Abstract
Milk containing antibiotics is used as cost-effective feed for calves, which may lead to antibiotic residues-associated food safety problems. This study aims to investigate the influence of antibiotics on rumen microbes. Through metagenomic sequencing, the rumen microbial communities of calves fed with pasteurized milk containing antibiotics (B1), milk containing antibiotics (B2) and fresh milk (B3) were explored. Each milk group included calves in 2 (T1), 3 (T2) and 6 (T3) months of age. Using FastQC software and SOAPdenovo 2, the filtered data, respectively, were performed with quality control and sequence splicing. Following KEGG annotation was conducted for the uploaded sequences using KAAS software. Using R software, both species abundance analysis and differential abundance analysis were performed. In the B1 samples, the species abundance of Bacteroidetes gradually decreased along with the extension of feeding time, while that of Fibrobacteres gradually increased. The species abundances of Proteobacteria (p value = 0.01) and Spirochaetes (p value = 0.03) had significant differences among T1, T2 and T3 samples. Meanwhile, only the species abundance of Spirochaetes (p value = 0.04) had significant difference among B1, B2 and B3 samples. Cell cycle involving GSK3β, CDK2 and CDK7 was significantly enriched for the differentially expressed genes in the T1 versus T2 and T1 versus T3 comparison groups. Milk containing antibiotics might have a great influence on these rumen microbes and lead to antibiotic residues-associated food safety problems. Furthermore, GSK3β, CDK2 and CDK7 in rumen bacteria might affect milk fat metabolism in early growth stages of calves.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Many kinds of microbes have the abilities of multi-drug resistance and broad-spectrum resistance (Davies and Davies 2010). Smillie et al have observed 42 common antibiotic resistance genes between livestocks and human beings, as well as 43 antibiotic resistance genes across geographic borders, indicating that antibiotic resistance genes can result in severe transnational consequences for human microbiome is globally spread (Smillie et al. 2011). Bovine rumen is inhabited by a large amount of microbiota (such as fungi, bacteria, archaea and protozoa) which contribute to degrade plant materials into digestible compounds (including bacterial proteins and volatile fatty acids) (Brulc et al. 2009). Therefore, rumen microbiota is important for the quality and production of meat and milk and consequently is also critical for human (Stevenson and Weimer 2007; Sundset et al. 2009; Welkie et al. 2010). At present, antibiotics have become the most economical and effective products for treating pneumonia, mastitis, hysteritis and other diseases in dairy cows (Goshen and Shpigel 2006; Katsuda et al. 2009; Barlow 2011). However, the milk produced by cows treated with antibiotics may have remaining antibiotics, which can lead to antibiotic residues-associated food safety problems (Oliver et al. 2011). For milk containing antibiotics has been used as cost-effective feed resource for newborn calves, it is necessary to explore the influence of antibiotics on rumen microbes.
Recently, bovine rumen microbiota has been extensively researched based on metagenomic sequencing. For example, Singh et al have analysed the microbial diversity in buffalo rumen using metagenomic sequencing and identified the typically microbial genes including bacterial virulence genes and antibiotic resistance genes (Singh et al. 2012). Using pyrosequencing of 16S rRNAs, quantitative real-time PCR and denaturing gradient gel electrophoresis, bacterial communities adhered to the rumen epithelial of 8 cattle were analysed in the transition process of forage to concentrated diet, in the process of acidosis, and when recovered (Petri et al. 2013). Pandya et al characterize the bacterial communities in rumen of 3 adult Surti buffaloes and differentiate 42 operational taxonomic units; additionally, the high coverage of 16S rRNA libraries (94.76%) suggests that sequences in the libraries can represent most of the bacterial diversity in rumen (Pandya et al. 2010). Through whole-genome shotgun and pyrosequencing of 16S rRNA, Li et al characterize the rumen microbiota of calves fed with milk replacer and identify 170 bacterial genera including 45 genera in the core microbiome of pre-ruminant calves (Li et al. 2012). Besides, the bacterial populations in rumen of 16 lactating cows have been studied by pyrosequencing, and different samples show 51% similarity in bacterial taxa (Jami and Mizrahi 2012). Nevertheless, the influence of milk containing antibiotics on rumen microbiota of newborn calves has not been explored yet.
