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Article

Differential Rumen Microbial Taxa in Charolais Bulls with Divergent Residual Feed Intake

by
Taylor Sidney
1,
Emily Treon
1,
Godstime Taiwo
1,
Samanthia Johnson
1,
Yarahy Leal
1,
Peixin Fan
2 and
Ibukun M. Ogunade
1,*
1
School of Agriculture and Food Systems, West Virginia University, Morgantown, WV 26506, USA
2
Department of Animal and Dairy Sciences, Mississippi State University, Starkville, MS 39762, USA
*
Author to whom correspondence should be addressed.
Appl. Microbiol. 2025, 5(3), 56; https://doi.org/10.3390/applmicrobiol5030056
Submission received: 14 April 2025 / Revised: 3 June 2025 / Accepted: 21 June 2025 / Published: 24 June 2025
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Abstract

The rumen microbiome impacts beef cattle feed efficiency, a key economic factor in production systems. This study investigated the rumen microbiome of Charolais bulls with divergent residual feed intake-expected progeny difference (RFI-EPD) values to identify microbial taxa associated with feed efficiency. Forty Charolais bulls were evaluated for feed intake and growth over 60 days, and RFI values were determined. The 10 most efficient (NegRFI) and 10 least efficient (PosRFI) bulls were selected for microbiome analysis. Rumen fluid samples were collected and analyzed via 16S rRNA gene sequencing. Microbial analysis revealed no significant differences in alpha or beta diversity between groups, but differential abundance analysis identified 20 operational taxonomic units (OTUs) as more prevalent in NegRFI bulls, while 15 OTUs were more abundant in PosRFI bulls. Two OTUs from the key genus Prevotella showed different relative abundances in the two RFI-EPD groups. NegRFI bulls had a higher relative abundance of Prevotella OTU 109358, while PosRFI bulls had more Prevotella OTU 626329. Additionally, OTUs from Ruminococcus, a genus involved in fiber degradation and volatile fatty acid (VFA) production, were more abundant in NegRFI bulls. In contrast, PosRFI bulls had a higher abundance of OTUs from Oscillospira and F16, both linked to butyrate production. The results of this study support the need for further exploration into the role of microbial taxa associated with feed efficiency. A deeper understanding of the functional profile of the microbiota could aid in the development of microbiome-informed strategies to enhance nutrient utilization and performance in beef cattle.

1. Introduction

In the beef industry, predicting the genetic potential of bulls and their progeny has long been a fundamental approach to selecting traits such as carcass merit, growth, and reproductive performance [1]. Due to economic pressures, particularly rising feed costs, production parameters that enhance profitability have become increasingly critical, with a growing emphasis on feed intake as a major input [2]. Residual feed intake (RFI), defined as the difference between an animal’s actual feed intake and the expected intake based on its maintenance and production requirements, is widely recognized as a valuable metric for evaluating feed efficiency because it is phenotypically correlated with feed intake but not growth intake, making it a reliable indicator of metabolic efficiency [3,4,5].
Residual feed intake is a moderately heritable trait (h2 ≈ 0.35) and has been successfully used to genetically select beef cattle that consume less feed while maintaining similar production outputs to less efficient cattle [3,6]. Residual feed intake can be integrated with genetic progeny testing to further predict progeny performance and select for more feed-efficient cattle. Expected progeny difference (EPD) is a widely used tool for predicting an animal’s genetic potential as a parent, aiding in the selection of traits that contribute to herd improvement [7]. Combining RFI with EPD, RFI-EPDs are employed in bull breeding programs to select genetically superior cattle for feed efficiency through pedigree analysis. This metric accounts for environmental influences, enabling comparisons across diverse settings and generating significant feed cost savings over time [8,9].
The rumen microbiome plays a crucial role in feed efficiency by breaking down plant material into digestible nutrients [10]. The rumen microbiota significantly influences carbohydrate and nitrogen metabolism, producing volatile fatty acids (VFAs) and microbial proteins [11]. Although feed intake affects microbial composition, a core microbiome is present in the rumen, dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria [12,13,14]. Omics-based studies have demonstrated that the rumen microbiome is heritable, suggesting that host genetics influence microbial composition and function [15]. Moreover, cattle selected for feed efficiency show differences in the relative abundance of specific microbial taxa, highlighting the potential to identify microbial signatures associated with feed efficiency [16,17].
While previous studies have explored associations between the rumen microbiome and feed efficiency, few studies have examined microbial associations with genetically informed metrics such as RFI-EPD in bulls. In this study, we employed 16S rRNA gene sequencing, a powerful tool for identifying and classifying microbial communities, to investigate differentially abundant microbial taxa in the rumen of Charolais cattle with divergent RFI values. We hypothesized that cattle identified as feed efficient or inefficient based on RFI would exhibit differences in the relative abundance of microbial taxa. Identifying microbial signatures associated with feed efficiency traits used in genetic selection, such as RFI-EPDs, could support the development of microbiome-informed strategies to enhance selection and improve long-term productivity in beef cattle

