Introduction
In recent years, the market demand for goat milk products has increased, and there has been a growing focus on how to improve the lactation performance of dairy goats. Farmers use feed additives to enhance the performance of their livestock. Yeast culture (YC) is a safe and reliable new feed additive that has been the focus of much attention in recent years. YC is a natural yeast fermentation product containing various bioactive substances such as yeast cells, vitamins, peptides, amino acids, proteins, organic acids, and oligosaccharides (Jensen et al. Reference Jensen, Patterson and Yoon2008). Dietary YC supplementation affects the performance by altering gastrointestinal microbial composition, which has been reported in dairy calves (Magalhaes et al. Reference Magalhaes, Susca and Lima2008), pigs (Lin et al. Reference Lin, Yu and Ma2022), poultry (Zhang et al. Reference Zhang, Chen and Zhang2020), and fattening lambs (Song et al. Reference Song, Wu and You2021). Moreover, supplementation of YC in dairy cows’ diets can also increase milk yield and improve milk quality (Halfen et al. Reference Halfen, Carpinelli and Pino F A B2021). However, the effect of YC is variable, and its mechanism in dairy goat production remains unclear. Therefore, the relationship between YC and lactation performance requires further exploration.
The rumen, as the bioreactor of ruminants, utilizes rumen microbes to degrade feed nutrients and produce volatile fatty acids (VFAs), peptides, and ammonia that directly or indirectly affect the lactation performance of the host (Russell et al. Reference Russell and Rychlik2001; Xue et al. Reference Xue, Sun and Wu2020). Thus, in complex digestive and metabolic processes, rumen microorganisms play a critical role. Diet is the main factor that affects the composition of the rumen microbiota (Ghaffari et al. Reference Ghaffari, Tahmasbi and Khorvash2014). In studies on the effects of YC on rumen microbial composition, more attention has been paid to bacteria (Jiang et al. Reference Jiang, Ogunade and Pech-Cervantes2020) and less to fungi. Fungi are the least characterized group of rumen microorganisms, but they are essential for plant digestion in the rumen (Wang et al. Reference Wang, Wang and Wu2022), and it has also been shown that rumen anaerobic fungi, which ferment cell wall carbohydrates (cellulose and hemicellulose) to produce VFAs, provide energy for the host animal and affect animal performance (Rabee et al. Reference Rabee, Forster and Elekwachi2019). A substantial degree of fiber degradation is achieved by species of Piromyces, Neocallimastix, Orpinomyces, and Ruminomyces (Wubah Reference Wubah2004). Both bacteria and fungi play an important role in the fermentation process in the rumen (Langda et al. Reference Langda, Zhang and Zhang2020). Therefore, the effect of YC on the composition of bacteria and fungi in rumen of lactating dairy goats deserves further attention.
Therefore, the effects of YC on the lactation performance of dairy goats during lactation were investigated in this experiment. The effects of YC on the composition of rumen bacteria and fungi were revealed by 16S and ITS (Internal Transcribed Spacer) assays, and the relationships between microbial changes and rumen environment, serum indices, and lactation performance were analyzed and discussed. This study provides a theoretical basis for clarifying the application value of YC in dairy goat production.
Materials and methods
Ethics approval
The Animal Care and Utilization Committee of Northwest Agriculture and Forestry University (Yangling, Shaanxi, China) approved all experimental protocols used in this study, including dairy goat feeding tests, rumen fluid collection, blood collection, and fecal collection (protocol no. DK2022008). The protocols conformed to the university’s guidelines for animal research.
Animals and experimental design
Twenty mid-lactation Saanen dairy goats (lactation period around 150 days) with similar health status and body weight, all of which in their second lactation, were selected from Xinlongmen Goat Farm, Lantian County, Xi’an, Shaanxi Province. They were randomly divided into two groups of ten animals each. The experimental groups were as follows: the control (CON) group was treated with a basal diet (concentrate: forage ratio of 4:6); the YC group was fed the basic diet supplemented with 10 g/kg YC. YC used in this study was provided by Xi’an Xinhanbao Biotechnology Co., Ltd. Table 1 presents the nutritional composition of YC, respectively. Table 2 shows the composition of the basal diet. Dairy goats were fed twice daily (8:00 am and 4:00 pm) and had free access to water in a clean and hygienic environment. After a 10-day adaption, daily feed intake and milk production were continuously recorded for 8 weeks.
