealing for 15 s at 60 C and extension at 72 C for 20 s. To determine the specificity of amplification, a melting curve of PCR products was monitored by slow heating with an increment of 1 C and holding for 10 s from 60 to 99 C, with fluorescence collection at 1 C intervals. Quantification of the selected bacteria was performed by dividing the number of the specific bacterial count obtained for each bacterium, using the appropriate set of primers, to the total bacterial count obtained by the amplification of the universal bacterial primer.
Fig. 2. Occurrence of OTUs across samples. Different OTUs were summed into categories according to their frequency of occurrence across cows. Each category’s percentage of the total OTU population was calculated. The X-axis represents the percentage of cows sharing a specific OTU. The Y-axis represents the percentage of OTUs in each category.
experimental conditions (see Section 2). Microbial DNA was extracted and subjected to ARISA, which is a high-resolution method capable of assessing bacterial diversity in environmental samples [14]. We counted a total of 250 OTUs across all 16 cows’ rumens and an average 149 OTUs per rumen sample. The number of OTUs was similar to the average in all samples except one (196) that had a higher OTU richness. We first performed a rarefaction analysis to assess whether our coverage depth of the OTU population was sufficient in our samples [18]. An asymptotic curve is obtained when a sufficient sampling size has been reached which represents most of the possible OTUs. As can be seen in Fig. 1, 90% of all OTUs were covered when the
3. Results 3.1. OTU diversity across individual cows Microbial cells were separated from the ruminal contents of 16 Holstein Friesian cows fed the same diet and held under the same
Fig. 1. Sample based rarefaction. This curve plots the number of OTUs as a function of the number of individuals sampled. Each samples added, adds to the plot the OTUs not yet seen in the previous samples. The curve becomes asymptotic as the OTU number saturates, and each sample adds an increasingly smaller number of new OTUs meaning that an adequate coverage was reached for the environment tested.
Fig. 3. Pairwise similarity measurements. Using the PAST program package, we calculated two similarity indexes for all samples. We calculated the average similarity and standard deviation of each sample relative to all of the others in each of the distance matrixes. The X-axis represents the cows’ serial numbers. The Y-axis represents the degree of similarity, with ‘0’ indicating no similarity between samples and 1 indicating that the samples are the same. The filled circles represent the degree of similarity using the Dice similarity index which only relates to the presence or absence of a given OTU. The empty circles represent the similarity using the BrayeCurtis similarity index, which also takes into account the abundance of each OTU.
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number of cows reached eight e a lower value than that obtained from our sampling effort. To investigate the distribution of the different OTUs across individual cows, and to determine whether there are any OTUs that are present in all samples (core microbiome), we evaluated the occurrence of each OTU in the different ruminal samples: the OTUs were binned according to their percentage of occurrence across cows (Fig. 1). We found 32% of the total OTUs (78 OTUs) present in over 90% of all samples (n > 15). This ensemble of OTUs had the largest number of OTUs of all conservation categories. A subset of OTUs in that group was present in 100% of the cows, constituting 19% of the total OTU population, hereafter referred to as the core microbiome group. 3.2. OTU similarity across individual cows We then investigated the degree of similarity of the bacterial communities between two individual cows. We performed pairwise analysis in which the similarity distance of each sample was measured with respect to all others. To this end, we compared two different similarity indices e Dice and BrayeCurtis. The Dice similarity coefficient only investigates the presence or absence of each OTU, while BrayeCurtis takes into account the abundance of each OTU. When the Dice coefficient was used, the similarity values
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ranged between 70% and 90% (Fig. 3). A significantly lower similarity (P < 0.001) was observed when the BrayeCurtis index was, with pairwise similarity values ranging between 50% and 75% (Fig. 3). This reduction in similarity values indicates differences in abundance of OTUs across the samples. 3.3. Quantification of specific selected bacteria across individual cows The fluctuation in the general bacterial populations observed by the ARISA was further examined by quantitatively monitoring specific bacterial species that have been previously studied and demonstrate specific enzymatic traits important for proper ruminal function. To estimate the abundance, degree of similarity and fluctuation that might occur for these specific species across a wide array of rumen samples, we performed quantitative real-time PCR analysis. The sum of the 13 bacterial strains quantified across the 16 cows’ rumens accounted for, on average, w3.4% of the total bacterial count although these species represent a small fraction of the total ruminal bacterial population their metabolic function are essential for proper plant mass breakdown and digestion (Fig. 4). This could be exemplified by the three cellulolytic species e Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens which are considered to be the major cellulolytic rumen
Fig. 4. Relative abundance of 13 selected functional bacterial species. Quantitative real-time PCR analysis was used to calculate the abundance of each species compared to the total bacterial population, represented as percentage on the X-axis. The boxes represent the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively) and the vertical line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Samples with a relative abundance of a given bacteria exceeding those values are represented as points beside the boxes.
