Abstract
The Nanjiang Yellow Goat (NJYG), Jintang Black Goat (JTBG), and
Jianzhou Da’er Goat (JZDEG) are representative local goat breeds for
meat production in Sichuan Province, China. This study conducted a
comprehensive evaluation of the meat quality of the longissimus dorsi
muscle of three goat breeds. Variations in meat quality were observed
in terms of meat pH, color, ash and fat content, water activity, and
muscle fiber structure. Quantitative proteomics analysis was employed
to identify biomarkers for goat meat quality, revealing hundreds of
differentially expressed proteins among three goat breeds. KEGG
enrichment analysis revealed enriched pathways including oxidative
phosphorylation, thermogenesis, citrate cycle (TCA cycle), fatty acid
degradation and metabolism, as well as valine, leucine, and isoleucine
degradation. Moreover, weighted protein co-expression network analysis
and protein–protein interaction analysis uncovered valuable biomarkers,
including GSTM3, NDUFS, OGDH, ACO2, HADH, ACAT1, ACADS, ACAA2, HSPG2,
ITGA7, PARVB, ALDH9A1, ADH5, and LOC102190016, for assessing goat meat
quality. This investigation highlighted the disparities in meat quality
among local goat breeds in Sichuan, China, and provided insights into
underlying biological pathways and valuable biomarkers for goat meat
quality.
1. Introduction
The global consumption of goat meat has significantly risen due to its
distinctive nutritional advantages over conventional red meats [[44]1].
In the past decade, China has contributed 40% to global goat meat
production [[45]2]. Recognized as a substantial protein source, goat
meat is characterized by lower levels of total fat, saturated fatty
acids, cholesterol, and a distinct flavor, aligning with the increasing
consumer demand for health-conscious dietary choices [[46]3]. Sichuan,
located in southwestern China, is celebrated for its diverse natural
geography and geomorphology, which comprises fertile plains, basins,
towering mountains, and deep valleys. The fertile plains, mountainous
landscapes, varied vegetation, and favorable climatic conditions in
Sichuan provide a range of rearing environments for diverse goat breeds
[[47]4,[48]5,[49]6,[50]7].
Proteomics technologies have been utilized to investigate molecular
mechanisms underlying divergences in meat quality. The high-quality
longissimus thoracis goat meat and low-quality external intercostals
goat meat display plenty of differentially expressed proteins (DEPs),
with enrichments in glycolysis and the tricarboxylic acid cycle
[[51]8]. These proteins regulate the rate and extent of pH decline in
meat postmortem, thereby determining the quality of goat meat. The
development of DFD (dark, firm, and dry) beef, a prototypical
low-quality beef, has been linked to pathways associated with energy
metabolism, cellular stress responses, and oxidoreductase activity
[[52]9], with several valuable biomarkers proposed for DFD beef
[[53]10]. Recent advancements in proteomic studies on sheep and goat
meat quality have been comprehensively reviewed [[54]11].
The Nanjiang Yellow Goat (NJYG), Jintang Black Goat (JTBG), and
Jianzhou Da’er Goat (JZDEG) are renowned goat breeds for meat
production in Sichuan Province. The NJYG predominantly inhabits in
northern region of Sichuan, shown in [55]Figure 1, characterized by
mountainous terrain ranging from 370 to 2507 meters (m) in altitude.
The JTBG is predominantly found in the western Chengdu Plain, thriving
in shallow and deep hill environments with altitudes ranging from 450
to 1090 m. The JZDEG is located in the Longquan Mountain area at an
altitude of around 1050 m. Several studies have examined these three
goat breeds, exploring the genetic composition of the JTBG [[56]12],
growth-related gene expression of the NJYG [[57]13], and muscle growth
of the JZDEG [[58]14]. Additionally, sausages derived from JZDEG meat
show a high quality, highlighting its superior goat meat quality
[[59]15]. Nevertheless, detailed data delineating characters of meat
quality from these goat breeds are currently missing. Hence, this study
aims to investigate meat quality characters of the NJYG, JTBG, and
JZDEG, and uncover underlying mechanisms, encompassing key pathways and
potential candidate genes as biomarkers of goat meat quality.
Figure 1.
[60]Figure 1
[61]Open in a new tab
The primary habitats of the Nanjiang Yellow Goat, Jintang Black Goat,
and Jianzhou Da’er Goat are depicted on a map of Sichuan, with
elevations indicated by color gradients.
