Abstract
Introduction
Bovine mastitis, especially subclinical mastitis (SCM), with minimal
clinical signs, is detrimental due to its resistance to treatment,
recurrence, and substantial economic impact on global dairy industry.
The modified form of Huangqi Shengmai Yin (HSY), classical traditional
herbal medicine renowned for its effects in antimicrobial and
circulatory-enhancing and thus beneficial for subclinical mastitis, has
been developed for treatment attempt of SCM, yet its therapeutic effect
and mechanism remains unclear. This study aims to investigate the
therapeutic effects of mHSY on SCM in cows, and elucidate its potential
therapeutic mechanism.
Methods
In this study, mHSY was given orally to cows with SCM. After a 3-day
treatment regimen, the therapeutic effects were evaluated. 16S
diversity sequencing and metabolomics were used to elucidate the
therapeutic mechanism of HSY.
Results
The SCM was significantly alleviated after the 3-day treatment with
HSY. In cows infected with SCM, there were significant alterations in
rumen fluid microbiota, particularly proportions of Enterobacter,
Desulfovibrio, and Flavonifractor, implying a pivotal role for these
bacteria in SCM. Furthermore, the therapeutic potential of HSY is
linked to improving the proportion of beneficial bacteria (e.g.,
Succinivibrionaceae_UCG-001) and re-establishing a balanced ruminal
bacterial profile. Modulation of fatty acid and amino acid metabolism,
as evidenced by changes in metabolite profiles, is a critical aspect of
SCM and can be markedly ameliorated with mHSY administration.
Conclusion
mHSY shows significant inhibitory effects on SCM, which may be
attributed to regulating ruminal microbiota and metabolic pathways in
vivo.
Keywords: subclinical bovine mastitis, Huangqi Shengmai Yin, microbiome
structure, metabolites activity, dairy cows
1. Introduction
Clinical mastitis, which has a high incidence and numerous causes, is
one of the main diseases affecting the global dairy industry ([41]1).
Mastitis in dairy cows is attributable to various factors, including
microbial infections (bacterial, fungal, mycoplasma, and viral),
environmental factors (hygiene, feed, temperature, humidity, etc.),
anthropogenic factors (mechanical injuries, milking stress, improper
feeding management, etc.), and the cow’s own factors (e.g., age,
lactation, feed, milk yield, and lactation period) ([42]2). Subclinical
mastitis, with minimal clinical signs and a high recurrence rate, also
poses a serious threat and causes substantial economic losses for dairy
farms ([43]3). Traditional treatments rely on large amounts of
antibiotics for lactating cows with clinical mastitis and at drying
off. However, bacteria with increasing resistance to a wide spectrum of
antibiotics are an impetus for effective and economical therapeutic
regimes for subclinical mastitis.
Traditional Chinese Medicine, renowned for its distinct mechanisms and
relatively low cost, is gaining momentum in modern pharmacy research.
Numerous studies have substantiated the profound therapeutic efficacy
TCM for treating diseases through regulation of immune and metabolic
systems. Consequently, this approach is posited to mitigate overuse of
antibiotics and reduce emergence of antimicrobial resistance ([44]4–6).
Huangqi Shengmai Yin is a classical formula derived from Traditional
Chinese Medicine, composed of Astragalus, pilose asiabell root, dwarf
lilyturf root, magnolia vine fruit, and southern magnolia vine fruit.
Officially documented in the Pharmacopeia of the People’s Republic of
China, it is renowned for its effects in tonifying qi, generating body
fluids, consolidating yin, and arresting sweating, traditionally used
to treat conditions arising from dual deficiency of qi and yin, such as
fatigue, palpitations, and shortness of breath ([45]7). Astragalus, the
principal herb in the formula, is sourced from the root of the
leguminous plant Astragalus membranaceus (Fisch.) Bge.var.mongholicus
(Bge.) Hsiao. It contains several bioactive compounds, including
formononetin, astragalosides, and astragalus polysaccharides, which
have been reported for their anti-inflammatory, antioxidant, and
intestinal microbiota-modulating properties ([46]8–10). Similarly,
bioactive constituents such as polysaccharides, lignans, and
triterpenoid saponins found in pilose asiabell root, dwarf lilyturf
root, magnolia vine fruit, and southern magnolia vine fruit contribute
to physiological homeostasis and cardioprotection by modulating immune
responses and metabolic pathways ([47]11–13). Previous studies
confirmed heart-nourishing and immunological regulation functions of
HSY, and it has been used to treat heart diseases like myocarditis,
heart failure, and coronary heart disease in humans and pigs ([48]14).
Building upon its classical foundation, a modified preparation of HSY
(commercially named as Ruyankang) was developed to address mammary
gland disorders, with a modified formula comprising Chinese angelica
root, Astragalus root, honeysuekle flower, forsythia fruit, Radix
Trichosanthis, Mongolian Snakegourd Root, Viola yedoensis Makino,
Houttuynia cordata Thunb., Taraxacum mongolicum, and Glycyrrhiza
uralensis Fisch. From a traditional medical perspective, this
reformulation shifts mHSY’s focus from qi and yin tonification to
heat-clearing, detoxifying, blood activating, and stasis removing,
namely antimicrobial and circulatory-enhancing ([49]12, [50]15–18).