2. Materials and Methods

2.1. Animals, Diet, RFI Determination, and Sampling

All animal care and use procedures were in accordance with the West Virginia University’s Institutional Animal Care and Use Committee Protocol (IACUC Protocol Number: 2206054350). Forty Charolais bulls (average initial body weight (BW) = 443 ± 64 kg; 365 ± 23 d of age) were fed a high-forage total mixed ration (TMR, primarily consisting of corn silage, hay, cracked corn, and a ration-balancing supplement; Table 1) in two pens (pen 1: n = 22; pen 2: n = 18) for a total of 60 days. Individual feed intake was monitored using two GrowSafe8000 intake nodes (GrowSafe Systems Ltd., Airdrie, Alberta, Canada) in each pen. In-Pen Weighing Positions (IPW, Vytelle LLC., Lenexa, KS, USA) were positioned in each pen at a water trough to measure the BW of individual animals several times daily and were reportedly used with sufficient accuracy in measuring feed efficiency over a testing period of 59 days [18,19].
After this test period, feed intake data and growth performance were collected. The bulls were then ranked and selected based on their RFI coefficients. Charolais bulls with the most negative RFI values (NegRFI; n = 10) and the most positive RFI values (PosRFI; n = 10) were identified and utilized for further analyses. Phenotypic RFI values of the bulls were identified and determined, as described previously by [19]. In brief, daily body weight (BW) was regressed on time to estimate the initial BW, mid-test BW, and average daily gain (ADG). Subsequently, ADG and metabolic mid-test BW (mid-test BW0.75) were regressed against individual daily dry matter intake (DMI) to calculate residual feed intake (RFI) as the difference between the predicted and observed DMI values, using the equation: Y = β0 + β1X1 + β2X2 + ε. Here, Y represents DMI (kg/d), β0 is the intercept, β1 and β2 are the partial regression coefficients, X1 denotes the metabolic mid-test BW (MMTW = mid-test BW0.75; kg), and X2 corresponds to ADG (kg/d) [19,20]. Vytelle (Vytelle Insight Beef Genetics) conducted genetic evaluation of the bulls for residual feed intake–expected progeny difference (RFI-EPD) determination via Vytelle SENSE systems and analyzed through Vytelle INSIGHT analytics services, incorporating at least three generations of pedigree information to determine RFI-EPD values.
Rumen fluid samples were collected prior to morning feeding on day 60 of the testing period. Approximately 200 mL of rumen fluid samples was collected from each of the 20 bulls using an orally administered stomach tube connected to a vacuum pump and placed in a 50 mL polypropylene conical bottom tube (Ruminator, Wittibruet, Bayern, Germany; www.profs-products.com (accessed on 25 August 2023)). The orogastric vacuum pump was washed between each bull to prevent contamination. To prevent saliva contamination, the first 200 mL of rumen fluid collected was discarded before the samples used in this study. The samples were then immediately placed on ice after collection and subsequently stored at −80 °C until further analyses.