2. Materials and Methods
2.1. Animals and Sampling
This study involved 12 male goats, with four of each breed: NJYGs with
a slaughter weight of 14.2 ± 2.1 kg were sourced from Sichuan Dejian
Nanjiang Huangyang Food Co., Ltd. Nanjiang, China, JTBGs with a
slaughter weight of 31.9 ± 1.7 kg were obtained from Chengdu Chuanmu
Black Goat Professional Cooperative, Chengdu, China and JZDEGs with a
slaughter weight of 40.2 ± 5.2 kg were acquired from Sichuan Jianyang
Dageda Animal Husbandry Co., Ltd, Jianyang, China. These goats were
raised under a semi-grazing system, which combines free-range grazing
and stall-feeding. During the grazing period, the goats had access to
natural pastures. In addition to the forage from grazing, goats were
supplemented with high quality feedstuffs, such as corn, wheat, soybean
meal, salt, and a mineral mix. Goats aged 12 months were ethically
slaughtered at local facilities after a 24 hour (h) fasting period, in
accordance with the Regulation of Experimental Animals at Chengdu
University (2016-4). The longissimus dorsi (LD) muscle was excised,
with visible connective tissue and fat meticulously removed. A section
was promptly frozen in liquid nitrogen and transferred to −80 °C
freezer for proteomic study, while remaining samples were transported
to the laboratory in ice boxes and stored at 4 °C for meat quality
analysis.
2.2. Eating Quality
2.2.1. Meat pH and Color
Meat pH was assessed at 0.5 h and 24 h after slaughter with a Testo 205
pH meter (Testo, Lenzkirch, Germany). Meat color was assessed employing
a CR-10 colorimeter (Konica Minolta, Osaka, Japan) under illuminant
D65, a 10° observer angle, and an 8 mm aperture. The lightness L*,
redness a*, and yellowness b* of meat sample were measured at 0.5 h and
24 h after slaughter. After calibration with a standard white plate,
three measurements were taken for each sample [[62]16]. The average
value and standard deviation (SD) were calculated based on four samples
for each breed.
2.2.2. Cooking Loss
A method adapted from Jo et al. [[63]17] was applied to determine the
cooking loss. Initially, a fresh meat sample, measuring 6 × 3 × 3 cm,
was weighed as m1 and subsequently sealed in a plastic bag. The samples
were cooked in water for 30 minutes (min) at 80 °C, followed by
overnight cooling at 4 °C. After removing surface water, the sample
weight was recorded as m2. The cooking loss value was calculated using
the following equation: Cooking loss
[MATH: % :MATH]
[MATH: = m1−m2m1 × 100 :MATH]
. This calculated the percentage of mass loss during the cooking
process of the meat sample.
2.2.3. Shear Force
Shear force was measured with a TA-XT Plus texture analyzer (Stable
Micro Systems, Surrey, UK) following cooking loss evaluation [[64]18].
Meat samples were sliced into 3 cm × 1 cm × 1 cm dimensions and
analyzed with a Warner-Bratzler Blade Set featuring a ‘V’ slot blade,
following a pre-test rate of 2.0 mm/s, a test rate of 1.0 mm/s, and a
post-test rate of 2.0 mm/s. Each sample was assessed in triplicate. The
average shear force in newton (N) and SD was calculated for each breed
based on four samples.
2.3. Proximate Analysis
Two grams of meat sample were extracted with petroleum ether solvent,
evaporated to remove the solvent, and dried to determine crude lipid in
accordance with the National Standard GB 5009.6-2016 [[65]19]. Crude
protein analysis was conducted based on National Standard GB
5009.5-2016 [[66]20]. The water activity measurement involved placing
five grams of meat sample in a water activity meter dish, as outlined
in National Standard GB 5009.238-2016 [[67]21]. Ash content was
determined using a muffle furnace following the National Standard GB
5009.4-2010 [[68]22]. These analyses were conducted in triplicate for
each sample, with the average and SD calculated based on four samples
for each breed.
2.4. Histology Analysis
For histological analysis, the meat sample was fixed with
paraformaldehyde for paraffin embedding. The 5 μm slices perpendicular
to muscle fibers were cut and stained with haematoxylin and eosin
(H&E). Subsequently, images were taken using BA210 digital microscope
(Motic, Xiamen, China). The diameter and area of muscle fibers were
quantified utilizing ImageJ software (v. 1.53c). Each sample had ten
values. The average and SD were calculated from four samples for each
breed.
2.5. Transmission Electron Microscope
Samples were fixed using glutaraldehyde and osmium tetroxide, followed
by dehydration with an acetone series and embedding in Epon 812.