Arguably, subclinical mastitis could be related to immune system
malfunction or perhaps affected by diet. Consequently, Chinese
veterinary researchers are exploring the potential of HSY for treating
bovine mastitis, especially subclinical mastitis. Previous research
correlated the therapeutic function of HSY components with their
effects on gut microbiome; the association between rumen microbiome and
metabolome has been verified, but few studies have elucidated the
relationship between HSY treatment and microbiome or metabolome
([51]19, [52]20). In this study, mHSY was giving orally order to assess
its therapeutic effects on SCM, with multi-omics used to elucidate
underlying mechanisms.
2. Materials and methods
2.1. Experimental design and sample collection
The mHSY used in this study was jointly screened by research groups led
by Li Yuqiong and Gao Jian. A suspension was prepared by mixing the
powdered drug, at the specified dose (5 g/kg for each cow each day,
from unpublished research of Li) with hot water, and then administered
through a gastrostomy tube to subjects at 8 AM, daily for 3 days. This
study was conducted on an intensively managed dairy farm in Ningxia
Province, China with over 1,000 lactating cattle.
Based on milk somatic cell counts (SCC ≥ 2 × 10^5 cells/mL), California
mastitis test (CMT) results (weakly positive or positive), and udder
clinical signs, 6 mid-lactation Holstein dairy cows with or without SCM
were selected as the treatment group (T group) and control group (C
group), respectively. Routine Dairy Herd Improvement (DHI) testing was
performed by the local DHI testing center, and DHI samples were
collected on Day 0 and Day 6. CMT scoring criteria followed the
criterion of previous study ([53]21), and SCC values from DHI report
were converted to somatic cell scores (SCS) using the standard formula
validated in dairy mastitis studies ([54]22):
[MATH: SCS=log2(SCC/100,0
00)+3 :MATH]
All cows received the same total mixed ration (TMR) and were housed in
free-stall barns with ad libitum access to feed and water. The TMR was
provided 3 times daily (07:30, 13:30, and 19:30). Samples of rumen
fluid were collected on Day 0, followed by a 3-day mHSY administration.
The cows were maintained on a standard diet throughout the experiment
and had ad libitum access to water. Rumen fluid was sampled again on
Day 6. Before sampling, cows were subjected to 12 h fasting to minimize
dietary interference with metabolites. After local anesthesia was
induced by subcutaneous injection of 2% procaine solution at a dosage
of 0.1 mL/kg body weight around the rumen puncture site, rumen fluid
samples (~ 10–20 mL) were collected through rumen puncture. In
addition, ~ 20–30 mL of milk was collected from each cow (samples were
a composite of milk from all 4 quarters) into sterile containers, and
immediately cryopreserved at −80°C to maintain compositional stability.
All samples were preserved in liquid nitrogen and subsequently analyzed
via 16S rDNA sequencing and metabolomics.
2.2. DNA extraction, amplification and sequencing
Extraction of DNA from fecal and rumen fluid samples was performed
using HiPure Stool DNA Kits (D3141, Guangzhou Meiji Biotechnology Co.,
Ltd., China) according to the kits’ instructions. Briefly, 150–200 mg
of sample was transferred to a 2 mL tube and 1.2 mL of Buffer SSL was
immediately added to the sample, followed by vortexing for 1 min at
maximum intensity to break up the sample. After 10 min in a water bath
at 70°C, the sample was vortexed for 15 s, then centrifuged at ≥14,000
g for 10 min at room temperature. Thereafter, 250 μL of supernatant was
transferred to a new 1.5 mL tube.
To extract DNA, 20 μL of Proteinase K and 250 μL of Buffer AL
supernatant were added to the sample, followed by gentle inversion
mixing for 10 times. The mixture was then incubated at 70°C for 10 min
to ensure complete protein digestion. Then, 250 μL of anhydrous ethanol
was added, and the mixture was inverted 10 times. The HiPure DNA Mini
Column I was loaded into a 2 mL collection tube, and the sample mixture
was transferred onto the column. The column was centrifuged at 10,000 g
for 30–60 s to bind the DNA to the silica membrane. The effluent was
discarded, and the column was placed back into the collection tube.
Next, 500 μL of Buffer GW1 was added to the column, followed by
centrifugation at 10,000 g for 30–60 s to wash the membrane. The
filtrate was discarded, and the column was returned to the collection
tube. A second wash was performed by adding 650 μL of Buffer GW2 to the
column and centrifuging at 10,000 g for 30–60 s. The column was then
transferred to a 1.5 mL centrifuge tube, and 50–200 μL of Buffer AE,
preheated to 70°C, was added to the center of the membrane. After
allowing the column to stand at room temperature for 2 min, it was
centrifuged at 13,000 g for 1 min to elute the purified DNA. The
purified DNA was then stored at −20°C.
Sample quality was measured using a NanoDrop (NanoDrop 2000, Thermo
Fisher Scientific, United States). Samples were amplified using a PCR
instrument, using the following primers: CCTACGGGGNGGCWGCAG; 806R:
GGACTACHVGGGTATCTAAT; 799F: AACMGGATTAGATACCCKG; 1193R:
ACGTCATCCCCACCTTCC; Arch519F: CAGCMGCCGCGGGTAA; and Arch915R:
GTGCTCCCCCGCCAATTCCT. Library quality testing was performed using the
ABI StepOnePlus Real-Time PCR System (Life Technologies, United
States), and up-sequencing was done using PE250 mode pooling with a
Novaseq 6000 (NovaSeq6000 S2 Reagent Kit v1.5, Illumina, United
States).
