Bacterial community and diversity in the rumen of 11 Mongolian cattle as revealed by 16S rDNA amplicon sequencing

doi:10.21203/rs.3.rs-2985630/v1

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Bacterial community and diversity in the rumen of 11 Mongolian cattle as revealed by 16S rDNA amplicon sequencing

https://doi.org/10.21203/rs.3.rs-2985630/v1

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published 18 Jan, 2024

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Through microorganisms in the rumen of ruminant, plant fiber can be converted to edible food such as meat and milk. This function is dependent on the microbe in the rumen. Ruminant have a rich and complex microbial community within the rumen, and the bacteria comprise the dominant proportion of the ruminal microbes. High-throughput sequencing offers a viable solution for the study of rumen microbes. In this study, rumen fluid samples were taken from 11 cattle from Inner Mongolian, the DNA of 11 rumen fluid samples were extracted and the V4 regions of 16Sr DNA amplicon sequenced. More than 90,000 raw reads and 60,000 effect Tags per sample were obtained. We observed 28,122 overall operational taxonomic units (2,557 ± 361 per sample), Bacteroidetes (44.41 ± 7.31%), Firmicutes (29.07 ± 3.78%), and Proteobacteria (7.18 ± 5.63%) were the dominant phyla in rumen bacteria, accounting for 82%. At the genus level, the highest relative abundance is Prevotella. We further predicted their functions using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The result showed that they include metabolism, genetic information processing, environmental information processing and cellular processes. It explored the bacterial community diversity and composition of the rumen of Mongolian cattle. On the whole, our research showed that there was a high diversity of rumen bacteria in Mongolian cattle. Meanwhile, these findings provided information for further studies on the relationship between the community, diversity, functions of rumen bacteria and the nutritional physiological functions of the host.

Biological sciences/Microbiology/Applied microbiology

Biological sciences/Biological techniques/Bioinformatics

Mongolian cattle

Rumen bacteria community

Species diversity

16S rDNA

Illumina NovaSeq

KEGG pathway

Ruminants were the source of dairy milk and meat. As an herbivore, the functionality of their digestive system can convert plant fiber into volatile fatty acids and other products that were easy to absorb. This capacity is of enormous significance to man, and its realization depended on the unique stomach of ruminants, particularly the rumen. About 75% of the dry fiber in the feed are digested in the rumen [1]. Rumen was a huge and complex microbial ecosystem, which depended on the joint action of bacteria, fungi, protozoa and other microorganisms. Among them, bacteria were the main component of rumen microorganisms, representing about 95% of the total microbes [2]. The rumen microorganisms served for the host as nutrition, immune regulation and physiological processes [3]. The composition and function of rumen microorganisms were not static, they would change with host, environment, diet and other factors.

As an anaerobic environment, many microorganisms in the rumen could not be cultured in laboratory, and only about 20% of rumen microorganisms were cultured conventionally and observed under microscope. Microgenome sequencing without culture made it possible to study microbial diversity, abundance and function for the uncultured microorganisms. The 16S rDNA gene was an important symbol for a unique microbial. As it included nine hypervariable regions in the gene sequence, and the sequences of the hypervariable regions in a 16S rDNA gene correspond to a unique bacterium [4]. Consequently, the 16S rDNA gene amplicon sequencing was a vital tool to study the communities and diversity of bacteria in rumen [5][6]. The 16S rRNA gene high-throughput sequencing was used for the microbial of the gastrointestinal in 2005 for the first time. It provided a new method for the identification of the uncultured microbial [7]. In order to study the changes of bile acid and intestinal microflora in the perinatal period of Shaling sows. Jie Wang [8] carried out 16s rDNA sequencing and bile acid targeted metabolome detection on the feces of 42 sows. The results showed that there was more much bile acid when the intestinal microflora was increasing in the perinatal period of Shaling sows. Dong SX [9] carried out 16S rDNA sequencing on the intestinal microorganisms of wild striped goose in Tibet Autonomous Region. A total of 513,505 original data reads were obtained. The results showed that the phylum Chlamydomonas (67.34%) was the dominant phylum, followed by Proteobacteria (29.03%) and Cyanophyta (1.97%). The findings were also very useful and valuable for the study of genetics and high altitude in Tibet Autonomous Region.

