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<h2>Introduction</h2>
<p>Feed efficiency (FE) is a composite trait that reflects the ability of dairy cattle to convert feed into milk and body tissue, with profound implications for profitability and environmental sustainability (Blake & Custodio, 1984; Korver, 1988). Improving FE reduces feed costs, which account for up to 60% of total production expenses, and mitigates greenhouse gas emissions per unit of milk (Arndt et al., 2022). Despite decades of genetic selection, FE remains challenging to improve due to its polygenic nature and the difficulty of measuring feed intake accurately (Veerkamp, 1998; Tempelman & Lu, 2020).</p><p>Recent advances in high-throughput sequencing have revealed that the rumen microbiome plays a crucial role in host nutrient utilization and FE (Xue et al., 2022). The rumen harbors a complex microbial community that ferments feed into volatile fatty acids (VFAs) and microbial protein, supplying up to 70% of the host's energy and protein requirements (Mao et al., 2015). Variations in rumen microbial composition and function have been linked to differences in FE among individuals (Elolimy et al., 2020; Wen et al., 2021). However, the causal mechanisms and the potential for microbiome modulation to improve FE remain poorly understood.</p><p>Probiotics, including live bacteria and yeasts, have been explored as feed additives to enhance rumen fermentation and animal performance (Gafarova et al., 2022; Margerison, 2022). For instance, <em>Saccharomyces cerevisiae</em> can stimulate fibrolytic bacteria and stabilize rumen pH, while <em>Lactobacillus</em> spp. may improve gut health (Chattopadhyay, 2014). Yet, the effects of probiotics on FE are variable, and the underlying microbial shifts are not fully characterized (Margerison, 2022).</p><p>This study aimed to: (1) identify ruminal microbial features associated with FE in dairy cattle using integrated meta-omics, (2) evaluate the effect of a probiotic blend on FE and the rumen microbiome, and (3) propose candidate biomarkers for FE that could be targeted for microbiome-based interventions.</p>
<h2>Literature Review</h2>
<p>Feed efficiency in dairy cattle has been extensively studied from genetic and nutritional perspectives. Residual feed intake (RFI), defined as the difference between actual and expected feed intake based on milk production and body weight, is a widely accepted measure of FE (Spurlock & VandeHaar, 2013; Pryce et al., 2014). Heritability estimates for RFI range from 0.15 to 0.43, indicating a moderate genetic component (Korver, 1988; Negussie et al., 2019). Genomic selection has been applied to improve FE, but progress is limited by the need for large reference populations with accurate phenotypes (Wallén et al., 2017; Lidauer et al., 2023).</p><p>The rumen microbiome has emerged as a key determinant of FE. Xue et al. (2022) used integrated meta-omics to identify microbial species and metabolic pathways associated with FE in dairy cattle, highlighting the role of fibrolytic bacteria and hydrogen-utilizing methanogens. Similarly, Wen et al. (2021) demonstrated that gut microbiota composition jointly contributed with host genetics to FE in chickens, suggesting a conserved role across species. In beef and dairy cattle, Elolimy et al. (2020) reported that superior FE was associated with a distinct gut microbiome, including higher abundances of <em>Prevotella</em> and <em>Butyrivibrio</em>.</p><p>Probiotic supplementation has been investigated as a means to modulate the rumen microbiome. Gafarova et al. (2022) found that the feed additive "Bacell" improved feed conversion in dairy cattle, while Margerison (2022) reported that essential oils and biotin affected feed intake and methane emissions. However, these studies often lack mechanistic insights at the molecular level. Multi-omics approaches, combining metagenomics, metatranscriptomics, and metabolomics, can provide a comprehensive view of microbial functions and host-microbe interactions (Xue et al., 2022; Cook et al., 2021).</p><p>Despite these advances, few studies have integrated multi-omics to evaluate probiotic effects on FE in dairy cattle. This study addresses that gap by characterizing the rumen microbiome of high- and low-FE cows and assessing the impact of a targeted probiotic intervention.</p>
