Abstract
Simple Summary
Low body weight is not conducive to the growth and development of
mammals in the subsequent stages of weaning. The rumen microbiota plays
an important role in the growth, development, production, and health of
ruminants. A comprehensive analysis was conducted using a combined 16S
rRNA and metabolomics analysis method to determine the impact of the
rumen microbiota and serum metabolites on goat kids before weaning
weight. This study may help to provide new insights for further
regulating the rumen microbiota to improve the body weight of animals.
Abstract
The critical role of the rumen microbiota in the growth performance of
livestock is recognized, yet its significance in determining the body
weight of goat kids before weaning remains less understood. To bridge
this gap, our study delved into the rumen microbiota, serum metabolome,
rumen fermentation, and rumen development in goat kids with contrasting
body weights before weaning. We selected 10 goat kids from a cohort of
100, categorized into low body weight (LBW, 5.56 ± 0.98 kg) and high
body weight (HBW, 9.51 ± 1.01 kg) groups. The study involved sampling
rumen contents, tissues, and serum from these animals. Our findings
showed that the HBW goat kids showed significant enrichment of
VFA-producing bacteria, particularly microbiota taxa within the
Prevotellaceae genera (UCG-001, UCG-003, and UCG-004) and the
Prevotella genus. This enrichment correlated with elevated acetate and
butyrate levels, positively influencing rumen papillae development.
Additionally, it was associated with elevated serum levels of glucose,
total cholesterol, and triglycerides. The serum metabonomic analysis
revealed marked differences in fatty acid metabolism between the LBW
and HBW groups, particularly in encompassing oleic acid and both
long-chain saturated and polyunsaturated fatty acids. Further
correlational analysis underscored a significant positive association
between Prevotellaceae_UCG-001 and specific lipids, such as
phosphatidylcholine (PC) (22:5/18:3) and PC (20:3/20:1) (r > 0.60, p <
0.05). In summary, this study underscores the pivotal role of the rumen
microbiota in goat kids’ weight and its correlation with specific serum
metabolites. These insights could pave the way for innovative
strategies aimed at improving animal body weight through targeted
modulation of the rumen microbiota.
Keywords: goat kids, body weight, rumen microbiota, serum metabolome
1. Introduction
Body weight before weaning influences the growth of suckling animals
and further affects the performance of animals in subsequent stages
[[36]1]. Previous studies on piglets have shown that low weaning weight
may negatively impact lifetime growth rate [[37]2], meat character
[[38]3], feed intake, and carcass traits [[39]4]. Studies have
demonstrated that low-weight piglets reduce their feed intake compared
to normal-weight piglets, and their digestive and immune systems were
incompletely developed after weaning [[40]5]. These conditions increase
the incidence of various diseases and microbiota infections due to the
vulnerability of low-weight piglets after weaning. Further research
indicates that low-weight mice exhibit imbalanced lipid metabolism and
insulin resistance in adulthood [[41]6,[42]7]. Hence, the low body
weight of animals in their infancy is not conducive to their health,
growth, and development.
The rumen harbors a highly diverse microbiota community contributing to
powerful functions, including bacteria, archaea, fungi, and ciliates.
This indicates that volatile fatty acids (VFAs), microbiota crude
protein, and B-group vitamins are synthesized through the fermentation
and transformation of feed by the rumen microbiota, serving as
essential nutrients for ruminants [[43]8]. Lopes et al. [[44]9] found
that Nellore cattle with high feed efficiency exhibited a relatively
high abundance of Anaerotruncus, Phocaeicola, unclassified Prevotella,
and lower levels of Bacteroidetes during the growth stage. High-growth
performance Yak calves [[45]10] and lambs [[46]11] with milk replacer
feeding were highly enriched in the Prevotella and
Christenselelaceae_R-7_group. The enrichment of Sediminibacterium and
Butyrivibrio in the intestine can promote weight gain in weaned
piglets, as shown in previous studies [[47]12]. Another study on meat
rabbits found that Blautia, Lachnoclostridium, and Butyricoccus with
high relative abundance in feces were not conducive to weaning weight
gain [[48]13]. These studies mainly address monogastric animals, and
the impact of the rumen microbiota on body weight in ruminant animals
before weaning is not clear.
The objective of this study was to investigate the rumen microbiota
taxa based on varying body weights and identify specific associations
with serum metabolites, intensively investigating the intricate
interactions between the microbiota and host. Hence, we hypothesized
that within a consistent feeding environment, the extent of rumen
fermentation and development is intricately linked to variations in the
rumen microbiota and that there exists a specific correlation with
serum metabolites. This research offers novel insights and serves as a
valuable reference for elucidating the potential impact of the rumen
microbiota on body weight, and it may contribute to strategies aimed at
enhancing animal weight through microbiota modulation.
2. Materials and Methods
2.1. Experimental Design, Animals, and Sample Collection
The experiment strictly adhered to Hainan University’s animal research
guidelines in Haikou, China, and received approval from the
institution’s Animal Protection Committee under approval number
(HNUAUCC-2024-00002). We utilized 100 Hainan black goat buck kids, all
21 days old with comparable birth weights, for this study. These goat
kids were housed in individual pens until they reached 90 days of age,
as detailed in [49]Table 1. Throughout the experiment, biweekly weight
measurements were conducted from birth until day 90. The goat kids were
delimited into two test groups according to their body weight at 90
days. Those weighing above the average for 90-day-old goat kids were
placed in the high body weight group (HBW, averaging 9.51 ± 1.01 kg).
