Jejunal Microbiome and Bile Acids Induced by Diets in Angus Beef Cattle

doi:10.21203/rs.3.rs-19919/v1

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Research Article

Jejunal Microbiome and Bile Acids Induced by Diets in Angus Beef Cattle

https://doi.org/10.21203/rs.3.rs-19919/v1

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published 16 Sep, 2020

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Background: The small intestine, while serving as the main absorption organ, also possesses a unique microbiome environment and holds the critical function of conversion of primary bile acids. Bile acids are, in turn, able to regulate microbiome structure and promote the growth of bacteria that convert primary bile acids to secondary bile acids. However, in beef cattle, few studies have explored the microbiome compositions of the jejunum and its relationships with bile acids. Therefore, the hypothesis is that microbiome compositions in the intestine are influenced by diet style and also related to bile acids.

Results: We demonstrated that the influences of diets on the intestinal microbiome could be observed in young beef cattle after weaning. A significantly higher level of microbial diversity was evidenced in feces of grass-fed animals comparing to grain-fed cattle. Top 20 essential genera were obtained with random forest analysis on the fecal microbiome to identify candidate microbial biomarkers. Moreover, the jejunal microbiome of adult Angus beef cattle exhibited significant differences in microbial composition and metabolic potential under different diets. Global balances and microbiome signatures of bile acids were identified.

Conclusions: The findings from this study provided deep insights into microbiome compositions in fecal and jejunal and identified relationships between jejunal microbiome and bile acids with diets in Angus beef cattle. Our results should help us better understand grass-fed beef in production practice and potential health benefits.

Animal Science

General Microbiology

16S rRNA gene sequencing

Angus beef cattle

Grass-fed beef

Jejunal microbiome

Microbiome signatures

Bile acids

Beef is a flavorful, nutritional, and expensive meat product than many other types of meat. In the beef cattle feeding industry, while the grain-fed method feeds young cattle in feedlots with high-grain diets, the pasture-fed mode allows cattle to graze naturally with grass-based land. As the demand for healthy and palatable beef is increasing worldwide, grass-fed beef products gradually attract more attention in the meat market, and a growing group of consumers is willing to pay a higher unit price for grass-fed beef than grain-fed beef [1]. Other than animal behavior aspects, the diet component is one of the main differences between grain-fed and grass-fed methods to determine cattle performance [2]. One critical influence of diets is that diets primarily learn gut microbiome structures and drive microbial adaption in animals and humans [3]. The compositions and functions of the gut microbiome in ruminant species are critical. Bacteria are the dominant kinds of microbes in the ruminant gut, which provide a significant contribution of metabolomic activities on cattle [4]. So far, many gut microbiome studies have focused on rumen microbiome to assess animal’s gut and calves performance, but roles of microbes in small intestine and colon are less explored. However, different segments of the ruminant abdomen possess unique ecological and microbial characteristics [5, 6]. After the digestion happening in the upper GI tract, peristalsis and a portion of undigested fibrous residuals together push digesta downward, to enter the small intestine and be in contact with villi of the intestinal epithelium where absorption happens. The small intestine is the main gut segment for the absorption of glucose, amino acids, fatty acids, and glycerol with the aid of bile and enzymes [4]. Because of the limited contact with the outside environment, low oxygen level, and little development time here, the small intestine of cattle GI tract is the segment where abundance and diversity of microorganisms are significantly reduced compared to rumen and hindgut (cecum and colon) in ruminant species [5, 7–9]. Although the abundance is relatively low, the microbiome in the small intestine played essential roles to aid digestion, including the conversion of primary bile acids, and the protection against pathogens in both human and animals [10, 11]. Importantly, by microbial actions in the small intestine, primary bile acids are mediated to produce secondary bile acids, deoxycholic acid (DCA) and lithocholic acid (LCA), which are conjugated and finally reabsorbed by the enterohepatic circulation for the recycling in the liver to regulate digestion [12, 13]. Diet, as the primary determinant of the gut microbiome, may also post significant influences on the microbiome structure of small intestine in cattle. Therefore, the small intestine microbiome and their relationships with bile acids are worth the exploration.

Previous studies of cattle microbial communities throughout the GI tract demonstrated compositional differences of the microbiome in the small intestine compared to other segments in both dairy and beef cattle [5, 14]. But not much information could be found to illustrate the influence of diet on the jejunal microbiome on beef cattle. In the case of grass-fed and grain-fed beef cattle, the jejunal microbiome is also critical to reveal diet influence on cattle performance. Besides, few studies showed the relationships between jejunal microbiome and bile acid concentration in cattle. In this study, we hypothesized that microbiome compositions in the intestine are associated with diet components. To test the hypothesis, we used Wye Angus beef cattle from the University of Maryland (UMD). The Angus herd in the Wye Angus farm has been closed to any animal importation for almost 70 years. Hence it produces very homogeneous progeny [1]. The heifers were treated and raised equally until weaning. After weaning, the heifers were randomly assigned to either grass or grain-fed style. Our previous studies using the same animals already pointed out several advantages of the grass-fed method over the grain-fed method [15, 16]. In this study, we would like to probe further from the aspect of the gut microbiome to explore the potential roles of microbiome in the jejunum and their relationships with bile acids to influence cattle metabolism. Since the influences may start from the early age of cattle and jejunum is not accessible in young animals, we first examined the fecal microbiome in the rectum to obtain an initial impression of the influence of the two different diets. After the reach of market weight, jejunal digesta were collected for exploration. In addition to the analyses of the microbial community in the jejunal and fecal microbiome, we also examined whether the components of bile acids associated with the composition of the intestinal microbiome. We profiled the microbial structure of jejunum with the 16S rRNA amplicon sequencing, together with bile acid quantification, to characterize the influences of different diets on the jejunal microbiome.