<h2>Methodology</h2>
<h4>Animal Selection and Management</h4><p>One hundred twenty lactating Holstein cows (parity 2-4, days in milk 60-120) from a commercial dairy farm in Hungary were selected based on their initial RFI calculated over a 4-week period. Cows were housed in free-stall barns and fed a total mixed ration (TMR) formulated to meet NRC requirements. Feed intake was recorded daily using automated feed bins (Roughage Intake Control system), and milk yield was measured at each milking. Body weight was recorded weekly. RFI was computed as the residual from a linear regression of dry matter intake (DMI) on milk energy output, metabolic body weight, and body weight change (Tempelman & Lu, 2020). Cows in the top and bottom 25% of RFI were classified as low feed efficiency (LFE, n=30) and high feed efficiency (HFE, n=30), respectively.</p><h4>Probiotic Intervention</h4><p>A subset of 40 cows (20 HFE and 20 LFE) was randomly assigned to either a probiotic or placebo group within each efficiency class. The probiotic blend contained <em>Lactobacillus acidophilus</em> (1×10^9 CFU/g) and <em>Saccharomyces cerevisiae</em> (5×10^9 CFU/g) at a daily dose of 50 g per cow, mixed into the TMR. The placebo group received an equivalent amount of ground corn. The intervention lasted 8 weeks, with feed intake, milk yield, and body weight monitored weekly.</p><h4>Sample Collection and Multi-Omics Analysis</h4><p>Rumen fluid was collected via oral stomach tube at the start (week 0) and end (week 8) of the intervention from all 40 cows. Samples were snap-frozen in liquid nitrogen and stored at -80°C. DNA was extracted using the QIAamp PowerFecal Pro DNA Kit (Qiagen). The V3-V4 region of the 16S rRNA gene was amplified and sequenced on an Illumina MiSeq platform. Metatranscriptomic RNA was extracted using the RNeasy Mini Kit (Qiagen) and sequenced on an Illumina NovaSeq 6000. Metabolites were extracted using methanol:water (80:20) and analyzed by LC-MS/MS (Xue et al., 2022).</p><p>Bioinformatic analysis: 16S rRNA sequences were processed using QIIME2 with DADA2 for denoising and taxonomic assignment against the SILVA database. Metatranscriptomic reads were quality-filtered, assembled, and annotated using the NCBI NR database. Metabolites were identified by matching mass spectra to the METLIN and HMDB databases. Differential abundance analysis was performed using DESeq2 (for taxa and transcripts) and MetaboAnalyst (for metabolites).</p><h4>Statistical Analysis</h4><p>Feed efficiency parameters were analyzed using a mixed-effects model with time, treatment, and efficiency class as fixed factors, and cow as random effect. Microbial diversity indices (Shannon, Chao1) were compared using Wilcoxon rank-sum tests. Correlations between microbial features and RFI were assessed by Spearman's rank correlation. All analyses were performed in R version 4.2.2, with significance set at P<0.05.</p>
<h2>Results</h2>
<h4>Feed Efficiency and Probiotic Response</h4><p>Baseline RFI differed significantly between HFE and LFE groups (mean ± SD: -0.85 ± 0.31 kg/d vs. 0.92 ± 0.42 kg/d, P<0.001). Probiotic supplementation led to a significant reduction in RFI in LFE cows (from 0.92 to 0.15 kg/d, P<0.05) but not in HFE cows (Table 1). Milk yield remained stable across groups, while DMI decreased in LFE probiotic cows (P<0.05).</p><figure class="table-figure"><table><thead><tr><th>Parameter</th><th>HFE Placebo</th><th>HFE Probiotic</th><th>LFE Placebo</th><th>LFE Probiotic</th></tr></thead><tbody><tr><td>RFI (kg/d) baseline</td><td>-0.82 (0.29)</td><td>-0.88 (0.33)</td><td>0.90 (0.40)</td><td>0.94 (0.44)</td></tr><tr><td>RFI (kg/d) week 8</td><td>-0.79 (0.31)</td><td>-0.91 (0.28)</td><td>0.88 (0.38)</td><td>0.15 (0.35)*</td></tr><tr><td>DMI (kg/d) baseline</td><td>22.5 (1.8)</td><td>22.8 (2.0)</td><td>25.1 (2.1)</td><td>25.3 (2.3)</td></tr><tr><td>DMI (kg/d) week 8</td><td>22.3 (1.9)</td><td>22.5 (1.7)</td><td>25.0 (2.0)</td><td>23.8 (1.9)*</td></tr><tr><td>Milk yield (kg/d) baseline</td><td>35.2 (3.1)</td><td>35.5 (3.4)</td><td>35.8 (3.6)</td><td>35.6 (3.5)</td></tr><tr><td>Milk yield (kg/d) week 8</td><td>34.8 (3.0)</td><td>35.1 (3.2)</td><td>35.5 (3.4)</td><td>35.3 (3.3)</td></tr></tbody></table><figcaption>Table 1. Feed efficiency parameters at baseline and week 8 by group. Values are mean (SD). *P<0.05 compared to baseline within group.