In contrast, those below the average were assigned to the low body
weight group (LBW, averaging 5.56 ± 0.98 kg). Each group of goat kids
adopts a simple random sampling method, with 5 biological replicates in
each group as representative samples. All goat kids were raised
alongside their ewes for the first 21 days post-birth. Following this,
there were separate feedings at 08:30 and 16:00 h of milk replacer
(Hangzhou Xiangmu Fresh Biotechnology Co., Ltd., Hangzhou, China) twice
per day (totaling 1 L/d) from days 21 to 90. Additionally, from the age
of 45 days, the goat kids were fed a mixed diet that gradually
increased by 5 g per day (starting from 5 g and reaching up to 220 g
per day by the experiment’s end) to ensure the absence of residual
feed; the experimental results are not biased by differences in dietary
intake. This mixed feed comprised 58% concentrate and 42% roughage on a
dry matter standard, as outlined in [50]Supplemental Table S1. Access
to fresh water was provided ad libitum. In the process of grouping, we
ensured that there were no dramatic differences in birth weight and
body weight at 21 days among the two experimental groups. This step was
crucial to obviate the potential influence of forage and beginning
weight before weaning body weight. The genetic background of the goat
kids in both groups was similar, with them all being offspring of the
same ram. To maintain the integrity of the experiment, the goat kids
were not exposed to antibiotics at any point, and uniform conditions
were maintained for all goat kids throughout the study.
Table 1.
The birth weight and weaning weight of the LBW and HBW goat kids.
Items ^1 Group ^2 SEM ^3 p-Value
HBW LBW
Birth BW (kg) 2.33 2.17 0.22 0.417
21-day BW (kg) 3.73 3.65 0.19 0.614
Weaning BW (kg) 9.51 5.56 0.69 0.034
[51]Open in a new tab
^1 BW, body weight. ^2 HBW = high body weight. LBW = low body weight.
^3 SEM, standard error of the mean.
After the experiment, blood samples were obtained from the jugular vein
of all grouped goat kids after fasting for 12 h, and they were then
euthanized and dissected for sampling. After centrifugation for 10 min
at 3100 rpm/min, the serum was divided into two parts. Chemical
indicators were measured and stored at −20 °C, while the other part was
analyzed for metabolomics and preserved at −80 °C.
We measured the pH value of the rumen after dissection using a pH meter
(DDS-307, Geomagnetic Company, Shanghai, China). The rumen contents
samples used for DNA extraction were stored in liquid nitrogen at −80
°C. The rumen fluid sample obtained through the use of a filtering
cheesecloth with four layers was preserved at −20 °C for follow-up
analysis and determination. The rumen of the individuals was weighed
after it was emptied, washed with tap water, and dried with a paper
towel.
The rumen epithelial samples (1.0 cm^2) collected from the experimental
goat kids were rinsed with sterile phosphate-buffered saline (PBS). The
rumen tissue used for the tissue morphology analysis was preserved in
4% paraformaldehyde.
2.2. Hematoxylin and Eosin (H&E) Staining
We evaluated the rumen morphology as described previously [[52]14].
Briefly, the rumen tissue was washed with PBS, dehydrated with ethanol,
embedded in paraffin, sliced, and finally stained with H&E
([53]Supplementary Figure S1). We measured the width, length, and
muscle layer thickness of the rumen papillae using IXplore Pro
microscope photography (Olympus China Group Co., Ltd., Shanghai,
China).
2.3. Rumen Fermentation Parameters
A gas chromatography system (Clarus 680, PerkinElmer, Inc, Waltham, MA,
USA) equipped with an Elite-FFAP column (0.25 mm i.d.; 30 m in length)
was used to determine the VFA content in the rumen as per the
guidelines of Chen et al. [[54]15]. The colorimetric method was used to
determine the ammonia nitrogen (NH[3]-N) content in the rumen fluid
[[55]16].
2.4. DNA Extraction, 16S rRNA Sequencing, and 16S rRNA Data Processing
In this study, we extracted the total genomic DNA from all samples by
utilizing the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany). The
concentration and purity of the abstracted genomic deoxyribonucleic
acid were rigorously assessed using a NanoDrop 2000 spectrophotometer
(Thermo Scientific, China Group Co, Ltd., Guangzhou, China). The
bacterial 16S rRNA gene’s V3-V4 hypervariable region was selectively
amplified by utilizing the universal primers 338F
(5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′),
including a 6-base pair error-correcting barcode, facilitating distinct
identification of each sample in subsequent analyses. We used the
Phusion High-Fidelity Polymerase Chain Reaction (PCR) Mastermix (New
England Biolabs Beijing Ltd., Beijing, China). The repeating parameters
were set as follows: an initial denaturation step for 5 min at 95 °C,
followed by 28 repeating cycles running at 95 °C for 45 s, 55 °C for 50
s, and 72 °C for 45 s. This was concluded with a final extension phase
of 72 °C for 10 min, ensuring thorough amplification of the target DNA
sequences. The PCR products underwent quantification using 1% agarose
gel electrophoresis, followed by a purification process utilizing the
Agencourt AMPure XP Kit (Beckman Colter Genomics, Indianapolis, IN,
USA). Quantification of amplicon libraries using a Qubit 2.0
fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) was performed
on all amplicons obtained from the samples. These libraries were then
sequenced on a MiSeq PE300 platform (Illumina, San Diego, CA, USA), a
procedure that generated paired-end reads of 2 × 250 base pairs each
[[56]17].
