Impact of virginiamycin on the ruminal microbiota of feedlot cattle

Bruna Parapinski Santos; Júlio Augusto Naylor Lisbôa; José Antônio Bessegatto; Carlos Henrique Montemor; Lais Resende Paulino; Amauri Alcindo Alfieri; J Scott Weese; Marcio Carvalho Costa

. 2024 Oct;88(4):114–122.

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Impact of virginiamycin on the ruminal microbiota of feedlot cattle

Bruna Parapinski Santos ¹, Júlio Augusto Naylor Lisbôa ¹, José Antônio Bessegatto ¹, Carlos Henrique Montemor ¹, Lais Resende Paulino ¹, Amauri Alcindo Alfieri ¹, J Scott Weese ¹, Marcio Carvalho Costa ^1,^✉

PMCID: PMC11418755 PMID: 39355681

Abstract

Growth-promoting antibiotics have been used in cattle, but concern about antimicrobial overuse has prompted a re-evaluation of this practice. To evaluate changes in the ruminal microbiota of feedlot cattle by virginiamycin, a total of 76 crossbreed beef cattle from 2 batches of cattle at different sampling periods (B1 and B2) were divided into 2 groups: one receiving virginiamycin in their diet (ATB) and the other receiving the same diet without any growth promoter (CON). The use of virginiamycin was associated with significant changes in the diversity and composition of the ruminal microbiota of cattle in B1, but not in cattle in B2. Several bacterial taxa were significantly more abundant in samples from CON, e.g., an unclassified genus of the TM7 phylum, whereas others were associated with the use of virginiamycin, e.g., Holdemania and Selenomonas spp. In conclusion, virginiamycin can affect bacterial diversity and composition in the rumen of feedlot cattle, but its effect is inconsistent in different seasons of the year.

Introduction

The ruminal microbiota is a complex ecosystem composed of bacteria, archaea, fungi, protozoa, and viruses. These microorganisms have a close relationship with the host and are the main providers of energy and protein to ruminants (1). DNA sequencing has been used to characterize the ruminal microbiota and factors such as age, diet, and geographic area have been shown to influence it (2,3). Furthermore, certain bacterial species have been associated with changes in milk yield and feed efficiency in cattle, highlighting the importance of ruminal microbiota on performance (4–6).

The use of antibiotics is an important factor that can impact the rumen and fecal microbiota of cattle (7,8). Sub-therapeutic doses of antibiotics are still extensively used in developing countries as antibiotic growth promoters (AGPs) in animals (9,10). In cattle, antibiotics added to the diet can provide better protein digestion and energy harvesting (11,12). Previous studies showed that some bacteria are related to better performance in domestic animals (13–15).

In fact, intestinal bacteria play a major role in food digestion, leading to obesity, and low doses of antibiotics can select bacterial species that can increase weight gain (16). Identifying bacterial species that are selected by antibiotics and associated with better performance could enhance animal production and decrease the use of antibiotics. Currently, there is global concern about overuse of antimicrobials and subsequent development of antimicrobial resistance (10,17).

Virginiamycin is a streptogramin antimicrobial produced by a mutant strain of Streptomyces virginiae, which affects mainly Gram-positive bacteria, both aerobe and anaerobe. This drug has been widely used in developing countries to improve growth performance, reduce the incidence and severity of liver abscesses, and reduce the risk of acidosis in cattle fed diets with high concentrations of non-structural carbohydrates (11,18–20).

Studies investigating the impact of virginiamycin on the ruminal microbiota are scarce and have used culture-based methods (19,21). The objective of this study was to use high-throughput DNA sequencing to evaluate the impact of virginiamycin when used as a growth promoter on the ruminal microbiota of feedlot cattle.

Materials and methods

Animals and samples

This study was approved by the Animal Care Committee of the Universidade Estadual de Londrina in Brazil (#23932.2015.95).

The study was conducted in a commercial feedlot facility in the northern region of Paraná State, Brazil. A total of 76 crossbreed cattle (Bos taurus taurus × Bos taurus indicus), approximately 18 mo of age, were enrolled. All cattle were kept on pasture and originated from different farms in the same region; they arrived at 2 different periods (batches B1 and B2). The first batch (B1 = 40 cattle) arrived at the feedlot during the dry season in the Southern Hemisphere (May to September) and the second batch (B2 = 36 cattle) arrived during the rainy season (January to May).

Upon arrival, cattle were randomly divided into 2 groups housed in neighboring pens: one group receiving virginiamycin (ATB) at the growth promoter’s dose [340 mg/360 kg body weight (BW) — estimated daily intake] and the other group receiving the same diet without any growth promoter (CON). The distribution of cattle in each group was as follows: ATB/B1 = 19, ATB/B2 = 19, CON/B1 = 20, CON/B2 = 18. Samples were collected from 20 steers and 20 heifers in B1 and from 36 steers in B2 and were used for DNA sequencing for microbiota analysis.

Cattle left the feedlot when they achieved the adequate amount of fat covering required for slaughtering, which was visually evaluated by the veterinarian responsible for the farm. Consequently, there were 3 slaughter dates in B1 (66 d, 88 d, and 116 d of confinement) and 2 slaughter dates in B2 (100 d and 122 d of confinement).

The cattle were fed a diet consisting of approximately 1.6% of body weight (dry matter) of corn silage (47.60%), concentrate (31.40%), and orange pulp (21%) calculated on natural matter. Unprocessed orange pulp was given to cattle in B1 and in the form of silage to cattle in B2. The concentrate mixture was composed of fat corn germ (42.90%), ground corn (49%), soybean meal (3.40%), urea (1.1%), encapsulated urea (0.8% Optigen; Alltech Brazil, Parana, Brazil), and a mineral supplement (2.8% Beephós 40; Nutristar, Paraná, Brazil). Virginiamycin was added to the mineral supplement during manufacturing.

