Abstract
Background
Rumen microbes play an important role in ruminant energy supply and animal performance. Previous studies showed that the rumen microbiome of Mongolian cattle has adapted to degrade the rough forage to provide sufficient energy to tolerate the harsh desert ecological conditions. However, little is known about the succession of rumen microbes in different developmental stages of post-weaning Mongolian cattle.
Methods
Here, we examined the succession of the rumen microbial composition and structure of 15 post-weaning Mongolian cattle at three developmental stages i.e., 5 months (RM05), 18 months (RM18) and, 36 months (RM36) by using the 16S rRNA gene sequencing method.
Results
We did not find any age-dependent variations in the ruminal concentrations of any volatile fatty acid (VFA) of Mongolian cattle. The diversity of the rumen bacterial community was significantly lower in RM05 group, which reached to stability with age. Bacteroidetes and Firmicutes were the two dominant phyla among all age groups. Phylum Actinobacteria was significantly higher in RM05 group, phyla Spirochaetes, and Tenericutes were highly abundant in RM18 group, and phyla Proteobacteria and Epsilonbacteraeota were enriched in RM36 group. Genera Prevotella_1, Bacteroides, and Bifidobacterium were abundant in RM05 group. The short chain fatty acid (SCFA) producing bacteria Rikenellaceae_RC9_gut_group showed high abundance in RM18 group and fiber degrading genus Alloprevotella was highly abundant in RM36 group. Random forest analysis identified Alloprevotella, Ileibacterium, and Helicobacter as important age discriminatory genera. In particular, the genera Ruminococcaceae_UCG-005, Bacteroides, Saccharofermentans, and Fibrobacter in RM05, genera [Eubacterium] coprostanoligenes_group, Erysipelotrichaceae_UCG-004, Helicobacter, Saccharofermentans, Papillibacter, and Turicibacter in RM18, and genera Rikenellaceae_RC9_gut_group, Lachnospiraceae_AC2044_group, and Papillibacter in RM36 showed the top interactions values in the intra-group interaction network.
Conclusions
The results showed that rumen microbiota of Mongolian cattle reached to stability and maturity with age after weaning. This study provides some theoretical evidence about the importance of functional specific rumen bacteria in different age groups. Further studies are needed to determine their actual roles and interactions with the host.
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Background
Ruminants have a complex rumen microbial community to help in adaptation to high fiber plants and provide energy in the form of VFA for the growth of the host by fermenting nutrients [1]. Rumen microbial community is known to be influenced by various factors such as diet, age, genetics, breed, and geography [2,3,4]. These factors directly or indirectly influence rumen microbiota that responds to variations in the environment and might change the physiological response of the host.
The development of rumen microbiota is linked to the structural variations of the rumen with age. The relationship between host and rumen microbiota occurs at birth as vertical transmission of microbes from the mother and is considered a crucial route for the establishment of microbiota in newborns [55]. The other two genera are known to produce butyrate by fermenting complex polysaccharides fiber and influence the rumen development and health of animal [56]. Overall, we assumed that these genera might be aiding in digestion of high fiber diet and maintaining the health of Mongolian cattle. The networks provide new dimensions to our understanding of the age-dependent variations in rumen bacterial community interactions by identifying keystone taxa.
Although three different age groups received the same diet, the variations in bacterial abundance might indicate that at 5 months of age rumen microbiota undergoes developmental changes independent of diet. The presence of Short-chain fatty acids producing bacteria in RM18 might be linked to its role in providing energy to the growing animals and hel** them to survive on shrubs and herbs during the cold and arid environment. While fiber degrading bacteria in RM36 might indicate the establishment of a stable and mature microbial community. The diet of Mongolian cattle is dominated by a variety of shrubs and halophytes, so rumen microbiota must be adapted to degrade such a recalcitrant diet which is rich in lignocellulosic materials. It is noteworthy that the age gap in this study is quite large and perhaps the analysis of smaller age would reveal more detailed variations in rumen microbiota with development.