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bacteria. This further emphasizes the vastness of the still mostly unknown rumen bacterial microbiota. The quantification analysis revealed that while some strains are relatively stable in their concentration across all rumen samples, such as Prevotella ruminicola and Selenomonas ruminantium, some have a high degree of variance in their relative abundance between different samples (Fig. 4). Although the high variations were observed mostly within the bacterial strains with low abundance, some species with low abundance, such as R. albus and Eubacterium ruminantium showed remarkable stability in abundance across all sampled cows.
bacterial community which varies considerably in abundance across animals. We also found that some specific bacteria with known metabolic functions show a high degree of variation between cows while others appear to be very stable. Understanding the reasons for these variations is important as they may be linked to the animal’s productivity. Furthermore, the identity of the core bacterial community and its existence and variance under other diets and at other time points are of great interest to the understanding of this important ecosystem and the way in which we study it.
4. Discussion
Acknowledgments
The aim of the current study was to explore the similarity in ruminal bacterial communities using a larger number of animals. This aspect is fundamental for an understanding of this complex ecosystem as well as for the design of future experimental procedures involving rumen bacterial communities. Sample based rarefaction analysis indicated that our sampling effort was sufficient and included both the most common and rarest OTUs existing in the ruminal bacterial community, as represented by the asymptotic shape of the generated curve (Fig. 1). We observed that 19% of the OTUs were found in all rumen samples and 32% were found in at least 90% (n > 15), implying a shared core community between different individual cows (Fig. 2), i.e. a core ruminal microbiome. This number might be an underestimate, due to the fact that when processing the retrieved ARISA data, we considered a signal lower than 0.1% as background noise, as previously described [15], and we may therefore have discarded some of the less abundant species that are potentially shared by all samples. Using pairwise comparison between samples with the Dice similarity index, we found a similarity between two individual cows of w75% (Fig. 3), in agreement with the value reported by Hernandez [19] who used the same similarity index with DGGE. This suggests comparable sensitivity of the ARISA and DGGE methods [19]. The Dice similarity index is a binary index reflecting only the presence or absence of an OTU in each sample. One advantage provided by ARISA is that each OTU has an abundance value represented by signal strength. Therefore, we used the BrayeCurtis similarity index which factors in the abundance of each OTU. When using this index, the average similarity decreased to less than 60% between individual samples which suggests a high degree of variation in abundance of some OTUs between samples (Fig. 3) We then used real-time PCR analysis to examine 13 bacterial species holding metabolic traits important for the rumen ecosystem, as described by Stevenson [12]. These metabolic traits include cellulose degradation in the case of R. albus, R. flavefaciens and F. succinogenes, and starch degradation in the case of Streptococcus bovis and Ruminobacter amylophilus [20,21]. We studied the variations of these specific bacterial strains in different rumen samples as percentage of the total bacteria present in each sample. All tested strains represented 2.7e4.3% of the total bacterial count. Our results indicated that certain bacterial strains are relatively stable in abundance across all tested rumen samples as demonstrated by E. ruminantium, a classic biohydrogenating species with hemicellulolytic activities, while others, such as Megasphaera elsdenii, a lactic-acid-fermenting bacteria, seem to exhibit a high degree of variation between samples, with values spreading across three orders of magnitude (Fig. 4).
We thank Amir Kovacs for His help and useful suggestions with the ARISA.
5. Conclusion Under the conditions of this study e 16 animals on a specific diet at a given time point e we established the existence of a core
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