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The Rumen Specific Bacteriome in Dry Dairy Cows and Its Possible animals Article The Rumen Specific Bacteriome in Dry Dairy Cows and Its Possible Relationship with Phenotypes 1, 2,3, 2 2 4 Shih-Te Chuang y, Shang-Tse Ho y, Po-Wen Tu , Kuan-Yi Li , Yu-Lun Kuo , Jia-Shian Shiu 5, Sheng-Yao Wang 2 and Ming-Ju Chen 2,* 1 Department of Veterinary Medicine, National Chung Hsing University, Taichung 402, Taiwan; [email protected] 2 Department of Animal Science and Technology, National Taiwan University, Taipei 106, Taiwan; [email protected] (S.-T.H.); [email protected] (P.-W.T.); [email protected] (K.-Y.L.); [email protected] (S.-Y.W.) 3 Department of Wood Based Materials and Design, National Chiayi University, Chiayi 600, Taiwan 4 Biotools Co., Ltd., New Taipei City 221, Taiwan; [email protected] 5 Hengchun Branch, Livestock Research Institute, Council of Agriculture, Executive Yuan, Pingtung 946, Taiwan; [email protected] * Correspondence: [email protected]; Tel.: +88-6-2-3366-4173 These authors contributed equally to this work. y Received: 13 August 2020; Accepted: 29 September 2020; Published: 1 October 2020 Simple Summary: The aim of this study was to investigate the importance of the relationships among hosts, microbiota, and diet composition in dry dairy cows. Therefore, the composition of the rumen microbiome in cows from three dairy farms was investigated to identify core bacteria contributing to various physiological roles during rumen fermentation in dry dairy cows. Our results revealed that the ruminal fluid in dry dairy cows from different regional farms had core rumen microbiota that could be clearly distinguished from that of cows of the other farms. Abstract: Most microbiome studies of dairy cows have investigated the compositions and functions of rumen microbial communities in lactating dairy cows. The importance of the relationships among hosts, microbiota, diet composition, and milk production remains unknown in dry dairy cows. Thus, in the present study, the composition of the rumen microbiome in cows from three dairy farms was investigated to identify core bacteria contributing to various physiological roles during rumen fermentation in dry dairy cows. The results indicated that ruminal fluid in dry dairy cows from different regional farms had core rumen microbiota that could be clearly distinguished from that of cows of the other farms. Further identification of key microorganisms associated with each farm revealed that Prevotella, Methanobrevibacter, Pseudobutyrivibrio, Ruminococcus, Bacteroides, and Streptococcus were major contributors. Spearman’s correlation indicated that the abundance of genera such as Prevotella and Ruminococcus in dry dairy cows could indicate milk yield in the previous lactating period. Functional pathway analysis of the rumen bacterial communities demonstrated that amino acid metabolism and carbohydrate metabolism were the major pathways. Our findings provide knowledge of the composition and predicted functions of rumen microbiota in dry dairy cows from regional farms, which underscore the importance of the relationships among hosts, microbiota, diet composition, and milk production. Keywords: dry period; rumen microbiota; physiological roles; Prevotella; Ruminococcus Animals 2020, 10, 1791; doi:10.3390/ani10101791 www.mdpi.com/journal/animals Animals 2020, 10, 1791 2 of 15 1. Background The rumen, the forestomach of ruminants, harbors bacteria, archaea, fungi, and ciliate protozoa [1]. An indispensable function of these microorganisms is to break down plant polymers into volatile fatty acids through various fermentation pathways to be absorbed and used by host animals [1,2]. The composition and function of rumen microbial communities are not only affecting growth and milk production but are also related to the host health and nutrient utilization in dairy cattle [3,4]. The unique metabolites (e.g., saturated fatty acids, organic acids, amine, or polysaccharide) produced by rumen microbes may play a vital role in ruminant physiology [5]. Jewell et al. [6] reported that some rumen bacteria are associated with milk production efficiency and ketosis. Many factors, including diet, species, age, and geographic location, can affect the composition and function of rumen microbiota [6,7]. Furthermore, the acidosis, subacidosis, and metabolic dysfunction of the rumen can change the composition and function of rumen microbiota [8–10]. Given the importance of rumen microbial communities on ruminant health and productivity, many researchers have attempted to manipulate rumen microbiota through various strategies, such as using chemical agents and enzymes as feed additives or probiotic supplements. However, modification of rumen microbial compositions in adult ruminants appears difficult. Changes in rumen microbiota from exogenous factors can be rapidly restored by eliminating the influential factors [11], but little is known of the ecological and physiological roles of predominant core bacteria in the rumen microbial ecosystem. In addition, the lactation cycle is split into lactation and dry