Skip to main content
Download PDF

Nutrient availability of roughages in isocaloric and isonitrogenous diets alters the bacterial networks in the whole gastrointestinal tract of Hu sheep

BMC Microbiology volume 23, Article number: 70 (2023) Cite this article

Abstract

Background

The nutrient availability of roughages could affect the dietary utilization efficiency of ruminants even in isocaloric and isonitrogenous diets. Here, we analyzed the bacterial composition and their metabolic pathways in the gastrointestinal tracts (GITs) of Hu sheep fed with wheat straw (WS) instead of alfalfa (AL) in isocaloric and isonitrogenous diets, trying to explore the reasons from the perspective of GITs bacterial network structure changes.

Results

We employed 16S rRNA gene sequencing in combination with the Kruskal–Wallis test, Spearman correlation analysis, and other statistical methods to describe the microbiota composition in the GITs of Hu sheep. The results showed after the roughage was replaced from AL to WS, the most positive response occurred in the rumen microbiota, resulting in a more obvious microbiological and functional redundancy phenomenon. Whereas extended biogeographic studies of the GITs bacterial community found opposite results for the hindgut microbiota and metabolism networks compared to the forestomach. The abundance of fiber-degrading bacteria such as Prevotella, Oscillospiraceae NK4A214 group, and Treponema was significantly increased in GITs, but low-efficiency crude fiber degradation inhibited energy use efficiency, the pentose phosphate pathway, gluconeogenesis, and volatile acid synthesis. In addition, dietary shifting from AL to WS decreased the abundance of beneficial bacteria such as the Lachnospiraceae NK3A20 group and Alistipes, thereby enhancing the underlying inflammatory response.

Conclusions

These findings suggest that feeding untreated WS affected the structure and function of the bacterial network in the GITs due to limited total digestible nutrients, and in particular increases the complexity of the rumen bacterial network, and limit the abundance of bacteria involved in the crude fiber degradation in the hindgut.

Peer Review reports

Background

Domesticated ruminants are crucial sources of animal meat and dairy products for humans, and their population would further increase with discretionary incomes and urbanization [1]. Roughages are important feeds for ruminants to provide the carbon skeleton of energy and amino acids for rumen microbes [2], maintain rumen health [3], and control feed intake through physical filling [4]. Within the roughages, alfalfa (Medicago sativa L.) is a high-quality legume forage rich in crude protein and a variety of growth-promoting factors [5] and easy to digest [6], which makes it to be a worldwide popular forage. However, its production could not meet the needs of animal husbandry and the amount of imported alfalfa is increasing in China. Recently, the increased price of alfalfa also damages the profits of the animal husbandry industry in China [7]. Thus, it's important to seek alternative roughage for alfalfa to meet the demand for roughage and reduce the feed costs for the ruminant industry. Wheat straw (WS) is the second most abundant agricultural residue in China [8]. Based on the high yield and low price of straws, WS could be the alternative roughage for ruminants to alleviate the shortage of high-quality forages such as alfalfa hay (AL) [9,10,11,12]. Previous studies reported that WS rich in effective neutral detergent fiber could maintain rumen health in ruminants fed high-grain diets [13]. The feed resources containing low nutrient density such as WS could also stimulate feed intake in cows during the dry period and promote a smooth transition of the cows to lactation [14].

However, the lignocellulosic structures in WS would cause a marked decrease in animal performance (e.g., feed intake and body weight decrease) and an increase in feed conversion ratio in ruminants [9, 10] compared with alfalfa, even under the isocaloric and isonitrogenous diets. The reason could be that lower contents of total digestible nutrients in WS would affect the composition and colonization of microbiota in the gastrointestinal tract (GITs) of ruminants. When untreated straw was used as forage, its indigestible fibrous structure increased the diversity and richness of the rumen bacterial community together with the relative abundance of Bacteroidetes, especially Prevotellaceae. However, it would reduce the relative abundance of Firmicutes and depress microbial protein synthesis resulting in the decline of nutrient utilization in the rumen, even under isocaloric and isonitrogenous diets [9, 15, 16]. Similar results were found in the in vitro rumen fermentation [17]. The changed bacterial community in the rumen ecosystem caused by the nutrient availability of roughages would further alter the co-abundances among microbes or metabolic pathways [18], due to the redundancies of microbial functions for complex carbohydrates in ruminants [19].

Current research on ruminant GITs' microbial responses to straw has mainly focused on the microbiota within the rumen which accounts for 60% to 80% of the total volume of the digestive system [20]. Some study suggests that the hindgut (cecum, colon) of ruminants is also rich in fiber-degrading microbiota [21]. However, there are few studies on the response of other parts of GITs microbiota to WS except the rumen. Thus, we hypothesized that the effect of dietary shifting of roughages from AL to WS on gastrointestinal microbiota was not limited to the rumen, but extended to the entire GITs in ruminants. This study aimed to investigate the dietary shifting of roughages from AL to WS on the bacterial community and their metabolic functions in different parts of GITs in sheep using the 16S rRNA sequencing and function predictions. Expanded biogeographic studies of the GITs in ruminants will help to profile further the microbial response to low-quality straws and increase the utilization of straws in ruminants to alleviate the shortage of high-quality roughages.

