Research Article Received: 4 August 2013
Revised: 4 November 2013
Accepted article published: 30 November 2013
Published online in Wiley Online Library: 29 December 2013
(wileyonlinelibrary.com) DOI 10.1002/jsfa.6508
Effect of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines in dairy cows Ruiyang Zhang,a Weiyun Zhu,a Wen Zhu,b Jianxin Liub and Shengyong Maoa∗ Abstract BACKGROUND: Fifteen lactating Holstein dairy cows were assigned to three diets in a 3 × 3 Latin square design to evaluate the effects of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines. Diets were isonitrogenous and isocaloric, with a forage/concentrate ratio of 45:55 (dry matter basis) but different main forage sources, namely cornstalk (CS), Leymus chinensis (LC) or alfalfa hay (AH). RESULTS: Pyrosequencing of the V3–V6 hypervariable coding region of 16S rRNA revealed that the rumen microbiota was significantly affected by forage sources. AH feeding increased the proportion of genera Prevotella and Selenomonas compared with the CS diet, while CS feeding increased the proportion of genera Anaerotruncus, Papillibacter, Thermoactimoyces, Bacillus and Streptomyces compared with the LC or AH diet. AH and LC feeding both increased the propionate concentration compared with the CS diet. AH feeding decreased the concentrations of tyramine, putrescine and histamine compared with the LC diet. CONCLUSION: These results indicate that a high proportion of alfalfa hay in the ration is beneficial for milk yield and a healthy and balanced rumen microbiota in lactating cattle. This can be attributed to the higher degradation of rumen organic matter and the more balanced carbohydrates and proteins for optimal rumen microbial growth. c 2013 Society of Chemical Industry Supporting information may be found in the online version of this article. Keywords: forage sources; dairy cows; microbiota; rumen fermentation; biogenic amines
INTRODUCTION
1886
Ruminant animals and ruminal microorganisms have evolved together for millions of years. The rumen is inhabited by diverse and interdependent populations of bacteria, protozoa and fungi.1 Within this microbiome, bacteria are the dominant domain and make the greatest contribution to digestion and conversion of feeds to volatile fatty acids (VFAs) and microbial proteins.2 The ruminal bacteria community is closely related to the diet, and changing the diet has a cascading effect on rumen metabolism, which can impact both quantity and quality of animal production.3 Forage quality is an important factor that affects the performance of ruminant production.4 Alfalfa is well known for its high quality and is used worldwide as an important dietary forage. While cornstalk is abundant and inexpensive and is consequently the predominant forage source for ruminants, it is typically considered as low-quality roughage. Leymus chinensis is a perennial species of Gramineae that is widely distributed throughout the eastern end of the Eurasian steppe zone, including the Songnen Plain and the eastern Inner Mongolian Plateau in China.5,6 It is an important forage in China and is considered as middle-quality roughage. Different quality forages have a large impact on ruminal metabolites, thereby affecting the composition of the rumen microbiota.4,7 Kong et al.8 used 16S rRNA gene-cloning library J Sci Food Agric 2014; 94: 1886–1895
technology to analyze the composition of the bacterial community structure in the rumen of cows fed different quality forages. They found that most of the species in the bacterial community with alfalfa fed as the diet (84%) were different from those in the community with triticale fed as the diet. Pitta et al.9 also reported that distinct bacterial communities developed in the rumen associated with bermudagrass hay feeding and winter wheat grazing in a cohort of yearling steers. According to these studies, most research exploring the relationship between different quality forages and rumen microbial community composition was mainly based on sole forage or high-forage diet (the proportion of forage in the ration is greater than 80%).8,9 Indeed, in the modern dairy industry the proportion of dietary forage is lower than 50% in high-producing lactating dairy cows. Therefore these studies based on sole forage or high proportion of dietary forage cannot
∗
Correspondence to: Shengyong Mao, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095 China. E-mail: [email protected]
a College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China b Institute of Dairy Science, Zhejiang University, Hangzhou, 310058, China
www.soci.org
c 2013 Society of Chemical Industry
Effect of dietary forage sources on dairy cow ruminal characteristics be used to reflect actual conditions. Thus it is necessary to explore whether there would be any differences in the rumen microbial community composition when the animals are fed diets with a proportion of dietary forage sources that more closely resembles actual conditions. Biogenic amines are nitrogen-containing aliphatic, aromatic or heterocyclic organic compounds widely present in various kinds of organism. Tyramine, putrescine, histamine, methylamine and tryptamine are the most common forms of biogenic amines. Smaller amounts of biogenic amines are usually metabolized in the organism without any impact on its health,10 but in larger quantities (1.4 g day−1 ) they become harmful to humans and livestock.11,12 The source of biogenic amines in the rumen of cattle is related to the diet and, most importantly, to the rumen microbiota, and a diet with highly degradable proteins may produce more biogenic amines.13 Compared with cornstalk and L. chinensis, alfalfa hay has higher contents of crude protein (CP), rumen-degradable protein (RDP) and rumen-undegradable protein (RUP).14 Additionally, alfalfa hay is easily broken down into smaller particles in the rumen because of its lower acid detergent fiber (ADF) and neutral detergent fiber (NDF). Therefore we postulated that a diet with alfalfa hay as the forage source might have higher levels of biogenic amines compared with a cornstalk or L. chinensis-based diet. In this study we hypothesized that diets with different forage sources will affect the rumen microbiota. Therefore the primary objective of the study was to evaluate the effects of diets with three different quality forage sources (alfalfa hay, L. chinensis and cornstalk) on the rumen microbiota of dairy cows. An additional aim was to investigate the effects of the diets on rumen fermentation and biogenic amine production.