Cattle were weighed at arrival and exit from the feedlot in order to calculate average daily weight gain (ADWG). After desensitization by stunning air, cattle were slaughtered by exsanguination and internal organs were removed. The rumen was opened between the dorsal and ventral sacs and ruminal contents were collected in 50-mL sterile plastic tubes. Samples were kept refrigerated until arrival at the laboratory (~1.5 h) and frozen at −80°C until processing.

Samples were thawed at 5°C for approximately 14 h and vigorously vortexed for 3 min for homogenization (22). Samples with a high proportion of solids were added to a sterile 0.9% saline solution in a 1:1 ratio before homogenization. Samples were then centrifuged at 500 × g at 4°C for 15 min to remove solid particles and the supernatant was used for DNA extraction (23).

DNA extraction and sequencing

DNA was extracted with 200 μL of the supernatant using a glass bead-based commercial kit (E.Z.N.A.; Omega Bio-tek, Norcross, Georgia, USA) according to the manufacturer’s instructions. The DNA concentration was estimated by spectrophotometry and diluted to 20 ng/μL.

The V4 hypervariable region of the gene 16S rRNA was amplified by polymerase chain reaction (PCR) in 2 steps. In the first step, 2.5 μL of DNA was added to a mixture containing 9 μL of water, 12.5 μL of Kapa 2X Ready Mix (Kapa Biosystems, Wilmington, Massachusetts, USA), and 0.5 μL (10 pmol/μL) of primer forward S-D-Bact-0564-a-S-15 and 0.5 μL (10 pmol/μL) of reverse primers S-D-Bact-0785-b-A-18 (24), both primers containing an overlapping region of the Illumina sequencing primers.

Polymerase chain reaction conditions consisted of an initial denaturing step at 94°C for 3 min, followed by 26 cycles at 94°C for 45 s, 53°C for 1 min for annealing, and 72°C for 90 s for elongation, and a final period at 72°C for 10 min and kept at 4°C. Products from PCR were purified with 20 μL of Agencourt AMPure XP magnetic beads (Beckman Coulter, Indianapolis, Indiana, USA) and eluted in 52.5 μL of Tris buffer (10 mM, pH 8.5).

The second PCR step was carried out by adding 4 μL of purified product to a mixture with 9.6 μL of water, 20 μL of 2X Ready Mix (Kapa Biosystems), and 3.2 μL of each Illumina index primer (2.5 pmol/μL). The PCR conditions consisted of 3 min at 94°C and 7 cycles of 45 s at 94°C, 60 s at 50°C, 90 s at 72°C, and a final period of 10 min at 72°C and kept at 4°C.

A second purification was carried out by using 40 μL of AMPure beads (Beckman Coulter) and eluted in 32 μL of Tris buffer (10 mM, pH 8.5). Sequencing was conducted with an Illumina MiSeq (Illumina San Diego, California, USA) with the V2 reagents kit for 250 cycles from each end at the Genomics Facility of the University of Guelph, Ontario.

Bioinformatic and statistical analyses

Bioinformatic analysis was carried out using the software Mothur following the protocol described by Kozich et al (25). Sequences were clustered using a reference-based clustering method and classified according to the Ribosomal Database Project (26). Relative abundance of the main phyla and genera (abundance > 1%) in each group was represented by column charts.

A subsample from the main dataset was used for alpha and beta diversity analyses to normalize sequence numbers and decrease bias caused by non-uniform sample sizes. In order to ensure that the cutoff adopted for subsampling was representative of the original samples, Good’s coverage was calculated. The Chao index was used to estimate richness and the inverse of the Simpson’s index to estimate diversity. After passing the normality test conducted using the Kolmogorov and Smirnov test, the groups (CON, ATB) were compared using a Student’s t-test, with each batch (B1 and B2) being considered individually.

Bacterial community membership that considers the different species present in each sample and community structure that takes into account the different taxa along with their evenness (relative abundance) were addressed by the classic Jaccard and the Yue and Clayton indexes, respectively. The similarity between community membership and structure within samples was represented by dendrograms visualized with FigTree and by the Principal Coordinate Analysis (PCoA) with 2 dimensions.

A statistical comparison between bacterial community membership and structure was carried out using the permutational multivariate analysis of variance (PERMANOVA) test with 10 000 permutations to investigate the impact of treatment with virginiamycin and the sampling period (B1 or B2), as well as the interaction between both variables. When adequate, the analysis of molecular variance (AMOVA) test was used for pairwise comparisons.

Average daily weight gain (ADWG) was calculated and compared between treatments in each batch using a Student’s t-test. The linear discriminant analysis (LDA) Effective Size (LefSe) algorithm (27) was used to identify genera statistically different between cattle with the highest and lowest ADWG and between treatments. In addition, all phyla and genera with relative abundances greater than 1% were compared using the Kruskal-Wallis or the Mann-Whitney test, considering a P-value of < 0.05 as significant. P-values were adjusted for false discovery rate using the Benjamini-Hochberg method.

Results

The body weight of cattle in B1 (335 ± 35.18 kg) and cattle in B2 (345 ± 16.34 kg) upon arrival at the feedlot was not statistically different (P = 0.082), but males in B1 were heavier (360.42 ± 27.18 kg) than those in B2 (345.00 ± 16.34 kg) (P = 0.018). Weight at slaughter was greater (P < 0.001) in cattle in B2 (497.53 ± 34.17 kg) than in cattle in B1 (431.92 ± 57.90 kg). Overall, ADWG during confinement was 1.07 ± 0.04 kg in cattle in B1 and 1.37 ± 0.03 kg in B2 (P < 0.001).

When only males were considered, there was a greater weight gain (P < 0.001) in cattle in B2 (1.37 ± 0.03 kg) than in cattle in B1 (1.12 ± 0.06 kg). The comparison of ADWG between cattle in ATB and CON within each batch revealed no statistical difference (1.16 ± 0.29 kg for ATB and 1.02 ± 0.32 kg for CON in B1, P = 0.296; and 1.44 ± 0.19 kg for ATB and 1.32 ± 0.20 kg for CON in B2, P = 0.065).