Materials and methods

Ethical approval

This experiment was approved by the Animal Care and Use Committee of Nanjing Agricultural University [SYXK (SU) 2021–0086]. All procedures followed the regulations of the Measures for the Management of Experimental Animals in Jiangsu province [22] and the ARRIVE guidelines (https://arriveguidelines.org/arrive-guidelines).

Experimental design and sampling

Twelve healthy male Hu sheep (5 months old, 40 ± 2 kg liveweight) selected as the experimental animals were purchased from the Institute of Animal Husbandry, Jiangsu Academy of Agricultural Sciences (Nanjing, China). Each animal was house-fed in a single cage and had free access to drinking water and feed. Table 1 showed the ingredients and nutrients of two experimental rations prepared according to the nutrient requirements of meat-type sheep and goats (NY/T 816–2021) [23]. During adaptation (30 days), all sheep were randomly divided into two groups and fed the alfalfa-based diet. After adaption, two groups of sheep were fed the alfalfa-based diet (AL group, n = 6) and wheat straw-based diet (WS group, n = 6) at 8:00 h and 17:00 h each day for 28 days. On day 28, all sheep were slaughtered before morning feeding, and the different segments of GITs were ligated, severed, and weighed including the rumen, reticulum, omasum, abomasum, jejunum, cecum, colon, and rectum. The GITs digesta and feces were collected and stored at -80 °C for further DNA extraction.

Table 1 Feed ingredient and nutrient composition
Full size table

DNA extraction and 16S rRNA sequencing of bacterial community

Total genomic DNA of microbes in digesta samples was extracted with a phenol/chloroform/isoamyl alcohol solution using a microbead beater (Biospec Products, OK, USA) as the method described by Sun et al. [24]. The solution was then precipitated with ethanol, and the precipitate was dissolved in 50 ml of pH 8.0 Tris–EDTA buffer. Quality and concentration of microbial DNA were determined by a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, MA, USA) and then stored at -20 °C for further analysis. The V4 region of bacterial 16S rRNA was amplified using the degenerate primers 515 F (5′-GTGCCAGCMGCCGCGTAA-3′) and 806 R (5′-GGACTACHVGGTWTCTAAT-3′) [25]. Both forward and reverse primers were tagged with Illumina adapters, pads, and linker sequences. The PCR reaction was conducted in a 50 μL reaction containing 30 ng of template, fusion PCR primers, and PCR master mix. The PCR cycling conditions were as follows: 95 °C for 3 min, 30 cycles of 95 °C for 45 s, 56 °C for 45 s, 72 °C for 45 s, and final extension for 10 min at 72 °C. The PCR products were purified using Agencourt AMPure XP beads and eluted in an elution buffer. Libraries were characterized by an Agilent Technologies 2100 Bioanalyzer. By the standard protocol of BGI (Shenzhen, China), the validated amplicon libraries were sequenced on the Illumina HiSeq 2500 platform using paired-end sequencing reads (2 × 250 bp).

Bioinformatics and Diversity Analyses

Raw reads were initially filtered using the fastp (v0.23.2) [26] with the following parameters: -q 30 -l 100, and then the trimmed reads were imported into QIIME 2 (v2022.2) [27]. Amplicon sequence variants (ASVs) were identified using the DADA2 tutorial (https://benjjneb.github.io/dada2/tutorial.html) after the removal of chimeras and filtration of low-quality sequences. The ASVs in at least 2 samples were retained to avoid accidental factors. Microbial taxonomy was aligned and annotated with the SILVA database (v138) [28]. Alpha and beta diversities were estimated using the q2-diversity method with a sample depth of 30,500. Linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify the differential bacteria and pathways between treatments [29]. Furthermore, the functional profiles of microbial communities were predicted using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) (https://github.com/picrust/picrust2) [30].

Statistical analyses and visualization

Concretely, The R software (v4.1.3) was employed for microbial community statistical analysis (Kruskal–Wallis test) and data visualization. The correlation networks among bacteria communities or metabolic pathways at level-3 Kyoto Encyclopedia of Genes and Genomes (KEGG) [31,32,33] were analyzed using the Spearman correlation analysis and visualized with GraphPad Prism (v8.0). Significant correlations with coefficient > 0.8 and P < 0.05 were visualized on the Gephi platform (v0.9.7). All data were shown as mean ± standard error of the mean (SEM), P < 0.05 or FDR adjusted P < 0.05 was considered statistically significant.

Results

GITs weight, bacterial richness and diversity

Dietary shifting of roughages from AL to WS caused a 59.4% increase in the full weight of the sheep rumen (P < 0.05, Table S1), whereas weights of other parts in GITs of sheep showed no difference between treatments (P > 0.10). For the bacteria community in GITs, Fig. S1 showed that the rarefaction curves stabilized when the sequences exceeded 30,000. The sequence coverage in all samples exceeded 99.9%, indicating a good sequencing depth for the further investigation of microbiota. Shifting roughages from AL to WS significantly increased the observed ASVs number, the faith-pd and the Shannon indices in the rumen, reticulum, and rectum, the Chao1 indices in the reticulum, colon, and rectum, and the faith-pd indices in the colon (P < 0.05) (Fig. 1).

