Effect of Grape Pomace Intake on the Rumen Bacterial Community of Sheep
by
, , , , , and
Michal Rolinec
1,*,
Juraj Medo
2,
Michal Gábor
1,
Martina Miluchová
1,
Milan Šimko
1,
Branislav Gálik
1,
Ondrej Hanušovský
1,
Zuzana Schubertová
3,
Daniel Bíro
1,
Luboš Zábranský
4 and
Miroslav Juráček
1,* 1
Institute of Nutrition and Genomics, Slovak University of Agriculture in Nitra, Trieda A. Hlinku 2, 94976 Nitra, Slovakia
2
Institute of Biotechnology, Slovak University of Agriculture in Nitra, Trieda A. Hlinku 2, 94976 Nitra, Slovakia
3
Institute of Plant and Environmental Sciences, Slovak University of Agriculture in Nitra, Trieda A. Hlinku 2, 94976 Nitra, Slovakia
4
FZT Department of Animal Husbandry Sciences, University of South Bohemia, Branišovská 1645/31a, 37005 České Budějovice, Czech Republic
*
Authors to whom correspondence should be addressed.
Diversity 2023, 15(2), 234; https://doi.org/10.3390/d15020234
Submission received: 29 December 2022
/
Revised: 3 February 2023
/
Accepted: 4 February 2023
/
Published: 7 February 2023
(This article belongs to the Topic Challenges of Animal Science Diversity in Times of Climate and Technological Change)
Abstract
:The performance of ruminants is affected mainly by the rumen bacterial community. The composition and properties of the rumen bacterial community depend largely on the diet components that are fed to the ruminant. The aim of this study was to determine the effect of grape pomace intake on the rumen bacterial community of sheep. Four different diets, two of which contained dried grape pomace (DGP), were used in this study. Rumen fluid samples from 12 wethers were used for 16S rRNA gene sequencing and subsequent bacterial identification. At the phylum level, Bacteroidetes and Firmicutes; at the family level, Prevotellaceae and Porphyromonadaeceae; and at the genera level, Prevotella and Verrucomicrobia_Subdivision5_genera_incertae_sedis were the most common regardless of the diet the animals were fed. After the addition of DGP to the diet, the relative abundance of Methanobrevibacter, Butytirivibrio, Fretibacterium, and Verrucomicrobia _Subdivision3 _genera_incertae_sedis significantly increased, whereas that of Succiniclasticum and Selenomonas significantly decreased. The upregulated pathway of methanogenesis from H2 and CO2 was supported by a significant increase in Methanobacteriaceae after the diet was supplemented with DGP. The rumen bacterial community diversity indices (Richness and Shannon) were significantly affected by diet composition as well as by the change of housing location. The addition of DGP into the wethers’ diet increased the richness of the rumen bacterial community, which is good for maintaining rumen bacterial homeostasis. No adverse effect of the addition of DPG on the rumen bacterial community was noted.
1. Introduction
The essential presumption of the full utilization of ruminants’ production potential is the maintenance of a stable rumen environment as well as high activity of rumen bacteria. From a nutritional point of view, the quantity and quality of ruminant performance is conditioned by feeds, which affects the production of volatile fatty acids and microbial protein in the rumen [1,2]. For this, a stable supply of nutrients to the rumen is required. Every change in ruminants’ diets causes alterations in the rumen bacterial population. If there is a negative alteration, the efficiency of production is also negatively affected [3].
Findings published by Clark [4] indicated that nutrition is a key factor affecting the rumen bacterial community. The utilization of by-products for feeding animals is preferred nowadays. Around the world, approximately 70 million tons of grapes are produced annually [5], which creates a huge amount of grape pomace. As stated in [6], grape by-products have potential applications in animal nutrition as a source of bioactive compounds. Grape pomace is a source of polyphenols, including tannins, which cause complexation between the tannin and protein, thus positively affecting the amount of dietary ruminal undegraded protein [7,8]. Grape pomace as part of a ruminant diet has been researched by many authors [9,10,11]. Additionally, the effect of grape pomace on the rumen microbiota has been researched. Vasta et al. [12] found that polyphenols can violate the rumen microbiota composition. On the other hand, Salami et al. [13] concluded that polyphenol-rich feed (cardoon meal) can be used for ruminants without negatively affecting the rumen microbiota. Biscarini et al. [14] found an increase in the diversity of rumen microbiota after feeding grape pomace as a source of natural polyphenols.
Research on the dietary effects on the rumen microbiota is highly topical, and the results of experiments with grape pomace and the gut bacterial community are not uniform; this study was designed to address some of these issues. The effects of dietary change, together with the shift of the location and the effect of the addition of grape pomace on the bacterial community of the rumen, were investigated.
2. Materials and Methods
2.1. Animals and Experiment Design
In this experiment, 12 wethers (Ile de France breed) with an average weight of 35.28 ± 2.04 kg were included. For bacterial community analyses, wethers were marked as individual A, B, C, … L. Wethers were housed in the University Farm (Slovak University of Agriculture in Nitra, Nitra, Slovakia) in Žirany before the experiment. The experiment was conducted at the barn for farm animals in Nitra (Slovak University of Agriculture in Nitra, Nitra, Slovakia). The feeding regime and rumen fluid collection procedure are shown in Figure 1. In each period of the experiment, the same twelve wethers were used. This provided the same effect because the animals’ genetic makeup, condition, and health status were the same for each experimental period. First, rumen fluid samples were collected immediately after the transport of wethers from University Farm in Žirany to the barn for farm animals in Nitra (Slovak University of Agriculture in Nitra, Nitra, Slovakia), where the experiment was carried out (Timepoint 1). After collection of the first sample, a 19-day preparatory period followed, feeding the wethers a control diet. After the end of the preparatory period, the second rumen fluid samples were collected (Timepoint 2). Subsequently, wethers were fed a control diet containing 1% DGP for 12 days. On the last day of the first experimental period, the third rumen fluid sample was collected (Timepoint 3). Then, the second experimental period started, where animals were fed with a control diet containing 2% DGP for 12 days. During the last day of the second experimental period, the fourth rumen fluid sample was collected (Timepoint 4) (Figure 1). The compositions of all diets are shown in Table 1. For all diets, mineral and vitamin licks and water were available ad libitum throughout the entire study period. DGP was obtained after the drying of pomace after grape (Pinot Gris variety) pressing. The pomace obtained after grape pressing consisted of skin, pulp, and seeds. Drying was conducted for 4 days at 55 ± 5 °C. The grape pomace was acquired from the Academic Winery of the Slovak University of Agriculture in Nitra, located in the village of Oponice (Slovakia). To ensure that the only feed intake that occurred was from the tested diets, from the start of the preparatory period to the end of the second experimental period, wethers were housed in a pen without any bedding. Wethers were kept under standard conditions and cared for by trained and experienced staff under the care of the contracted veterinary doctor. The protection standards for animals used for scientific and teaching purposes were in accordance with national directive no. 2010/63/EU [15].