Conclusions
In conclusion, the rumen microbiota of Mongolian cattle reached to stability and maturity with age after weaning. The diversity of the rumen bacterial community was lower at a young age which becomes stable with age. Bacteroidetes and Firmicutes were the core phyla in all age groups. We identified functional-specific bacterial genera in three age groups. Genera Prevotella_1, Bacteroides, and Bifidobacterium were abundant in RM05. The Short-chain fatty acids producing bacteria Rikenellaceae_RC9_gut_group showed high abundance in the RM18 group and the fiber degrading genus Alloprevotella was highly abundant in the RM36 group. The genera Ruminococcaceae_UCG-005, Bacteroides, Saccharofermentans, and Fibrobacter in RM05, genera [Eubacterium] coprostanoligenes_group, Erysipelotrichaceae_UCG-004, Helicobacter, Saccharofermentans, Papillibacter, and Turicibacter in RM18, and genera Rikenellaceae_RC9_gut_group, Lachnospiraceae_AC2044_group, and Papillibacter in RM36 showed the top interactions values in the intra-group interaction network. This study provides some preliminary information about the structure and composition of rumen microbiota in Mongolian cattle from weaning to adulthood. Further studies are needed to determine their actual roles and interactions with the host.
Methods
Site description
The trial was conducted at Alashan, which is located in the westernmost part of Inner Mongolia Autonomous Region, bordered in the north by Mongolia, in the south and west by Gansu province, China during the winter of 2020. Alashan has a continental climate, which is dry and windy. Winter is cold and summer is hot. Vegetation in this area is dominated by Salix cupularis, Haloxylon ammodendron, Caragana jubata and Kobresia spp. The precipitation on the Alashan is under 150 mm/year, while annual temperature ranges from 6 °C-8°C on average.
Animals and sample collection
Fifteen Ujumqin Mongolian female cattle from three different age groups i.e., 5 months old (RM05), 18 months old (RM18), and 36 months old (RM36) were randomly selected from a farm in Alashan Mongolia and were individually penned until sampling. The selected animals shared the same raising protocols i.e., all animals were naturally weaned at 5 months of age and before weaning they were purely on mother milk. After weaning, animals were allowed to graze the natural alpine shrub grasslands year round and drank water from the local river. None of the studied animals were pregnant or given birth before. The animals used in this study were not genetically related or receiving antibiotic treatment. All the animals were purely grazing and were not provided with any supplements. The body weight of animals from each age group was measured at the time of sample collection by using electronic weighing scale (Shanghai Yaohua Urban Systems Co., Ltd. Shanghai, China).
Animals from RM18 and RM36 were restrained in a veterinary crush before sampling to ensure the safety of animals, while animals from RM05 group were straddled between the handler’s legs and their shoulders were firmly squeezed between legs to avoid movement and misplacement of the oral tube. Rumen content (70 mL/animal, liquid part) was collected using an oral stomach tube from each animal before morning grazing and snap-frozen in liquid nitrogen and then stored at − 80 °C until use. Polyvinyl chloride oral tube (length = 125 cm) with small side holes (7 mm in diameter) located at the insertion end was used for young animals [57]. The length of the tube to be inserted was measured as the distance from the tip of the calf’s nose to the point of its elbow behind the front leg and marked on the tube with a piece of tape i.e., approximately 45 cm. For adult animals, stainless steel rumen fluid extractor (Chengdu Huazhi Kaiwu Technology Co., Ltd., Chengdu, China) was used for sampling and approximately 200 cm of the tube was inserted to reach the center of the rumen. Each time before taking the new sample, the tube was thoroughly cleaned with fresh warm water and about 10-15 ml of the sample from each cattle was always discarded to prevent saliva contamination [58].
Analysis of rumen volatile fatty acids
The frozen rumen fluid sample was thawed at 4 °C and thoroughly mixed by vortexing. After that, 10 mL of rumen fluid was centrifuged at 3000 g for 10 min, and 1 mL of the supernatant was transferred to a 1.5 mL centrifuge tube, along with 0.2 mL of a metaphosphoric acid solution containing the internal standard 2-ethylbutyric acid. The sample was mixed, placed in an ice-water bath for 30 min, and centrifuged at 10,000 × g at 4 °C. The supernatant was transferred to a new 1.5 mL centrifuge tube and placed at 4 °C for testing. The volatile fatty acids (VFA) concentration was determined by gas chromatography (Agilent Technologies 7820A GC system, Santa Clara, CA) equipped with a 30 m × 0.25 mm × 0.33 μm fused silica column (AE-FFAP, Atech Technologies Co. Ltd., Shanghai, China). The gas chromatographic conditions and subsequent test procedures were conducted as described previously [59].