A total of 6 350 151 good-quality sequences remained after data cleaning (median: 83 627 reads per sample; min-max: 38 934 to 129 103 reads). A subsample of 38 934 reads per sample was randomly selected for the analysis to normalize the number of sequences. Overall, Good’s coverage median was 95.76% (94.00 to 97.70). Alpha diversity results represented by the number of genera and the Chao, Simpson, and Shannon indexes are provided in Table I.

Table I.

Mean ± standard deviation of the number of genera and the Chao, Simpson, and Shannon indexes in the rumen of feedlot cattle treated with virginiamycin (ATB) and controls (CON) in 2 different batches (dry and rainy season).

	Dry season (B1)			Rainy season (B2)

	COM	ATB	P-value	COM	ATB	P-value
Number of genera	3613^a ± 540.52	3005.8^b ± 558.30	0.001	3009.61^a ± 552.08	3107.17^a ± 473.84	0.573
Chao	6754.3^a ± 948.36	5682.9^b ± 1045.5	0.002	5666.6^a ± 1176.9	5899^a ± 966.33	0.521
Simpson	92.782^a ± 40.892	83.811^a ± 36.651	0.470	74.937^a ± 32.291	87.462^a ± 36.662	0.284
Shannon	6.106^a ± 0.3601	5.823^b ± 0.3803	0.002	5.772^a ± 0.3946	5.886^a ± 0.3817	0.385

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^a,b

Different letters in the same line express statistical differences (P < 0.05).

P-values were obtained from statistical comparisons between groups within each season.

Cattle from ATB in the B1 batch had higher richness addressed by the number of genera (P = 0.001) and the Chao index (P = 0.002) and higher diversity addressed by the Shannon index (P = 0.002). No differences were determined in alpha diversity indicators between cattle from the CON and ATB groups in B2.

Relative abundances at the phylum level in the rumen of each group of cattle are presented in Table II, as well as the results of statistical analysis comparing the main phyla between treatments (> 1%). Sequences were classified into 23 different phyla, of which only 9 had relative abundances > 1%. The 2 dominant phyla observed in cattle in all groups and treatments were Firmicutes and Bacteroidetes, with Firmicutes representing more than 50% of bacteria in all groups. The third most abundant phylum was Fibrobacteres in B1 and Proteobacteria in B2.

Table II.

Median, interquartile range (Q1 to Q3) values of relative abundance of the main phyla present in the rumen of feedlot cattle treated with virginiamycin (ATB) and controls (CON) in 2 different batches (dry and rainy season).

	Dry season (B1)			Rainy season (B2)

	CON	ATB	P-value	CON	ATB	P-value
Firmicutes	59.03^a	59.65^a	0.879	52.63^a	53.40^a	0.923
Firmicutes	55.62 to 63.43	55.15 to 61.68		48.46 to 57.23	49.75 to 55.80
Unclassified	9.65^a	9.99^a	0.879	9.42^a	10.03^a	0.923
Unclassified	7.66 to 10.51	5.88 to 11.10		7.03 to 11.21	6.48 to 11.58
Bacteroidetes	7.36^a	9.35^a	0.272	10.79^a	11.99^a	0.354
Bacteroidetes	6.30 to 8.32	7.75 to 10.58		9.19 to 11.62	11.26 to 13.13
Proteobacteria	3.11^a	2.75^a	0.580	7.18^a	6.95^a	0.987
Proteobacteria	2.36 to 3.59	2.56 to 3.19		3.15 to 18.58	4.05 to 14.44
Fibrobacteres	8.80^a	7.96^a	0.580	0.16^a	0.21^a	0.923
Fibrobacteres	4.98 to 16.01	4.61 to 12.63		0.052 to 7.17	0.041 to 3.11
Verrucomicrobia	2.48^a	3.25^a	0.203	4.62^a	4.59^a	0.925
Verrucomicrobia	2.03 to 4.16	2.72 to 5.57		3.29 to 7.43	1.17 to 6.94
Spirochaetes	2.87^a	3.89^a	0.110	3.83^a	4.04^a	0.923
Spirochaetes	2.21 to 3.85	2.89 to 6.51		2.82 to 4.91	2.94 to 6.47
Tenericutes	1.18^a	0.67^a	0.580	0.965^a	0.818^a	0.987
Tenericutes	0.81 to 1.96	0.044 to 1.44		0.44 to 1.80	0.40 to 2.02
TM7	1.43^a	0.477^b	0.020	0.67^a	0.65^a	0.923
TM7	0.92 to 1.87	0.25 to 0.67		0.27 to 0.80	0.52 to 1.06
Actinobacteria	0.145^a	0.11^a	0.580	0.0898^a	0.096^a	0.923
Actinobacteria	0.09 to 0.22	0.07 to 0.35		0.05 to 0.11	0.07 to 0.17

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^a,b

Different letters in the same line express statistical differences (P < 0.05).

Overall, 529 genera were identified. The genera with relative abundance > 1% are presented in Table III and Figure 1. Sequences classified as Clostridiales were the most abundant in all groups. The second most abundant taxon in B1 was Saccharofermentans spp. and an unclassified Lachnospiraceae in B2. Genera that were statistically different in cattle in the CON and ATB groups in B1, as indicated by the LEfSe analysis, are listed in Figure 2. No bacterial genera were associated with the use of virginiamycin in cattle in B2.

Table III.

Median, interquartile range (Q1 to Q3) values of relative abundance of the main genera present in the rumen of feedlot cattle treated with virginiamycin (ATB) and controls (CON) in 2 different batches (dry and rainy season).