2.2. DNA Extraction and 16s rRNA Gene Sequencing

Prior to microbial DNA extraction, rumen fluid samples collected from the Charolais bulls divergent in RFI status were thawed at room temperature. Microbial DNA was extracted from the rumen fluid samples using the Qiagen DNeasy Powersoil Pro DNA Isolation Kit following the manufacturer’s instructions (Qiagen; catalog number: 47014, Germantown, MD, USA). Total DNA purity was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). All microbial DNA samples had an A260/A280 ratio ranging from 1.8 to 2.0. The 16S rRNA gene sequencing was performed at SeqCenter, LLC (SeqCenter, LLC, Pittsburg, PA, USA). The samples were prepared using Zymo Research Quick-16S Library Prep Kit (Zymo Research; catalog number: D6400, Irvine, CA, USA) to target the V3-V4 regions of the 16S gene. The forward primer sequence used was 5′-CCTACGGGDGGCWGCAG CCTAYGGGGYGCWGCAG-3′, and the reverse primer sequence was 5′-GACTACNVGGGTMTCTAATCC-3′. Subsequently, the prepared samples were cleaned and normalized before being sequenced on a P1 600cyc NextSeq2000 Flowcell to generate 2 × 301 bp paired end (PE) reads.

2.3. Data and Statistical Analysis

The growth performance data, including dry matter intake (DMI), average daily gain (ADG), initial and final body weight (BW), residual feed intake (RFI), and residual feed intake–expected progeny difference (RFI-EPD) values of the PosRFI and NegRFI Charolais bulls, were analyzed using the GLIMMIX procedure in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Animals were included as a random effect, nested within RFI-EPD. The RFI-EPD status was included as a fixed effect, and initial body weight values were included as a covariate for the final body weight. Results were considered significant when p ≤ 0.05.
For microbiome analysis, the 16S rRNA gene sequencing data were analyzed as described previously by [17]. Quality control and adapter trimmings of the raw sequence files were performed using default parameters with the Illumina Binary base call (BCL) Convert v3.9.3 (Illumina, San Diego, CA, USA). Fastq files were imported into QIIME2, and primer sequences were removed using the cutadapt plugin within QIIME2 [21,22]. Sequences were denoised using the dada2 plugin and subsequently assigned operational taxonomic units (OTUs) with a 97% sequence similarity threshold using the Silva database (v138.1). This classification was accomplished with the VSEARCH utility within QIIME’s feature-classifier plugin. The OTUs were then collapsed into their respective taxonomic units and converted into relative frequencies for each sample.
The OTU data were then uploaded into the online platform MicrobiomeAnalyst (www.microbiomeanalyst.ca (accessed on 2 August 2024); Supplementary File S1) [23] for alpha diversity (Chao1) and beta diversity (Bray–Curtis distance matrix-based principal coordinate analysis (PCoA)) analyses. Permutational Multivariate Analysis of Variance (PERMANOVA) was set at 999 permutations and was utilized to test the difference in beta diversity distance between the two groups of samples. Lastly, differential abundance analysis (Wald test) was performed using CLC Microbial Genomics Module (Qiagen; Supplementary File S2). Operational taxonomic units were aligned and assigned to genera, based on sequence similarity. Differentially abundant OTUs and their respective genera between the two groups of bulls were identified using FDR ≤ 0.05.

3. Results

3.1. Growth Performance

The results of the growth performance for Charolais bulls selected for divergent RFI status are presented in Table 2. As intended, bulls selected for NegRFI exhibited significantly lower RFI-EPD and phenotypic RFI values than PosRFI bulls (p < 0.0001 and p = 0.004, respectively) Additionally, NegRFI bulls consumed less dry matter per day (p = 0.006), yet no differences were observed in initial weight (p = 0.539), final weight (p = 0.065), or average daily gain (p = 0.073) between groups. These results highlight effective stratification based on feed efficiency.