Fecal microbiome structure influenced by diets in young beef cattle

Microbial composition of the feces in the rectum of calves aged seven months old was examined based on the OTU table generated from the QIIME (Quantitative Insights Into Microbial Ecology) closed reference pipeline [17]. In total, there were 19 microbial phyla identified from grass-fed and grain-fed groups (Figure S1). The most abundant phylum was Firmicutes, ranging from 38.36–68.42% of relative abundance percentages, followed by Bacteroidetes (37.77%), Proteobacteria (3.96%), and Verrucomicrobia (1.20%).

Microbiome diversity indices were examined for the fecal microbiome of grass-fed and grain-fed cattle. Alpha-diversity indices, including Chao1, Shannon, Simpson, and Phylogenetic diversity (PD_whole_tree) were calculated (Table 1). A Welch’s t-test was used to perform a differential test between the two groups and obtain p-values. In general, the grass-fed group had higher alpha diversity values of indices than the grain-fed group, and p-values of all four indices showed significant differences (p < 0.05). Therefore, it was suggested that diets significantly influenced microbial diversity between the two groups, and the grass-fed group tended to have higher microbial diversity than the grain-fed group (p < 0.05). PCA (Principal component analysis) was also performed to examine beta diversity to explore the differences between groups based on the most abundant microbial families, such as Ruminococcaceae (10.92%), Rikenellaceae (6.20%), Lachnospiraceae (4.61%), and Paraprevotellaceae (4.34%), etc. The biplot of PCA (Fig. 1) showed a clear separation of the fecal microbiome of young beef cattle under grass-fed and grain-fed diets.

Differentially abundant taxa and important microbial features in fecal microbiome under different diets

The difference in fecal microbiome composition between grass-fed and grain-fed groups was also examined. Relative abundances of taxa were computed by QIIME and analyzed with the Linear Discriminant Analysis (LDA) Effect Size (LEfSe) algorithm [18]. Fourteen phyla were identified to be differentially abundant (LDA score ≥ 2.0) (Fig. 2A). As for the family level, 47 families showed differences in relative abundances. A cladogram was plotted at the family level with a notation of differential taxa under different diets in grass-fed and grain-fed groups (Fig. 2B). Among these differential families, Ruminococcaceae, BS11, and Porphyromonadaceae were the top three discriminative features in the grass-fed group. At the same time, Succinivibrionaceae, S24-7, and Lachnospiraceae were the top three discriminative families in the grain-fed group. At the OTU level, among the detected 4182 OTU, 402 OTU had a significant difference in relative abundance (absolute LDA score log10 ≥ 2.0). Of them, 144 OTU showed enrichment in the grain-fed group, and the other 258 OTU showed higher plenty in the grass-fed group. The top 20 most abundant significant OTU were listed in Additional file 1: Table S1. The abundance value based on the genus level was further evaluated by applying a random forest analysis for the group classification (m_try = 7, ntree = 500), and predictive accuracy of 100% regarding grass-fed and grain-fed group was achieved (Fig. 3). The decrease in mean accuracy measured the importance of features. The top 20 most important elements were plotted, and their abundance levels were noted on the left side of the plot. The results suggested that these taxa held the highest discriminatory power between grass-fed and grain-fed groups and may be of interest as microbial biomarkers.

Jejunal Microbiome Structure In Beef Cattle Under Different Diets

After the two groups of young animals reached market weight, cattle were slaughtered, and the jejunal microbiome was examined for comparison. QIIME closed reference pipeline was used to analyze 16S-seq data of cattle jejunal contents with Greengenes Database [17, 19], identifying 24 phyla, 44 classes, 77 orders, 149 families, and 263 genera collectively of the two cattle groups. Of the 24 recognized phyla (Additional file 1: Figure S2), the most abundant phylum detected in cattle jejunal microbiome was Firmicutes that accounted for 63.11–98.21% in relative abundances. Besides Firmicutes, some phyla with high abundances included Proteobacteria (6.14%), Bacteroidetes (2.52%), Verrucomicrobia (1.92%), Actinobacteria (1.66%), and Elusimicrobia (0.89%). Among the 149 assigned families, eight families possessed a relative abundance higher than 1%, including Clostridiaceae (33.82%), Peptostreptococcaceae (27.87%), Ruminococcaceae (6.03%), Enterobacteriaceae (5.69%), Lachnospiraceae (5.62%), Turicibacteraceae (4.60%), RFP12 (1.76%), Bacillaceae (1.68%). The next abundant family, Bacteroidaceae, was also typical in cattle, which accounted for approximately 0.99% abundance of jejunal microbiome families.

Also, jejunal microbial diversity was analyzed, including alpha and beta diversities. Common microbial diversity indices were evaluated (Additional file 1: Table S2). No significant differences in diversity indices were detected between grass-fed and grain-fed groups (p values > 0.05). The average values between alpha diversity indices of two groups were examined, the grass-fed group always had higher average values of the alpha index than the grain-fed group. For example, the PD_whole_tree value was 49.02 ± 9.46 (mean ± SD) for the grass-fed group, 66.77 ± 18.04 for the grain-fed group. We also performed a rarefaction analysis based on Chao1 values of grass-fed and grain-fed groups. We plotted the rarefaction curve, which suggested that the sequencing depth in the current study was enough (Additional file 1: Figure S3). Jejunal microbiome in grass-fed cattle also showed a higher average of Chao1 than grain-fed animals during a random sampling of rarefaction process. Our results suggested that the grass-fed group tended to have a higher microbial diversity than the grain-fed group, although the diversity was not statistically significant (p values > 0.05). As for the beta diversity analysis, biplot of PCA was plotted based on identified top abundant families across the two groups, which demonstrated a distinct difference in jejunum microbial composition between grass-fed and grain-fed individuals (Fig. 4).