Nutrient level of yeast cultures
1 CP, crude protein;
2 EE, ether extract;
3 CF, crude fiber.
Ingredients and nutrient levels in the basal diet, % (as-fed basis)
1 The premix provided the following per kilogram of the diet; Vitamin A 3 000 IU, Vitamin D 300 IU, Vitamin E 15 IU, Fe 30 mg, Cu 8 mg, Zn 30 mg, Mn 40 mg, I 0.25 mg, Se 0.1 mg, Co 0.1 mg.
2 Nutrient levels were calculated values; NEm. was a calculated value according to ingredient composition, while the others were measured values.
Milk production and composition
Milk production was recorded daily during the experiment. Milk samples were collected in the morning and afternoon on days 0, 14, 28, 42, and 56. The samples were mixed in a 3:2 ratio, treated with preservatives, and sent to the nearest Xi’an Animal Husbandry Station for milk composition and somatic cell count (SCC) analysis.
Feed sampling, fecal sampling, and chemical analysis
Feed intake was measured and recorded daily at the beginning of the study. During the last 3 days of the experiment, the remaining feed and fecal samples were collected continuously. Fecal samples up to 10 g were collected with Polyethylene (PE) gloves and stored at −20℃. The samples were dried in an oven at 105℃ ± 2℃ under atmospheric pressure to constant weight and the dry matter content was determined. Crude protein (CP) was determined according to AOAC Official Method 988.05 Protein (Crude) in Animal Feed and Pet Food, and crude fat (EE) was determined according to the standard method (ISO 6492:1999). Neutral detergent fiber (NDF) was determined according to AOAC Official Method 2002.04, acid detergent fiber (ADF) was determined according to AOAC Official Method 973.18, and ash insoluble in hydrochloric acid was used as a marker in the digestibility study and determined by reference to ISO (5985:2002), and then the apparent digestibility of CP, NDF, and ADF was calculated using the following formula:
\begin{equation*}{\text{Apparent digestibility}}\,\left( \% \right) = \left( {1 - \,\frac{{{\text{a }}\left( {\text{\% }} \right)}}{{{\text{c }}\left( {\text{\% }} \right)}} \times \frac{{{\text{b }}\left( {\text{\% }} \right)}}{{{\text{d }}\left( {\text{\% }} \right)}}} \right) \times 100\end{equation*}The letters in the formula mean that a = fecal nutrients, b = dietary acid insoluble ash, c = fecal acid insoluble ash, and d = dietary nutrients.
Serum indices
On the 56th day of the test, 10 mL of blood was collected from the jugular vein using a coagulant tube, and the serum was separated by centrifugation at 3,000 × g for 10 min at 4℃ and stored at −20℃ until analysis. Total protein (TP), albumin (ALB), creatinine (CR), blood urea nitrogen (BUN), γ-glutamyl transferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glucose (Glu), triglyceride (TG), and total cholesterol (TCHO) were detected by a fully automatic biochemical analyzer (Roche cobasc800). Kits for the detection of superoxide dismutase (SOD), malondialdehyde (MDA), total antioxidant capacity (T-AOC), glutathione peroxidase (GSH), catalase (CAT), immunoglobulin A (IgA), immunoglobulin M (IgM), and immunoglobulin G (IgG) were purchased from Shanghai Preferred Bioscience & Technology Co. Ltd in China. Kits for the determination of estrogen (E) and prolactin (PRL) were purchased from Nanjing Jiancheng Reagent Company in China. All experimental methods and steps were performed according to the instructions provided with the kits.
Rumen fermentation
On the 56th day of the experiment, 50 mL of rumen fluid was collected from each goat through a rumen catheter, and its pH was immediately determined using a pH-3B high precision pH meter. NH3-N was determined by the phenol-sodium hypochlorite colorimetric method (Mendoza et al. Reference Mendoza, Cajarville and Perez-Ruchel2011), and VFA was determined by gas chromatography (Agilent 7890A, USA). The remaining rumen fluid samples were placed in liquid nitrogen and finally stored at −80℃ for subsequent analysis of rumen microbial composition.