3.2. Rumen Bacterial Community

A total of 20 microbial DNA samples were collected and sequenced, yielding an average of 955,433 ± 266,363 read pairs per sample. Sequencing depth was assessed using rarefaction curves, which showed that the rate of OTU accumulation decreased as the number of reads per sample increased, eventually reaching a plateau (Figure 1), indicating sufficient sequencing coverage.
Analysis of alpha diversity, measured by the Chao1 index, revealed no significant differences in microbial richness between bulls divergent in RFI (p = 0.92; Figure 2). Additionally, alpha diversity measured using the Shannon (p = 0.68; Supplementary Figure S1a) and Simpson (p = 0.48; Supplementary Figure S1b) indices revealed no significant differences within the rumen of Charolais bulls. Lastly, beta diversity, assessed using Bray–Curtis-based PCoA, showed no significant variation between the two groups (p = 0.397; Figure 3).
Differential abundance analysis (Table 3) identified 20 OTUs and their correspondingly assigned genera as more prevalent in NegRFI bulls, including Clostridiales OTU 133719, Ruminococcus OTU 823658, S24-7 OTU 4479793, and Prevotella OTU 109358. In contrast, 15 OTUs, such as F16 OTU 277519, Clostridiales OTU 207713, Prevotella OTU 626329, and Oscillospira OTU 290253, were found to be more abundant in PosRFI bulls.