>The diet is the primary determinant of jejunal microbial composition

The difference in microbial composition between grass-fed and grain-fed groups was examined. Relative abundances of taxa were computed by QIIME and examined with LEfSe [18]. Even though there was limited access to the external environment and low microbial abundance in the small intestine, diets still exerted several critical influences on microbial composition. Nine discriminative taxa at phylum level were depicted (Fig. 5A). At the family level, 67 taxa showed significant differences in relative abundance between the two groups. For example, identified families Enterobacteriaceae, Turicibacteraceae, RFP12, Elusimicrobiaceae, and Bifidobacteriaceae showed higher abundance in the grain-fed group, whereas Bacteroidaceae, Rikenellaceae, Paraprevotellaceae, BS11, and Nocardioidaceae were significantly higher in abundance in the grass-fed group. A cladogram based on the family level was depicted (Fig. 5B), displaying taxa with significant differences in the jejunal microbiome. Forty-six named genera showed substantial differences between the two groups (absolute LDA score log10 ≥ 2.0). For example, Lactobacillus and Ruminococcus were significantly higher in the grain-fed cattle jejunal microbiome, whereas Solibacillus had substantially higher abundance in the grass-fed group (Fig. 6). At the OTU level, 291 OTUs were significantly different in wealth between the grass-fed and grain-fed groups (absolute LDA score log10 ≥ 2.0).

In comparison, 215 OTUs had higher relative abundance in the grass-fed group, and 76 OTUs showed higher relative abundance in the grain-fed group. Selected significantly different OTUs impacted by diets between the two groups were listed in Table 2 with relative abundance (mean ± SD) in the grain-fed and grass-fed group. The LDA log10 score was calculated using the LefSe algorithm.

Potential Jejunal Microbial Pathways Inferred From The 16s Data

Differences in microbial communities are always associated with different biological functions of microorganisms. Therefore, in this study, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) method [20] was used to predict functional profiling of the jejunal microbiome between grass-fed and grain-fed group based on 16S rRNA marker gene sequences. After normalization of read counts in the OTU table from QIIME pipeline output, a total of 6909 Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology gene families were identified. Of them, five KEGG gene families showed significant differences in abundance between grass-fed and grain-fed groups using the LEfSe method [18] with a cutoff value of Linear Discriminant Analysis (LDA) score log10 ≥ 2.0. Specifically, only one KEGG, insertion element IS1 protein InsB (K07480), was more abundant in the grain-fed group. In contrast, methyl-accepting chemotaxis protein (K03406), RNA polymerase sigma-70 factor, ECF subfamily (K03088), DNA topoisomerase III [EC:5.99.1.2] (K03169), and ABC-2 type transport system permease protein (K01992) had significantly higher abundance in the grass-fed group. In total, as for predicted KEGG pathways, there were 328 identified microbial biological pathways. Some abundant functional pathways in the jejunal microbiome included membrane transport such as ABC transporters, genetic information processing such as DNA repair and recombination proteins, and nucleotide metabolisms. LEfSe analysis identified seven pathways that had significantly different abundance between grass-fed and grain-fed groups (Fig. 7).

Associations Between The Gut Microbiome And Bile Acids

Bile acids from gallbladder samples of eight cattle in each group were measured using an LC-MS/MS system. In total, 21 bile acids were identified and quantified, including both primary and secondary bile acids and bile acid conjugates. Among them, nine were significantly different between grass-fed and grain-fed groups (Table 3). The conjugated form of cholic acid and deoxycholic acid was detected at a relatively high µmol/mL concentrations. For example, the level of taurocholic acid, cholic, and glycocholic acids were significantly higher in the grass-fed than the grain-fed group (Table 3; p < 0.05).

Further, at least six bile acids, including the conjugated form of primary, secondary bile acids, such as lithocholic acid and deoxycholic acid, were significantly higher in the grain-fed group. The other 11 detected bile acids were not significantly different between grass-fed and grain-fed groups (Additional file 1: Table S3), such as the secondary bile acids, deoxycholic acid, ursodeoxycholic acid, indicating their relatively low susceptibility to diet influences in the gut of beef cattle. Together, our data suggest that the grain diet may promote bacterial activities in converting primary to secondary bile acids.

A critical concept of compositional balance has been introduced [21, 22]. The identification of the global microbial balance is to find predictive microbial signatures of a phenotype of interest by Selbal [22]. In our study, the predictive microbiome signatures were most likely secondary bile acids, obtained by using Selbal with default parameters. In the process, six secondary bile acids related to bacterial bile acid conversion activities were used as the response variables for prediction in Selbal. Each time, one of six bile acids was tested using the microbial abundance data at the genus level to perform modeling and variable selection. In total, twelve different taxa were identified among all the taxa, with some of them being selected in more than one balance for different bile acids (Table 4, Additional file 1: Figure S4). The taxa in the numerator and denominator of the global balances predictive of the corresponding bile acids were listed. As expected, among these genera, there were known bile acid producers, Clostridiaceae, Clostridium, and Veillonellaceae [23–25]. For example, the balance (log ratio) of SMB53 (numerator) and Clostridium (denominator) were identified as a microbial signature that could readily help the prediction of glycodeoxycholic acid. The results suggested that these taxa likely played vital global roles to influence bile acids composition in beef cattle under different diets and were worthy of further investigation.