2.2. Diet Composition and Analysis
The nutritional characteristics of the diets fed to wethers are shown in Table 1. Diets were analyzed in the Laboratory of Quality and Nutritional Value of Feeds at the Institute of Nutrition and Genomics (Slovak University of Agriculture in Nitra), according to official AOAC laboratory methods [16].
2.3. Rumen Fluid Sample Collection
Rumen fluid was collected on the morning of the last day of each period, before feeding. After the wether had been secured, 2 mL rumen fluid was collected using duodenal sound type Levin CH14 (Unomedical, ConvaTec, Greenboro, NC, USA). Sampled rumen fluid was mixed with 2 mL of DNA/RNA preservation reagent (DNA/RNA ShieldTM, Zymoresearch, Irvine, CA, USA). Rumen fluid samples were stored at −80 °C.
2.4. DNA Analysis
Bacterial DNA extraction was performed using the EZ10 bacterial extraction kit (BioBasic, Markham, ON, Canada). For assessment of the bacterial community, the V4 part of the 16S gene was amplified using barcoded primers 515F and 806R [17]. The reaction mixture (25 µL) contained 1U of Q5 DNA polymerase (New England Biolabs, Inc., Ipswich, MA, USA), 12.5 µL Q5 PCR buffer (New England Biolabs, Inc., Ipswich, MA, USA), 5 µL of each primer (2 µM) (Generi Biotech, Hradec Králové, Czech Republic), and 2 µL of DNA. The thermal cycling conditions were as follows: initial denaturation 98 °C for 3 min; 30 cycles of 98 °C denaturation for 3 s; 65 °C annealing for 10 s; and amplification at 72 °C for 20 s. PCR products were checked on an agarose gel, purified by Ampure XP (Beckman Coulter, Brea, CA, USA), quantified by qubit (Invitrogen, Carlsbad, CA, USA), and pooled in equimolar ratios. Sequencing libraries were prepared using the Illumina TruSeq PCR free library prep kit (Illumina, San Diego, CA, USA). Sequencing was performed on Illumina MiSeq using a V3 2x300 bp kit. Acquired sequences were initially processed using SEED 2.1 software [18], where sequences were demultiplexed according to their barcodes and quality-filtered. Then, the DADA2 algorithm in QIIME 2 [19] was used for sequence denoising and amplicon sequence variant (ASV) calling. ASVs were identified using the Ribosomal Database Project (RDP) Classifier ver. 2.13. [20]. Indexes of diversity (richness, Pielou evenness, and Shannon’s diversity index) and a Unifrac similarity matrix were calculated using QIIME 2. Picrust2 was used for the prediction of active metabolic pathways annotated by the MetaCYC database.
2.5. Statistical Analysis
Statistical analysis was performed in R [21]. The abundances of particular ASVs, genera, families, or phyla were analyzed using DeSeq2 [22]. Diversity indices among timepoints were compared using paired t-tests. Similarities in community composition among samples were visualized using NMDS and statistically analyzed by PERMANOVA using a two-factor model involving variability in timepoints and between individuals. PERMANOVA, pairwise comparisons, and betadisper homogeneity of dispersion was analyzed using the vegan package [23]. Metabolic pathways predicted in PICRUSt2 [24] were statistically compared between timepoints using DeSeq2, and volcano plots were created using ggplot in R.
3. Results
In total, 2236 ASVs were identified in rumen fluid, regardless of the diet that was fed and irrespective of the sampling timepoint. When the distribution of ASVs at each timepoint was considered, the high proportion of ASVs was specific for timepoint 1 and timepoint 4. Only 6.2% of ASVs were common across all four sampling timepoints (Figure 2).
The Bacteroidetes, Firmicutes, and Verrucomicrobia phyla were the most abundant in rumen fluid. These were followed by Proteobacteria, Euryarchaeota, Synergistetes, SR1, and Candidatus Saccharibacteria. The phyla with an abundance of at least 1% in at least one sampling timepoint were Actinobacteria, Fibrobacteres, Spirochaetes, Chloroflexi, and Tenericutes. The change of housing together with the change from the diet fed at the University Farm in Žirany to the control diet (TP1 to TP2) caused a shift in the relative abundance of some bacteria, which was significant for 9 of the 13 most abundant phyla. On the other hand, the addition of 1% DGP to the control diet (TP2 to TP3) caused only one significant shift by Synergistetes (p < 0.001). An increase in DGP in the diet from 1% to 2% (TP3 to TP4) caused four significant changes (Euryarchaeota, Candidatus Saccharibacteria, Fibrobacteres, and Verrucomicrobia). In comparison with the control diet, in the control diet with the addition of 2% DGP (TP2 to TP4), the abundance of Euryarchaeota and Candidatus Saccharibacterium increased, whereas that of Spirochaetes, Tenericutes, and Verrucomicrobia decreased (Table 2).
The rumen families with an abundance of at least 10% in at least one sampling timepoint were Prevotellaceae, Porphyromonadaceae, Verrucomicrobia_Subdivision5_genera_incertae_sedis, and Ruminococcaceae. In total, 25 rumen bacterial families exhibited an abundance of at least 1% at at least one sampling timepoint.
Most (13 of 25 of the most abundant rumen bacterial families) significant abundance shifts occurred after the change of housing, together with the change from the diet fed at the University Farm in Žirany to the control diet (TP1 to TP2). The addition of DGP in an amount of 1% to the control diet (TP2 to TP3) caused two significant shifts by Acidaminococcaceae and Synergistaceae. After an increase in the level of DGP in the diet from 1% to 2% (TP3 to TP4), 10 significant shifts were detected, and 9 significant shifts occurred in families with a relative abundance of over 1.0% when comparing TP2 with TP4 (Table 3).