Sequence processing involving demultiplexing and quality filtering via
QIIME [[57]18] ([58]http://qiime.org, accessed on 7 May 2023),
retaining only bases with a quality score exceeding 20 and reads longer
than 200 bp. FLASH [[59]19], was utilized for merging paired-end reads
into tags, necessitating a minimum 10-base sequence overlap. Chimeric
sequences were identified and eliminated using UCHIME [[60]20].
Operational taxonomic units (OTUs) [[61]21] were clustered at a 97%
similarity threshold with UPARSE2 [[62]22] and classified taxonomically
using the Ribosomal Database Project classifier [[63]23] against the
SILVA 138.1 database [[64]24]. After the post-removal of singleton and
doubleton OTUs via UCLUST [[65]25], the representative OTU table was
finalized. For the analyses of α-diversity, β-diversity, and taxonomic
classification, normalized OTU counts per sample were examined using R
3.6.0 [[66]26].
2.5. Serum Biochemistry Indices
The serum levels of glucose, blood urea nitrogen (BUN), alanine
transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase
(ALP), lactic dehydrogenase (LDH), total cholesterol (TC),
triglycerides (TG), and protein were determined using a Cobus-Mira-Plus
automatic clinical chemistry analyzer (Cobus-Mira-Plus; Roche
Diagnostic Systems Inc., Zurich, Swiss Confederation). The serum
globulin content was computed by deducting the albumin level from the
total protein.
2.6. Serum Metabolome
In this study, we employed a cutting-edge approach to analyze the serum
metabolome, leveraging the precision of a UHPLC system [[67]27]
(Vanquish, Thermo Fisher Scientific) integrated with a Q Exactive HFX
mass spectrometer (Orbitrap MS, Thermo). We initiated our protocol by
treating 100 µL of serum with 300 µL of a methanol–acetonitrile
mixture. The samples underwent a thorough vortex process, followed by
two-hour incubation at −20 °C. Post-incubation, they were centrifuged,
freeze-dried, and redissolved.
The serum samples’ intricate analysis was executed using ProteoWizard
for data conversion and a bespoke R-based program, harnessing XCMS for
peak analysis. Our identification strategy hinged on matching both the
mass spectrum and retention index. We adopted a stringent criterion,
discarding metabolite peaks found in less than half of our samples. To
address missing data, we filled gaps with half the minimum value,
ensuring a comprehensive analysis. Altogether, 570 serum metabolites
were characterized, so that data normalization processing uses the sum
of features of each sample for downstream analysis.
In our study, we employed the “cluster” package (v3.6.0) in R for
hierarchical clustering of pre-treatment serum metabolome samples,
applying orthogonal partial least-squares discriminant analysis
(OPLS-DA). We pinpointed differential metabolites based on a two-fold
criterion: a variable importance in projection (VIP) score exceeding
one from the OPLS-DA model and a p-value below 0.05 from the univariate
statistical analysis. Subsequently, we conducted a comprehensive
functional analysis of these metabolites using MetaboAnalyst 5.0
([68]https://www.metaboanalyst.ca, accessed on 24 May 2023) [[69]28],
which provided valuable insights into their biological roles and
potential impact.
2.7. Correlation Analysis
Utilizing the “vegan” R package (v3.6.0), we executed a Procrustes test
to assess the alignment of two-dimensional shapes derived from PCoA
analyses across two datasets. We applied Spearman correlation analysis
to investigate relationships among distinct bacteria, rumen
fermentation parameters, rumen morphology, serum biochemical indices,
and serum metabolites. Emphasis was placed on significant correlations,
defined by a threshold of an absolute Spearman rank-order correlation
coefficient (|r|) greater than 0.6 and a p-value less than 0.05. These
significant correlations were then effectively visualized using the
“pheatmap” package in R.
2.8. Statistical Analysis
We utilized R version 4.2.3 for analyzing body weight, serum
parameters, rumen morphology, rumen fermentation parameters, rumen
microbiota, and serum metabolites by employing a linear mixed-effects
model:
[MATH:
Yik=μ+Oi+<
msub>LK+eik :MATH]
The dependent variable is represented as
[MATH:
Yik :MATH]
, and the overall average is represented as
[MATH: μ :MATH]
.
[MATH: Oi
:MATH]
is the effect of the group,
[MATH: Lk
:MATH]
is the random effect of goat kids, and
[MATH:
eik :MATH]
is the residual error.
For the analysis of body weight, rumen morphology, serum biochemical
indices, and rumen fermentation parameters across different groups, we
conducted independent t-tests using SPSS 21.0
([70]https://www.ibm.com/cn-zh/spss, accessed on 12 August 2023). The
alpha diversity indices of the rumen microbiota were assessed using the
Wilcoxon rank-sum test, considering p < 0.05 as the threshold for
statistical significance. Additionally, permutational multivariate
analysis of variance (PERMANOVA) was performed using the vegan package
in R, focusing on genus-level bacterial taxa.