Mongolian cattle are mainly fed in the north of China, Mongolia and the east of Russia [10], where the altitude here is high, and the weather is cold and dry. Mongolian cattle are hardy, disease resistant, adaptable to harsh environmental conditions and have relatively low food requirements, being able to digest roughage with a high fibre content. It is a treasure of microbial community research because of its unique living environment setting and eating habits [11].

In order to further study the composition and structure of microorganisms in the rumen, we analyzed the sequencing results of 16S rDNA gene amplifiers of 11 Mongolian cattle rumen fluid. The findings would be helpful for the study of rumen microbial diversity of Mongolian cattle, and they would provide basis for future research on rumen bacteria of Mongolian cattle.

1.1 Sample collection

A total of 11 Mongolian cattle with two years old were used in this study. And these cattle were originated from Zhenglan Banner, Xilin Gol League, Inner Mongolia, in China (115°00′-116°42′E, 41°56′-43°11′N). All the animals were raised under the same conditions. The experimental cattle were slaughtered in the same abattoir, where 11 rumen fluid samples were collected immediately. It was necessary to wear sterile gloves when collecting rumen fluid samples. Rumen fluid (approximately 50 mL) was strained through four layers of cheesecloth and centrifuged at 13,000 ×g for 20 min at 4℃ to collect the supernatant. They were labeled as N1 to N11.

1.2 Methods

1.2.1 Genome DNA extraction and PCR amplification

Total genome DNA of rumen fluid from 11 samples was extracted by Genome DNA Extraction Kit (Qiagen, Germany). The concentration and purity of the genome DNA were monitored using 1% agarose gels. For the procedure of Polymerase Chain Reaction (PCR), DNA was diluted to 1ng µL^− 1 using sterile water according to the concentration.

Specific primers 515F-806R were used to amplify the 16S rRNA gene of the 16S V4 region. All PCR reactions were carried out with 15 µL of Phusion® High-Fidelity PCR Master Mix (New England Biolabs). In each reaction forward and reverse primers were 2µM, and template DNA was 10 ng. Thermal cycling was followed below: an initial denaturation at 98℃ for 1 min; 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s; 72℃ for 5 min. The concentration and purity of the PCR products were monitored on 2% agarose gel. The mixture of PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany).

1.2.2 Library preparation and sequencing

Genome DNA libraries of 11 rumen fluids were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) and index codes were added. At last, 11 sequencing libraries were sequenced through an Illumina NovaSeq platform. 250 bp paired-end reads were generated and were named raw data.

1.2.3 Processing of sequencing data

The raw data were spliced and were controlled on quality to obtain clean tags. Chimerism was filtered to obtain effective tags. Effective tags were clustered with 97% identity and named OTUs (operational taxonomic units). Representative sequences for each OTU were screened and annotated taxonomic information according to Silva Database (http://www.arb-silva.de/) [19]. MUSCLE (Multiple Protein Sequence Alignment) software (Version 3.8.31, http://www.drive5.com/muscle/) was used to analyze the relationship of different OTUs and the dominant species among 11 samples [20].

1.2.4 Alpha diversity

For studying species diversity for each sample, alpha diversity was used on 6 indices, such as Observed-species, Chao1, Shannon, Simpson, ACE, and Good-coverage. The 6 indices in the 11 samples were calculated with QIIME (Quantitative Insights into Microbial Ecology) (Version 1.7.0) and displayed with R software (Version 2.15.3). Two indices were selected to identify community richness: the Chao1 estimator (http://www.mothur.org/wiki/Chao), and the ACE estimator (http://www.mothur.org/wiki/Ace). Two indices were used to identify community diversity: the Shannon index (http://www.mothur.org/wiki/Shannon), and the Simpson index (http://www.mothur.org/wiki/Simpson). One index to characterized Sequencing depth: the Good’s coverage (http://www.mothur.org/wiki/Coverage)

1.2.5 Beta diversity

In order to analyze the species complexity among the 11 samples, beta diversity analysis was used. Beta diversity included weighted and unweighted unifrac. And weighted and unweighted unifrac were calculated using QIIME software (Version 1.9.1).