16S rRNA and ITS gene sequencing
To determine rumen microbiota, rumen fluid samples were thawed overnight at 4℃. Rumen fluid DNA from the test dairy goats was extracted using the TIANamp Stool DNA Kit. DNA quality was detected by electrophoresis and NanoDrop 2000 spectrophotometer. After quality control, the DNA was divided into two parts for the determination of 16S and ITS, respectively. Amplification primers used for 16S and ITS sequencing were different, 341F and 806R for 16S rRNA (V3–V4 region) and ITS1-1F and ITS1-1R for ITS (ITS1 region), and specific primer information was listed in Table S1. The library was then constructed using the TruSeq® DNA PCR-Free Sample Preparation Kit, the NovaSeq6000 was used for onboard sequencing, and 250 bp paired-end reads were generated. Next, the data were subjected to paired-end reads assembly and quality control, including Data split, Sequence assembly (Mago et al. Reference Mago and Salzberg2011), Data filtration (Bokulich et al. Reference Bokulich, Subramanian and Faith2013; Caporaso et al. Reference Caporaso, Kuczynski and Stombaugh2010), and Chimera removal (Edgar et al. Reference Edgar, Haas and Clemente2011), to obtain effective tags. Operational taxonomic units (OTUs) were clustered with a 97% identity and analyzed for species classification based on the clean data. According to the clustering results of OTUs, the representative sequences of each OTU were annotated with species, and the corresponding species information and species-based abundance distribution were obtained, which were used for the calculation and analysis of α-diversity, β-diversity, and principal coordinate analysis (PCoA). Alpha diversity indices in our samples were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 2. 15.3). Beta diversity were calculated by QIIME software (Version 1.9. 1). PCoA analysis was displayed by ade4 package and ggplot2 package in R software (Version 2. 15.3).
Statistical analysis
Differential microbial composition was analyzed by Wilcoxon rank-sum test. Spearman correlation analysis was conducted to generate bacterial–fungal interaction network. SPSS was used to process and analyze the experimental data. The effects of YC on lactation performance indices, serum indices, and rumen fermentation parameters were analyzed by t-test. GraphPad Prism 8.0 was used to perform Pearson correlation analysis to study the relationship between microorganisms and lactation performance, rumen environment, and other factors.
Results
Lactation performance
Lactation performance was measured, and the results showed that feeding YC could improve dry matter intake (DMI), milk yield, and milk lactose yield but decreased milk protein rate (P < 0.05). There were no significant differences in milk fat rate, milk fat yield, milk protein yield, and milk lactose rate (Fig. 1A, P > 0.05). There were no significant differences in SCC, CP, ADF, and NDF of dairy goats (Table S2).
Effect of yeast cultures on lactation performance of dairy goat. Changes in indicators related to lactation performance at weeks 0, 2, 4, 6, and 8 mean value for each group. *P < 0.05, **P < 0.01, ***P < 0.001.
Serum indices
Compared with the CON group, dairy goats fed with YC had a higher levels of TP, ALB, CR, Glu, SOD, and CAT (Fig. 2A, P < 0.05), whereas no significant differences for BUN, GGT, ALT, AST, TG, TCHO, MDA, T-AOC, GSH, E, PRL, IgA, IgM, and IgG were observed among the two groups (Table S3).
Serum parameter difference and correlation analysis. Serum indicators of differences between YC and CON. *P < 0.05, **P < 0.01, ***P < 0.001.
Rumen fermentation parameters
To investigate the impact of changes in rumen microbial composition on the rumen environment, we measured the pH, NH3-N, and VFA content of rumen fluid. The results showed that the pH and NH3-N levels in the YC group were significantly higher than those in the CON group (Fig. 3A, P < 0.05), and concentrations of total VFA, acetate, propionate, and butyrate in YC group were significantly reduced (Fig. 3B, P < 0.05), while the levels of iso-butyrate, valerate, iso-valerate, and acetate:propionate were not significantly different (Table S4, P > 0.05).
Rumen fermentation parameters and correlation analysis. (A) NH3-N and pH levels in the rumen of CON and YC. (B) Concentration of volatile fatty acids in the rumen.
