Abstract
The milk flavor can be attributed to the presence of numerous flavor
molecules and precursors. In this study, we employed widely targeted
metabolomic analysis techniques to analyze the metabolic profiles of
various milk samples obtained from goats, sheep, dairy cows, and
buffaloes. A total of 631 metabolites were identified in the milk
samples, which were further categorized into 16 distinct classes.
Principal component analysis (PCA) suggested that the metabolite
profiles of samples from the same species exhibit clustering, while
separated patterns of metabolite profiles are observed across goat,
sheep, cow, and buffalo species. The differential metabolites between
the groups of each species were screened based on fold change and
variable importance in projection (VIP) values. Five core differential
metabolites were subsequently identified, including
3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5′-triphosphate,
methylcysteine, N-cinnamylglycine, and small peptide
(L-tyrosine–L-aspartate). Through multiple comparisons, we also
screened biomarkers of each type of milk. Our metabolomic data showed
significant inter-species differences in the composition and
concentration of some compounds, such as organic acids, amino acids,
sugars, nucleotides, and their derivatives, which may affect the
overall flavor properties of the milk sample. These findings provided
insights into the molecular basis underlying inter-species variations
in milk flavor.
Keywords: goat, milk, UPLC–ESI–MS/MS-based metabolomics, flavor profile
1. Introduction
Milk from goats, sheep, and cows show differences in composition that
set the different varieties apart from each other. For instance, goat
milk exhibits a higher mineral content, increased calcium levels
[[42]1], and a distinct fatty acid composition [[43]2,[44]3]. In
addition, due to the presence of its natural antibiotics and
antioxidants, goat milk is also believed to have a certain degree of
immune-boosting effects [[45]4]. Previous studies primarily focused on
conducting a comparative analysis of the nutrient composition between
goat milk and cow milk [[46]5,[47]6], as well as comparing the
individual or multiple flavor-related compounds across species.
However, few studies have investigated the role of metabolites in the
determination of milk flavor.
Due to the intricate chemical composition of livestock products, such
as milk and meats, their flavor is typically attributed not to a
singular component but rather to a diverse array of molecules [[48]7].
Previous studies have indicated that the flavor profile of milk
encompasses a wide range of compounds, including free fatty acids,
sulfur compounds, terpenoids, aromatic compounds, esters, ethers,
aldehydes, ketones, alkanes, alcohols, and lactones [[49]8,[50]9].
Changes in the abundance and composition of these compounds may explain
the distinct flavors observed in milk from different species. Some
studies have identified branched-chain fatty acids as the primary
compounds responsible for the characteristic “goaty” flavor of goat
milk and sheep milk [[51]5,[52]10,[53]11]. Additionally, the presence
of 3-methylindole and 4-methyl phenol [[54]12], along with oxidation
byproducts of fatty acids, such as aldehydes, ketones, lactones, and
stearic acid, also play a role in determining milk flavor [[55]13].
Metabolomics provides a powerful tool for investigating intricate
metabolite profiles, facilitating comprehensive investigations into the
growth and development of plants and animals [[56]14,[57]15], stress
response [[58]16,[59]17,[60]18], immune interactions [[61]19], mutant
phenotypes [[62]20,[63]21], nutrient composition [[64]22], bioactive
compounds [[65]23], fermentation flavor [[66]24], brewing technology
[[67]25], etc. Recently, extensive investigations have been conducted
on the correlation between microbial metabolite activity in soil, air,
water, and animal intestines and human health [[68]26,[69]27].
Metabolomics has been employed for the analysis of food composition,
identification of food quality, monitoring of food consumption, and
assessment of nutrition [[70]28]. In the field of food metabolomics, an
increasing number of studies have begun to focus on the application of
metabolomics in food flavor analysis. Many studies have quantified
various compounds in food, including sugars, amino acids, and organic
acids, to assess their contribution to the formation of food flavor
[[71]29]. For instance, the identification of major flavor compounds
associated with wines [[72]24] and radish taproots [[73]30] has been
accomplished through the application of a widely targeted metabolomics
technique. The application of metabolomic analysis has also been
extended to the characterization of flavor profiles in animal-derived
foods. Zhang et al. used nuclear magnetic resonance (NMR) to
investigate metabolites of dry-cured hams, revealing that amino acids
and organic acids played a predominant role in determining the taste
profile [[74]31]. Based on liquid chromatography mass spectrometry
(LC-MS) techniques, Wang et al. compared metabolites in three types of
goat meat and revealed that fatty acids, aldehydes, ketones, lactones,
alkaloids, flavonoids, and phenolics were responsible for the nuances
of their flavors [[75]32].