Principal Coordinate Analysis (PCoA) was a visualized form of the sequencing data to show the principal coordinate. In the principal coordinate analysis, a set of orthogonal axe was obtained from a distance matrix of weighted or unweighted unifrac among the 11 samples. The maximum variation factor was demonstrated by first principal coordinate, and the second maximum one by the second principal coordinates, and so on. PCoA analysis was displayed by WGCNA (Weighted correlation network analysis) package, stat packages and ggplot2 package in R software (Version 2.15.3).

Unweighted Pair-group Method with Arithmetic Means (UPGMA) clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage and was conducted by QIIME software (Version 1.9.1).

1.2.6 Function prediction

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was a group of pathways showing the relation networks for: 1. Metabolism 2. Gene information transduction 3. Environmental information processing 4. Cell signal transduction 5. Organism Systems 6. Human diseases 7. Drug development.

KEGG pathways were built using the Tax4Fun program. Tax4Fun function prediction was analyzed by the nearest neighbor method based on the minimum 16S rRNA sequence similarity. The procedure of the Tax4Fun function prediction was followed below: firstly, extracting the whole genome 16S rRNA gene sequence of prokaryotes in KEGG database, secondly comparing the 16S rRNA gene sequence o SILVA SSU Ref NR database by BLASTN algorithm (BLAST bitscore > 1500), thirdly establishing a correlation matrix, fourthly functional annotating using functional information of KEGG database annotated by UProC and PAUDA methods and the SILVA database. The OTUs of samples with SILVA database were used as reference sequences in the annotation of functional prediction.

2.1 Sequencing data analysis

A total of 1,153,578 raw reads were obtained from 11 samples, with an average of 104,870 ± 4,224 sequences for each sample (Table 1). The original reads were spliced and filtered to obtain effective data. On average, 102,823 ± 4,091 raw tags, 101,953 ± 4,032 clean tags, 65,898 ± 2,766 effective tags were obtained (Table 1). The average length of effective tags was 253 nucleotides (nt). The Q20 and Q30 of effect tags were 99.04 ± 0.06% and 97.44 ± 0.13%, respectively (Table 1).

Table 1

Summary of sequencing data.
Sample Name	Raw PE (#)	Raw reads	Raw Tags (#)	Clean Tags (#)	Effective Tags (#)	AvgLen (nt)	Q20	Q30	OTU
N1	103,427	103,427	101,540	100,716	63,575	253	99.14	97.66	2,564
N2	109,111	109,111	106,926	106,037	67,386	253	99.06	97.5	2,293
N3	110,156	110,156	108,182	107,308	69,210	253	99.04	97.45	2,565
N4	106,948	106,948	104,616	103,671	69,076	253	99.01	97.42	2,455
N5	98,612	98,612	96,775	96,053	62,793	253	99.06	97.5	2,283
N6	96,928	96,928	95,168	94,395	61,201	253	98.97	97.21	2,319
N7	105,998	105,998	103,870	102,934	63,458	253	98.95	97.25	2,490
N8	105,701	105,701	103,341	102,336	67,656	253	98.99	97.38	3,539
N9	105,878	105,878	103,806	102,910	67,805	253	99.03	97.44	2,723
N10	108,598	108,598	106,474	105,567	67,589	253	99.03	97.44	2,630
N11	102,221	102,221	100,352	99,555	65,124	253	99.11	97.58	2,261
Note: Q20 and Q30 refer to the percentage of bases with the quality score greater than 20 (sequencing error rate less than 1%) and 30 (sequencing error rate less than 0.1%) in the effective tag, respectively.

2.2 Alpha diversity analysis

Operational taxonomic units (OTUs) clustering and species analysis were conducted based on the effect tags. Based on 97% nucleotide sequence identity, the total number of OTUs reached 28,122 with an average of 2,557 ± 361 per sample. As shown in Fig. 1A, as the number of sequences increased, the rarefaction curve flattens out indicating that the depth of sequencing is sufficient to accurately represent the bacterial composition of the rumen (with the Good’s coverage > 99%). Furthermore, rumen microbial diversity and richness were estimated using alpha diversity index (Fig. 1B), including Shannon, Observed species, PD whole tree, Simpson, Chao1, and ACE.