Microbial composition of rumen of dairy goats
To investigate the effect of YC on the rumen environment of dairy goats, 16S sequencing and ITS sequencing were conducted. As shown in Fig. 4A and 4B, the rank abundance curves of bacteria and fungi leveled off, indicating sufficient and feasible sequencing data with more than 99.5% coverage of bacteria and fungi (Table 4). The α-diversity indices, including observed_species, Shannon, Simpson, Chao1, and ACE indices, indicated no significant difference between the YC and CON groups (P > 0.05) (Table 3). PCoA showed that there was no significant separation between the YC and CON groups based on the bacterial results obtained by 16S sequencing (Fig. 4C) and the fungal results obtained by ITS sequencing (Fig. 4D). At the phylum level, Firmicutes (average of two groups, 47.66%), Bacteroidota (32.89%), and Euryarchaeota (7.90%) were the dominant bacterial communities in the rumen of dairy goats (Fig. 4E). At the genus level, the dominant bacterial genera were Rikenellaceae_RC9_gut_group (11.57%), followed by Prevotella (11.47%), Ruminococcus (8.02%), and Methanobrevibacter (7.83%) (Fig. 4F). At the phylum level of fungi, the dominant microbial community was Basidiomycota (54.17%) and Ascomycota (11.22%) (Fig. 4G), and at the genus level, the dominant fungi were Wallemia (52.87%), followed by Dipodascus (2.21%) and Aspergillus (1.49%) (Fig. 4H).
Effect of yeast cultures on the composition of rumen microbiota. (A) Rank abundance curves of bacteria. (B) Rank abundance curves of fungi. (C) PCOA results for bacteria. (D) PCoA results for fungi. (E) Taxonomic analysis of rumen bacterial groups at the phylum level. (F) Taxonomic analysis of rumen bacterial groups at the genus level. (G) Taxonomic analysis of rumen fungi groups at the phylum level. (H) Taxonomic analysis of rumen fungi groups at the genus level.
Effect of YC on α-diversity of bacteria and fungi in rumen
Differences in rumen microbial composition
To compare the effect of YC on the microbial composition of the rumen, Wilcoxon rank-sum test analysis revealed that, at the genus level, 19 bacterial genera differed by relative abundance. The YC group showed an increase in 15 bacterial genera and a decrease in 4 bacterial genera compared to the CON group (Fig. 5A, P < 0.05). At the genus level, 24 distinct fungal genera were found, with the YC increasing the relative abundance of 13 fungal genera and decreasing the relative abundance of 11 fungal genera (Fig. 5B, P < 0.05).
Differences at the genus level between fungi and bacteria. (A) Significantly different genus of bacteria, P < 0.05. (B) Significantly different genus of fungi, P < 0.05.
Microbial function prediction
Through PICRUSt2 function prediction and differential pathway enrichment analysis (Fig. S1), we focused on 22 differential metabolic pathways (Fig. 6A, P < 0.05). We noted that in the YC group, the flavone and flavonol biosynthesis, valine, leucine and isoleucine degradation, betalain biosynthesis, limonene and pinene degradation, fatty acid degradation, butanoate metabolism, benzoate degradation, folate biosynthesis, beta-alanine metabolism, ascorbate and aldarate metabolism, glutathione metabolism, phenylalanine metabolism, metabolism of xenobiotics by cytochrome P450, tyrosine metabolism, and inositol phosphate metabolism metabolic pathways were upregulated, and carbon fixation in photosynthetic organisms, terpenoid backbone biosynthesis, biosynthesis of various secondary metabolites – part 1, biosynthesis of ansamycins, D-alanine metabolism, biosynthesis of secondary metabolites, and peptidoglycan biosynthesis were downregulated. With respect to FunGuild functional prediction of fungi, the results of t-test showed that compared with the CON group, the enrichment of plant-undefined-saprotroph and plant-pathogen in the guild classification and pathotroph in the mode classification were significantly reduced (Fig. 6B, P < 0.05).
Microbial function prediction. (A) Differential enrichment pathway (level 3) analysis of PIRCUSt2 based on 16S sequencing data. (B) FUNGuild differential function prediction analysis based on ITS sequencing data.