MATERIALS AND METHODS Animals involved in this study were cared for according to the guidelines of the Zhejiang University Animal Care and Use Committee. The committee reviewed and approved the experiment and all procedures carried out in the study.
J Sci Food Agric 2014; 94: 1886–1895
Table 1. Ingredients and chemical composition of diets Dieta Item Ingredients (% DM) Ground corn grain Wheat bran Soybean meal Cottonseed meal DDGSb Limestone Dicalcium phosphate Sodium bicarbonate Salt Premix Ca salts of long-chain FAs Maize silage Cornstalk Leymus chinensis Alfalfa hay Nutrient composition (% DM) CP RDP NDF NFCc Ca P Ash NEL (Mcal kg−1 DM)
LC
CS
AH
26.6 3.00 12.0 3.50 6.00 0.80 0.85 0.75 0.50 1.00 0.00 19.00 0.00 21.00 5.00
28.0 0.00 13.0 3.50 6.00 0.75 1.20 0.75 0.50 1.00 0.30 21.00 19.00 0.00 5.00
28.0 4.00 10.0 3.50 6.00 0.50 0.75 0.75 0.50 1.00 0.00 19.00 0.00 9.00 17.00
16.1 9.86 36.0 35.9 0.76 0.59 5.83 1.59
16.2 9.79 36.4 35.2 0.85 0.58 4.74 1.59
16.3 9.75 33.0 38.7 0.78 0.56 5.69 1.61
a LC, diet with Leymus chinensis as main forage source; CS, diet with cornstalk as main forage source; AH, diet with alfalfa hay as main forage source. b Distiller’s dried grains with solubles. c Non-fiber carbohydrates, calculated as 100 − (% NDF + % CP + % ether extract + % ash).
of different treatment groups had the same forage/concentrate ratio (45:55, dry matter basis). Diets were provided ad libitum as a total mixed ration (TMR) to avoid selection of dietary components. Cows were fed at 06:00, 13:30 and 20:00 and had free access to drinking water throughout the experiment. Each experimental period comprised a 14 day treatment adaptation followed by a 7 day sampling period. Sample collection Full details of the sampling procedures are provided in Zhu et al.4 Briefly, ruminal content samples were collected at 0 and 2 h following the morning feeding (06:00) on day 21 of each experimental period. Rumen contents were orally collected from the ventral sac of the rumen. After collection, the rumen contents were divided into two portions. The first portion (∼150 mL) was stored at −20 ◦ C for microbial profiling. The second portion was filtered through four layers of cheesecloth and the rumen fluid pH was measured immediately. Then the filtrate was centrifuged at 2000 × g for 15 min at 4 ◦ C and the supernatant was stored at −20 ◦ C for analyses of VFAs, ammonia-nitrogen (NH3 -N) and biogenic amines. Chemical analytical procedures Dry matter (DM; method 934.01), ash (method 942.05) and N (method 984.13) were determined according to AOAC methods.15
c 2013 Society of Chemical Industry
wileyonlinelibrary.com/jsfa
1887
Animals and experimental design This study was part of a larger experiment investigating the effect of dietary forage sources, specifically alfalfa hay (AH), L. chinensis (LC) and cornstalk (CS), on the performance of lactating dairy cows.4 The hypothesis tested in the main study was that diets with different forage sources will affect the production of microbial protein and milk protein percentages, and the details have been reported previously.4 The present research mainly focused on the effect of dietary forage sources on rumen fermentation, rumen biogenic amines and the bacterial community. The mean body weight and days in milk (both determined at the beginning of the study) of the cows during the study were 552 ± 16.0 kg and 45 ± 6.0 days respectively. The design of the experiment was a replicated 3 × 3 Latin square balanced for residual effects. Treatments were three different types of forage: diet LC (47% L. chinensis, 11% alfalfa hay and 42% maize silage), diet CS (42% cornstalk, 11% alfalfa hay and 47% maize silage) and diet AH (38% alfalfa hay, 20% L. chinensis and 42% maize silage). Diets (Table 1) were formulated to meet or exceed the requirements (at yielding 30 kg day−1 ; 3.5% milk fat; 3.1% milk protein) recommended by the National Research Council (NRC, 2001). All diets were similar in their CP, RDP and net energy of lactation (NEL ), but different in their forage sources. Diets