	Dry season (B1)			Rainy season (B2)

	CON	ATB	P-value	CON	ATB	P-value
Unclassified Clostridiales	13.55^a	12.18^a	0.740	11.16^a	11.58^a	0.937
Unclassified Clostridiales	12.23 to 14.92	11.03 to 13.49		8.92 to 12.91	10.74 to 14.09
Unclassified	9.64^a	9.99^a	0.876	9.41^a	10.03^a	0.937
Unclassified	7.67 to 10.51	5.89 to 11.10		7.03 to 11.21	6.48 to 11.58
Saccharofermentans	8.32^a	8.84^a	0.876	4.94^a	5.69^a	0.937
Saccharofermentans	6.76 to 11.24	6.58 to 12.50		3.84 to 8.00	4.67 to 7.15
Unclassified Lachnospiracea	8.21^a	6.92^a	0.740	6.54^a	6.85^a	0.937
Unclassified Lachnospiracea	6.62 to 8.99	5.69 to 9.08		5.46 to 7.09	6.03 to 7.13
Unclassified Ruminococcus	7.07^a	6.81^a	0.740	6.12^a	5.14^a	0.937
Unclassified Ruminococcus	6.32 to 7.69	5.05 to 7.55		5.29 to 7.50	4.37 to 7.47
Unclassified Firmicutes	6.20^a	6.16^a	0.876	6.25^a	6.07^a	0.937
Unclassified Firmicutes	4.97 to 7.16	4.86 to 7.17		4.80 to 7.50	5.30 to 7.26
Fibrobacter	8.80^a	7.96^a	0.740	0.16^a	0.212^a	0.937
Fibrobacter	4.93 to 16.01	4.61 to 12.62		0.052 to 7.17	0.041 to 3.11
Prevotella	3.71^a	3.87^a	0.740	4.53^a	4.95^a	0.937
Prevotella	2.41 to 4.22	2.88 to 5.61		3.38 to 7.30	4.36 to 7.28
5_genus_incertae_sedis	2.41^a	3.21^a	0.345	4.45^a	4.18^a	0.937
5_genus_incertae_sedis	2.17 to 4.10	2.70 to 5.28		3.04 to 7.35	0.97 to 6.83
Treponema	2.85^a	3.87^a	0.345	3.78^a	4.01^a	0.937
Treponema	2.19 to 3.79	2.84 to 6.45		2.81 to 4.80	2.84 to 6.37
Succiniclasticum	3.94^a	3.52^a	0.345	3.33^a	3.13^a	0.937
Succiniclasticum	3.01 to 4.72	2.93 to 4.36		2.81 to 3.94	2.80 to 4.95
Ruminococcus	2.50^a	2.28^a	0.740	2.13^a	2.09^a	0.962
Ruminococcus	1.81 to 3.93	1.66 to 3.26		1.49 to 3.73	1.79 to 3.23

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Letter in the same line expresses statistical differences (P < 0.05).

Relative abundance of the genera in the rumen of feedlot cattle treated with virginiamycin (ATB) and controls (CON) from 2 different batches: a) dry season (B1); b) rainy season (B2).

LEfSe analysis indicating ruminal bacterial taxa associated with treatment with virginiamycin (ATB) and control feedlot animals (CON) in beef cattle during the dry season (B1).

Results of the PERMANOVA test for bacterial community membership, which considers only the presence or absence of the different genera, showed a significant impact of the sampling period (P = 0.001) and treatment with antibiotics (P = 0.016), but there was no interaction between both variables (P = 0.069). Likewise, for community structure, considering the relative abundance of each genus, there was a significant impact of the sampling period (P = 0.001) and treatment with antibiotics (P = 0.020), but there was no interaction between both variables (P = 0.078). The similarity between bacterial community membership and structure within each batch (B1 and B2) is illustrated in Figure 3.

PCoA plots representing similarities in membership of bacterial communities (Jaccard index) and structure (Yue and Clayton index) in the rumen of feedlot cattle treated with virginiamycin (red) and control cattle (blue): a) and b) bacterial community membership and structure, respectively, in cattle during the dry season (B1); c) and d) bacterial community membership and structure, respectively, in cattle during the rainy season (B2).

Because there the sampling period elicited an effect, pairwise comparisons of each period were carried out separately using the AMOVA test. The use of virginiamycin significantly impacted bacterial community membership and structure in the rumen of treated cattle compared to the controls in B1 (both P < 0.001), but not in B2 (P = 0.953 and P = 0.816 for membership and structure, respectively). The comparison of bacterial community membership between batches is further represented by dendrograms in Figure 4.

Dendrograms representing similarities in a) membership of bacterial communities, and b) structure in the rumen of feedlot cattle treated with virginiamycin (dark red lines) and control cattle (dark blue lines) during the dry season (B1) and in cattle treated with virginiamycin (light red lines) and control cattle (light blue lines) during the rainy season (B2).

The average daily weight gain (ADWG) in the 5 cattle with the highest weight gain in B1 was 1.48 kg/d (± 0.07 kg), which differed from the group with the lowest weight gain (0.86 kg/d ± 0.29 kg; P = 0.002). In B2, there was also a significant difference (P < 0.001) in cattle with the highest ADWG (1.69 kg/d ± 0.08 kg) compared to the lowest weight gain (1.07 kg/d ± 0.08 kg). LEfSe analysis was used to identify bacterial taxa associated with increased performance, with each batch considered separately (Figure 5).

Ruminal bacterial taxa associated with high and low average daily weight gain in feedlot cattle during a) the dry season (B1), and b) the rainy seasons (B2), as indicated by the LEfSe analysis.

Discussion

The use of virginiamycin was associated with changes in bacterial community membership and structure and reduced richness and diversity in the rumen of antibiotic-treated cattle from the first batch (B1) analyzed in this study. This demonstrated the selective action of this antimicrobial against certain species of bacteria present in this complex ecosystem.

Virginiamycin did not change the most prevalent phyla and genera but had a marked effect in decreasing the relative abundance of phylum TM7. This was further supported by the LEfSe analysis, which indicated that a genus of this phylum was overrepresented in the CON group of cattle. This widely distributed group of bacteria has been reported in the bovine rumen (28,29) and is thought to have limited fermentative capacity (30). The effects on the rumen ecosystem of depleting this phylum in antibiotic-treated animals should be further investigated.