To investigate the differences of microbial communities in the rumen of calves fed with pasteurized milk containing antibiotics (B1), milk containing antibiotics (B2) and fresh milk (B3), species abundance analysis and differential abundance analysis of the microbial community in ruminal fluids were conducted. This study may provide scientific basis for the rational use of milk containing antibiotics and calves breeding.
Materials and methods
Sample collection, DNA extraction and next-generation sequencing
Animal procedures were approved by the Ethical Committee of Livestock Research Institute of Heilongjiang Bayi Agricultural University. A total of 54 newborn calves (without significantly different birth weights) were selected from spark dairy farm in Daqing City. The experiments continued for 6 months. During the study period, the calves had different milks before 2 months of age, and then were weaned and fed with the same amount of granules under the same feeding conditions. Through puncturing, the ruminal fluids were extracted from calves fed with pasteurized milk containing antibiotics, milk containing antibiotics and fresh milk. Each milk group included calves in 2 months of age (T1), 3 months of age (T2) and 6 months of age (T3). The calves in different ages in each milk group had 6 repeats. The milk containing antibiotics was produced by cows treated with intravenous drip of 500 ml Nacl and 2 packages of ceftiofur sodium (Zhengzhou Bairui Animal Pharmaceutical Co., Ltd, Zhengzhou, China; 0.5 g/package) once a day. The filtered ruminal fluids extracted from calves of the same age in each milk group were mixed and stored in liquid nitrogen for following sequencing. Based on the manufacturer’s instructions, genomic DNA (gDNA) in ruminal fluids was isolated by the TIANamp Stool DNA Kit (Tiangen, Beijing, China). Subsequently, the sequencing library was constructed according to the manufacturer instruction of library preparation kits (New England Biolabs, Inc., Beverly, MA, USA). In addition, the library was performed with paired-end sequencing based on the platform of Illumina NextSeq 500 (Illumina, CA, USA). The sequencing data were uploaded to the public National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/) database (Wheeler et al. 2007), and the accession number was SRP075724.
Data filtering, quality control and sequence splicing
Using cutadapt-1.2.1 software (https://pypi.python.org/pypi/cutadapt/1.2.1) (Martin 2011), adapters were removed from the raw data. Then, the data were performed with quality screen, with average mass fraction ≥Q20 (5-bp non-overlap window) as the threshold. To filter out the fragments of host genome, the clean reads were mapped to the host genome by Burrows–Wheeler Aligner (BWA) software (http://bio-bwa.sourceforge.net) (Li and Durbin 2009). Moreover, FastQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) (Andrew 2010) was applied to perform quality control for the data. Additionally, the preprocessed data were spliced by SOAPdenovo 2 (http://soap.genomics.org.cn/soapdenovo.html) (Luo et al. 2012) to obtain contigs and scaffolds.
KEGG annotation
Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg) database can be utilized for the annotation and classification of metabolic pathways from cellular processes, human diseases, metabolism, environmental information processing, organismal systems and genetic information processing aspects (Kanehisa et al. 2012). Using KEGG Automatic Annotation Server (KAAS, http://www.genome.jp/tools/kaas) software (Moriya et al. 2007), KEGG annotation was performed for the uploaded gene sequences. Metagenomes for prokaryotes were selected, and other parameters were set as defaults.