2.3. Data processing, annotation and statistical analysis
Amplicons were evaluated with 2% agarose gels and purified using AMPure
XP Beads (Beckman, CA, United States) according to the manufacturer’s
instructions. Sequencing libraries were generated using Illumina DNA
Prep Kit (Illumina, San Diego, CA, United States) following
manufacturer’s recommendation. Library quality was assessed with ABI
StepOnePlus Real-Time PCR System (Life Technologies, Foster City, CA,
United States). At the end, 2 × 250 bp paired-end reads were generated
by sequencing on the Novaseq 6,000 platform. Raw reads were deposited
into the NCBI Sequence Read Archive (SRA) database (Accession Number:
SRR32089890 – SRR32089913). Raw reads were further filtered according
to the following rules using FASTP (Version 0.18.0) to get high-quality
clean reads ([55]23). Paired reads were overlapped as raw tags using
FLASH (version 1.2.11) with a minimum overlap of 10 bp and mismatch
error rates of 2% ([56]24). Noisy sequences of raw tags were filtered
under specific filtering conditions to obtain high-quality clean tags
([57]25) that were clustered into operational taxonomic units (OTUs) of
≥ 97% similarity using UPARSE (version 9.2.64) pipeline. All chimeric
tags were removed using the UCHIME algorithm, and finally effective
tags were obtained for further analysis.
Within each cluster, the tag sequence with the highest abundance was
selected as the representative sequence ([58]26, [59]27).
Representative OTU sequences or ASV sequences were classified into
organisms by a naive Bayesian model using RDP classifier (version 2.2)
based on SILVA database (version 138.1) or UNITE database (version
8.3), with confidence threshold value of 0.8 ([60]28, [61]29).
Abundance statistics of each taxonomy were visualized using Krona
(version 2.6) ([62]30). The stacked bar plot of the community
composition was visualized in the R project ggplot2 package (version
2.2.1). Pearson correlation analysis of species was calculated in the R
project psych package (version 1.8.4). Species comparison between
groups was calculated by Welch’s t-test and Wilcoxon rank test in the R
project Vegan package (version 2.5.3). Biomarker features in each group
were screened by LEfSe software (version 1.0), random forest package
(version 4.6.12) in the R project, pROC package (version 1.10.0) in the
R project, and lands package (version 2.0-1) in R project. Chao1, ACE,
Shannon, Simpson, Good’s coverage, and Pielou’s evenness index were
calculated in QIIME (version 1.9.1). Alpha index comparison among
groups was computed by Tukey’s HSD test and Kruskal-Wallis H test in R
project Vegan package (version 2.5.3). Jaccard and Bray-Curtis distance
matrix calculated in R project Vegan package (version 2.5.3). FAPROTAX
database (Functional Annotation of Prokaryotic Taxa) and associated
software (version 1.0) were used for generating ecological functional
profiles of bacteria ([63]31).
2.4. Metabolite extraction and LC–MS/MS detection
For ruminal fluid and milk samples, ~ 100 mg of each sample was ground
under liquid nitrogen and weighed. Subsequently, 1 mL of a pre-cooled
methanol-acetonitrile-aqueous solution (v/v, 2:2:1) was added, and the
mixture vortexed to ensure thorough mixing. Samples were then subjected
to low-temperature ultrasound treatment for two 30 min intervals,
followed by a 60-min static incubation at −20°C. After incubation,
samples were centrifuged at 14,000 g for 20 min at 4°C, and the
supernatant (equivalent to 50 mg of original sample) was collected. The
supernatant was dried under vacuum, and mass spectrometry (MS) analysis
was performed after reconstituting dried samples with 100 μL of an
acetonitrile-water mixture (acetonitrile: water = 1:1, v/v) and
vortexing. For further analysis, samples were re-dissolved with 100 μL
of acetonitrile-water solution (acetonitrile: water = 1:1, v/v),
vortexed, and centrifuged at 14,000 g for 15 min at 4°C, with
supernatant used for subsequent analysis. Quality control (QC) samples
were prepared by pooling equal volumes from samples to be tested. These
QC samples were used to assess instrument status, to balance the
chromatography-mass spectrometry system before injection, and to
evaluate system performance throughout the experiment.
2.5. Metabolomics data preprocessing, annotation and analysis
Positive ion mode (POS) and negative ion mode (NEG) were both used to
detect metabolites, improving metabolite coverage and the detection
effect. In subsequent data analyzes, positive and negative ion models
were analyzed separately. QC samples are usually used for quality
control when the study of metabolomics is based on mass spectrometry.
Theoretically, QC samples are the same. However, there could be
systematic errors in sample extraction, detection or analysis, which
would lead to differences among QC samples.
Principle component analysis (PCA) was performed by R language models
(v2.18.1). Orthogonal partial least squares discriminant analysis
(OPLS-DA) is a supervised dimensionality reduction method in which
class memberships are coded in matrix form into Y to better distinguish
the metabolomics profiles of two groups by screening variables
correlated with class memberships. OPLS-DA was applied in comparison
groups using R package ropls.[64]^1 A variable importance in projection
(VIP) score of PLS model was applied to rank metabolites that were best
distinguished between the 2 groups.
To understand regulation of differential metabolites, fold changes of
abundance between 2 groups were further calculated to draw the volcano
plot. Kyoto Encyclopedia of Genes and Genomes (KEGG) is the major
public pathway-related database that includes genes and metabolites.
Metabolites were mapped to KEGG metabolic pathways for annotation and
enrichment analysis. Pathway enrichment analysis identified
significantly enriched metabolic pathways or signal transduction
pathways in differential metabolites compared to the whole background
([65]32, [66]33).