The consistency of the impact of virginiamycin on the ruminal microbiota was tested in this study by repeating the experiment in 2 consecutive batches of cattle (B1 and B2) entering the same facility. It would be expected that the species selected by the drug would be consistent in the 2 different batches, but no differences in bacterial community membership or structure were observed in cattle in either B2 or in B1.

This could potentially be explained by the different seasons of the year in which the trials occurred: B1 took place from May to September, which is dry winter in Brazil, and B2 from January to May, which is the summer rainy season. The greater food intake of cattle in B1 could have been affected by lower winter temperatures and the higher rainfall experienced by cattle in B2 could have altered the bioavailability of virginiamycin or even washed the drug out of the troughs since pens and feeders were not covered, although this is less likely.

Furthermore, unprocessed orange pulp from the juice industry was fed to cattle in B1 and in the form of silage to cattle in B2. The composition of the feed may have been changed by the fermentation of the substrate and the conservation of the feedstuff, which could have favored certain species of bacteria (31). It is also possible, although not likely, that the orange pulp arrived from the factory with different water content, degree of spoiling, or palatability.

Finally, differences in microbial communities in the rumen of cattle in the 2 batches might have contributed to the lack of action of virginiamycin in cattle in B2, as in theory their microbiota could be less susceptible to the action of the drug. The factors that overwrote the antibiotic effect on the ruminal microbiota could not be determined with the proposed study design, as individual antibiotic intake was not measured.

The data from this study highlight the need for controlled environments and standard populations in microbiota studies, which may partially explain the discrepancies between groups. In fact, even different methodologies for microbiota sequencing and data analysis can introduce bias to the final results of microbiota studies (32).

Comparing cattle with the highest and lowest ADWG revealed that Paraeggerthella and Holdemania were strongly associated with more efficiency in cattle in batches B1 and B2. Interestingly, Holdemania was also pointed out by LEfSe as a marker of cattle receiving virginiamycin in B1. This bacterial genus is reported in humans who have high blood cholesterol and other parameters of lipid metabolism that are known predictors of metabolic syndrome (33). This finding might help in understanding the mechanisms of action of antibiotic growth promoters (AGPs) on the ruminal microbiota and emphasize the importance of developing alternatives to AGPs in order to decrease the overuse of antimicrobials in food animals.

The genus Megasphaera was associated with a greater ADWG in cattle in B1. This genus belongs to the Firmicutes phylum and is comprised of M. elsdenii and other species, which are lactate-consuming bacteria present in diets rich in grains, and that are used as probiotics to prevent acidosis (34). The Megasphaera genus was reported to be more abundant in animals that produce less methane and may be associated with better performance due to lower carbon loss (35,36).

Conversely, Subdivision 5, a fastidious bacterium of the Verrucomicrobia phylum that was recently cultured (37), was associated with lower ADWG in cattle in B1. However, its abundance could be associated with methodological biases, as small differences in primer selection could lead to greater relative abundance of certain taxa of this phylum (38). The genus Pseudobutyrivibrio was also associated with lower ADWG in cattle in B2. Interestingly, Pseudobutyrivibrio has been associated with weight loss in mice consuming black tea polyphenols (39).

At the end of the study, the ADWG was greater in cattle in B2 than those in B1, even though the diets of the 2 batches were very similar in composition. There are many reasons for this difference. The fact that the cattle in B2 weighed less than those in B1 at the beginning of the study may have contributed to a greater final ADWG in B2 cattle. Another factor is that the cattle in B1 consisted of mixed genders and this contact with females may have led the males to ingest less dry matter, which would lower their ADWG.

Inconsistences in ADWG and feed efficiency between different batches of cattle treated with virginiamycin have been reported before, which suggests that other factors can interfere with ingestion of dry matter, ADWG, and feed efficiency (19). It is possible that differences in microbiota composition may influence these inconsistencies, as some species of bacteria extract energy more efficiently than others (4–6).

Succinivibrio spp. was representative of cattle in B2 and has been reported in cattle with better performance in terms of ADWG and dry matter intake (4,5). In addition, the Succinivibrionaceae family has been associated with high milk yield in dairy cows (40). It should also be mentioned that, as studies evaluating zootechnical indices of performance require a larger sample size than what was used in our study, these results should be interpreted with caution.

In conclusion, virginiamycin can affect bacterial diversity and composition in the rumen of feedlot cattle, but its effect is inconsistent in different seasons of the year. This highlights the importance of standardizing research conditions and using caution in extrapolating and comparing results of different studies.