Microbial network
To assess the differential microbes in the center, a network of bacteria, fungi, and bacteria–fungi correlations was constructed by using Spearman correlation analysis (|R| > 0.6, P < 0.05). The analysis showed that in the bacterial network, Woeseia, Roseivivax, Leisingera, Rubitalea, Cyanobium_PCC-6307, and Vibrio were located at the core position (degree = 13) (Fig. 7A). The network results for fungi indicated that the core fungal genera were Sordaria (degree = 19) and Xeromyces (degree = 17) (Fig. 7B). Next, we further focus on the major bacterial genera correlated with fungi (Fig. 7C) found that Vibrio (degree = 13) and UCG (degree = 18) have a strong correlation with fungi. On the contrary, fungi Galactomyces (degree = 17), Neocollimasix (degree = 15), Byssochlamys (degree = 14), Gibberella (degree = 13), and Dipodascus (degree = 14) have a strong correlation with bacteria (Fig. 7C).
Differential microbial interaction networks. (A) Differential core bacterial genera Spearman correlation analysis (|R| > 0.6, P < 0.05), line red represents positive correlation; blue represents negative correlation; the darker the red color of the node, the closer it is to the core of the microbial network; node size represents closeness centrality. (B) Spearman correlation analysis of different fungal core fungi (|R| > 0.6, P < 0.05). (C) Heatmap of bacterial and fungal correlations, *P < 0.05, **P < 0.01, ***P < 0.001.
Correlation analysis
Next, we wanted to investigate the relationship between rumen microbial composition and rumen fermentation parameters. Correlation analyses showed that Xeromyces was positively correlated with pH and negatively correlated with propionate concentration (P < 0.05). Roseivivax, Leisingera, and Xeromyces were negatively correlated with acetate (P < 0.05). Vibrio, Cyanobium_PCC_6307, Xeromyces, and Galactomyces were negatively correlated with total VFA (P < 0.05). Xeromyces is negatively correlated with butyrate (P < 0.05). Gibberella was positively correlated with total VFA and butyrate, and Galactomyces was positively correlated with pH (Fig. 8A, P < 0.05). Redundancy analysis (RDA) showed that bacteria contributed significantly to pH, while fungi contributed significantly to VFA and NH3-N (|R| > 0.6, P < 0.05) (Fig. 8B). Correlation analysis of microorganisms with blood parameters and lactating performance reveals that milk fat rate was positively correlated with Gibberella level. Milk protein yield was negatively correlated with Dipodascus. CP and ADF were negatively correlated with Neocallimastix, and NDF was negatively correlated with Rubritalea and Cyanobium_PSC-6307 (Fig. 8C, P < 0.05). TP was negatively correlated with Gibberella (P < 0.05). Glucose was negatively correlated with Gibberella and Dipodascus and positively correlated with Sordaria (P < 0.05). There was a significant positive correlation between SOD and Neocallimastix, while CAT is positively associated with Rubritalea, Vibrio, Cyanobium_PCC-6307, Galactomyces, and Byssochlamys (P < 0.05).
(A) Pearson correlation analysis of rumen core differential microorganisms and differential fermentation parameters, *P < 0.05, **P < 0.01, ***P < 0.001. (B) Redundancy analysis (RDA) of microbial community changes with rumen environmental variables. (C) Correlating rumen microbiological with lactation performance and serum indicators, *P < 0.05, **P < 0.01, ***P < 0.001.
Discussion
The composition of YC is primarily constituted by yeast extracellular metabolites, which are variable components of the culture medium following fermentation. Additionally, it comprises cell wall components, as well as a limited number of inactive yeast cells (Wang et al. Reference Wang, Li and Zhang2023). YCs are employed as a source of protein in lieu of fishmeal and as an immunomodulator for fish (Xv et al. Reference Xv, Zhong and Wei2021). The effects of YC on rumen pH regulation, ruminal environmental stability, VFA absorption by ruminal epithelium, and the reduction of inflammatory responses associated with high-concentrate feeding have been confirmed in dairy cows (Li et al. Reference Li, Yoon and Scott2016). Studies have shown that YC modulates rumen pH, alters fermentation patterns, and improves productivity (Mohamed et al. Reference Mohamed, Maareck and Abdel-Magid2009; Ranjan et al. Reference Ranjan, Sahoo and Singh2013), and Carpinelli et al. showed that YC affects rumen microbial composition and lactation performance in dairy cows (Carpinelli et al. Reference Carpinelli, Halfen and Trevisi2021). Current studies on the effects of YC on rumen microorganisms have focused on bacteria, but little has been reported on fungi, so it is worthwhile to investigate the relationship between fungi and lactation performance. This study analyzed the effects of YC on rumen microbial composition in mid-lactation dairy goats using 16S and ITS sequencing. The results showed that feeding YC could decrease the relative abundance of Dipodascus and Gibberella, decrease VFA concentrations, increase serum antioxidant levels and increase milk yield. Correlation analyses showed that the rumen fungi Dipodascus and Gibberella were at the center of a network of differential microbial interactions, and their proportions were significantly negatively correlated with milk protein yield. The relationship between YC and lactation performance has been relatively understudied. The present study found that YC improves lactation performance in dairy goats, and alterations in rumen fungi appear to play an important role in this process.
