miRNA-microbiome interplay is related to Bos indicus feed efficiency

miRNA-microbiome interplay is related to Bos indicus feed efficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article miRNA-microbiome interplay is related to Bos indicus feed efficiency Priscila Silva Neubern De Oliveira, Bruno Gabriel Nascimento Andrade, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4744784/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The fecal microbiome is emerging as an essential component of the gut microbiota and host metabolism, whereas in cattle, fecal microbiome characterization is still needed. Recent evidence indicates that small RNAs, such as miRNAs, may be isolated from feces and involved in host–microbe interactions. In this study, fecal samples were collected from the rectal ampulla of Nelore bulls phenotypic divergent for residual feed intake (RFI). miRNA sequencing and 16S rRNA gene (V3-V4 region) were performed to reveal the associations between host miRNAs and microbiome composition and their relationships with the feed efficiency phenotype. Among the 162 identified fecal miRNAs, seven were more expressed in the inefficient group: bta-miR-27b, bta-miR-30a, bta-miR-126, bta-miR-143, bta-miR-155, bta-miR-205 and bta-miR-196a. Using metabarcoding sequencing, we identified 5,005 bacterial ASVs, and after filtering, we used 357 ASVs in subsequent analyzes. Weighted gene coexpression network analysis (WGCNA) was used to identify miRNA and microbiome interactions. We observed significant correlations between fecal miRNA expression and microbiota composition. The differentially expressed fecal miRNAs were correlated with some taxa as Prevotella, Anaerorhabdus furcosa , Bifidobacterium, Bacillales , Succinispira mobilis, Peptostreptococcaceae and Coriobacteriaceae , suggesting that they may play a role in the expression of feed efficiency-related miRNAs. Our results provide a new perspective for exploring host-microbiome interactions that affect FE traits. Taken together, these results point to miRNAs and taxa identified here as potential regulators of feed efficiency, which may provide the knowledge needed to develop future strategies to manipulate the microbiome. Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Molecular biology bovine microbiome interaction residual feed intake Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The gut hosts a complex community of microorganisms that comprises several species of bacteria, archaea, and eukaryotes, and recently, the holobiont theory has emerged as a way to explain the interactions between hosts and their associated microbial communities 1 . This relationship has been associated with animal development, metabolic processes and diseases, and our understanding of its importance for livestock production is increasing quickly. Due to its pivotal role, it is crucial to understand the mechanisms underlying microbiota regulation by its host, as well as to identify strategies to successfully manipulate the microbiota. Animal feed is one of the most significant costs in production systems, in addition to livestock competing for resources, e.g., cereals and grains, with humans 2 , which means excessive water consumption and deforestation. This turns feed efficiency into a trait with economic, environmental and sustainability impacts since it is a complex measurement of how efficient the animals are in metabolizing feed into livestock products. Additionally, feed efficiency is frequently associated with lower methane emissions 3 , another important sustainability trait. Recent findings have indicated that the ruminal microbiota partially controls the variability in feed efficiency in ruminants; as a result, the microbiome has been proposed as a proxy to predict feed efficiency traits 4 . Associations between the rumen microbiota and feed efficiency in beef cattle have demonstrated that differences in the abundance and diversity of microbial phylotypes exist between efficient and inefficient individuals. Recently, Conteville et al. (2023) 5 noted that the abundance of the Eubacterium genus was associated with both inefficient and high methane emitters animals, while Delgado et al. (2019) noted that Methanobacteria and Methanobrevibacter were less abundant in efficient cattle. These results suggest a link between efficient cattle and lower methane emissions. Thus, modulation of the microbiota composition can promote more sustainable and efficient livestock production while reducing the negative environmental impacts of beef cattle production. The fecal microbiome, although less studied than the ruminal microbiome, is emerging as an important component of host metabolism, and recent evidence indicates that microRNAs (miRNAs) may be involved in host–microbe interactions. miRNAs are small noncoding RNA molecules that play a role in the post transcriptional/translational regulation of gene expression influencing many biological processes in mammals. A key attribute that has started to be considered recently is that miRNAs can be transferred to other cells by extracellular vesicles (EVs), constituting an essential mode of inter-cell communication with the potential to shape microbial communities and host–microbe interactions 6 . In addition, studies have reported that changes in the gut microbiota are influenced by secreted host miRNAs 7 . Fecal miRNAs have been identified as potential indicators of the host-microbe interface 8 , with miRNAs produced by the host’s intestinal epithelial cells regulating bacterial gene transcripts and affecting bacterial growth 7 . Fecal miRNAs have also been characterized in bovine feces as potential biomarkers of diseases 9 , and there is also evidence that the gut microbiota can influence the expression of miRNAs, suggesting a possible new route of communication between microbiota and host. Thus, regarding feed efficiency, the known difference in gut microbiota composition between efficient and inefficient subjects could also drive differential in miRNA expression. Although miRNAs have been widely identified in bovines, the functional role of fecal miRNAs in host-microbe communication is yet to be understood, and based on observations from other species, we hypothesized that miRNAs from the host could play a regulatory role in the microbiome. Conversely, the gut microbiota can regulate host gene expression through miRNAs, and both mechanisms could contribute to feed efficiency variability. Thus, a detailed understanding of the molecular mechanisms affecting feed efficiency may provide a means to improve the productivity and sustainability of ruminant’s production to meet global food production demands. Results Phenotypic and miRNA data Phenotypic data on the residual feed intake (RFI; kg/d) of Nelore cattle belonging to the National Program for the Evaluation of Young Bulls (PNAT) of the Brazilian Association of Zebu Breeders (ABCZ) were obtained from a total of 91 bulls, from which 16 extreme animals were selected for feed efficiency: 8 efficient (negative RFI) and 8 inefficient (positive RFI) bulls. Table 1 presents the raw phenotypic data of residual feed intake (RFI; kg/day), dry matter intake (DMI; kg/day), metabolic body weight (MBW; kg), average daily gain (ADG; kg/day), feed efficiency (FE; kg/kg) and feed conversion (FC; kg/kg) used for the selection of 16 contrasting Nelore animals and the number of 16S RNA gene reads and miRNA reads mapped for each sample. Student’s t test was performed to evaluate the mean phenotypic differences between the efficient and inefficient RFI groups, and significant differences (p < 0.05) were observed for RFI, metabolic live weight and feed conversion phenotypes. Table 1 Phenotypic data of residual feed intake (RFI; kg/d) and their components dry matter intake (DMI; kg/day), metabolic live weight (MLW; kg), average daily gain (ADG; kg/day), feed efficiency (FE; kg/kg), feed conversion (FC; kg/kg) and number of mapped miRNA reads and 16S rRNA gene reads for efficient and inefficient Nelore cattle groups. Group RFI (kg/day) DMI (kg/day) MLW (kg) ADG (kg/day) FE (kg/kg) FC (kg/kg) Mapped miRNA reads 16S rRNA gene reads Efficient73 -1.87 11.57 131.01 1.44 0.12 8.02 6,120,000 91,053 Efficient16 -1.70 9.83 98.28 1.82 0.18 5.39 9,600,000 90,409 Efficient72 -1.51 11.78 123.60 1.64 0.13 7.18 8,200,000 86,151 Efficient25 -1.28 10.62 111.25 1.52 0.14 6.97 3,600,000 98,179 Efficient34 -1.27 12.29 119.31 1.89 0.15 6.50 7,640,000 96,322 Efficient62 -1.03 13.18 123.31 2.00 0.15 6.56 2,460,000 89,585 Efficient4 -0.95 9.59 106.11 1.16 0.12 8.22 2,530,000 91,919 Efficient13 -0.90 12.46 113.49 2.01 0.16 6.17 4,560,000 92,928 Mean -1.32 a 11.42 a 115.80 a 1.68 a 0.14 a 6.88 a 5,340,000 92,068,25 Inefficient32 0.69 13.07 113.55 1.62 0.12 8.04 3,240,000 95,434 Inefficient30 0.74 10.66 108.73 0.83 0.07 12.85 3,180,000 93,653 Inefficient47 0.03 13.13 125.41 1.53 0.11 8.58 8,780,000 88,604 Inefficient46 0.48 14.49 127.13 1.79 0,12 8.06 7,640,000 94,641 Inefficient37 0.40 13.68 121.09 1.71 0.12 7.96 4,830,000 86,082 Inefficient41 0.50 12.19 115.91 1.67 0.13 7.29 9,790,000 94,825 Inefficient40 0.25 12.95 120.32 1.51 0.11 8,54 4,950,000 95,179 Inefficient55 0.67 16.85 129.02 2.18 0.12 7.70 7,850,000 85,783 Mean 0.47 b 13.38 a 120.151 b 1.60 a 0.12 a 8.63 b 4,950,000 91,775,12 Efficient group animal IDs: Efficient73, Efficient16, Efficient72, Efficient25, Efficient34, Efficient62, Efficient4 and Efficient13. Inefficient group animal IDs: Inefficient32, Inefficient30, Inefficient47, Inefficient46, Inefficient37, Inefficient41, Inefficient40 and Inefficient55. a,b means with different letters had significant differences (p < 0.05) according to the student's test. miRNA sequencing of fecal samples from these Nelore cattle yielded 186,700,000 sequences ranging from 20–25 bp in length. On average, 50% of miRNA reads were mapped to the Bos taurus genome (ARS-UCD1.2). In total, 162 mature miRNAs were detected by STAR software (Table S2), which were further included in the differential expression analysis (Table S3). Identification and functionality of the bovine fecal miRNA profile Among the 162 expressed fecal miRNAs, 7 were differentially expressed between the RFI comparison groups (FDR < 0.1) and were upregulated in the inefficient group. To better understand the potential functional impact of the seven detected upregulated fecal DE miRNAs on the host, we assessed the biological pathways with overrepresentation enrichment analysis (ORA) performed by WebGestalt software and using the list of bovine genes targeted by the DE miRNAs. This analysis identified significant (FDR ≤ 0.05) signaling pathways related to RFI (Table 2 , Table S4). Table 2 Fecal miRNAs differentially expressed in inefficient and efficient Nelore cattle groups, respective fold-change, false discovery rate (FDR), number of target genes and significant signaling pathways related to residual feed intake (RFI). miRNA Fold Change a FDR b Inefficient c Efficient d Target genes e Significant signaling pathways related to RFI bta-mir-126 2.63 0.0019 8.1262 3.8865 4250 mTOR signaling pathway FoxO signaling pathway Focal adhesion MAPK signaling pathway bta-mir-30a 2.02 0.0042 17.4249 11.6890 1467 **** bta-mir-196a 1.64 0.0164 5.5739 1.5020 257 Ras signaling pathway bta-mir-205 2.01 0.0401 2.4378 0.7773 542 Rap1 signaling pathway bta-mir-27b 0.49 0.0517 443.5984 343.5702 110 Type II diabetes mellitus Insulin Resistance TNF signaling pathway Insulin signaling pathway bta-mir-143 1.06 0.0965 47.5615 45.2055 437 EGFR tyrosine kinase inhibitor resistance Regulation of actin cytoskeleton PI3K-Akt signaling pathway bta-mir-155 1.37 0.0942 2.8775 0.7773 498 B-cell receptor signaling pathway T-cell receptor signaling pathway mTOR signaling pathway a Fold Change of Inefficient to Efficient Nelore cattle groups, b False discovery rate adjusted p values by Benjamini‒Hochberg (1995) methodology, c,d Normalized mean counts of inefficient and efficient Nelore cattle groups, e Number of predicted target genes. Nelore fecal microbiome composition and taxonomy Sequencing of amplicons from the fecal samples of 16 Nelore cattle yielded a total of 2,821,494 paired end reads for bacteria and archaea. Quality control, denoising and chimera exclusion retained a total of 1,462,354 sequences resolved in 5,005 ASVs. A total of 357 ASVs were retained after the exclusion of singletons (Table S5). The rarefaction curves based on the Shannon‒Wiener alpha diversity metrics reached a plateau, which indicated that the sampling depth was adequate and that additional sequences were unlikely to result in additional features (Figure S1 ). The taxonomic profile of the microbiome of Nelore bulls from fecal samples was mainly composed of bacteria (mean ± sd: 98.4 ± 1.30%) and a small fraction of archaea (mean ± sd: 1.6 ± 1.20%). Because of the small fraction of Archaea present in the Nelore bull microbiome, we focused the following analysis on the bacterial fecal microbiomes. In the bacterial fecal microbiomes, seven phyla, 12 classes, 13 orders, 20 families, 27 genera and 26 species were identified. The most abundant phyla of both feed efficiency groups (Fig. 1 ) were Firmicutes (65.97% for the efficient group and 66.93% for the inefficient group), Proteobacteria (21.40% for the efficient group and 17.97% for the inefficient group), Bacteroidetes (10.69% for the efficient group and 13.10% for the inefficient group) and Euryachaeota ( 1.17% for the efficient group and 0.90% for the inefficient group). Nelore fecal microbiome diversity To compare the microbiome diversity between feed efficiency groups, the data were rarefied to 5,000 reads. Comparison of samples from different groups using richness (observed) and alpha diversity metrics (Chao 1, ACE, Shannon, Simpson, inverted Simpson and Fisher indices) revealed no significant difference (p > 0.01) in the richness or diversity of bacterial populations between the efficient and inefficient groups (Fig. 2 ). miRNA-microbiome network analysis To investigate miRNA-microbiome interactions in feces from divergent RFI animals, we applied the weighted gene coexpression network analysis (WGCNA) method to microbial communities 10 . To this end, miRNA and ASV networks were constructed separately. After quality control, the expression data of 58 miRNAs were used to construct a miRNA network, and the abundance data of 358 ASVs were used for the construction of the ASV network. Coexpression network analysis revealed eight miRNA module eigengenes (Figure S2) and six ASV MEs (Figure S3) in the efficient group. In the inefficient group, six miRNA MEs (Figure S4) and seven ASV MEs were identified (Figure S5). Relating modules to feed efficiency and identifying hub miRNAs and ASVs We also aimed to identify miRNA and ASV modules significantly associated with the RFI phenotype (Fig. 3 ). Among the eight WGCNA modules identified in the miRNA network from the efficient group, no modules were correlated with RFI, while among the six identified miRNA modules within the inefficient group, one module was negatively correlated (MEturquoise; cor=-0.9, p value = 0.02) with RFI. For the ASV network analysis, two out of the six identified modules of the efficient group MEred (cor=-0.87, p value = 0.06) and MEblack (cor = 0.81, p value = 0.09) were correlated with RFI, while in the inefficient group, no modules were correlated with RFI. Hub genes are defined as the genes that are most strongly correlated with features, i.e. , miRNAs or ASVs within each candidate module 11 . Table 3 shows the hub miRNAs and ASVs from modules eigengene associated with RFI phenotype from efficient and inefficient groups of Nelore bulls. Table 3 Hub miRNAs and ASVs (Amplicom Sequencing Variants) from modules eigengene (ME) associated with residual feed intake (RFI) phenotype from efficient and inefficient groups of Nelore bulls. ASV ME Hub ASV Taxonomic classification Efficient black ASV 741 k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales red ASV 376 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Succinispira; s__mobilis Inefficient miRNA ME Hub miRNA turquoise bta-mir-16a *** miRNA-microbiome interactions To investigate whether there were any direct correlations between microbiome composition and miRNA expression, miRNA and ASV modules that were positively or negatively correlated and had p values < = 0.1 were selected for further investigation (Fig. 4 ) According to these criteria, in the efficient group, we observed significant negative correlations between the miRNA and ASV modules in the range from − 0.8 to -0.7 and significant positive correlations in the range from 0.9 to 1 (Table S6). In the inefficient group, we observed significant negative correlations in the range of -0.8 to -0.7 and significant positive correlations in the range of 0.8 to 0.9 (Table S6). We