Abundance analysis
Using BLAST (McGinnis and Madden 2004), the filtered contigs were aligned to the sequences of bacteria, fungi, archaea and viruses which were extracted from the NCBI nucleotide (nt) database (version: 2014-10 -19). The e value ≤1e−5 was taken as the cut-off criterion. By the locally adaptive clustering (LAC) algorithm (Parvin et al. 2011), the taxonomic rank before the first branch was selected as the species annotation information of a certain sequence. Combined with the statistical information of sequences in the level of phylum, ANOVA test (Campbell and Lele 2013) and Tukey HSD multiple comparison test (Conagin et al. 2008) of species abundance were performed among multiple groups using R software package. The species with p value <0.05 had significant differences among the groups. Principal components analysis (PCA) is a multivariate statistical technique that reduces dimensionality and retains variation in data (Ringnér 2008). Using R software package (Mevik 2007), PCA was performed based on the species abundance in the level of genus.
Differential analysis
After the data were normalized, the differentially expressed genes (DEGs) in the T1 versus T2, T1 versus T3, B1 versus B2 and B1 versus B3 comparison groups were identified by paired t test command (Zhou and Wang 2007) in R software package. The genes with p value <0.05 and |log2 fold change (FC)| > 1 were taken as DEGs. Finally, the DEGs in different comparison groups were performed with KEGG pathway enrichment analysis, respectively.
Results
Data analysis
After the raw data were preprocessed, more than 96% high-quality reads were selected (Table 1). Then, the reads were performed with quality control, and the results of base mass distribution, base content distribution, GC content distribution and sequence base quality indicated a high quality. Through sequence splicing, a total of 3,498,534 contigs and 262,753 scaffolds were obtained.
KEGG annotation
KEGG annotation was performed for the uploaded gene sequences, and the results are shown in Fig. 1. The enriched terms were mainly associated with cellular processes, environmental information processing, genetic information processing, human diseases, metabolism and organismal systems.
Abundance analysis
In the level of phylum, the relative species abundances of the samples are exhibited in Fig. 2. In the samples, the species abundances of Bacteroidetes (mean abundance = 69.8%), Firmicutes (mean abundance = 9.8%), Proteobacteria (mean abundance = 9.2%), Fibrobacteres (mean abundance = 4.9%) and Actinobacteria (mean abundance = 2.8%) were relatively higher in the level of phylum. In the B1T1, B1T2 and B1T3 samples, the species abundances of Bacteroidetes and Firmicutes in the level of phylum gradually decreased along with the extension of feeding time, while those of Proteobacteria, Fibrobacteres and Actinobacteria were just the opposite. In the B2T1, B2T2 and B2T3 samples, the species abundances of Proteobacteria and Verrucomicrobia in the level of phylum gradually increased along with the extension of feeding time, while those of other species had no rules. In the B3T1, B3T2 and B3T3 samples, the species abundance of Proteobacteria in the level of phylum gradually increased along with the extension of feeding time; however, those of other species had no rules. In general, B3T1 sample with 11,754,882 species and B2T1 sample with 11,753,187 species had relatively higher species abundances in the level of phylum, which might be caused by that microbe inherent in B1 had been killed by pasteurization.
In the level of genus, the species abundances of Prevotella, Bacteroides, Fibrobacter, Barnesiella and Alistipes in all of the samples were relatively higher. In the B1T1, B1T2 and B1T3 samples, the species abundance of Fibrobacter in the level of genus gradually increased along with the extension of feeding time, while those of Prevotella, Bacteroides and Barnesiella were just the opposite. In the B2T1, B2T2 and B2T3 samples, the species abundance of Fibrobacter also gradually increased along with the extension of feeding time. However, the species abundance of Fibrobacter gradually decreased along with the extension of feeding time in the B3T1, B3T2 and B3T3 samples (Fig. 3).
With p value <0.05 as threshold, variance analysis of species abundance was performed among multiple groups. The species abundances of Proteobacteria (p value = 0.01) and Spirochaetes (p value = 0.03) had significant differences among T1, T2 and T3 samples. Meanwhile, only the species abundance of Spirochaetes (p value = 0.04) had significant difference among B1, B2 and B3 samples. Additionally, the result of PCA showed that the samples could be significantly divided into 3 parts according to feeding time (Fig. 4).