DNA extraction and sequencing
Genomic deoxyribonucleic acid (DNA) from rumen fluid was extracted by cetyltrimethylammonium bromide method [60] and pure DNA was eluted in 150 µL of elution buffer and stored at − 20 °C until use. DNA quality and quantity were checked by 1.5% agarose gel electrophoresis and NanoPhotometer® spectrophotometer (Implen, Westlake Village, CA, USA), respectively [61]. Polymerase chain reaction (PCR) amplification of the V3-V4 region of the 16S rRNA gene was performed for bacterial analysis by using universal primer pairs (343F (5'-TACGGRAGGCAGCAG-3')-798R (5'-AGGGTATCTAATCCT-3')) with barcodes [62]. PCR amplification was performed by using Phusion® High-Fidelity PCR Master Mix with GC Buffer from New England BioLabs. Briefly, PCR amplifications were done in duplicate with 25 μL reaction mix containing 2X phusion master mix, 0.5 µM forward and reverse primers, and 20 ng of genomic DNA. The thermal cycling procedure consisted of an initial denaturation step at 98 °C for 30 s, followed by 25 cycles of 98 °C for 10 s, 56 °C for 30 s, and 72 °C for 20 s, and a final extension at 72 °C for 10 min. The amplicons were visualized using 1.5% agarose gel electrophoresis and purified with AMPure XP beads (Agencourt) according to the manufacturer's instructions [63]. The purified products were used for second round of PCR for the enrichment of ampilcons having adapters on both sides using TruSeq™ DNA sample preparation kit (Illumina Inc, San Diego, CA) according to the manufacturer’s protocol and quantified using Qubit dsDNA Assay kit (Thermo Fisher) [64]. Paired-end sequencing was carried out according to the standard protocol using the Illumina HiSeq2500 PE250 method by commercial company (Oebiotech, Shanghai, China) [65].
Bioinformatics analysis
After sequencing, barcodes and primer sequences were truncated. The QIIME (Quantitative Insights Into Microbial Ecology, Version 1.9.1) software was used to remove low-quality sequences from the raw data to get clean tags [66]. Chimera sequences were removed from clean tags using UCHIME (version 2.4.2) software to get valid tags [67]. Valid tags were clustered into Operational Taxonomic Unit (OTUs) using Vsearch (version 2.4.2) software according to 97% similarity [68]. The representative sequences of the OTUs were used to classify bacterial taxa using against Silva database (Version 123) (https://www.arb-silva.de/) using RDP Naive Bayesian classifier algorithm [69, 70]. Rarefaction curve was constructed in QIIME software, while bargraphs at phylum, family, and genus levels were created using GraphPad Prism version 8.00 for Windows (www.graphpad.com/). The alpha diversity indices such as Chao1, Shannon, Simpson, and Goods-coverage were calculated using QIIME software (Version 1.9.1). Unweighted and weighted uniFrac distance based principal coordinates analysis (PCoA) plots were drawn in R studio (Version 2.15.3) (http://www.rstudio.com/) using vegan package to demonstrate the difference between samples [71]. A correlation heat map was generated in GraphPad Prism version 8.00 for Windows (www.graphpad.com/).