Fig. 1
figure 1

Comparison of alpha diversity between treatments. a observed ASVs index; b Chao 1 index; c Shannon index; d Faith-pd. WS, wheat straw group; AL, alfalfa group. *: P < 0.05; **: P < 0.01; ns: no significance

Full size image

Based on the principal co-ordinates analysis, shifting roughages had less contribution to the bacterial community than the spatial distributions in the major parts of GITs (Fig. 2a). This shows that the microbes of each GIT are mainly compartmentalized in the forestomach (rumen, reticulum, omasum, and abomasum), small intestine (jejunum) and hindgut (cecum, colon, and rectum), and the effect of feed composition differentiation is not obvious. Shifting roughages from AL to WS caused more independent ASVs in GITs of sheep, especially in the forestomach (Fig. S2). Compared with the other GITs, the jejunum had the greatest number of unique ASVs, with 342 independent ASVs in the WS group and 294 in the AL group, and there were only 20 ASVs shared in each group (Fig. 2b).

Fig. 2
figure 2

Beta diversity and ASVs composition analysis. a PCoA of gut microbes using Bray–Curtis dissimilarity values (PERMANOVA; P = 0.001). b Venn diagram for the bacterial ASVs in each part of GITs. WS, wheat straw group; AL, alfalfa group

Full size image

Compositions of the bacteria community in GITs

Figure 3a showed that Bacteroidota was the predominant phylum in the bacteria community of forestomach (48.37%-65.31%), whereas Firmicutes was the most common phylum in the jejunum (84.64%-87.06%), hindguts, and feces (51.70%-67.23%) of sheep. Shifting roughages from AL to WS significantly increased the relative abundances of Bacteroidota, Spirochaetota (PFDR < 0.05), and decreased the relative abundances of Firmicutes, Actinobacteriota and Planctomycetota at phylum levels in the rumen of sheep (PFDR < 0.05, Fig. 3b). It also significantly increased the relative abundance of Chloroflexi in the reticulum, omasum, abomasum and jejunum (PFDR < 0.05), and the relative abundance of Desulfobacterota in the cecum, colon, and feces (PFDR < 0.05). Besides, shifting roughages from AL to WS decreased the cecal Actinobacteriota (PFDR = 0.010).

Fig. 3
figure 3

Bacterial phylum profiles in the gastrointestinal tracts of Hu sheep. a relative abundances of bacterial phyla; b differential bacterial phyla between treatments based on the analysis of linear discriminant analysis (LDA) effect size (relative abundance > 1%, LDA > 2, FDR adjusted P < 0.05). WS, wheat straw group; AL, alfalfa group

Full size image

At the genus level, Prevotella was the predominant bacteria in the rumen, reticulum, omasum, and abomasum (Fig. 4a). Shifting roughages from AL to WS increased the Prevotella relative abundance in the rumen and reticulum (PFDR < 0.05), but decreased the relative abundances of Lachnospiraceae NK3A20 group and Unclassified Lachnospiraceae in rumen and reticulum (PFDR < 0.05) (Fig. 4b). The predominant bacterial genus in jejunum were different between the WS group (Unclassified Ruminococcaceae) and AL group (Ruminococcus), while the predominant bacterial genus in the cecum, colon, rectum, and feces of sheep were Oscillospiraceae UCG-005. Meanwhile, shifting roughages from AL to WS increased the Oscillospiraceae NK4A214 group in the jejunum, colon, and rectum, and decreased the Alistipes in the colon, rectum, and feces of sheep (PFDR < 0.05).

Fig. 4
figure 4

Bacterial genus profiles in the gastrointestinal tracts of Hu sheep. a relative abundances of bacterial genera; b differential bacterial genera between treatments based on the analysis of linear discriminant analysis (LDA) effect size (relative abundance > 1%, LDA > 2, FDR adjusted P < 0.05). WS, wheat straw group; AL, alfalfa group

Full size image

Co-abundance network of bacterial community in ruminal contents and feces

Bacterial networks achieved by the Spearman correlation analysis in rumen and feces were selected to represent the forestomach and hindgut based on the differential bacterial community at genus levels (Fig. 5). Shifting roughages from AL to WS caused more complex networks between rumen genera. A total of 18 co-abundances were found in the rumen bacterial community of sheep fed WS with 9 positive and 9 negative co-abundances, while just 3 positive and 3 negative co-abundances were identified in sheep fed AL. The negative co-abundance between Prevotellaceae UCG-001 and Unclassified Clostridia was found in the rumen of both treatments. For dietary WS, ruminal genera belonging to Firmicutes had the most co-abundances (Fig. 5a). For example, the relative abundance of Prevotella had a negative correlation with Clostridia UCG-014. The Christensenellaceae R-7 group had a positive correlation with Unclassified Clostridia and negative correlations with Succinivibrionaceae UCG-002 and Prevotellaceae UCG-001. However, ruminal genera belonging to Firmicutes and Bacteroidota showed negative correlations with each other in sheep fed AL (Fig. 5b), indicating a different rumen bacterial network compared with sheep fed WS.

Fig. 5
figure 5

Co-abundance networks of the bacterial community in the rumen and feces of Hu sheep. Only the co-abundance with spearman correlation coefficient > 0.8, P < 0.05 were illustrated in the network; the circles represent bacterial genera

Full size image

The bacterial networks in sheep feces became simpler after shifting roughages from AL to WS (Fig. 5c, d), which differed from those in sheep rumen. A total of 9 co-abundances (5 positives and 4 negatives) between feces bacterial genera were obtained in sheep fed WS, while 18 co-abundances (15 positives and 3 negatives) were found in sheep fed AL. Feces bacterial genera belonging to Firmicutes showed the most co-abundances in the feces of both treatments. For example, Alistipes had positive correlations with Bacilli RF39 and Lachnospiraceae NK3A20 groups (Fig. 5d), and Bacilli RF39 had positive correlations with Lachnospiraceae NK3A20 group, Bacteroides and Oscillospiraceae UCG-005, but negatively correlated with Treponema in the feces of sheep fed wheat straw. For dietary AL, the relative abundance of Treponema had negative correlations with Bacilli RF39, Bacteroides, and Oscillospiraceae UCG-005, respectively.