2.3 Beta diversity analysis

In order to carry out a comparative analysis of the microbial community composition of the different samples, beta diversity analysis was performed. The samples with high similarity in community structure were clustered together, and the samples with large community differences will be far apart in the polt. As shown in Fig. 2A, the first and second principal coordinates of the PCoA diagram are 45.16% and 19.13% respectively. Differences in rumen microbiota between samples were evaluated further using a UPGMA clustering tree based on weighted UniFrac distances in Fig. 2B. Similar results to PCoA mentioned above, samples were divided into three groups, (A) N1, N2, and N4, (B) N3, N5, N6, N7, N9, N10, and N11, and (C) only N8.

2.4 The bacteria composition and community of rumen in Mongolian cattle

In order to intuitively view the species with high relative abundance at different taxonomic levels and their proportion in each sample, Cumulative bar charts of relative species abundance at the taxonomic level of the phylum (Fig. 3A) and genus (Fig. 3B) were produced. The top ten genera in terms of relative abundance were Prevotella (14.30 ± 4.13%), Succinimonas (2.42 ± 4.19%), Rikenellaceae_RC9_gut_group (6.66 ± 1.25%), Succinivibrionaceae_UCG-002 (1.65 ± 2.23%), Lactobacillus (4.57 ± 2.62%), Escherichia-Shigella (0.79 ± 1.96%), Saccharofermentans (1.98 ± 1.03%), Christensenellaceae_R-7_group (2.44 ± 0.60%), unidentified_F082 (0.96 ± 0.82%), and NK4A214_group (1.88 ± 0.48%) (Fig. 3B and D). These genera accounted for 37.66 ± 6.16% of total bacterial sequences and belonged to the three phyla with the highest relative abundance (Fig. 3A): Bacteroidetes (44.41 ± 7.31%), Firmicutes (29.07 ± 3.78%), and Proteobacteria (7.18 ± 5.63%). Firmicutes and Bacteroides were the core phylum in the rumen accounting for 73.48 ± 5.60%. In addition, the ratio of Firmicutes to Bacteroidetes is about 1.57.

To visualize the different species clustered in the samples, the top 35 genera in terms of abundance were selected and clustered at both species and sample levels based on their abundance information in each sample, plotted as heat maps (Fig. 3C), The relative abundances of Micrococcus, Enterococcus, and Morganella in sample N2 were higher than those in other samples. But the relative abundance of Christensenellaceae_ R-7_ group and NK4A214_ group were lower than others. The relative abundance of Methanobrevibacter, Veilone laceae-ucg-001, and Saccharofermentans in sample N8 were higher than that in other samples. But the relative abundance of Prevotrlla, Prevotrllaceae_Ucg-001, and Prevotrllaceae_ Ucg-003 were lower than that in other samples.

2.5 Functional annotation

To understand the potential function of bacteria in the samples, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway at level 1 and 2 were performed estimated by Tax4Fun. Functional relative abundance annotation results showed that the pathways were extraordinary analogous among samples. At KEGG level 1 (Fig. 4A), the biological pathways of the samples mainly included metabolism (45.09 ± 0.39%), genetic information processing (25.13 ± 0.61%), environmental information processing (11.72 ± 0.74%), cellular processes (7.82 ± 0.15%), Unclassified (5.31 ± 0.12%), human diseases (2.86 ± 0.06%), and organism systems (2.06 ± 0.07%). The top three functional paths in terms of relative abundance accounting for more than 80%.

Hierarchical clustering heat map showing abundance information for the top 35 relative abundances of KEGG pathways in different samples (Fig. 4B). Like the results of level 1, the functional pathways of each sample were similar among 11 samples. The KEEG pathways with high relative abundance in the samples mainly included carbohydrate metabolism, replication and repair, translation, membrane transport, and amino acid metabolism. The cellular community prokaryotes, membrane transport, transcription, poorly characterized, and infectious diseases were the mainly pathways in the samples N1, N4, N8, and N9. In the sample N4, the cellular community prokaryotes, membrane transport and poorly characterized were play an import role. The folding, sorting and degradation, lipid metabolism, and metabolism of terpenoids and polyketides were the mainly metabolic pathways in the sample N3.

3.1 Microbial diversity of samples

The sequencing platform and the sample numbers usually affected the quality of the 16S rRNA data. Meanwhile, if more than 10,000 reads were obtained for each sample was sufficient for accurate and reliable results [12][13][14]. In this study the 16S rRNA gene of 11 samples were sequenced by Illumina NovaSeq platform. And every sample had obtained more than 90,000 reads. And the amount of the 16SrRNA data was accurate and reliable.