2.6. Statistical analyzes
The t-test and VIP were used as a univariate analysis for screening
differential metabolites. Metabolites with a p-value of t-test < 0.05
and VIP ≥ 1 were considered differential metabolites between the 2
groups. The SCS and SCC data are presented in the form of mean ± SD.
Milk components and SCS data of the 2 groups of cows were analyzed
using one-way ANOVA and Student’s t-test with SPSS Statistics 22 (IBM,
Chicago, United States). Significance was declared at p < 0.05, and
0.05 < p < 0.10 represented a tendency.
3. Results
3.1. mHSY mitigated bovine subclinical mastitis and altered milk components
Six healthy cows and 6 SCM cows were selected for the experiment
([67]Figure 1A). Both groups were given mHSY for 3 consecutive days,
followed by an additional 3 days of observation. Samples of rumen fluid
were collected on Day 0 and Day 6 for analysis of bacterial diversity
and metabolomic profiling. Concurrently, the CMT test and milk somatic
cell count were performed daily from Day 0 to Day 6.
Figure 1.
[68]Diagram depicts an experimental process and results. **Panel A**:
Illustration of a cow receiving drugs, with samples collected for
testing. Timeline shows treatments for control and HQSMY groups over
six days, with observations noted.**Panel B**: Line graph showing
number of positive CMT results over six days, contrasting control and
SCM groups. The control group remains at zero, while the SCM group
decreases from six to zero.**Panel C**: Line graph of SCS values over
seven days for control and SCM groups, showing varied trends and error
bars.Legend indicates orange for control and blue for SCM.
[69]Open in a new tab
Cow with subclinical mastitis given a Traditional Chinese Medicine
(mHSY). (A) Schematic diagram of the experimental procedure. (B) CMT
test results. (C) Milk somatic cell count test results. All results are
mean ± SD (n = 6 per group).
The number of CMT-positive cows had minimal change during the initial
2 days and a slight decline from Day 2 to Day 4 ([70]Figure 1B).
However, a marked decrease was observed in cows with SCM on Day 5. In
contrast, the number of presumably healthy cows remained consistent
throughout the experiment. The curve of milk SCC had a similar pattern,
decreasing from > 75 × 10^4 cells/mL to ~ 25 × 10^4 cells/mL, with a
minor peak on Day 2. The SCC of healthy cows was consistently <
25 × 10^4 cells/mL ([71]Figure 1C).
Variations in milk composition were also analyzed through one-way ANOVA
and Student’s t-test ([72]Table 1). Milk fat and urea concentrations
were not different (p > 0.05) between healthy and SCM cows, both prior
to and after mHSY treatment. Conversely, there was a significant
disparity between the 2 groups in milk protein percentage and SCC on
Day 0; however, by Day 6, these parameters were not different between
the 2 groups (p < 0.05). Interestingly, the percentage of lactose in
milk exhibited an inverse trend, with no significant difference on Day
0, yet a divergence between the 2 groups was noted on Day 6 (p < 0.05).
Table 1.
Milk composition in healthy and SCM cows before and after Traditional
Chinese Medicine (mHSY) treatment.
Items Experimental treatments SEM P-value
C-D0[73]^1 C-D6 T-D0[74]^2 T-D6
(n = 6) (n = 6) (n = 6) (n = 6)
Milk fat (%) 2.01 1.19 1.75 1.52 0.17 0.637
Milk protein (%) 3.20^a 3.24^a 3.71^b 3.23^ab 0.12 0.049
Lactose (%) 4.13^ab 4.21^a 3.95^b 3.90^b 0.07 0.038
Milk SCC[75]^3 (×10^3/mL) 99.17^b 36.67^c 717^a 144.5^b 157.45 <0.001
Urea (mg/dL) 6.15 8.41 8.36 5.84 0.69 0.088
[76]Open in a new tab
^1
C = healthy cattle.
^2
T = cattle with subclinical mastitis.
^3
SCC = somatic cell count.
^a,b,csuperscripts of the significant difference between different
treatment groups in the same row (p < 0.05), different letters indicate
significant difference, and the same letter indicates no significant
difference.
3.2. mHSY reversed the SCM-induced negative impact on the richness,
diversity, and composition of the rumen microbial community
A total of 6,193,093 high-quality 16S rRNA gene sequences were obtained
from 24 rumen fluid samples, with Good’s coverage of 99% across all
samples. Rarefaction curves indicated a gradual plateau in the increase
of species numbers and diversity indices with the rise in the number of
reads sampled, suggesting that sequencing data were adequate to capture
the majority of microbial diversity. Significant differences were
observed between healthy and SCM groups on Day 0 in the Sobs, Chao1,
and ACE indices ([77]Figure 2A), with no significant differences
between the 2 groups in Shannon or Simpson indices. After mHSY
treatment, Sobs, Chao1, and ACE indices of SCM cows had significant
increases, whereas those of the control group remained stable. In
contrast, Shannon and Simpson indices for healthy cows were
significantly decreased. On Day 6, the Simpson index for SCM cows was
significantly higher compared to that of healthy cows. With the
exception of the SCM group on Day 0, there were no significant
differences among groups in Good’s coverage.
Figure 2.
[78](A) Box plots showing microbial diversity indices (Sob, Chao1, ACE,
Shanon, Simpson, and Good's coverage) for different sample groups with
statistical significance indicated by p-values. (B) Stacked bar chart
displaying relative abundance at the phylum level across sample groups,
with a color legend for different phyla. (C) Stacked bar chart showing
relative abundance at the genus level with a corresponding color
legend. (D) PCoA plot illustrating sample clustering based on microbial
composition, differentiated by color-coded groups. (E) Box plot
comparing microbial composition between sample groups. (F) Box plots
showing different microbial metrics with p-values indicating
statistical analysis across sample groups.