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4.Hernandez-Sanabria E, Goonewardene LA, Wang Z, Durunna ON, Moore SS, Guan LL. Impact of feed efficiency and diet on adaptive variations in the bacterial community in the rumen fluid of cattle. Appl Environ Microbiol. 2012;78:1203–1214. doi: 10.1128/AEM.05114-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS One. 2014;9:e85423. doi: 10.1371/journal.pone.0085423. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Martinez-Álvaro M, Mattock J, González-Recio Ó, et al. Including microbiome information in a multi-trait genomic evaluation: A case study on longitudinal growth performance in beef cattle. Genet Sel Evol. 2024;56:19. doi: 10.1186/s12711-024-00887-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Ji S, Jiang T, Yan H, et al. Ecological restoration of antibiotic-disturbed gastrointestinal microbiota in foregut and hindgut of cows. Front Cell Infect Microbiol. 2018;8:79. doi: 10.3389/fcimb.2018.00079. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Bessegatto JA, Paulino LR, Lisbôa JAN, et al. Changes in the fecal microbiota of beef cattle caused by change in management and the use of virginiamycin as a growth promoter. Res Vet Sci. 2017;114:355–362. doi: 10.1016/j.rvsc.2017.06.011. [DOI] [PubMed] [Google Scholar]
9.Van TTH, Yidana Z, Smooker PM, Coloe PJ. Antibiotic use in food animals worldwide, with a focus on Africa: Pluses and minuses. J Glob Antimicrob Resist. 2020;20:170–177. doi: 10.1016/j.jgar.2019.07.031. [DOI] [PubMed] [Google Scholar]
10.Sinha I, Dayama S. Antimicrobial stewardship in low- and middle-income countries: Developing a broader perspective through an ethical analysis. Indian J Med Ethics. 2024;IX:35–40. doi: 10.20529/IJME.2023.072. [DOI] [PubMed] [Google Scholar]
11.Benatti JMB, Alves Neto JA, de Oliveira IM, de Resende FD, Siqueira GR. Effect of increasing monensin sodium levels in diets with virginiamycin on the finishing of Nellore cattle. Anim Sci J. 2017;88:1709–1714. doi: 10.1111/asj.12831. [DOI] [PubMed] [Google Scholar]
12.Spears JW. Ionophores and nutrient digestion and absorption in ruminants. J Nutr. 1990;120:632–638. doi: 10.1093/jn/120.6.632. [DOI] [PubMed] [Google Scholar]
13.Matthews C, Crispie F, Lewis E, Reid M, O’Toole PW, Cotter PD. The rumen microbiome: A crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency. Gut Microbes. 2019;10:115–132. doi: 10.1080/19490976.2018.1505176. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Myer PR, Smith TP, Wells JE, Kuehn LA, Freetly HC. Rumen microbiome from steers differing in feed efficiency. PLoS One. 2015;10:e0129174. doi: 10.1371/journal.pone.0129174. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Nathani NM, Patel AK, Mootapally CS, et al. Effect of roughage on rumen microbiota composition in the efficient feed converter and sturdy Indian Jaffrabadi buffalo (Bubalus bubalis) BMC Genomics. 2015;16:1116. doi: 10.1186/s12864-015-2340-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Cox LM, Yamanishi S, Sohn J, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell. 2014;158:705–721. doi: 10.1016/j.cell.2014.05.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Centner TJ. Recent government regulations in the United States seek to ensure the effectiveness of antibiotics by limiting their agricultural use. Environ Int. 2016;94:1–7. doi: 10.1016/j.envint.2016.04.018. [DOI] [PubMed] [Google Scholar]
18.Ortiz J, Montano M, Plascencia A, Salinas J, Torrentera N, Zinn RA. Influence of kaolinite clay supplementation on growth performance and digestive function in finishing calf-fed Holstein steers. Asian-Australas J Anim Sci. 2016;29:1569–1575. doi: 10.5713/ajas.16.0162. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Rogers JA, Branine ME, Miller CR, et al. Effects of dietary virginiamycin on performance and liver abscess incidence in feedlot cattle. J Anim Sci. 1995;73:9–20. doi: 10.2527/1995.7319. [DOI] [PubMed] [Google Scholar]
20.Salinas-Chavira J, Lenin J, Ponce E, Sanchez U, Torrentera N, Zinn RA. Comparative effects of virginiamycin supplementation on characteristics of growth-performance, dietary energetics, and digestion of calf-fed Holstein steers. J Anim Sci. 2009;87:4101–4108. doi: 10.2527/jas.2009-1959. [DOI] [PubMed] [Google Scholar]
21.Krause DO, Denman SE, Mackie RI, et al. Opportunities to improve fiber degradation in the rumen: Microbiology, ecology, and genomics. FEMS Microbiol Rev. 2003;27:663–693. doi: 10.1016/S0168-6445(03)00072-X. [DOI] [PubMed] [Google Scholar]
22.Peng S, Yin J, Liu X, et al. First insights into the microbial diversity in the omasum and reticulum of bovine using Illumina sequencing. J Appl Genet. 2015;56:393–401. doi: 10.1007/s13353-014-0258-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.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;75:165–174. doi: 10.1007/s00253-006-0802-y. [DOI] [PubMed] [Google Scholar]
24.Klindworth A, Pruesse E, Schweer T, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1. doi: 10.1093/nar/gks808. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–5120. doi: 10.1128/AEM.01043-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Cole JR, Wang Q, Fish JA, et al. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642. doi: 10.1093/nar/gkt1244. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Golder HM, Denman SE, McSweeney C, Celi P, Lean IJ. Ruminal bacterial community shifts in grain-, sugar-, and histidine-challenged dairy heifers. J Dairy Sci. 2014;97:5131–5150. doi: 10.3168/jds.2014-8003. [DOI] [PubMed] [Google Scholar]
29.He X, McLean JS, Edlund A, et al. Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci U S A. 2015;112:244–249. doi: 10.1073/pnas.1419038112. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–538. doi: 10.1038/nbt.2579. [DOI] [PubMed] [Google Scholar]
31.Belanche A, Newbold CJ, Lin W, Rees Stevens P, Kingston-Smith AH. A systems biology approach reveals differences in the dynamics of colonization and degradation of grass vs. hay by rumen microbes with minor effects of Vitamin E supplementation. Front Microbiol. 2017;8:1456. doi: 10.3389/fmicb.2017.01456. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Forry SP, Servetas SL, Kralj JG, et al. Variability and bias in microbiome metagenomic sequencing: An interlaboratory study comparing experimental protocols. Sci Rep. 2024;14:9785. doi: 10.1038/s41598-024-57981-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lippert K, Kedenko L, Antonielli L, et al. Gut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults. Benef Microbes. 2017;8:545–556. doi: 10.3920/BM2016.0184. [DOI] [PubMed] [Google Scholar]
34.Klieve AV, Hennessy D, Ouwerkerk D, Forster RJ, Mackie RI, Attwood GT. Establishing populations of Megasphaera elsdenii YE 34 and Butyrivibrio fibrisolvens YE 44 in the rumen of cattle fed high grain diets. J Appl Microbiol. 2003;95:621–630. doi: 10.1046/j.1365-2672.2003.02024.x. [DOI] [PubMed] [Google Scholar]
35.Shabat SK, Sasson G, Doron-Faigenboim A, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958–2972. doi: 10.1038/ismej.2016.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Kamke J, Kittelmann S, Soni P, et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome. 2016;4:56. doi: 10.1186/s40168-016-0201-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Spring S, Bunk B, Spröer C, et al. Characterization of the first cultured representative of Verrucomicrobia subdivision 5 indicates the proposal of a novel phylum. ISME J. 2016;10:2801–2816. doi: 10.1038/ismej.2016.84. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Walters W, Hyde ER, Berg-Lyons D, et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems. 2016;1:9–15. doi: 10.1128/mSystems.00009-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Henning SM, Yang J, Hsu M, et al. Decaffeinated green and black tea polyphenols decrease weight gain and alter microbiome populations and function in diet-induced obese mice. Eur J Nutr. 2018;57:2759–2769. doi: 10.1007/s00394-017-1542-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Indugu N, Vecchiarelli B, Baker LD, Ferguson JD, Vanamala JKP, Pitta DW. Comparison of rumen bacterial communities in dairy herds of different production. BMC Microbiol. 2017;17:190. doi: 10.1186/s12866-017-1098-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b1-cjvr_04_114] 1.Russell JB, Rychlik JL. Factors that alter rumen microbial ecology. Science. 2001;292:1119–1122. doi: 10.1126/science.1058830. [DOI] [PubMed] [Google Scholar]