www.soci.org
www.soci.org Analysis of NDF was carried out according to Van Soest et al.16 using an Ankom 220 Fiber Analyzer (Ankom Technology Corp., Fairport, NY, USA). Sodium sulfite and heat-stable amylase were used in the analysis of NDF, which was expressed inclusive of residual ash. VFA concentrations in rumen fluids were determined by gas chromatography as described by Qin.17 NH3 -N concentrations in rumen fluids were measured by the indophenol method as described by Weatherburn.18 The concentrations of amines in rumen fluids were determined by adapting a high-performance liquid chromatography (HPLC) method for detecting caecal amines used by Bailey et al.19 The intra-assay coefficients of variation for each of the rumen biogenic amines in the mixture of standards ranged from 2.14 to 6.04% and the inter-assay coefficients of variation fell between 7.23 and 13.82%. The intra-assay coefficients of variation in rumen samples were between 3.26 and 7.24%. The detection limits for each amine varied between 0.05 and 0.20 µmol L−1 and the recoveries of the standard amines added to rumen samples ranged from 92.9 to 95.5%. The evaluation of feed protein and carbohydrate fractions was carried out according to the Cornell Net Carbohydrate and Protein System (CNCPS). This system divides protein fractions into soluble non-protein nitrogen (PA), soluble true protein (PB1), rapidly degradable true protein (PB2), slowly degradable protein (PB3) and undegradable protein (PC). The carbohydrates are fractionated into soluble sugars of rapid ruminal degradation (CA), starch and pectin (CB1), potentially degradable fiber (CB2) and unavailable cell wall (CC) (Table S1).20
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DNA extraction and 16S rRNA gene amplicon pyrosequencing Samples of rumen contents collected at 0 h on day 21 before the morning feeding were thawed at 37 ◦ C and fully vortexed. A 1 mL aliquot of ruminal content was used for DNA extraction. The DNA was extracted by a bead-beating method using a minibead beater (BioSpec Products, Bartlesville, OK, USA), followed by phenol/chloroform extraction.21 The solution was precipitated with ethanol and the pellets were suspended in 50 µL of Tris-EDTA buffer. DNA was quantified with a Nanodrop spectrophotometer (Nyxor Biotech, Paris, France) following staining using a Quant-iT PicoGreen dsDNA Kit (Invitrogen, Paisley, UK). DNA samples were stored at −80 ◦ C until further processing. The following universal primers were applied for the amplification of the V3–V6 region of the 16S rRNA gene: forward primer 5 -CCATCTCATCCCTGCGTGTCTCCGACTCAGNNNNNNACTCCTACG GGAGGCAGCAG-3 (the italicized sequence is 454 Life Sciences primer A and the bold sequence is the broadly conserved bacterial primer 338F; NNNNNN designates the sample-specific six-base barcode used to tag each polymerase chain reaction (PCR) product); reverse primer 5 -CCTATCCCCTGTGTGCCTTGGCA GTCTCAGCRRCACGAGCTGACGAC-3 (the italicized sequence is 454 Life Sciences primer B and the bold sequence is the broadly conserved bacterial primer 1061R). The cycling parameters were as follows: initial denaturation at 95 ◦ C for 5 min; 25 cycles of denaturation at 95 ◦ C (30 s), annealing at 55 ◦ C (30 s) and elongation at 72 ◦ C (30 s); final extension at 72 ◦ C for 5 min. Three separate PCRs for each sample were pooled for pyrosequencing. The PCR products were separated by 10 g L−1 agarose gel electrophoresis and purified using a QIAquick Gel Extraction Kit (Qiagen, Valencia, CA). Amplicons were quantified using a QuantiT PicoGreen dsDNA Assay Kit (Invitrogen). Equal concentrations of amplicons were pooled from each sample. The amplicons were sequenced as recommended by the manufacturer.
wileyonlinelibrary.com/jsfa
R Zhang et al.