Alpha diversity indices included four main indices such as Chao1, ACE, Shannon and PD whole tree. The Chao1 and ACE indices reflected the richness of the microbial communities. The Shannon indices reflected the diversity, and the PD whole tree reflected the genetic relationship of species in the community. The larger index values of Chao1, ACE, and Shannon indicated that the more abundant and diverse species were observed in the samples [15]. Higher alpha diversity indicates a more diverse and complex microbial composition in the rumen. It could enhance the resistance to environment and the adaptability of the host’ health [16]. The rumen microbiota can regulate physiological processes that provide protection to the host, such as the production of antimicrobial substances and inhibition of the growth of digestive pathogens [17], ensuring proper rumen function [18]. A good rumen microbial ecosystem can improve the growth performance of the host [19]. Dietary variation may be a major source of new microbial diversity, with alpha diversity often changing dynamically with diet [16]. In the present study, the 11 Mongolian cattle rumen samples showed great similarity in terms of overall microbial diversity. The Alpha diversity indices of sample N8 was higher than that of other samples, indicating that N8 had a higher richness of species diversity.

3.2 Microbial community structure of Mongolian cattle

In our study, the Bacteroidetes, Firmicutes, and Proteobacteria were found in all samples. Firmicutes and Bacteroides were the dominant flora in the rumen of mammals, they help animals digest plant-derived feed [20][21][22]. When the diet of adult cows was changed to a high fibre diet, the abundance of the phylum Bacteroides increased considerably and the composition of the rumen microbiota changes. Consistently, our findings also show Firmicutes and Bacteroides account for 70% of rumen bacteria of Mongolian cattle, and the ratio of Firmicutes to Bacteroidetes was about 1.57. In the study of Elie Jam [23], they found the abundance of Bacteroidetes were inversely proportional to the fat. The results found that when the fat in the blood and tissue was increased, the abundance of Bacteroidetes was decreased. The change of this ratio affects the metabolic potential of the mouse gut microbiota [1]. Similarly, Turnbaugh [24] found that the ability of the obesity microbiome to obtain energy from the diet was increased and, more importantly, that this feature was transmissible: the mice had an increase in whole body fat following the introduction of microbiota positively associated with obesity in germ-free mice, suggesting that these floras were a cause of obesity. This was of great importance for the breeding of Mongolian cattle, as the ratio of the two floras could be adjusted to regulate the fat content and thus the health and cold tolerance of the animal [25]. These results suggested that the ratio of the relative abundance of Firmicutes and Bacteroides was probably related to obesity. Proteobacteria is the phylum with the third highest relative abundance in the Mongolian cattle. Proteobacteria comprises a large number of bacteria that can catabolize feedstuff components [26], including fiber plant [27]. The result also demonstrated that the rumen microorganisms contained Fibrobacteres, accounting for nearly 1%. It was an important phylum of cellulose-degrading bacteria [28]. This phylum included one cellulase producing bacteria genus named Fibrobacter. And the genus named Fibrobacter consisted of two cellulose-degrading species named Fibrobacter succinogenes and Fibrobacter enterica.

In our study, the top ten species with relative abundance at the genus level include Prevotella, Rikenellaceae_RC9_gut_group, Succinivibrionaceae_UCG_002, Lactobacillus, Escherichia Shigella, Saccharofermentans, Christensenellaceae_R-7_group、unidentified_F082、NK4A214_group. These genera belong to three phyla——Bacteroidetes, Firmicutes, and Proteobacteria.

Prevotella is the genus with the highest relative abundance among rumen bacteria [29]. Although Pratylenchus cannot degrade cellulose, it can degrade other polysaccharides such as xylan. As the main proteolytic bacteria in the rumen, Prevotella could use peptides and ammonia as nitrogen source. In addition to that it could also produce a lot of complex enzymes to degrades starch [30]. Meanwhile, Prevotella were associated with the production of propionate in the rumen [19]. Propionate was negatively correlated with methane production [21][31], this mean that an increase in the relative abundance of Prevotella favored a reduction in methane emissions. Furthermore, this had an important role to play in reducing greenhouse gas emission from ruminants.