Widely targeted metabolomics represents an advancement technique in the
field of metabolomics, integrating the merits of both targeted and
untargeted methods. It offers remarkable advantages, such as high
throughput, enhanced sensitivity, and extensive coverage
[[76]33,[77]34]. By utilizing a self-constructed compound database and
employing the multiple reaction monitoring (MRM) scanning mode of mass
spectrometry, this method facilitates both qualitative and quantitative
identification of over a thousand metabolites. This tool enables us to
investigate the metabolic profiles of milk from different species and
the identification of biomarkers associated with milk flavor.
In this work, we employed a widely targeted metabolomic mean to compare
the composition and relative abundance of milk metabolites across
multiple species, including goats, sheep, cows, and buffaloes. Notably,
characteristic metabolites specific to each type of milk and their
associated metabolic pathways were identified. The findings are
anticipated to contribute to a more comprehensive understanding of the
regulation of milk flavor and propose further research for manipulating
the flavor of milk products.
2. Materials and Methods
2.1. Milk Samples
Xinong Saanen dairy goats (approximately 3–4 years old, 2 parities) and
Holstein cows (approximately 4–5 years old, 2 parities) utilized in
this study were selected from the experimental farm located at
Northwest A&F University, Yangling, Shaanxi Province, China. The East
Friesian dairy sheep (approximately 3 to 4 years old, 2 parities)
utilized in this research were selected from Yuan Sheng Nong Mu Co.,
Ltd., Jinchang, Gansu Province, China. The buffaloes (approximately 4
to 5 years old, 2 parities) utilized in this study were selected from a
commercial farm located in Guangxi, Province, China. The animals were
all managed similarly and were provided with a mixed diet consisting of
corn, soybean meal, bran, rapeseed meal, and a mineral-vitamin premix.
The milk samples were collected from each dairy animal during the peak
lactation period (60 days postpartum; 6 goats, GMM group; 6 sheep, SMM
group; 6 cows, CMM group; 6 buffaloes, BMM) ([78]Table 1), with a
sample volume of 100 mL. Subsequently, the collected samples were
divided into centrifuge tubes of 50 mL capacity, ensuring secure seals.
Finally, the samples were stored at −80 °C in a refrigerator.
Table 1.
Sample information.
Species Type of Samples Name of Samples Group
Goat Milk GMM1 GMM
Goat Milk GMM2 GMM
Goat Milk GMM3 GMM
Goat Milk GMM4 GMM
Goat Milk GMM5 GMM
Goat Milk GMM6 GMM
Sheep Milk SMM1 SMM
Sheep Milk SMM2 SMM
Sheep Milk SMM3 SMM
Sheep Milk SMM4 SMM
Sheep Milk SMM5 SMM
Sheep Milk SMM6 SMM
Cow Milk CMM1 CMM
Cow Milk CMM2 CMM
Cow Milk CMM3 CMM
Cow Milk CMM4 CMM
Cow Milk CMM5 CMM
Cow Milk CMM6 CMM
Buffalo Milk BMM1 BMM
Buffalo Milk BMM2 BMM
Buffalo Milk BMM3 BMM
Buffalo Milk BMM4 BMM
Buffalo Milk BMM5 BMM
Buffalo Milk BMM6 BMM
[79]Open in a new tab
2.2. Sample Preparation and Extraction
The samples stored at −80 °C were thawed on ice until there was no ice
in the sample and vortexed for 10 s. Subsequently, 50 μL of the sample
and 300 μL of extraction solution (ACN:methanol = 1:4, v/v) containing
internal standards were added to a 2 mL microcentrifuge tube. The
sample was vortexed for 3 min and then centrifuged at 12,000 rpm for 10
min (at a temperature of 4 °C). A volume of 200 μL of the supernatant
was collected and placed in a freezer set at −20 °C for a duration of
30 min, followed by centrifugation at 12,000 rpm for another period of
three minutes (at a temperature of 4 °C). An aliquot consisting of 180
μL from the supernatant was transferred for LC-MS analysis. The sample
extracts were analyzed using ultra-performance liquid chromatography
(UPLC, ExionLC AD (AB SCIEX Pet. Ltd., Framingham, MA, USA),
[80]https://sciex.com.cn/, accessed on 1 December 2022) and tandem mass
spectrometry (MS/MS, QTRAP^® (AB SCIEX Pet. Ltd., Framingham, MA, USA),
[81]https://sciex.com/, accessed on 1 December 2022).