[79]Open in a new tab
Alpha and beta diversity of the microbiota between healthy and SCM cows
before and after Traditional Chinese Medicine (mHSY) treatment. (A)
Alpha diversity indices and Good’s coverage of rumen fluid microbiomics
in dairy cows, analyzed by Tukey-HSD analysis (Sob, Chao1, ACE,
Simpson, Shannon, Good’s coverage). (B,C) Microbiota composition of
rumen fluid at phylum level (B) and genus level (C). (D) Principal
co-ordinates analysis (PCoA) of ruminal microbiota based on an
unweighted unifrac of OTUs. (E) Beta diversity results analyzed by
Anosim based on an unweighted unifrac of OTUs. (F) Beta diversity
results analyzed by Welch’s t-test. C, healthy cattle; T, cattle with
subclinical mastitis.
After sequence clustering and quality filtering, a total of 11,005 OTUs
with > 97% similarity were identified. Using the Unweighted Pair Group
Method with Arithmetic Mean (UPGMA) for taxonomic analysis of similar
OTU representative sequences, 28 phyla and 338 genera of bacteria were
identified. At the phylum level, predominant groups before and 3 days
post-administration were Bacteroidota (51.29% ± 5.62%), Firmicutes
(23.45% ± 5.99%), Proteobacteria (16.84% ± 12.08%), Patescibacteria
(2.39% ± 0.76%), and Cyanobacteria (1.71% ± 0.97%) ([80]Figure 2B). At
the genus level, the most abundant microorganisms included Prevotella
(37.20% ± 3.71%), Succinivibrionaceae_UCG-001 (14.53% ± 13.43%),
Succiniclasticum (5.24% ± 2.74%), Rikenellaceae_RC9_gut_group
(3.06% ± 1.12%), and Treponema (1.44% ± 0.61%) ([81]Figure 2C).
Principal coordinates analysis (PCoA) of rumen microbial communities
was conducted based on the Unweighted UniFrac distance algorithms
([82]Figure 2D); differences were assessed using the Anosim test, with
R acting as a statistic. In [83]Figure 2E, an R > 0 indicated that the
intra-group distances were smaller than inter-group distances, which
validated groupings. Furthermore, microbial composition differed
(p = 0.001) between control and mHSY treatment groups, with Welch’s
t-test used to provide more detailed comparisons. A difference in beta
diversity between the control group and the SCM group (p < 0.05)
implied that the etiology of SCM may be rooted in alterations within
the rumen microbiome. After mHSY treatment, the lack of a difference
(p > 0.05) between the control group before and after mHSY
administration implied that mHSY was safe, as it did not appear to
disrupt the original rumen microbiome. However, after mHSY
administration, there was a difference (p < 0.05) in microorganism
diversity within the SCM group. Moreover, the lack of a difference
between the 2 groups on Day 6 (p > 0.05) implied that mHSY
significantly restored the impaired rumen microbiome induced by SCM
toward a healthier state ([84]Figure 2F).
3.3. mHSY altered ruminal bacteria composition at phylum and genus levels
A non-parametric Kruskal-Wallis sum-rank test was used to detect
differences in ruminal bacteria between the 2 groups. Significantly
diverse microbes (genus level) were assembled in 14 phyla, including
Firmicutes, Bacteroidota, Proteobacteria, Planctomycetota,
Euryarchaeota, Actinobacteriota, Desulfobacterota, etc. Significantly
different bacteria at the genus level were filtered with linear
discriminant analysis effect size (LEfSe), as shown in [85]Figure 3A,
with the top 5 on the Linear discriminant analysis (LDA) score listed
in [86]Figure 3B.
Figure 3.
[87]Composite image showing three data visualizations. A: A color-coded
cladogram indicating bacterial taxa distribution over four conditions
(C-D0, C-D6, T-D0, T-D6). Each segment represents distinct groups with
color associations. B: A bar graph displaying LDA scores of various
bacteria across the same four conditions, highlighting significant
differences. C: A bar and dot plot illustrating some statistical
comparisons across the four conditions with specific numeric values.
Each graph is color-coded according to the legend for conditions.
[88]Open in a new tab
(A) Cladogram indicating linear discriminant analysis effect size
(LEfSe) analysis of different ruminal microbiota between healthy and
subclinical mastitis (SCM) cows before Traditional Chinese Medicine
(mHSY) treatment. (B) Linear discriminant analysis (LDA) score plot
indicating the effects of top 5 microbiota on difference on genus level
between healthy and SCM cows before mHSY treatment. (C) Upset diagram
of shared ruminal bacteria taxa between healthy and SCM cows before and
after mHSY administration.
The upset diagram displayed the number of unique microorganisms on the
genus level ([89]Figure 3C). A total of 127 genera were shared by the
control and SCM groups before and after mHSY administration, which was
subjected to correlation analysis with ruminal metabolites. SCM cows
harbored 50 unique genera, whereas healthy cows had only 2 distinctive
genera, with no genus shared by the 2 groups. Therefore, there was a
dramatic difference between SCM cows and healthy individuals. However,
after mHSY treatment, the healthy group had 3 unique genera and shared
2 unique genera with the SCM group, whereas 6 unique genera were
exclusive to the SCM group. The rumen microbiome of the SCM group at
Day 0 was obviously distinct from that of the other groups; however,
mHSY treatment appeared to bridge this gap.