4. Discussion

The rumen microbiota is a complex ecosystem influenced by various factors, such as diet, feed intake, and breed diversity [17,24]. Additionally, host genetics can shape rumen bacterial communities, impacting feed efficiency and overall animal performance [25]. To support this, ref. [17] reported differential abundance in rumen bacterial composition associated with the feed efficiency phenotype and observed bacterial taxa associated with energy metabolism and VFA synthesis in feed-efficient steers, compared to their inefficient counterparts. In the present study, microbial diversity metrics indicated no difference in overall community structure, indicating the overall richness and evenness of the rumen microbiome were comparable in bulls divergent with feed efficiency. As such, subsequent analyses focused on taxonomic-level differences, where the differential abundance of specific genera provided more biologically relevant insight into microbial associations with feed efficiency. Given this, there were differences in the relative abundance of specific bacterial genera OTUs, underscoring potential links between the rumen microbiome and feed efficiency. Twenty OTUs, including Clostridiales OTU 133719, Prevotella OTU 109358, S24-7 OTU 4479793, and Ruminococcus OTU 823658, were more abundant in NegRFI bulls, while 15 OTUs, such as F16 OTU 277519, Oscillospira OTU 290253, Clostridiales OTU 207713, and Prevotella OTU 626329, were more prevalent in PosRFI bulls.
The 16S rRNA gene is a widely used marker for characterizing microbial communities due to its presence in nearly all bacteria and its combination of conserved and variable regions [26]. However, this method has limited taxonomic resolution, particularly at the species and strain levels, because closely related organisms can share highly similar or even identical 16S rRNA gene sequences and are typically clustered into operational taxonomic units (OTUs), based on sequence similarity [27]. As a result, multiple OTUs may be assigned to the same genus, despite representing distinct bacterial lineages [28]. In this study, several genera including Prevotella and Clostridiales were each represented by different OTUs that exhibited differential abundance between RFI groups, suggesting possible differences in the underlying phylogenetic and functional diversity within these genera. To combat the lower taxonomic resolution of 16s rRNA gene sequencing, future studies should incorporate additional methodologies, including whole-genome shotgun sequencing, which has previously been reported to provide greater taxonomic precision [29]
The genus Prevotella was represented by different OTUs in the two RFI-EPD groups. For instance, the relative abundance of Prevotella OTU 109358 is higher in NegRFI bulls while that of Prevotella OTU 626329 is greater in PosRFI bulls. Prevotella is a predominant rumen genus that plays a key role in carbohydrate and nitrogen metabolism [30]. Prevotella produces propionate, an important energy substrate supporting gluconeogenesis in cattle [31]. Similarly, Clostridiales OTU 133719 and Clostridiales OTU 207713 were differentially abundant in NegRFI and PosRFI bulls, respectively. The Clostridiales order includes taxa known for their role in fiber degradation and short-chain fatty acid (SCFA) production, particularly propionate and butyrate [32]. Additionally, previous studies have reported differential abundance in these two genera as associated with feed efficiency phenotypes in cattle, specifically due to their increased capacity to ferment carbohydrates and subsequent high propionate production [33,34,35]. While the specific roles of these OTUs remain unknown, their differential abundance within these genera suggests functional specialization related to feed efficiency.
The S24-7 OTU 4479793 and Ruminococcus OTU 823658 were found to be greater in bulls selected for NegRFI, compared to PosRFI. S24-7, more commonly known as Muribaculaceae or Candidatus Homeothermaceae, is a bacterium classified within the Bacteroidetes family [36]. Previous research has reported that the S24-7 specializes in complex carbohydrate fermentation, the production of VFA like propionate, and to be positively associated with production measures like milk yield and feed efficiency phenotypes in dairy cows [37,38,39]. Additionally, the Ruminococcus genus is a Gram-positive, spherical-shaped bacterium that belongs to the Firmicutes phylum and is commonly found within the gastrointestinal tract of ruminants [40]. Most species within the genus Ruminococcus are recognized for their cellulolytic capabilities, enabling them to efficiently degrade harsh and fibrous components of forages like cellulose and hemicellulose [41]. Due to their high capacity for fiber degradation, Ruminococcus species enhance the availability of simpler carbohydrates for fermentation, thereby increasing the production of VFA [42,43]. Other reports have identified Ruminococcus as positively associated with performance and feed efficiency within ruminants [44,45,46]. The increased relative abundance of both S24-7 OTU 4479793 and Ruminococcus OTU 823658 in NegRFI Charolais bulls could suggest a microbial community optimized for fiber degradation and enhanced carbohydrate fermentation, potentially contributing to improved energy utilization and production efficiency.
Operational taxonomic units (OTUs) from Oscillospira and F16 genera were more abundant in PosRFI bulls, compared to NegRFI bulls. Oscillospira is known for butyrate production [47], a VFA that is primarily used for ruminal epithelial maintenance [48]. In addition, the presence of F16, an underexplored microbial group often associated with Oscillospira, further suggests rumen fermentation that favors increased butyrate production [49,50]. While butyrate contributes to systemic energy metabolism, it is primarily associated with ruminal tissue maintenance and is less efficient than propionate in supporting production-related energy needs such as growth and feed efficiency [51,52]. In this study, PosRFI bulls consumed significantly more feed than NegRFI bulls yet achieved similar growth rates, indicating reduced metabolic efficiency. In contrast, NegRFI bulls harbored a greater abundance of propionate-associated microbes, including Prevotella OTU 109358, Ruminococcus OTU 823658, and Clostridiales OTU 133719, genera commonly linked to ATP generation and enhanced energy metabolism [30,31,32]. Although limited research exists on the role of Oscillospira and F16 in relation to feed efficiency, Oscillospira has previously been associated with feed inefficiency in cattle [53]. Interestingly, it has also been positively linked to gut health in other species, including rabbits and humans [47,54]. While specific VFA profiles were not measured in this study, the increased abundance of butyrate-producing microbes in PosRFI bulls may reflect a ruminal environment that favors epithelial maintenance over efficient energy extraction, contributing to their less efficient feed utilization.
Taken together, the microbial differences observed between NegRFI and PosRFI bulls suggest that feed efficiency may be driven by subtle shifts in specific microbial populations rather than broad changes in overall community diversity. Bulls with greater feed efficiency (NegRFI) harbored a higher relative abundance of genera and corresponding OTUs commonly associated with fiber degradation and VFA production, particularly propionate, which supports improved energy utilization and gluconeogenesis. In contrast, PosRFI bulls had a greater abundance of genera linked to butyrate production. While butyrate contributes to systemic energy metabolism, it is primarily utilized by ruminal epithelial cells and plays a central role in epithelial maintenance. This microbial profile may indicate a shift in energy partitioning that favors tissue maintenance over energy availability for production parameters like feed efficiency. While both groups harbored members of genera such as Prevotella and Clostridiales, the presence of differentially abundant OTUs within these genera may reflect compositional differences among closely related microbial populations, potentially indicative of functional divergence within genera that could influence fermentation profiles and energy utilization. While the literature cited in this study linked these genera to specific fermentation patters, VFA production was not quantified in the present study, and, therefore, functional implications are inferred based on taxonomic abundance and known microbial roles supported by the literature.