2.3. Ultra-Performance Liquid Chromatography Conditions
The Ultra-Performance Liquid Chromatography (UPLC) conditions were as
follows: the chromatographic column used was Waters ACQUITY UPLC HSS T3
C18, with a particle size of 1.8 µm and dimensions of 2.1 mm × 100 mm;
the mobile phase consisted of ultra-pure water (supplemented with 0.1%
formic acid) as phase A and acetonitrile (supplemented with 0.1% formic
acid) as phase B; the gradient program employed was Water/Acetonitrile,
starting at a ratio of 95:5 v/v at 0 min, transitioning to a ratio of
10:90 v/v at 11.0 min, maintaining this ratio until 12.0 min, then
returning to a ratio of 95:5 v/v at 12.1 min and continuing until the
endpoint at 14.0 min; the flow rate utilized was set at a constant
value of 0.4 mL/min; and the column temperature was maintained at a
steady level of 40 °C throughout analysis.
2.4. Tandem Mass Spectrometry Conditions
LIT and triple quadrupole (QQQ) scans were acquired on a triple
quadrupole-linear ion trap mass spectrometer (QTRAP), QTRAP^® LC-MS/MS
System (AB Sciex, Shanghai, China), equipped with an ESI Turbo
Ion-Spray interface (AB SCIEX Pet. Ltd., Framingham, MA, USA) operating
in positive and negative ion mode and controlled by Analyst 1.6.3
software (Sciex, AB SCIEX Pet. Ltd., Framingham, MA, USA). Electrospray
ionization (ESI) was performed at a temperature of 500 °C, with the
mass spectrum voltage set to 5500 V (positive) and −4500 V (negative).
The ion source gas I (GSI) pressure was maintained at 55 psi, while the
gas II (GS II) pressure was set to 60 psi. Additionally, the curtain
gas (CUR) pressure was adjusted to 25 psi. For collision-activated
dissociation (CAD), the parameter set was optimized for high
performance. In a triple quadrupole (Qtrap), each ion pair underwent
scanning based on an optimized declustering potential (DP) and
collision energy (CE).
2.5. Unsupervised Principal Component Analysis
The principal component analysis (PCA) was conducted using the
statistical function prcomp in R ([82]www.r-project.org, accessed on 1
May 2023), with the parameter “scale” set to “True”. Unit variance
scaling is calculated by centralizing the raw data and dividing it by
the standard deviation of the variable. The calculating formula is as
follows:
[MATH: x′=x−uσ :MATH]
Here,
[MATH: u :MATH]
is the mean, and
[MATH: σ
:MATH]
is the standard deviation.
2.6. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA)
The partial least squares-discriminant analysis (PLS-DA) is a
supervised multivariate statistical analysis method employed for
pattern recognition. This specific approach involves extracting the
components of both the independent variable X and dependent variable Y,
followed by calculating their correlation. OPLS-DA integrates
orthogonal signal correction (OSC) and PLS-DA methods to decompose the
X matrix information into two components, one related to Y and the
other unrelated to Y, effectively capturing relevant variance while
removing irrelevant differences [[83]35]. OPLS-DA further applies
centralized processing after the log2 transformation of the original
data. Here, X represents the matrix of sample quantitative information,
while Y represents the matrix of sample grouping information.
