Abstract
The effects of yeast culture (YC) on dairy goat milk yield and
potential effects of rumen microbial population changes on rumen
fermentation are poorly understood. This study aimed to evaluate the
effects of YC on milk yield and rumen fermentation in dairy goats and
explore the potential microbial mechanisms. Forty Laoshan dairy goats
with a weight of 51.23 ± 2.23 kg and daily milk yield of 1.41 ± 0.26 kg
were randomly divided into 4 groups: control (no YC), YC1 (10 g/day per
goat), YC2 (25 g/day per goat), and YC3 (40 g/day per goat). The
pre-feeding period was 15 days, and the official period was 60 days.
Laoshan dairy goats were milked twice daily, and the individual milk
yield was recorded. On the last day of the official period, rumen fluid
was collected to measure rumen fermentation, perform quantitative
polymerase chain reaction (PCR), and detect metabolites. Compared to
the control group, the YC group had greater milk yield; higher acetic
acid, butyric acid, and total volatile fatty acid contents; and lower
ammonia-N (NH[3]-N) content in the rumen (p < 0.05). YC increased the
abundance of Clostridia_UCG-014 and Paraprevotella (p < 0.05).
Differential metabolites L-leucine and aspartic acid were screened.
This study revealed the microbial mechanisms linking the relative
abundance of Paraprevotella and Clostridia_UCG-014 to L-leucine and
aspartic acid utilization. These results describe the potential
benefits of supplementing 10 g/day per goat YC in the diets of Laoshan
dairy goats for improving the rumen environment and milk yield.
Keywords: dairy goats, production performance, rumen fermentation
parameters, rumen microflora, rumen metabolism
1. Introduction
Increasing dairy production efficiency is a key goal in dairy goat
nutrition, and dietary interventions play a crucial role. Yeast culture
(YC) has been reported to improve milk yield ([35]1, [36]2), stabilize
rumen pH, and promote consistent environments for rumen fermentation
([37]3–5).
Commercial YC and yeast-containing feed ingredients vary in many
characteristics, including the yeast strain, viability, culture and
associated media, and post-fermentation processing [e.g., fractionated
yeast ([38]6)]. YC is unique among yeast products because it contains
yeast biomass and fermentation metabolites ([39]6). The composition and
characteristics of the fermentation metabolites are highly dependent on
the medium used to grow the yeast. The freeze-drying method of yeast
culture can also retain a small amount of active yeast and the
fermentation activity of yeast ([40]6, [41]7). The benefits of YC have
been attributed to the presence of functional metabolites (e.g.,
organic acids, B vitamins, and enzymes) that may influence ruminal
fermentation by supplying key nutrients that are otherwise scarce in
the ruminal environment ([42]8, [43]9).
The abundance of various rumen bacterial taxa is correlated with
production performance and rumen fermentation parameters, indicating
that bacterial communities play important roles in regulating host
physiological parameters ([44]10, [45]11). According to
Chaucheyras-Durand et al. ([46]12), the main effects of YC are related
to rumen fermentation and benefit key microbial populations and their
metabolism, increase fiber degradation, and stabilize rumen pH. In vivo
and in vitro experiments have shown that YC can stimulate the growth of
rumen cellulolytic bacteria ([47]13), promote fiber degrading bacteria
establishment in the digestive tract of lambs, and accelerate microbial
activity in the rumen ([48]14). Rumen cellulolytic bacteria can
decompose dietary macromolecular carbohydrates into glucose, which is
then fermented to produce volatile fatty acids (VFAs), such as acetic
acid, propionic acid, and butyric acid. These VFAs contain large
amounts of energy and are the main energy sources for dairy cows
([49]15, [50]16). In addition, studies have reported that the ratios of
these VFAs are affected by changes in microbial metabolism and species
([51]17). However, the effects of YC on the production performance and
rumen fermentation parameters of dairy goats and the exact microbial
mechanisms underlying these effects are unclear. Therefore,
understanding the microbial mechanisms underlying the effects of YC on
the rumen is important for optimizing the utilization of YC for
ruminant nutrition.
Therefore, the current study evaluated the effects of YC on the
production performance, rumen fermentation characteristics, rumen
microorganisms, and metabolites of dairy goats. Moreover, the main
metabolites and metabolic pathways affecting rumen fermentation
parameters were explored through the weighted gene correlation network
analysis (WGCNA) method to further understand the microbial mechanisms
underlying the effect of YC on rumen fermentation parameters in dairy
goats.
2. Materials and methods
2.1. Ethical considerations and location
This study was conducted at the Aote Breeder Goat Co., Ltd., Qingdao
City, Shandong Province (120°36′E, 36°58′N), and the experiments were
performed in strict accordance with the guidance of the National
Council for the Control of Animal Experimentation under an experimental
protocol approved by the Animal Science and Technology College of
Qingdao Agricultural University, Qingdao, Shandong (protocol code
DKY20200701, dated July 1, 2020).
2.2. Diet and livestock management
The studied YC, which was provided by Danongwei Technology (Shenzhen)
Co., Ltd., is a concentrated and dried product of Saccharomyces
cerevisiae after fermentation. It is mainly composed of yeast cell
lysate, yeast cell wall (e.g., mannan-oligosaccharide, β-glucan, and
chitin), post-fermentation denaturation medium, and extracellular
metabolites (e.g., nucleosides, organic acids, proteins, peptides,
plant Zi alcohol, natural antioxidants, and digestive enzymes). The
crude protein (CP) content was 16.2%, crude fat (EE) content was 1.53%,
ash content was 7.71%, and moisture content was 9.87%.
A completely randomized experimental design was adopted. Forty Laoshan
adult dairy goats (born in late April 2020) that had previously given
birth twice and exhibited good body condition, (51.23 ± 2.23 kg body
weight; 40 ± 4 days in milk; 1.41 ± 0.26 kg/d milk yield) were selected
and randomly divided into four groups. Each treatment consisted of 10
Laoshan dairy goats.