then further explored the correlated modules and calculated specific Spearman’s correlations between miRNA expression and ASV abundance, further selecting the differentially expressed miRNAs along with their top five correlations with ASVs within each previously correlated module in the efficient (Table 4 ) and inefficient group (Table 5). Table 4 Negative and positive correlations between differentially expressed miRNAs and ASVs within correlated modules in efficient Nelore cattle group and taxonomic classification of each ASV inside the module. Efficient MEbrown miRNAs r MEred ASVs Taxonomic classification bta-mir-205 -0.9 ASV_307 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Prevotellaceae; g__Prevotella; bta-mir-205 -0.9 ASV_321 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales bta-mir-205 -0.9 ASV_336 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-205 -0.8 ASV_261 k__Bacteria; p__Firmicutes bta-mir-205 -0.7 ASV_4 k__Bacteria; p__Proteobacteria; c__Gammaproteobacteria MEbrown miRNAs r MEblue ASVs Taxonomic classification bta-mir-205 0.9 ASV_157 k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae; g__Hespellia; s__porcina bta-mir-205 0.9 ASV_281 k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae; g__Alistipes bta-mir-205 0.9 ASV_7 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae bta-mir-205 0.9 ASV_128 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-205 0.8 ASV_355 k__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Clostridium; s__saccharogumia MEblue miRNAs r MEyellow ASVs Taxonomic classification bta-mir-155 -0.7 ASV_161 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-155 -0.7 ASV_207 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-155 -0.7 ASV_250 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-155 -0.7 ASV_283 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-155 -0.6 ASV_492 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae MEblue miRNAs r MEred ASVs Taxonomic classification bta-mir-155 0.9 ASV_61 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-155 0.9 ASV_77 k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae; g__Clostridium bta-mir-155 0.9 ASV_292 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales bta-mir-155 0.9 ASV_1015 k__Bacteria;p__Actinobacteria; c__Coriobacteriia; o__Coriobacteriales; f__Coriobacteriaceae bta-mir-155 0.9 ASV_22 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae MEturquois miRNAS r MEyellow ASVs Taxonomic classification bta-mir-126 0.9 ASV_150 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-126 0.8 ASV_93 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales bta-mir-126 0.8 ASV_33 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-126 0.7 ASV_30 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-126 0.7 ASV_108 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales Table 5. Negative and positive correlations between differentially expressed miRNAs and ASVs within correlated modules in inefficient Nelore cattle group and taxonomic classification of each ASV inside the module. Inefficient MEyellow miRNAs r MEyellow ASVs Taxonomic classification bta-mir-196a -0.9 ASV_160 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-196a -0.9 ASV_184 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-196a -0.9 ASV_283 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-196a -0.9 ASV_386 k__Bacteria; p__Firmicutes bta.mir.196a -0.9 ASV_235 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae MEyellow miRNAs r MEturquoi ASVs Taxonomic classification bta-mir-196a 0.9 ASV_6 k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Bacillaceae bta-mir-196a 0.9 ASV_90 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium bta-mir-196a 0.9 ASV_524 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-196a 0.8 ASV_81 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-196a 0.8 ASV_900 k__Bacteria; p__Firmicutes MEblue miRNAs r MEred ASVs Taxonomic classification bta-mir-126 -0.9 ASV_62 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__ bta-mir-126 -0.9 ASV_949 k__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Anaerorhabdus; s__furcosa bta-mir-126 -0.9 ASV_613 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae bta-mir-126 -0.9 ASV_426 k__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium bta-mir-126 -0.9 ASV_429 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae MEturquoi r MEyellow Taxonomic classification bta-mir-30a 0.6 ASV_235 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales bta-mir-30a 0.5 ASV_573 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales bta-mir-30a 0.3 ASV_283 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae bta-mir-30a 0.3 ASV_386 k__Bacteria; p__Firmicutes bta-mir-30a 0.3 ASV_291 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales Discussion The gut microbiota and miRNAs are emerging as promising targets for managing and preventing inflammatory and metabolic disorders in mammals. In the present study, we identified functional fecal miRNAs associated with the feed efficiency phenotype residual feed intake and linkages between the miRNA profile and microbiome gut composition of Nelore cattle belonging to divergent feed efficiency groups. The bovine gut microbiome consists of trillions of microorganisms, most of which are bacteria 12 . As expected, in ruminants, Firmicutes , Proteobacteria , and Bacteroidetes were the main bacterial phyla found in the microbiomes of the efficient and inefficient groups of Nelore bulls. Firmicutes is a phylum in which many members produce butyrate, an important substance that keeps the colon healthy and plays a significant role in animal health 13 . They also breakdown carbohydrates that cannot be digested by enzymes in the gut, such as dietary fiber and resistant starch 14 . Proteobacteria is a phylum that digests/degrades proteins through the process of decarboxylation of amino acids 15 , while Bacteroidetes is also one of the predominant phyla with fermentative characteristics and the ability to modulate the immune system 16 . Here, we did not observe significant differences in the richness of bacterial populations between efficient and inefficient animals, which could have been influenced by the small sample size used in this study. Nonetheless, Clemons et al. 17 suggested that the lack of diversity differences regarding feed efficiency phenotypes may be due to dissimilarities at a finer resolution, such as for individual taxa and metabolites, rather than global changes in microbial communities and metabolites. Functionality of fecal miRNAs Fecal miRNAs have been characterized in bovine feces and identified as biomarkers for intestinal diseases 18 , however, little is known about fecal miRNAs and their relationship with feed efficiency traits in bovines. The bta-miR-27b, bta-miR-30a, bta-miR-126, bta-miR-143, bta-miR-155, bta-miR-205 and bta-miR-196a were all up-regulated in the inefficient group. The target genes of bta-miR-126 and bta-miR-155 were predicted to be involved in signal transduction pathways associated with muscle development, such as the mTOR and Wnt signaling pathways. Mammalian target of rapamycin (mTOR) regulates cell proliferation, autophagy, and apoptosis by participating in multiple signaling pathways, including the phosphoinositide-3-kinase (PI3K)/Akt and AMPK pathways 19 . mTOR in conjunction with Akt, a protein kinase B, is required for skeletal muscle cell development 20 . Based on the higher expression of bta-miR-126 and bta-miR155 and given the most canonical post transcriptional downregulation mechanism of miRNA-mRNA interaction, we can speculate that the muscle development pathway is downregulated in inefficient animals, corroborating the idea that they exhibit less muscle in the adult phase than the efficient ones. The target genes of bta-miR-126 were also related to the focal adhesion pathway. Focal adhesions (FAs) are points of contact between the cell and the extracellular matrix that regulate cell communication with the extracellular environment and cellular processes 21 . The digestion and absorption capacities of the small intestine are closely related to feed efficiency traits in pigs, and small intestine structures such as microvilli, focal adhesions, and intestinal mucosa are important factors affecting the absorption of nutrients in the intestine 22 . Pathways associated with small intestinal structure also include those involved in the regulation of the actin cytoskeleton, adherens and tight junctions 22 . Thus, the bta-miR-205 could also have a role in this structure by targeting genes associated with the adherens junction pathway. In cattle, the microarchitecture of the small intestine is related to improved feed efficiency. Greater cellularity indicates a more metabolically active small intestine in cattle with higher feed efficiency 23 . Some upregulated miRNAs in inefficient animals were predicted to play important roles in metabolic homeostasis, including insulin and glucose metabolism. Among them, bta-miR-143 and bta-miR-27b were also upregulated in inefficient cattle in a previous study 24 . These authors speculated that the increased expression of btamiR-143 impaired insulin and glucose homeostasis by targeting the insulin signaling pathway and its regulation. This miRNA has also been reported with a role in intestinal epithelium regeneration by modulating the insulin growth factor signaling pathway 25 . In this study, bta-mir-143 was predicted to regulate genes related to EGFR tyrosine kinase inhibitor resistance, regulation of the actin cytoskeleton and the PI3K-Akt signaling pathway, while bta-miR-27b was predicted to regulate genes associated with type II diabetes mellitus, insulin resistance and insulin pathway. The regulation of feed intake and feed efficiency by insulin has been described in many species, including cattle 26 and pigs 27 . Here, the predicted downregulation of the insulin pathway by bta-miR-27b in inefficient animals is consistent with findings in the literature that indicate increased insulin metabolism with reduced feed intake in efficient animals 26 . The target genes of bta-miR-126 were associated with the FoxO signaling pathway. FoxO transcription factors regulate genes associated with glucose metabolism and resistance to oxidative stress 28 , and this pathway has already been associated with increased feed efficiency in Nelore cattle. Similarly, Casal et al. 29 reported that efficient steers had better hepatic oxidative status associated with greater antioxidant ability and reduced oxidative stress, which would reduce maintenance requirements due to lower protein and lipid turnover, resulting in better energy use efficiency. Therefore, the downregulation of the FoxO signaling pathway in inefficient animals may result in higher oxidative stress, lowering feed efficiency in Nelore bulls. Other enriched signaling pathways related to RFI through bta-mir-205 and bta-mir- 196a target genes were Rap 1 and Ras-related protein 1, respectively. Ras-proximate-1 or Ras-related protein 1 (Rap1) are small cytosolic proteins that act as cellular switches, being essential for effective signal transduction 30 and related to leptin, which regulates body weight and feed intake in bovines. Both pathways were previously associated with increased feed efficiency in Nelore cattle 31 . In our study, based on the upregulation of bta-mir-205 and bta-mir-196 in the inefficient group, the Rap 1 and Ras signaling pathways were predicted to be downregulated, suggesting a mechanism for the previously observed differences in pathway modulation. Relating modules to feed efficiency and finding potential biomarkers Module–trait relationships are estimated by Spearman’s correlations between the MEs and the animals’ phenotypic information to select potential biologically interesting modules that could explain the phenotypic differences between groups, while hub miRNAs or ASVs are those with the highest correlation within the module. Therefore, hub miRNAs and hub ASVs identified by WGCNA can be considered principal components and, consequently, potential biomarkers for feed efficiency. In the efficient group, negative and positive correlations, respectively, were detected between the RFI and hub ASVs classified as Bacillales from MEblack and Succinispira mobilis from MEred, whereas in the inefficient group, negative correlations between the RFI and the hub bta-mir-16a from MEturquoise were detected. Bacillales is an order of gram-positive bacteria from the phylum Firmicutes , and representative genera, including Bacillus are the core of the human gut microbiome and are found in bovine feces 32 . This genus was reported to have antimicrobial activity against microbes that promote nutrient absorption, and the order Bacillales was associated with inefficient beef cattle 32 . Succinispira mobilis is a succinate-decarboxylating anaerobic bacterium 33 , and previous reports mention acetate and succinate (a precursor of propionate) as the major products of ruminants fed high-starch diets 34 ; therefore, S. mobilis might play a role in propionate synthesis, thereby improving feed efficiency in efficient Nelore bulls. The bta-miR-16a and bta-miR-16b have been reported to regulate milk fat metabolism, with a negative effect on fatty acid metabolism and adipocyte differentiation 35 . The biological mechanisms driving the synthesis of fatty acids and triacylglycerols are complex and partially regulated by miRNAs. Several miRNAs, including miR-16b, were predicted to target genes related to lipid metabolism and/or adipogenesis, and as the adipose tissue modulates a variety of processes related to feed intake, energy homeostasis, and physiology, are also associated with feed efficiency traits 36 . Previous studies also indicate a potential role for miR16 in inflammatory processes, with this miRNA increasing T-cell subtypes, and influencing the degradation of mRNAs from immune response pathways 37 . These results indicate that bta-miR-16a may contribute to reduced feed efficiency due to its functional effects on fatty acid metabolism and the immune response. miRNA-microbiome interactions The relationship between host miRNAs and the gut microbiota has been investigated, being Liu et al. 7 the first to propose a linkage between miRNA expression and the gut microbiota composition (and its metabolites). Since then, many manuscripts have been published 38 – 41 and, to support this, in this study, we identified high and significant correlations between miRNA expression and the gut microbiome and its relationship with feed efficiency in Nelore cattle. According to the canonical view, eukaryotic miRNAs negatively regulate mRNA translation via complementary binding to 3’ untranslated regions (UTRs), which results in either translation repression or degradation of the mRNA transcript 42 . However, the role of miRNAs in bacterial gene regulation is yet to be fully understood. Host miRNAs can enter bacteria in different ways, including through extracellular vesicles, and can specifically regulate bacterial gene transcripts that control bacterial growth 7 . Conversely, changes in the microbiome may also induce differences in miRNA expression 43 , demonstrating the power of miRNA-microbiome interactions. In coexpression analysis, module eigengenes are considered important biological clusters, and microorganisms in the same modules have strong relationships, which provides an opportunity to investigate and explore highly related taxa within a microbial community 44 . The roles of miRNAs in regulating host–microbe interactions were further evaluated, exploring the relationships among the expression of miRNAs and bacterial composition. No direct relation between the microbiome and the described miRNAs has been reported in literature. miRNA-microbiome interactions in the efficient group In the efficient group, DE bta-mir-205 from MEbrown was negatively correlated with Prevotella , Clostridiales , Lachnospiraceae , Firmicutes , and Gammaproteobacteria from MEred. With a role in digesting complex polysaccharides, such as cellulose and hemicellulose, the genus Prevotella has been associated with lower feed efficiency in cattle 45 and pigs 46 . However, Prevotella was recently identified as a potential biomarker for efficient beef cattle 47 . The Prevotella genus, with 29 known species, contains cellulolytic bacteria that degrade cellulose into acetic, isobutyric, isovaleric, and lactic acid, providing energy for the host 48 . In addition to increasing glycogen storage and glucose tolerance, Prevotella -rich microbiota can improve growth performance, which is important for regulating RFI in beef cattle 49 . In our study, we are still determining which species of Prevotela was identified as, in general, 16S rRNA gene sequences allow differentiation