The prediction parameters of the OPLS-DA evaluation model encompass
R^2X, R^2Y, and Q^2. Herein, R^2X and R^2Y denote the explanatory power
of the constructed model for X and Y matrices respectively, while Q^2
represents the predictive capability of the model. The closer these
three indicators approach 1, the greater stability and reliability
exhibited by the model. A Q^2 value greater than 0.5 indicates an
effective model, while a Q^2 value exceeding 0.9 signifies an excellent
model.
2.7. Differential Metabolites Selected
The differential metabolites for different types of milk were
identified based on the criteria of VIP ≥ 1 and |Log2FC| ≥ 1.0. VIP
values are extracted from OPLS-DA results by using the R software
package “MetaboAnalystR”, which includes score plots and permutation
plots. The data underwent logarithmic transformation (log2) and mean
centering before OPLS-DA. To avoid overfitting, we performed a
permutation test (200 permutations).
2.8. Kyoto Encyclopedia of Genes and Genomes (KEGG) Annotation and Enrichment
Analysis
The identified metabolites were annotated using the KEGG Compound
database ([84]http://www.kegg.jp/kegg/compound/, accessed on 1 May
2023) and subsequently mapped to the KEGG Pathway database
([85]http://www.kegg.jp/kegg/pathway.html, accessed on 1 May 2023). The
identified pathways, which exhibited significant regulation of
metabolites, were subsequently subjected to metabolite set enrichment
analysis (MSEA) using a hypergeometric test to determine their
statistical significance. The calculating formula is as follows:
[MATH: P=1−∑i=0m−1<
mfrac>MiN−Mn−iNn :MATH]
Among these, N represents the total number of metabolites annotated in
the KEGG database, n represents the count of differential metabolites
among all annotated metabolites, M denotes the overall number of
metabolites within a specific KEGG pathway, and m signifies the count
of differential metabolites within the pathway.
3. Results
3.1. Metabolite Profiles of Milk Derived from Goat, Sheep, Cow, and Buffalo
Species
To gain deeper insights into the metabolite variations among milk from
diverse species, we conducted the widely targeted UPLC-MS/MS
metabolomic analysis on a total of 24 distinct milk samples. A total of
631 metabolites were identified and classified into 16 categories
([86]Table S1), including 182 amino acids and their derivatives
(28.84%), 91 organic acid and their derivatives (14.42%), 75
nucleotides and derivatives (11.82%), 57 glycerophospholipids (GP,
9.03%), 51 fatty acyls (FA, 8.08%), 48 carbohydrates and its
metabolites (7.61%), 36 heterocyclic compounds (5.71%), 31 benzene and
substituted derivatives (4.91%), 23 alcohol and amines (3.65%), 13
coenzyme and vitamins (2.06%), 10 bile acids (1.58%), 6 hormones and
hormone-related compounds (0.95%), 2 sphingolipids (SL, 0.32%), 2
tryptamines, cholines, and pigments (0.32%), and others ([87]Figure 1).
Figure 1.
[88]Figure 1
[89]Open in a new tab
The proportion of different classes of compounds for the metabolites
detected in milk samples obtained from goats, sheep, cows, and
buffaloes using a widely targeted metabolomic approach.
The principal component analysis (PCA) [[90]36] was conducted using the
prcomp function in R ([91]www.r-project.org, accessed on 1 May 2023).
We observed that the metabolite compositions of the four types of milk
exhibited clear separation, with PC1 and PC2 explaining 25.44% and
19.82% of the total variance, respectively ([92]Figure 2A). The PCA
plot also demonstrated that the quality control (QC) samples, prepared
from a mixture of samples, exhibited clustering within the same region
and even some overlap, thereby indicating their similarity in metabolic
profiles and affirming the stability and reproducibility of our
analysis ([93]Figure S1).
Figure 2.