The diets were formulated based on the nutrient requirements of
lactating goats reported by Cannas et al. ([52]18, [53]19). The goats
were fed a basal diet ad libitum and allowed approximately 5% orts
twice a day at 08:00 and 14:30. The daily feeding quantity was 3.5% of
the body weight. The dietary composition and nutritional levels are
shown in [54]Table 1.
Table 1.
Ingredients and chemical composition of diets (DM basis).
Item g/100 g
Ingredients
Corn silage 16.46
Peanut vine 20.77
Pomace 6.21
Garlic stalks 3.53
Hay 2.16
NaCl 0.29
Mineral-vitamin-protein mix[55] ^1 50.58
Total 100
Nutrient content[56] ^2, dry matter basis
DE, MJ/kg 13.85
CP, % 13.95
NDF, % 47.59
ADF, % 22.19
[57]Open in a new tab
^1
Mineral-vitamin-protein mix was purchased from Oulifeide Feed Science
and Technology Co, Ltd. (Shandong, China), and it contained corn, corn
dry alcohol grains, spray corn husk, corn germ meal, soybean meal,
bran, cotton meal, calcium bicarbonate, stone powder, sodium chloride,
sugarcane molasses, palm fat powder, sodium bicarbonate, minerals, and
vitamin ingredients. The feed had the following contents, crude protein
(CP), 20.1%; Ash, 9.7% and NaCl, 1%.
^2
For the nutrient levels, the digestible energy (DE) was based on the
calculated value. CP, neutral detergent fiber (NDF), and acid detergent
fiber (ADF) were the measured value.
The control group (CON) was fed a basic diet, while the YC1, YC2, and
YC3 groups were fed the basic diet and 10, 25, and 40 g YC/day per
goat, respectively. To ensure that the YC groups ingested sufficient YC
per day per goat, YC was mixed with a small amount of basic diet and
fed to each goat separately every morning. After each goat had finished
all of the basic diet with YC, the remaining feed would be given to
them. The YC dose was based on the recommendations of the supplier,
which suggested a range of 10–40 g/day per goat. The adaptation period
was 15 days, and the experimental period was 60 days. During the test
period, all Laoshan dairy goats were allocated to individual pens with
natural light and cool environmental temperatures and provided ad
libitum access to drinking water.
2.3. Data and sample collection
Laoshan dairy goats were milked twice daily at approximately 06:00 and
18:00, and individual milk yield was recorded using an Afikim milking
system (AfiFlo milk meters, S.A.E. Afikim, Israel). The milk samples
(morning and afternoon) of each animal were mixed to form a composite
sample, which was placed in plastic containers with Brono-pol^®
preservative and stored in a freezer at −20°C until the chemical
composition (50 mL sample) was analyzed. Milk fat concentrations were
analyzed once weekly using the Gerber method ([58]20). The 4% fat
corrected milk (FCM) yield was calculated as follows: [(0.4 × milk
yield) + (15 × milk fat percentage × milk yield)] ([59]21).
The rumen fluid was collected from 40 Laoshan dairy goats on the final
day of the study period. Laoshan dairy goats were sampled in the
afternoon via an esophageal tube at 14:00. The esophageal tubing
apparatus was assembled by coupling the esophageal tube to a metal
strainer ([60]22) at one end and the opposite handle side of a manual
vacuum pump (Med-Eze stomach pump, MAI Animal Health, China) at the
other end. The rumen fluid samples were collected by passing the fluid
through the hollow shaft of the pump into a plastic beaker. After
discarding the first 200 mL of fluid to minimize salivary
contamination, approximately 50 mL of rumen fluid was collected. After
collection, the pH was immediately measured using a pH meter
(Waterproof pH Testr 30, Oakton Instruments, United States), and two
aliquots (10 mL) were acidified with either 200 μL of 50% sulfuric acid
or 2 mL of 25% meta-phosphoric acid and stored at −20°C until analysis
of ammonia-N (NH[3]-N) and VFAs, respectively. In addition, 2 mL of
rumen fluid samples were collected and immediately frozen in liquid
nitrogen and stored at −80°C until DNA isolation and subsequent
relative abundance analysis of bacteria species, which was performed
via the quantitative polymerase chain reaction (qPCR) method. Detection
of metabolites was performed via non-targeted metabolomics analysis on
Ultra-high-performance Liquid Chromatography–Tandem Mass Spectrometry
(UHPLC–MS/MS) system (ExionLC AD, SCIEX, United States; QTRAP^®, SCIEX,
United States).
Rumen fluid samples preserved in 50% sulfuric acid and 25%
meta-phosphoric acid were thawed and transferred into 2 mL
microcentrifuge tubes. Then, the samples were centrifuged at 30,000 × g
for 20 min at 4°C (model 5403, Eppendorf, Germany), and the supernatant
from samples in sulfuric acid was used to analyze NH[3]-N using the
colorimetric assay described by Chaney and Marbach ([61]23). The
supernatant of rumen fluid containing 25% meta-phosphoric acid was
analyzed for the acetic acid, propionic acid, butyric acid, and total
VFA (TVFA) concentrations using an automated gas chromatograph (model
689, Hewlett-Packard, Juyi Hui supply chain Co., Ltd., China) equipped
with a 0.25 mm i.d × 15-m column (Nukol 24106-U, Supelco, Inc., United
States), and the internal standard was 2-ethylbutyrate.