[b2-cjvr_04_114] 2.Han X, Yang Y, Yan H, Wang X, Qu L, Chen Y. Rumen bacterial diversity of 80 to 110-day-old goats using 16S rRNA sequencing. PLoS One. 2015;10:e0117811. doi: 10.1371/journal.pone.0117811. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b3-cjvr_04_114] 3.Henderson G, Cox F, Ganesh S, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep. 2015;5:14567. doi: 10.1038/srep14567. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b4-cjvr_04_114] 4.Hernandez-Sanabria E, Goonewardene LA, Wang Z, Durunna ON, Moore SS, Guan LL. Impact of feed efficiency and diet on adaptive variations in the bacterial community in the rumen fluid of cattle. Appl Environ Microbiol. 2012;78:1203–1214. doi: 10.1128/AEM.05114-11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b5-cjvr_04_114] 5.Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS One. 2014;9:e85423. doi: 10.1371/journal.pone.0085423. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b6-cjvr_04_114] 6.Martinez-Álvaro M, Mattock J, González-Recio Ó, et al. Including microbiome information in a multi-trait genomic evaluation: A case study on longitudinal growth performance in beef cattle. Genet Sel Evol. 2024;56:19. doi: 10.1186/s12711-024-00887-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b7-cjvr_04_114] 7.Ji S, Jiang T, Yan H, et al. Ecological restoration of antibiotic-disturbed gastrointestinal microbiota in foregut and hindgut of cows. Front Cell Infect Microbiol. 2018;8:79. doi: 10.3389/fcimb.2018.00079. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b8-cjvr_04_114] 8.Bessegatto JA, Paulino LR, Lisbôa JAN, et al. Changes in the fecal microbiota of beef cattle caused by change in management and the use of virginiamycin as a growth promoter. Res Vet Sci. 2017;114:355–362. doi: 10.1016/j.rvsc.2017.06.011. [DOI] [PubMed] [Google Scholar]

[b9-cjvr_04_114] 9.Van TTH, Yidana Z, Smooker PM, Coloe PJ. Antibiotic use in food animals worldwide, with a focus on Africa: Pluses and minuses. J Glob Antimicrob Resist. 2020;20:170–177. doi: 10.1016/j.jgar.2019.07.031. [DOI] [PubMed] [Google Scholar]

[b10-cjvr_04_114] 10.Sinha I, Dayama S. Antimicrobial stewardship in low- and middle-income countries: Developing a broader perspective through an ethical analysis. Indian J Med Ethics. 2024;IX:35–40. doi: 10.20529/IJME.2023.072. [DOI] [PubMed] [Google Scholar]

[b11-cjvr_04_114] 11.Benatti JMB, Alves Neto JA, de Oliveira IM, de Resende FD, Siqueira GR. Effect of increasing monensin sodium levels in diets with virginiamycin on the finishing of Nellore cattle. Anim Sci J. 2017;88:1709–1714. doi: 10.1111/asj.12831. [DOI] [PubMed] [Google Scholar]

[b12-cjvr_04_114] 12.Spears JW. Ionophores and nutrient digestion and absorption in ruminants. J Nutr. 1990;120:632–638. doi: 10.1093/jn/120.6.632. [DOI] [PubMed] [Google Scholar]

[b13-cjvr_04_114] 13.Matthews C, Crispie F, Lewis E, Reid M, O’Toole PW, Cotter PD. The rumen microbiome: A crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency. Gut Microbes. 2019;10:115–132. doi: 10.1080/19490976.2018.1505176. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b14-cjvr_04_114] 14.Myer PR, Smith TP, Wells JE, Kuehn LA, Freetly HC. Rumen microbiome from steers differing in feed efficiency. PLoS One. 2015;10:e0129174. doi: 10.1371/journal.pone.0129174. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b15-cjvr_04_114] 15.Nathani NM, Patel AK, Mootapally CS, et al. Effect of roughage on rumen microbiota composition in the efficient feed converter and sturdy Indian Jaffrabadi buffalo (Bubalus bubalis) BMC Genomics. 2015;16:1116. doi: 10.1186/s12864-015-2340-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b16-cjvr_04_114] 16.Cox LM, Yamanishi S, Sohn J, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell. 2014;158:705–721. doi: 10.1016/j.cell.2014.05.052. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b17-cjvr_04_114] 17.Centner TJ. Recent government regulations in the United States seek to ensure the effectiveness of antibiotics by limiting their agricultural use. Environ Int. 2016;94:1–7. doi: 10.1016/j.envint.2016.04.018. [DOI] [PubMed] [Google Scholar]

