Background: Microbiome studies need to analyze massive sequencing data, which requires the use of sophisticated bioinformatics pipelines. Up to date, several tools are available, although the literature is scarce on studies that compare the performance of different bioinformatics pipelines on rumen microbiota when 16S rRNA amplicons are analyzed. The impact of the pipeline on the outcome of the results is also unknown, mainly in terms of the output from studies using these tools as an intermediate phenotype (pseudophenotypes). This study compares two commonly used software (Quantitative Insights Into Microbial Ecology) (QIIME) and mothur, and two microbial gene data bases (GreenGenes and SILVA) for 16S rRNA gene analysis, using metagenome read data collected from rumen content of a cohort of dairy cows. Results: We compared the relative abundance (RA) of the identified OTUs at the genus level. Both tools presented a high degree of agreement at identifying the most abundant genera: Bifidobacterium, Butyrivibrio, Methanobrevibacter, Prevotella, and Succiniclasticum (RA > 1%), regardless the database. There were no statistical differences between mothur and QIIME (P > 0.05) at estimating the overall RA of the most abundant (RA > 10%) genera, either using SILVA or GreenGenes. However, differences were found at RA < 10% (P < 0.05) when using GreenGenes as database, with mothur assigning OTUs to a larger number of genera and in larger RA for these less frequent microorganisms. With this database mothur resulted in larger richness (P < 0.05), more favorable rarefaction curves and a larger analytic sensitivity. These differences caused significant and relevant differences between tools at identifying the dissimilarity of microbiotas between pairs of animals. However, these differences were attenuated, but not erased, when SILVA was used as the reference database. Conclusion: The findings showed that the SILVA database seemed a preferred reference dataset for classifying OTUs from rumen microbiota. If this database was used, both QIIME and mothur produced comparable richness and diversity, and also in the RA of most common rumen microbes. However, important differences were found for less common microorganisms which impacted on the beta diversity calculated between pipelines. This may have relevant implications at studying global rumen microbiota.

The objective of this study was to investigate the effects of chitosan (CHI) on ruminal and cecal fermentation and on apparent digestibility in sheep. In a first trial, 4 ruminally fistulated sheep fed an alfalfa hay and concentrate diet formulated to meet 1.2 times their maintenance energy requirements were assigned to 2 treatments [no-additive control (CTR), or 136 mg/kg of BW of CHI] for 19 d in a 2 × 2 crossover design. Samples of ruminal fluid and feces were taken for VFA and NH₃-N concentrations. At the end of each period of the crossover, ruminal fluid was used as inoculum for batch cultures (24 and 96 h), and the effects on rate and extent of gas production and on fermentation variables for starch, cellulose, and the same diet fed to the animals were calculated. In a second trial, 4 sheep fed the alfalfa hay and concentrate diet were assigned to 2 treatments (CTR and CHI), as for the first trial, but this time for 14 d in a 2 × 2 crossover design. Apparent digestibility was studied. In the first trial, in terms of ruminal fermentation, CHI did not affect (P = 0.12) total VFA concentration or acetate proportion, but increased propionate proportion (P = 0.004) and propionate-to-acetate ratio (C3:C2; P = 0.007) and decreased branched-chain VFA (BCVFA) proportion (P = 0.003) and NH₃-N concentration (P = 0.045). In feces, CHI decreased total VFA concentration (P = 0.01) and acetate (P = 0.045) and butyrate (P = 0.025) proportions, and increased propionate (P = 0.04), valerate (P = 0.01), and BCVFA (P = 0.032) proportions and C3:C2 ratio (P = 0.045). In batch cultures, when starch was used as substrate, CHI decreased acetate proportion (P = 0.016) and methane production (P = 0.03) and increased propionate and valerate proportions and C3:C2 ratio (P < 0.001). When cellulose was used as substrate, CHI decreased in vitro OM digestibility (P = 0.033), total VFA concentration, propionate proportion (P < 0.001), C3:C2 ratio (P = 0.006), methane production (P < 0.001), NH₃-N concentration (P = 0.019), and gas production rate (P = 0.002), and increased butyrate (P = 0.029) and BCVFA (P = 0.004) proportions and pH (P < 0.001). When the alfalfa hay and concentrate diet was used as substrate, CHI decreased butyrate (P < 0.001) and BCVFA (P = 0.008) proportions, methane (P = 0.005) and asymptotic gas production (P = 0.02), and gas production rate (P = 0.05). In the second trial, CHI decreased NDF apparent digestibility (P = 0.09). In conclusion, CHI shifted ruminal fermentation toward energetically more efficient routes, without reducing OM apparent digestibility. Copyright © 2010. American Society of Animal Science .