We compared the relative abundance of genera (with relative abundances
exceeding 0.10% in at least 70% of samples) using the Wilcoxon rank-sum
test. p-values were adjusted for false discovery rate, with a threshold
of p < 0.05 after correction deemed significant for our study.
3. Results
3.1. Rumen Fermentation Parameters of Goat Kids
In our findings, significant differences were observed in rumen NH[3]-N
levels between the HBW and LBW groups (p < 0.05, [71]Table 2).
Furthermore, goat kids with HBW exhibited a significantly elevated
concentration of total VFA, encompassing acetate, butyrate, and
valerate, in comparison to goat kids with LBW, a statistically
significant difference (p < 0.05).
Table 2.
Rumen fermentation parameters between the HBW and LBW goat kids.
Items ^1 Group ^2 SEM ^3 p-Value
HBW LBW
pH 6.13 6.84 0.29 0.57
NH[3]-N (mg/dL) 4.79 3.42 0.23 0.01
Acetate (mmol/L) 21.37 13.54 5.81 0.02
Propionate (mmol/L) 8.13 6.48 2.25 0.23
Butyrate (mmol/L) 5.98 3.03 2.01 0.05
Isobutyrate (mmol/L) 0.82 0.60 0.05 0.14
Valerate (mmol/L) 0.72 0.39 0.15 0.03
Isovalerate (mmol/L) 1.32 1.05 0.18 0.53
AP (mmol/L) 2.63 2.08 0.27 0.12
TVFA ^4 (mmol/L) 38.37 25.12 10.49 0.02
[72]Open in a new tab
^1 AP, the acetate-to-propionate ratio; TVFAs, total VFAs. ^2 HBW =
high body weight; LBW = low body weight. ^3 SEM, standard error of the
mean. ^4 TVFA, total volatile fatty acids.
3.2. The Difference in the Rumen Morphology of the Goat Kids
The study revealed that the length and width of the rumen papillae in
the HBW group are more developed than those in the LBW group, with
statistical significance (p < 0.05, [73]Supplementary Table S2).
Nevertheless, the groups characterized by HBW and LBW demonstrated
comparable developmental progress in aspects such as rumen weight and
muscle thickness (p > 0.05, [74]Supplementary Table S2).
3.3. Serum Biochemical Indicators of the Goat Kids
Serum biochemical analysis revealed elevated levels of key nutrients,
including glucose, TC, and TG, in the HBW group when compared to the
LBW group (p < 0.05, [75]Supplementary Table S3).
3.4. Rumen Microbiota Sequencing and Composition
From the ten samples analyzed, we initially obtained 539,364 raw
bacterial sequences. Following quality control and equal-depth
clustering with 97% identical sequences (53,936 reads per sample), the
study identified a total of 7067 operational taxonomic units (OTUs).
These OTUs were classified into 25 phyla, 48 classes, 84 orders, 139
families, and 298 genera. Rarefaction curves, as shown in
[76]Supplementary Figure S2, confirmed that the sampling depth
adequately represented the rumen microbiota, evidenced by a plateau in
the number of new OTUs despite increasing sequence counts per sample.
The Good’s coverage exceeded 99.03%, further supporting the sufficiency
of our sequencing depth for this microbiota study.
Bacteroidetes, Firmicutes, and Proteobacteria emerged as advantageous
groups at the phylum level ([77]Supplementary Figure S3A). The most
prevalent genera included F082, Bacteroides,
Rikenellaceae_RC9_gut_group, Prevotella, Ruminococcus, Muribaculaceae,
and Oscillospiraceae_UCG-005 ([78]Supplementary Figure S3B).
3.5. Comparison of the Rumen Microbiota between Goat Kids with Different Body
Weights
In assessing microbiota α-diversity using the number of OTUs, the Chao1
index, and the Shannon index through Wilcoxon rank-sum tests,
significant differences emerged between the HBW and LBW groups.
Specifically, the HBW group exhibited notably higher values in these
indices (p < 0.05, [79]Supplementary Table S4), suggesting reduced
α-diversity in LBW goat kids compared to their HBW counterparts.
To determine the profile of the rumen microbiota communities in the LBW
and HBW groups, β-diversity was assessed using the Procrustes test and
visualized using a PCoA plot. As shown in the PCoA plot, the rumen
samples of the HBW and LBW groups cross each other and overlap
([80]Figure 1). To further compare the significant difference in the
rumen microbiota composition between the tested groups, PERMANOVA
analysis was used. PERMANOVA analysis showed that there was a
significant difference in microbiota composition between the HBW and
LBW groups (p = 0.028).
Figure 1.
[81]Figure 1
[82]Open in a new tab
Comparison of the composition of the microbiota in the rumen of the
goat kids. The PERMANOVA analysis with 999 permutations is shown. The
microbiota composition of the HBW and LBW rumen samples based on OTUs
was visualized using principal coordinate analysis (PCoA).