The rumen genera with an abundance of at least 5% at at least one sampling timepoint were Prevotella, Verrucomicrobia_Subdivision5_genera_incertae_sedis, Acetobacteroides, Succiniclasticum, Fretibacterium, and SR1_genera_incertae_sedis. In total, 38 rumen bacterial genera revealed abundances of at least 0.9% at at least one of the sampling timepoints (Table 4). The change of housing and the change in diet between TP1 and TP2 caused a significant abundance shift in 19 of the 38 most abundant rumen genera. The addition of 1% DGP to the control diet (TP2 to TP3) resulted in increases in the abundance of Paraprevotella, Chelonobacter, Fretibacterium, and Verrucomicrobia_Subdivision3_genera_incertae_sedis, as well as decreases in the abundance of Barnesiella, Tannerella, Succiniclasticum, and Selenomonas. Increases in DGP, from 1% to 2% (TP3 to TP4), resulted in increases in the abundance of Methanobrevibacter, Barnesiella, and Butyrivibrio, as well as decreases in the abundance of Mangroviflexus, Alloprevotella, Paraprevotella, Fibrobacter, Schwartzia, Chelonobacter, and Verrucomicrobia_Subdivision5_genera_incertae_sedis. Twelve significant shifts occurred in the relative abundance of dominant genera, when comparing TP2 with TP4 (Table 4).
Diversity indices were significantly affected by the timepoint (richness p = 0.000014; Shannon p = 0.00336); individual effects were not significant in any index. The addition of DGP into the wethers’ diets increased richness in the rumen bacterial community. The average values of rumen bacterial community diversity indices and comparisons between timepoints are summarized in Table 5.
Beta diversity analysis performed using NMDS based on unifrac distances showed a clear separation of samples based on timepoint (Figure 3). Timepoint 1 samples were clustered as a separated group, and differences among other timepoints were lower. According to the PERMANOVA, 28% of the variability in the microbiome composition of ruminal fluid was explained by the timepoint (p = 0.001), whereas differences between individual wethers explained 22% of the variability (p = 0.002).
In silico metabolic predictions using Picrust2 were performed to determine metagenome functions. Figure 4 shows the predicted functions, which were a result of changes in the diet composition. The addition of DGP into the wethers’ diets increased the amino acid degradation pathway, as well as acetone, menaquinol, and methane production, but decreased pathways for the degradation of fucose. Pairwise ANOSIM comparison showed that all timepoints significantly affected the composition; all p values were 0.001. The strongest difference estimated by the R value was between timepoints 1 and 3, whereas differences between timepoints 2 and 3 and timepoints 3 and 4 were lower (Table 6).
4. Discussion
Bacteroidetes and Firmicutes have been reported to be the dominant rumen bacterial phyla [25]. Li et al. [26] and Liu et al. [27] reported that Bacteroidetes and Firmicutes were the most abundant sheep rumen phyla in addition to Proteobacteria, Verrucomicrobiota, Spirochaetes, and Fibrobacteres, which is similar to the results of this study. Additionally, in the study by Liu et al. [27], Prevotella was detected as the predominant sheep rumen genus. The addition of DGP into the diet did not affect the abundance of Prevotella (Table 4). According to Liu et al. [27], Prevotella has high genetic variability, which enables this genus to occupy different ecological niches within the rumen. It seems that the abundance of Prevotella remains unchanged after the intake of polyphenol-rich feed, such as a daily diet containing DGP in amounts of 1% or 2%. Succiniclasticum has mainly been identified in the liquid fraction of the rumen, and is specialized in fermenting succinate to propionate [12,28]. Diaz Carrasco et al. [28] demonstrated an increase (from 1.99% to 3.99%) in Succiniclasticum in the rumen after the supplementation of a diet with tannin at a concentration of 2 g per kilogram of feed. This contrasted with the results of this study, where the abundance of Succiniclasticum decreased from 5.10% to 2.88% (p = 0.001) after the ingestion of feed containing a polyphenol, i.e., DGP. Similar differences in results were also seen with Selenomonas, which are starch- and sugar-degrading bacteria and possess lipolytic activity. In this study, higher abundances of Butyrivibrio and Methanobrevibacter were determined in rumen fluid after the addition of grape pomace into the wethers’ diets. Butyrivibrio, a bacterium involved in fiber, pectin, and urea degradation in the rumen, was shown to increase in this study after the addition of DGP to the diet. It can be hypothesized that the addition of DGP into the diet provides substances supporting the growth of these two genera in the rumen. However, Diaz Carrasco et al. [28] claimed that the abundance of Butyrivibrio was negatively affected by tannin treatment. It is known that grape pomace contains tannins, as well as other bioactive substances [29] which, in adequate concentrations, can have a positive influence. However, the increased intake of DGP (from 1% to 2%) had a significantly negative effect on other rumen cellulolytic bacteria. The abundance of Ruminococcus decreased from 0.46% to 0.27%, and the abundance of Fibrobacter decreased from 0.64% to 0.25% (Table 4). Decreases in these rumen cellulolytic bacteria after the increased intake of feed containing polyphenols, such as tannins, are in accordance with the findings of Vasta et al. [12], Frutos et al. [30], and Jones et al. [31]. Ma et al. [32] concluded that the addition of 25 mg of resveratrol to the diet significantly reduced the abundance of Methanobrevibacter, which resulted in decreased methane production [32,33]. Tavendale et al. [34] found that tannins completely inhibited methane production by limiting the growth of Methanobrevibacter strains YLM-1 and DSM1093. Additionally, Newbold and Ramos-Morales [35] claimed that plant polyphenol compounds have demonstrated potential to decrease methane emissions from ruminants. In this study, the intake of feed with increased amounts of DGP, which contains several polyphenols, including tannin and resveratrol, resulted in upregulation of the methane production pathway. This finding was supported by an increase in methanogens, such as Methanobrevibacter, but also by a decrease in the genus Ruminococcus (Table 4), which, according to Ross et al. [36], is important for decreasing methane after the addition of grape pomace to the diet.
The greatest number of significant changes in the abundance of rumen phyla, families, and genera were observed following changes in housing and diet (TP1 to TP2) (Table 2, Table 3, Table 4 and Table 5). A change in housing conditions and diet composition will cause a change in the rumen bacterial community. Such findings have also been published by Wu et al. [37] and Henderson et al. [38].
The change of the housing and the diet fed at the University Farm in Žirany to the housing and the control diet fed during the experiment (TP1 to TP2) decreased the richness, as well as the Shannon index, of the rumen bacterial community (Table 5). The addition of DGP changed the richness and evenness diversity indices, whereas the Shannon index remained unchanged as compared with the control diet (Table 5). Liu et al. [39] experimented with a diet with a similar concentrate-to-forage ratio as that used in this study. They determined the Shannon index to be >8, which is a much higher result compared with the control diet and the diet containing DGP assessed in this study. Li et al. [26] found the Shannon index to be 5.48 for lambs with a low subacute ruminal acidosis risk. Belanche et al. [40] determined a Shannon index of 6.35 and evenness of 0.83 for grazing sheep. These results are comparable with the results of this study. However, Langda et al. [41] estimated a Shannon index of 5.5 for grazing sheep, which was associated with the richness and evenness of rumen bacteria; this was lower compared with the results of this study. A stable rumen bacterial community will provide beneficial effects on the health and performance of ruminants. The addition of DGP into the wethers’ diet increased the richness of the rumen bacterial community, which is favorable for maintaining rumen bacterial homeostasis. Vašeková et al. [42] determined total polyphenols to be 27.38 mg GAE/g in the DGP diets which were fed to wethers in this study. Consequently, the calculated intakes of total polyphenols in the diets containing 1% or 2% DGP were 282 or 564 mg GAE, respectively, which were higher than the control diet.