In the present study, the relative abundance of Succinimonas and Succinivibrionaceae_UCG_002 was accounting for 4%. Succinimonas were anaerobic organisms that break down starch, ferment glucose, maltose, dextrin, or starch, but not other carbohydrates, the metabolites are a large amount of succinic acid and a small amount of acetic acid [32]. The relative abundance of Succinivibrionaceae_UCG_002 was higher than others in the Succinivibrionaceae family. The abundances of all the Succinivibrionaceae members were positively correlated with the emission of methane. Because its members mainly produce succinate, competition with methane production, thus reducing the release of hydrogen [33][34]. Some studies have found that the presence of Succinivibrionaceae_UCG_002 and Succinivibrio was inversely proportional to feed efficiency [35][36]. Some species of the Succinivibrionaceae family from the tammar wallaby (Macropus eugenii) manipulated methane and therefore it drew attention to methane emissions in ruminant livestock [37].

The genus with the second highest relative abundance in the sample was Rikenellaceae_RC9_gut_group, lower pH will reduce the amount of this genus. And the relative abundance varies by sex, with lower abundance of the genus in female cows [38]. A recent study [31] found its abundance was also associated with rumen epithelial morphology, the rumen epithelium is important for the absorption of volatile fatty acids from feed digestion, with approximately 50–80% of volatile fatty acids (VFA) being absorbed directly by the epithelium, resulting in increased absorption of feed by the host [39]. Overall, Rikenellaceae_RC9_gut_group in the rumen has an important role in the degradation of crude fibre and in the morphological structure of the rumen epithelium.

Lactobacillus is composed of over 170 species and 17 subspecies [40]. As a typical bacterium of Lactobacillus, Lactobacilli have long been used to make dairy products such as cheese and yogurt. They also have a high tolerance for very low pH conditions, especially those used to ferment foodstuffs such as mustard, cabbage, and olives, which optimizes their travel through the stomach. In the gut, Lactobacillus adhesion to the mucus layer of the gut wall is mediated by a protein surface layer called the S-layer. In addition, some strains of lactobacilli produce antioxidants. Ljungh and Wadström [41] also review perhaps the most interesting probiotic potential of lactobacilli, the ability to immunomodulate human cells to achieve an anti-inflammatory response. In addition, Lactobacilli can produce strain-specific bacteriocins and bacteriocin-like products that can inhibit the growth of other organisms [42]. Lactobacillus is a partly anaerobic or strictly anaerobic bacterium that and is involved in the hydrolysis of proteins and lipids. In particular, they can continue to multiply after participating in the formation of cheese and thus produce contamination [43]. Lactobacillus fermenting glucose produces lactic or acetic acid, which produces an acidic environment that inhibits the growth of some harmful bacteria, which is helpful for the health of the host. In addition to this, Lactobacillus can also alleviate respiratory diseases and regulate respiratory immunity [44]. and provide some relief from the symptoms of cardiovascular disease [45]. But the genus Lactobacillus is not only a beneficial organism, but can also act as a pathogen for serious infections. If Lactobacillus enters the bloodstream, it may be associated with sepsis in immunocompromised patients.

Escherichia Shigella is a facultative anaerobic microorganism as well as a pathogenic bacterium. The rapid increase of Escherichia Shigella abundance in the intestine and stomach is one of the signs of igan production, which leads to the imbalance of intestinal ecology of patients and the decline of immunity [46]. The study of Lee [47] showed that diarrhoea caused by Escherichia-Shigella is not associated with changes in children's weight, but can slow height growth in children. The presence of large amounts of Escherichia-Shigella is detrimental to the health of the host, there is a small relative abundance of Escherichia-Shigella among samples, accounting for less than 0.00002%. It shows that the 11 samples in this study are all from healthy Mongolian cattle.