Prediction of the effect on the function profiles in different parts of GITs

Based on the predicted bacterial functions, shifting roughages from AL to WS increased the carbohydrate metabolism in the rumen and reticulum, and decreased the energy metabolism in the rumen and abomasum (PFDR < 0.05, LDA score > 2) (Fig. S3). Meanwhile, it decreased the metabolism of cofactors and vitamins and lipid metabolism in the rumen as well as reticulum, omasum, and jejunum, respectively (PFDR < 0.05, LDA score > 2). Notably, no significant difference was found in the predicted metabolic pathways of the bacterial community in the hindguts (cecum, colon, and rectum) of sheep (PFDR > 0.05).

The transition of roughages from AL to WS increased the complexity of the rumen metabolic pathway network, while the pathway network in the sheep feces became simpler (Fig. 6), which was similar to the bacterial networks. The pathways of carbohydrate metabolism, amino acid metabolism, and glycan biosynthesis and metabolism had the most contribution to the pathway networks in the rumen or feces of both treatments. A total of 203 positive and 215 negative pathway co-abundances were identified among ruminal bacteria of sheep fed wheat straw, while dietary AL caused 155 positive and 110 negative pathway co-abundances among ruminal bacteria.

Fig. 6
figure 6

Co-abundance networks of predicted metabolic pathways in the rumen and feces of Hu sheep. Only the co-abundance with spearman correlation coefficient > 0.8, P < 0.05 were illustrated in the network. Circles represent the KEGG pathways at level 3. WS, wheat straw group; AL, alfalfa group. All the KEGG identifiers were from (http://www.kegg.jp/kegg/kegg1.html) [31,32,33]

Full size image

Among level-3 pathways within carbohydrate metabolism, amino sugar and nucleotide sugar metabolism had the greatest positive correlations with other pathways, while pyruvate metabolism showed the most negative correlations with others in the rumen of sheep fed WS. Unlike feeding WS, the pathways of butanoate metabolism and pentose phosphate pathway were respectively the most linked nodes for positive and negative correlations in the rumen of sheep fed AL. The transition of roughages from AL to WS decreased the bacterial pathway co-abundances of bacterial metabolism at KEGG level-3 in sheep feces (489 vs. 129). The top three ranking of node numbers in the feces of both treatments were the same as the rumen bacterial network in sheep fed AL. Within carbohydrate metabolism pathways at level 3, the pathways of pyruvate metabolism and pentose phosphate pathway were respectively the most linked nodes for positive and negative correlations in the feces of sheep fed WS, while the starch and sucrose metabolism and the carbohydrate digestion and absorption were those nodes in the feces of sheep fed AL.

Discussions

Roughages are the main feed and fundamental nutrients for ruminants, and the ability of ruminants to degrade the roughage is endowed by the function of microbiota in GITs [34]. Results of the present study showed that the transition from AL to WS mainly caused responses in rumen microbiota even under the isocaloric and isonitrogenous diets. The most dramatic changes in rumen microbiota structure were observed together with the predicted functions of carbohydrate metabolism, which were caused by the nutrient availability of roughages [35]. Typically, more than 50% of the forage is digested in the rumen. However, the untreated WS has a more recalcitrant structure rich in structural carbohydrates, which may make Hu sheep GITs employ more species for microbial co-degradation [36]. Previous results revealed that the increased NDF in the diet would decrease the circulation rate of indigestible NDF in the rumen, which increased the whole rumen weight [37]. These results were in agreement with our results that the rumen weights of sheep fed WS were greater than AL. The high content of NDF in the WS suppressed the passage rate of the feed in the rumen, resulting in the retention of coarse material. The low nutrient availability of WS enriched greater abundances of fiber-degrading bacteria, such as Treponema [35]. In addition, the differential bacteria communities in the reticulum and omasum were similar to those in the rumen after the transition of low-quality wheat straw. The reason could be that these GITs are the major sites for microbial digestion of dietary crude fiber in host animals [38]. Results of the present experiment also indicated that microbes of each GITs were compartmentalized in the forestomach, small intestine, and hindguts, rather than differentiated by forage composition, which was consistent with previous findings [39].

The GITs function is inextricably linked to microbiota composition, hence, there is a need for a better understanding and a thorough analysis of those processes. At the genus level, the relative abundance of Prevotella was significantly increased in the rumen and reticulum in the WS group compared to the AL group. Members of Prevotella have glycolytic potential and could metabolize sugars to acetate, propionate, and succinate [34]. Therefore, the increased relative abundance of Prevotella after feeding WS was inseparable from the enhanced carbohydrate metabolism in the rumen and reticulum. Meanwhile, previous results indicated that this genus is also involved in glycan biosynthesis [40]. However, more Prevotella is involved in the degradation of refractory wheat straw in the forestomach, also increasing the functional redundancy of these GITS, thereby reducing the efficiency of degradation.