Statistical analysis
Before any statistical analyses were conducted, all data were checked for normality using Shapiro–Wilk test using SPSS software (Version 20.0, IBM, Armonk, NY, United States). The Kruskal Wallis test was used to compare VFA across different groups using R software (Version 2.15.3) [72]. Analysis of similarities (ANOSIM) analysis was performed by using the ANOSIM function of the R vegan package to confirm statistically significant differences between groups [73]. The linear discriminant analysis effect size (LEfSe) method was used to examine age-dependent variations at phylum and genus levels using a linear discriminant analysis (LDA) score equal to 4 as a thresholds value [74]. Microbial interactions within studied age groups (Intra-group interaction) were determined by Spearman’s correlation coefficient of 40 top rumen bacterial genera to identify keystone species. Only genera showing P < 0.05 were further selected to plot the network by using the cytoscape (Version 3.6.13) [75]. Random forest analysis was performed to identify important age discriminatory bacterial genera by using randomForest function in the R package [76]. All P-values were adjusted using false discovery rate to remove false-positive results and significance was declared at P < 0.05.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the National Center for Biotechnology Information (NCBI) repository under BioProject ID PRJNA762300 (https://www.ncbi.nlm.nih.gov/bioproject/762300).
References
Flint HJ, Scott KP, Louis P, Duncan SH. The role of the gut microbiota in nutrition and health. Nat Rev Gastroenterol Hepatol. 2012;9(10):577.
Ahmad AA, Yang C, Zhang J, Kalwar Q, Liang Z, Chen L, et al. Effect of dietary energy levels on rumen fermentation, microbial diversity and feed efficiency of yaks (Bos grunniens). Front Microbiol. 2020;11:625.
Henderson G, Cox F, Ganesh S, Jonker A, Young W, Abecia L, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep. 2015;5:14567.
Li F, Li C, Chen Y, Liu J, Zhang C, Irving B, et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome. 2019;7(1):92.
Odamaki T, Kato K, Sugahara H, Hashikura N, Takahashi S, **ao JZ, et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC microbiol. 2016;16(1):1–12.
Minato H, Otsuka M, Shirasaka S, Itabashi H, Mitsumori M. Colonization of microorganisms in the rumen of young calves. J Gen Appl Microbiol. 1992;38(5):447–56.
Fonty G, Gouet P, Jouany J-P, Senaud J. Establishment of the microflora and anaerobic fungi in the rumen of lambs. Microbiol Mol biol Rev. 1987;133(7):1835–43.
Li RW, Connor EE, Li C, Baldwin V, Ransom L, Sparks ME. Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol. 2012;14(1):129–39.
Jami E, Israel A, Kotser A, Mizrahi I. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J. 2013;7(6):1069–79.
Liu C, Meng Q, Chen Y, Xu M, Shen M, Gao R, et al. Role of age-related shifts in rumen bacteria and methanogens in methane production in cattle. Front Microbiol. 2017;8:1563.
Li RW, Connor EE, Li C, Baldwin V, Ransom L, Sparks ME. Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol. 2012;14(1):129–39.
Meale SJ, Li S, Azevedo P, Derakhshani H, DeVries T, Plaizier J, et al. Weaning age influences the severity of gastrointestinal microbiome shifts in dairy calves. Sci Rep. 2017;7(1):1–13.
Amin N, Schwarzkopf S, Kinoshita A, Tröscher-Mußotter J, Dänicke S, Camarinha-Silva A, et al. Evolution of rumen and oral microbiota in calves is influenced by age and time of weaning. Animal microbiome. 2021;3(1):1–15.
Yin X, Ji S, Duan C, Tian P, Ju S, Yan H, et al. Age-related changes in the ruminal microbiota and their relationship with rumen fermentation in lambs. Front Microbiol. 2021;12:679135.
Kodithuwakku H, Maruyama D, Owada H, Watabe Y, Miura H, Suzuki Y, et al. Alterations in rumen microbiota via oral fiber administration during early life in dairy cows. Sci Rep. 2022;12(1):1–12.
Meale SJ, Li S, Azevedo P, Derakhshani H, Plaizier JC, Khafipour E, et al. Development of ruminal and fecal microbiomes are affected by weaning but not weaning strategy in dairy calves. Front Microbiol. 2016;7:582.
Jami E, Israel A, Kotser A, Mizrahi I. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J. 2013;7(6):1069–79.
Van F, Tjan C, Zhun V. Mongolian cattle improvement and beef breeding planning (in Chieneese). Anim Husbandry feed Sci. 2004;6:67–8.