Pyrosequencing data analysis Sequences were processed using the MOTHUR program (http://www.mothur.org). The 16S rRNA reads were decoded based on the 6 bp sample-specific barcodes and processed to remove poor quality sequences. To reduce sequencing errors, the shhh.flows command was applied, which is the MOTHUR implementation of the AmpliconNoise algorithm.22 Quality filters were applied to trim and remove sequences with sequences less than 200 bp in length, average quality score less than 35, homopolymers longer than eight nucleotides and more than two different bases to the primer. To obtain a non-redundant set of sequences, unique sequences were determined and used to align against the SILVA reference alignment database;23 chimeras were removed using chimera.uchime (http://drive5.com/uchime); sequences identified as being of eukaryotic origin were removed; candidate sequences were screened and preclustered to eliminate outliers; a distance matrix was generated from the resulting sequences. Sequences were clustered into operational taxonomic units (OTUs) using the furthest-neighbor algorithm. Representative sequences from OTUs at a 0.03 distance were obtained and classified using the RDP Bayesian classifier. Rarefaction curves and Good’s coverage were calculated to quantify the coverage and sampling effort. Community diversity was estimated using the Chao 1 and Berger–Parker indices.24,25 The unweighted UniFrac distance method was used to perform a principal coordinate analysis (PCoA), and a distance-based molecular variance analysis (AMOVA) was conducted to assess significant differences between samples.26 A double hierarchical analysis was conducted using the unweighted pair group (UPGMA) method and Manhattan distance with no scaling in the Number Cruncher Statistical System (NCSS 2007) software (NCSS, Kaysville, UT, USA). Statistical analyses The rumen pH, VFA and biogenic amine data were analyzed with the MIXED procedure of SPSS Version 18 (SPSS, Chicago, IL, USA) according to the model Yijklm = µ + Gi + C (G)ij + Pk + τl + Dm + τ Pkl + eijklm where Yijklm is the dependent variable, µ is the overall mean, Gi is the group, C(G)ij is the cow within group, Pk is the kth period, τ l is the lth treatment, Dm is the time effect, τ Pkl is the period × treatment interaction and eijklm is the error term, assumed to be normally distributed with mean zero and constant variance. Group and cow within group were random effects, whereas all others were fixed. Measurements collected at different times on the same cow were considered to be repeated measures in the analysis of variance (ANOVA). Post hoc multiple comparisons were made to compare the means using Fisher’s least significant difference (LSD). Statistical differences were declared at P < 0.05. Differences between treatments at 0.05 ≤ P ≤ 0.10 were considered a trend toward significance. The microbial data were analyzed with the general linear model (GLM) procedure in SPSS Version 18 according to the model Yijkl = µ + Gi + C (G)ij + Pk + τl + τ Pkl + eijkl where Yijkl is the dependent variable, µ is the overall mean, Gi is the group, C(G)ij is the cow within group, Pk is the kth period, τ l is the lth treatment, τ Pkl is the period × treatment interaction and eijkl is the error term, assumed to be normally distributed
c 2013 Society of Chemical Industry
J Sci Food Agric 2014; 94: 1886–1895
Effect of dietary forage sources on dairy cow ruminal characteristics
www.soci.org
Table 2. Ruminal parameters of dairy cows fed different forage sources Dieta Parameter
LC
pH Acetate (mmol L−1 ) Propionate (mmol L−1 ) Butyrate (mmol L−1 ) Valerate (mmol L−1 ) Total branched-chain VFAs (mmol L−1 ) Total VFAs (mmol L−1 ) NH3 -N (mmol L−1 ) Acetate/propionate ratio
Diet effect
CS
6.46 65.26a 16.4a 13.27a 1.07ab 3.19 99.19a 8.63 4.02
AH
6.51 60.55b 15.04b 11.26c 0.99b 2.98 90.82b 7.36 4.05
6.50 62.63ab 15.9a 12.36ab 1.1a 3.03 95.02ab 9.08 4.00
SEMb 0.031 1.190 0.300 0.308 0.029 0.089 1.746 0.640 0.054
P value 0.602 0.024 0.008
Revised: 4 November 2013
Accepted article published: 30 November 2013
Published online in Wiley Online Library: 29 December 2013
(wileyonlinelibrary.com) DOI 10.1002/jsfa.6508
Effect of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines in dairy cows Ruiyang Zhang,a Weiyun Zhu,a Wen Zhu,b Jianxin Liub and Shengyong Maoa∗ Abstract BACKGROUND: Fifteen lactating Holstein dairy cows were assigned to three diets in a 3 × 3 Latin square design to evaluate the effects of dietary forage sources on rumen microbiota, rumen fermentation and biogenic amines. Diets were isonitrogenous and isocaloric, with a forage/concentrate ratio of 45:55 (dry matter basis) but different main forage sources, namely cornstalk (CS), Leymus chinensis (LC) or alfalfa hay (AH). RESULTS: Pyrosequencing of the V3–V6 hypervariable coding region of 16S rRNA revealed that the rumen microbiota was significantly affected by forage sources. AH feeding increased the proportion of genera Prevotella and Selenomonas compared with the CS diet, while CS feeding increased the proportion of genera Anaerotruncus, Papillibacter, Thermoactimoyces, Bacillus and Streptomyces compared with the LC or AH diet. AH and LC feeding both increased the propionate concentration compared with the CS diet. AH feeding decreased the concentrations of tyramine, putrescine and histamine compared with the LC diet. CONCLUSION: These results indicate that a high proportion of alfalfa hay in the ration is beneficial for milk yield and a healthy and balanced rumen microbiota in lactating cattle. This can be attributed to the higher degradation of rumen organic matter and the more balanced carbohydrates and proteins for optimal rumen microbial growth. c 2013 Society of Chemical Industry Supporting information may be found in the online version of this article. Keywords: forage sources; dairy cows; microbiota; rumen fermentation; biogenic amines