It is worth noting that the Unclassified Lachnospiraceae and Lachnospiraceae NK3A20 group was decreased in the rumen and reticulum of the WS group in the present experiment. Some members of this family can protect against inflammation by producing butyrate [41]. Therefore, the lower abundance of this genus in the WS group implies that feeding WS may cause potential damage to animal health. Similarly, the WS group had a significant reduction of Prevotellaceae UCG-001 in the abomasum and feces, as well as Alistipes in the colon, rectum, and feces, compared to the AL group. Some studies have found that Prevotellaceae UCG-001 is negatively correlated with markers of glucose and lipid metabolism disorders [42], and Alistipes is also an important genus of anti-inflammatory bacteria [43]. The decrease of these bacterial genera possibly increased the inflammation levels in the GITs of sheep fed WS, thereby affecting their growth performance. Besides, feeding WS significantly reduced the lipid metabolism of jejunal bacteria, which would further affect the lipid deposition and absorption in the small intestine of sheep [44, 45].

Carbohydrate metabolism and amino acid metabolism are major bacterial functions in all GITs. Results of the present experiment found significant differences in these pathways between treatments in the rumen and feces of sheep. Generally, 70 to 85% of the digestible dry matter in the diet can be fully fermented and digested by microbiota in the rumen, and the whole process involves a rigorous carbohydrate metabolism process [46], while feces are generally hindgut representatives in microbiological research [47], therefore a more detailed analysis of rumen and feces. Feed nutrient composition is related to the type of fermentation in the rumen [34], with feeds with higher fiber content producing lower levels of propionate and butyrate [48]. Thus, most metabolic pathways of rumen bacteria had positive correlations with the ruminal butyrate concentration in sheep fed AL. The decreased correlations between the pyruvate metabolism pathway and other pathways after shifting roughages to WS would reduce the precursor for various volatile fatty acids [48], and consequently affect the dietary energy utilization in ruminants. The pentose phosphate pathway is closely related to gluconeogenesis and was the most negatively related metabolism in the feces after feeding WS in the present experiment. This result indicated that the harmful impacts of WS on bacterial energy metabolism is not only limited to the forestomach but also extended to the hindguts.

Opposite results were found in the co-abundance networks of bacterial communities and functions between bacterial communities in the rumen and feces of sheep in the present experiment. The transition of roughage from AL to WS increased the microbial diversity of the rumen microbiota and complicated the microbial and metabolic network. The increased diversity caused by WS would further result in the functional redundancy of rumen bacteria, especially the fiber-degrading bacteria belonging to the genus Prevotella. However, nutrients would be preferentially used by the excess rumen bacteria and their redundant functions rather than the host animals, which thereby reduced the utilization efficiency of dietary energy [49]. This agreed with our results that feeding WS reduced the energy metabolism of the rumen bacterial community.

After digesting in the forestomach and small intestine, the roughage residues would enter the hindgut and be utilized by the hindgut microbes. Unlike AL, WS could not provide enough digestible material to satisfy hindgut microbial degradation, which caused the simplified networks of hindgut microbes and their metabolic pathways. Despite less diversity of hindgut bacteria would improve the efficiency of energy metabolism [47], the nutrient utilization in sheep fed WS was still limited and not as good as AL. Our study found that even under isocaloric and isonitrogenous diets, the transition of roughage from AL to WS still caused significant changes in the Hu sheep microbiota in GITs, which affects the bacteria function and degradation effect of the GITs. The reason for this phenomenon is inseparable from the refractory structural carbohydrate composition of wheat straw, resulting in low nutrient availability. However, although our study has analyzed the relationship between GITs and function, and inferred the effect of feeding WS on the growth performance of Hu sheep, the KEGG metabolic pathway is only obtained through prediction. In the follow-up, it is necessary to measure the nutrient content of the two diets after they reach the intestinal tract after digestion, which is helpful to further understand the impact of WS feeding on Hu sheep. Appropriate physical, chemical, and biological pretreatment of straw is worth considering if we want to further improve the availability of WS feeding in roughage substitution. The destruction of the cross-linked state within structural carbohydrates would improve the palatability and nutrient availability of WS, which can make it more suitable for feed utilization [50, 51].

Conclusions

In conclusion, our results showed that feeding Hu sheep with untreated WS instead of alfalfa as roughage caused changes in the bacterial community in the GITs of Hu sheep, even under the isocaloric and isonitrogenous diet conditions. This alteration of the bacterial community is distinct in the forestomach and hindgut and mainly in the rumen. Microbiota diversity and functional analyzes found that feeding untreated WS might reduce the abundance of anti-inflammatory bacteria in the whole GITs and potentially affect fatty acid synthesis. Further studies need to focus on the pretreated straw and the suitable ratio of straw to alfalfa in the feed to understand the role of straw replacing alfalfa in ruminant feed utilization.

Availability of data and materials

The datasets supporting the findings of this study are available in the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA861451), https://www.ncbi.nlm.nih.gov/sra/PRJNA861451.