The results of this study, as well as comparison with other published articles, indicate the various effects of specific polyphenols on rumen bacteria. However, a previous study [43] supported the hypothesis that including grape pomace in the diet of ruminants would increase organic matter and neutral detergent fiber digestibility. From this point of view, the addition of DGP has a positive effect on the performance of ruminants; however, the costs of drying fresh grape pomace may be problematic.
5. Conclusions
A change in housing, as well as addition of dried grape pomace, significantly affected the rumen bacterial phyla, families, and genera, as well as the rumen bacterial community indices. No adverse effect of the addition of dried grape pomace on the rumen bacterial community was noted. Future studies should investigate exactly which species and strains are susceptible to intakes of specific polyphenols and what concentrations or combinations of polyphenols are the most beneficial for rumen bacteria.
Author Contributions
Conceptualization, M.R., J.M. and M.J.; data curation, M.R., J.M., M.G. and M.M.; formal analysis, J.M., M.G., M.M. and Z.S.; funding acquisition, B.G.; investigation, M.R.; methodology, M.G., D.B. and M.J.; project administration, B.G. and Z.S.; resources, M.R. and B.G.; software, O.H., Z.S. and L.Z.; supervision, D.B.; validation, M.Š., B.G. and D.B.; visualization, J.M. and Z.S.; writing—original draft preparation, M.R. and J.M.; writing—review and editing, O.H., L.Z. and M.J. All authors have read and agreed to the published version of the manuscript.
Funding
This study and the APC were funded by the Slovak Research and Development Agency, grant number APVV-16-0170 (by-products from grape processing as a bioactive substance source in animal nutrition).
Institutional Review Board Statement
The conditions of animal care, manipulations, and use adhered to guidance of the Ethics Committee of the Slovak University of Agriculture in Nitra, Protocol No. 48/2013. According to the State Veterinary and Food Administration of the Slovak Republic, the given study has been evaluated as an inexperienced agricultural practice that does not fall under the legislation of Government Regulation of the Slovak Republic 377/2012 of 14 November 2012, laying down requirements for the protection of animals used for scientific or educational purposes.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to acknowledge the University Farm (Slovak University of Agriculture in Nitra, Slovakia) for establishing the appropriate conditions for this experiment.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Hanušovský, O.; Bíro, D.; Šimko, M.; Gálik, B.; Juráček, M.; Rolinec, M.; Majlát, M.; Herkeľ, R. Continual monitoring of reticulorumenal pH of dairy cows during 45 days. Acta Fytotechn. Zootechn. 2015, 18, 53–55. [Google Scholar] [CrossRef]
- Imrich, I.; Čopík, Š.; Mlyneková, E.; Mlynek, J.; Haščík, P.; Kanka, T. The effect of Saccharomyces cerevisiae additive to cattle ration on milk yield of dairy cows. Acta Fytotechn. Zootechn. 2021, 24, 45–48. [Google Scholar] [CrossRef]
- Hanušovský, O.; Bíro, D.; Šimko, M.; Gálik, B.; Juráček, M.; Rolinec, M.; Balušíková, Ľ. The dynamic of the ruminal content pH change and its relationship to milk composition. Acta Vet. Brno 2018, 87, 119–126. [Google Scholar] [CrossRef]
- Clark, H.J. Lactational responses to postruminal administration of proteins and amino acids. J. Dairy Sci. 1975, 58, 1178–1197. [Google Scholar] [CrossRef] [PubMed]
- OIV. International Organisation of Vine and Wine (OIV). 2018. Available online: https://www.oiv.int/what-we-do/global-report?oiv (accessed on 20 January 2023).
- Hanušovsky, O.; Gálik, B.; Bíro, D.; Šimko, M.; Juráček, M.; Rolinec, M.; Zábranský, L.; Philipp, C.; Puntigam, R.; Slama, J.A.; et al. The nutritional potential of grape by-products from the area of Slovakia and Austria. Emirates J. Food. Agri. 2020, 32, 1–10. [Google Scholar] [CrossRef]
- Makkar, H.P.S. Effects and fate of tannins in ruminant animals, adaptation to tannins, and strategies to overcome detrimental effects of feeding tannin-rich feeds. Small Rum. Res. 2003, 49, 241–256. [Google Scholar] [CrossRef]
- Mezzomo, R.; Paulino, P.V.R.; Barbosa, M.M.; Martins, T.S.; Pereira, L.G.R.; Silva, J.C.; Paulino, M.F.; Silva, M.C.; Serão, N.V. Protein dietary efficiency and methane emission in cattle fed soybean meal treated with tannins. Anim. Prod. Sci. 2017, 58, 2233–2241. [Google Scholar] [CrossRef]
- Nudda, A.; Correddu, F.; Marzano, A.; Battacone, G.; Nicolussi, P.; Bonelli, P.; Pulina, G. Effects of diets containing grape seed, linseed, or both on milk production traits, liver and kidney activities, and immunity of lactating dairy ewes. J. Dairy Sci. 2015, 98, 1157–1166. [Google Scholar] [CrossRef] [PubMed]
- Ianni, A.; Martino, G. Dietary Grape Pomace Supplementation in Dairy Cows: Effect on Nutritional Quality of Milk and Its Derived Dairy Products. Foods 2020, 9, 168. [Google Scholar] [CrossRef]
- Bennato, F.; Ianni, A.; Florio, M.; Grotta, L.; Pomilio, F.; Saletti, M.A.; Martino, G. Nutritional properties of milk from dairy ewes fed with a diet containing grape pomace. Foods 2022, 11, 1878. [Google Scholar] [CrossRef]
- Vasta, V.; Daghio, M.; Cappucci, A.; Buccioni, A.; Serra, A.; Viti, C.; Mele, M. Invited review: Plant polyphenols and rumen microbiota responsible for fatty acid biohydrogenation, fiber digestion, and methane emission: Experimental evidence and methodological approaches. J. Dairy Sci. 2019, 102, 3781–3804. [Google Scholar] [CrossRef] [PubMed]
- Salami, S.A.; Valenti, B.; Luciano, G.; Lanza, M.; Umezurike-Amahah, N.M.; Kerry, J.P.; O’Grady, M.N.; Newbold, C.J.; Priolo, A. Dietary cardoon meal modulates rumen biohydrogenation and bacterial community in lambs. Sci. Rep. 2021, 11, 16180. [Google Scholar] [CrossRef] [PubMed]
- Biscarini, F.; Palazzo, F.; Castellani, F.; Masetti, G.; Grotta, L.; Cichelli, A.; Martino, G. Rumen microbiome in dairy calves fed copper and grape-pomace dietary supplementations: Composition and predicted functional profile. PLoS ONE 2018, 13, e0205670. [Google Scholar] [CrossRef] [PubMed]
- Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes (Text with EEA relevance). Off. J. Europ. Union 2010, L276.