Fedotova G, Slozhenkina M, Natyrov A, Erendzhenova M, editors. Comparative analysis of economic and biological features of Kalmyk and Mongolian cattle breeds. In IOP Conference Series: Earth and Environmental Science, vol. 548, no. 8, p. 082076. IOP Publishing; 2020. https://iopscience.iop.org/article/10.1088/1755-1315/548/8/082076.
**ng Y, Qi Y, Purev C, Wang S, Wang H, Wu K, et al. Structural variants in Mongolian originated ruminant: role in adaptation of extreme-environment. Research Square. 2020.
Zhang X. Northern China. Grasslands and Grassland Sciences in Northern China: The national academies press. 1992. p. 39–54.
Vasseur E, Borderas F, Cue RI, Lefebvre D, Pellerin D, Rushen J, et al. A survey of dairy calf management practices in Canada that affect animal welfare. J Dairy Sci. 2010;93(3):1307–16.
Schwarzkopf S, Kinoshita A, Kluess J, Kersten S, Meyer U, Huber K, et al. Weaning Holstein calves at 17 weeks of age enables smooth transition from liquid to solid feed. Animals. 2019;9(12):1132.
Ochirkhuu N, Konnai S, Odbileg R, Nishimori A, Okagawa T, Murata S, et al. Detection of bovine leukemia virus and identification of its genotype in Mongolian cattle. Adv Virol. 2016;161(4):985–91.
Hu L, Brito LF, Abbas Z, Sammad A, Kang L, Wang D, et al. Investigating the short-term effects of cold stress on metabolite responses and metabolic pathways in inner-mongolia sanhe cattle. Animals. 2021;11(9):2493.
Wang Y, Zhang H, Zhu L, Xu Y, Liu N, Sun X, et al. Dynamic distribution of gut microbiota in goats at different ages and health states. Front Microbiol. 2018;9:2509.
Lane M, Baldwin Rt, Jesse BJJoas. Sheep rumen metabolic development in response to age and dietary treatments. 2000;78(7):1990-6
Han X, Yang Y, Yan H, Wang X, Qu L, Chen Y. Rumen bacterial diversity of 80 to 110-day-old goats using 16S rRNA sequencing. PLoS ONE. 2015;10(2):e0117811.
Petri R, Schwaiger T, Penner G, Beauchemin K, Forster R, McKinnon J, et al. Changes in the rumen epimural bacterial diversity of beef cattle as affected by diet and induced ruminal acidosis. Appl Environ Microbiol. 2013;79(12):3744–55.
Malmuthuge N, Liang G. Regulation of rumen development in neonatal ruminants through microbial metagenomes and host transcriptomes. Genome Biol. 2019;20(1):172.
Pitta DW, Indugu N, Kumar S, Vecchiarelli B, Sinha R, Baker LD, et al. Metagenomic assessment of the functional potential of the rumen microbiome in Holstein dairy cows. Anaerobe. 2016;38:50–60.
Kumar S, Indugu N, Vecchiarelli B, Pitta DW. Associative patterns among anaerobic fungi, methanogenic archaea, and bacterial communities in response to changes in diet and age in the rumen of dairy cows. Front Microbiol. 2015;6:781.
Zhang B, Wu X, Tai X, Sun L, Wu M, Zhang W, et al. Variation in actinobacterial community composition and potential function in different soil ecosystems belonging to the arid Heihe River Basin of Northwest China. Front Microbiol. 2019;10:2209.
Paster B, Canale-Parola E. Physiological diversity of rumen spirochetes. Appl Environ Microbiol. 1982;43(3):686–93.
Wang Y, Huang J-M, Zhou Y-L, Almeida A, Finn RD, Danchin A, et al. Phylogenomics of expanding uncultured environmental Tenericutes provides insights into their pathogenicity and evolutionary relationship with Bacilli. BMC Genomics. 2020;21(1):1–12.
Shin N-R, Whon TW, Bae J-W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 2015;33(9):496–503.
Waite DW, Vanwonterghem I, Rinke C, Parks DH, Zhang Y, Takai K, et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front Microbiol. 2017;8:682.