2.4. Ruminal bacteria DNA isolation and qPCR amplification of 16S rDNA genes
Microbiome DNA was extracted from each sample (250 μL of rumen fluid
was used) using a PowerSoil DNA Isolation Kit (MoBio Laboratories,
Inc., Canada), following the manufacturer’s instructions. The extracted
DNA was detected using a NanoDrop 2000 (ThermoFisher Scientific, Inc.,
United States) to determine the DNA quality and concentration. The
samples qualified for quality inspection were stored at −20°C for use
in follow-up experiments.
The V3-V4 region of the 16Sr RNA gene of bacteria was amplified by
primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R
(5′-GGACTACHVGGGTWTCTAAT-3′). Eight base-pair barcode sequences were
added to the 5′ ends of upstream and down-stream primers to distinguish
different samples. The PCR reaction system contained the following
(25 μL total volume): 12.5 μL 2× Taq Plus Master Mix II (Vazyme Biotech
Co., Ltd., China), 3 μL BSA (2 ng/μL), 1 μL forward primer (5 μM), 1 μL
reverse [rimer (5 μM), 2 μL DNA (total amount of added DNA was 30 ng)],
and 5.5 μL dd H[2]O to a volume of 25 μL. The reaction parameters were
pre-denatured at 95°C for 45 min, denatured at 95°C for 45 s, annealed
at 55°C for 50 s, annealed at 55°C for 45 s, extended at 72°C for 45 s
for 28 cycles, and extended at 72°C for 10 min. The PCR products were
amplified using an ABI 9700 PCR instrument (Thermo Fisher Scientific,
Inc., United States). The size of the amplified bands was detected by
1% agarose gel electrophoresis, and the bands were purified using an
Agencourt AMPure XP nucleic acid purification kit (Beckman Coulter,
Inc., United States).
The library was constructed using the NEB Next Ultra II DNA Library
Prep Kit (New England Biolabs, Inc., United States), which is a library
building kit, and paired-end sequencing was performed using the
Illumina MiSeq PE300 (Illumina, Inc., United States) high-throughput
sequencing platform. Trimmomatic software was used to control the
quality of Fastq data, and it used the sliding window strategy, a
window size of 50 mbp, average quality value of 20, and minimum
reserved sequence length of 120. Using Pear (v0.9.6), the minimum
overlap was set to 10 bp, and the mismatch rate was 0.1. After
splicing, Vsearch (v2.7.1) software was used to remove sequences whose
length was less than 230 bp, and the UCHIME method was used to remove
chimera sequences according to the Gold Database. Using the Vsearch
(v2.7.1) software uparse algorithm for operational taxonomic unit (OTU)
clustering of high-quality sequences. Valid tags with sequence
similarity thresholds ≥97% were assigned to the same taxon (OTU), and
the tag sequence with the highest abundance was selected as the
representative sequence in each OTU cluster. The BLAST algorithm with
Silva138 was used to annotate the species classification. The α
diversity index results were analyzed by QIIME (v2.0.0) software, and
the α diversity among groups was compared by Wilcoxon rank test using
the R package ggpubr (0.4.0). Based on the species annotation and
relative abundance results, R (v3.6.0) software was used to analyze the
histogram of species composition. QIIME (v2.0.0) was used to calculate
the beta diversity distance matrix. NMDS analysis and mapping were
performed using the RPG plot2 (3.3.2) and vegan software packages. The
Sankey diagram of the species community composition was visualized
using the R-package ggplot2. Short time-series expression miner (STEM)
([62]24). The abundance distributions of all OTUs were analyzed.
Co-occurrence network analysis was performed using the R-package Psych
and visualized using the Cytoscape software (version 3.7.1).
2.5. Metabolite extraction and UHPLC–MS/MS analysis
Rumen fluid samples were collected in 1.5 mL Eppendorf miniature
centrifuge tubes, and 1 mL 70% methanol internal standard extract was
added. The samples were oscillated for 5 min, maintained on ice for
15 min, centrifuged at 12000 × g for 10 min at 4°C. The supernatant was
then extracted, and 400 μL was placed in a corresponding EP tube after
centrifugation. The supernatant was then placed in a −20°C refrigerator
overnight. Then, it was centrifuged at 12000 × g for 3 min at 4°C.
Subsequently, 200 μL of supernatant was placed into the inner liner of
the corresponding injection bottle for UHPLC–MS/MS analysis, which
combined ultra-performance liquid chromatography (UPLC) (ExionLC AD,
SCIEX, United States) with MS/MS (QTRAP^®, SCIEX, United States).
Samples collected from the supernatant mixture were used as quality
control samples. During the instrument analysis, one quality control
sample was injected into every six test samples to monitor the
repeatability and stability of the instrument.
The original data obtained from the UHPLC–MS/MS platform were converted
into the TXT format using MSconventer software. Based on the self-built
target database MWDB (software database), the software Analyst1.6.3 was
used to qualitatively analyze the information and secondary spectrum
data according to the retention time (RT), parent ion pair, and
secondary spectrum data. Heat maps of different metabolites were drawn
using the R-package pheatmap. Metabolites were enriched using
MetaboAnalyst4.0.[63]^1
The WGCNA method is a correlation-based method that describes and
visualizes networks of data points, regardless of whether they are
estimates of gene expression, metabolite concentration, or other
phenotypic data ([64]25, [65]26). WGCNA can identify a module by
building a metabolite correlation network and deriving the
characteristic metabolite score (related to the first principal
component) ([66]27) from the identified module. To explore the
mechanism of rumen acid-related metabolism, WGCNA was used to identify
highly related metabolite modules based on annotated metabolites. These
modules were associated with the rumen acid indexes determined in this
study. The parameters used were a soft power of five and a minimum
module size of 10 metabolites. The modules with p < 0.05 and R > 0.5
were considered to be related to the rumen acid index. The molecule
pathway database MetaboAnalyst4.0[67] ^2 was used to analyze the
pathway enrichment of metabolites in the rumen acid-related modules.