- AOAC. Official Methods of Analysis AOAC, 17th ed.; Association of Official Analytical Chemists: Gaithersburg, MD, USA, 2000. [Google Scholar]
- Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Lozupone, C.A.; Turnbaugh, P.J.; Fierer, N.; Knight, R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 2011, 108, 4516–4522. [Google Scholar] [CrossRef]
- Větrovský, T.; Baldrian, P.; Morais, D. SEED 2: A user-friendly platform for amplicon high-throughput sequencing data analyses. Bioinformatics 2018, 34, 2292–2294. [Google Scholar] [CrossRef]
- Boylen, E.; Ram Rideout, J.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef]
- Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Env. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020. [Google Scholar]
- Love, M.I.; Huber, W.; Andres, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
- Oksanen, J.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’hara, R.; Simpson, G.L.; Solymos, P.; Stevens, M.H.H.; Wagner, H. Package ‘Vegan’. Community Ecology Package, Version 2.5-6. 2019. Available online: https://CRAN.R-project.org/package=vegan (accessed on 2 October 2022).
- Douglas, G.M.; Maffei, V.J.; Zaneveld, J.R.; Yurgel, S.N.; Brown, J.R.; Taylor, C.M.; Huttenhower, C.; Langille, M.G.I. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020, 38, 685–688. [Google Scholar] [CrossRef]
- Morgavi, D.P.; Kelly, W.J.; Janssen, P.H.; Attwood, G.T. Rumen microbial (meta)genomics and its application to ruminant production. Animal 2013, 7, 184–201. [Google Scholar] [CrossRef] [PubMed]
- Li, F.; Wang, Z.; Dong, C.; Li, F.; Wang, W.; Yuan, Z.; Mo, F.; Weng, X. Rumen Bacteria Communities and Performances of Fattening Lambs with a Lower or Greater Subacute Ruminal Acidosis Risk. Front. Microbiol. 2017, 12, 2506. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.; Xu, T.; Xu, S.; Ma, L.; Han, X.; Wang, X.; Zhang, X.; Hu, L.; Zhao, N.; Chen, Y.; et al. Effect of dietary concentrate to forage ratio on growth performance, rumen fermentation and bacterial diversity of Tibetan sheep under barn feeding on the Qinghai-Tibetan plateau. PeerJ 2019, 5, e7462. [Google Scholar] [CrossRef] [PubMed]
- Diaz Carrasco, J.M.; Cabral, C.; Redondo, L.M.; Pin Viso, N.D.; Colombatto, D.; Farber, M.D.; Fernández Miyakawa, M.E. Impact of chestnut and quebracho tannins on rumen microbiota of bovines. BioMed. Res. Int. 2017, 2017, 9610810. [Google Scholar] [CrossRef]
- Ivanišová, E.; Terentjeva, M.; Kántor, A.; Frančáková, H.; Kačániová, M. Phytochemical and antioxidant profile of different varietes of grape from the Small Carpathians wine region of Slovakia. Erwerbs Obstbau 2019, 61, 53–59. [Google Scholar] [CrossRef]
- Frutos, P.; Hervas, G.; Giraldez, F.J.; Mantecon, A.R. Review. Tannins and ruminant nutrition. Span J Agric Res 2004, 2, 191–202. [Google Scholar] [CrossRef]
- Jones, G.A.; McAllister, T.A.; Muir, A.D.; Cheng, K.-J. Effects of sainfoin (Onobrichys viciifolia Scop.) condensed tannins on growth and proteolysis by four strains of ruminal bacteria. Appl. Env. Microbiol. 1994, 60, 1374–1378. [Google Scholar] [CrossRef] [PubMed]
- Ma, T.; Wu, W.; Tu, Y.; Zhang, N.; Diao, Q. Resveratrol affects in vitro rumen fermentation, methane production and prokaryotic community composition in a time- and diet-specific manner. Microb. Biotecnol. 2020, 13, 1118–1131. [Google Scholar] [CrossRef]
- Witzig, M.; Zeder, M.; Rodehutscord, M. Effect of the ionophore monensin and tannin extracts supplemented to grass silage on populations of ruminal cellulolytics and methanogens in vitro. Anaerobe 2018, 50, 44–54. [Google Scholar] [CrossRef]
- Tavendale, M.H.; Meagher, L.P.; Pacheco, D.; Walker, N.; Attwood, G.T.; Sivakumaran, S. Methane production from in vitro rumen incubations with Lotus pedunculatus and Medicago sativa, and effects of extractable condensed tannin fractions on methanogenesis. Anim. Feed Sci. Technol. 2005, 123, 403–419. [Google Scholar] [CrossRef]
- Newbold, C.J.; Ramos-Morales, E. Review: Ruminal microbiome and microbial metabolome: Effects of diet and ruminant host. Animal 2020, 14 (Suppl. S1), 78–86. [Google Scholar] [CrossRef] [PubMed]
- Ross, E.M.; Moate, P.J.; Marett, L.; Cocks, B.G.; Hayes, B.J. Investigating the effect of two methane- mitigating diets on the rumen microbiome using massively parallel sequencing. J. Dairy Sci. 2013, 96, 6030–6046. [Google Scholar] [CrossRef] [PubMed]
- Wu, D.; Vinitchaikul, P.; Deng, M.; Zhang, G.; Sun, L.; Gou, X.; Mao, H.; Yang, S. Host and altitude factors affect rumen bacteria in cattle. Braz J. Microbiol. 2020, 51, 573–1583. [Google Scholar] [CrossRef] [PubMed]
- Henderson, G.; Cox, F.; Ganesh, S.; Jonker, A.; Young, W.; Janssen, P.H. 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. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.H.; Bian, G.R.; Zhu, W.Y.; Mao, S.Y. High-grain feeding causes strong shifts in ruminal epithelial bacterial community and expression of Toll-like receptor genes in goats. Front. Microbiol. 2015, 6, 167. [Google Scholar] [CrossRef]
- Belanche, A.; Kingston-Smith, A.H.; Griffith, G.W.; Newbold, C.J. A Multi-Kingdom Study Reveals the Plasticity of the Rumen Microbiota in Response to a Shift From Non-grazing to Grazing Diets in Sheep. Front. Microbiol. 2019, 10, 122. [Google Scholar] [CrossRef]
- Langda, S.; Zhang, C.; Zhang, K.; Gui, B.; Ji, D.; Deji, C.; Cuoji, A.; Wang, X.; Wu, Y. Diversity and Composition of Rumen Bacteria, Fungi, and Protozoa in Goats and Sheep Living in the Same High-Altitude Pasture. Animals 2020, 10, 186. [Google Scholar] [CrossRef]
- Vašeková, P.; Juráček, M.; Biro, D.; Šimko, M.; Gálik, B.; Rolinec, M.; Hanušovský, O.; Kolláthová, R.; Ivanišová, E. Bioactive compounds and fatty acid profile of grape pomace. Acta Fytotechn Zootechn 2020, 23, 230–235. [Google Scholar] [CrossRef]
- Juráček, M.; Vašeková, P.; Massányi, P.; Kováčik, A.; Bíro, D.; Šimko, M.; Gálik, B.; Rolinec, M.; Hanušovský, O.; Kolláthová, R.; et al. The effect of dried grape pomace feeding on nutrients digestibility and serum biochemical profile of wethers. Agriculture 2021, 11, 1194. [Google Scholar] [CrossRef]
Figure 1.