between organisms at the genus level. Gammaproteobacteria is a class of Proteobacteria identified in a study of feed efficiency phenotypes in beef cattle and the relative abundance of this phylum has also been associated with high-efficiency steers 17 . The DE bta-mir-205 was positively correlated with Hespellia porcina , Alistipes, Peptostreptococcaceae, Ruminococcaceae and Clostridium saccharogumia from ASV MEblue. Alistipes is a genus of bacteria in the phylum Bacteroidetes that colonizes the human gastrointestinal tract and has protective effects against intestinal inflammation 50 , while the species Clostridium saccharogumia is associated with increased body weight and abdominal fat in chickens 51 . ​ In a study with efficient steers, Lourenco et al. 52 demonstrated increased Peptostreptococcaceae and Ruminococcaceae populations. The greater abundance of some members of the Peptostreptococcaceae family may contribute to increased ammonia availability in the hindgut, allowing for the development of structural carbohydrate-fermenting bacteria in more efficient steers 52 . Ruminococcaceae is a family composed of both fibrolytic organisms and involved in starch hydrolysis, which produces acetate, formate, and succinate. contributing to increased feed efficiency. In our study, Ruminococcaceae from MEyellow was a unique taxon negatively correlated with DE bta-mir-155 from MEblue. On the other hand, in the efficient Nelore bulls, DE bta-mir-155 was positively correlated with Coriobacteriaceae from MEred. This family of bacteria and different phylotypes are considered regulatory targets for improving host feed efficiency, as they are more abundant in efficient steers 53 . DE bta-miR-126 from MEturq was positively correlated with Lachnospiraceae, Bacteroidale and Clostridiales from MEyellow. Myer et al. 54 also reported that Lachnospiraceae and Clostridiales were more abundant in efficient steers. Acetogens can be found in the Lachnospiraceae and Ruminococcaceae families and serve as hydrogen sinks, which may increase with reduced methane production 55 . The relationship between methane production and feed efficiency is known, where the energy not lost as methane can be converted into weight gain, increasing animal efficiency 32 . Furthermore, the ASV MEred was negatively correlated with RFI in the module-trait association analysis. Overall, the positive effect of these microorganisms on feed efficiency biological processes indicate that these miRNAs and these taxa might contribute to increased feed efficiency in Nelore cattle. miRNA-microbiome interactions in the inefficient group In the inefficient group, DE bta-miR-196a from MEyellow was negatively correlated with Lachnospiraceae and Ruminococcaceae families from MEyellow, and the DE bta-miR 126 from MEblue was negatively correlated with Anaerorhabdus furcosa and Bifidobacterium , in addition to the Lachnospiraceae and Ruminococcaceae families. The miR-126 was recently implicated as potential biomarker in an inflammatory bowel disease (IBD) study, reported to inhibit leukocyte adhesion pathways 56 . A. furcosa has been associated with human infection and the production of short-chain fatty acids (SCFAs). SCFA production improves intestinal homeostasis and weaning stress in piglets and is associated with the modulation of intestinal microbiota composition and immune system genes 57 . Bifidobacterium species are known to produce carbohydrate-degrading enzymes, which facilitate carbohydrate metabolism and efficiently extract energy, contributing to the host's feed efficiency 58 . Furthermore, Bifidobacterium is also a significant producer of SCFAs and decreased in abundance in a study of IBD patients 56 . SCFAs may affect the differentiation of epithelial cells, which are known to play an essential role in intestinal homeostasis. In IBD patients, the host inflammatory response produces oxidative stress for the host and the intestinal microbiota, leading to intestinal dysbiosis with a reduced abundance of Firmicutes and Bacteroidetes species 56 . Based on the idea that inefficient animals may present intestinal dysbiosis due to metabolic processes of oxidative stress, we can speculate that in our study, decreased Bifidobacterium and A. furcosa populations may reflect the effect of bta-miR-126 in the inefficient animals. Consistent with our results, E. Hernandez-Sanabria et al. 34 also found that Bifidobacterium was associated with inefficient steers, while A. furcosa spp. have never been linked to feed efficiency. In addition to the negative correlations in the inefficient group, we found most of the positive correlations of DE bta-miR-30a from MEturquoise and bta-mir-196a with ASVs classified as Lachnospiraceae and Ruminococcaceae families, and with Bacteroidales, Bacillaceae and Clostridium. Furthermore, the miRNA MEturquoise was negatively correlated with RFI in the module-trait association analysis. Bacteroidales is an order of bacteria that includes the genus Prevotella and is commonly associated with feed efficiency in bovines 47 . This genus is one of the most abundant taxa in the rumen, with species that grow on starch, protein, peptides, hemicellulose, and pectin and, similar to what we found in our study, can be both positively and negatively correlated with FE in beef and dairy cattle 59 . The order Bacillales and the family Bacillaceae have been associated with inefficient cattle 32 , while the presence of the Clostridiaceae family in the digestive tract of ruminants is well documented. Clostridiaceae are essential commensals in the digestion of carbohydrates and proteins, and numerous species are involved in bile acid metabolism, being related to a higher feed efficiency 60 . The genus Clostridium is more frequently associated with feed efficiency in poultry 61 . Considering that the Lachnospiraceae and Ruminococcaceae families exhibited both positive and negative correlations in the feed efficiency groups, we suggest that these ASVs may belong to different genera, species or lineages and be physiologically different within the groups, which could not be observed here due to the limitations of 16S taxonomic signals. Final considerations The role of miRNAs and their interactions with the host and its microbiota have been gaining prominence, and several studies have demonstrated that miRNAs can modulate the intestinal microbiota, while the intestinal microbiota, in turn, may regulate miRNA expression. Fecal miRNAs can regulate bacterial composition by targeting bacterial genes, and conversely, the gut microbiota can regulate host gene expression and miRNAs through gut microbiota metabolites 38 . In humans, miRNAs have been associated with several biological processes, such as the immune system, cancer, and obesity, and due to their increasing relevance, in the last decade, they have been associated with production traits in livestock species. Some studies in beef cattle have implicated miRNAs as potential regulators of important biological pathways related to feed efficiency, such as muscle development and adipogenesis. In this study, some of the upregulated miRNAs correlated with bacteria that contribute to lower feed efficiency in the inefficient group were also correlated with bacterial microbiomes that increased feed efficiency in the efficient group, suggesting that these miRNAs and bacteria are somehow related to biological processes that influence feed efficiency. Furthermore, differences in richness and diversity between feed efficiency groups would be expected from the correlations found with miRNAs. However, the expected effects of miRNA would be on gene expression and thus on the functionality of the microbiome. This hypothesis could not be confirmed as the method used to access microbiomes in our study does not allow for identification of functional differences. Also, if slight differences in individual microorganisms’ abundance would result from this modulation, they would probably not have overpassed the multiple tests correction due to the limited sample size of the study, since the number of microorganisms was far higher than the number of miRNAs per sample. Our results suggest a complex link between host miRNAs and the bovine microbiota and the taxa Prevotella, Ruminococcaceae , Lachnospiraceae , Anaerorhabdus furcosa , Bifidobacterium, Bacillales , Succinispira mobilis, Peptostreptococcaceae and Coriobacteriaceae appear to influence feed efficiency. The miRNAs and taxa identified from network analyses may serve as potential candidates for exploring host–microbe interactions. Although our exploratory study has limitations, our findings could serve as a basis for future studies on the development of strategies to manipulate the microbiome and improve feed efficiency traits of bovines. Conclusions Based on our results, we conclude that there is an interplay among miRNAs identified in feces and the fecal microbiome composition. We quantified high correlations between fecal miRNAs and bacterial microbiomes in Nelore cattle. The differentially expressed fecal miRNAs and taxa identified play a role in biological processes related to residual feed intake and their interactions may affect feed efficiency in beef cattle. However, the underlying mechanisms involving gut miRNAs and microbiota interactions, and their effects on feed efficiency, must be further investigated. Methods Ethics Approval Statement All experimental procedures were conducted in accordance with animal welfare and humane slaughter guidelines and were approved by Associated Colleges Of Uberaba, Ethics Committee On The Use Of Animals/CEUA-FAZU,CIAEP (Protocol No 01.0593.2019). All methods were performed in accordance with relevant guidelines and regulations. Methods are reported in the manuscript following the recommendations in the ARRIVE guidelines. Animals and Experimental Design The National Young Bulls Evaluation Program (PNAT) is a young sire evaluation test run by the Brazilian Zebu Breeders Association (ABCZ) that selects registered Nelore bulls between 18 and 30 months of age based on an index that considers growth, carcass, reproductive, morphological and feed efficiency traits. The Nelore bulls belonging to the PNAT were housed in the feedlot of “Faculdades Associadas de Uberaba” - FAZU, Uberaba/MG, for a period of approximately 21 days for adaptation and 70 days for effective evaluation. For this study, 16 animals, out of 91 belonging to the age group of 21 to 24 months, were selected to represent extreme values for residual feed intake (RFI). The feedlot diet, which consisted of corn silage, commercial concentrate in the proportion of 60/40, and sodium monensin, was formulated to obtain an average daily gain (ADG) of 1.3 kg/day. The animals were fed “ad libitum” in four daily treatments with 10% leftovers. Individual dry matter intake (DMI) data were obtained from an Intergado System (Intergado Ltd., Contagem, Minas Gerais, Brazil). All animals adapted to the management and diet, and there were no complications in the consumption measurement system during the test. The residual feed intake (RFI, kg/day) phenotypes were computed as the residuals from a multivariate linear regression of dry matter intake (DMI; kg/day), taking into account the metabolic body weight (MBW) in the middle of the test (on the 35th day) and average daily gain (ADG; kg/day). The 91 animals were ranked according to RFI phenotypic value and 16 extreme animals were chosen from each tail from distribution (efficient, n = 8; inefficient, n = 8). Where possible animals that had common sires were sampled only when they belonged to different tails of the RFI distribution. A Student’s t-test was performed to evaluate the mean differences between the efficient and inefficient RFI groups. Fecal sample collection Fecal samples from the experimental population were collected from the rectal ampulla in the final feedlot evaluation period of 2019. No animal exhibited a significant change in health status, although there was individual variation in fecal consistency. After retrieval, the samples were stored in liquid nitrogen and kept at -80°C until DNA/RNA extraction. RNA sampling and extraction Total RNA extraction was performed on fecal samples for miRNA sequencing using TRIzol™ Reagent (Invitrogen). 1 mL of Trizol was added to each 150 to 200 mg of fecal sample, after maceration in liquid nitrogen with the aid of mortar and pistil. After homogenizing the sample with TRIzol™ Reagent, chloroform was added, and the homogenate was separated into a clear upper aqueous layer (containing RNA), an interphase, and a red lower organic layer (containing the DNA and proteins). RNA was precipitated from the aqueous layer with isopropanol. The precipitated RNA was washed to remove impurities, and then resuspended in 50 µL of RNAse-free deionized water and stored at -80 ° C until miRNA sequencing. The total RNA concentration was measured by Nanodrop 1000 spectrophotometer, and quality was verified initially by the 260:280 ratio, followed by assessment of integrity by agarose gel electrophoresis. Only intact samples, with a RNA 260:280 ratio greater than 1.8, were used. Before sequencing, samples were randomly chosen to double-check RNA quality on the Agilent 2100 Bioanalyzer System. The RNA Integrity Number for all samples was higher than 7. miRNA data collection and analysis miRNA library preparation and sequencing For miRNA libraries, 1 ug of total stool RNA from each animal was treated with 1U of DNase I amplification grade enzyme (Invitrogen). Subsequent procedures were performed according to the protocol described by Illumina. Briefly, 3-prime end-specific adapters were ligated to miRNAs using T4 RNA Ligase 2 enzyme, Deletion Mutant (Epicentre - LR2D1132K). Then 3-prime and 5-prime RNA adapters were ligated using the same enzyme. The synthesis of the first strand cDNA was generated using the SuperScript II Reverse Transcriptase enzyme (Invitrogen − 18064014). The cDNA was amplified and analyzed using the High Sensitivity DNA chip (Agilent- 5067 − 4626). Amplified cDNA samples were size selected (18–24 bp) and recovered from polyacrylamide, validated using the DNA 1000 chip (Agilent − 5067 − 1504) and submitted to sequencing. Sequencing of the fecal miRNA samples were performed using the Illumina Miseq 2500 platform, with a throughput of 8.000.000 paired-end reads per sample. miRNA sequencing quality check We used FastQC 62 as a supported tool in MultiQC tools ( https://multiqc.info/ ) to verify the sequence quality according to the following parameters: [-q 28] = minimum quality score to keep; [-p 70] = minimum percentage of bases that must have [-q] quality. Reads with noncanonical letters or with low quality were removed, 3′ adapters were trimmed with Cutadapt, and sequences shorter than 18 nt were discarded. After quality control, the reads were subjected to alignment against the Bos taurus genome (ARS-UCD1.2) with STAR software 63 . Differentially expressed fecal miRNAs Differentially expressed (DE) miRNAs were identified from a total of 16 small RNA libraries derived from fecal samples of efficient (n = 8) and inefficient (n = 8) Nelore cattle using DESeq2 software 64 . The read count data were filtered as follows: i) miRNAs with zero counts were removed; ii) miRNAs for which fewer than 1/5 of the samples had 0 counts were removed. We used the Benjamini–Hochberg method 65 to control for the rate of false positives (FDR; 10%). We set a p-value threshold of 0.1 (i.e., 10% of false positives are expected) to avoid losing too much information and in this way expand the biological response. The target genes of the identified DE fecal miRNAs were predicted with TargetScan 66 . The TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites. The conserved (across most mammals, but usually not beyond placental mammals) miRNAs family threshold was used and customized by species (cow). Functional enrichment analysis of target genes was performed by WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) 67 using B. taurus organisms and the overrepresentation enrichment analysis (ORA) method. Fecal DNA Extraction The total DNA of the 16 fecal samples was extracted using the ZR Fecal DNA Kit MiniPrep following the standard protocol (ZYMO Research Corp., Irvine, CA). In brief, cells were mechanically lysed using the bead beating process. The total DNA obtained was then subjected to several filtering steps to obtain ultrapure DNA in accordance with the manufacturer's instructions. DNA quality and integrity checks were performed with Nanodrop and agarose gel electrophoresis. 