Grass-fed and grain-fed methods are two feeding regimens in beef cattle production. As diets are directly related to the microbiome, exploration of diet influences on the gut microbiome is necessary to guarantee a proper functioning gut and determine its further impacts on cattle performance. Because gaining access to forestomach and small intestine is difficult in live animals before slaughtering, the effects of diets on the gut microbiome are always determined using rectal microbial communities as baseline data. Besides, microbial communities and their potential functions are critical to aid nutrient absorption in lower gut segments of cattle. In the jejunum, the large number of villi and microvilli increase the surface area of the small intestine, enhancing the chance of food particles to encounter bile acids and digestive enzymes. Microbes function actively to convert primary bile acids into secondary bile acids to aid digestion, especially the lipid. Nutrients could then be absorbed across epithelium by passive or active transport to blood vessels [26]. In this study, we first focused on the fecal microbiome in 7-month young cattle as a baseline to initially explore the influence of diets on young beef cattle and identify candidate biomarkers. Then we measured jejunal microbiome and examined its association with bile acids when these cattle reached market weight. The results of our study indicated grass-fed and grain-fed regimens altered microbial community’s structure, diversity, and biological functional categories of the microbiome in feces and jejunum. Also, bile acids were influenced by diets, which exerted an influence on the jejunal microbiome.

From the feces of 7-month-old young cattle under the two different diets, significant microbiome differences could already be observed from the phylum level to the OTU level. The dominant phyla were in agreement with previous studies of cattle gut microbiome that Firmicutes was the most abundant phylum near the colon, followed by Bacteroidetes and Proteobacteria [8, 27]. We identified significantly different taxa in relative abundance between grass-fed and grain-fed. Specifically, Firmicutes were almost five-fold higher in abundance in the grass-fed group than the grain-fed group in LDA value (Log₁₀ LDA = 4.7), whereas Bacteroidetes (Log₁₀ LDA = 4.4) and Proteobacteria (Log₁₀ LDA = 4.2) was approximately 4-fold higher in the grain-fed group. These phyla were commonly found within the digestive tract of animals, and also were found in colon and feces of steers, and these results were also in agreement with similar studies about influences of diets and fed in cattle [28, 29]. At higher taxonomic ranks, several common taxa were identified to be significantly different between grass-fed and grain-fed cattle fecal microbiome (Log₁₀ LDA ≥ 2.0). For example, Ruminococcaceae, the top abundant identified family in the fecal microbiome, was ~ 5 fold higher in effect size in the grass-fed group than the grain-fed group; Ruminococcaceae was also the essential family identified by random forest classification analysis and had a higher abundance in the grass-fed group, as it relies on dietary fiber as an energy source [30]. Some named genera were also identified to provide clues of possible influences of diets on fermentation strains in the upper forestomach. For example, a named species Succinivibrio was analyzed to be 2-fold greater in effect size of abundance in the grain-fed group than the grass-fed group of cattle fecal microbiome (Log₁₀ LDA = 2.3). Succinivibrio species ferments glucose to produce high amounts of succinic acid as their main products [31]. It was also found that when fed cattle with diets that contain high levels of rapidly fermented carbohydrates, mainly starch, Succinivibrio species would always exist in cattle rumen [31, 32]. Our study indicated that this trend of Succinivibrio could already be observed in the fecal microbiome of 7-months young cattle under different diets, and, as expected, it was higher in fecal microbiome of the grain-fed group. These fecal microbiome explorations served as a baseline data and provided a reference for the jejunal microbiome characterization.

As for the small intestine, the unique environment and function of the small intestine made microbiome here to be distinctive with the upper digestive tract [14]. It was not surprising that microbial profiles we analyzed within jejunum also had various unique characteristics. From several previous studies, microbial diversities were all observed to decrease in this segment of cattle gut. Possible reasons could be that diets already went through several sequential digestions in the upper gut segments, there was limited contact with the outside environment, and a reduced oxygen level in the small intestine [5, 9, 14, 27]. In our study, we also observed that Firmicutes became the only one dominant phylum in the jejunal microbiome. The microbiome diversity analysis was then calculated. Alpha diversity indices of the jejunal microbiome did not achieve statistically significant differences between the two groups. A relatively small sample size in this study and animal variations within the same group could potentially be the reasons. Another possible reason was that the unfavorable environment of the small intestine itself and fast rate of digesta passage was infeasible for microbial growth compared to rumen and rectum, so significant differences in diversity might not be statically obtained. However, we did observe a clear separation of the two groups in beta-diversity analysis, demonstrating with a biplot of PCA (Fig. 4). In higher taxonomic levels, many named genera and species were identified. It was observed that families such as Clostridiaceae, Peptostreptococcaceae, and Ruminococcaceae, genera Clostridium, Butyrivibrio, and Ruminococcus were common dominant taxa in the jejunal microbiome of beef cattle. This was also in agreement with previous studies regarding the profiles of the ruminant small intestine microbiome [14, 27, 28].

Besides, significant differences in the relative abundance of specific taxa in the jejunal microbiome were observed between the two groups and posted health implications for beef cattle production. The genera, Lactobacillus, Streptococcus, and Bifidobacterium that can produce lactic acid [33, 34], and the genera, Megasphaera, a commonly known lactic acid utilizing genus [35], all showed higher relative abundance levels in the jejunal microbiome of grain-fed cattle. These genera have long been observed to have a higher abundance in high-grain diet rumen, and in animals with rumen acidosis [36, 37]. Our study further showed that from the rumen to the jejunum, these higher abundances in grain-fed cattle remained significant to have more senior activities related to lactate. Another genus, Solibacillus, was observed to have reduced abundance in the grain-fed group. It was previously reported that in dairy cows, Solibacillus was decreased significantly in abundance when diets were shifted from control diets into sub-acute ruminal acidosis induction diets, and it was potentially related to valerate metabolism in the gut [38]. A similar result that Solibacillus had reduced abundance under high-grain diets was observed in the jejunal microbiome in our study. From the aspect of cattle health, these observations, all together, suggested that grain-fed cattle tend to cause an acidic gut environment globally, and influence significantly not only the rumen but also jejunal microbiome. The grass-fed method could be a better choice to avoid this phenomenon.