3.4. Metabolic profiling of ruminal fluid samples
An untargeted metabolomics analysis based on liquid chromatography-mass
spectrometry (LC–MS) technology was used to examine metabolite profiles
in rumen fluid. Through overlapping comparison, the response intensity
and retention time of each chromatographic peak were essentially
overlapped, which reflected reliability of data quality. After
filtering and optimizing, a total of 23,528 ruminal metabolites (13,220
and 10,308 in positive and negative ion modes, respectively) were
identified from the 24 samples. An orthogonal partial least squares
discriminant analysis (OPLS-DA) was conducted based on a supervised
multivariate statistical analysis method to reflect overall differences
among various groups and variability within sample groups. Rumen fluid
samples were clearly separated according to their metabolic profiles
across groups, as evidenced by the OPLS-DA score plot in the merging
ion mode ([90]Figure 4A). The heat map, which displays relative
abundance of various metabolites and their top 30 enriched pathways
based on the adjusted p-value, for samples from healthy and SCM cows in
merging ion mode, is presented in [91]Figures 4B,[92]C.
Figure 4.
[93]Panel A shows four OPLS-DA score plots with T scores on the x-axis
and orthogonal T scores on the y-axis, comparing control (orange) and
treatment (blue) groups at different time points. Panel B is a heatmap
displaying gene expression data with red indicating high expression and
blue indicating low expression, for control and treatment groups. Panel
C presents a bar chart of Metabolite Sets Enrichment Overview showing
various metabolic pathways with enrichment scores on the x-axis,
distinguished by p-value color gradients.
[94]Open in a new tab
Ruminal metabolites were analyzed with OPLS-DA, heatmap, and MSEA
pathway enrichment. (A) OPLS-DA of metabolic sets between healthy and
subclinical mastitis (SCM) cows before and after Traditional Chinese
Medicine (mHSY) administration. (B) Heatmap of significantly
differential metabolites between healthy and SCM cows before mHSY
administration. (C) MSEA pathway enrichment of significantly
differential metabolites between healthy and SCM cows before mHSY
administration.
3.5. Significantly different metabolites in ruminal fluid of cows given mHSY
Differential ruminal and mammary metabolites with a VIP value of >1, a
fold change of >1.5 or <0.67, and p < 0.05 were identified among the
control group and SCM group before and after mHSY administration. Ionic
strengths from merging mode of metabolites between groups are shown in
[95]Figures 5A,[96]B. A total of 372 and 393 metabolites in the rumen
fluid of healthy and SCM cows, respectively, underwent significant
changes during mHSY treatment, with 135 shared metabolites used for
further analysis ([97]Figure 5C). After mHSY treatment, expression of
most ruminal metabolites was upregulated in both groups; however,
expression of Creatine, Creatinine, Dihydrouracil, Quinacrine,
Polydatin, Tepraloxydim, 9-aminocamptothecin, Sepiapterin, and
Morroniside was downregulated in both groups. Expression patterns of
Oleyl anilide, L-carnitine, Gatifloxacin, Costunolide, Anabasine,
L-methionine, Erioglaucine, 10-hydroxydecanoate,
4-imidazoleacrylicacid, Ng,ng-dimethyl-l-arginine, Dacarbazine,
N-methyltyramine, Tyramine, 1 h-indole-1-pentanoic acid,
3-[(4-chloro-1-naphthalenyl)carbonyl]-, and Lactulose were divergent
between healthy and SCM cows, with the majority except for Oleyl
anilide decreasing in healthy cows but increasing in cows with SCM
([98]Figure 5E). Metabolite Set Enrichment Analysis (MSEA) was
conducted on rumen fluid metabolites from the Control group before and
after mHSY treatment, as well as that from the SCM group, with the top
30 metabolites pathways sorted from highest to lowest according to
FDR-adjusted p-value. The most significantly enriched pathways included
amino acid metabolism, nucleotide metabolism, metabolism of cofactors
and vitamins, and lipid metabolism are shown in [99]Figure 5D.
Figure 5.
[100]Heatmaps A and B show metabolomic profiles with varying
intensities of red and blue, indicating levels of expression over time.
Panel C presents a Venn diagram comparing metabolite differences
between groups, highlighting 135 common features. Panel D is a bar
chart of metabolite set enrichment, with p-values indicated by color
intensity. Panel E displays a detailed heatmap of specific metabolites,
with a color key showing expression levels.
[101]Open in a new tab
Significantly differential metabolites between healthy and subclinical
mastitis (SCM) cows before and after Traditional Chinese Medicine
(mHSY) administration. (A,B) Significantly differential metabolites of
healthy cows (A) and SCM cows (B) before and after mHSY administration
(VIP > 1, p value <0.05, 0.67 < FDR < 1.5). (C) Venn diagram of
significantly differential metabolites before and after mHSY treatment.
(D) MSEA pathway enrichment of significantly differential metabolites
before and after mHSY treatment. (E) Heatmap of significantly
differential metabolites among groups.
3.6. Correlations between differential rumen microbiota, ruminal and mammary
metabolites, and DHI
Before analyzing the correlation between differential microbiota and
ruminal and mammary metabolites, unknown differential metabolites
lacking CAS codes, unclassified microbiota and those with a relative
abundance < 0.1% were excluded, whereas only DHI indices that
demonstrated significant differences were included in the analysis. The
Pearson correlation coefficient model and genus-level heat maps were
mapped ([102]Figure 6A), including only metabolites and bacteria with
an absolute Pearson correlation coefficient value >0.5 and ranking in
the top100 by absolute value. The Pearson correlation coefficient model
and corresponding correlation heat maps among ruminal and mammary
metabolites are in [103]Figure 6B. The Sankey diagram, depicting
interrelationships among ruminal microbiota, ruminal and mammary
metabolites, and DHI, was mapped ([104]Figure 6C).