5. Conclusions

This study highlights the potential role of the rumen microbiome in influencing feed efficiency in Charolais bulls. Although microbial diversity was comparable between groups, differences in the relative abundance of specific bacterial genera were observed, suggesting that the microbial composition of the rumen may contribute to variations in feed efficiency. Operational taxonomic units and their respective genera, such as Prevotella, Clostridiales, S24-7, and Ruminococcus, were differentially abundant between NegRFI and PosRFI bulls, with distinct OTUs of Prevotella observed in each group. The higher abundance of fiber-degrading OTUs like the genus Ruminococcus and carbohydrate-fermenting OTUs such as genus S24-7, in NegRFI bulls may indicate a rumen microbial community optimized for fiber degradation and energy utilization, potentially contributing to improved metabolic efficiency. Conversely, the increased prevalence of butyrate-producing OTUs and their assigned genera such as Oscillospira and F16 in PosRFI bulls suggests that feed-inefficient cattle may have a rumen environment favoring butyrate production, which could influence energy partitioning. As a limitation of using 16S rRNA gene sequencing, these findings highlight the need for further investigation into the functional roles of specific microbial taxa in relation to feed efficiency. Future studies incorporating multi-omics approaches, such as shotgun metagenomics, will allow for a more comprehensive understanding of the rumen microbiome’s contribution to feed utilization and subsequent animal performance. Furthermore, future studies should integrate VFA quantification alongside microbial profiling to more accurately characterize how rumen microbiome composition influences fermentation patterns and contributes to feed efficiency phenotypes. Understanding these microbial signatures could enhance the development of microbiome-based strategies for improving feed efficiency in cattle.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/applmicrobiol5030056/s1, Supplementary File S1: Microbiome Analyst; Supplementary File S2: OTU (Table, Differential Abundance Analysis; CLC Genomics Module); Supplementary Figure S1a,b: Alpha Diversity Shannon and Simpson Indices.

Author Contributions

Data curation, T.S.; formal analysis, T.S.; investigation, T.S.; methodology, T.S.; writing—original draft preparation, T.S.; writing—review and editing, T.S.; data curation, E.T.; investigation, E.T.; methodology, E.T.; data curation, G.T.; investigation, G.T.; methodology, G.T.; data curation, S.J.; investigation, S.J.; methodology, S.J.; data curation, Y.L.; investigation, Y.L.; methodology, Y.L.; writing—review and editing, P.F.; conceptualization, I.M.O.; data curation, I.M.O.; funding acquisition, I.M.O.; investigation, I.M.O.; methodology, I.M.O.; project administration, I.M.O.; resources, I.M.O.; supervision, I.M.O.; validation, I.M.O.; visualization, I.M.O.; writing—review and editing, I.M.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research study was funded by the U.S. Department of Agriculture hatch multi-state regional project W-3010.