periods. Most studies have focused on investigating rumen microbiota during lactation; for instance, studies have assessed how rumen microbiota respond to exogenous butyrate [12] and the composition of rumen microbiota [13,14]. The dry period is a crucial rest period for cows in which new hormonal stimulation is gained for subsequent lactation. The precise interaction between a rumen microbial community and the host animal should be investigated for the whole lactation cycle to investigate the possibility of maintaining the health and productivity of ruminants by manipulating rumen microbial composition. Nevertheless, limited information has been reported concerning rumen microbiomes and their functions during dry periods. Thus, in the present study, the composition of rumen microbiomes from three dairy farms in the northern, middle, and southern regions of Taiwan, respectively, were investigated. We identified core bacteria that contribute to various physiological roles during rumen fermentation in dry cows. The exogenous factors affecting rumen microbiomes were also studied. Fundamental knowledge of rumen core microbiomes and their relationship to physiological functionality in rumen microbial ecosystems during dry periods can provide insight into potential manipulation of rumen microbiota to enhance dairy performance, such as milk production and cow health. 2. Methods All experimental procedures were conducted according to guidelines from the Institutional Animal Care and Use Committee of National Taiwan University (IACUC number: NTU105-EL-00022). The owners’ consent was verbally confirmed as part of routine veterinary operation. 2.1. Animal Sources and Sampling All experimental procedures were conducted according to guidelines from the Institutional Animal Care and Use Committee of National Taiwan University (IACUC number: NTU105-EL-00022). In total, 15 Holstein cows in the second week of their dry periods from three farms were sampled in Taipei (experimental farm of the National Taiwan University, Farm A, N = 5), Changhua (private farm, Farm B, N = 5), and Tainan (Lin Fengying’s daily farm, Farm C, N = 5). The animals were fed with a mixed forage–concentrate diet with different diet compositions among the farms (Farm A: 10.71% crude protein (CP) and 60.90% crude fiber (CF); Farm B: 9.92% CP and 66.79% CF; Farm C: 13.20% CP and 43.08% CF). The forage-to-concentrate ratios (F:C) in diet dry matter (DM) basis of Farms A, B and C were 85.7:14:3, 88.8:11.2, and 67.5:32.5, respectively (Table1). Average milk production during the Animals 2020, 10, 1791 3 of 15 previous lactating period for the cows of Farms A, B, and C were 24.1 8.3, 22.5 7.6, and 20.1 8.5 kg, ± ± ± respectively. The cows’ entire mid-rumen fluid contents (approximately 200 mL) were collected using a stainless-steel stomach tube by a veterinarian, and their entire rumen fluid volume was filtered with gauze to remove solids. After this study, all experimental animals were released and then maintained by the original farms. Table 1. The dietary information of dry cows in different farms. Farm A Major ingredient Dry cow concentrate, Bermuda grass F:C (DM) 85.7:14:3 Crude protein (% DM) 10.71 Crude fiber (% DM) 60.90 Calcium (% DM) 0.60 Phosphorus (% DM) 0.25 Farm B Major ingredient Dry cow concentrate, Bermuda grass, Pennisetum grass F:C (DM) 88.8:11.2 Crude protein (% DM) 9.92 Crude fiber (% DM) 66.79 Calcium (% DM) 0.66 Phosphorus (% DM) 0.34 Farm C Major ingredient Dry cow concentrate, Bermuda grass, corn silage, oat grass F:C ratio 67.5:32.5 Crude protein (% DM) 13.20 Crude fiber (% DM) 43.08 Calcium (% DM) 0.45 Phosphorus (% DM) 0.39 F:C = forage-to-concentrate ratios; DM = dry matter. 2.2. DNA Extraction and Next-Generation Sequencing Rumen samples were immediately frozen in a 80 C freezer and then freeze-dried. DNA − ◦ was extracted from 100 mg of homogenized rumen sample through bead beating followed by phenol–chloroform extraction [5]. The resulting solution was precipitated with ice-cold isopropanol (Sigma-Aldrich, St. Louis, MO, USA) to obtain the DNA pellet. The pellet was washed with 70% ethanol twice and suspended in 200 µL of ddH2O. The DNA was quantified using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) and stored in a 20 C freezer until − ◦ analyzed. For next-generation sequencing (NGS), the 16S ribosomal RNA (rRNA) gene regions were amplified for further analysis. The primer set used in this study was 16S, 515F, and 806R for amplification of the V4 16S rRNA gene region. The PCR reaction was conducted in a PCR machine (Biometra T3000 thermocycler, Analytik Jena, Göttingen, Germany); the PCR conditions are shown in Supplementary Materials Table S1. 2.3. Bioinformatics Amplicon sequencing was performed using the paired-end Illumina HiSeq platform to generate 250 bp paired-end raw reads, and the entire set of paired-end reads was assembled using FLASH Animals 2020, 10, 1791 4 of 15 v.1.2.7 [15]. Demultiplexing was conducted from barcode identification. For quality control, low-quality reads (Q < 20) were discarded in the QIIME 1.9.1 pipeline [16].
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