The microbiome of the small intestine was associated with bile acid composition. Bile acids were secreted from the duodenum and flow along the small pipe to aid lipid digestion, and processed into secondary bile acids, and finally could get recycled through the enterohepatic circulation. In this study, bile acids were measured in the gallbladder of cattle in two groups, and the data showed significant differences in both primary and secondary bile acids. Among the nine significantly different bile acids, common conjugated forms of secondary bile acids, taurolithocholic acid, glycolithocholic acid, glycodeoxycholic acid, and taurodeoxycholic acid, all showed significantly elevated levels in the grain-fed group than the grass-fed group. Taxa that were potentially related to secondary bile acids conversion were examined, and selbal was used to find predictive microbiome signatures for significantly different secondary bile acids. Six OTUs belong to family Ruminococcaceae were found to be more abundant in the grain-fed group, such as the genus flavefaciens. It has been reported that gram-positive bacteria in the order of Clostridiales, including families of Ruminococcaceae, could perform 7α-dehydroxylation to convert primary bile acids into secondary bile acids [39]. These higher amounts of OTUs belonging to Ruminococcaceae in the grain-fed cattle jejunal microbiome might contribute to the higher secondary bile acids recycled from the intestine. Besides, specific genera, such as Lactobacillus, Clostridium, and Bifidobacterium, could potentially mediate bile acids by performing deconjugation with bile salt hydrolases (BSH) to make bile acids available for further biotransformation [39]. As mentioned above, Lactobacillus and Bifidobacterium had increased abundance in the grain-fed jejunal microbiome in our study.

Moreover, Lactobacillus was identified to be the numerator that constructed the global microbial balance predictive of glycolithocholic acid. Clostridium was also recognized in the microbial balances regarding glycodeoxycholic acid and the total of tauroursodeoxycholic/taurohyodeoxycholic acid. These increased bacteria in grain-fed cattle jejunum could potentially aid to deconjugate the amide bond between bile acids with glycine or taurine, to make bile acids available as substrates for further biotransformation by other microbes [39]. Also, this elevated level of bile acids and bile acid utilizing taxa in grain-fed cattle might give us clues about its less healthy fatty acids profile in grain-fed beef, which needed further explorations focusing on these associations. Overall, these identified microbial taxa, both in the differential analysis and selbal analysis, indicated novel associations between the gut microbiome and higher secondary bile acids in the grain-fed cattle and were also suggestive in other ruminant species.

The jejunal microbiome shared many similar functional categories between grass-fed and grain-fed cattle. Only a few KEGG gene families and potential pathways of the jejunal microbiome showed significant differences between the two groups. Proteins involving microbial genetic activities were higher in the grass-fed group, such as DNA topoisomerase III [EC:5.99.1.2] and RNA polymerase sigma-70 factor, ECF subfamily. By using the PICRUSt algorithm [20], lots of core microbiome were identified to be associated with these KEGGs in contribution analysis of OTU, including 19 phyla such as Firmicutes, Bacteroidetes, Actinobacteria, and Cyanobacteria. These could also explain the increased abundance of the functional category, transcription machinery, in the jejunal microbiome of the grass-fed cattle. The lipid metabolism pathway, glycerophospholipid metabolism, was the only pathway that showed a significant increase in the grain-fed group. Glycerophospholipids were the dominant class of complex lipids, which were composed of glycerol, two fatty acids, phosphate, and amino alcohol [40]. Grain-fed regimen contained higher fats, together with digestion by bile acids, could provide higher levels of glycerol and fatty acids in the small intestine that serves the organ of nutrient absorption, triggering more activities of glycerophospholipids metabolisms of microbes in grain-fed cattle jejunum.

In ruminants, few studies have focused on the lower GI tract; and a comprehensive survey of microbial composition in the small intestine digest of grain- and grass-fed beef cattle is lacking. In this study, microbiome profile and candidate microbial biomarkers were identified in fecal contents of 7-month animals, suggesting an early influence of diet on the microbiome in beef cattle. The jejunal microbiome was observed to have lower microbial diversities to have only Firmicutes accounts for more than 90% relative abundance, which potentially caused by the lower pH and higher rate of digesta passage. Differences in microbial diversities between the grass-fed and grain-fed were not significant, but microbial composition and pathways differed significantly for the jejunal microbiome.

Most importantly, the gut microbiome, in association with bile acids and microbiome signatures were discovered as a novel aspect. There were increased secondary bile acids in the grain-fed group, as well as increased bacteria populations that could modulate bile acids. Besides, microbiome signatures were identified with minimal taxa but could still be associated with secondary bile acids. Furthermore, a microbial pathway, glycerophospholipid metabolism, which was related to lipid metabolism, was also enriched in the grain-fed group. These phenomena reflected the effects of the high-concentrate dietary components in the grain-fed diet. Our results could provide important clues about the microbiome in the lower GI tract of beef cattle and help the comprehensive understanding of the jejunal microbiome and bile acid metabolism with much higher resolution.

UMD Wye Angus herd and experimental design

Steers selected for this study came from a population of cattle in the University of Maryland, Wye Angus beef cattle herd. This herd has been kept closed for over 50 years and maintained a very similar genetic background for all offspring, which reduces the genetic variations of groups for our study. Two kinds of feeding methods, grain-fed and free-range grass-fed methods, were used in this herd as two experimental groups in this study. The grain-fed group received finishing diets, including corn silage, shelled corn, soybean, and trace minerals, which were high in energy and protein. The grass-fed group had free access to grazed alfalfa and consumed bailage during cold seasons. The hay contained no fertilizers, pesticides, or other artificial chemicals. Steers from the grass-fed group in this herd were not fed with any animal, agricultural, or industrial byproducts, and were not supplied with any types of grain. Each steer’s date of birth, birth weight, dam, and sire information were all recorded. Every 24 to 28 days, body weight was measured, and average daily gain (ADG) was calculated. All these metadata of the steers were kept in record.