At the phylum and genus levels, assessed via Wilcoxon rank-sum tests,
we observed significant disparities in microbiota abundance between the
groups. In HBW goat kids, there was a significantly higher relative
abundance of Bacteroidetes (p = 0.011) and a significantly lower
relative abundance of Firmicutes (p = 0.05, [83]Table 3). Other
dominant phyla like Proteobacteria, Desulfobacterota, and Spirochaetota
showed no significant differences (p > 0.05, [84]Table 3).
Table 3.
Comparison of the relative abundance of the rumen microbiota at the
phylum level (relative abundances > 0.10% in at least 70% of samples)
of weaning goat kids.
Phylum Group ^1 SEM ^2 p-Value ^3
HBW LBW
Bacteroidetes 57.60 28.10 20.94 0.01
Firmicutes 23.36 38.07 12.47 0.05
Proteobacteria 12.78 28.10 16.08 0.13
Desulfobacterota 0.94 2.52 1.11 0.45
Spirochaetota 0.42 1.70 0.90 0.83
[85]Open in a new tab
^1 HBW = high body weight; LBW = low body weight. ^2 SEM, standard
error of the mean. ^3 Adjusted p-value = false discovery rate-adjusted
p-value; the p-values between the HBW and LBW goat kids were obtained
using the Wilcoxon rank-sum test.
Significant differences were observed at the genus level between the
HBW and LBW groups. In the HBW group, genera such as F082,
Rikenellaceae_RC9_gut_group, Prevotella, and several Prevotellaceae
subgroups (UCG-001, UCG-003, UCG-004), along with Syntrophococcus,
Lachnospiraceae_UCG-002, Pseudobutyrivibrio, Ruminococcus,
Veillonellaceae_UCG-001, and Eubacterium_nodatum_group, showed
significantly higher relative abundance compared to the LBW group (p <
0.05, [86]Table 4). Conversely, the abundance of Muribaculaceae,
Escherichia-Shigella, Bacteroides, Alistipes, Butyricimonas,
Ruminococcus_torques_group, and Oscillospiraceae_UCG-005 was
significantly lower in the HBW group compared to the LBW group (p <
0.05, [87]Table 4). This indicates a distinct microbiota composition
associated with body weight variations.
Table 4.
Comparison of the relative abundance of the rumen microbiota at the
genus level (relative abundances > 0.10% in at least 70% of samples) of
weaning goat kids.
Phylum Genus Group ^1 SEM ^2 p-Value ^3
HBW LBW
Bacteroidetes Bacteroides 3.03 10.28 3.62 0.01
Bacteroidales_UCG-001 1.31 0.01 0.45 0.05
F082 19.61 1.35 9.15 0.03
Butyricimonas 0.15 1.61 0.72 0.05
Prevotellaceae_UCG-001 4.88 0.13 2.37 0.01
Prevotellaceae_UCG-003 3.32 0.04 1.35 0.05
Prevotellaceae_UCG-004 0.83 0.03 0.25 0.02
Prevotella 0.45 0.23 0.14 0.04
Alistipes 0.76 3.74 1.49 0.05
Rikenellaceae_RC9_gut_group 9.70 1.85 3.92 0.05
Firmicutes Clostridium_sensu_stricto_1 0.18 1.20 0.51 0.05
Ruminococcus_torques_group 0.23 3.01 1.39 0.01
Monoglobus 2.52 4.42 0.50 0.03
Oscillospiraceae_UCG-005 1.49 7.00 2.75 0.05
Ruminococcus 1.92 0.07 0.55 0.02
Pseudobutyrivibrio 0.43 0.01 0.14 0.05
Defluviitaleaceae_UCG-011 0.12 0.01 0.03 0.04
Veillonellaceae_UCG-001 0.37 0.01 0.13 0.03
Eubacterium_nodatum_group 0.27 0.01 0.10 0.04
Lachnospiraceae_UCG-002 0.23 0.01 0.02 0.05
Syntrophococcus 0.17 0.08 0.06 0.02
Proteobacteria Escherichia-Shigella 1.40 17.47 8.03 0.03
[88]Open in a new tab
^1 HBW = high body weight; LBW = low body weight. ^2 SEM, standard
error of the mean. ^3 Adjusted p-value = false discovery rate-adjusted
p-value; the p-values between the HBW and LBW goat kids were obtained
using the Wilcoxon rank-sum test.
3.6. Correlation Analysis of Rumen Microbiota and Rumen Fermentation
Parameters, Rumen Morphology, and Serum Biochemical Indicators
The Spearman correlation result demonstrated that acetate and butyrate
were significantly and positively correlated with the relative
abundance of Prevotellaceae_UCG-003, Prevotellaceae_UCG-001,
Prevotella, and F082 (r > 0.60, p < 0.05, [89]Figure 2). The rumen
papillae length and width were significantly and positively correlated
with the relative abundance of Rikenellaceae_RC9_gut_group,
Prevotellaceae_UCG-001, and Pseudobutyrivibrio (r > 0.60, p < 0.05,
[90]Figure 2). Glucose, TC, and TG were significantly and positively
correlated to the relative abundance of Prevotellaceae_UCG-001,
Prevotellaceae_UCG-003, Rikenellaceae_RC9_gut_group, and
Pseudobutyrivibrio (r > 0.60, p < 0.05, [91]Figure 2). The relative
abundance of F082, Ruminococcus, and Veillonellaceae_UCG-001 was
positively correlated with pH, while the relative abundance of
Alistipes, Butyricimonas, and Oscillospiraceae_UCG-005 was negatively
correlated with pH (r < −0.60, p < 0.05, [92]Figure 2). In addition,
the enrichment of Pseudobutyrivibrio was significantly and positively
correlated with NH[3]-N (r > 0.60, p < 0.05, [93]Figure 2).