Figure 6.
[105]Panel A displays a clustered heatmap illustrating metabolite
interactions, with red and blue indicating varying levels of
significance. Panel B is a bar graph showing metabolite levels for
lactose and cells, using a similar color code. Panel C is a Sankey
diagram linking bacterial groups to metabolites, highlighting
connections to cells and lactose.
[106]Open in a new tab
Correlation analysis between ruminal microbiome, metabolome and milk
components before and after Traditional Chinese Medicine (mHSY)
treatment. (A) Correlation heatmap of significantly differential
ruminal bacteria and metabolites before and after mHSY treatment. (B)
Correlation heatmap of significantly differential ruminal metabolites
and milk components. (C) Sankey diagram of correlations among ruminal
microbiome, metabolome and milk components.
4. Discussion
Subclinical mastitis causes large milk production losses in the dairy
sector worldwide. Unlike clinical mastitis, it presents a diagnostic
challenge due to the lack of overt clinical signs (e.g., udder swelling
and redness), and has a much higher prevalence than clinical mastitis.
Furthermore, SCM can progress to clinical mastitis ([107]34).
Traditionally, antibiotic therapy has been the primary method for
managing SCM; however, antibiotics may disrupt the rumen microbiome and
can lead to other health disorders ([108]35). Conversely, TCM, mainly
composed of medical plants and minerals, usually have less side
effects. Their beneficial effects on managing various diseases are
achieved by modulating the animal’s physiological state and intestinal
microbiota, thereby providing a holistic therapeutic approach. This
character makes for their widespread application as dietary additives
to enhance immunoregulation, anti-inflammation, anti-stress, and
prevent underlying diseases in many species ([109]8, [110]36, [111]37).
Moreover, TCM promises to decrease antibiotic use, which is
particularly vital if there is increasing antibiotic resistance
([112]38–40). Therefore, TCM should improve animal welfare and address
some environmental issues.
In recent years, the entero-mammary pathway theory has gained traction,
with research increasingly focused on uncovering links between
microbiomes and metabolomes in the mammary gland and gastrointestinal
tract ([113]41–43). Studies of SCM often examine shifts in the
metabolome and microbiota of either the udder or intestine
([114]44–46). Admittedly, SCM tends to lead to a divergent microbial
landscape within the intestines, resembling the dysbiosis present in
inflammatory bowel disease, which disrupts the intestinal microbiome
([115]21, [116]47). Unlike monogastric animals, ruminants have a
rumen—a fermentation chamber housing diverse microorganisms crucial for
digestion. Within these populations, Succiniclasticum, Prevotella,
Treponema, Dorea, Lachnospiraceae and Ruminococcus are regarded as
beneficial due to their roles in breaking down cellulose,
hemicellulose, starch, and proteins into short-chain fatty acids
(SCFAs) and amino acids, which are energy sources for the host and milk
precursors ([117]48–50). The gastrointestinal barrier, vital to
preventing endogenous pathogen and virulence factors from entering
mammary gland through blood and thus avoid inflammation, also relies on
sufficient SCFAs ([118]21). In this study, mHSY promoted a shift toward
commensal microbiota dominated by SCFA-producing bacteria in both
groups. Critically, in cows with SCM, this microbial restructuring was
accompanied by significantly reduced SCC. This improvement in udder
health is proposed to mechanistically correlate with enhanced
production of ruminal SCFAs through the mechanism mentioned above.
Competitive exclusion of pro-inflammatory bacteria in SCM pathogenesis
may also happened during this microbial alteration.
In association with alternations in ruminal microbial composition,
levels of differential metabolites associated with energy and vitamin
metabolism in both healthy and SCM cows were also observed after mHSY
treatment, indicating changed energy metabolism and biosynthesis
pathways in the rumen. Notably, mHSY appeared to reverse the
downregulated amino acid metabolism observed in cows with SCM, such as
methionine, histidine, tryptophan and arginine, essential for milk
protein production ([119]51). Besides, altered steroidogenesis, fatty
acid metabolism, oxidation of branched-chain fatty acids and very long
chain fatty acids revealed that mHSY may modulate energy supply and
reproductive function in dairy cows ([120]52). Androgen and estrogen
metabolism was also significantly affected by mHSY, which may in turn
regulate proliferation rates of mammary epithelial cells ([121]53).
Correlation analysis revealed that specific rumen bacteria, including
Treponema, Succinivibrionaceae_UCG-001, Lachnospira, and
Shuttleworthia, were positively associated with metabolites such as
uric acid, sebacic acid, and N-acetylhistidine, which are integral to
amino acid, lipid, and nucleic acid metabolism ([122]54, [123]55).
Conversely, other bacteria like Butyrivibrio, Ruminococcus, and
Succiniclasticum had negative correlations with energy metabolism,
possibly due to niche competition among microbial populations
([124]56). Furthermore, different microorganisms may get involved in
diverse parts of the same pathway, thus leading to seemingly opposite
functions in metabolism correlation ([125]57). This suggests that
different microorganisms may exert diverse and sometimes opposing
effects on metabolic pathways, highlighting the complexity of rumen
microbiota interactions. SCC in milk tends to correlate negatively with
lactose concentration, contributing to the contradicted correlation in
some ruminal metabolites ([126]58). However, few studies have
elucidated the mechanism underlying the correlation between the ruminal
organisms and metabolites and milk components shown in our study. In
addition, we inferred that the rumen metabolic profile and microbial
structure in cows with SCM are closely linked to milk quality. mHSY had
substantial potential in alleviating SCM and enhancing milk quality,
likely through its role in modulating the ruminal microbiome and
subsequently regulating metabolism. Further studies are needed to
investigate effects of mHSY’s individual components on the ruminal
microbiome, especially Succiniclasticum, to elucidate its
pharmacological mechanisms and specific therapeutic target.