Sample collection of fecal and jejunal contents

Twenty-two young male cattle were obtained and raised at UMD Wye Angus herd. Twelve animals were fed with grass ad libitum. The other ten cattle were fed with grain diets. Around seven months old, feces at the rectum of both groups of animals were sampled at UMD Wye Angus farm (Queenstown, MD) and immediately frozen in dry ice and stored at – 80 °C freezer in the lab until microbial DNA extraction. After the steers have reached market weight, they were slaughtered, and jejunal contents were immediately collected for microbial DNA extraction.

DNA extraction, 16 s rRNA gene amplification, and sequencing

Microbial DNA from feces and jejunal contents was extracted using QIAamp DNA stool kit (Qiagen, Valencia, CA) with slice modifications that replaced the lysis procedure in protocol with an eight-minute 95 °C incubation in a water bath. DNA concentration was then measured using NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA USA). 16S rRNA gene sequencing was performed as previously described [40]. Briefly, the QIAamp DNA stool kit (Qiagen, Valencia, CA) with slice modifications was used for microbial DNA extraction. DNA concentration was then measured using NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA USA). 16S rRNA gene sequencing was performed. Hypervariable V3-V4 regions of the 16S rRNA gene were amplified through PCR from 20 ng of total DNA with PAGE-purified Illumina platform-compatible adaptor oligos. DNA concentration and sizes were quantified using a BioAnalyzer 2000 (Agilent, Palo Alto, CA). Amplicons were finally purified using Agencourt AMPure XP bead kits (Beckman Coulter Genomics, Danvers, MA). The library pool was sequenced in an Illumina MiSeq sequencer with an Illumina MiSeq Reagent Kit according to the manufacture’s protocol.

Bile acids measurements

Bile samples were measured in a commercial lab (Creative Proteomics, Shirley, NY) according to a procedure published previously [41]. After ultrasonication and centrifugation, five µl of cattle bile was mixed with 995 µl of methanol in Eppendorf tubes. Supernatant 8 µl of each sample was combined with 992 µL of 50% methanol. After preprocessing, 20 µl aliquots were used for quantitation by UPLC-(-)ESI-MRM/MS according to a procedure published previously[55]. Standard curves for bile acid concentrations were performed, as followed: an STD mix containing standard substances of all the quantified bile acids was dissolved in 50% methanol, and this standard solution was used as S1. The S1standard solution was diluted in a series at the same dilution ratio of 1 to 4 with 50% methanol to have standard solutions of S2 to S10. 50 µl of each of S1 to S10 was mixed with 50 µl of 14 D4-bile acid SIS mix (as internal standards) and 100 µl of 50% methanol. Twenty µl aliquots were injected to record the data files to prepare calibration curves using linear regression (As/Ai peak area ratio versus concentration) for calculation of bile acid concentrations measured in the individual samples. SciexMultiQuant was used for data processing. T-test was used to compare the difference of bile acid from grass-fed to grain-fed group with a selected threshold for p-values < 0.05.

Bioinformatics and data analysis

Raw sequences were first analyzed with FastQC (version 0.11.5) to examine the quality of sequencing. The first four maximally degenerate bases (“NNNN”) at the most 5’ end, designed to maximize the diversity during sequencing run to identify unique clusters better and improve base-calling accuracy, were trimmed with Trimmomatic version 0.38 [42]. Then, the processed pair-end reads were merged using PandaSeq v 2.11 with default parameters to join the reads into representative complete nucleotide sequences (contigs) [43]. Overlapped reads with high mismatches and low-quality scores were filtered and removed during this process. QIIME (Quantitative Insights Into Microbial Ecology) version 1.9.1 pipeline was used to analyze the processed 16S rRNA gene sequencing data [44]. OTU was identified using a closed-reference OTU picking protocol with a threshold of 97% similarity. The taxonomic assignment was performed based on the GreenGenes database v13.8 [45]. Alpha (α)-diversity and beta (β)-diversity were calculated and examined using QIIME, and R. PCA (Principal component analysis) was performed, and graphs were plotted with R packages ade4 [46] and factoextra [47]. PICRUSt (v1.1.2) was used with default parameters to predict metagenomics, KEGGs gene families with regarding contributions of OTU, and functional categories [20]. The software takes the OTU table from QIIME as input. After reads normalization with normalize_by_copy_number.py, the workflow predict_metagenomes.py was applied to predict metagenomes and obtain KEGG Orthologs. Predicted metagenome functions were finally analyzed by categorize_by_function.py to collapse thousands of KEGG Orthologs into higher functional categories (pathways). LEfSe algorithm [18] was used in the differential analysis to identify significantly different OTUs, KEGGs, and pathways between the grass-fed group and grain-fed groups. LEfSe can identify abundant differences of characteristic features between two or more sample conditions, considering samples’ biological categories as well as statistical significance to perform comparative analysis based on feature abundance. Non-parametric factorial Kruskal-Wallis (KW) sum-rank test, Wilcoxon rank-sum test, and a Linear Discriminant Analysis were used together to identify differential features and effect sizes [18]. Selbal [22] was used to identify global balances of the two groups and identify microbiome signatures predictive of significantly different secondary bile acids.

Ethics approval and consent to participate

The animal handling and experiments in this study were approved by the Beltsville Area Animal Care and Use Committee and the Institutional Animal Care and Use Committee at the University of Maryland (UMCP-IACUC) with the protocol #R-11-72.

Consent for publication

Not applicable.

Availability of data and material

The 16S rRNA gene sequencing data are available in the NCBI Sequence Read Archive (SRA) under Bioproject PRJNA608270.