Figure 2.
[94]Figure 2
[95]Open in a new tab
Correlation between the different genera and rumen fermentation
parameters, rumen morphology, and serum biochemical indicators. Yellow
represents a positive correlation, and blue represents a negative
correlation. Only strong (Spearman r > 0.6, r < −0.6) and significant
(p < 0.05) correlations are displayed.
3.7. Differences in the Serum Metabolomics and Metabolic Pathways of Goat
Kids with Different Weaning Weights
To investigate metabolic changes between the two groups, we conducted
LC-MS analysis on serum metabolites. The OPLS-DA models showed
excellent interpretative and predictive abilities, with R^2Y and Q^2
values of 0.999 and 0.417 in both positive and negative ion modes
([96]Supplementary Figure S4). A distinct separation in the OPLS-DA
plots between the HBW and LBW groups was observed, indicating
differences in their metabolic profiles ([97]Supplementary Figure S4).
In the present study, we identified 570 metabolites. Notably, 20
metabolites, including diacetone alcohol, citraconic acid, 16-hydroxy
hexadecanoic acid, cis,cis-muconic acid, 3-guanidinopropionate,
2-hydroxybutyric acid, methylimidazole acetaldehyde, bovinic acid,
myristoleic acid, 2-hydroxymyristic acid, adipic acid, homocitrulline,
gamma-aminobutyric acid, alpha-linolenic acid, PC (20:3/20:1), and PC
(22:5/18:3), were found in higher concentrations in the HBW group.
Conversely, lsoliquiritgenin, 2-methylpiperidine, and two other
phosphatidylcholines were more abundant in the LBW group (p < 0.05,
[98]Figure 3A). KEGG pathway analysis revealed that the above
differential metabolites were mainly concentrated in “valine, leucine,
and isoleucine biosynthesis”, “linoleic acid metabolism”, “sphingolipid
metabolism”, “biosynthesis of unsaturated fatty acids”, “propanoate
metabolism”, and “alpha-linolenic acid metabolism” pathway (pathway
impact > 0.1, p < 0.05, [99]Figure 3B). This highlights a pronounced
metabolic divergence between HBW and LBW goat kids.
Figure 3.
[100]Figure 3
[101]Open in a new tab
Serum metabolites of the HBW and LBW goat kids. (A) Error bars display
significant metabolite differences in the serum between the HBW and LBW
goat kids (red, HBW is significantly greater than LBW; green, HBW is
significantly less than LBW). (B) Pathway enrichment analysis was
performed using the significantly different serum metabolites between
the HBW and LBW goat kids. Larger sizes, darker colors, and higher
column numbers represent greater pathway enrichment, more significant
enrichment, and higher pathway impact values, respectively.
3.8. Correlation between the Major Rumen Microbiota and Different Serum
Metabolites
To elucidate the potential interplay between the rumen microbiota and
serum metabolism, we employed the Spearman correlation results, which
examined the associations between the rumen microbiota and serum
metabolites ([102]Figure 4). The analysis revealed notable
correlations. Specifically, the relative abundance of
Defluviitaleaceae_UCG-011 exhibited a positive correlation with certain
lipids and lipid-like molecules. These include bovinic acid (r = 0.82,
p = 0.01), myristoleic acid (r = 0.72, p = 0.04), citraconic acid (r =
0.83, p = 0.01), and 2-hydroxymyristic acid (r = 0.80, p = 0.01).
Figure 4.
[103]Figure 4
[104]Open in a new tab
Interactions between the rumen microbiota and serum metabolites.
Spearman correlations between significantly different rumen microbiota
and serum metabolites (r > 0.6, r < −0.6; “*” represents p < 0.05 and
“**” represents p < 0.01).
Additionally, Prevotellaceae_UCG-001 was found to be positively
correlated with four lipid metabolites, namely adipic acid (r = 0.81, p
= 0.01), PC (20:3/20:1) (r = 0.80, p = 0.01), PC (18:2/15:0) (r = 0.83,
p = 0.01), and cis, cis-muconic acid (r = 0.76, p = 0.03). Furthermore,
the relative concentration of PC (18:2/15:0) showed a positive
correlation with the abundance of Bacteroides (r = 0.81, p = 0.01) and
the Ruminococcus_torques_group (r = 0.82, p = 0.01).
The research also identified a positive correlation between the
relative concentration of PC (18:2/14:0) and the abundance of
Butyricimonas (r = 0.67, p = 0.03). Conversely, a negative correlation
was observed between the relative concentration of 2-hydroxymyristic
acid and the abundance of Bacteroides (r = −0.66, p = 0.03) and
Oscillospiraceae_UCG-005 (r = −0.61, p = 0.04). These findings provide
insights into the complex relationships between the rumen microbiota
and host metabolic processes.