16S rRNA library preparation, sequencing and data analysis PCR target amplification of the bacterial and archaeal 16S rRNA coding genes was performed using the following primers 341-b-S-17F and 785-a-A-21R for bacteria, Ar915aF and Ar1386R for archaea 68 (Table S1 ). Amplicons were sequenced on an Illumina HiSeq platform (2 × 250 bp) using an Illumina V3 sequencing kit at the ESALQ Genomics Center (Piracicaba, SP, Brazil). The raw reads were filtered for quality (> Q25) and trimmed at positions 220 (forward) and 175 (reverse) using QIIME 2 version 2018.8 69 . These positions were selected based on aggregation plots generated by QIIME 2. The filtered data were submitted to the DADA2 package to generate amplicon sequence variants (ASVs) with the option just concatenate and exclude chimeric sequences 70 . Bacterial sequences were annotated using the SILVA database version 138.1 71 . To avoid spurious correlations, only ASVs identified in 10% of the samples with at least 100 sequences in total and were considered for microbiome analysis. ASV features were transformed using centered log ratio (CLR) transformation. The resulting ASV table was used to determine alpha (number of ASVs, Chao I, ACE, Shannon‒Wiener, Simpson, inverted Simpson and Fisher indices) and beta diversities (unweighted UniFrac distance) with QIIME 2, following Andrade et al 68 . miRNA-microbiome interaction networks Weighted gene coexpression network analysis (WGCNA) and WGCNA applied to microbial communities 10 . were used to construct separate miRNA and ASV networks. To this end, the adjacency matrix was created from the pairwise Spearman’s correlation coefficients between all miRNA/ASV pairs using a power β to reach a scale-free topology criterion. All miRNAs/ASVs were hierarchically clustered based on topological overlap measure (TOM) dissimilarity. The modules, consisting of groups of miRNAs/ASVs with similar expression/abundance profiles between samples, correspond to the branches of the dendrogram and were selected using the dynamic tree cut algorithm. MiRNA network construction and module detection used the step-by-step network construction with a soft threshold of β = 6 (R 2 > 0.90) and a minimum module size of 5 and ASV network construction used the step-by-step network construction with a soft threshold of β = 6 (R 2 > 0.91) and a minimum module size of 30. The topological overlap distance calculated from the adjacency matrix is then clustered with the average linkage hierarchical clustering. The default minimum cluster merge height of 0.25 was retained.The module eigengene (ME), the first principal component of each module, represents the module’s expression/abundance profile. Additionally, the gene significance (GS) was calculated as the absolute value of the correlation between the expression/abundance profile and the trait. Hub miRNAs and ASVs were selected based on module membership (MM), defined as the Spearman correlation coefficient between the expression/abundance profile and each ME. An integrated network (microbiome-miRNA interaction network) was constructed by correlating the MEs of miRNAs with the MEs of ASVs. Modules with positive and negative correlation and p value < 0.10 were used for functional enrichment analysis. Declarations Competing interests The author(s) declare no competing interests. Funding Statement This research was conducted with funding from CAPES (Coordination of Superior Level Staff Improvement grant number 88887.473152/2020-00) for scholarship to PSNO, ABCZ (Brazilian Zebu Breeders Association) for animal samples and phenotypic data, FAPESP (São Paulo Research Foundation grant number 2019/04089-2) and CNPq (National Council for Scientific and Technological Development grant number 428153/2018) for funding sequencing data analysis. Author Contribution PSNO, BGNA, JMR, and LCAR conceived the experiment; JGP, LAJ, LFA, HTV, GAM, JJB and MJr performed the experiments; PSNO, BGNA, TFC, LCC and GACP performed analysis; PSNO, BGNA, TFC, LCC, GBM, LLC, JM and LCAR interpreted the results and PSNO, BGNA, TFC, LCC, JJB, LLC, JR and LCAR drafted revised the manuscript. All authors read and approved the final manuscript. Acknowledgement The authors thank CAPES (Coordination of Superior Level Staff Improvement grant number 88887.473152/2020-00) for scholarship to PSNO, ABCZ (Brazilian Zebu Breeders Association) for collecting sample data and the staff of the Embrapa Southeast Cattle Animal biotechnology laboratory for assistance Data Availability The raw datasets generated and/or analyzed during the present study are not publicly available due to license from the Embrapa Southeast Livestock Research Center and Brazilian Association of Zebu Breeders (ABCZ), but are available from the corresponding author upon reasonable request. All data generated or analyzed during this study are included in this published article (and its Supplementary Information files). References Simon, J. C., Marchesi, J. R., Mougel, C. & Selosse, M. A. Host-microbiota interactions: From holobiont theory to analysis. Microbiome 7, 1–5 (2019). Li, F., Hitch, T. C. A., Chen, Y., Creevey, C. J. & Guan, L. L. 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Assessing the relationship between the rumen microbiota and feed efficiency in Nellore steers. J. Anim. Sci. Biotechnol. 12, 1–17 (2021). Myer, P. R., Smith, T. P. L., Wells, J. E., Kuehn, L. A. & Freetly, H. C. Rumen microbiome from steers differing in feed efficiency. PLoS One (2015) doi: 10.1371/journal.pone.0129174 . Zhou, Q. et al. Genetic and microbiome analysis of feed efficiency in laying hens. Poult. Sci. 102, 1–12 (2023). Andrews, S. FASTQC A Quality Control tool for High Throughput Sequence Data. Babraham Inst. (2015). Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. (2014) doi: 10.1186/s13059-014-0550-8 . Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (1995) doi: 10.2307/2346101 . McGeary, S. E. et al. The biochemical basis of microRNA targeting efficacy. Science (80-.). 366, (2019). Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019). Andrade, B. G. N. et al. The structure of microbial populations in Nelore GIT reveals inter-dependency of methanogens in feces and rumen. J. Anim. Sci. Biotechnol. 11, 1–10 (2020). Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. doi: 10.1038/s41587-019-0190-3 . Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods (2016) doi: 10.1038/nmeth.3869 . Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. doi: 10.1093/nar/gks1219 . Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS6.xlsx SupplementaryInformationScientificReports.docx Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Dec, 2024 Reviews received at journal 22 Dec, 2024 Reviewers agreed at journal 11 Dec, 2024 Reviews received at journal 01 Aug, 2024 Reviewers agreed at journal 28 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers invited by journal 22 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Editor invited by journal 17 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 15 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4744784","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":337015132,"identity":"aba4c462-8685-4414-8a31-62c379a20c49","order_by":0,"name":"Priscila Silva Neubern De Oliveira","email":"","orcid":"","institution":"Federal University of São Carlos","correspondingAuthor":false,"prefix":"","firstName":"Priscila","middleName":"Silva Neubern","lastName":"De Oliveira","suffix":""},{"id":337015134,"identity":"0269bdf8-4951-4d0d-9746-36b8e0b613d8","order_by":1,"name":"Bruno Gabriel Nascimento Andrade","email":"","orcid":"","institution":"Munster Technological University","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Gabriel Nascimento","lastName":"Andrade","suffix":""},{"id":337015135,"identity":"ee249c73-361f-4b41-b823-cd7c6b2a844c","order_by":2,"name":"Tainã Ferreira Cardoso","email":"","orcid":"","institution":"Embrapa Southeast-Cattle Research Center","correspondingAuthor":false,"prefix":"","firstName":"Tainã","middleName":"Ferreira","lastName":"Cardoso","suffix":""},{"id":337015136,"identity":"e4ddee05-f7a5-4ebd-9bfc-564996a122a0","order_by":3,"name":"Liliane Costa Conteville","email":"","orcid":"","institution":"Embrapa Southeast-Cattle Research Center","correspondingAuthor":false,"prefix":"","firstName":"Liliane","middleName":"Costa","lastName":"Conteville","suffix":""},{"id":337015138,"identity":"4da8808a-3455-4ec0-9716-a29349bc3832","order_by":4,"name":"Gabriel Alexander Colmenarez Pena","email":"","orcid":"","institution":"Embrapa Southeast-Cattle Research 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Regitano","email":"data:image/png;base64,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","orcid":"","institution":"Embrapa Southeast-Cattle Research Center","correspondingAuthor":true,"prefix":"","firstName":"Luciana","middleName":"Correia de Almeida","lastName":"Regitano","suffix":""}],"badges":[],"createdAt":"2024-07-15 18:21:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4744784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4744784/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20408-9","type":"published","date":"2025-10-21T16:16:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62098274,"identity":"2c57a8fc-dcc4-484a-8667-dcde08d49bbf","added_by":"auto","created_at":"2024-08-09 09:10:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":340518,"visible":true,"origin":"","legend":"\u003cp\u003eBar plot showing the relative abundance of the main phyla from the fecal bacterial microbiomes of Nelore cattle groups whose feed efficiency was measured as the residual feed intake (RFI).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/d25c47cec2fe4a0aafb4bb48.jpg"},{"id":62098276,"identity":"e3933c9d-0bd3-4845-9116-993050ac2176","added_by":"auto","created_at":"2024-08-09 09:10:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":248754,"visible":true,"origin":"","legend":"\u003cp\u003eRichness (observed) and alpha diversity metrics (Chao I, ACE, index and Shannon, Simpson, inverted Simpson and Fisher indices) revealed no significant difference (p \u0026gt; 0.01) in the richness and diversity of bacterial populations between the efficient and inefficient Nelore cattle groups, whose feed efficiency was measured as the residual feed intake (RFI).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/8f48178f5da6a7de685d774f.jpg"},{"id":62098277,"identity":"00db8ac8-7820-4bcf-b425-91d17fd2fc00","added_by":"auto","created_at":"2024-08-09 09:10:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61902,"visible":true,"origin":"","legend":"\u003cp\u003eModule-trait association visualization plots. Each row corresponds to\u003cstrong\u003e \u003c/strong\u003ea miRNA module in the efficient (\u003cstrong\u003ea\u003c/strong\u003e) and inefficient (\u003cstrong\u003eb\u003c/strong\u003e) group, and ASV module in the efficient (\u003cstrong\u003ec\u003c/strong\u003e) and inefficient group (\u003cstrong\u003ed\u003c/strong\u003e), and the column color corresponds to the residual feed intake (RFI) phenotype correlation. Each cell was labeled by the corresponding correlation coefficient (above) and p value (below). Positive interconnectedness is indicated by progressively more saturated red color, and negative interconnectedness is indicated by progressively more saturated green color.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/181a931d26c2dd356aef606a.jpg"},{"id":62097610,"identity":"2f04ca96-3f68-4400-bd2a-e1c4c7e3e6f0","added_by":"auto","created_at":"2024-08-09 09:02:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78427,"visible":true,"origin":"","legend":"\u003cp\u003emiRNA- microbiome interaction plot. Each row corresponds to\u003cstrong\u003e \u003c/strong\u003ea miRNA module and each \u0026nbsp;column color corresponds to ASV module in the efficient (\u003cstrong\u003ea\u003c/strong\u003e) and inefficient (\u003cstrong\u003eb\u003c/strong\u003e) group. \u0026nbsp;Each cell was labeled by the corresponding correlation coefficient (above) and p value (below). Positive interconnectedness is indicated by progressively more saturated red color, and negative interconnectedness is indicated by progressively more saturated green color.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/9c8563ea2d5d1b6efd73f317.jpg"},{"id":94490538,"identity":"c8f8c6e3-6d2e-49a0-b69c-81f6561f261d","added_by":"auto","created_at":"2025-10-27 17:11:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2535657,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/1e72f6d4-85cd-405d-9908-d8a909ed6eb3.pdf"},{"id":62097608,"identity":"06d765ae-0377-4aac-b43f-32b39215f599","added_by":"auto","created_at":"2024-08-09 09:02:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9703,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/027da16b67d8cfb2e6da4900.xlsx"},{"id":62097619,"identity":"7e965003-dbde-4b07-887d-cd448b5e9207","added_by":"auto","created_at":"2024-08-09 09:02:56","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2755536,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/3f24f2132d9996f8a7f90c5a.xlsx"},{"id":62098275,"identity":"52234e93-44bc-4acc-be43-f2cbf82a60dc","added_by":"auto","created_at":"2024-08-09 09:10:55","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26700,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/d830335fc0bf122d176f0917.xlsx"},{"id":62097618,"identity":"69ff3ed0-340c-4144-b234-f16473144127","added_by":"auto","created_at":"2024-08-09 09:02:56","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":147786,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/d70268baf496a34b3fa9a8dd.xlsx"},{"id":62097612,"identity":"3d559c00-8078-4e6d-ac0b-2eb1ce4187c4","added_by":"auto","created_at":"2024-08-09 09:02:56","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":43433,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/301776cd8498a8a8e469c881.xlsx"},{"id":62098846,"identity":"4901345d-37e0-48a8-bd5b-37f0962330c9","added_by":"auto","created_at":"2024-08-09 09:18:56","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":19293,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/03d6e5fbc01b893123269a8a.xlsx"},{"id":62098845,"identity":"bac298b7-cc66-4c02-a09d-475c0d04ba4d","added_by":"auto","created_at":"2024-08-09 09:18:56","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":19293,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/513cf6bb5d6ee901940856bf.xlsx"},{"id":62098278,"identity":"36b0bfc9-4acb-4f01-bcd0-a89e20ea534d","added_by":"auto","created_at":"2024-08-09 09:10:56","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":757500,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-4744784/v1/257b5e407f1f3f4863527e7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"miRNA-microbiome interplay is related to Bos indicus feed efficiency","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut hosts a complex community of microorganisms that comprises several species of bacteria, archaea, and eukaryotes, and recently, the holobiont theory has emerged as a way to explain the interactions between hosts and their associated microbial communities\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This relationship has been associated with animal development, metabolic processes and diseases, and our understanding of its importance for livestock production is increasing quickly. Due to its pivotal role, it is crucial to understand the mechanisms underlying microbiota regulation by its host, as well as to identify strategies to successfully manipulate the microbiota.\u003c/p\u003e \u003cp\u003eAnimal feed is one of the most significant costs in production systems, in addition to livestock competing for resources, e.g., cereals and grains, with humans\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which means excessive water consumption and deforestation. This turns feed efficiency into a trait with economic, environmental and sustainability impacts since it is a complex measurement of how efficient the animals are in metabolizing feed into livestock products. Additionally, feed efficiency is frequently associated with lower methane emissions\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, another important sustainability trait.\u003c/p\u003e \u003cp\u003eRecent findings have indicated that the ruminal microbiota partially controls the variability in feed efficiency in ruminants; as a result, the microbiome has been proposed as a proxy to predict feed efficiency traits\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Associations between the rumen microbiota and feed efficiency in beef cattle have demonstrated that differences in the abundance and diversity of microbial phylotypes exist between efficient and inefficient individuals. Recently, Conteville et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e noted that the abundance of the \u003cem\u003eEubacterium\u003c/em\u003e genus was associated with both inefficient and high methane emitters animals, while Delgado et al. (2019) noted that \u003cem\u003eMethanobacteria\u003c/em\u003e and \u003cem\u003eMethanobrevibacter\u003c/em\u003e were less abundant in efficient cattle. These results suggest a link between efficient cattle and lower methane emissions. Thus, modulation of the microbiota composition can promote more sustainable and efficient livestock production while reducing the negative environmental impacts of beef cattle production.