INTRODUCTION
1886
Ruminant animals and ruminal microorganisms have evolved together for millions of years. The rumen is inhabited by diverse and interdependent populations of bacteria, protozoa and fungi.1 Within this microbiome, bacteria are the dominant domain and make the greatest contribution to digestion and conversion of feeds to volatile fatty acids (VFAs) and microbial proteins.2 The ruminal bacteria community is closely related to the diet, and changing the diet has a cascading effect on rumen metabolism, which can impact both quantity and quality of animal production.3 Forage quality is an important factor that affects the performance of ruminant production.4 Alfalfa is well known for its high quality and is used worldwide as an important dietary forage. While cornstalk is abundant and inexpensive and is consequently the predominant forage source for ruminants, it is typically considered as low-quality roughage. Leymus chinensis is a perennial species of Gramineae that is widely distributed throughout the eastern end of the Eurasian steppe zone, including the Songnen Plain and the eastern Inner Mongolian Plateau in China.5,6 It is an important forage in China and is considered as middle-quality roughage. Different quality forages have a large impact on ruminal metabolites, thereby affecting the composition of the rumen microbiota.4,7 Kong et al.8 used 16S rRNA gene-cloning library J Sci Food Agric 2014; 94: 1886–1895
technology to analyze the composition of the bacterial community structure in the rumen of cows fed different quality forages. They found that most of the species in the bacterial community with alfalfa fed as the diet (84%) were different from those in the community with triticale fed as the diet. Pitta et al.9 also reported that distinct bacterial communities developed in the rumen associated with bermudagrass hay feeding and winter wheat grazing in a cohort of yearling steers. According to these studies, most research exploring the relationship between different quality forages and rumen microbial community composition was mainly based on sole forage or high-forage diet (the proportion of forage in the ration is greater than 80%).8,9 Indeed, in the modern dairy industry the proportion of dietary forage is lower than 50% in high-producing lactating dairy cows. Therefore these studies based on sole forage or high proportion of dietary forage cannot
∗
Correspondence to: Shengyong Mao, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing 210095 China. E-mail: [email protected]
a College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China b Institute of Dairy Science, Zhejiang University, Hangzhou, 310058, China
www.soci.org
c 2013 Society of Chemical Industry
Effect of dietary forage sources on dairy cow ruminal characteristics be used to reflect actual conditions. Thus it is necessary to explore whether there would be any differences in the rumen microbial community composition when the animals are fed diets with a proportion of dietary forage sources that more closely resembles actual conditions. Biogenic amines are nitrogen-containing aliphatic, aromatic or heterocyclic organic compounds widely present in various kinds of organism. Tyramine, putrescine, histamine, methylamine and tryptamine are the most common forms of biogenic amines. Smaller amounts of biogenic amines are usually metabolized in the organism without any impact on its health,10 but in larger quantities (1.4 g day−1 ) they become harmful to humans and livestock.11,12 The source of biogenic amines in the rumen of cattle is related to the diet and, most importantly, to the rumen microbiota, and a diet with highly degradable proteins may produce more biogenic amines.13 Compared with cornstalk and L. chinensis, alfalfa hay has higher contents of crude protein (CP), rumen-degradable protein (RDP) and rumen-undegradable protein (RUP).14 Additionally, alfalfa hay is easily broken down into smaller particles in the rumen because of its lower acid detergent fiber (ADF) and neutral detergent fiber (NDF). Therefore we postulated that a diet with alfalfa hay as the forage source might have higher levels of biogenic amines compared with a cornstalk or L. chinensis-based diet. In this study we hypothesized that diets with different forage sources will affect the rumen microbiota. Therefore the primary objective of the study was to evaluate the effects of diets with three different quality forage sources (alfalfa hay, L. chinensis and cornstalk) on the rumen microbiota of dairy cows. An additional aim was to investigate the effects of the diets on rumen fermentation and biogenic amine production.