Timepoints of rumen fluid sampling.
Figure 2.
Venn diagram of amplicon sequence variants (ASVs) exhibited at different rumen fluid sampling timepoints.
Figure 2.
Venn diagram of amplicon sequence variants (ASVs) exhibited at different rumen fluid sampling timepoints.
Figure 3.
Non-metric multidimensional scaling (NMDS) plot of microbial community composition in the rumen fluid samples, according to sampling timepoints.
Figure 3.
Non-metric multidimensional scaling (NMDS) plot of microbial community composition in the rumen fluid samples, according to sampling timepoints.
Figure 4.
Volcano plot of Deseq analysis of metabolic pathways predicted by Picrust2 and annotated by MetaCYC, between: (a) TP1 and TP2; (b) TP2 and TP3; and (c) TP3 and TP4.
Figure 4.
Volcano plot of Deseq analysis of metabolic pathways predicted by Picrust2 and annotated by MetaCYC, between: (a) TP1 and TP2; (b) TP2 and TP3; and (c) TP3 and TP4.
Table 1.
Diet compositions before and during the experiment.
| Diet Fed at Farm in Žirany | Control Diet | Control Diet with 1% DGP | Control Diet with 2% DGP | |
|---|---|---|---|---|
| Feeds (g) | ||||
| Maize silage | 400 | - | - | - |
| Alfalfa silage | 300 | - | - | - |
| Barley grounded | 100 | - | - | - |
| Meadow hay | 500 | 700 | 700 | 700 |
| Wheat grounded | 100 | 118.6 | 118.6 | 118.6 |
| Soybean meal | - | 238.6 | 238.6 | 238.6 |
| DGP | - | - | 10.3 * | 20.6 ** |
| Nutrient concentrations in diet (g) | ||||
| Dry matter | 838.4 | 934.1 | 943.8 | 953.5 |
| Crude protein | 88.1 | 179.0 | 180.0 | 181.0 |
| Ether extract | 16.4 | 13.0 | 13.9 | 14.8 |
| Crude fiber | 265.8 | 288.0 | 289.9 | 291.8 |
| ADF | 310.5 | 351.4 | 355.3 | 359.2 |
| NDF | 474.8 | 529.8 | 534.5 | 539.3 |
| NFE | 491.2 | 520.6 | 526.8 | 532.9 |
| NFC | 282.2 | 278.8 | 282.1 | 285.4 |
| Organic matter | 861.4 | 1000.6 | 1010.5 | 1020.4 |
| Ash | 49.0 | 56.6 | 57.0 | 57.4 |
| Ca | 4.2 | 4.1 | 4.1 | 4.2 |
| P | 2.4 | 3.9 | 4.0 | 4.0 |
| Mg | 1.5 | 2.1 | 2.1 | 2.1 |
| Na | 0.3 | 0.3 | 0.3 | 0.3 |
| K | 11.1 | 15.5 | 15.6 | 15.8 |
* indicates 1% from daily dry matter intake; ** indicates 2% from daily dry matter intake; DGP, dried grape pomace; ADF, acid detergent fiber; NDF, neutral detergent fiber; NFE, nitrogen-free extract; NFC, non-fiber carbohydrates.
Table 2.
Dominant phyla with a relative abundance of over 1.0% at one or more of the sampling timepoints.
Table 2.
Dominant phyla with a relative abundance of over 1.0% at one or more of the sampling timepoints.
| Phylum (%) | TP1 | TP2 | TP3 | TP4 | p-Value TP1 to TP2 | p-Value TP2 to TP3 | p-Value TP3 to TP4 | p-Value TP2 to TP4 |
|---|---|---|---|---|---|---|---|---|
| Actinobacteria | 2.50 | 0.15 | 0.03 | 0.29 | <0.001 | NA | NA | 0.291 |
| Bacteroidetes | 47.99 | 61.35 | 58.35 | 56.07 | 0.946 | 0.953 | 0.838 | 0.561 |
| Candidatus Saccharibacteria | 2.98 | 0.30 | 0.34 | 1.97 | <0.001 | 0.893 | 0.006 | 0.001 |
| Chloroflexi | 0.30 | 1.28 | 0.87 | 1.30 | 0.052 | 0.632 | 0.996 | 0.395 |
| Euryarchaeota | 1.34 | 1.30 | 2.20 | 7.74 | 0.946 | 0.976 | <0.001 | <0.001 |
| Fibrobacteres | 1.64 | 0.46 | 0.64 | 0.25 | <0.001 | 0.976 | 0.006 | 0.261 |
| Firmicutes | 23.85 | 20.15 | 15.59 | 16.92 | 0.005 | 0.428 | 0.644 | 0.056 |
| Proteobacteria | 3.59 | 1.56 | 3.80 | 3.83 | 0.001 | 0.368 | 0.514 | 0.516 |
| Spirochaetes | 1.59 | 0.82 | 0.08 | 0.11 | 0.001 | 0.367 | 0.838 | <0.001 |
| SR1 | 5.25 | 0.17 | 0.51 | 0.18 | <0.001 | 0.976 | 0.609 | 0.326 |
| Synergistetes | 0.90 | 1.44 | 5.19 | 4.57 | <0.001 | <0.001 | 0.838 | n. d. |
| Tenericutes | 1.02 | 0.18 | 0.21 | 0.13 | 0.011 | 0.976 | 0.060 | 0.001 |
| Verrucomicrobia | 6.87 | 10.44 | 11.36 | 5.86 | 0.478 | 0.989 | <0.001 | <0.001 |
TP, timepoint; n. d., not defined.