Differential analysis
There were 318 and 1398 DEGs in the T1 versus T2 and T1 versus T3 comparison groups, respectively. A total of 9 and 33 pathways, respectively, were significantly enriched for the DEGs in the T1 versus T2 and T1 versus T3 comparison groups, including 2 common pathways of cell cycle (Fig. 5; which involved glycogen synthase kinase-3β, GSK3β; cyclin-dependent kinase 2, CDK2; and cyclin-dependent kinase 7, CDK7) and biosynthesis of amino acids (Fig. 6). A total of 30 and 24 DEGs were identified for the B1 versus B2 and B1 versus B3 comparison groups, respectively. There, respectively, were 9 and 1 pathways significantly enriched for the DEGs in the B1 versus B2 and B1 versus B3 comparison groups. However, no common pathways were found for the DEGs in the two comparison groups.
Discussion
After preprocessing, more than 96% high-quality reads were identified from the raw data. Through sequence splicing, a total of 3,498,534 contigs and 262,753 scaffolds were obtained. The result of PCA showed that the samples could be significantly divided into 3 parts according to feeding time. The species abundances of Fibrobacteres and Fibrobacter were relatively higher in the samples. The phylum Fibrobacteres consists of one genus (Fibrobacter) and two species (Fibrobacter intestinalis and F. succinogenes) and is known for degrading cellulosic plant biomass in the gut of herbivore, suggesting that cellulose degradation may be a potential characteristic of the phylum (Ransom-Jones et al. 2012, 2014; Rosenberg 2014). As effective cellulolytic bacteria, the members of Fibrobacteres act in rumen function and serve as promising sources of novel enzymes that can be used for bioenergy applications (Jewell et al. 2013). Previous study detects relatively more members of the phylum Fibrobacteres in cellulolytic ecosystems through the 16S rRNA-based environmental surveys (Ransom-Jones et al. 2014). Physiological and ecological characterization shows that F. succinogenes is an important cellulolytic microbe and plays an essential role in fibre digestion in the rumen (Kobayashi et al. 2008; Shinkai et al. 2010). F. succinogenes degrades plant cell walls via arabinofuranosidase(s), esterases, glucanases and xylanases, opening its way through the complex matrix of hemicellulose and cellulose (Jun et al. 2007). The phylum Bacteroidetes includes 4 classes (Bacteroidia, Cytophagia, Flavobacteria and Sphingobacteria), and approximately 7000 species, in particular Flavobacteria is the largest class which comprises of more than four times of species than other classes (Whitman et al. 2012). Members of the phylum Bacteroidetes are the major microbes in the gastrointestinal tract and are increasingly considered as special decomposers of high molecular weight organic matter, such as carbohydrates and proteins (Thomas et al. 2011; Fernández-Gómez et al. 2013). In the B1T1, B1T2 and B1T3 samples, the species abundance of Bacteroidetes gradually decreased along with the extension of feeding time, while that of Fibrobacteres was just the opposite. These were consistent with the results in the level of genus. These declared that Bacteroidetes and Fibrobacteres contributed to digestion in the rumen, and Bacteroidetes was more sensitive to pasteurized milk containing antibiotics.