This pathway was defined as significantly enriched at p < 0.05.
Coefficients of the Spearman correlation of acetic acid, propionic
acid, and butyric acid with key metabolites were analyzed using the
R-package Psych (2.0.7) and visualized using the R-package pheatmap
(1.0.12). This correlation was statistically significant (p < 0.05).
2.6. Statistical analysis
Data on the production performance and rumen fermentation parameters
were analyzed using one-way ANOVA and Tukey’s honest significant
difference test using SPSS statistical software (version 20.0; SPSS
Inc., Chicago, IL, USA). Statistical significance was set at p < 0.05.
The results are expressed as the mean ± standard deviation. The
experimental units were replicates, and the statistical model used was
as follows:
[MATH: Yij=μ+
Ai+eij :MATH]
where Y[ij] represents an observation, μ is the overall mean, A[i]
represents the effect of YC, and e[ij] represents random error.
3. Results
3.1. Milk yield
The effects of YC on the average daily 4% FCM yield of Laoshan dairy
goats are presented in [68]Table 2. At weeks 4, 5, and 10, the average
daily 4% FCM yield of YC1 was significantly greater than that of CON,
YC2, and YC3 (p < 0.05). At week 4, the average daily 4% FCM yield of
YC1 surpassed that of CON, YC2, and YC3 by 12.94, 14.29, and 9.09%,
respectively; at week 5, it reached 16.77, 17.47, and 10.80%,
respectively; and at week 10, it further rose to 24.55, 26.06, and
18.18%, respectively. At the 9th week, the average daily 4% FCM yield
of YC1 was significantly greater than that of CON and YC2 (p < 0.05),
with increases of 23.84 and 18.99%, respectively. However, no
significant differences were observed between the YC1 and YC3 groups
(p > 0.05).
Table 2.
Effects of yeast culture on the milk yield of Laoshan dairy goats,
kg/d.
Items[69] ^1 CON YC1 YC2 YC3 p-value
1st week 1.75 ± 0.17 1.78 ± 0.06 1.76 ± 0.03 1.83 ± 0.09 0.76
2nd week 1.76 ± 0.17 1.73 ± 0.12 1.71 ± 0.14 1.82 ± 0.17 0.81
3rd week 1.87 ± 0.31 1.78 ± 0.09 1.69 ± 0.13 1.88 ± 0.12 0.56
4th week 1.70 ± 0.04^b 1.92 ± 0.10^a 1.68 ± 0.59^b 1.76 ± 0.12^b <0.05
5th week 1.67 ± 0.15^b 1.95 ± 0.03^a 1.66 ± 0.07^b 1.76 ± 0.04^b <0.05
6th week 1.77 ± 0.90 1.95 ± 0.11 1.76 ± 0.11 1.83 ± 0.20 0.21
7th week 1.74 ± 0.07 2.07 ± 0.27 1.74 ± 0.11 1.85 ± 0.20 0.12
8th week 1.74 ± 0.08 2.18 ± 0.30 1.84 ± 0.11 1.90 ± 0.86 0.07
9th week 1.72 ± 0.13^b 2.13 ± 0.23^a 1.79 ± 0.08^b 1.89 ± 0.20^ab <0.05
10th week 1.67 ± 0.16^b 2.08 ± 0.17^a 1.65 ± 0.11^b 1.76 ± 0.69^b <0.05
[70]Open in a new tab
^a,bValues within the same row with different superscripts are
significantly different (p < 0.05).
^1
CON, control diet; YC1, CON + 10 g YC/d/goat; YC2, CON + 25 g
YC/d/goat; YC3, CON + 40 g YC/d/goat; YC; yeast culture.
3.2. Rumen fermentation
The effects of YC on ruminal fermentation parameters in Laoshan dairy
goats are shown in [71]Table 3. Significant differences in ruminal pH
were not observed between the CON, YC1, YC2, and YC3 groups (p > 0.05).
Compared with the CON group, a significant decrease in ruminal NH[3]-N
content was observed in the YC2 group (p < 0.05). However, significant
differences were not observed in ruminal NH[3]-N content between the
YC1, YC2, and YC3 groups (p > 0.05). Compared with the CON group,
significant increases in ruminal acetic acid, butyric acid, and TVFA
contents were observed among the YC1, YC2, and YC3 groups (p < 0.05),
whereas no significant differences in ruminal acetic acid, butyric
acid, and TVFA contents were observed between the YC1, YC2, and YC3
groups (p > 0.05).
Table 3.
Effects of yeast culture on the rumen fermentation parameters of
Laoshan dairy goats.
Items[72] ^1 CON YC1 YC2 YC3 p-value
pH 5.90 ± 0.18 5.89 ± 0.22 6.03 ± 0.16 6.22 ± 0.35 0.22
NH[3]-N, mg/dL 13.72 ± 2.42^a 11.01 ± 1.97^ab 9.28 ± 1.24^b
11.04 ± 0.44^ab <0.05
Acetic, mmol/L 36.83 ± 6.09^b 51.94 ± 6.82^a 50.39 ± 6.57^a
52.67 ± 11.33^a <0.05
Propionate, mmol/L 16.21 ± 2.11 22.05 ± 3.67 21.21 ± 2.90 21.33 ± 3.51
0.07
Butyrate, mmol/L 11.33 ± 1.21^b 16.85 ± 3.48^a 15.11 ± 1.94^a
16.48 ± 2.14^a <0.05
TVFA, mmol/L 64.39 ± 9.35^b 90.84 ± 13.92^a 86.72 ± 11.25^a
90.48 ± 16.96^a <0.05
Acetic/Propionate 2.26 ± 0.13 2.37 ± 0.08 2.38 ± 0.13 2.45 ± 0.13 0.21
[73]Open in a new tab
^a,bValues within the same row with different superscripts are
significantly different (p < 0.05).