Abbreviations

GITs:

Gastrointestinal tracts

WS:

Wheat straw

AL:

Alfalfa hay

NDF:

Neutral detergent fiber

ADF:

Acid detergent fiber

LDA:

Linear discriminant analysis

LEfSe:

LDA effect size

KEGG:

Kyoto Encyclopedia of Genes and Genomes

PICRUSt2:

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2

SEM:

Standard error of the mean

References

  1. Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat Commun. 2018;9(1):870. https://doi.org/10.1038/s41467-018-03317-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Li Q, Xue B, Zhao Y, Wu T, Liu H, Yi X, et al. In situ degradation kinetics of 6 roughages and the intestinal digestibility of the rumen undegradable protein. J Anim Sci. 2018;96(11):4835–44. https://doi.org/10.1093/jas/sky298.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chibisa GE, Beauchemin KA, Koenig KM, Penner GB. Optimum roughage proportion in barley-based feedlot cattle diets: total tract nutrient digestibility, rumination, ruminal acidosis, short-chain fatty absorption, and gastrointestinal tract barrier function. J Anim Sci. 2020;98(6):skaa160. https://doi.org/10.1093/jas/skaa160.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Jardstedt M, Hessle A, Norgaard P, Frendberg L, Nadeau E. Intake and feed utilization in two breeds of pregnant beef cows fed forages with high-fiber concentrations. J Anim Sci. 2018;96(8):3398–411. https://doi.org/10.1093/jas/sky199.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Liu J, Liu X, Zhang Q, Li S, Sun Y, Lu W, et al. Response of alfalfa growth to arbuscular mycorrhizal fungi and phosphate-solubilizing bacteria under different phosphorus application levels. AMB Express. 2020;10(1):200. https://doi.org/10.1186/s13568-020-01137-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Agarussi MCN, Pereira OG, de Paula RA, da Silva VP, Roseira JPS, Silva FFE. Novel lactic acid bacteria strains as inoculants on alfalfa silage fermentation. Sci Rep. 2019;9(1):8007. https://doi.org/10.1038/s41598-019-44520-9.

    Article  CAS  Google Scholar 

  7. He Y, Cone JW, Hendriks WH, Dijkstra J. Corn stover usage and farm profit for sustainable dairy farming in China. Anim Biosci. 2021;34(1):36–47. https://doi.org/10.5713/ajas.19.0222.

    Article  CAS  PubMed  Google Scholar 

  8. Zhang L, Chen K, He L, Peng L. Reinforcement of the bio-gas conversion from pyrolysis of wheat straw by hot caustic pre-extraction. Biotechnol Biofuels. 2018;11(1):72. https://doi.org/10.1186/s13068-018-1072-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang B, Sun H, Wang D, Liu H, Liu J. Constraints on the utilization of cereal straw in lactating dairy cows: A review from the perspective of systems biology. Anim Nutr. 2022;9:240–8. https://doi.org/10.1016/j.aninu.2022.01.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang B, Zhao FQ, Zhang BX, Liu JX. An insufficient glucose supply causes reduced lactose synthesis in lactating dairy cows fed rice straw instead of alfalfa hay. J Anim Sci. 2016;94(11):4771–80. https://doi.org/10.2527/jas.2016-0603.

    Article  CAS  PubMed  Google Scholar 

  11. Cai J, Zhao FQ, Liu JX, Wang DM. Local mammary glucose supply regulates availability and intracellular metabolic pathways of glucose in the mammary gland of lactating dairy goats under malnutrition of energy. Front Physiol. 2018;9:1467. https://doi.org/10.3389/fphys.2018.01467.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wang DM, Cai J, Zhao FQ, Liu JX. Low-quality rice straw forage increases the permeability of mammary epithelial tight junctions in lactating dairy cows. J Sci Food Agric. 2019;99(4):2037–41. https://doi.org/10.1002/jsfa.9330.

    Article  CAS  PubMed  Google Scholar 

  13. Eastridge ML, Starkey RA, Gott PN, Oelker ER, Sewell AR, Mathew B, et al. Dairy cows fed equivalent concentrations of forage neutral detergent fiber from corn silage, alfalfa hay, wheat straw, and corn stover had similar milk yield and total tract digestibility. Anim Feed Sci Tech. 2017;225:81–6. https://doi.org/10.1016/j.anifeedsci.2017.01.013.

    Article  CAS  Google Scholar 

  14. Havekes CD, Duffield TF, Carpenter AJ, DeVries TJ. Effects of wheat straw chop length in high-straw dry cow diets on intake, health, and performance of dairy cows across the transition period. J Dairy Sci. 2020;103(1):254–71. https://doi.org/10.3168/jds.2019-17033.

    Article  CAS  PubMed  Google Scholar 

  15. Du CM, Nan XM, Wang K, Zhao YG, Xiong BH. Evaluation of the digestibility of steam-exploded wheat straw by ruminal fermentation, sugar yield and microbial structure in vitro. RSC Adv. 2019;9(71):41775–82. https://doi.org/10.1039/c9ra08167d.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang LY, Shang Y, Li JC, Fu T, Lian HX, Gao TY, et al. Comparison of feeding diets including dried or ensiled peanut vines as forage sources on the growth performance, ruminal fermentation, and bacterial community in young Holstein bulls. Anim Sci J. 2022;93(1):e13675. https://doi.org/10.1111/asj.13675.