4. Discussion
In this research, we detail the rumen microbiota profiles in LBW and
HBW goat kids, identifying significant links between the rumen
microbiota, growth performance, and host metabolism. Our findings
revealed that (i) the rumen microbiota in HBW goat kids was
predominantly composed of bacteria adept at VFA production and (ii) a
strong correlation exists between the rumen fermentation parameters,
serum glucose, and rumen microbiota composition.
VFAs, a crucial energy source for ruminants, significantly contribute
to their metabolic energy, accounting for up to 50% of their energy
intake [[105]29,[106]30]. Research by Górka et al. [[107]31,[108]32]
demonstrated that sodium butyrate enhances rumen development and
expedites the growth of the rumen papilla in calves. Additionally,
intraluminal infusion of acetate has been found to stimulate rumen
development in non-lactating cows [[109]33]. In our research, the HBW
goat kids exhibited superior levels of butyrate and acetate, along with
increased rumen papilla length and width, compared to the LBW goat
kids. This suggests that HBW goat kids possess a more advanced rumen
fermentation capacity, which in turn enhances the development of rumen
papillae.
In the current study, we observed a notable disparity in the microbiota
composition at both the phylum and genus levels between the LBW and HBW
goat kids, aligning with the findings of Ding et al. [[110]12] and Fang
et al. [[111]13]. Notably, the genus Ruminococcus was significantly
more abundant in HBW goat kids, corroborating the findings of Wang et
al. [[112]34]. As identified by Gaffney et al. [[113]35], Ruminococcus
is known for its butyrate-producing capabilities. Additionally, we
detected a significantly higher relative abundance of
Prevotellaceae_UCG-001, Prevote-llaceae_UCG-004,
Prevotellaceae_UCG-003, and Prevotella in the HBW goat kids. These
bacteria, as detailed by Seshadri et al. [[114]36] and Accetto et al.
[[115]37], are adept at polysaccharide degradation and produce acetate
and butyrate. Previous research, such as the work by Chiquette et al.
[[116]38], demonstrated that the introduction of Prevotella bryantii
25A into the rumen of dairy cows via a rumen fistula led to increased
butyrate levels. Similarly, Takizawa et al. [[117]39] found that
microbiota translocation in Japanese black cattle resulted in a higher
abundance of Prevotellaceae_UCG-004, subsequently elevating acetate and
butyrate levels. This was further supported by Zhao et al. [[118]40],
who confirmed through in vitro fermentation tests that
Prevotellaceae_UCG-001 contributes to lignocellulose degradation and
elevates VFA concentrations. Moreover, our study revealed a positive
correlation between the relative abundance of Prevotella,
Prevotellaceae_UCG-003, and butyrate levels, echoing the findings of
Wang et al. [[119]41] in young goats, who also reported a positive
correlation between the abundance of these bacteria and the
concentrations of acetate and butyrate. These findings collectively
suggest that the enriched presence of VFA-producing bacteria, including
Prevotellaceae_UCG-001, Prevotellaceae_UCG-003, Prevotellaceae_UCG-004,
and Prevotella, in the HBW goat kids is instrumental in enhancing rumen
fermentation ability and the development of the rumen epithelium.
Glucose is a crucial nutrient for ruminants, with it playing a pivotal
role in their metabolic processes. According to Leskova et al.
[[120]42], serum glucose levels in ruminants are regulated by
propionate levels, a primary substrate for hepatic gluconeogenesis. In
our investigation, we observed higher concentrations of VFA and
propionate in the rumen of the HBW group compared to the LBW group.
Correspondingly, the HBW goat kids exhibited significantly higher serum
levels of glucose, TC, and TG when compared with the LBW group. Recent
research by Xu et al. [[121]43] aligns with our findings, demonstrating
that rumen microbiota enhancement leads to increased production of
propionate and VFAs. This, in turn, elevates serum glucose levels,
subsequently boosting the average daily weight gain in Holstein heifer
calves pre-weaning. Furthermore, elevated serum glucose levels have
been implicated in increasing substrate concentrations across various
metabolic pathways. This enhances the synthesis of TG and TC in serum,
factors integral to growth, development, and metabolism, as supported
by studies from Ma et al. [[122]44] and Schade et al. [[123]45]. A
noteworthy aspect of our study is the Spearman analysis, which revealed
a simultaneous and positive correlation between glucose, TG, and TC and
the relative abundance of specific rumen bacteria, namely
Prevotellaceae_UCG-001 and Prevotellaceae_UCG-004. These findings
suggest a potential mechanistic link whereby higher VFA-producing
bacteria in the rumen may elevate serum glucose levels, contributing
significantly to the growth of the animals.
Lipid metabolism plays a critical role in body weight regulation.