Competing interests

The authors declare that they have no competing interests.

Funding

The work was supported by the Maryland Agricultural Experiment Station (MAES) and Jorgensen Endowment Funds.

Authors' contributions

JS and RWL designed the experiment. JL, FL, WC, CJ, YB, and WZ participated in the data collection and performed the experiments. JL wrote the manuscript, and JS and RWL revised the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We thank the UMD Wye Angus farm and Old Line slaughterhouse and their staff for helping with animal slaughtering and sample collection.

Xue H, Mainville D, You W, Nayga RM: Consumer preferences and willingness to pay for grass-fed beef: Empirical evidence from in-store experiments. Food Quality and Preference 2010, 21(7):857-866.
Thomas HS, Black B: Storey's guide to raising beef cattle : health, handling, breeding, 4th edition. edn. North Adams, MA: Storey Publishing; 2018.
Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, Henrissat B, Knight R, Gordon JI: Diet Drives Convergence in Gut Microbiome Functions Across Mammalian Phylogeny and Within Humans. 2011, 332(6032):970-974.
Van Soest PJ: Nutritional ecology of the ruminant, 2nd ed. edn. Ithaca: Comstock Pub.; 1994.
Mao S, Zhang M, Liu J, Zhu WJSr: Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: membership and potential function. 2015, 5:16116.
Wang J, Fan H, Han Y, Zhao J, Zhou Z: Characterization of the microbial communities along the gastrointestinal tract of sheep by 454 pyrosequencing analysis. In: Asian-Australasian journal of animal sciences. vol. 30; 2017: 100-110.
Khafipour E, Li S, Tun H, Derakhshani H, Moossavi S, Plaizier JJAF: Effects of grain feeding on microbiota in the digestive tract of cattle. 2016, 6(2):13-19.
Myer P, Freetly H, Wells J, Smith T, Kuehn LJJoas: Analysis of the gut bacterial communities in beef cattle and their association with feed intake, growth, and efficiency. 2017, 95(7):3215-3224.
Myer PR, Wells JE, Smith TP, Kuehn LA, Freetly HC: Microbial community profiles of the jejunum from steers differing in feed efficiency. Journal of animal science 2016, 94(1):327-338.
Deusch S, Tilocca B, Camarinha-Silva A, Seifert J: News in livestock research—use of Omics-technologies to study the microbiota in the gastrointestinal tract of farm animals. Computational and structural biotechnology journal 2015, 13:55-63.
Kamada N, Chen GY, Inohara N, Núñez G: Control of pathogens and pathobionts by the gut microbiota. Nature immunology 2013, 14(7):685.
Sheriha GM, Waller GR, Chan T, Tillman ADJL: Composition of bile acids in ruminants. 1968, 3(1):72-78.
Bauchart DJJods: Lipid absorption and transport in ruminants. 1993, 76(12):3864-3881.
Durso LM, Miller DN, Schmidt TB, Callaway T: Tracking Bacteria through the Entire Gastrointestinal Tract of a Beef Steer. ael 2017, 2(1).
Carrillo JA, He Y, Li Y, Liu J, Erdman RA, Sonstegard TS, Song JJSr: Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare. 2016, 6:25948.
Li Y, Carrillo JA, Ding Y, He Y, Zhao C, Liu J, Liu GE, Zan L, Song J: Transcriptomic Profiling of Spleen in Grass-Fed and Grain-Fed Angus Cattle. PloS one 2015, 10(9):e0135670-e0135670.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI: QIIME allows analysis of high-throughput community sequencing data. Nature methods 2010, 7(5):335.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C: Metagenomic biomarker discovery and explanation. Genome biology 2011, 12(6):R60.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006, 72(7):5069-5072.
Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Thurber RLV, Knight R: Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology 2013, 31(9):814-821.
Hensley-McBain T, Wu MC, Manuzak JA, Cheu RK, Gustin A, Driscoll CB, Zevin AS, Miller CJ, Coronado E, Smith E: Increased mucosal neutrophil survival is associated with altered microbiota in HIV infection. PLoS pathogens 2019, 15(4):e1007672.
Rivera-Pinto J, Egozcue J, Pawlowsky-Glahn V, Paredes R, Noguera-Julian M, Calle M: Balances: a new perspective for microbiome analysis. MSystems 2018, 3(4):e00053-00018.
Jergens AE, Guard BC, Redfern A, Rossi G, Mochel JP, Pilla R, Chandra L, Seo Y-J, Steiner JM, Lidbury J et al: Microbiota-Related Changes in Unconjugated Fecal Bile Acids Are Associated With Naturally Occurring, Insulin-Dependent Diabetes Mellitus in Dogs. Frontiers in Veterinary Science 2019, 6(199).
Kang JD, Myers CJ, Harris SC, Kakiyama G, Lee I-K, Yun B-S, Matsuzaki K, Furukawa M, Min H-K, Bajaj JS: Bile acid 7α-dehydroxylating gut bacteria secrete antibiotics that inhibit Clostridium difficile: role of secondary bile acids. Cell chemical biology 2019, 26(1):27-34. e24.
Kakiyama G, Pandak WM, Gillevet PM, Hylemon PB, Heuman DM, Daita K, Takei H, Muto A, Nittono H, Ridlon JM et al: Modulation of the fecal bile acid profile by gut microbiota in cirrhosis. J Hepatol 2013, 58(5):949-955.
Nickel R, Schummer A, Seiferle E, Sack WO: The viscera of the domestic mammals, vol. 2: Springer; 1979.
de Oliveira MNV, Jewell KA, Freitas FS, Benjamin LA, Tótola MR, Borges AC, Moraes CA, Suen GJVm: Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. 2013, 164(3-4):307-314.
Myer PR, Wells JE, Smith TP, Kuehn LA, Freetly HCJS: Microbial community profiles of the colon from steers differing in feed efficiency. 2015, 4(1):454.
Shanks OC, Kelty CA, Archibeque S, Jenkins M, Newton RJ, McLellan SL, Huse SM, Sogin ML: Community Structures of Fecal Bacteria in Cattle from Different Animal Feeding Operations. 2011, 77(9):2992-3001.
Jensen BB, Jørgensen H: Effect of dietary fiber on microbial activity and microbial gas production in various regions of the gastrointestinal tract of pigs. Appl Environ Microbiol 1994, 60(6):1897-1904.
Bryant MP, Small N: CHARACTERISTICS OF TWO NEW GENERA OF ANAEROBIC CURVED RODS ISOLATED FROM THE RUMEN OF CATTLE. Journal of Bacteriology 1956, 72(1):22-26.
Lau SKP, Teng JLL, Chiu TH, Chan E, Tsang AKL, Panagiotou G, Zhai SL, Woo PCY: Differential Microbial Communities of Omnivorous and Herbivorous Cattle in Southern China. Computational and structural biotechnology journal 2018, 16:54-60.
Yang HE, Zotti CA, McKinnon JJ, McAllister TAJFim: Lactobacilli Are Prominent Members of the Microbiota Involved in the Ruminal Digestion of Barley and Corn. 2018, 9:718.
Goad D, Goad C, Nagaraja TJJoas: Ruminal microbial and fermentative changes associated with experimentally induced subacute acidosis in steers. 1998, 76(1):234-241.
Kung Jr L, Hession AO: Preventing in vitro lactate accumulation in ruminal fermentations by inoculation with Megasphaera elsdenii. Journal of Animal Science 1995, 73(1):250-256.
Silva LD, Pereira OG, Silva TC, Leandro ES, Paula RA, Santos SA, Ribeiro KG, Valadares Filho SC: Effects of <i>Lactobacillus buchneri</i> isolated from tropical maize silage on fermentation and aerobic stability of maize and sugarcane silages. Grass and Forage Science 2018, 73(3):660-670.
Nagaraja T, Titgemeyer EJJoDS: Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. 2007, 90:E17-E38.
Mao S, Zhang R, Wang D, Zhu WJBVR: The diversity of the fecal bacterial community and its relationship with the concentration of volatile fatty acids in the feces during subacute rumen acidosis in dairy cows. 2012, 8(1):237.
Urdaneta V, Casadesús JJFim: Interactions between bacteria and bile salts in the gastrointestinal and hepatobiliary tracts. 2017, 4:163.
Tocher DR: Glycerophospholipid metabolism. In: Biochemistry and molecular biology of fishes. vol. 4: Elsevier; 1995: 119-157.
Han J, Liu Y, Wang R, Yang J, Ling V, Borchers CHJAc: Metabolic profiling of bile acids in human and mouse blood by LC–MS/MS in combination with phospholipid-depletion solid-phase extraction. 2014, 87(2):1127-1136.
Bolger AM, Lohse M, Usadel BJB: Trimmomatic: a flexible trimmer for Illumina sequence data. 2014, 30(15):2114-2120.
Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD: PANDAseq: paired-end assembler for illumina sequences. BMC bioinformatics 2012, 13(1):31.
Navas-Molina JA, Peralta-Sánchez JM, González A, McMurdie PJ, Vázquez-Baeza Y, Xu Z, Ursell LK, Lauber C, Zhou H, Song SJ: Advancing our understanding of the human microbiome using QIIME. Methods in enzymology 2013, 531:371.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GLJAEM: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. 2006, 72(7):5069-5072.
Dray S, Dufour A-BJJoss: The ade4 package: implementing the duality diagram for ecologists. 2007, 22(4):1-20.
Kassambara A, Mundt FJRpv: Factoextra: extract and visualize the results of multivariate data analyses. 2016, 1(3).