^1
CON, control diet; YC1, CON + 10 g YC/d/goat; YC2, CON + 25 g
YC/d/goat; YC3, CON + 40 g YC/d/goat; YC, yeast culture; NH[3]-N,
ammonia-N; TVFA, total volatile fatty acids.
3.3. Characteristic analysis of OTUs
A total of 1,734,238 tags were generated from 16 samples (with an
average of 106,831 tags). These samples were obtained by randomly
selecting four samples from each treatment group. After performing
quality control, denoising, splicing, and de-chimerism, and eliminating
singleton OTUs, 16 samples (average 96,824) generated 1,564,718
high-quality data points (valid tags). At a 97% similarity level, a
total of 33,640 OTUs were described. The abundance of 33,640 OTUs was
analyzed using STEM software. Six significantly enriched modules were
identified ([74]Figure 1A). Upset diagrams ([75]Figure 1B) depict the
number of OTUs that are unique to each group or shared among multiple
groups. The CON, YC1, YC2, and YC3 groups had 17,820, 18,883, 19,329,
and 17,254 unique OTUs, respectively, with 7,961 OTUs in total.
Figure 1.
[76]Figure 1
[77]Open in a new tab
Characteristic analysis of operational taxonomic units (OTUs). (A)
Temporal expression cluster analysis of OTUs. The color module showed a
significant enrichment trend (p < 0.05). (B) Upset map of rumen fluid
OTUs. The left bar chart shows the total elements in each original data
set; vertical lines connect points to show intersections between data
sets, and values represent common OTUs. (C) Circos map of OTUs in rumen
fluid samples. The outermost circle on the left is sample grouping, on
the right is phylum species, and the innermost part is the relative
abundance percentage circle. The lines indicate the species and
relative abundance information in the samples. (D) Change in the
relative abundance of the genus. Using the Spearman test method, the
top 20 genera in samples were selected for correlation analysis, and
corresponding phyla were used as the legend. Results with p values
greater than 0.05 were filtered out. The size of the point represents
abundance, the thickness of the line represents correlation, and the
color of the point represents the phylum. Red lines indicate positive
correlations and blue lines indicate negative correlations.
3.4. Abundance of ruminal bacteria
The α diversity of microbiota in the rumen fluid of dairy goats was
analyzed by calculating the Chao1, Simpson, Shannon, Observed_species,
PD_whole_tree, and Goods_coverage indexes. The results showed that the
six α diversity indexes did not significantly differ among the
different groups (p > 0.05) ([78]Figure 2A). β diversity analyses were
then performed, and the results showed that there were significant
differences in phylogenetic distance among the four groups. Principal
component analysis (PCA) showed that CON, YC1, YC2, and YC3 could not
be distinguished, indicating that different treatments did not change
the microbial diversity of the rumen fluid of dairy goats as a whole
([79]Figure 2B). However, the partial least squares discriminant
analysis (PLS-DA) model showed significant differences among CON, YC1,
YC2, and YC3 ([80]Figure 2C).
Figure 2.
[81]Figure 2
[82]Open in a new tab
Abundance of ruminal bacteria. (A) Alpha diversity of rumen fluid
microbiota of dairy goats with different treatments. The horizontal bar
in the box represents the average. The top and bottom of the box
represent the upper and lower quartiles, respectively. Single asterisk
(*) means p < 0.05, and no asterisk means p > 0.05. (B) Principal
component analysis (PCA) and (C) Partial least squares discriminant
analysis (PLS-DA) models. Different colors represent samples from
different groups of rumen liquid from dairy goats. The distance between
the points on the map represents the similarity of all samples in terms
of microflora composition and abundance. (D) Sankey diagram of species
composition at the phylum level. Different colors represent different
phyla. (E) Sankey diagrams of species composition at the genus level.
Different colors represent different genera.
Changes in the composition of different microorganisms were analyzed at
the phylum and genus levels. The results showed that Bacteroidota and
Firmicutes were the two main phyla observed, and in CON, YC1, YC2, and
YC3. Compared to that in CON, YC1 and YC2 showed relative increases in
the abundance of Bacteroidota but decreases in the abundance of
Firmicutes ([83]Figure 2D). At the genus level, compared with the CON
group, the relative abundance of Prevotella increased in the YC1 and
YC2 groups but decreased in YC3, whereas the relative abundance of
Rikenellaceae_RC9_gut_group increased in YC3. The relative abundances
of NK4A214_group and F082 were stable ([84]Figure 2E). In summary, YC
supplementation increased the abundance of the phylum Bacteroidota and
genera Prevotella and Rikenellaceae_RC9_gut_group in the rumen of
Laoshan dairy goats. Microbial co-occurrence networks have been widely
used to explore the relationships in microbial communities. The Circos
species relationship diagram at the phylum level of microbial community
composition ([85]Figure 1C) revealed that the CON, YC1, YC2, and YC3
groups mainly contained Bacteroidota and Firmicutes. An analysis of
changes in relative abundance revealed that the genera Prevotella,
Rikenellaceae_RC9_gut_group, Bacteroi-dales_RF16_group, F082, p_251_o5,
Muribaculaceae, and Prevotellaceae_UCG_001 of the phylum Bacteroidota
and genera NK4A214_group, Christensenellaceae_R_7_group, and
Ruminococcus of the phylum Firmicutes were highly abundant ([86]Figure
1D). As shown in [87]Figure 1D, the red lines between phyla represent
positive correlations. Notably, the network demonstrated a multitude of
positive correlations, as indicated by the dense web of red edges,
indicating the existence of synergistic interactions among the phyla
Firmicutes, Spirochaetota, Bacteroidota, Patescibacteria, and
Synergistota.