Ley RE. Prevotella in the gut: choose carefully. Nat Rev Gastroenterol Hepatol. 2016;13(2):69–70.
Wexler HM. Bacteroides: the good, the bad, and the nitty-gritty. Clin Microbiol Rev. 2007;20(4):593–621.
Collado MC, Meriluoto J, Salminen S. Measurement of aggregation properties between probiotics and pathogens: in vitro evaluation of different methods. J Microbiol Methods. 2007;71(1):71–4.
Timmerman H, Koning C, Mulder L, Rombouts F, Beynen A. Monostrain, multistrain and multispecies probiotics—a comparison of functionality and efficacy. Int J Food Microbiol. 2004;96(3):219–33.
Bi Y, Zeng S, Zhang R, Diao Q, Tu Y. Effects of dietary energy levels on rumen bacterial community composition in Holstein heifers under the same forage to concentrate ratio condition. BMC Microbiol. 2018;18(1):69.
Holman DB, Gzyl KE. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS microbiology ecology. 2019;95(6):fiz072.
Downes J, Dewhirst FE, Tanner AC, Wade WG. WG. Description of Alloprevotella rava gen. nov., sp. nov., isolated from the human oral cavity, and reclassification of Prevotella tannerae Moore et al. 1994 as Alloprevotella tannerae gen. nov., comb. nov. Int J Syst Evol Microbiol. 2013;63(Pt 4):1214.
Jiang W, Wu N, Wang X, Chi Y, Zhang Y, Qiu X, et al. Dysbiosis gut microbiota associated with inflammation and impaired mucosal immune function in intestine of humans with non-alcoholic fatty liver disease. Sci Rep. 2015;5(1):1–7.
Neumann AP, McCormick CA, Suen G. Fibrobacter communities in the gastrointestinal tracts of diverse hindgut-fermenting herbivores are distinct from those of the rumen. Environ Microbiol. 2017;19(9):3768–83.
Zhang Y, Zhang X, Li F, Li C, Li G, Zhang D, et al. Characterization of the rumen microbiota and its relationship with residual feed intake in sheep. Animal. 2021;15(3):100161.
Bi Y, Cox MS, Zhang F, Suen G, Zhang N, Tu Y, et al. Feeding modes shape the acquisition and structure of the initial gut microbiota in newborn lambs. Environ Microbiol. 2019;21(7):2333–46.
Li L, Batt SM, Wannemuehler M, Dispirito A, Beitz DC. Effect of feeding of a cholesterol-reducing bacterium, Eubacterium coprostanoligenes, to germ-free mice. Comp Med. 1998;48(3):253–5.
Wang H, **a P, Lu Z, Su Y, Zhu W. Metabolome-Microbiome Responses of Growing Pigs Induced by Time-Restricted Feeding. Front Vet Sci. 2021;8:681202. https://doi.org/10.3389/fvets.2021.681202.
Li F, Wang Z, Dong C, Li F, Wang W, Yuan Z, et al. Rumen bacteria communities and performances of fattening lambs with a lower or greater subacute ruminal acidosis risk. Front Microbiol. 2017;8:2506.
Huo W, Zhu W, Mao S. Impact of subacute ruminal acidosis on the diversity of liquid and solid-associated bacteria in the rumen of goats. World J Microbiol Biotechnol. 2014;30(2):669–80.
Martínez I, Wallace G, Zhang C, Legge R, Benson AK, Carr TP, et al. Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut microbiota. Appl Environ Microbiol. 2009;75(12):4175–84.
Van den Abbeele P, Belzer C, Goossens M, Kleerebezem M, De Vos WM, Thas O, et al. Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J. 2013;7(5):949–61.
Asma Z, Sylvie C, Laurent C, Jérôme M, Christophe K, Olivier B, et al. Microbial ecology of the rumen evaluated by 454 GS FLX pyrosequencing is affected by starch and oil supplementation of diets. FEMS Microbiol Ecol. 2013;83(2):504–14.
Sagheddu V, Patrone V, Miragoli F, Puglisi E, Morelli L. Infant early gut colonization by Lachnospiraceae: high frequency of Ruminococcus gnavus. Front Pediatr. 2016;4:57.