    Article  CAS  PubMed  Google Scholar 

  17. Saleem AM, Ribeiro GO, Sanderson H, Alipour D, Brand T, Hunerberg M, et al. Effect of exogenous fibrolytic enzymes and ammonia fiber expansion on the fermentation of wheat straw in an artificial rumen system (RUSITEC). J Anim Sci. 2019;97(8):3535–49. https://doi.org/10.1093/jas/skz224.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Solden LM, Naas AE, Roux S, Daly RA, Collins WB, Nicora CD, et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat Microbiol. 2018;3(11):1274–84. https://doi.org/10.1038/s41564-018-0225-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Weimer PJ. Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front Microbiol. 2015;6:296. https://doi.org/10.3389/fmicb.2015.00296.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Arshad MA, Hassan FU, Rehman MS, Huws SA, Cheng YF, Din AU. Gut microbiome colonization and development in neonatal ruminants: Strategies, prospects, and opportunities. Anim Nutr. 2021;7(3):883–95. https://doi.org/10.1016/j.aninu.2021.03.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ma YH, Deng XT, Yang X, Wang JK, Li T, Hua GY, et al. Characteristics of bacterial microbiota in different intestinal segments of Aohan fine-wool sheep. Front Microbiol. 2022;13. https://doi.org/10.3389/fmicb.2022.874536.

  22. People’s Government of Jiangsu Province: Measures for the Management of Experimental Animals in Jiangsu province. http://www.jiangsu.gov.cn/art/2008/9/2/art_46143_2543850. html?gqnahi=affiy2 (2008). Accessed 12 Jan 2023.

  23. Ministry of Agriculture of the People’s Republic of China. Feeding Standard of Meat-producing Sheep and Goats (NY/T 816–2021). Beijing, China: China Agriculture Press; 2021.

  24. Sun YZ, Mao SY, Yao W, Zhu WY. DGGE and 16S rDNA analysis reveals a highly diverse and rapidly colonising bacterial community on different substrates in the rumen of goats. Animal. 2008;2(3):391–8. https://doi.org/10.1017/s1751731107001231.

    Article  CAS  PubMed  Google Scholar 

  25. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci USA. 2011;108:4516–22. https://doi.org/10.1073/pnas.1000080107.

    Article  PubMed  Google Scholar 

  26. Chen SF, Zhou YQ, Chen YR, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):884–90. https://doi.org/10.1093/bioinformatics/bty560.

    Article  CAS  Google Scholar 

  27. Bolyen E, Rideout JR, Dillon MR, Bokulich N, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7. https://doi.org/10.1038/s41587-019-0209-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(D1):D590–6. https://doi.org/10.1093/nar/gks1219.

    Article  CAS  PubMed  Google Scholar 

  29. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. https://doi.org/10.1186/gb-2011-12-6-r60.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38(6):685–8. https://doi.org/10.1038/s41587-020-0548-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30. https://doi.org/10.1093/nar/28.1.27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51. https://doi.org/10.1002/pro.3715.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2022;51(D1):D587–92. https://doi.org/10.1093/nar/gkac963.

    Article  PubMed Central  Google Scholar 

  34. Mizrahi I, Wallace RJ, Morais S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19(9):553–66. https://doi.org/10.1038/s41579-021-00543-6.

    Article  CAS  PubMed  Google Scholar 

  35. Liu JH, Zhang ML, Xue CX, Zhu WY, Mao SY. Characterization and comparison of the temporal dynamics of ruminal bacterial microbiota colonizing rice straw and alfalfa hay within ruminants. J Dairy Sci. 2016;99(12):9668–81. https://doi.org/10.3168/jds.2016-11398.

    Article  CAS  PubMed  Google Scholar 

  36. Wilkinson T, Korir D, Ogugo M, Stewart RD, Watson M, Paxton E, et al. 1200 high-quality metagenome-assembled genomes from the rumen of African cattle and their relevance in the context of sub-optimal feeding. Genome Biol. 2020;21(1):229. https://doi.org/10.1186/s13059-020-02144-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Linton JAV, Allen MS. Nutrient demand interacts with forage family to affect nitrogen digestion and utilization responses in dairy cows. J Dairy Sci. 2009;92(4):1594–602. https://doi.org/10.3168/jds.2008-1327.

    Article  CAS  Google Scholar 

  38. Xue D, Chen H, Luo XL, Guan JQ, He YX, Zhao XQ. Microbial diversity in the rumen, reticulum, omasum, and abomasum of yak on a rapid fattening regime in an agro-pastoral transition zone. J Microbiol. 2018;56(10):734–43. https://doi.org/10.1007/s12275-018-8133-0.

    Article  CAS  PubMed  Google Scholar 

  39. Freetly HC, Dickey A, Lindholm-Perry AK, Thallman RM, Keele JW, Foote AP, et al. Digestive tract microbiota of beef cattle that differed in feed efficiency. J Anim Sci. 2020;98(2). https://doi.org/10.1093/jas/skaa008.

  40. Wang LZ, Jin L, Xue B, Wang ZS, Peng QH. Characterizing the bacterial community across the gastrointestinal tract of goats: Composition and potential function. Microbiologyopen. 2019;8(9). https://doi.org/10.1002/mbo3.820.

  41. Surana NK, Kasper DL. Moving beyond microbiome-wide associations to causal microbe identification. Nature. 2017;552(7684):244. https://doi.org/10.1038/nature25019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Song XF, Zhong L, Lyu N, Liu F, Li BX, Hao YN, et al. Inulin can alleviate metabolism disorders in ob/ob mice by partially restoring leptin-related pathways mediated by gut microbiota. Genom Proteom & Bioinf. 2019;17(1):64–75. https://doi.org/10.1016/j.gpb.2019.03.001.