3.5. Rumen fluid metabolic spectrum
In this study, a UPLC-MS/MS non-targeted metabolomics analysis method
was used to study the rumen fluid samples from dairy goats subjected to
different treatments. Based on the results of secondary quality
determination, 366 metabolites were annotated, of which 224 were
annotated based on the HMDB database,[88] ^3 and 203 were annotated
based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A
heat map of the expression profiles of the 366 metabolites was
generated ([89]Figure 3A). The PCA and PLS-DA maps were consistent with
the results for the microorganisms ([90]Figures 3B,[91]C). The KEGG
enrichment analysis of 336 metabolites showed that the metabolites were
mainly concentrated in the primary metabolic pathways. The pathways
with significant enrichment were purine metabolism; valine, leucine,
and isoleucine bio-synthesis; phenylalanine metabolism; pyrimidine
metabolism; aminoacyl-tRNA biosynthesis; alanine, aspartate, and
glutamate metabolism; arginine biosynthesis; histidine metabolism;
vitamin B6 metabolism; glycine, serine, and threonine metabolism;
phenyl-alanine, tyrosine, and tryptophan biosynthesis; and pantothenate
and CoA biosynthesis ([92]Figure 3D).
Figure 3.
[93]Figure 3
[94]Open in a new tab
Rumen fluid metabolic spectrum. (A) Cluster analysis. Metabolite
changes from green to red are shown for different samples. Darker red
indicates greater abundance, darker green indicates lower abundance.
(B) PCA and (C) PLS-DA diagrams. Different colors represent different
groups of rumen fluid samples from dairy goats. The distance between
points represents the similarity in microflora composition and
abundance. (D) Kyoto encyclopedia of genes and genomes (KEGG)
enrichment analysis, showing primary and tertiary pathways and KOID for
each metabolic pathway. “All detected” indicates the number of
metabolites annotated by KEGG data; “enrichment” indicates the number
of differential metabolites enriched in the rumen acid pathway.
3.6. Weighted co-expression network analysis of metabolomics and rumen
fermentation parameters
To explore the relationship between metabolites and rumen acid indexes,
366 metabolites were analyzed using WGCNA, and highly related
metabolite modules were identified and correlated with the acetic acid,
propionic acid, and butyric acid contents. WGC-NA used a soft threshold
(power) of 5 to cluster 366 metabolites into 14 modules ([95]Figures
4A,[96]B). Among the 14 modules, 21 metabolites in the red module were
significantly correlated with acetic, propionic, and butyric acids
(p < 0.05, |r| > 0.5) ([97]Figure 4C). The red module was negatively
correlated with acetic, propionic, and butyric acids, indicating that
the metabolites in this module may be the substrates used in the
synthesis of acetic, propionic, and butyric acids. The abundances of 21
metabolites in the red module that were significantly related to rumen
fluid were plotted. The results showed that the aspartic acid and
L-leucine levels in the YC1 group were significantly lower than those
in the CON group ([98]Figure 4D).
Figure 4.
[99]Figure 4
[100]Open in a new tab
Weighted co-expression network analysis (WGCNA) of metabonomic and
rumen fermentation parameters. (A) Determination of the soft threshold
for WGCNA analysis. The scale-free fitting index and average
connectivity show that a soft threshold greater than 5 satisfies a
scale-free topology greater than 0.8. (B) Clustering tree map of
different metabolites based on topological overlap. The dynamic cutting
method identifies modules, shown in different colors below the tree
view. (C) Heatmap diagram visualization module-feature association.
Each row is a module characteristic, and each column is a feature, with
correlation and p value in each unit. Red and green represent positive
and negative correlations, respectively, with darker colors indicating
stronger correlations. (D) Violin diagram of the abundance of 21
metabolites. p < 0.05 indicates a significant difference.
3.7. Pathway enrichment analysis of important modules
Twenty metabolic pathways were identified, among which five were
significant (p < 0.05) ([101]Figure 5A): aminoacyl-tRNA biosynthesis;
valine, leucine, and isoleucine biosynthesis; valine, leucine, and
isoleucine degradation; arginine biosynthesis; and histidine
metabolism. To further explore the potential correlation between these
metabolites and related bacteria, R-package Psych software was used to
calculate the Spearman correlation coefficients. [102]Figure 5B shows
the relationship between the key metabolites and bacteria. The results
revealed a significant positive correlation between Acetitomaculum and
aspartic acid (p < 0.05), a significant negative correlation between
Clostridia_UCG-014 and aspartic acid (p < 0.05), a significant positive
correlation between Acetitomaculum, Blautia,
Lachno-spiraceae_NK3A20_group, and Butyrivibrio and L-leucine
(p < 0.05); and a significant negative correlation between
Paraprevotella and L-leucine (p < 0.05). As shown in [103]Table 4, the
abundance of Acetitomaculum in the YC1, YC2, and YC3 groups was
significantly greater than that in the CON group (p < 0.05); the
abundance of Clostridia_UCG-014 in the YC3 group was significantly
greater than that in the CON group (p < 0.05); and the abundance of
Blautia and Paraprevotella in the YC1 and YC2 groups was significantly
greater than that in the CON group (p < 0.05).
Figure 5.
[104]Figure 5
[105]Open in a new tab
Pathway enrichment analysis of important modules. (A) KEGG enrichment
analysis of differential metabolites significantly related to rumen
acid. The x-axis shows the rich factor of each pathway, the y-axis
shows the pathway names, and the color of the dot is the p value. A
redder color indicates more significant enrichment. The size of the
point represents the number of differential metabolites enriched. (B)
Correlation analysis between rumen fluid-related metabolites and top 20
most abundant bacteria. Blue represents a negative correlation, red
represents a positive correlation, and a darker color indicates a more
significant correlation. An asterisk (^*) indicates significance at
p < 0.05, and no asterisk indicates p > 0.05.