The β-lactam antibiotics (such as clavulanate, carbapenem, cephalosporin, nocardicin, monobactam and penicillins) are appropriate for the therapy of bacterial infection for they have unparalleled broad antibacterial spectrum and clinical safety (Testero et al. 2010). Due to the appearance and fast spreading of antibiotic-resistant pathogens, the issue of antibiotic resistance has attracted much attention (Aminov and Mackie 2007). Bacteria can acquire resistance to β-lactam antibiotics through utilizing β-lactam-insensitive cell wall transpeptidases, producing β-lactam-hydrolyzing β-lactamase enzymes, or expulsing β-lactam molecules from Gram-negative cells (Wilke et al. 2005). The antibiotic resistances are reported to mainly be acquired resistance, resulting by antibiotic resistance genes transferred from other taxonomically and ecologically distant bacteria (Aminov and Mackie 2007). The phylum Proteobacteria contains most diverse phylogenetic lineage (such as Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria and Epsilonproteobacteria), which possesses excessive metabolic diversity and has important biological significance (Kersters et al. 2006). Spirochaetes are spiral-shaped, Gram-stain-negative and motile cells, and most Spirochaetes have periplasmic flagella containing organelles of motility (Paster 2010). Anaerobic Spirochaetes belonging to the genus Brachyspira have been regarded as key pathogens of pig gut for long, but they are increasingly considered as causes of disease in other animals (Hampson and Ahmed 2009). The species abundances of Proteobacteria (p value = 0.01) and Spirochaetes (p value = 0.03) had significant differences among T1, T2 and T3 samples. Meanwhile, only the species abundance of Spirochaetes (p value = 0.04) had significant difference among B1, B2 and B3 samples and was more abundant in B2 samples, suggesting that the abundant Spirochaetes contributes to the antibiotic resistance. Thus, milk containing antibiotics might have influence on rumen microbial community and might lead to antibiotic residues-associated food safety problems through affecting pathogenic Spirochaetes.
There were 318, 1398, 30 and 24 DEGs in the T1 versus T2, T1 versus T3, B1 versus B2 and B1 versus B3 comparison groups, respectively. Cell cycle (which involved GSK3β, CDK2 and CDK7) and biosynthesis of amino acids were the two common pathways, which were significantly enriched for the DEGs, respectively, in the T1 versus T2 and T1 versus T3 comparison groups. Reportedly, GSK3β could mediate milk synthesis and the proliferation of mammary epithelial cells in dairy cow (Zhang et al. 2014). In addition, phosphorylation of GSK3 by the prolactin receptor (PRLR) is highly associated with milk production during lactation (Shi et al. 2016). These suggest the involvement of GSK3β in cell cycle and proliferation regulation. Unfortunately, expression of GSK3β in rumen tissue has not yet been reported. CDK2 plays an important role in cell growth and division (Lu et al. 2012). Reduced activity of CDK2 induces the G1 cell cycle arrest of trans-10 conjugated linoleic acid (CLA), which presents less than 1% in the CLA isomer in milk fat that is generated by rumen bacteria (Rosbergcody et al. 2007). CDK7 is another CDKs family member relating to cell cycle regulation (Fisher 2005). Based on our study, the three above genes were all differentially expressed among three stages in rumen bacteria fed with different milk. These collectively hint that rumen bacteria might influence the milk fat metabolism via cell cycle regulation, in the early growth stages (2–6 months) of calves, whatever the milk feeding is.
In conclusion, a total of 3,498,534 contigs and 262,753 scaffolds were obtained. Bacteroidetes and Fibrobacteres contributed to digestion in the rumen, and Bacteroidetes was more sensitive to pasteurized milk containing antibiotics. Besides, milk containing antibiotics had influence on rumen microbial community and might lead to antibiotic residues-associated food safety problems through affecting pathogenic Spirochaetes. Furthermore, GSK3β, CDK2 and CDK7 in rumen bacteria might affect the cell cycle of milk fat metabolism in early growth stages (2–6 months) of calves, regardless of different milk feedings.