MATERIALS AND METHODS Animals involved in this study were cared for according to the guidelines of the Zhejiang University Animal Care and Use Committee. The committee reviewed and approved the experiment and all procedures carried out in the study.
J Sci Food Agric 2014; 94: 1886–1895
Table 1. Ingredients and chemical composition of diets Dieta Item Ingredients (% DM) Ground corn grain Wheat bran Soybean meal Cottonseed meal DDGSb Limestone Dicalcium phosphate Sodium bicarbonate Salt Premix Ca salts of long-chain FAs Maize silage Cornstalk Leymus chinensis Alfalfa hay Nutrient composition (% DM) CP RDP NDF NFCc Ca P Ash NEL (Mcal kg−1 DM)
LC
CS
AH
26.6 3.00 12.0 3.50 6.00 0.80 0.85 0.75 0.50 1.00 0.00 19.00 0.00 21.00 5.00
28.0 0.00 13.0 3.50 6.00 0.75 1.20 0.75 0.50 1.00 0.30 21.00 19.00 0.00 5.00
28.0 4.00 10.0 3.50 6.00 0.50 0.75 0.75 0.50 1.00 0.00 19.00 0.00 9.00 17.00
16.1 9.86 36.0 35.9 0.76 0.59 5.83 1.59
16.2 9.79 36.4 35.2 0.85 0.58 4.74 1.59
16.3 9.75 33.0 38.7 0.78 0.56 5.69 1.61
a LC, diet with Leymus chinensis as main forage source; CS, diet with cornstalk as main forage source; AH, diet with alfalfa hay as main forage source. b Distiller’s dried grains with solubles. c Non-fiber carbohydrates, calculated as 100 − (% NDF + % CP + % ether extract + % ash).
of different treatment groups had the same forage/concentrate ratio (45:55, dry matter basis). Diets were provided ad libitum as a total mixed ration (TMR) to avoid selection of dietary components. Cows were fed at 06:00, 13:30 and 20:00 and had free access to drinking water throughout the experiment. Each experimental period comprised a 14 day treatment adaptation followed by a 7 day sampling period. Sample collection Full details of the sampling procedures are provided in Zhu et al.4 Briefly, ruminal content samples were collected at 0 and 2 h following the morning feeding (06:00) on day 21 of each experimental period. Rumen contents were orally collected from the ventral sac of the rumen. After collection, the rumen contents were divided into two portions. The first portion (∼150 mL) was stored at −20 ◦ C for microbial profiling. The second portion was filtered through four layers of cheesecloth and the rumen fluid pH was measured immediately. Then the filtrate was centrifuged at 2000 × g for 15 min at 4 ◦ C and the supernatant was stored at −20 ◦ C for analyses of VFAs, ammonia-nitrogen (NH3 -N) and biogenic amines. Chemical analytical procedures Dry matter (DM; method 934.01), ash (method 942.05) and N (method 984.13) were determined according to AOAC methods.15
c 2013 Society of Chemical Industry
wileyonlinelibrary.com/jsfa
1887
Animals and experimental design This study was part of a larger experiment investigating the effect of dietary forage sources, specifically alfalfa hay (AH), L. chinensis (LC) and cornstalk (CS), on the performance of lactating dairy cows.4 The hypothesis tested in the main study was that diets with different forage sources will affect the production of microbial protein and milk protein percentages, and the details have been reported previously.4 The present research mainly focused on the effect of dietary forage sources on rumen fermentation, rumen biogenic amines and the bacterial community. The mean body weight and days in milk (both determined at the beginning of the study) of the cows during the study were 552 ± 16.0 kg and 45 ± 6.0 days respectively. The design of the experiment was a replicated 3 × 3 Latin square balanced for residual effects. Treatments were three different types of forage: diet LC (47% L. chinensis, 11% alfalfa hay and 42% maize silage), diet CS (42% cornstalk, 11% alfalfa hay and 47% maize silage) and diet AH (38% alfalfa hay, 20% L. chinensis and 42% maize silage). Diets (Table 1) were formulated to meet or exceed the requirements (at yielding 30 kg day−1 ; 3.5% milk fat; 3.1% milk protein) recommended by the National Research Council (NRC, 2001). All diets were similar in their CP, RDP and net energy of lactation (NEL ), but different in their forage sources. Diets
