Introduction

The fecal microbiome consists of a complex community of microorganisms and represents a central issue in relation to cattle welfare and feed efficiency. In particular, the associations between fecal microbiome and animal health have been shown in the intestinal microbiota of calves (Oikonomou et al. 2013). The main factor that influences fecal microbiome composition is animal diet. Callaway et al. (2010) carried out an evaluation of bacterial diversity of 6 cattle (3 Jersey cows and 3 Angus steers) through a comparison of 3 different diets in terms of amount of dried distillers grain; Shanks et al. (2011) analyzed the structure of fecal community in 30 adult beef cattle equally divided in 3 diet groups; and Rice et al. (2012) evaluated the influence of different types and amount of distillers grains on fecal microbial assemblages in 20 crossbreed cattle. The forage to concentrates ratio in the diet is the major factor affecting fecal microbiome composition in cattle (Kim et al. 2014). According to the meta-analysis of Kim and Wells (2016), the fecal cattle microbiome is composed of 10 phyla, 17 classes, 28 orders, 59 families, and 100 genera. Firmicutes is the most represented phylum, followed by Bacteroidetes and Proteobacteria. Within Firmicutes, Clostridia and Fecalibacterium are the largest class and genus, respectively. Within Bacteroidetes, Bacteroidia is the largest class and Prevotella the largest genus. Finally, Proteobacteria includes Gammaproteobacteria and Succinivibrio as the most abundant class and genus, respectively. To investigate the microbiome in cattle, most studies have used DNA-based methodologies such as Sanger sequencing technology, quantitative real-time polymerase chain reaction, and phylogenetic microarrays (Kim et al. 2017; Mende et al. 2012). Currently, the next-generation sequencing (NGS) is considered the most reliable approach to evaluate the diversity of bacteria, both in rumen and feces of cattle (Kim et al. 2017). The 16S rRNA is widely used as reference gene to determine the composition of bacterial community due to its phylogenetic variability (Tringe and Hugenholtz 2008); indeed, it includes 9 hypervariable regions and 10 conserved regions. The conserved regions (C1 to C10) are shared among bacterial and archaeal species, whereas 16S rRNA hypervariable regions (V1 to V9) are different. The latter can be targeted to identify individual bacterial or archaeal species using PCR with species-specific primers for the 16S rRNA gene. Data analysis assigns 16S rRNA sequences to operational taxonomic units (OTUs) that can be identified according to available database. The literature reports differences of fecal microbiome composition within beef cattle breeds, across dairy and beef cattle breeds, and within crossbreed cattle (Durso et al. 2012). Comparisons between heifers and cows of dairy and dual-purpose cattle breeds are currently lacking. The aim of this study was to characterize and analyze the difference of the fecal microbiome community of heifers and cows of dairy and dual-purpose cattle breeds, targeting the hypervariable regions of the bacterial 16S; moreover, we evaluated if the microbiome composition is conserved between the breeds that underwent to different management and diet composition. Gaining knowledge on these aspects is expected to be beneficial to investigate changes in methane emissions and variation of feed efficiency, as well as to develop non-invasive routine controls to evaluate animal welfare and health.

Materials and methods

Sample collection and DNA extraction

Fecal samples were collected through rectal picking during routine health monitoring of animals by authorized veterinarians of the Breeders Association of Veneto Region (Italy). Twenty individual samples (one sample per animal) from 2 single-breed herds (one rearing Holstein-Friesian and the other Simmental breed) were collected for microbiome analysis, considering two categories: cows and heifers. Animals were divided in 2 experimental groups: (1) Holstein-Friesian heifers (HFH, n = 5) and Holstein-Friesian cows (HFC, n = 5); (2) Simmental heifers (SIH, n = 5) and Simmental cows (SIC, n = 5). The sample size was chosen after a literature review (Callaway et al. 2010; Sandri et al. 2018). A description of rations used in the 2 farms is presented in Table 1.

Table 1 Diet composition (kg/day) in each farm and cattle category
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Feces were stored at −80 °C within 1 h from sampling. DNA extraction was performed through AllPrep PowerFecal® DNA/RNA Kit (Qiagen, Hilden, Germany), and the quantity and quality of total DNA were checked through spectrophotometer assay (Multiscan Sky, Thermo Fisher Scientific, USA).

Next-generation sequencing

Total genomic DNA was amplified by using a standard protocol and modified primers (Takahashi et al. 2014). Amplicons were purified through magnetic beads Agencourt XP 0.8× (Beckman Coulter, Brea, CA, USA) and amplified through HiSeq by using Index Nextera XT kit (Illumina, San Diego, CA, USA). All amplified sequences were normalized by SequalPrep (Thermo Fisher, Waltham, MA, USA) and precipitated through magnetic beads Agencourt XP 0.8×. Libraries were loaded onto MiSeq (Illumina, San Diego, CA, USA) and sequenced following V3-300PE strategy.