\u003c/p\u003e \u003cp\u003eThe fecal microbiome, although less studied than the ruminal microbiome, is emerging as an important component of host metabolism, and recent evidence indicates that microRNAs (miRNAs) may be involved in host\u0026ndash;microbe interactions. miRNAs are small noncoding RNA molecules that play a role in the post transcriptional/translational regulation of gene expression influencing many biological processes in mammals. A key attribute that has started to be considered recently is that miRNAs can be transferred to other cells by extracellular vesicles (EVs), constituting an essential mode of inter-cell communication with the potential to shape microbial communities and host\u0026ndash;microbe interactions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, studies have reported that changes in the gut microbiota are influenced by secreted host miRNAs\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFecal miRNAs have been identified as potential indicators of the host-microbe interface\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, with miRNAs produced by the host\u0026rsquo;s intestinal epithelial cells regulating bacterial gene transcripts and affecting bacterial growth\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Fecal miRNAs have also been characterized in bovine feces as potential biomarkers of diseases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and there is also evidence that the gut microbiota can influence the expression of miRNAs, suggesting a possible new route of communication between microbiota and host. Thus, regarding feed efficiency, the known difference in gut microbiota composition between efficient and inefficient subjects could also drive differential in miRNA expression.\u003c/p\u003e \u003cp\u003eAlthough miRNAs have been widely identified in bovines, the functional role of fecal miRNAs in host-microbe communication is yet to be understood, and based on observations from other species, we hypothesized that miRNAs from the host could play a regulatory role in the microbiome. Conversely, the gut microbiota can regulate host gene expression through miRNAs, and both mechanisms could contribute to feed efficiency variability. Thus, a detailed understanding of the molecular mechanisms affecting feed efficiency may provide a means to improve the productivity and sustainability of ruminant\u0026rsquo;s production to meet global food production demands.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic and miRNA data\u003c/h2\u003e \u003cp\u003ePhenotypic data on the residual feed intake (RFI; kg/d) of Nelore cattle belonging to the National Program for the Evaluation of Young Bulls (PNAT) of the Brazilian Association of Zebu Breeders (ABCZ) were obtained from a total of 91 bulls, from which 16 extreme animals were selected for feed efficiency: 8 efficient (negative RFI) and 8 inefficient (positive RFI) bulls. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the raw phenotypic data of residual feed intake (RFI; kg/day), dry matter intake (DMI; kg/day), metabolic body weight (MBW; kg), average daily gain (ADG; kg/day), feed efficiency (FE; kg/kg) and feed conversion (FC; kg/kg) used for the selection of 16 contrasting Nelore animals and the number of 16S RNA gene reads and miRNA reads mapped for each sample. Student\u0026rsquo;s t test was performed to evaluate the mean phenotypic differences between the efficient and inefficient RFI groups, and significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed for RFI, metabolic live weight and feed conversion phenotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhenotypic data of residual feed intake (RFI; kg/d) and their components dry matter intake (DMI; kg/day), metabolic live weight (MLW; kg), average daily gain (ADG; kg/day), feed efficiency (FE; kg/kg), feed conversion (FC; kg/kg) and number of mapped miRNA reads and 16S rRNA gene reads for efficient and inefficient Nelore cattle groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRFI\u003c/p\u003e \u003cp\u003e(kg/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMI (kg/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMLW (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADG (kg/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFE\u003c/p\u003e \u003cp\u003e(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003cp\u003e(kg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMapped miRNA reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16S rRNA gene reads\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6,120,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91,053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,600,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90,409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86,151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,600,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98,179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,640,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96,322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,460,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89,585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,530,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91,919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficient13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,560,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92,928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.32\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.42\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e115.80\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.68\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e6.88\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5,340,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e92,068,25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,240,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95,434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3,180,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93,653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8,780,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88,604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,640,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94,641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,830,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86,082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9,790,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94,825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,950,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95,179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInefficient55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7,850,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e85,783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13.38\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e120.151\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.60\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8.63\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4,950,000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e91,775,12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eEfficient group animal IDs: Efficient73, Efficient16, Efficient72, Efficient25, Efficient34, Efficient62, Efficient4 and Efficient13.\u0026nbsp; Inefficient group animal IDs: Inefficient32, Inefficient30, Inefficient47, Inefficient46, Inefficient37, Inefficient41, Inefficient40 and Inefficient55.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea,b\u003c/sup\u003e means with different letters had significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) according to the student's test.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003emiRNA sequencing of fecal samples from these Nelore cattle yielded 186,700,000 sequences ranging from 20\u0026ndash;25 bp in length. On average, 50% of miRNA reads were mapped to the \u003cem\u003eBos taurus\u003c/em\u003e genome (ARS-UCD1.2). In total, 162 mature miRNAs were detected by STAR software (Table S2), which were further included in the differential expression analysis (Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and functionality of the bovine fecal miRNA profile\u003c/h2\u003e \u003cp\u003eAmong the 162 expressed fecal miRNAs, 7 were differentially expressed between the RFI comparison groups (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) and were upregulated in the inefficient group. To better understand the potential functional impact of the seven detected upregulated fecal DE miRNAs on the host, we assessed the biological pathways with overrepresentation enrichment analysis (ORA) performed by WebGestalt software and using the list of bovine genes targeted by the DE miRNAs. This analysis identified significant (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05) signaling pathways related to RFI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFecal miRNAs differentially expressed in inefficient and efficient Nelore cattle groups, respective fold-change, false discovery rate (FDR), number of target genes and significant signaling pathways related to residual feed intake (RFI).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold Change\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFDR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInefficient\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEfficient\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTarget genes\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant signaling pathways related to RFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003emTOR signaling pathway\u003c/p\u003e \u003cp\u003eFoxO signaling pathway\u003c/p\u003e \u003cp\u003eFocal adhesion\u003c/p\u003e \u003cp\u003eMAPK signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e****\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRas signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRap1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-27b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443.5984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e343.5702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eType II diabetes mellitus\u003c/p\u003e \u003cp\u003eInsulin Resistance\u003c/p\u003e \u003cp\u003eTNF signaling pathway\u003c/p\u003e \u003cp\u003eInsulin signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.2055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEGFR tyrosine kinase inhibitor resistance\u003c/p\u003e \u003cp\u003eRegulation of actin cytoskeleton\u003c/p\u003e \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB-cell receptor signaling pathway\u003c/p\u003e \u003cp\u003eT-cell receptor signaling pathway\u003c/p\u003e \u003cp\u003emTOR signaling pathway\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eFold Change of Inefficient to Efficient Nelore cattle groups, \u003csup\u003eb\u003c/sup\u003eFalse discovery rate adjusted p values by Benjamini‒Hochberg (1995) methodology, \u003csup\u003ec,d\u003c/sup\u003eNormalized mean counts of inefficient and efficient Nelore cattle groups, \u003csup\u003ee\u003c/sup\u003eNumber of predicted target genes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNelore fecal microbiome composition and taxonomy\u003c/h2\u003e \u003cp\u003eSequencing of amplicons from the fecal samples of 16 Nelore cattle yielded a total of 2,821,494 paired end reads for bacteria and archaea. Quality control, denoising and chimera exclusion retained a total of 1,462,354 sequences resolved in 5,005 ASVs. A total of 357 ASVs were retained after the exclusion of singletons (Table S5). The rarefaction curves based on the Shannon‒Wiener alpha diversity metrics reached a plateau, which indicated that the sampling depth was adequate and that additional sequences were unlikely to result in additional features (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe taxonomic profile of the microbiome of Nelore bulls from fecal samples was mainly composed of bacteria (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd: 98.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30%) and a small fraction of archaea (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd: 1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20%). Because of the small fraction of Archaea present in the Nelore bull microbiome, we focused the following analysis on the bacterial fecal microbiomes. In the bacterial fecal microbiomes, seven phyla, 12 classes, 13 orders, 20 families, 27 genera and 26 species were identified. The most abundant phyla of both feed efficiency groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were \u003cem\u003eFirmicutes\u003c/em\u003e (65.97% for the efficient group and 66.93% for the inefficient group), \u003cem\u003eProteobacteria\u003c/em\u003e (21.40% for the efficient group and 17.97% for the inefficient group), \u003cem\u003eBacteroidetes\u003c/em\u003e (10.69% for the efficient group and 13.10% for the inefficient group) and \u003cem\u003eEuryachaeota (\u003c/em\u003e1.17% for the efficient group and 0.90% for the inefficient group).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNelore fecal microbiome diversity\u003c/h2\u003e \u003cp\u003eTo compare the microbiome diversity between feed efficiency groups, the data were rarefied to 5,000 reads. Comparison of samples from different groups using richness (observed) and alpha diversity metrics (Chao 1, ACE, Shannon, Simpson, inverted Simpson and Fisher indices) revealed no significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01) in the richness or diversity of bacterial populations between the efficient and inefficient groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-microbiome network analysis\u003c/h2\u003e \u003cp\u003eTo investigate miRNA-microbiome interactions in feces from divergent RFI animals, we applied the weighted gene coexpression network analysis (WGCNA) method to microbial communities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. To this end, miRNA and ASV networks were constructed separately. After quality control, the expression data of 58 miRNAs were used to construct a miRNA network, and the abundance data of 358 ASVs were used for the construction of the ASV network. Coexpression network analysis revealed eight miRNA module eigengenes (Figure S2) and six ASV MEs (Figure S3) in the efficient group. In the inefficient group, six miRNA MEs (Figure S4) and seven ASV MEs were identified (Figure S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRelating modules to feed efficiency and identifying hub miRNAs and ASVs\u003c/h2\u003e \u003cp\u003eWe also aimed to identify miRNA and ASV modules significantly associated with the RFI phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the eight WGCNA modules identified in the miRNA network from the efficient group, no modules were correlated with RFI, while among the six identified miRNA modules within the inefficient group, one module was negatively correlated (MEturquoise; cor=-0.9, p value\u0026thinsp;=\u0026thinsp;0.02) with RFI. For the ASV network analysis, two out of the six identified modules of the efficient group MEred (cor=-0.87, p value\u0026thinsp;=\u0026thinsp;0.06) and MEblack (cor\u0026thinsp;=\u0026thinsp;0.81, p value\u0026thinsp;=\u0026thinsp;0.09) were correlated with RFI, while in the inefficient group, no modules were correlated with RFI.\u003c/p\u003e \u003cp\u003eHub genes are defined as the genes that are most strongly correlated with features, \u003cem\u003ei.e.