Garcia et al. [[124]46] previously demonstrated that an increase in
linoleic acid and α-linolenic acid levels in the blood before weaning
can enhance the growth of Holstein calves. In our study, we observed
significant alterations in fatty acid profiles, marked by a
substantially higher concentration of various lipid metabolites in the
serum of HBW goat kids compared to LBW goat kids. These metabolites
include cholesterol esters, triglycerides, phospholipids, PC,
acylcarnitine, sphingomyelins, and ceramides. Building upon previous
research, Sherratt et al. [[125]47] highlighted that PC is a vital
lipid molecule for animal growth, influencing fatty acid oxidation and
lipid transport. Furthermore, Liu et al. [[126]48] found that dietary
supplementation of PC in weaned piglets promoted lipid synthesis while
inhibiting lipid catabolism. Consistent with these findings, our data
revealed that in the HBW group, serum levels of specific PC species,
namely PC (20:3/20:1) and PC (22:5/18:3), were significantly elevated
and positively correlated with the abundance of Prevotellaceae_UCG-001.
Interestingly, the work of Li et al. [[127]49] supports the notion that
the enrichment of Prevotellaceae_UCG-001 in the intestines of mice,
particularly those fed with Jerusalem artichoke and inulin, is closely
associated with increased PC levels. Additionally, our study revealed
that HBW goat kids exhibit elevated relative concentrations of adipic
acid and 2-hydroxymyristic acid. These increases are positively
correlated with the relative abundance of Prevotellaceae_UCG-001.
Vlaeminck et al. [[128]50] previously showed that
Prevotellaceae_UCG-001 plays a role in the hydrogenation of long-chain
fatty acids and is strongly associated with odd and branched-chain
fatty acids. Further supporting the role of Prevotellaceae_UCG-001, Ma
et al. [[129]51] demonstrated that its enrichment in the intestines of
young rabbits leads to increased concentrations of short-chain fatty
acids such as acetate, propionate, and butyrate. Similarly, Li et al.
[[130]52] found that the intestinal microbiota of mice enhances fatty
acid synthesis by supplying a high level of acetate, a precursor for
the synthesis of palmitate and stearate. In humans, Van der Beek et al.
[[131]53] observed that acetate infusion in the distal colon alters
systemic lipid metabolism, impacting plasma triglyceride levels and
body weight. Therefore, the growth rate of individual animals can be
influenced by variations in their microbiota and metabolic profiles
[[132]54]. Drawing from these findings, we speculate that
Prevotellaceae_UCG-001 elevates VFA concentrations in the rumen, which
in turn augments the lipid content in the serum of goat kids before
weaning. This intricate interplay between the rumen microbiota and
lipid metabolism offers valuable insights into the mechanisms governing
early growth and development in goat kids.
5. Conclusions
Under the same feeding background, there were differences in the rumen
microbiota and serum metabolites between the HBW and LBW goat kids, and
there are metabolic interactions, suggesting that these differences may
be important factors affecting body weight. Nevertheless, additional
investigation is warranted to substantiate the speculation derived from
these experimental results. The present study enhances our
understanding of rumen microbiota in goat kids of different weights,
providing insights for optimizing ruminant growth through targeted
microbiota manipulation in animal husbandry practices.
Supplementary Materials
The following supporting information can be downloaded at:
[133]https://www.mdpi.com/article/10.3390/ani14030425/s1. Figure S1:
Histomorphometry measurements performed in the rumen of goat kids at 90
days. Ruminal sections were stained with a mixture of hematoxylin and
eosin. HBW = high body weight; LBW = low body weight; Figure S2:
Rarefaction curves of OTU number in the rumen fluid samples. Figure S3:
The stacked bar plot shows the rumen microbiota composition of HBW and
LBW goat kids. (A) The average relative abundance of phyla (relative
abundance > 1% for all samples). (B) The average relative abundance of
genera (relative abundance > 1% for all samples); Figure S4: OPLS-DA
scores plot between the HBW and LBW groups serum based on LC/MS. (A)
The positive and (B) negative modes of ionization. Table S1:
Composition and nutrient levels of the basal diet (DM basis); Table S2:
Differences in the rumen morphology of goat kids with different body
weights; Table S3: The serum biochemical indicators of LBW and HBW goat
kids; Table S4: Valid operational taxonomic units (OTUs) and
α-diversity indices of rumen microbiota in HBW and LBW goat kids.
[134]Click here for additional data file.^ (3.7MB, zip)
Author Contributions
G.Z. and S.H. conceived and designed the study; G.Z., M.W. and K.M.
collected all samples used in this study; G.Z. performed the data
analysis and wrote the manuscript with contributions from W.X., G.Z.,
S.H., J.W. and D.W. reviewed and revised the manuscript; S.H. provided
the funding. All authors have read and agreed to the published version
of the manuscript.
Institutional Review Board Statement
The experimental protocol for this study received approval from the
Institutional Animal Use and Care Committee at Hainan University,
Haikou, China. This approval signifies compliance with the stringent
guidelines for the ethical review of animal welfare as outlined in the
GB/T 35892-2018 standard, ensuring adherence to the highest standards
of laboratory animal care and ethical conduct (ethical approval number:
HNUAUCC-2024-00002).
Data Availability Statement
The raw datasets analyzed in this study are accessible in the SRA
(Sequence Read Archive) of the NCBI (National Center for Biotechnology
Information) database. These datasets are cataloged under the specific
Bio project accession number PRJNA978501, facilitating easy retrieval
and review for interested parties.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This study was supported by the Hainan Provincial Natural Science
Foundation of China (322QN240) and the Foundation of Hainan University
(RZ2200001242).
Footnotes
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References