Klopp R, Oconitrillo M, Sackett A, Hill T, Schlotterbeck R, Lascano G. A simple rumen collection device for calves: an adaptation of a manual rumen drenching system. J Dairy Sci. 2018;101(7):6155–8.
Shen J, Chai Z, Song L, Liu J, Wu Y. Insertion depth of oral stomach tubes may affect the fermentation parameters of ruminal fluid collected in dairy cows. J Dairy Sci. 2012;95(10):5978–84.
Isac MD, García MA, Aguilera JF, Alcaide EM. A comparative study of nutrient digestibility, kinetics of digestion and passage and rumen fermentation pattern in goats and sheep offered medium quality forages at the maintenance level of feeding. Arch Anim Nutr. 1994;46(1):37–50.
Minas K, McEwan NR, Newbold CJ, Scott KP. Optimization of a high-throughput CTAB-based protocol for the extraction of qPCR-grade DNA from rumen fluid, plant and bacterial pure cultures. FEMS Microbiol Lett. 2011;325(2):162–9.
Lee PY, Costumbrado J, Hsu C-Y, Kim YH. Agarose gel electrophoresis for the separation of DNA fragments. J Vis Exp. 2012;62:3923.
Nossa CW, Oberdorf WE, Yang L, Aas JA, Paster BJ, DeSantis TZ, et al. Design of 16S rRNA gene primers for 454 pyrosequencing of the human foregut microbiome. World J Gastroenterol. 2010;16(33):4135.
Schofield BJ, Lachner N, Le OT, McNeill DM, Dart P, Ouwerkerk D, et al. Beneficial changes in rumen bacterial community profile in sheep and dairy calves as a result of feeding the probiotic Bacillus amyloliquefaciens H57. J Appl Microbiol. 2018;124(3):855–66.
Armbrecht M. Economic DNA Determination in the Eppendorf BioSpectrometer® fluorescence Using Qubit™ Assay kits. Eppendorf Application Note 402; www.eppendorf.com, https://handling-solutions.eppendorf.com/fileadmin/Community/Sample_Handling/Amplification/Application_Note_402.pdf.
Holm JB, Humphrys MS, Robinson CK, Settles ML, Ott S, Fu L, et al. Ultrahigh-throughput multiplexing and sequencing of> 500-base-pair amplicon regions on the Illumina HiSeq 2500 platform. MSystems. 2019;4(1):e00029-e119.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200.
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7.
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. 2012;41(D1):D590–6.
Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Package “vegan” Title Community Ecology Package. 2019.
Hollander M, Wolfe DA, Chicken E. Nonparametric statistical methods: John Wiley & Sons. 2013.
Anderson MJ, Walsh DC. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 2013;83(4):557–74.
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):1–18.
Griffith DM, Veech JA, Marsh CJ. Cooccur: probabilistic species co-occurrence analysis in R. J Stat Softw. 2016;69:1–17.
Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002;2(3):18–22.
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Funding
We acknowledge the funding support from the Innovation Program of Chinese Academy of Agricultural Sciences (25-LZIHPS-04) and the international cooperation and exchange program of the National Natural Science Foundation of China (31461143020).
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A.A, J.Z, Z.L, M.D, Y.Y and J.Z performed exxperiment. A.A.wrote the main manuscript text. A.A, J.Z, and Z.L prepared figures. All authors reviewed the manuscript. P.Y, R.L, B.T, J.H and X.D conceived and designed the experiments. The author(s) read and approved the final manuscript.
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The animals were provided as experimental animals by the Cattle owner and after the completion of the experiment, all animals keep on living for other experimental work.
All procedures involving animals were approved by Animal Administration and Ethics Committee of Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Science, Lanzhou, China (2019–0004).
All experiments were performed in accordance with relevant guidelines and regulations. The study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).
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Ahmad, A.A., Zhang, J., Liang, Z. et al. Age-dependent variations in rumen bacterial community of Mongolian cattle from weaning to adulthood. BMC Microbiol 22, 213 (2022). https://doi.org/10.1186/s12866-022-02627-6
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DOI: https://doi.org/10.1186/s12866-022-02627-6