\u003c/em\u003e, miRNAs or ASVs within each candidate module\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the hub miRNAs and ASVs from modules eigengene associated with RFI phenotype from efficient and inefficient groups of Nelore bulls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHub miRNAs and ASVs (Amplicom Sequencing Variants) from modules eigengene (ME) associated with residual feed intake (RFI) phenotype from efficient and inefficient groups of Nelore bulls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASV ME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHub ASV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaxonomic classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEfficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eblack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASV 741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASV 376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Veillonellaceae; g__Succinispira; s__mobilis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emiRNA ME\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHub miRNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eturquoise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebta-mir-16a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003emiRNA-microbiome interactions\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo investigate whether there were any direct correlations between microbiome composition and miRNA expression, miRNA and ASV modules that were positively or negatively correlated and had p values\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.1 were selected for further investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e) According to these criteria, in the efficient group, we observed significant negative correlations between the miRNA and ASV modules in the range from \u0026minus;\u0026thinsp;0.8 to -0.7 and significant positive correlations in the range from 0.9 to 1 (Table S6). In the inefficient group, we observed significant negative correlations in the range of -0.8 to -0.7 and significant positive correlations in the range of 0.8 to 0.9 (Table S6).\u003c/p\u003e \u003cp\u003eWe then further explored the correlated modules and calculated specific Spearman\u0026rsquo;s correlations between miRNA expression and ASV abundance, further selecting the differentially expressed miRNAs along with their top five correlations with ASVs within each previously correlated module in the efficient (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and inefficient group (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNegative and positive correlations between differentially expressed miRNAs and ASVs within correlated modules in efficient Nelore cattle group and taxonomic classification of each ASV inside the module.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEfficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMEbrown miRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEred\u003c/p\u003e \u003cp\u003eASVs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eTaxonomic classification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Prevotellaceae; g__Prevotella;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Proteobacteria; c__Gammaproteobacteria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEbrown miRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMEblue\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae; g__Hespellia; s__porcina\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae; g__Alistipes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Clostridium; s__saccharogumia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEblue miRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMEyellow\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEblue miRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMEred\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae; g__Clostridium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_1015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria;p__Actinobacteria; c__Coriobacteriia; o__Coriobacteriales; f__Coriobacteriaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEturquois miRNAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMEyellow ASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eASV_108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003ctable\u003e\u003ctbody\u003e\u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTable\u0026nbsp;5.\u003c/b\u003e Negative and positive correlations between differentially expressed miRNAs and ASVs within correlated modules in inefficient Nelore cattle group and taxonomic classification of each ASV inside the module.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEyellow\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emiRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMEyellow\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta.mir.196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEyellow miRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMEturquoi ASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; f__Bacillaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Clostridiaceae; g__Clostridium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-196a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEblue\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emiRNAs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMEred ASVs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae; g__; s__\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Anaerorhabdus; s__furcosa\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Actinobacteria; c__Actinobacteria; o__Bifidobacteriales; f__Bifidobacteriaceae; g__Bifidobacterium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMEturquoi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003er\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMEyellow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTaxonomic classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Ruminococcaceae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ebta-mir-30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eASV_291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ek__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gut microbiota and miRNAs are emerging as promising targets for managing and preventing inflammatory and metabolic disorders in mammals. In the present study, we identified functional fecal miRNAs associated with the feed efficiency phenotype residual feed intake and linkages between the miRNA profile and microbiome gut composition of Nelore cattle belonging to divergent feed efficiency groups.\u003c/p\u003e \u003cp\u003eThe bovine gut microbiome consists of trillions of microorganisms, most of which are bacteria\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As expected, in ruminants, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eBacteroidetes\u003c/em\u003e were the main bacterial phyla found in the microbiomes of the efficient and inefficient groups of Nelore bulls. \u003cem\u003eFirmicutes\u003c/em\u003e is a phylum in which many members produce butyrate, an important substance that keeps the colon healthy and plays a significant role in animal health\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. They also breakdown carbohydrates that cannot be digested by enzymes in the gut, such as dietary fiber and resistant starch\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eProteobacteria\u003c/em\u003e is a phylum that digests/degrades proteins through the process of decarboxylation of amino acids\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, while \u003cem\u003eBacteroidetes\u003c/em\u003e is also one of the predominant phyla with fermentative characteristics and the ability to modulate the immune system\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Here, we did not observe significant differences in the richness of bacterial populations between efficient and inefficient animals, which could have been influenced by the small sample size used in this study. Nonetheless, Clemons et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e suggested that the lack of diversity differences regarding feed efficiency phenotypes may be due to dissimilarities at a finer resolution, such as for individual taxa and metabolites, rather than global changes in microbial communities and metabolites.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctionality of fecal miRNAs\u003c/h2\u003e \u003cp\u003eFecal miRNAs have been characterized in bovine feces and identified as biomarkers for intestinal diseases\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, however, little is known about fecal miRNAs and their relationship with feed efficiency traits in bovines.\u003c/p\u003e \u003cp\u003eThe bta-miR-27b, bta-miR-30a, bta-miR-126, bta-miR-143, bta-miR-155, bta-miR-205 and bta-miR-196a were all up-regulated in the inefficient group. The target genes of bta-miR-126 and bta-miR-155 were predicted to be involved in signal transduction pathways associated with muscle development, such as the mTOR and Wnt signaling pathways. Mammalian target of rapamycin (mTOR) regulates cell proliferation, autophagy, and apoptosis by participating in multiple signaling pathways, including the phosphoinositide-3-kinase (PI3K)/Akt and AMPK pathways\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. mTOR in conjunction with Akt, a protein kinase B, is required for skeletal muscle cell development\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Based on the higher expression of bta-miR-126 and bta-miR155 and given the most canonical post transcriptional downregulation mechanism of miRNA-mRNA interaction, we can speculate that the muscle development pathway is downregulated in inefficient animals, corroborating the idea that they exhibit less muscle in the adult phase than the efficient ones.\u003c/p\u003e \u003cp\u003eThe target genes of bta-miR-126 were also related to the focal adhesion pathway. Focal adhesions (FAs) are points of contact between the cell and the extracellular matrix that regulate cell communication with the extracellular environment and cellular processes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The digestion and absorption capacities of the small intestine are closely related to feed efficiency traits in pigs, and small intestine structures such as microvilli, focal adhesions, and intestinal mucosa are important factors affecting the absorption of nutrients in the intestine\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Pathways associated with small intestinal structure also include those involved in the regulation of the actin cytoskeleton, adherens and tight junctions\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Thus, the bta-miR-205 could also have a role in this structure by targeting genes associated with the adherens junction pathway. In cattle, the microarchitecture of the small intestine is related to improved feed efficiency. Greater cellularity indicates a more metabolically active small intestine in cattle with higher feed efficiency\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome upregulated miRNAs in inefficient animals were predicted to play important roles in metabolic homeostasis, including insulin and glucose metabolism. Among them, bta-miR-143 and bta-miR-27b were also upregulated in inefficient cattle in a previous study\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These authors speculated that the increased expression of btamiR-143 impaired insulin and glucose homeostasis by targeting the insulin signaling pathway and its regulation. This miRNA has also been reported with a role in intestinal epithelium regeneration by modulating the insulin growth factor signaling pathway\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In this study, bta-mir-143 was predicted to regulate genes related to EGFR tyrosine kinase inhibitor resistance, regulation of the actin cytoskeleton and the PI3K-Akt signaling pathway, while bta-miR-27b was predicted to regulate genes associated with type II diabetes mellitus, insulin resistance and insulin pathway. The regulation of feed intake and feed efficiency by insulin has been described in many species, including cattle\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and pigs\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Here, the predicted downregulation of the insulin pathway by bta-miR-27b in inefficient animals is consistent with findings in the literature that indicate increased insulin metabolism with reduced feed intake in efficient animals\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe target genes of bta-miR-126 were associated with the FoxO signaling pathway. FoxO transcription factors regulate genes associated with glucose metabolism and resistance to oxidative stress\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and this pathway has already been associated with increased feed efficiency in Nelore cattle. Similarly, Casal et al.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e reported that efficient steers had better hepatic oxidative status associated with greater antioxidant ability and reduced oxidative stress, which would reduce maintenance requirements due to lower protein and lipid turnover, resulting in better energy use efficiency. Therefore, the downregulation of the FoxO signaling pathway in inefficient animals may result in higher oxidative stress, lowering feed efficiency in Nelore bulls.\u003c/p\u003e \u003cp\u003eOther enriched signaling pathways related to RFI through bta-mir-205 and bta-mir- 196a target genes were Rap 1 and Ras-related protein 1, respectively. Ras-proximate-1 or Ras-related protein 1 (Rap1) are small cytosolic proteins that act as cellular switches, being essential for effective signal transduction\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and related to leptin, which regulates body weight and feed intake in bovines. Both pathways were previously associated with increased feed efficiency in Nelore cattle\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In our study, based on the upregulation of bta-mir-205 and bta-mir-196 in the inefficient group, the Rap 1 and Ras signaling pathways were predicted to be downregulated, suggesting a mechanism for the previously observed differences in pathway modulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelating modules to feed efficiency and finding potential biomarkers\u003c/h2\u003e \u003cp\u003eModule–trait relationships are estimated by Spearman’s correlations between the MEs and the animals’ phenotypic information to select potential biologically interesting modules that could explain the phenotypic differences between groups, while hub miRNAs or ASVs are those with the highest correlation within the module. Therefore, hub miRNAs and hub ASVs identified by WGCNA can be considered principal components and, consequently, potential biomarkers for feed efficiency. In the efficient group, negative and positive correlations, respectively, were detected between the RFI and hub ASVs classified as \u003cem\u003eBacillales\u003c/em\u003e from MEblack and \u003cem\u003eSuccinispira mobilis\u003c/em\u003e from MEred, whereas in the inefficient group, negative correlations between the RFI and the hub bta-mir-16a from MEturquoise were detected.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacillales\u003c/em\u003e is an order of gram-positive bacteria from the phylum \u003cem\u003eFirmicutes\u003c/em\u003e, and representative genera, including \u003cem\u003eBacillus\u003c/em\u003e are the core of the human gut microbiome and are found in bovine feces\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This genus was reported to have antimicrobial activity against microbes that promote nutrient absorption, and the order \u003cem\u003eBacillales\u003c/em\u003e was associated with inefficient beef cattle\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSuccinispira mobilis\u003c/em\u003e is a succinate-decarboxylating anaerobic bacterium\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and previous reports mention acetate and succinate (a precursor of propionate) as the major products of ruminants fed high-starch diets\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e; therefore, \u003cem\u003eS. mobilis\u003c/em\u003e might play a role in propionate synthesis, thereby improving feed efficiency in efficient Nelore bulls.\u003c/p\u003e \u003cp\u003eThe bta-miR-16a and bta-miR-16b have been reported to regulate milk fat metabolism, with a negative effect on fatty acid metabolism and adipocyte differentiation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The biological mechanisms driving the synthesis of fatty acids and triacylglycerols are complex and partially regulated by miRNAs. Several miRNAs, including miR-16b, were predicted to target genes related to lipid metabolism and/or adipogenesis, and as the adipose tissue modulates a variety of processes related to feed intake, energy homeostasis, and physiology, are also associated with feed efficiency traits\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Previous studies also indicate a potential role for miR16 in inflammatory processes, with this miRNA increasing T-cell subtypes, and influencing the degradation of mRNAs from immune response pathways\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These results indicate that bta-miR-16a may contribute to reduced feed efficiency due to its functional effects on fatty acid metabolism and the immune response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-microbiome interactions\u003c/h2\u003e \u003cp\u003eThe relationship between host miRNAs and the gut microbiota has been investigated, being Liu et al.