The results revealed that YC altered the rumen microbial composition, and 19 microbial genera with different relative abundance were identified at the bacterial genus level. Moreover, analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment results revealed that YC significantly upregulated 15 metabolic pathways. Among these pathways, the flavone and flavonol biosynthesis metabolic pathway belongs to the phenylpropanoid metabolic pathway, which produces a large amount of phenolic acids, lignans, stilbenes, and other polyphenols (Ma et al. Reference Ma, Zhang and Shen2017). These compounds help to limit the proliferation of undesirable microorganisms (Xia et al. Reference Xia, Wu and Zhang2023). The upregulation of the valine, leucine, and isoleucine degradation pathway indicates an increase in rumen metabolism (Zhuang et al. Reference Zhuang, Lv and Cui2023). Fatty acid degradation and butanoate metabolism can directly impact the concentrations of VFA in the rumen. Benzoate degradation pathway is associated with Pseudomonas abundance (Wang et al. Reference Wang, Morimoto and Ogawa2011). Additionally, an increased folate biosynthesis pathway is associated with the improved production performance of ruminants (Abbasi et al. Reference Abbasi, Abbasi and Wang2018; Cheng et al. Reference Cheng, Wang and Zhang2020). Therefore, YC affects the microbial composition of the rumen, leading to changes in the intensity of metabolic pathways, among which the inhibition of undesirable microbial proliferation by flavone and flavonol biosynthesis pathway may be one of the reasons for improving production performance.
Despite representing only 20% of the rumen biomass, fungi are the most efficient contributors to fiber degradation (Rezaeian et al. Reference Rezaeian, Beakes and Parker2004). Next, we focused on changes in the composition of fungi in the rumen. At the fungal level, 24 different genera were identified. Of these, the reduced Dipodascus and Gibberella induced by YC were the center of a network of interactions. The presence of pathogenic species in Dipodascus (Kulesza et al. Reference Kulesza, Biedunkiewicz and Nowacka2021) can lead to fungal infections in some patients with low immunity (Lakshmi et al. Reference Lakshmi, Prasad and Prasad2023). Gibberella is also a genus of pathogenic fungi (Gao et al. Reference Gao, Ye and Mu2023), inducing liver cancer in rodents (Perera et al. Reference Perera, Al-hebshi and Perera2017) and causing a carcinogenic effect on animals and humans (Kondratiuk et al. Reference Kondratiuk, Beregova and Ostapchenko2017).
Functional classification of fungi revealed a significant decrease in plant_pathogen and pathotroph. It has been shown that Dipodascus includes pathogenic species (Kulesza et al. Reference Kulesza, Biedunkiewicz and Nowacka2021) and Gibberella species are destructive plant pathogens (Karlsson et al. Reference Karlsson, Edel-Hermann and Gautheron2016), and the reduced abundance of both genera may explain the decrease in phytopathogenic and pathogenic trophic fungi. Thus, the study speculated that the decrease in the abundance of Dipodascus and Gibberella might be caused by the increase of flavone and flavonol biosynthesis pathway in YC. A reduction in the abundance of these two fungal genera is likely to have a beneficial effect on the health of dairy goats. In this study, rumen fermentation parameters were also examined, and the result showed that feeding YC had a significant effect on ruminal environmental indices, pH, NH3-N levels increased, total VFA, acetate, propionate and butyrate levels decreased, acetate and propionate ratio did not change significantly, and fermentation pattern was not altered. The extant study corroborates earlier research, which demonstrated that YC increased pH and reduced acetate levels in dairy cows (Halfen et al. Reference Halfen, Carpinelli and Pino F A B2021), and that yeast supplementation also reduced rumen butyrate levels in dairy heifers (Lascano et al. Reference Lascano and Heinrichs2009). However, under the condition of heat stress in bulls, after supplementation of YC, pH, NH3-N, butyrate, or acetate/propionate did not differ significantly, while the rumen acetate, propionate, and total VFA content increased significantly (Zhang et al. Reference Zhang, Liang and Xu2022). However, dietary with YC had no significant effect on rumen fermentation parameters of Baluchi lambs (Malekkhahi et al. Reference Malekkhahi, Tahmasbi and Naserian2015). The different effects of feeding YC in different studies may be due to different species and physiological conditions. In addition, RDA analysis showed that among the differential microbes, differential fungi contributed more to the rumen environment, including Gibberella and Dipodascus.