[b18-cjvr_04_114] 18.Ortiz J, Montano M, Plascencia A, Salinas J, Torrentera N, Zinn RA. Influence of kaolinite clay supplementation on growth performance and digestive function in finishing calf-fed Holstein steers. Asian-Australas J Anim Sci. 2016;29:1569–1575. doi: 10.5713/ajas.16.0162. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b19-cjvr_04_114] 19.Rogers JA, Branine ME, Miller CR, et al. Effects of dietary virginiamycin on performance and liver abscess incidence in feedlot cattle. J Anim Sci. 1995;73:9–20. doi: 10.2527/1995.7319. [DOI] [PubMed] [Google Scholar]

[b20-cjvr_04_114] 20.Salinas-Chavira J, Lenin J, Ponce E, Sanchez U, Torrentera N, Zinn RA. Comparative effects of virginiamycin supplementation on characteristics of growth-performance, dietary energetics, and digestion of calf-fed Holstein steers. J Anim Sci. 2009;87:4101–4108. doi: 10.2527/jas.2009-1959. [DOI] [PubMed] [Google Scholar]

[b21-cjvr_04_114] 21.Krause DO, Denman SE, Mackie RI, et al. Opportunities to improve fiber degradation in the rumen: Microbiology, ecology, and genomics. FEMS Microbiol Rev. 2003;27:663–693. doi: 10.1016/S0168-6445(03)00072-X. [DOI] [PubMed] [Google Scholar]

[b22-cjvr_04_114] 22.Peng S, Yin J, Liu X, et al. First insights into the microbial diversity in the omasum and reticulum of bovine using Illumina sequencing. J Appl Genet. 2015;56:393–401. doi: 10.1007/s13353-014-0258-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b23-cjvr_04_114] 23.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;75:165–174. doi: 10.1007/s00253-006-0802-y. [DOI] [PubMed] [Google Scholar]

[b24-cjvr_04_114] 24.Klindworth A, Pruesse E, Schweer T, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1. doi: 10.1093/nar/gks808. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b25-cjvr_04_114] 25.Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–5120. doi: 10.1128/AEM.01043-13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b26-cjvr_04_114] 26.Cole JR, Wang Q, Fish JA, et al. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642. doi: 10.1093/nar/gkt1244. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b27-cjvr_04_114] 27.Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b28-cjvr_04_114] 28.Golder HM, Denman SE, McSweeney C, Celi P, Lean IJ. Ruminal bacterial community shifts in grain-, sugar-, and histidine-challenged dairy heifers. J Dairy Sci. 2014;97:5131–5150. doi: 10.3168/jds.2014-8003. [DOI] [PubMed] [Google Scholar]

[b29-cjvr_04_114] 29.He X, McLean JS, Edlund A, et al. Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci U S A. 2015;112:244–249. doi: 10.1073/pnas.1419038112. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b30-cjvr_04_114] 30.Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–538. doi: 10.1038/nbt.2579. [DOI] [PubMed] [Google Scholar]

[b31-cjvr_04_114] 31.Belanche A, Newbold CJ, Lin W, Rees Stevens P, Kingston-Smith AH. A systems biology approach reveals differences in the dynamics of colonization and degradation of grass vs. hay by rumen microbes with minor effects of Vitamin E supplementation. Front Microbiol. 2017;8:1456. doi: 10.3389/fmicb.2017.01456. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b32-cjvr_04_114] 32.Forry SP, Servetas SL, Kralj JG, et al. Variability and bias in microbiome metagenomic sequencing: An interlaboratory study comparing experimental protocols. Sci Rep. 2024;14:9785. doi: 10.1038/s41598-024-57981-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b33-cjvr_04_114] 33.Lippert K, Kedenko L, Antonielli L, et al. Gut microbiota dysbiosis associated with glucose metabolism disorders and the metabolic syndrome in older adults. Benef Microbes. 2017;8:545–556. doi: 10.3920/BM2016.0184. [DOI] [PubMed] [Google Scholar]

[b34-cjvr_04_114] 34.Klieve AV, Hennessy D, Ouwerkerk D, Forster RJ, Mackie RI, Attwood GT. Establishing populations of Megasphaera elsdenii YE 34 and Butyrivibrio fibrisolvens YE 44 in the rumen of cattle fed high grain diets. J Appl Microbiol. 2003;95:621–630. doi: 10.1046/j.1365-2672.2003.02024.x. [DOI] [PubMed] [Google Scholar]

[b35-cjvr_04_114] 35.Shabat SK, Sasson G, Doron-Faigenboim A, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958–2972. doi: 10.1038/ismej.2016.62. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b36-cjvr_04_114] 36.Kamke J, Kittelmann S, Soni P, et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome. 2016;4:56. doi: 10.1186/s40168-016-0201-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b37-cjvr_04_114] 37.Spring S, Bunk B, Spröer C, et al. Characterization of the first cultured representative of Verrucomicrobia subdivision 5 indicates the proposal of a novel phylum. ISME J. 2016;10:2801–2816. doi: 10.1038/ismej.2016.84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b38-cjvr_04_114] 38.Walters W, Hyde ER, Berg-Lyons D, et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems. 2016;1:9–15. doi: 10.1128/mSystems.00009-15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b39-cjvr_04_114] 39.Henning SM, Yang J, Hsu M, et al. Decaffeinated green and black tea polyphenols decrease weight gain and alter microbiome populations and function in diet-induced obese mice. Eur J Nutr. 2018;57:2759–2769. doi: 10.1007/s00394-017-1542-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b40-cjvr_04_114] 40.Indugu N, Vecchiarelli B, Baker LD, Ferguson JD, Vanamala JKP, Pitta DW. Comparison of rumen bacterial communities in dairy herds of different production. BMC Microbiol. 2017;17:190. doi: 10.1186/s12866-017-1098-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Impact of virginiamycin on the ruminal microbiota of feedlot cattle

Bruna Parapinski Santos

Júlio Augusto Naylor Lisbôa

José Antônio Bessegatto

Carlos Henrique Montemor

Lais Resende Paulino

Amauri Alcindo Alfieri

J Scott Weese

Marcio Carvalho Costa

Abstract

Résumé

Introduction