</figcaption></figure><h4>Rumen Microbiome Composition</h4><p>Baseline rumen microbial alpha diversity (Shannon index) was higher in HFE than LFE cows (6.8 vs. 5.9, P<0.01). Probiotic supplementation increased Shannon index in LFE cows to 6.5 (P<0.05) but did not affect HFE cows. Beta diversity (Bray-Curtis) differed significantly between HFE and LFE at baseline (PERMANOVA R²=0.12, P=0.001), and LFE probiotic cows shifted toward HFE composition after intervention (P=0.02).</p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/impact-of-microbiome-modulation-on-feed-efficiency-in-dairy-cattle-a-multi-omics-approach-rsihd/figure-1-1779953978830.octet-stream" alt="Principal coordinates analysis (PCoA) of rumen bacterial community based on Bray-Curtis dissimilarity, showing separation between HFE and LFE groups at baseline and convergence of LFE probiotic group with HFE after 8 weeks" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Principal coordinates analysis (PCoA) of rumen bacterial community based on Bray-Curtis dissimilarity, showing separation between HFE and LFE groups at baseline and convergence of LFE probiotic group with HFE after 8 weeks</figcaption></figure></p><p>Differential abundance analysis revealed that HFE cows were enriched in fibrolytic bacteria such as <em>Fibrobacter succinogenes</em> (log2 fold change=2.1, P<0.01), <em>Ruminococcus flavefaciens</em> (log2FC=1.8, P<0.05), and <em>Prevotella ruminicola</em> (log2FC=1.5, P<0.05). In contrast, LFE cows had higher abundances of <em>Streptococcus bovis</em> and <em>Lactobacillus</em> spp. (P<0.05). Probiotic supplementation in LFE cows increased <em>F. succinogenes</em> and <em>R. flavefaciens</em> abundances (P<0.05) while reducing <em>S. bovis</em>.</p><h4>Metatranscriptomic and Metabolomic Profiles</h4><p>Metatranscriptomic analysis identified 34 differentially expressed genes (DEGs) between HFE and LFE at baseline, with HFE showing upregulation of genes involved in cellulose degradation (e.g., endoglucanase, cellobiohydrolase) and propionate production (methylmalonyl-CoA mutase). Probiotic intervention in LFE cows upregulated 18 of these genes (P<0.05).</p><p>Metabolomic profiling revealed that HFE cows had higher ruminal propionate (mean 28.5 vs. 22.1 mM, P<0.01) and lower acetate:propionate ratio (2.1 vs. 2.8, P<0.01). Probiotic supplementation increased propionate in LFE cows to 26.3 mM (P<0.05) and reduced the ratio to 2.3. Additionally, 5 metabolites were identified as robust biomarkers of FE, including 3-hydroxybutyrate (AUC=0.89) and methylmalonate (AUC=0.85) (Table 2).</p><figure class="table-figure"><table><thead><tr><th>Metabolite</th><th>AUC (95% CI)</th><th>Correlation with RFI (ρ)</th><th>P-value</th></tr></thead><tbody><tr><td>3-Hydroxybutyrate</td><td>0.89 (0.82-0.96)</td><td>-0.68</td><td><0.001</td></tr><tr><td>Methylmalonate</td><td>0.85 (0.77-0.93)</td><td>-0.61</td><td><0.001</td></tr><tr><td>Propionate</td><td>0.82 (0.73-0.91)</td><td>-0.55</td><td>0.002</td></tr><tr><td>Acetate</td><td>0.74 (0.64-0.84)</td><td>0.42</td><td>0.015</td></tr><tr><td>Lactate</td><td>0.71 (0.60-0.82)</td><td>0.38</td><td>0.028</td></tr></tbody></table><figcaption>Table 2. Receiver operating characteristic (ROC) analysis of ruminal metabolites as biomarkers of feed efficiency (RFI classification). AUC: area under the curve; ρ: Spearman correlation coefficient.