Table 3.
Dominant families with a relative abundance of over 1.0% at one or more of the sampling timepoints.
Table 3.
Dominant families with a relative abundance of over 1.0% at one or more of the sampling timepoints.
| Family (%) | TP1 | TP2 | TP3 | TP4 | p-Value TP1 to TP2 | p-Value TP2 to TP3 | p-Value TP3 to TP4 | p-Value TP2 to TP4 |
|---|---|---|---|---|---|---|---|---|
| Acidaminococcaceae | 3.80 | 5.21 | 2.88 | 2.49 | 0.760 | 0.005 | 0.559 | <0.001 |
| Anaerolineaceae | 0.29 | 1.28 | 0.87 | 1.30 | 0.088 | 0.994 | 0.964 | n. d. |
| Bdellovibrionaceae | 1.56 | 0.33 | 0.97 | 0.22 | 0.009 | 0.994 | 0.016 | 0.332 |
| Bifidobacteriaceae | 2.44 | 0.00 | 0.00 | 0.02 | <0.001 | 0.994 | 0.964 | 0.738 |
| Chitinophagaceae | 1.40 | 0.40 | 0.13 | 0.43 | 0.005 | 0.487 | 0.051 | 0.719 |
| Fibrobacteraceae | 1.64 | 0.46 | 0.64 | 0.25 | <0.001 | 0.994 | 0.003 | 0.222 |
| Flavobacteriaceae | 1.20 | 1.00 | 0.87 | 1.20 | 0.453 | 0.994 | 0.559 | 0.791 |
| Gracilibacteraceae | 1.10 | 1.23 | 1.75 | 1.69 | 0.657 | 0.994 | 0.964 | n. d. |
| Lachnospiraceae | 3.35 | 2.46 | 2.38 | 3.89 | 0.001 | 0.994 | <0.001 | 0.011 |
| Marinilabiliaceae | 1.65 | 2.34 | 1.97 | 1.05 | 0.760 | 0.994 | 0.001 | <0.001 |
| Methanobacteriaceae | 1.07 | 1.06 | 1.53 | 6.85 | 0.981 | 0.994 | <0.001 | <0.001 |
| Pasteurellaceae | 0.43 | 0.13 | 1.00 | 0.04 | 0.099 | 0.348 | 0.010 | n. d. |
| Porphyromonadaceae | 8.39 | 23.11 | 17.75 | 22.23 | 0.006 | 0.866 | 0.509 | 0.004 |
| Prevotellaceae | 32.30 | 28.80 | 33.38 | 24.44 | 0.002 | 0.387 | 0.003 | 0.796 |
| Prolixibacteraceae | 0.45 | 1.00 | 0.55 | 1.10 | 0.387 | 0.866 | 0.009 | 0.981 |
| Rikenellaceae | 1.58 | 3.61 | 2.80 | 3.45 | 0.001 | 0.994 | 0.723 | 0.875 |
| Ruminococcaceae | 11.23 | 6.50 | 5.21 | 5.12 | 0.004 | 0.994 | 0.964 | 0.745 |
| Saccharibacteria_genera_incertae_sedis | 2.98 | 0.30 | 0.34 | 1.97 | <0.001 | 0.994 | 0.062 | 0.002 |
| Sphingobacteriaceae | 0.52 | 0.49 | 0.80 | 1.35 | 0.970 | 0.866 | 0.032 | 0.705 |
| Spirochaetaceae | 1.59 | 0.82 | 0.08 | 0.11 | <0.001 | 0.387 | 0.964 | <0.001 |
| SR1_genera_incertae_sedis | 5.25 | 0.17 | 0.51 | 0.18 | <0.001 | 0.994 | 0.964 | 0.413 |
| Synergistaceae | 0.09 | 1.44 | 5.19 | 4.57 | <0.001 | <0.001 | 0.964 | <0.001 |
| Syntrophorhabdaceae | 0.00 | 0.02 | 0.63 | 2.71 | 0.981 | 0.994 | 0.509 | 0.280 |
| Veillonellaceae | 3.00 | 4.18 | 2.89 | 2.84 | 0.803 | 0.994 | 0.964 | 0.997 |
| Verrucomicrobia _Subdivision5_genera_incertae_sedis | 6.79 | 10.23 | 10.37 | 4.99 | 0.466 | 0.994 | 0.001 | 0.023 |
TP, timepoint; n. d., not defined.
Table 4.
Dominant genera with relative abundance over 0.9% at one or more of the sampling timepoints.
Table 4.
Dominant genera with relative abundance over 0.9% at one or more of the sampling timepoints.