[94]Figure 2
[95]Open in a new tab
Principal component analysis (PCA), correlation analysis, and cluster
analysis were conducted to understand the overall metabolite
differences between the groups of milk samples and the variation
between the samples within the groups. (A) PCA plot showing the
different metabolic profiles of milk samples. (B) Clustering heat map
showing the Pearson correlation coefficients of metabolites in 24 milk
samples. (C) The cluster heat map displaying the accumulation levels of
each metabolite across four different types of milk. SMM1-6, the
samples of sheep milk metabolites. BMM1-6, the samples of buffalo milk
metabolites. GMM1-6, the samples of goat milk metabolites. CMM1-6, the
samples of cow milk metabolites.
Pearson correlation coefficients between samples were calculated using
the cor function in R. The results depicted in [96]Figure 2B
demonstrate a strong correlation coefficient among the samples,
indicating the robust reproducibility of our analysis and providing a
high level of confidence in discerning differences between milk samples
from distinct species ([97]Table S2). A hierarchical cluster analysis
was performed after the data was processed by unit variance scaling.
The cluster heat map was drawn by using the R program script. It was
observed that milk samples of the same type exhibited clustering
tendencies ([98]Figure 2C). These findings indicated that there are
significant differences in metabolite profiles between the four types
of milk.
To investigate the trend of relative abundance changes in metabolites
from BMM, CMM, and SMM to GMM, we conducted a K-Means cluster analysis
on the standardized data of all metabolites. The result showed that the
metabolites were classified into nine subclasses based on the trend of
relative abundance changes ([99]Figure 3 and [100]Table S3). The number
of metabolites in subclasses 3 and 9 reached the highest count,
totaling 89 for both subclasses, while subclass 2 exhibited the lowest
enrichment of metabolites, with a total of only 38. In subclasses 1 and
4, the GMM group showed higher metabolite abundance levels compared to
the other three groups. In subclass 3, the CMM group exhibited elevated
metabolite abundance levels in comparison to the other three groups. In
subclass 8, the SMM group displayed higher relative abundance levels of
metabolites when compared to the remaining three groups. In subclasses
6 and 9, the BMM group demonstrated higher relative abundance levels of
metabolites than the other three groups. These up-regulated metabolites
may play a role in determining the distinct flavor profiles of the
various types of milk ([101]Figure 3).
Figure 3.
[102]Figure 3
[103]Open in a new tab
K-Means analysis. The X-axis represents the sample groups, while the
Y-axis denotes the standardized relative content of metabolites.
Additionally, sub-class indicates metabolites exhibiting similar
trends.
3.2. Differential Metabolite Screening
An OPLS-DA [[104]37] analysis was employed to accurately identify the
differential metabolites in the comparison groups. The Q^2 values of
all comparison groups exceeded 0.9, indicating the robustness and
reliability of these models for further screening of differential
metabolites ([105]Figure S2). Then, we integrated the fold change with
VIP values derived from the OPLS-DA model for the identification of
differentially expressed metabolites (DEMs). The DEMs were identified
based on a threshold of VIP ≥ 1.0, with fold changes ≥2 and ≤0.5. We
obtained six DEM sets, including GMM vs. CMM, GMM vs. SMM, GMM vs. BMM,
CMM vs. SMM, CMM vs. BMM, and SMM vs. BMM. There were 256 DEMs (109
up-regulated and 147 down-regulated) in the GMM vs. CMM, 170 DEMs (109
up-regulated and 61 down-regulated) in the GMM vs. SMM, 265 DEMs (140
up-regulated and 125 down-regulated) in GMM vs. BMM, 257 DEMs (145
up-regulated and 112 down-regulated) in CMM vs. BMM, 275 DEMs (173
up-regulated and 102 down-regulated) in CMM vs. SMM, and 259 DEMs (126
up-regulated and 133 down-regulated) in SMM vs. BMM ([106]Figure 4 and
[107]Tables S4–S9). The results revealed that the dissimilarities in
the number of differential metabolites between goat milk and sheep milk
were comparatively less than those observed between goat milk and cow
milk, as well as buffalo milk.
Figure 4.
[108]Figure 4
[109]Open in a new tab
Histogram of the differential metabolites. The numbers of up-regulated
(represented in green) and down-regulated (represented in purple)
metabolites between each pair of experimental groups.