www.soci.org
www.soci.org Analysis of NDF was carried out according to Van Soest et al.16 using an Ankom 220 Fiber Analyzer (Ankom Technology Corp., Fairport, NY, USA). Sodium sulfite and heat-stable amylase were used in the analysis of NDF, which was expressed inclusive of residual ash. VFA concentrations in rumen fluids were determined by gas chromatography as described by Qin.17 NH3 -N concentrations in rumen fluids were measured by the indophenol method as described by Weatherburn.18 The concentrations of amines in rumen fluids were determined by adapting a high-performance liquid chromatography (HPLC) method for detecting caecal amines used by Bailey et al.19 The intra-assay coefficients of variation for each of the rumen biogenic amines in the mixture of standards ranged from 2.14 to 6.04% and the inter-assay coefficients of variation fell between 7.23 and 13.82%. The intra-assay coefficients of variation in rumen samples were between 3.26 and 7.24%. The detection limits for each amine varied between 0.05 and 0.20 µmol L−1 and the recoveries of the standard amines added to rumen samples ranged from 92.9 to 95.5%. The evaluation of feed protein and carbohydrate fractions was carried out according to the Cornell Net Carbohydrate and Protein System (CNCPS). This system divides protein fractions into soluble non-protein nitrogen (PA), soluble true protein (PB1), rapidly degradable true protein (PB2), slowly degradable protein (PB3) and undegradable protein (PC). The carbohydrates are fractionated into soluble sugars of rapid ruminal degradation (CA), starch and pectin (CB1), potentially degradable fiber (CB2) and unavailable cell wall (CC) (Table S1).20
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DNA extraction and 16S rRNA gene amplicon pyrosequencing Samples of rumen contents collected at 0 h on day 21 before the morning feeding were thawed at 37 ◦ C and fully vortexed. A 1 mL aliquot of ruminal content was used for DNA extraction. The DNA was extracted by a bead-beating method using a minibead beater (BioSpec Products, Bartlesville, OK, USA), followed by phenol/chloroform extraction.21 The solution was precipitated with ethanol and the pellets were suspended in 50 µL of Tris-EDTA buffer. DNA was quantified with a Nanodrop spectrophotometer (Nyxor Biotech, Paris, France) following staining using a Quant-iT PicoGreen dsDNA Kit (Invitrogen, Paisley, UK). DNA samples were stored at −80 ◦ C until further processing. The following universal primers were applied for the amplification of the V3–V6 region of the 16S rRNA gene: forward primer 5 -CCATCTCATCCCTGCGTGTCTCCGACTCAGNNNNNNACTCCTACG GGAGGCAGCAG-3 (the italicized sequence is 454 Life Sciences primer A and the bold sequence is the broadly conserved bacterial primer 338F; NNNNNN designates the sample-specific six-base barcode used to tag each polymerase chain reaction (PCR) product); reverse primer 5 -CCTATCCCCTGTGTGCCTTGGCA GTCTCAGCRRCACGAGCTGACGAC-3 (the italicized sequence is 454 Life Sciences primer B and the bold sequence is the broadly conserved bacterial primer 1061R). The cycling parameters were as follows: initial denaturation at 95 ◦ C for 5 min; 25 cycles of denaturation at 95 ◦ C (30 s), annealing at 55 ◦ C (30 s) and elongation at 72 ◦ C (30 s); final extension at 72 ◦ C for 5 min. Three separate PCRs for each sample were pooled for pyrosequencing. The PCR products were separated by 10 g L−1 agarose gel electrophoresis and purified using a QIAquick Gel Extraction Kit (Qiagen, Valencia, CA). Amplicons were quantified using a QuantiT PicoGreen dsDNA Assay Kit (Invitrogen). Equal concentrations of amplicons were pooled from each sample. The amplicons were sequenced as recommended by the manufacturer.
wileyonlinelibrary.com/jsfa
R Zhang et al.