This study has several limitations that warrant consideration. Given
the association between the rumen microbiome and mastitis, this study
emphasized the rumen microbiome and metabolome when investigating SCM,
aligning with findings from other bovine metabolic diseases ([127]42).
Community richness was significantly lower in rumen fluid of cows with
SCM compared to healthy individuals, consistent with that of Zhu et al.
([128]59). However, diversity levels between groups were similar, in
contrast to Zhong et al. ([129]60). Though overt clinical issues were
not observed in this study, the observed ruminal alterations in healthy
cows necessitate more comprehensive evaluation including longitudinal
clinical monitoring of rumen fermentation function, metabolic profiles,
and general health indicators to fully establish safety. The relatively
small sample size may have reduced statistical power and introduced
bias toward individual variability. Additionally, the absence of an
untreated SCM control group limits our ability to definitively
attribute the observed reductions in SCC and changes in ruminal
parameters solely to the administration of mHSY. Future research should
address these limitations through randomized controlled trials
incorporating untreated and placebo control groups, systematic
collection of comprehensive production data, and expanded sample sizes
to validate these preliminary findings. It is important to note that
this study primarily focused on SCC as the key indicator of SCM. This
study was primarily designed to investigate the effects of mHSY on SCC
and the ruminal microbiome and metabolome in SCM cows. As a result,
production parameters such as individual milk yield before and after
treatment were not systematically collected in this study. While the
randomization process aimed to minimize bias, the absence of these
potential confounding factors limits our ability to fully account for
their influence on treatment response. Future studies incorporating
comprehensive production records and detailed individual histories
would be valuable to confirm and extend these findings. Moreover,
long-term studies should be conducted to determine impacts of mHSY
treatment on the recurrence rate and chronicity of subclinical
mastitis, which would provide valuable insights into long-term efficacy
and potential side effects of mHSY.
In summary, this study demonstrated the potential of mHSY, a TCM with
efficacy in treating myocarditis, heart disease, and allergic shock, as
a novel therapeutic agent for SCM in dairy cows ([130]14). In this
study, mHSY significantly alleviated SCM parameters in dairy cows, with
no detectable adverse effects on SCC observed in healthy cows,
confirming its favorable safety profile. We inferred that mHSY may have
anti-inflammatory properties and protect the blood-milk barrier
([131]9, [132]11, [133]61). mHSY has promising effects in modulating
the ruminal microbial composition and metabolome, critical factors in
the pathogenesis of SCM. In practical veterinary applications, mHSY has
potential as an alternative or complementary approach to conventional
antibiotic therapies by improving the overall health and productivity
of dairy herds. The optimal dosing regimen, which remains to be
determined through further studies, will be crucial in maximizing the
efficacy and minimizing potential adverse effects of mHSY. Furthermore,
validation of mHSY’s efficacy would be a valuable advance in management
of SCM, contributing to improved animal welfare and enhanced
productivity in the dairy industry.
5. Conclusion
This study was a comprehensive analysis of the gastrointestinal
microbiome and associated metabolite alterations in dairy cows with SCM
and their response to mHSY treatment. There were significant
alterations in Enterobacter, Desulfovibrio and Flavonifractor
populations in rumens of affected cows, implying a crucial role in
disease pathogenesis. Furthermore, mHSY demonstrated therapeutic
potential by normalizing the gastrointestinal microbiota. Additionally,
there were significant shifts in lipid and amino acids metabolism,
which are pivotal for the pathophysiology of subclinical mastitis and
are substantially ameliorated with mHSY intervention. These findings
underscored the remarkable efficacy of mHSY in managing SCM.
Funding Statement
The author(s) declare that financial support was received for the
research and/or publication of this article. This study was financially
supported by the National Natural Science Foundation of China
(U21A20262 and 32273082).
Footnotes
^1 [134]http://www.r-project.org/
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found at: [135]https://www.ncbi.nlm.nih.gov/,
SRR32089890 – SRR32089913.
Ethics statement
The animal study was approved by the Ethical Committee of the College
of Veterinary Medicine at China Agricultural University approved all
animal procedures (ID: AW71214202-2-02). All procedures adhered to
standard ethical guidelines implemented by CAU. The study was conducted
in accordance with the local legislation and institutional
requirements.
Author contributions
CZ: Data curation, Formal analysis, Methodology, Software,
Visualization, Writing – original draft. BZ: Data curation, Formal
analysis, Software, Writing – original draft. YL: Conceptualization,
Data curation, Investigation, Methodology, Project administration,
Resources, Writing – review & editing. JK: Supervision, Visualization,
Writing – review & editing. XL: Software, Writing – review & editing.
XT: Software, Writing – review & editing. AY: Software, Writing –
review & editing. CX: Resources, Supervision, Writing – review &
editing. BH: Resources, Supervision, Writing – review & editing. JG:
Conceptualization, Data curation, Investigation, Project
administration, Resources, Supervision, Validation, Writing – review &
editing.
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.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this
manuscript.
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
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
References