Statistical analyses

The OTUs obtained from 16S rRNA sequencing results were filtered for 0.005% frequency, and organized in an OTU table. The taxonomic survey was obtained from a cross comparison between the QIIME2 software package (http://qiime.org/) and the two databases SILVA v.1.132 and Geengenes v.13.8 (the last as a comparison); clustered OTUs were matched against references from databases. Alpha-diversity analyses were conducted considering Observed OTUs, Shannon Index, Pielou’s Evenness, and Faith’s Phylogenetic Diversity Index using QIIME2 platform. All sequences were clustered with representative OTUs and cleaned considering 97% of identity as cutoff. The statistical significance of each index was analyzed by Kruskal-Wallis non-parametric test, comparing cows and heifers within the same breed. Beta-diversity was calculated through the Bray-Curtis Metric, Jaccard Metric, and the UniFrac Metric (weighted and unweighted) to evaluate the dissimilarity and distance between the animals of the same breed. Dissimilarities in fecal bacteria and archaea communities were visualized using principal coordinate analysis (PCoA) method. The permutational analysis of variance (PERMANOVA) (Anderson 2005) and analysis of similarities (ANOSIM) were performed in R-vegan package adonis (Oksanen et al. 2017). Finally, differential abundance test was performed using ANCOM packages of R software (Team R, Core Team 2015). Significance was determined through W statistic, which indicates the number of times the null hypothesis was rejected. Positive values of W statistic correspond to more abundant taxa in the comparison of HFH vs HFC, and SIH vs SIC. The test was performed accounting for the percentile abundance.

Results

Taxonomic identification

The total OTUs obtained (2,302) were clustered trough SILVA and Geengenes for the taxonomic analysis; this identified the presence of 2 kingdoms, 14 phyla, 22 classes, 34 orders, 74 families, 212 genera, and 350 species, while the remaining sequences were not assigned to known phyla (Table 2).

Table 2 Average number of sequences (standard deviation) per taxon obtained from fecal samples of Holstein-Friesian heifers (HFH), Holstein-Friesian cows (HFC), Simmental heifers (SIH), and Simmental cows (SIC)
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Archaea were represented by Euryarchaeota phylum which includes 5 genera equally distributed among the 2 experimental groups (Table 2). Within this phylum, we found several microorganisms that colonize the rumen and are involved in methane production (Holmes and Smith 2016). It is worth noting that the difference regarding Methanosphaera genus was larger for HFC (141 sequences) and SIC (157 sequences) than HFH (22 sequences) and SIH (74 sequences), likely due to the physiological status and diet composition (Hook et al. 2010). As expected, Bacteria was the largest domain in all experimental groups, representing about two-thirds of the total microbiome (Fig. 1).

Fig. 1
figure 1

Next-generation sequencing (NGS) relative abundance at the kingdom, phylum, and class levels. The figure represents 5 experimental replicates for each experimental group (x-axis). Annotation was done using SILVA database: kingdoms are shown on the left side of the figure, phyla on the central part, and classes on the right side

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Abundance of bacterial and archeal communities differs between heifers and lactating cows

As expected, within bacteria domain, Firmicutes represented the most abundant phylum (Table 2 and Fig. 1). In HFH, Paeniclostridium and Romboutsia were the largest genera, covering 1,543 and 1,119 sequences, respectively, followed by Clostridium, Eubacterium, and Turicibacter. The same order of abundance was maintained in SIH and SIC; however, in HFC, Anaerovibrio (97 sequences), Blautia (137 sequences), Marvinbryantia (157 sequences), Oscillibacter (148 sequences), Roseburia (202 sequences), and Lachnospiraceae AC2044group (274 sequences) showed high abundance. In SIH, the Candidatus class showed good number of sequences (138 sequences), while in SIC Syntrophococcus sequences were greater than the other groups. Moreover, Fourneriella genus was identified only in HFC (13 sequences) and SIC (12 sequences), being probably related to a diet rich in corn. Intestinimonas was represented only in HFH (3 sequences) and SIH (6 sequences).

Bacteroidetes represented the second most abundant phylum with 12,529 sequences in HFH; 10,308 in HFC; 9,997 in SIH; and 10,019 in SIC (Table 2). Prevotellaceae was the largest family and in all groups comprised more than 3,000 sequences, with a particular relative abundance of some genera in heifers, as Prevotellaceae UCG-004, or in cows, as Prevotellaceae UCG-003. Rikenellaceae RC9 gut group was the second largest genus and comprised 2,380 sequences in HFH; 2,015 in HFC; 2,238 in SIH; and 1,918 in SIC. The third largest genus identified was Bacteroides with high differences between heifers and cows; indeed, in HFH (1,715 sequences) and SIH (1,759 sequences), the abundance was lower compared with HFC (2,807 sequences) and SIC (2,350 sequences). Alistipes was another important genus (Table 2) that showed great variability among the experimental groups: 1,489 sequences in HFH; 866 in HFC; 1,108 in SIH; and 709 in SIC, and it is typical component of gut microbiome (** minimally invasive and routine screening system to collect information on microbiome composition at population level. This would open new opportunities to select for more efficient and healthy animals.