Pyrosequencing data analysis Sequences were processed using the MOTHUR program (http://www.mothur.org). The 16S rRNA reads were decoded based on the 6 bp sample-specific barcodes and processed to remove poor quality sequences. To reduce sequencing errors, the shhh.flows command was applied, which is the MOTHUR implementation of the AmpliconNoise algorithm.22 Quality filters were applied to trim and remove sequences with sequences less than 200 bp in length, average quality score less than 35, homopolymers longer than eight nucleotides and more than two different bases to the primer. To obtain a non-redundant set of sequences, unique sequences were determined and used to align against the SILVA reference alignment database;23 chimeras were removed using chimera.uchime (http://drive5.com/uchime); sequences identified as being of eukaryotic origin were removed; candidate sequences were screened and preclustered to eliminate outliers; a distance matrix was generated from the resulting sequences. Sequences were clustered into operational taxonomic units (OTUs) using the furthest-neighbor algorithm. Representative sequences from OTUs at a 0.03 distance were obtained and classified using the RDP Bayesian classifier. Rarefaction curves and Good’s coverage were calculated to quantify the coverage and sampling effort. Community diversity was estimated using the Chao 1 and Berger–Parker indices.24,25 The unweighted UniFrac distance method was used to perform a principal coordinate analysis (PCoA), and a distance-based molecular variance analysis (AMOVA) was conducted to assess significant differences between samples.26 A double hierarchical analysis was conducted using the unweighted pair group (UPGMA) method and Manhattan distance with no scaling in the Number Cruncher Statistical System (NCSS 2007) software (NCSS, Kaysville, UT, USA). Statistical analyses The rumen pH, VFA and biogenic amine data were analyzed with the MIXED procedure of SPSS Version 18 (SPSS, Chicago, IL, USA) according to the model Yijklm = µ + Gi + C (G)ij + Pk + τl + Dm + τ Pkl + eijklm where Yijklm is the dependent variable, µ is the overall mean, Gi is the group, C(G)ij is the cow within group, Pk is the kth period, τ l is the lth treatment, Dm is the time effect, τ Pkl is the period × treatment interaction and eijklm is the error term, assumed to be normally distributed with mean zero and constant variance. Group and cow within group were random effects, whereas all others were fixed. Measurements collected at different times on the same cow were considered to be repeated measures in the analysis of variance (ANOVA). Post hoc multiple comparisons were made to compare the means using Fisher’s least significant difference (LSD). Statistical differences were declared at P < 0.05. Differences between treatments at 0.05 ≤ P ≤ 0.10 were considered a trend toward significance. The microbial data were analyzed with the general linear model (GLM) procedure in SPSS Version 18 according to the model Yijkl = µ + Gi + C (G)ij + Pk + τl + τ Pkl + eijkl where Yijkl is the dependent variable, µ is the overall mean, Gi is the group, C(G)ij is the cow within group, Pk is the kth period, τ l is the lth treatment, τ Pkl is the period × treatment interaction and eijkl is the error term, assumed to be normally distributed
c 2013 Society of Chemical Industry
J Sci Food Agric 2014; 94: 1886–1895
Effect of dietary forage sources on dairy cow ruminal characteristics
www.soci.org
Table 2. Ruminal parameters of dairy cows fed different forage sources Dieta Parameter
LC
pH Acetate (mmol L−1 ) Propionate (mmol L−1 ) Butyrate (mmol L−1 ) Valerate (mmol L−1 ) Total branched-chain VFAs (mmol L−1 ) Total VFAs (mmol L−1 ) NH3 -N (mmol L−1 ) Acetate/propionate ratio
Diet effect
CS
6.46 65.26a 16.4a 13.27a 1.07ab 3.19 99.19a 8.63 4.02
AH
6.51 60.55b 15.04b 11.26c 0.99b 2.98 90.82b 7.36 4.05
6.50 62.63ab 15.9a 12.36ab 1.1a 3.03 95.02ab 9.08 4.00
SEMb 0.031 1.190 0.300 0.308 0.029 0.089 1.746 0.640 0.054
P value 0.602 0.024 0.008