Due to technical limitations the Tables are available as downloads in the Supplementary Files>

Table 1 Alpha diversity indices in cattle fecal microbiome.

Table 2 Select significantly different OTUs in relative abundance in jejunal microbiome of grain-fed and grass-fed cattle.

Table 3 Nine significantly different bile acids between grass-fed and grain-fed cattle (concentration unit: μmol/ml).

Table 4 Predictive global balances of jejunal microbiome for significantly different secondary bile acids.

Additional file 1 as pdf

Additional file 1: Figure S1 Phylum-level fecal microbial composition in young beef cattle. Table S1 Selected 20 significantly different OTUs in relative abundance in the rectal microbiome of grain-fed and grass-fed Angus beef cattle. Figure S2 Phylum-level microbial composition of jejunal digesta. Table S2 Alpha diversity indices of jejunum microbiota in grass-fed and grain-fed Angus beef cattle. Figure S3 Rarefaction curves of jejunal microbiome based on Chao1 values of grass-fed (blue) and grain-fed (red) groups. Figure S4 Demonstration of global balances for secondary bile acids quantities of grass-fed and grain-fed cattle.

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Jejunal Microbiome and Bile Acids Induced by Diets in Angus Beef Cattle

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Fecal microbiome structure influenced by diets in young beef cattle

Differentially abundant taxa and important microbial features in fecal microbiome under different diets

Jejunal Microbiome Structure In Beef Cattle Under Different Diets

>The diet is the primary determinant of jejunal microbial composition

Potential Jejunal Microbial Pathways Inferred From The 16s Data

Associations Between The Gut Microbiome And Bile Acids

Discussion

Conclusions

Methods

UMD Wye Angus herd and experimental design

Sample collection of fecal and jejunal contents

DNA extraction, 16 s rRNA gene amplification, and sequencing

Bile acids measurements

Bioinformatics and data analysis

Declarations

References

Tables

Additional File Legend

Supplementary Files

Status:

Journal Publication

Version 1