| Genera (%) | TP1 | TP2 | TP3 | TP4 | p-Value TP1 to TP2 | p-Value TP2 to TP3 | p-Value TP3 to TP4 | p-Value TP2 to TP4 |
|---|---|---|---|---|---|---|---|---|
| Acetobacteroides | 0.45 | 10.57 | 9.23 | 10.80 | <0.001 | 0.994 | 0.650 | 0.928 |
| Alloprevotella | 0.92 | 0.43 | 0.61 | 0.10 | <0.001 | 0.994 | <0.001 | 0.139 |
| Barnesiella | 1.16 | 3.43 | 0.94 | 4.24 | 0.148 | 0.007 | <0.001 | 0.560 |
| Bifidobacterium | 2.44 | 0.00 | 0.00 | 0.02 | <0.001 | 0.994 | 0.954 | 0.825 |
| Butyrivibrio | 0.73 | 0.66 | 1.26 | 2.12 | 0.096 | 0.099 | <0.001 | <0.001 |
| Centipeda | 0.31 | 1.15 | 0.64 | 0.70 | 0.148 | 0.994 | 0.985 | <0.001 |
| Clostridium IV | 2.05 | 0.94 | 0.19 | 0.34 | <0.001 | 0.081 | 0.242 | 0.097 |
| Chelonobacter | 0.34 | 0.00 | 1.00 | 0.02 | <0.001 | <0.001 | 0.001 | 0.400 |
| Falsiporphyromonas | 2.93 | 4.97 | 6.17 | 4.54 | 0.913 | 0.253 | 0.588 | 0.003 |
| Fibrobacter | 1.64 | 0.46 | 0.64 | 0.25 | <0.001 | 0.994 | 0.012 | 0.079 |
| Flavonifractor | 1.19 | 0.63 | 0.91 | 0.45 | 0.400 | 0.994 | 0.954 | n. d. |
| Fretibacterium | 0.04 | 1.36 | 5.15 | 4.56 | <0.001 | <0.001 | 0.954 | <0.001 |
| Gracilibacter | 1.05 | 1.21 | 1.72 | 1.68 | 0.594 | 0.984 | NA | n. d. |
| Mangroviflexus | 0.26 | 1.09 | 1.03 | 0.30 | <0.001 | 0.994 | <0.001 | <0.001 |
| Methanobrevibacter | 1.07 | 1.06 | 1.53 | 6.84 | 0.873 | 0.994 | <0.001 | <0.001 |
| Methanomassiliicoccus | 0.27 | 0.18 | 0.66 | 0.90 | 0.647 | 0.692 | 0.808 | 0.057 |
| Mucinivorans | 1.58 | 3.55 | 2.75 | 3.41 | 0.067 | 0.994 | 0.422 | 0.441 |
| Ornatilinea | 0.29 | 1.12 | 0.87 | 1.29 | 0.319 | 0.994 | 0.954 | n. d. |
| Oscillibacter | 0.75 | 1.50 | 0.52 | 0.46 | 0.924 | 0.994 | 0.954 | n. d. |
| Parafilimonas | 1.40 | 0.40 | 0.13 | 0.39 | 0.001 | 0.467 | 0.062 | n. d. |
| Paraprevotella | 3.33 | 2.02 | 3.38 | 1.47 | <0.001 | 0.031 | <0.001 | <0.001 |
| Petrimonas | 0.03 | 0.68 | 0.96 | 0.77 | <0.001 | 0.185 | 0.097 | n. d. |
| Prevotella | 28.05 | 26.35 | 29.39 | 22.86 | <0.001 | 0.692 | 0.377 | 0.169 |
| Pseudosphingobacterium | 0.46 | 0.42 | 0.74 | 1.04 | 0.396 | 0.902 | 0.327 | n. d. |
| Ruminococcus | 1.85 | 0.43 | 0.46 | 0.27 | <0.001 | 0.976 | 0.741 | 0.361 |
| Saccharibacteria_genera_incertae_sedis | 2.98 | 0.30 | 0.34 | 1.97 | <0.001 | 0.994 | 0.062 | 0.008 |
| Saccharofermentans | 2.80 | 0.87 | 0.91 | 1.16 | <0.001 | 0.994 | 0.479 | 0.376 |
| Schwartzia | 0.85 | 0.80 | 1.09 | 0.36 | 0.027 | 0.976 | 0.002 | <0.001 |
| Selenomonas | 1.31 | 1.84 | 0.13 | 0.20 | 0.373 | 0.009 | 0.741 | <0.001 |
| Sporobacter | 0.40 | 0.57 | 1.09 | 1.11 | 0.873 | 0.994 | 0.954 | 0.155 |
| SR1_genera_incertae_sedis | 5.25 | 0.17 | 0.51 | 0.18 | <0.001 | 0.994 | 0.954 | 0.629 |
| Succiniclasticum | 3.78 | 5.10 | 2.88 | 2.48 | 0.755 | 0.001 | 0.954 | <0.001 |
| Syntrophorhabdus | 0.00 | 0.02 | 0.63 | 2.71 | 0.924 | 0.994 | 0.422 | 0.365 |
| Tannerella | 3.30 | 2.63 | 0.30 | 1.71 | 0.179 | 0.016 | 0.077 | n. d. |
| Treponema | 1.44 | 0.80 | 0.07 | 0.10 | 0.001 | 0.383 | 0.954 | <0.001 |
| Vampirovibrio | 1.56 | 0.33 | 0.97 | 0.22 | 0.005 | 0.994 | 0.059 | 0.194 |
| Verrucomicrobia _Subdivision3_genera_incertae_sedis | 0.00 | 0.06 | 0.98 | 0.85 | 0.265 | <0.001 | 0.954 | n. d. |
| Verrucomicrobia _Subdivision5_genera_incertae_sedis | 6.79 | 10.23 | 10.37 | 4.99 | 0.924 | 0.994 | 0.003 | n. d. |
TP, timepoint; n. d., not defined.
Table 5.
Rumen bacterial community diversity indices according to sampling timepoint.
| Indices (Mean ± SD) | TP1 | TP2 | TP3 | TP4 |
|---|---|---|---|---|
| Richness | 275 ± 39 a | 144 ± 45 b | 192 ± 30 c | 204 ± 75 c |
| Evenness | 0.88 ± 0.023 ab | 0.90 ± 0.013 a | 0.89 ± 0.013 ab | 0.87 ± 0.047 b |
| Shannon | 7.17 ± 0.32 a | 6.39 ± 0.46 b | 6.73 ± 0.21 ab | 6.61 ± 0.74 b |
TP, timepoint; SD, standard deviation; a,b,c, values within the same row bearing different superscript letters are significantly different at p < 0.05.
Table 6.
ANOSIM comparison of rumen microbial community sampling timepoints.
| ANOSIM | TP1 | TP2 | TP3 | TP4 |
|---|---|---|---|---|
| TP1 | p = 0.001 | p = 0.001 | p = 0.001 | |
| TP2 | R = 0.819 | p = 0.001 | p = 0.001 | |
| TP3 | R = 0.958 | R = 0.306 | p = 0.001 | |
| TP4 | R = 0.934 | R = 0.588 | R = 0.320 |
TP, timepoint.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Rolinec, M.; Medo, J.; Gábor, M.; Miluchová, M.; Šimko, M.; Gálik, B.; Hanušovský, O.; Schubertová, Z.; Bíro, D.; Zábranský, L.; et al. Effect of Grape Pomace Intake on the Rumen Bacterial Community of Sheep. Diversity 2023, 15, 234. https://doi.org/10.3390/d15020234
AMA Style
Rolinec M, Medo J, Gábor M, Miluchová M, Šimko M, Gálik B, Hanušovský O, Schubertová Z, Bíro D, Zábranský L, et al. Effect of Grape Pomace Intake on the Rumen Bacterial Community of Sheep. Diversity. 2023; 15(2):234. https://doi.org/10.3390/d15020234
Chicago/Turabian StyleRolinec, Michal, Juraj Medo, Michal Gábor, Martina Miluchová, Milan Šimko, Branislav Gálik, Ondrej Hanušovský, Zuzana Schubertová, Daniel Bíro, Luboš Zábranský, and et al. 2023. "Effect of Grape Pomace Intake on the Rumen Bacterial Community of Sheep" Diversity 15, no. 2: 234. https://doi.org/10.3390/d15020234
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