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e the first to propose a linkage between miRNA expression and the gut microbiota composition (and its metabolites). Since then, many manuscripts have been published \u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e–\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and, to support this, in this study, we identified high and significant correlations between miRNA expression and the gut microbiome and its relationship with feed efficiency in Nelore cattle. According to the canonical view, eukaryotic miRNAs negatively regulate mRNA translation via complementary binding to 3’ untranslated regions (UTRs), which results in either translation repression or degradation of the mRNA transcript\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, the role of miRNAs in bacterial gene regulation is yet to be fully understood. Host miRNAs can enter bacteria in different ways, including through extracellular vesicles, and can specifically regulate bacterial gene transcripts that control bacterial growth\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Conversely, changes in the microbiome may also induce differences in miRNA expression\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, demonstrating the power of miRNA-microbiome interactions.\u003c/p\u003e \u003cp\u003eIn coexpression analysis, module eigengenes are considered important biological clusters, and microorganisms in the same modules have strong relationships, which provides an opportunity to investigate and explore highly related taxa within a microbial community\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The roles of miRNAs in regulating host–microbe interactions were further evaluated, exploring the relationships among the expression of miRNAs and bacterial composition. No direct relation between the microbiome and the described miRNAs has been reported in literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-microbiome interactions in the efficient group\u003c/h2\u003e \u003cp\u003eIn the efficient group, DE bta-mir-205 from MEbrown was negatively correlated with \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eClostridiales\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eGammaproteobacteria\u003c/em\u003e from MEred. With a role in digesting complex polysaccharides, such as cellulose and hemicellulose, the genus \u003cem\u003ePrevotella\u003c/em\u003e has been associated with lower feed efficiency in cattle\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and pigs \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, \u003cem\u003ePrevotella\u003c/em\u003e was recently identified as a potential biomarker for efficient beef cattle\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003ePrevotella\u003c/em\u003e genus, with 29 known species, contains cellulolytic bacteria that degrade cellulose into acetic, isobutyric, isovaleric, and lactic acid, providing energy for the host\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In addition to increasing glycogen storage and glucose tolerance, \u003cem\u003ePrevotella\u003c/em\u003e-rich microbiota can improve growth performance, which is important for regulating RFI in beef cattle \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In our study, we are still determining which species of \u003cem\u003ePrevotela\u003c/em\u003e was identified as, in general, 16S rRNA gene sequences allow differentiation between organisms at the genus level. \u003cem\u003eGammaproteobacteria\u003c/em\u003e is a class of \u003cem\u003eProteobacteria\u003c/em\u003e identified in a study of feed efficiency phenotypes in beef cattle and the relative abundance of this phylum has also been associated with high-efficiency steers\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The DE bta-mir-205 was positively correlated with \u003cem\u003eHespellia porcina\u003c/em\u003e, \u003cem\u003eAlistipes, Peptostreptococcaceae, Ruminococcaceae\u003c/em\u003e and \u003cem\u003eClostridium saccharogumia\u003c/em\u003e from ASV MEblue. \u003cem\u003eAlistipes\u003c/em\u003e is a genus of bacteria in the phylum \u003cem\u003eBacteroidetes\u003c/em\u003e that colonizes the human gastrointestinal tract and has protective effects against intestinal inflammation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, while the \u003cem\u003especies Clostridium saccharogumia\u003c/em\u003e is associated with increased body weight and abdominal fat in chickens \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003e​\u003c/em\u003e In a study with efficient steers, Lourenco et al.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e demonstrated increased \u003cem\u003ePeptostreptococcaceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e populations. The greater abundance of some members of the \u003cem\u003ePeptostreptococcaceae\u003c/em\u003e family may contribute to increased ammonia availability in the hindgut, allowing for the development of structural carbohydrate-fermenting bacteria in more efficient steers\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eRuminococcaceae\u003c/em\u003e is a family composed of both fibrolytic organisms and involved in starch hydrolysis, which produces acetate, formate, and succinate. contributing to increased feed efficiency. In our study, \u003cem\u003eRuminococcaceae\u003c/em\u003e from MEyellow was a unique taxon negatively correlated with DE bta-mir-155 from MEblue. On the other hand, in the efficient Nelore bulls, DE bta-mir-155 was positively correlated with \u003cem\u003eCoriobacteriaceae\u003c/em\u003e from MEred. This family of bacteria and different phylotypes are considered regulatory targets for improving host feed efficiency, as they are more abundant in efficient steers\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. DE bta-miR-126 from MEturq was positively correlated with \u003cem\u003eLachnospiraceae, Bacteroidale\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e from MEyellow. Myer et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e also reported that \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eClostridiales\u003c/em\u003e were more abundant in efficient steers. Acetogens can be found in the \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e families and serve as hydrogen sinks, which may increase with reduced methane production\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The relationship between methane production and feed efficiency is known, where the energy not lost as methane can be converted into weight gain, increasing animal efficiency\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, the ASV MEred was negatively correlated with RFI in the module-trait association analysis. Overall, the positive effect of these microorganisms on feed efficiency biological processes indicate that these miRNAs and these taxa might contribute to increased feed efficiency in Nelore cattle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003emiRNA-microbiome interactions in the inefficient group\u003c/h2\u003e \u003cp\u003eIn the inefficient group, DE bta-miR-196a from MEyellow was negatively correlated with \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e families from MEyellow, and the DE bta-miR 126 from MEblue was negatively correlated with \u003cem\u003eAnaerorhabdus furcosa\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e, in addition to the \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e families. The miR-126 was recently implicated as potential biomarker in an inflammatory bowel disease (IBD) study, reported to inhibit leukocyte adhesion pathways\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eA. furcosa\u003c/em\u003e has been associated with human infection and the production of short-chain fatty acids (SCFAs). SCFA production improves intestinal homeostasis and weaning stress in piglets and is associated with the modulation of intestinal microbiota composition and immune system genes \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBifidobacterium\u003c/em\u003e species are known to produce carbohydrate-degrading enzymes, which facilitate carbohydrate metabolism and efficiently extract energy, contributing to the host's feed efficiency\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Furthermore, \u003cem\u003eBifidobacterium\u003c/em\u003e is also a significant producer of SCFAs and decreased in abundance in a study of IBD patients \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. SCFAs may affect the differentiation of epithelial cells, which are known to play an essential role in intestinal homeostasis. In IBD patients, the host inflammatory response produces oxidative stress for the host and the intestinal microbiota, leading to intestinal dysbiosis with a reduced abundance of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e species\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Based on the idea that inefficient animals may present intestinal dysbiosis due to metabolic processes of oxidative stress, we can speculate that in our study, decreased \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eA. furcosa\u003c/em\u003e populations may reflect the effect of bta-miR-126 in the inefficient animals. Consistent with our results, E. Hernandez-Sanabria et al.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e also found that \u003cem\u003eBifidobacterium\u003c/em\u003e was associated with inefficient steers, while \u003cem\u003eA. furcosa\u003c/em\u003e spp. have never been linked to feed efficiency.\u003c/p\u003e \u003cp\u003eIn addition to the negative correlations in the inefficient group, we found most of the positive correlations of DE bta-miR-30a from MEturquoise and bta-mir-196a with ASVs classified as \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e families, and with \u003cem\u003eBacteroidales, Bacillaceae\u003c/em\u003e and \u003cem\u003eClostridium.\u003c/em\u003e Furthermore, the miRNA MEturquoise was negatively correlated with RFI in the module-trait association analysis. \u003cem\u003eBacteroidales\u003c/em\u003e is an order of bacteria that includes the genus \u003cem\u003ePrevotella\u003c/em\u003e and is commonly associated with feed efficiency in bovines\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. This genus is one of the most abundant taxa in the rumen, with species that grow on starch, protein, peptides, hemicellulose, and pectin and, similar to what we found in our study, can be both positively and negatively correlated with FE in beef and dairy cattle\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The order \u003cem\u003eBacillales\u003c/em\u003e and the family \u003cem\u003eBacillaceae\u003c/em\u003e have been associated with inefficient cattle\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, while the presence of the \u003cem\u003eClostridiaceae\u003c/em\u003e family in the digestive tract of ruminants is well documented. \u003cem\u003eClostridiaceae\u003c/em\u003e are essential commensals in the digestion of carbohydrates and proteins, and numerous species are involved in bile acid metabolism, being related to a higher feed efficiency\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. The genus \u003cem\u003eClostridium\u003c/em\u003e is more frequently associated with feed efficiency in poultry\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Considering that the \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e families exhibited both positive and negative correlations in the feed efficiency groups, we suggest that these ASVs may belong to different genera, species or lineages and be physiologically different within the groups, which could not be observed here due to the limitations of 16S taxonomic signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFinal considerations\u003c/h2\u003e \u003cp\u003eThe role of miRNAs and their interactions with the host and its microbiota have been gaining prominence, and several studies have demonstrated that miRNAs can modulate the intestinal microbiota, while the intestinal microbiota, in turn, may regulate miRNA expression. Fecal miRNAs can regulate bacterial composition by targeting bacterial genes, and conversely, the gut microbiota can regulate host gene expression and miRNAs through gut microbiota metabolites \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn humans, miRNAs have been associated with several biological processes, such as the immune system, cancer, and obesity, and due to their increasing relevance, in the last decade, they have been associated with production traits in livestock species. Some studies in beef cattle have implicated miRNAs as potential regulators of important biological pathways related to feed efficiency, such as muscle development and adipogenesis. In this study, some of the upregulated miRNAs correlated with bacteria that contribute to lower feed efficiency in the inefficient group were also correlated with bacterial microbiomes that increased feed efficiency in the efficient group, suggesting that these miRNAs and bacteria are somehow related to biological processes that influence feed efficiency. Furthermore, differences in richness and diversity between feed efficiency groups would be expected from the correlations found with miRNAs. However, the expected effects of miRNA would be on gene expression and thus on the functionality of the microbiome. This hypothesis could not be confirmed as the method used to access microbiomes in our study does not allow for identification of functional differences. Also, if slight differences in individual microorganisms’ abundance would result from this modulation, they would probably not have overpassed the multiple tests correction due to the limited sample size of the study, since the number of microorganisms was far higher than the number of miRNAs per sample.\u003c/p\u003e \u003cp\u003eOur results suggest a complex link between host miRNAs and the bovine microbiota and the taxa \u003cem\u003ePrevotella, Ruminococcaceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eAnaerorhabdus furcosa\u003c/em\u003e, \u003cem\u003eBifidobacterium, Bacillales\u003c/em\u003e, \u003cem\u003eSuccinispira mobilis, Peptostreptococcaceae\u003c/em\u003e and \u003cem\u003eCoriobacteriaceae\u003c/em\u003e appear to influence feed efficiency. The miRNAs and taxa identified from network analyses may serve as potential candidates for exploring host–microbe interactions. Although our exploratory study has limitations, our findings could serve as a basis for future studies on the development of strategies to manipulate the microbiome and improve feed efficiency traits of bovines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on our results, we conclude that there is an interplay among miRNAs identified in feces and the fecal microbiome composition. We quantified high correlations between fecal miRNAs and bacterial microbiomes in Nelore cattle. The differentially expressed fecal miRNAs and taxa identified play a role in biological processes related to residual feed intake and their interactions may affect feed efficiency in beef cattle. However, the underlying mechanisms involving gut miRNAs and microbiota interactions, and their effects on feed efficiency, must be further investigated.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eEthics Approval Statement\u003c/h2\u003e\u003cp\u003e All experimental procedures were conducted in accordance with animal welfare and humane slaughter guidelines and were approved by Associated Colleges Of Uberaba, Ethics Committee On The Use Of Animals/CEUA-FAZU,CIAEP (Protocol No 01.0593.2019). All methods were performed in accordance with relevant guidelines and regulations. Methods are reported in the manuscript following the recommendations in the ARRIVE guidelines.