Many studies have shown that the genus Christensenellaceae R7 group was found in the intestinal tract and played an important role in amino acid and lipid metabolisms [48]. It was recently found Christensenellaceae was associated with the health of digestive system for humans and mice [49]. The Christensenellaceae_R-7_group is an important part of the Christensenellaceae, and Christensenellaceae is importantly associated with mammal health. The relative abundance of Christensenellaceae was inversely proportional to fat content, with higher abundance being beneficial to health, and interestingly the relative abundance of Christensenellaceae is higher in centenarians [48]. The fermented complete feed significantly affected the microbial diversity in pig manure, with the most affected genera including Christensenellaceae_R-7_group, whose relative abundance was significantly reduced compared to the control group [50]. Yang’s studies indicated that the abundance of the Christensenellaceae_R-7_group was increased in the rumen as the content of n3-poly unsaturated fatty acids in longissimus dorsi muscle was increased [51]. And it may play an important role in the low-density fatty acids metabolism for mammals health. In present study, the relative abundances of Christensenellaceae_R-7_group were very close among 11 samples, averagely accounting for 4.5%.

3.3 Functional pathways of rumen bacteria in Mongolian cattle

According to the function prediction analysis of KEGG, it had found that the rumen bacteria in Mongolian cattle play roles in the metabolism of gene, cell, carbohydrate, amino acid, energy and vitamine. All findings indicated that bacteria played an important role in nutrient digestion and absorption of ruminants. The fiber was digested through the bacteria in the rumen into volatile fatty acids, such as acetic acid, propionic acid, and butyric acid [52][53]. Meanwhile, the bacteria could convert the nitrogen-containing substances into amino acids and provided the nitrogen source for ruminants. Consequently, the abundance and composition of the rumen bacterial population was closely correlated with the health of the ruminants [54][55]. Research had shown that the diversity and abundance of rumen bacteria was closely related to the composition and type of feed [55], and that highly nutritious feeds could reduce the variety of rumen microorganisms and even cause subacute ruminal acidosis (SARA), which was detrimental to the health of cattle and could even cause death [56]. Therefore, ruminal microbial composition could be controlled through scientific feed ratios to improve the stability of the rumen microbial community and increase ruminant production [57]. For example, when the ratio of concentrate to roughage in feeds was changed, it could maximize productivity, reduce energy losses and decrease the methane emission [58].

In this study, the diversity and abundance of rumen microorganisms were investigated by sequencing the 16S rDNA gene of 11 Mongolian cattle rumen bacteria, and function was predicted through KEGG. Our findings showed that there was higher diversity, richness, and evenness of microbial communities in the rumen. The predominant phylum were Bacteroidetes and Firmicutes in the rumen, Prevotella was the most predominant genus in the rumen, and the biological pathways in microbial communities of rumen mainly included metabolism, environmental information processing, and genetic information processing. Nevertheless, obtained functional profiles are merely a prediction; detailed analyses are still needed to elucidate this aspect. Further studies are warranted to determine the contributions of the bacterial taxa and metabolic pathways to the health, development, and physiology of cattle.

Acknowledgements Financial support by the Natural Science Foundation of Inner Mongolia Autonomous Region is gratefully acknowledged. The first author thanks Dr. YLS for her guidance.

Authors' contributions YJJ collected the samples，performed the data analysis and wrote the manuscript. YLS and HYQ contributed to critical discussions. YLS revised and reviewed the manuscript. All authors read and approved the final manuscript.

Availability of data The raw sequencing data have been deposited on the Sequencing Reads Archive (SRA), and SRA accession numbers are PRJNA955685 (https://www.ncbi.nlm.nih.gov/sra/PRJNA955685).

Competing interests The authors declare that they have no competing interests.

Ethics approval and consent to participate This article does not contain any studies with human participants or animals performed by any of the authors.

Funding This work was supported by Natural Science Foundation of Inner Mongolia Autonomous Region (No. 2020MS03030).

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Bacterial community and diversity in the rumen of 11 Mongolian cattle as revealed by 16S rDNA amplicon sequencing

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Journal Publication

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Abstract

Figures

Introduction

1 Methods

1.1 Sample collection

1.2 Methods

1.2.1 Genome DNA extraction and PCR amplification

1.2.2 Library preparation and sequencing

1.2.3 Processing of sequencing data

1.2.4 Alpha diversity

1.2.5 Beta diversity

1.2.6 Function prediction

2 Results

2.1 Sequencing data analysis

2.2 Alpha diversity analysis

2.3 Beta diversity analysis

2.4 The bacteria composition and community of rumen in Mongolian cattle

2.5 Functional annotation

3. Discussion

3.1 Microbial diversity of samples

3.2 Microbial community structure of Mongolian cattle

3.3 Functional pathways of rumen bacteria in Mongolian cattle

4. Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

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