References
Aminov RI, Mackie RI (2007) Evolution and ecology of antibiotic resistance genes. FEMS Microbiol Lett 271:147–161
Andrew S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/
Barlow J (2011) Mastitis therapy and antimicrobial susceptibility: a multispecies review with a focus on antibiotic treatment of mastitis in dairy cattle. J Mammary Gland Biol Neoplasia 16:383–407
Brulc JM et al (2009) Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci 106:1948–1953
Campbell D, Lele S (2013) An ANOVA test for parameter estimability using data cloning with application to statistical inference for dynamic systems. Comput Stat Data Anal 70:257–267
Conagin A, Barbin D, Demétrio CGB (2008) Modifications for the Tukey test procedure and evaluation of the power and efficiency of multiple comparison procedures. Sci Agric 65:428–432
Davies J, Davies D (2010) Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74:417–433
Fernández-Gómez B et al (2013) Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J 7:1026–1037
Fisher RP (2005) Secrets of a double agent: cDK7 in cell-cycle control and transcription. J Cell Sci 118:5171–5180
Goshen T, Shpigel NY (2006) Evaluation of intrauterine antibiotic treatment of clinical metritis and retained fetal membranes in dairy cows. Theriogenology 66:2210–2218
Hampson DJ, Ahmed N (2009) Spirochaetes as intestinal pathogens: lessons from a Brachyspira genome. Gut pathogens 1:1
Jami E, Mizrahi I (2012) Composition and similarity of bovine rumen microbiota across individual animals. PLoS ONe 7:e33306
Jewell KA, Scott JJ, Adams SM, Suen G (2013) A phylogenetic analysis of the phylum Fibrobacteres. Syst Appl Microbiol 36:376–382
Jun H, Qi M, Ha J, Forsberg C (2007) Fibrobacter succinogenes, a dominant fibrolytic ruminal bacterium: transition to the post genomic era. Asian Australas J Anim Sci 20:802
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114
Katsuda K, Kohmoto M, Mikami O, Uchida I (2009) Antimicrobial resistance and genetic characterization of fluoroquinolone-resistant Mannheimia haemolytica isolates from cattle with bovine pneumonia. Vet Microbiol 139:74–79
Kersters K, De Vos P, Gillis M, Swings J, Vandamme P, Stackebrandt E (2006) Introduction to the proteobacteria. In: Dworkin M, Falkow S, Rosenberg E (eds) The prokaryotes. Springer, Berlin, pp 3–37
Kobayashi Y, Shinkai T, Koike S (2008) Ecological and physiological characterization shows that Fibrobacter succinogenes is important in rumen fiber digestion: review. Folia Microbiol 53:195–200
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25:1754–1760
Li RW, Connor EE, Li C, Baldwin V, Ransom L, Sparks ME (2012) Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol 14:129–139
Lu J, Zhao H, Xu J, Zhang L, Yan L, Shen Z (2012) Elevated cyclin D1 expression is governed by plasma IGF-1 through Ras/Raf/MEK/ERK pathway in rumen epithelium of goats supplying a high metabolizable energy diet. J Anim Physiol A Anim Nutr 97:1170–1178
Luo R et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1:1–6
Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12
McGinnis S, Madden TL (2004) BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 32:W20–W25
Mevik Br (2007) The PLS package: principal component and partial least squares regression in R. J Stat Softw 18:1–24
Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:W182–W185
Oliver SP, Murinda SE, Jayarao BM (2011) Impact of antibiotic use in adult dairy cows on antimicrobial resistance of veterinary and human pathogens: a comprehensive review. Foodborne Pathog Dis 8:337–355
Pandya P et al (2010) Bacterial diversity in the rumen of Indian Surti buffalo (Bubalus bubalis), assessed by 16S rDNA analysis. J Appl Genet 51:395–402
Parvin H, Minaei-Bidgoli B, Alizadeh H (2011) A new clustering algorithm with the convergence proof. In: Jordanov I, Jain RJHL (eds) Knowledge-based and intelligent information and engineering systems. Springer, Berlin, pp 21–31