The aim of this trial was to study the agreement between the non-dispersive infrared methane analyzer (NDIR) method and the hand held laser methane detector (LMD). Methane (CH₄) was measured simultaneously with the two devices totaling 164 paired measurements. The repeatability of the CH₄ concentration was greater with the NDIR (0.42) than for the LMD (0.23). However, for the number of peaks, repeatability of the LMD was greater (0.20 vs. 0.14, respectively). Correlation was moderately high and positive for CH₄ concentration (0.73 and 0.74, respectively) and number of peaks (0.72 and 0.72, respectively), and the repeated measures correlation and the individual-level correlation were high (0.98 and 0.94, respectively). A moderate concordance correlation coefficient was observed for the CH₄ concentration (0.62) and for the number of peaks (0.66). A moderate-high coefficient of individual agreement for the CH₄ concentration (0.83) and the number of peaks (0.77) were observed. However, CH₄ concentrations population means and all variance components differed between instruments. In conclusion, methane concentration measurements obtained by means of NDIR and LMD cannot be used interchangeably. The joint use of both methods could be considered for genetic selection purposes or for mitigation strategies only if sources of disagreement, which result in different between-subject and within-subject variabilities, are identified and corrected for.

Records of methane emissions from 1,501 cows on 14 commercial farms in 4 regions of Spain were collected from May 2018 to June 2019. Methane concentrations (MeC) were measured using a nondispersive infrared methane detector installed within the feed bin of the automatic milking system during 14- to 21-d periods. Rumination time (RT; min/d) was collected using collars with a tag that registered time (minutes) spent eating and ruminating. The means of MeC and methane production (MeP) were 1,254.28 ppm and 182.49 g/d, respectively; mean RT was 473.38 min/d. Variance components for MeC, MeP, and RT were estimated with REML using pedigree and genomic information in a single-step model. Heritabilities for MeC and MeP were 0.11 and 0.12, respectively. Rumination time showed a slightly larger heritability estimate (0.17). The genetic correlation between MeP and MeC was high (>0.95), suggesting that selection on either trait would lead to a positive correlated response on the other. Negative correlations were estimated between RT and MeC (-0.24 ± 0.38) and MeP (-0.43 ± 0.35). Methane concentration and MeP had slightly positive correlations with milk yield (0.17 ± 0.39 and 0.21 ± 0.36), protein percentage (0.08 ± 0.32 and 0.30 ± 0.45), protein yield (0.22 ± 0.41 and 0.31 ± 0.35), fat percentage (0.02 ± 0.40 and 0.27 ± 0.36), and fat yield (0.27 ± 0.28 and 0.29 ± 0.28) from bivariate analyses. Rumination time had positive correlations with milk yield (0.41 ± 0.75) and protein yield (0.26 ± 0.57) and negative correlations with fat yield (-0.45 ± 0.32), protein percentage (-0.15 ± 0.38), and fat percentage (-0.40 ± 0.47). A positive approximated genetic correlation was estimated between fertility and MeC (0.10 ± 0.05) and MeP (0.18 ± 0.05), resulting in slightly higher CH4 production when selecting for better fertility [days open estimated breeding values (EBV) are expressed with mean 100 and SD 10, inversely related to days from calving to conception; that is, greater days open EBV implies better fertility]. Positive correlations were also estimated for stature with MeC and MeP (0.30 ± 0.04 and 0.43 ± 0.04, respectively). Other type traits (chest width, udder depth, angularity, and capacity) were positively correlated with methane traits, possibly because of higher milk yield and higher feed intake from these animals. Rumination time showed positive EBV correlations with production traits and type traits, and negative correlations with somatic cell count and body condition score. Based on the genetic correlations and heritabilities estimated in this study, methane is measurable and heritable, and estimates of genetic correlations suggest no strong opposition to current breeding objectives in Spanish Holsteins.

This study aimed to evaluate whether the host genotype exerts any genetic control on the microbiome composition of the rumen in cattle. Microbial DNA was extracted from 18 samples of ruminal content from 2 breeds (Holstein and Brown Swiss). Reads were processed using mothur (https://www.mothur.org/) in 16S and 18S rRNA gene-based analyses. Then, reads were classified at the genus clade, resulting in 3,579 operational taxonomic units (OTU) aligned against the 16S database and 184 OTU aligned against the 18S database. After filtering on relative abundance (>0.1%) and penetrance (95%), 25 OTU were selected for the analyses (17 bacteria, 1 archaea, and 7 ciliates). Association with the genetic background of the host animal based on the principal components of a genomic relationship matrix based on single nucleotide polymorphism markers was analyzed using Bayesian methods. Fifty percent of the bacteria and archaea genera were associated with the host genetic background, including Butyrivibrio, Prevotella, Paraprevotella, and Methanobrevibacter as main genera. Forty-three percent of the ciliates analyzed were also associated with the genetic background of the host. In total, 48% of microbes were associated with the host genetic background. The results in this study support the hypothesis and provide some evidence that there exists a host genetic component in cattle that can partially regulate the composition of the microbiome.