Serum levels of TP, ALB and CR reflect the metabolism of dietary proteins and the immune status of the body (He et al. Reference He, Niu and Qiu2018). The results of the present study found that feeding YC increased the serum levels of TP, ALB, CR and glucose in dairy goats, which suggests that YC improves metabolism levels of dairy goats. Propionate is an important substrate for glucose (Kawas et al. Reference Kawas, Garcia-Castillo and Garza-Cazares2007). Thus, the elevated serum glucose in the present study was partly due to increased fatty acid utilization in the rumen of ruminants, which may also contribute to the reduction in rumen VFA. YC increased serum SOD and CAT levels in dairy goats, possibly due to the antioxidant content from YC (Krizková Reference Krizková2001). This is consistent with previous studies, indicating that YC has resulted in increased antioxidant activity in organisms (Bu et al. Reference Bu, Lian and Wang2019; Xv et al. Reference Xv, Zhong and Wei2021). Correlation analysis showed that SOD was positively correlated with Neocallimastix, which is an anaerobic fungus in rumen (Comlekcioglu et al. Reference Comlekcioglu, Ozkose and Akyol2010). CAT was positively correlated with Galactomyces and Vibrio. Galactomyces can be isolated from rumen fluid and it has potential for use as a feed additive in ruminant production (Suntara et al. Reference Suntara, Cherdthong and Wanapat2021). Vibrio is a rumen microorganism associated with rumen fermentation (Ramos et al. Reference Ramos, Kim and Jeong2022).
YC increased DMI, milk yield, and milk lactose yield in dairy goats, which is consistent with the results of YC studies in cows (Bruno et al. Reference Bruno, Rutigliano and Cerri2009) and Nili-Ravi buffaloes (Ali et al. Reference Ali, Ahmed and Ali2023). Correlation analysis results found that milk yield was negatively correlated with Gibberella and Dipodascus (P > 0.05). Milk lactose yield was negatively correlated with Gibberella and Dipodascus (P > 0.05), and milk fat rate was positively correlated with Gibberella proportions. As the relationship between these two fungi and lactation performance has not been reported, we speculated that feeding YC to dairy goats resulted in changes in the core differential microorganisms, Gibberella and Dipodascus, in the rumen of dairy goats, which further affected the bacterial–fungal interaction network and ultimately led to a decrease in VFA levels and an increase in serum glucose levels, finally increased lactose production, milk yield, and milk protein yield. Correlation analysis indicated that there was a stronger association between fungal diversity and dairy goat lactation index. Correlation analyses remain a nascent field of study, and the relationship between rumen microbes and lactation performance is yet to be fully elucidated.
Conclusions
The addition of YC to the diet improved lactation performance by altering the rumen bacterial and fungal composition of lactating dairy goats, affecting rumen fermentation parameters, increasing serum TP, CR, and Glu levels, and increasing serum levels of antioxidant indices such as SOD and CAT. Importantly, we further revealed that the improved lactation performance induced by YC was associated with decreased abundances of Dipodascus and Gibberella in the rumen.
Author contributions
Huaiping Shi: project administration, data curation, supervision. Liyan Ge and Shuying Bai: conceptualization, validation, visualization, writing – original draft, software. Huijun Shen and Kela Sha: validation, methodology, writing – original draft. Yuexin Shao: methodology, formal analysis. The authors have all read and approved the manuscript.
Financial support
This study was supported by National Key Research and Development Plan of China (2022YFD1300200) and the National Natural Science Foundation of China (32272828).
Conflict of interest
The authors declare no competing financial interest.
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