Informed Consent Statement

The research procedures were approved by the Institutional Animal Care and Use Committee of West Virginia University (IACUC Protocol Number: 2206054350).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

Thank you to everyone who contributed to this work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applmicrobiol 05 00056 g001
Figure 1. Rarefaction curve for sequences from NegRFI (blue) and PosRFI (red) samples.
Figure 1. Rarefaction curve for sequences from NegRFI (blue) and PosRFI (red) samples.
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Applmicrobiol 05 00056 g002
Figure 2. Alpha diversity profiling (Chao1 Index; p = 0.92) of the rumen microbial community in Charolais bulls (PosRFI: Red; NegRFI: Green) with divergent residual feed intake. Black diamond represents group mean.
Figure 2. Alpha diversity profiling (Chao1 Index; p = 0.92) of the rumen microbial community in Charolais bulls (PosRFI: Red; NegRFI: Green) with divergent residual feed intake. Black diamond represents group mean.
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Applmicrobiol 05 00056 g003
Figure 3. Beta diversity (PCoA; R-Squared: 0.05; PERMANOVA: p = 0.397) in Charolais bulls (PosRFI: Red; NegRFI: Green) selected for divergent residual feed intake.
Figure 3. Beta diversity (PCoA; R-Squared: 0.05; PERMANOVA: p = 0.397) in Charolais bulls (PosRFI: Red; NegRFI: Green) selected for divergent residual feed intake.
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Table 1. Ingredient and chemical composition of the basal diet fed to Charolais bulls.
Table 1. Ingredient and chemical composition of the basal diet fed to Charolais bulls.
Ingredients (%DM)Inclusion (% of Dietary DM)
Corn silage65.61
Hay a14.93
Cracked corn14.50
Concentrate/vitamin supplement b4.69
Mineral supplement c0.27
Nutrient AnalysisValue
DM, %48.3
Crude Protein, % 11.6
NDF, % 38.5
NFC, % 42.0
Fat, % 3.59
Calcium, % 0.57
Phosphorus, %0.37
Potassium, %1.28
Magnesium, %0.15
a Contains a mixture of Smooth Broom hay, Timothy hay, and Orchard Grass. b 50% Concentrate Supplement (Kalmbach Feeds, Pennsylvania, PA, USA) contained soybean meal, corn dried distillers grains (DDGS), soybean hulls, lime-calcium supplement, urea, mold star dry, salt, monocal (containing monofluorophosphate and calcium carbonate), magnesium oxide, K-Dairy Premix (containing vitamin A, vitamin D, vitamin, E, antioxidant, manganese, zinc, iron, copper, iodine, cobalt, magnesium, and selenium), Zinpro Availa 4 (containing zinc, manganese, copper, and cobalt), Rumensin 90 (containing monensin; 90.7 g/lb), selenium, vitamin A, Tylan 40 (containing tylosin phosphate; 40 g/lb), Alkosel (containing selenium enriched yeast; 3000 ppm Se), vitamin D3, vitamin E, Kem Trace chromium; guaranteed analysis: 44% crude protein; 2.6% crude fat; 9.7% crude fiber; 13.2% ADF; 18.9% NDF; 10.4% non-protein nitrogen. c Contains calcium, phosphorus, magnesium, potassium, ash, sulfur, sodium, chloride, iron, manganese, zinc, copper, and molybdenum; guaranteed analysis (% DM): 5.77% ash; 0.57% Ca; 0.37% P; 0.15% Mg; 1.28% K, 0.16% Na.
Table 2. Growth performance of Charolais bulls with divergent residual feed intake.