Table 4.
Differences in abundance of bacteria related to aspartic acid and
L-leucine.
Items[106] ^1 CON YC1 YC2 YC3 p-value
Acetitomaculum 1.32 ± 0.03^c 1.58 ± 0.07^a 1.48 ± 0.04^b 1.43 ± 0.05^b
<0.01
Clostridia_UCG-014 2.22 ± 0.16^b 2.42 ± 0.03^b 2.33 ± 0.05^b
3.01 ± 0.14^a <0.01
Blautia 1.32 ± 0.09^b 1.63 ± 0.11^a 1.57 ± 0.06^a 1.36 ± 0.11^b 0.01
Lachnospiraceae_NK3A20_group 2.65 ± 0.55 2.06 ± 0.91 3.06 ± 0.97
3.02 ± 0.85 0.47
Butyrivibrio 2.77 ± 0.70 2.76 ± 0.83 3.50 ± 0.99 4.83 ± 0.83 0.06
Paraprevotella 1.42 ± 0.01^b 1.86 ± 0.09^a 1.84 ± 0.01^a 1.07 ± 0.04^c
<0.01
[107]Open in a new tab
^a,bValues within the same row with different superscripts are
significantly different (p < 0.05).
^1
CON, control diet; YC1, CON + 10 g YC/d/goat; YC2, CON + 25 g
YC/d/goat; YC3, CON + 25 g YC/d/goat; YC; yeast culture.
4. Discussion
The findings showed that dietary supplementation with YC has positive
and significant effects on the milk yield of Laoshan dairy goats, which
is consistent with the results of Khan et al. ([108]28), who studied
the effects of yeast supplementation on Beetal goats during early
lactation and concluded that dietary yeast supplementation had
beneficial effects on the milk yield. Moreover, the finding is
consistent with that of Zaworski et al. ([109]29), Dias et al. ([110]4,
[111]5), Nocek et al. ([112]30), Halfen et al. ([113]31), and Shi et
al. ([114]32) for cows and Baiomy ([115]33) and Zicarelli et al.
([116]34) for goats. In this study, the increased milk yield of dairy
goats owing to YC supplementation was attributed to elevated levels of
acetate, propionate, butyrate, and TVFAs in the rumen, which enhanced
the fermentation activity of cellulolytic bacteria ([117]35). In
contrast, our findings are inconsistent with the results of
Hadjipanayiotou et al. ([118]36), who evaluated the effect of YC on
milk yield in Damascus goats and reported that the milk yield did not
differ between animals. The variance in results might be attributed to
differences in the yeast strains and animal species.
Dietary carbohydrates are fermented by ruminal bacteria, fungi, and
protozoa into end products, including VFA (e.g., acetate, propionate,
and butyrate), which constitute nearly 50% of the energy requirements
for ruminants ([119]37). In this study, dietary YC supplementation
increased the ruminal acetic, butyrate, and TVFA contents. These
findings are consistent with those of Carpinelli et al. ([120]38), Zhu
et al. ([121]14), Sun et al. ([122]39) for cows, and Xue et al.
([123]40), Özsoy et al. ([124]41), and Ogbuewu et al. ([125]42) for
goats. In our study, we observed that YC supplementation increased the
abundance of fiber degrading bacteria (Bacteroidetes and Prevotella) in
the goat rumen, thereby promoting cellulose metabolism, which
ultimately led to an increase in the ruminal acetate, butyrate, and
TVFA contents ([126]43). Studies have shown that YC supplementation
reduces NH[3]-N levels in ruminants. For instance, a study on dairy
cows observed a tendency for lower rumen NH[3]-N concentrations with YC
compared to the control ([127]44). In this study, among the 21
metabolites that presented significant correlations with rumen
fermentation parameters, aspartic acid and L-leucine contents in the
rumen of the YC group were significantly reduced. Furthermore, KEGG
analysis of these 21 metabolites highlighted significant amino acid
synthesis pathways, including aminoacyl-tRNA biosynthesis, valine,
leucine, arginine biosynthesis, and histidine metabolism. These results
suggest that YC supplementation enhances the efficiency of microbial
protein synthesis in the rumen, thereby enabling more effective
utilization of NH[3]-N ([128]45).
Higher microbial diversity in the mammalian gastrointestinal system is
often associated with a stronger metabolic capacity ([129]46). In this
study, β-diversity analyses showed that microbial diversity was
increased in the YC groups. Furthermore, studies have shown that feed
efficiency in dairy cows was significantly associated with lower
microbial diversity in the rumen ([130]47). However, YC supplementation
increased the abundance of the phylum Bacteroidota and genera
Prevotella and Rikenellaceae_RC9_gut_group in the rumen of goats,
mitigating the negative impacts on milk production and feed efficiency.
The rumen is a complex microbial anaerobic fermentation chamber that
harbors one of the most diverse intestinal microbial communities in the
animal kingdom ([131]48). Firmicutes and Bacteroidota are the dominant
species in the goat rumen ([132]49). The positive correlations observed
among the phyla Firmicutes, Spirochaetota, Bacteroidota,
Patescibacteria, and Synergistetes in this study are consistent with
the synergistic interactions among related taxa identified in previous
studies ([133]50). These relationships may arise from shared metabolic
pathways or mutualistic interactions, such as cross-feeding, in which
one genus produces a metabolite utilized by another ([134]51).
Moreover, the relative abundance of Bacteroidetes increased in the
rumen of the dairy goats treated with YC in this study, which is
similar to the findings of Li et al. ([135]52) regarding cows. The
positive effects of YC on Prevotella growth have also been previously
documented ([136]53) and supported by the results of the current study.
Such effects are related to YC growth factors (i.e., organic acids, B
vitamins, and AA) that stimulate fiber-digesting bacteria, such as
Bacteroidetes and Prevotella ([137]54, [138]55).