    Article  Google Scholar 

  43. Fernandez J, de la Fuente VG, Garcia MTF, Sanchez JG, Redondo BI, Villar CJ, et al. A diet based on cured acorn-fed ham with oleic acid content promotes anti-inflammatory gut microbiota and prevents ulcerative colitis in an animal model. Lipids Health Dis. 2020;19(1):28. https://doi.org/10.1186/s12944-020-01205-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zeng T, Huang L, Ren J, Chen L, Tian Y, Huang Y, et al. Gene expression profiling reveals candidate genes related to residual feed intake in duodenum of laying ducks. J Anim Sci. 2017;95(12):5270–7. https://doi.org/10.2527/jas2017.1714.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zeng Y, Zeng D, Ni XQ, Zhu H, Jian P, Zhou Y, et al. Microbial community compositions in the gastrointestinal tract of Chinese Mongolian sheep using Illumina MiSeq sequencing revealed high microbial diversity. AMB Express. 2017;7(1):75. https://doi.org/10.1186/s13568-017-0378-1.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Li LZ, Qu MR, Liu CJ, Xu LJ, Pan K, Song XZ, et al. Expression of a recombinant lentinula edodes xylanase by Pichia pastoris and its effects on ruminal fermentation and microbial community in in vitro incubation of agricultural straws. Front Microbiol. 2018;9. https://doi.org/10.3389/fmicb.2018.02944.

  47. Teseo S, Otani S, Brinch C, Leroy S, Ruiz P, Desvaux M, et al. A global phylogenomic and metabolic reconstruction of the large intestine bacterial community of domesticated cattle. Microbiome. 2022;10(1):22. https://doi.org/10.1186/s40168-022-01357-1.

    Article  Google Scholar 

  48. Ungerfeld EM. Metabolic hydrogen flows in rumen fermentation: Principles and possibilities of interventions. Front Microbio. 2020;11. https://doi.org/10.3389/fmicb.2020.00589.

  49. Moraïs S, Mizrahi I. The road not taken: the rumen microbiome, functional groups, and community states. Trends Microbiol. 2019;27(6):538–49. https://doi.org/10.1016/j.tim.2018.12.011.

    Article  CAS  PubMed  Google Scholar 

  50. Nayan N, Sonnenberg ASM, Hendriks WH, Cone JW. Prospects and feasibility of fungal pretreatment of agricultural biomass for ruminant feeding. Anim Feed Sci Tech. 2020;268:114577.

    Article  CAS  Google Scholar 

  51. Wang YQ, Luo Y, Luo LL, Zhang H, Liao YC, Gou CL. Enhancement of the nutritional value of fermented corn stover as ruminant feed using the fungi Pleurotus spp. Sci Rep. 2021;11(1):11961. https://doi.org/10.1038/s41598-021-90236-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the National Key R&D Program of China (2021YFD1300302), the National Natural Science Foundation of China (32061143034 and 32161143028), and the Regional Science and Technology Collaborative Innovation Project (QYXTZX-NQ2021-01).

Author information

Author notes

  1. Yuqi Li and Jian Gao are contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Gastrointestinal Microbiology, National Center for International Research On Animal Gut Nutrition, Nanjing Agricultural University, Nanjing, 210095, China

    Yuqi Li, Jian Gao, Yihan Xue, Ruolin Sun, Xiaoni Sun, Zhanying Sun, Weiyun Zhu & Yanfen Cheng

  2. College of Animal Science, Tibet Agricultural and Animal Husbandry University, Nyingchi, 860000, China

    Suozhu Liu & Zhankun Tan

Authors
  1. Yuqi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yihan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruolin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoni Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhanying Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Suozhu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhankun Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Weiyun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yanfen Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.L: Writing – Original Draft; J.G: Visualization; Y.X and R.S: Writing – Review & Editing; X.S, Z.S, S.L and Z.T: Investigation and Resources; W.Z, Supervision, Project Administration; Y.C: Funding acquisition and Conceptualization. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yanfen Cheng.

Ethics declarations

Ethics approval and consent to participate

The animal experiment was approved by the Animals Care and Use Committee of Nanjing Agricultural University (SYXK (SU) 2021–0086). All procedures were followed with the regulations of the Measures for the Management of Experimental Animals in Jiangsu province and the ARRIVE guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:  Table S1.

The results of different parts of weight of Hu sheep gastrointestinal tracts. Figure S1. Rarefaction curves at 100% similarity for each treatment. Figure S2. Venn diagram of ASVs in each gastrointestinal tract. WS, wheat straw group; AL, alfalfa group. Figure S3. Level 2 KEGG pathway predictions in different groups. KEGG pathway with significant differences (LDA > 2), results are presented when proportion >1%, FDR adjusted P < 0.05. WS, wheat straw group; AL, alfalfa group. All the KEGG identifiers were from (http://www.kegg.jp/kegg/kegg1.html) .

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Gao, J., Xue, Y. et al. Nutrient availability of roughages in isocaloric and isonitrogenous diets alters the bacterial networks in the whole gastrointestinal tract of Hu sheep. BMC Microbiol 23, 70 (2023). https://doi.org/10.1186/s12866-023-02814-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12866-023-02814-z

Keywords

BMC Microbiology

ISSN: 1471-2180

Contact us