3.3. KEGG Enrichment Analysis of Differential Metabolites
To obtain comprehensive insights into the metabolic pathways of
differential metabolites, we performed KEGG enrichment analysis on 460
differential metabolites among four species ([110]Figure 5A–F). The
enrichment analysis revealed that the differential metabolites in the
GMM/CMM and GMM/BMM groups were primarily associated with purine
metabolism and nucleotide metabolism ([111]Figure 5C,E). Moreover,
these two metabolic pathways also showed up in the SMM/CMM and SMM/BMM
groups ([112]Figure 5B,F). The differential metabolites in the GMM/SMM
set were mainly involved in fatty acid biosynthesis, one carbon pool by
folate, and linoleic acid metabolism ([113]Figure 5A). The differential
metabolites in the CMM/BMM set showed significant enrichment in bile
secretion, salivary secretion, and riboflavin metabolism ([114]Figure
5D). The findings suggest that these pathways may play a critical role
in modulating flavor characteristics and have significant biological
implications.
Figure 5.
[115]Figure 5
[116]Open in a new tab
KEGG enrichment analysis of the differential metabolites. (A) The top
20 metabolic pathways with the lowest q-values in the GMM/SMM set. (B)
The top 20 metabolic pathways with the lowest q-values in the CMM/SMM
set. (C) The top 20 metabolic pathways with the lowest q-values in the
GMM/CMM set. (D) The top 20 metabolic pathways with the lowest q-values
in the CMM/BMM set. (E) The top 20 metabolic pathways with the lowest
q-values in the GMM/BMM set. (F) The top 20 metabolic pathways with the
lowest q-values in the SMM/BMM set.
3.4. Identification of Characteristic Metabolites of Each Type of Milk
We conducted a comparative analysis of the DEMs in six DEM sets and
screened five core differential metabolites, namely
3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5′-triphosphate,
methylcysteine, N-cinnamylglycine, and Tyr-Asn ([117]Figure 6). The
findings imply that these compounds could potentially account for the
variations in milk flavor observed among the species.
Figure 6.
[118]Figure 6
[119]Open in a new tab
Venn diagram of differential metabolites for each comparison group of
GMM, SMM, CMM, and BMM.
To identify the characteristic metabolites of goat milk, we performed
multiple comparisons of up-regulated DEMs among the GMM/CMM, GMM/SMM,
and GMM/BMM sets. A total of seven characteristic metabolites were
identified in goat milk, including N-(3-indolylacetyl)-L-alanine,
pyridoxine 5′-phosphate, ADP-ribose, N-acetytryptophan,
3-methylcrotonyl glycine, dihydro-D-sphingosine, and N-cinnamylglycine
([120]Figure 7A). These compounds were enriched in goat milk compared
to other types of milk, suggesting their potential as biomarkers for
screening goat milk.
Figure 7.
[121]Figure 7
[122]Open in a new tab
Network Venn diagram showing the characteristic metabolites of each
type of milk. (A) The characteristic metabolites of goats. (B) The
characteristic metabolites of sheep. (C) The characteristic metabolites
of cows. (D) The characteristic metabolites of buffaloes.
Based on the multiple comparisons of up-regulated DEMs among the
SMM/CMM, SMM/GMM, and SMM/BMM sets, a total of 18 characteristic
metabolites were identified ([123]Figure 7B). These included eight
small peptides, five nucleotides and their metabolites, one amino acid,
one amino derivative, one amine, one organic acid derivative, and one
heterocyclic compound. The relatively elevated levels of
methylcysteine, adenine, 5′-deoxy-5′-(methylthio) adenosine,
N-alpha-acetyl-L-asparagine, cytidine 2′, 3′-cyclomonophosphate,
oxypurinol, 8-Azaguanine,
1,4-Dihydro-1-Methyl-4-Oxo-3-Pyridinecarboxamide,
3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid, and
N(alpha)-acetyl-epsilon-(2-propenal) lysine, in comparison to other
metabolites, can serve as potential biomarkers of sheep milk.