The metabolites in the rumen mainly include nutrients that can be used
by the host and rumen microorganisms, and differences in the levels of
ruminal metabolites are associated with changes in the microbiota
([139]56). We identified 13 tertiary metabolic pathways, which
primarily included secondary metabolic pathways of lipid metabolism,
glycan biosynthesis and metabolism, amino acid metabolism, and cofactor
and vitamin metabolism. These findings are consistent with the results
of Li et al. ([140]57). As the main pathways of microbial VFA
production, lipid metabolism, glycan biosynthesis and metabolism, and
carbohydrate metabolism play important roles in the rumen ([141]3,
[142]58, [143]59). In addition, microorganisms satisfy the host’s
nutritional needs by performing amino acid metabolism and cofactor and
vitamin metabolism to produce amino acids, vitamins, and cofactors
([144]60). This study observed the consistency between the differences
in metabolite types between the YC and CON groups and the differences
in microbial populations, indicating that the diversity of microbial
populations also affects the diversity of rumen metabolite types.
Similarly, Xue et al. ([145]61) also reached similar conclusions in a
study on the impact of different feed types on the rumen microbiome and
serum metabolome in lambs.
Goat rumen metabolites were significantly negatively correlated with
acetate, butyrate, and propionate, which is consistent with a previous
study in which negative correlations were observed between specific
metabolites and rumen fermentation parameters ([146]62). This study
found that among the 21 metabolites negatively correlated with ruminal
acetate, propionate, and butyrate, YC supplementation significantly
reduced the ruminal aspartic acid and L-leucine. This observation is in
line with a previous study in which YC significantly influence the
metabolite profiles in the rumen of dairy cows, thereby affecting the
fermentation processes ([147]63). Studies have also shown that
L-leucine could be converted into branched chain VFAs, such as acetic
acid and butyric acid, during oxidative deamination ([148]64). In
addition, Jalc and Ceresnáková ([149]65) investigated the effect of
aspartic acid on rumen fermentation and found that aspartate influenced
propionate production during rumen fermentation. To explore the
microbes that caused the differences in ruminal aspartic acid and
L-leucine levels, this study conducted a correlation analysis between
aspartic acid and L-leucine and the top 20 most abundant genera in the
rumen, and then it performed an analysis of variance among the groups
for the correlated microbial populations. The negative correlation
between aspartic acid and L-leucine and Clostrid-ia_UCG-014 and
Paraprevotella and the greater abundance of Clostridia_UCG-014 and
Paraprevotella in the YC groups revealed that differences in these
genera in the rumen lead to the differences in ruminal aspartic acid
and L-leucine levels.
Further investigation into these specific metabolites could reveal more
about their roles and how they influence the rumen’s acidic
environment. Among the five significant pathways identified in this
study, aspartic acid directly participates in the amino-acyl-tRNA
biosynthesis and arginine biosynthesis pathways and L-leucine directly
participates in the aminoacyl-tRNA biosynthesis, valine, leucine, and
isoleucine biosynthesis, and valine, leucine, and isoleucine
degradation pathways. Moreover, research has shown that aminoacyl-tRNA
biosynthesis is the foundation of microbial protein synthesis in the
rumen, supporting their growth and metabolism, which in turn influences
the fermentation process in the rumen ([150]66). In addition,
microorganisms can use L-leucine to produce propionyl-CoA and
acetyl-CoA through valine, leucine, and isoleucine degradation
metabolic pathways, and then propionyl-CoA and acetyl-CoA can be
further metabolized into VFAs, such as propionic acid and acetate, thus
affecting rumen fermentation parameters ([151]67). Integrating the
findings of this study with previous research reveals that the impact
of YC on ruminal fermentation parameters in goats was attributed to its
promotional effect on Clostridia_UCG-014 and Paraprevotella, which then
utilize more aspartic acid and L-leucine through pathways such as
aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine
degradation. These changes ultimately lead to alterations in ruminal
fer-mentation parameters.
5. Conclusion
Yeast culture dietary supplementation at 10 g/day per goat improved the
milk yield and ruminal fermentation parameters in Laoshan dairy goats.
Moreover, YC increased the ruminal Clostridia_UCG-014 and
Paraprevotella abundance, which facilitated aspartic acid and L-leucine
utilization by these genera, thereby enhancing the ruminal acetic,
butyrate, and TVFA contents and reducing the ruminal NH[3]-N content.
Data availability statement
The original contributions presented in the study are included in the
article/supplementary material, further inquiries can be directed to
the corresponding author.
Ethics statement
The animal study was approved by the Animal Administration and Ethics
Committee of Qingdao Agricultural University, Animal Science and
Technology College. The study was conducted in accordance with the
local legislation and institutional requirements.
Author contributions
ZL: Writing – original draft, Writing – review & editing. YH: Data
curation, Formal analysis, Writing – review & editing. HL:
Investigation, Writing – review & editing. YL: Funding acquisition,
Project administration, Resources, Writing – review & editing. MC:
Supervision, Writing – review & editing. FZ: Methodology, Writing –
review & editing. YG: Methodology, Project administration, Writing –
review & editing.
Funding Statement
The author(s) declare that financial support was received for the
research, authorship, and/or publication of this article. This study
was financially supported by the Technological System of Modern
Agricultural Industry in Shandong Province Goat Industry Innovation
team (SDAIT-10), the Qingdao Agricultural University Doctoral Start Up
Fund (663/1120009), and the Major Scientific and Technological
Innovation Project of Shandong Province (2019JZZY020609-02).
Footnotes
^1 [152]www.metaboanalyst.ca/
^2 [153]https://smpdb.ca/
^3 [154]https://hmdb.ca/metabolites
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
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References