Table 2. Growth performance of Charolais bulls with divergent residual feed intake.
ItemPosRFINegRFISEMp-Value
RFI-EPD, kg/d0.18−0.110.02<0.0001
RFI, kg/d0.71−0.820.460.004
Initial Weight, kg42744826.470.539
Final Weight, kg 15415248.400.065
ADG, kg/d1.751.490.140.073
DMI, kg/d10.559.390.370.006
RFI-EPD, residual feed intake–expected progeny difference; RFI, residual feed intake; SEM, standard error of the mean; ADG, average daily gain; DMI, dry matter intake. 1 Covariate adjusted by initial body weight.
Table 3. Differentially abundant OTUs in Charolais bulls with divergent residual feed intake.
Table 3. Differentially abundant OTUs in Charolais bulls with divergent residual feed intake.
Genus; OTUFold Change 1FDR p-Value 2
F16; 277519−110.580.007
Oscillospira; 290253−25.380.007
Clostridiales; 207713−29.590.009
Prevotella; 626329−20.110.01
YRC22; 544154−18.980.03
Ruminococcaceae; 196905−12.190.03
Prevotella; 296753−11.170.03
Prevotella; 169950−9.380.03
Prevotella; 593357−6.930.03
Bacteroidales; 152221−18.980.03
Bacteroidales; 346529−12.760.03
Bacteroidales; 325575−5.190.04
BS11; 288543−19.660.04
LD1-PB3; 329608−19.300.04
BS11; 354615−9.930.05
Clostridiales; 13371917.820.007
Prevotella; 10935815.950.008
S24-7; 447979315.480.009
Ruminococcus; 82365829.630.009
Prevotella; 26390541.040.009
Bacteroidales; 25424861.290.01
Treponema; 69992710.300.01
Clostridiales; 52919225.640.01
Clostridiales; 15754648.120.01
Prevotella; 10683930.200.01
Clostridiales; 6095895.920.02
Lachnospiraceae; 28906416.190.03
Bacteroidales; 3130839.810.03
Ruminococcus; 59059510.050.03
Oscillospira; 27086621.270.03
[Paraprevotellaceae]; 22016.160.03
Succinogenes; 3029066.800.03
Bacteroidales; 55642471.540.04
BS11; 24016822.440.05
Prevotella; 32362511.350.05
1 Fold change: Negative fold change value indicates the specific taxa is more abundant in PosRFI Charolais bulls. Positive fold change value indicates the specific taxa is more abundant in NegRFI Charolais bulls, compared to PosRFI bulls. 2 FDR p-value: false discovery ratio ≤ 0.05.
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Sidney, T.; Treon, E.; Taiwo, G.; Johnson, S.; Leal, Y.; Fan, P.; Ogunade, I.M. Differential Rumen Microbial Taxa in Charolais Bulls with Divergent Residual Feed Intake. Appl. Microbiol. 2025, 5, 56. https://doi.org/10.3390/applmicrobiol5030056

AMA Style

Sidney T, Treon E, Taiwo G, Johnson S, Leal Y, Fan P, Ogunade IM. Differential Rumen Microbial Taxa in Charolais Bulls with Divergent Residual Feed Intake. Applied Microbiology. 2025; 5(3):56. https://doi.org/10.3390/applmicrobiol5030056

Chicago/Turabian Style

Sidney, Taylor, Emily Treon, Godstime Taiwo, Samanthia Johnson, Yarahy Leal, Peixin Fan, and Ibukun M. Ogunade. 2025. "Differential Rumen Microbial Taxa in Charolais Bulls with Divergent Residual Feed Intake" Applied Microbiology 5, no. 3: 56. https://doi.org/10.3390/applmicrobiol5030056

APA Style

Sidney, T., Treon, E., Taiwo, G., Johnson, S., Leal, Y., Fan, P., & Ogunade, I. M. (2025). Differential Rumen Microbial Taxa in Charolais Bulls with Divergent Residual Feed Intake. Applied Microbiology, 5(3), 56. https://doi.org/10.3390/applmicrobiol5030056

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