\u003c/p\u003e\u003ch2\u003eAnimals and Experimental Design\u003c/h2\u003e\u003cp\u003eThe National Young Bulls Evaluation Program (PNAT) is a young sire evaluation test run by the Brazilian Zebu Breeders Association (ABCZ) that selects registered Nelore bulls between 18 and 30 months of age based on an index that considers growth, carcass, reproductive, morphological and feed efficiency traits. The Nelore bulls belonging to the PNAT were housed in the feedlot of “Faculdades Associadas de Uberaba” - FAZU, Uberaba/MG, for a period of approximately 21 days for adaptation and 70 days for effective evaluation. For this study, 16 animals, out of 91 belonging to the age group of 21 to 24 months, were selected to represent extreme values for residual feed intake (RFI). The feedlot diet, which consisted of corn silage, commercial concentrate in the proportion of 60/40, and sodium monensin, was formulated to obtain an average daily gain (ADG) of 1.3 kg/day. The animals were fed “ad libitum” in four daily treatments with 10% leftovers. Individual dry matter intake (DMI) data were obtained from an Intergado System (Intergado Ltd., Contagem, Minas Gerais, Brazil). All animals adapted to the management and diet, and there were no complications in the consumption measurement system during the test. The residual feed intake (RFI, kg/day) phenotypes were computed as the residuals from a multivariate linear regression of dry matter intake (DMI; kg/day), taking into account the metabolic body weight (MBW) in the middle of the test (on the 35th day) and average daily gain (ADG; kg/day). The 91 animals were ranked according to RFI phenotypic value and 16 extreme animals were chosen from each tail from distribution (efficient, n = 8; inefficient, n = 8). Where possible animals that had common sires were sampled only when they belonged to different tails of the RFI distribution. A Student’s t-test was performed to evaluate the mean differences between the efficient and inefficient RFI groups.\u003c/p\u003e\u003ch2\u003eFecal sample collection\u003c/h2\u003e\u003cp\u003eFecal samples from the experimental population were collected from the rectal ampulla in the final feedlot evaluation period of 2019. No animal exhibited a significant change in health status, although there was individual variation in fecal consistency. After retrieval, the samples were stored in liquid nitrogen and kept at -80°C until DNA/RNA extraction.\u003c/p\u003e\u003ch2\u003eRNA sampling and extraction\u003c/h2\u003e\u003cp\u003eTotal RNA extraction was performed on fecal samples for miRNA sequencing using TRIzol™ Reagent (Invitrogen). 1 mL of Trizol was added to each 150 to 200 mg of fecal sample, after maceration in liquid nitrogen with the aid of mortar and pistil. After homogenizing the sample with TRIzol™ Reagent, chloroform was added, and the homogenate was separated into a clear upper aqueous layer (containing RNA), an interphase, and a red lower organic layer (containing the DNA and proteins). RNA was precipitated from the aqueous layer with isopropanol. The precipitated RNA was washed to remove impurities, and then resuspended in 50 µL of RNAse-free deionized water and stored at -80 ° C until miRNA sequencing. The total RNA concentration was measured by Nanodrop 1000 spectrophotometer, and quality was verified initially by the 260:280 ratio, followed by assessment of integrity by agarose gel electrophoresis. Only intact samples, with a RNA 260:280 ratio greater than 1.8, were used. Before sequencing, samples were randomly chosen to double-check RNA quality on the Agilent 2100 Bioanalyzer System. The RNA Integrity Number for all samples was higher than 7.\u003c/p\u003e\u003ch2\u003emiRNA data collection and analysis\u003c/h2\u003e\u003ch2\u003emiRNA library preparation and sequencing\u003c/h2\u003e\u003cp\u003eFor miRNA libraries, 1 ug of total stool RNA from each animal was treated with 1U of DNase I amplification grade enzyme (Invitrogen). Subsequent procedures were performed according to the protocol described by Illumina. Briefly, 3-prime end-specific adapters were ligated to miRNAs using T4 RNA Ligase 2 enzyme, Deletion Mutant (Epicentre - LR2D1132K). Then 3-prime and 5-prime RNA adapters were ligated using the same enzyme. The synthesis of the first strand cDNA was generated using the SuperScript II Reverse Transcriptase enzyme (Invitrogen − 18064014). The cDNA was amplified and analyzed using the High Sensitivity DNA chip (Agilent- 5067 − 4626). Amplified cDNA samples were size selected (18–24 bp) and recovered from polyacrylamide, validated using the DNA 1000 chip (Agilent − 5067 − 1504) and submitted to sequencing. Sequencing of the fecal miRNA samples were performed using the Illumina Miseq 2500 platform, with a throughput of 8.000.000 paired-end reads per sample.\u003c/p\u003e\u003ch2\u003emiRNA sequencing quality check\u003c/h2\u003e\u003cp\u003eWe used FastQC\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e as a supported tool in MultiQC tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://multiqc.info/\u003c/span\u003e\u003cspan address=\"https://multiqc.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to verify the sequence quality according to the following parameters: [-q 28] = minimum quality score to keep; [-p 70] = minimum percentage of bases that must have [-q] quality. Reads with noncanonical letters or with low quality were removed, 3′ adapters were trimmed with Cutadapt, and sequences shorter than 18 nt were discarded. After quality control, the reads were subjected to alignment against the \u003cem\u003eBos taurus\u003c/em\u003e genome (ARS-UCD1.2) with STAR software \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eDifferentially expressed fecal miRNAs\u003c/h2\u003e\u003cp\u003eDifferentially expressed (DE) miRNAs were identified from a total of 16 small RNA libraries derived from fecal samples of efficient (n = 8) and inefficient (n = 8) Nelore cattle using DESeq2 software\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. The read count data were filtered as follows: i) miRNAs with zero counts were removed; ii) miRNAs for which fewer than 1/5 of the samples had 0 counts were removed. We used the Benjamini–Hochberg method \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e to control for the rate of false positives (FDR; 10%). We set a p-value threshold of 0.1 (i.e., 10% of false positives are expected) to avoid losing too much information and in this way expand the biological response. The target genes of the identified DE fecal miRNAs were predicted with TargetScan \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites. The conserved (across most mammals, but usually not beyond placental mammals) miRNAs family threshold was used and customized by species (cow). Functional enrichment analysis of target genes was performed by WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e using \u003cem\u003eB. taurus\u003c/em\u003e organisms and the overrepresentation enrichment analysis (ORA) method.\u003c/p\u003e\u003ch2\u003eFecal DNA Extraction\u003c/h2\u003e\u003cp\u003eThe total DNA of the 16 fecal samples was extracted using the ZR Fecal DNA Kit MiniPrep following the standard protocol (ZYMO Research Corp., Irvine, CA). In brief, cells were mechanically lysed using the bead beating process. The total DNA obtained was then subjected to several filtering steps to obtain ultrapure DNA in accordance with the manufacturer's instructions. DNA quality and integrity checks were performed with Nanodrop and agarose gel electrophoresis.\u003c/p\u003e\u003ch2\u003e16S rRNA library preparation, sequencing and data analysis\u003c/h2\u003e\u003cp\u003ePCR target amplification of the bacterial and archaeal 16S rRNA coding genes was performed using the following primers 341-b-S-17F and 785-a-A-21R for bacteria, Ar915aF and Ar1386R for archaea\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Amplicons were sequenced on an Illumina HiSeq platform (2 × 250 bp) using an Illumina V3 sequencing kit at the ESALQ Genomics Center (Piracicaba, SP, Brazil). The raw reads were filtered for quality (\u0026gt; Q25) and trimmed at positions 220 (forward) and 175 (reverse) using QIIME 2 version 2018.8\u003csup\u003e69\u003c/sup\u003e. These positions were selected based on aggregation plots generated by QIIME 2. The filtered data were submitted to the DADA2 package to generate amplicon sequence variants (ASVs) with the option just concatenate and exclude chimeric sequences\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Bacterial sequences were annotated using the SILVA database version 138.1\u003csup\u003e71\u003c/sup\u003e. To avoid spurious correlations, only ASVs identified in 10% of the samples with at least 100 sequences in total and were considered for microbiome analysis. ASV features were transformed using centered log ratio (CLR) transformation. The resulting ASV table was used to determine alpha (number of ASVs, Chao I, ACE, Shannon‒Wiener, Simpson, inverted Simpson and Fisher indices) and beta diversities (unweighted UniFrac distance) with QIIME 2, following Andrade et al\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003emiRNA-microbiome interaction networks\u003c/h2\u003e\u003cp\u003eWeighted gene coexpression network analysis (WGCNA) and WGCNA applied to microbial communities\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. were used to construct separate miRNA and ASV networks. To this end, the adjacency matrix was created from the pairwise Spearman’s correlation coefficients between all miRNA/ASV pairs using a power β to reach a scale-free topology criterion. All miRNAs/ASVs were hierarchically clustered based on topological overlap measure (TOM) dissimilarity. The modules, consisting of groups of miRNAs/ASVs with similar expression/abundance profiles between samples, correspond to the branches of the dendrogram and were selected using the dynamic tree cut algorithm. MiRNA network construction and module detection used the step-by-step network construction with a soft threshold of β = 6 (R 2 \u0026gt; 0.90) and a minimum module size of 5 and ASV network construction used the step-by-step network construction with a soft threshold of β = 6 (R 2 \u0026gt; 0.91) and a minimum module size of 30. The topological overlap distance calculated from the adjacency matrix is then clustered with the average linkage hierarchical clustering. The default minimum cluster merge height of 0.25 was retained.The module eigengene (ME), the first principal component of each module, represents the module’s expression/abundance profile. Additionally, the gene significance (GS) was calculated as the absolute value of the correlation between the expression/abundance profile and the trait. Hub miRNAs and ASVs were selected based on module membership (MM), defined as the Spearman correlation coefficient between the expression/abundance profile and each ME. An integrated network (microbiome-miRNA interaction network) was constructed by correlating the MEs of miRNAs with the MEs of ASVs. Modules with positive and negative correlation and p value \u0026lt; 0.10 were used for functional enrichment analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThis research was conducted with funding from CAPES (Coordination of Superior Level Staff Improvement grant number 88887.473152/2020-00) for scholarship to PSNO, ABCZ (Brazilian Zebu Breeders Association) for animal samples and phenotypic data, FAPESP (S\u0026atilde;o Paulo Research Foundation grant number 2019/04089-2) and CNPq (National Council for Scientific and Technological Development grant number 428153/2018) for funding sequencing data analysis.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePSNO, BGNA, JMR, and LCAR conceived the experiment; JGP, LAJ, LFA, HTV, GAM, JJB and MJr performed the experiments; PSNO, BGNA, TFC, LCC and GACP performed analysis; PSNO, BGNA, TFC, LCC, GBM, LLC, JM and LCAR interpreted the results and PSNO, BGNA, TFC, LCC, JJB, LLC, JR and LCAR drafted revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank CAPES (Coordination of Superior Level Staff Improvement grant number 88887.473152/2020-00) for scholarship to PSNO, ABCZ (Brazilian Zebu Breeders Association) for collecting sample data and the staff of the Embrapa Southeast Cattle Animal biotechnology laboratory for assistance\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw datasets generated and/or analyzed during the present study are not publicly available due to license from the Embrapa Southeast Livestock Research Center and Brazilian Association of Zebu Breeders (ABCZ), but are available from the corresponding author upon reasonable request. All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSimon, J. C., Marchesi, J. R., Mougel, C. \u0026amp; Selosse, M. A. Host-microbiota interactions: From holobiont theory to analysis. Microbiome 7, 1\u0026ndash;5 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, F., Hitch, T. C. A., Chen, Y., Creevey, C. J. \u0026amp; Guan, L. L. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle 06 Biological Sciences 0604 Genetics 06 Biological Sciences 0605 Microbiology. 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Methods (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.3869\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.3869\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuast, C. \u003cem\u003eet al.\u003c/em\u003e The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gks1219\u003c/span\u003e\u003cspan address=\"10.1093/nar/gks1219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bovine, microbiome, interaction, residual feed intake","lastPublishedDoi":"10.21203/rs.3.rs-4744784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4744784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fecal microbiome is emerging as an essential component of the gut microbiota and host metabolism, whereas in cattle, fecal microbiome characterization is still needed. Recent evidence indicates that small RNAs, such as miRNAs, may be isolated from feces and involved in host\u0026ndash;microbe interactions. In this study, fecal samples were collected from the rectal ampulla of Nelore bulls phenotypic divergent for residual feed intake (RFI). miRNA sequencing and 16S rRNA gene (V3-V4 region) were performed to reveal the associations between host miRNAs and microbiome composition and their relationships with the feed efficiency phenotype. Among the 162 identified fecal miRNAs, seven were more expressed in the inefficient group: bta-miR-27b, bta-miR-30a, bta-miR-126, bta-miR-143, bta-miR-155, bta-miR-205 and bta-miR-196a. Using metabarcoding sequencing, we identified 5,005 bacterial ASVs, and after filtering, we used 357 ASVs in subsequent analyzes. Weighted gene coexpression network analysis (WGCNA) was used to identify miRNA and microbiome interactions. We observed significant correlations between fecal miRNA expression and microbiota composition. The differentially expressed fecal miRNAs were correlated with some taxa as \u003cem\u003ePrevotella, Anaerorhabdus furcosa\u003c/em\u003e, \u003cem\u003eBifidobacterium, Bacillales\u003c/em\u003e, \u003cem\u003eSuccinispira mobilis, Peptostreptococcaceae\u003c/em\u003e and \u003cem\u003eCoriobacteriaceae\u003c/em\u003e, suggesting that they may play a role in the expression of feed efficiency-related miRNAs. Our results provide a new perspective for exploring host-microbiome interactions that affect FE traits. Taken together, these results point to miRNAs and taxa identified here as potential regulators of feed efficiency, which may provide the knowledge needed to develop future strategies to manipulate the microbiome.\u003c/p\u003e","manuscriptTitle":"miRNA-microbiome interplay is related to Bos indicus feed efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 09:02:50","doi":"10.21203/rs.3.rs-4744784/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-24T07:08:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-22T11:55:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288957342657187199670192854868996396816","date":"2024-12-11T23:03:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-01T13:07:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266342874479443820657981560795079250147","date":"2024-07-28T06:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89892318752643132752482494476730218701","date":"2024-07-23T06:51:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-23T00:34:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T22:57:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-17T14:28:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-17T04:17:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-15T18:18:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cfe31bf-2df4-4938-acae-0c9b4c33369e","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35696745,"name":"Biological sciences/Biotechnology"},{"id":35696746,"name":"Biological sciences/Genetics"},{"id":35696747,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-27T16:33:14+00:00","versionOfRecord":{"articleIdentity":"rs-4744784","link":"https://doi.org/10.1038/s41598-025-20408-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-21 16:16:06","publishedOnDateReadable":"October 21st, 2025"},"versionCreatedAt":"2024-08-09 09:02:50","video":"","vorDoi":"10.1038/s41598-025-20408-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-20408-9","workflowStages":[]},"version":"v1","identity":"rs-4744784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4744784","identity":"rs-4744784","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}