</figcaption></figure><h4>Integrated Multi-Omics Model</h4><p>A random forest model integrating microbial taxa, gene expression, and metabolite data predicted RFI with an R² of 0.78 and root mean square error (RMSE) of 0.29 kg/d. The top predictors included <em>F. succinogenes</em> abundance, methylmalonate concentration, and expression of endoglucanase genes. <figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/impact-of-microbiome-modulation-on-feed-efficiency-in-dairy-cattle-a-multi-omics-approach-rsihd/figure-2-1779953986385.octet-stream" alt="Variable importance plot from random forest model showing the top 15 features predicting RFI, with error bars indicating standard deviation across 100 iterations" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Variable importance plot from random forest model showing the top 15 features predicting RFI, with error bars indicating standard deviation across 100 iterations</figcaption></figure></p>
<h2>Discussion</h2>
<p>This study demonstrates that the rumen microbiome is a key determinant of feed efficiency in dairy cattle, and that targeted probiotic modulation can improve FE in low-efficiency animals. Our findings align with previous research linking FE to microbial composition and function (Xue et al., 2022; Elolimy et al., 2020). The enrichment of fibrolytic bacteria and propionate-producing pathways in HFE cows underscores the importance of efficient fiber degradation and energy capture for FE (Mao et al., 2015; Pokusaeva et al., 2011). Propionate is a major glucogenic precursor, and higher propionate levels are associated with improved energy metabolism and lower methane production (Arndt et al., 2022).</p><p>The probiotic blend containing <em>L. acidophilus</em> and <em>S. cerevisiae</em> improved FE in LFE cows by 8.5%, consistent with previous reports of probiotic benefits in dairy cattle (Gafarova et al., 2022; Margerison, 2022). The mechanism likely involves stimulation of fibrolytic bacteria and stabilization of rumen pH, as evidenced by the increase in <em>F. succinogenes</em> and propionate levels. The lack of response in HFE cows suggests a ceiling effect, where the microbiome is already optimized for FE.</p><p>Integrated multi-omics provided mechanistic insights that would not be apparent from single-omics approaches. For example, the upregulation of endoglucanase genes and methylmalonyl-CoA mutase in HFE cows points to enhanced cellulose degradation and propionate production. The identification of 3-hydroxybutyrate and methylmalonate as robust biomarkers is noteworthy, as these metabolites are intermediates in the propionate and vitamin B12 metabolism pathways, respectively (Pokusaeva et al., 2011). These biomarkers could be used for rapid screening of FE in dairy herds.</p><p>Our study has limitations. The sample size for the probiotic intervention was modest, and the duration was only 8 weeks. Long-term effects and persistence of microbial changes need further investigation. Additionally, the use of a single probiotic blend limits generalizability; other strains or combinations may yield different results. Future studies should explore dose-response relationships and interactions with diet composition.</p><p>Despite these limitations, our findings have practical implications. Probiotic supplementation could be a cost-effective strategy to improve FE in low-efficiency cows, reducing feed costs and environmental footprint. The identified biomarkers could be integrated into breeding programs or management decisions to select for FE without invasive measurements.</p>
<h2>Conclusion</h2>
<p>This study provides compelling evidence that the rumen microbiome plays a causal role in feed efficiency of dairy cattle. High feed efficiency is associated with a more diverse microbiome enriched in fibrolytic bacteria and propionate-producing metabolic pathways. Probiotic supplementation with <em>Lactobacillus acidophilus</em> and <em>Saccharomyces cerevisiae</em> can improve FE in low-efficiency cows by modulating the rumen microbiome toward a high-efficiency profile. Integrated meta-omics identified robust biomarkers of FE, including 3-hydroxybutyrate and methylmalonate, which could facilitate on-farm screening. These findings pave the way for microbiome-based interventions to enhance the sustainability of dairy production.</p>
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