Paster BJ (2010) Phylum XV: spirochaetes garrity and holt 2001. In: Vos P, Garrity G, Jones D, Krieg NR, Ludwig W, Rainey FA, Schleifer K-H, Whitman W (eds) Bergey’s manual® of systematic bacteriology. Springer, Berlin, pp 471–566
Petri R et al (2013) Changes in the rumen epimural bacterial diversity of beef cattle as affected by diet and induced ruminal acidosis. Appl Environ Microbiol 79:3744–3755
Ransom-Jones E, Jones DL, McCarthy AJ, McDonald JE (2012) The Fibrobacteres: an important phylum of cellulose-degrading bacteria. Microb Ecol 63:267–281
Ransom-Jones E, Jones DL, Edwards A, McDonald JE (2014) Distribution and diversity of members of the bacterial phylum Fibrobacteres in environments where cellulose degradation occurs. Syst Appl Microbiol 37:502–509
Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26:303–304
Rosbergcody E, Johnson MC, Fitzgerald GF, Ross PR, Stanton C (2007) Heterologous expression of linoleic acid isomerase from Propionibacterium acnes and anti-proliferative activity of recombinant trans-10, cis-12 conjugated linoleic acid. Microbiology 153:2483–2490
Rosenberg E (2014) The phylum Fibrobacteres. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds) The prokaryotes. Springer, Berlin, pp 641–642
Shi H, Zhang T, Yi Y, Wang H, Luo J (2016) Long form PRLR (lPRLR) regulates genes involved in the triacylglycerol synthesis in goat mammary gland epithelial cells. Small Rumin Res 139:7–14
Shinkai T, Ueki T, Kobayashi Y (2010) Detection and identification of rumen bacteria constituting a fibrolytic consortium dominated by Fibrobacter succinogenes. Anim Sci J 81:72–79
Singh K, Jakhesara S, Koringa P, Rank D, Joshi C (2012) Metagenomic analysis of virulence-associated and antibiotic resistance genes of microbes in rumen of Indian buffalo (Bubalus bubalis). Gene 507:146–151
Smillie CS, Smith MB, Friedman J, Cordero OX, David LA, Alm EJ (2011) Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480:241–244
Stevenson DM, Weimer PJ (2007) Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol 75:165–174
Sundset MA et al (2009) Molecular diversity of the rumen microbiome of Norwegian reindeer on natural summer pasture. Microb Ecol 57:335–348
Testero SA, Fisher JF, Mobashery S (2010) β-Lactam antibiotics. In: Abraham DJ, Rotella DP (eds) Burger’s medicinal chemistry, drug discovery and development. Wiley, Hoboken, pp 259–404
Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G (2011) Environmental and gut Bacteroidetes: the food connection. Front Microbiol 2:93
Welkie DG, Stevenson DM, Weimer PJ (2010) ARISA analysis of ruminal bacterial community dynamics in lactating dairy cows during the feeding cycle. Anaerobe 16:94–100
Wheeler DL et al (2007) Database resources of the national center for biotechnology information. Nucleic Acids Res 35:D5–D12
Whitman WB et al (2012) Bergey’s manual of systematic bacteriology, vol 5. Springer, New York
Wilke MS, Lovering AL, Strynadka NC (2005) β-Lactam antibiotic resistance: a current structural perspective. Curr Opin Microbiol 8:525–533
Zhang X et al (2014) GSK3β regulates milk synthesis in and proliferation of dairy cow mammary epithelial cells via the mTOR/S6K1 signaling pathway. Molecules 19:9435–9452
Zhou N, Wang L (2007) A modified T-test feature selection method and its application on the HapMap genotype data. Genomics Proteomics Bioinform 5:242–249
Acknowledgements
This study was funded by National Natural Science Foundation of China (Grant Number 31340031); Synergetic Innovation Center of Food Safety and Nutrition, National Key Technologies R&D Program (Grant Numbers 2012BAD12B05-1 and 2012BAD12B02).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by Yusuf Akhter.
Rights and permissions
About this article
Cite this article
Li, W., Han, Y., Yuan, X. et al. Metagenomic analysis reveals the influences of milk containing antibiotics on the rumen microbes of calves. Arch Microbiol 199, 433–443 (2017). https://doi.org/10.1007/s00203-016-1311-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00203-016-1311-8