By conducting multiple comparisons of up-regulated DEMs among the
CMM/GMM, CMM/SMM, and CMM/BMM sets, we observed 14 characteristic
metabolites in cow milk. These included five carbohydrates and their
metabolites, three nucleotides and their metabolites, two amino acids
and their derivatives, two organic acid and their derivatives, one
coenzyme and vitamin, and one fatty acyl ([124]Figure 7C). The levels
of these 14 metabolites were found to be significantly elevated in cow
milk, indicating their potential as biomarkers for the screening of cow
milk.
A total of 38 metabolites were identified as characteristic metabolites
in buffalo milk through multiple comparisons of up-regulated DEMs among
the BMM/GMM, BMM/SMM, and BMM/CMM sets. These included thirty
glycerophospholipids, three amino acids and their metabolites, two
fatty acyls, one hormone, one carbohydrate and its metabolite, and one
organic acid ([125]Figure 7D). We found that glycerophospholipids are
the most important characteristic metabolites in buffalo milk,
especially the relatively high levels of lysophosphatidylcholine (LPC),
including PC (O-16:0/O-1:0), LPC (16:0/0:0), LPC (0:0/16:0), LPC
(O-16:0/2:0), LPC (18:0/0:0), and LPC (0:0/18:0).
4. Discussion
The milk derived from goats, sheep, cows, and buffaloes serves as a
significant protein source for human consumption. Previous research has
primarily focused on conducting comparative analyses of the nutrient
composition between goat milk and cow milk [[126]6], as well as
identifying individual or multiple compounds that contribute to their
flavors [[127]10,[128]38]. The current knowledge regarding the global
metabolic profiles among different dairy animals is limited. In this
study, we focused on the overall differences in metabolic profiles
among different milk types obtained from distinct species. Furthermore,
we conducted a comprehensive and in-depth analysis of the flavor
profiles and metabolic characteristics of four milk types while also
screening their metabolic markers specific to each type.
By conducting a principal component analysis (PCA) and a cluster
analysis on the population sample, we found that the metabolite
profiles of milk from goats and sheep were more similar than those of
milk from cows and buffaloes. The different metabolites between goat
milk and milk from cow and buffalo are mainly concentrated in amino
acids and their derivatives, nucleotides, and their metabolites, as
well as organic acids and their derivatives. This difference also
exists in sheep milk. The difference in metabolites between goat milk
and sheep milk is mainly reflected in the components of amino acids and
their derivatives. KEGG pathway enrichment analysis showed that the
differential metabolites of goat milk were significantly enriched into
two pathways, namely purine metabolism and nucleotide metabolism,
compared with cow milk and buffalo milk. Compared with cow milk and
buffalo milk, the differential metabolites of sheep milk were
significantly enriched in three pathways, namely purine metabolism,
nucleotide metabolism, amino sugar metabolism, and nucleotide sugar
metabolism. The shared pathways of goat and sheep milk mainly include
purine metabolism and nucleotide metabolism. The findings provide novel
perspectives for comprehending the variations in metabolic profiles
among diverse milk types derived from distinct species.
The presence of organic acids, amino acids, and nucleotides plays an
important role in determining the flavor characteristics
[[129]10,[130]39,[131]40]. In this study, our results identified five
core differential metabolites in four types of milk, including
3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5′-triphosphate,
methylcysteine, N-cinnamylglycine, and small peptide (Tyr-Asn).
Previous studies have revealed that the abnormal
3-(3-hydroxyphenyl)-3-hydroxypropanoic acid concentrations in the body
are correlated with dysregulation of the intestinal microbiota and a
variety of neurological diseases [[132]41]. Inosine 5′-triphosphate
(ITP) can support the initiation of effector systems [[133]42]. In
addition, the interaction of proteins and fats with volatile flavor
compounds also affects humans’ perception of flavor [[134]43,[135]44].
Our metabolomic analysis revealed distinct profiles of lipids, as well
as organic acids and their derivatives in goat milk compared to that of
cows and buffaloes. Specifically, the levels of FAs (carnitine C6:0,
carnitine C8:0, carnitine C7:0, and carnitine C4: DC) and short-chain
fatty acids and their derivatives (methylmalonic acid, tricarballylic
acid, 5-hydroxyhexanoic acid, 2-hydroxyisocaproic acid, and
8-aminooctanoic acid) in goat milk were significantly higher than those
found in other species. Sheep milk exhibited significantly higher
levels of FAs (carnitine C6:0, carnitine C16:0, carnitine C7:0), as
well as short-chain fatty acids and their derivatives
(3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, tricarballylic acid, and
glycerophosphoric acid) than other type milk. We also found variations
in the composition and concentration of sugars and organic acids in
four types of milk, which may be another factor in the distinctive
flavor profiles [[136]45]. Taken together, the differential profiles of
organic acids, amino acids, and nucleotides across the four types of
milk could potentially contribute to their flavor variations.
The present study reveals a wider range of metabolites in milk compared
to the previous investigation and encompasses a higher multitude of
metabolites. A total of 631 distinct metabolites were detected across
various milk varieties. Our metabolomic data have revealed significant
inter-breed variations in the compositions and concentrations of
organic acids, amino acids, sugars, and nucleotides, which potentially
contribute to the overall flavor attributes of milk samples. We
observed that methylmalonic acid was relatively more abundant in goat
milk compared to sheep milk, cow milk, and dairy milk. The activation
of methylmalonic acid by gene ACSF3 (Acyl-CoA synthetase family member
3) leads to the production of methyl malonyl-CoA, which serves as a
precursor for the synthesis of methyl-branched fatty acids [[137]46].
Some studies have identified branched-chain fatty acids as the primary
compounds responsible for the characteristic “goaty” flavor of goat
milk [[138]10]. The main source of methylmalonic acid is the metabolism
of propionic acid and the catabolism of branched-chain amino acids,
indicating that these pathways may play a significant role in the
development of characteristic flavor in goat milk. Additionally, the
main sugar in milk is lactose, which is slightly lower in goat milk
than in cow milk. However, goat milk contains high levels of
oligosaccharides and sugar complexes [[139]47], aligning with our
findings. These compounds play important roles in various biological
processes, such as cell signaling and energy metabolism [[140]48]. The
high concentration of these sugars in goat milk may have implications
for human health. The levels of UDP-sugars, such as UDP-glucose,
UDP-galactose, and UDP-xylose, were found to be higher in goat milk
compared to other types of milk in this study.
Despite being an emerging omics technique with the ability to
qualitatively and quantitatively analyze a wide range of low molecular
weight metabolites in biological samples, widely targeted metabolomics
is still in its nascent stage and encounters several challenges. For
example, there is no single technique that can analyze all the
compounds in the metabolome at the same time. In this study, we
exclusively identified one ester (ethyl hydrogen malonate), while no
aldehydes and ketones were detected, despite their known association
with flavor [[141]49]. In future studies, this can be solved using
selective extraction techniques combined with parallel analysis of
various analysis techniques. In addition, milk proteins are also
important factors influencing flavor production and release
[[142]50,[143]51], and we will further integrate metabolomics and
proteomics to systemically reveal flavor markers in four types of milk.
5. Conclusions
Using widely targeted metabolomic technology, the metabolic profiles of
goat milk, sheep milk, cow milk, and buffalo milk were systematically
compared. A total of 631 metabolites were identified and classified
into 16 categories. Among these, amino acids and their derivatives
accounted for the highest proportion (28.84%), followed by organic
acids and their derivatives (14.42%). Principal component analysis and
hierarchical cluster analysis revealed that the metabolites of goat
milk and sheep milk exhibited similar characteristics. Five
metabolites, including 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid,
inosine 5′-triphosphate, methylcysteine, N-cinnamylglycine, and small
peptide (Tyr-Asn), were core differential metabolites in four types of
milk. The biomarkers for each type of milk were obtained through a
systematic comparison of the metabolic profiles derived from goats,
sheep, cows, and buffaloes. Our metabolomic data have revealed
significant inter-breed variations in the compositions and
concentrations of organic acids, amino acids, sugars, and nucleotides.
These differences could potentially contribute to the overall flavor
attributes of milk samples. The present findings are expected to
contribute to a more comprehensive understanding of the regulation of
milk flavor and to support further research on manipulating the flavor
of milk products.
Acknowledgments