Semi-thin and ultra-thin sections were stained with methylene blue and
uranyl acetate with lead citrate, respectively. Subsequently, images
were captured using a JEM-1400 Flash transmission electron microscope
(JEOL, Tokyo, Japan). For sarcomere length analysis, one hundred
measurements were recorded for each sample using ImageJ software, and
the average along with SD was calculated based on four samples per
breed.
2.6. Proteomics
2.6.1. Protein Extraction and Digestion
Three meat samples for each breed were used for proteomic analysis. The
crushed sample was incubated in lysis solution for 5 min and subjected
to ultrasonication on ice for 10 min, followed by a 20 min
centrifugation at 13,000× g at 4 °C. The resulting supernatant was
mixed with four volumes of acetone at −20 °C for 2 h. The protein
pellet obtained after a second centrifugation was dried and dissolved
in a buffer (8 mol/L (M) urea, 100 mM triethylammonium bicarbonate, pH
8.0). The protein solution was treated with 10 mM DTT at 56 °C for 30
min, 50 mM iodoacetamide for 30 min in darkness. The protein solution
was digested with 50 times trypsin (w/w) at 37 °C for 16 h, followed by
desalting using a C18 cartridge and drying with a vacuum concentrator.
2.6.2. Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS)
The digested peptides were analyzed with a nanoElute UHPLC (Bruker
Daltonics, Bremen, Germany) coupled with a hybrid timsTOF Pro 2 mass
spectrometer (Bruker Daltonics). Mobile phases A and B comprised 0.1%
formic acid in water and 0.1% formic acid in ACN, respectively. The
gradient of mobile phase B increased from 2% to 22% over 45 min, then
to 35% over following 5 min, and further to 80% in 5 min, maintaining
at 80% for an additional 5 min. The flow rate was set at 0.3 μL/min.
The capillary voltage was set at 1400 V. The MS and MS/MS spectra were
acquired in the mass range from 100 to 1700 m/z. The MS raw data
underwent analysis using FragPipe (version 17.1), utilizing MSFragger
for qualitative assessment and Phosopher for validation and filtering.
These tools, in conjunction with the Uniprot-goat database, containing
20,425 entries, ensured a false discovery rate of <1% at both protein
and peptide levels. Label-free quantitation was conducted using
IonQuant. Proteins exhibiting a significant up-regulation with a fold
change (FC) > 1.20 or a down-regulation with a FC < 0.83 were deemed as
DEPs.
2.7. Bioinformatics Analysis
The functions of DEPs were analyzed with Gene Ontology (GO) database
([69]http://geneontology.org/ accessed on 15 July 2024) and the Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway database
([70]https://www.genome.jp/kegg/ accessed on 20 August 2024).
Additionally, the DAVID database ([71]https://david.ncifcrf.gov/
accessed on 25 June 2024) was also utilized for enrichment analysis.
Weighted protein co-expression network analysis (WPCNA) was performed
using the Metware platform ([72]https://cloud.metware.cn accessed on 3
July 2024). Furthermore, protein–protein interactions were visualized
using the STRING-db server ([73]http://string-db.org/ accessed on 14
July 2024).
2.8. Data Analysis
The data were statistically analyzed utilizing Prism GraphPad 9.0
software to conduct one-way analysis of variance (ANOVA) and Tukey’s
HSD tests. The data were presented as average ± SD. A statistical
significance level of p < 0.05 was applied in this study.
3. Results and Discussion
3.1. Quality Traits
3.1.1. Eating Quality and Chemical Composition
The quality traits of NJYG, JTBG, and JZDEG LD meat are shown in
[74]Table 1. The NJYG LD meat demonstrated a lower pH[24h] than JZDEG,
while exhibiting the highest L*[24h] and the lowest a*[24h] and b*[24h]
among three goat breeds (p < 0.05). Myosin, the primary muscle protein,
possesses a known isoelectric point (pI) of 5.4 [[75]23]. Postmortem
muscle pH gradually approximates meat pI, causing a decreased water
retention in meat and water expulsion from meat [[76]24]. This water
increased the moisture content on meat surface and L* value. JZDEG LD
muscle exhibited a higher b*[0.5h] value than JTBG and a higher b*[24h]
value than NJYG. Consistent with previous report, meat pH significantly
influences meat color [[77]25], which is crucial in shaping consumer
perceptions of meat quality as color is closely associated with meat
freshness.
Table 1.
Quality traits of longissimus dorsi meat.
Parameters NJYG JTBG JZDEG
pH[0.5h] 6.65 ± 0.19 6.59 ± 0.26 6.55 ± 0.38
L*[0.5h] 34.68 ± 4.47 34.64 ± 0.89 33.65 ± 2.02
a*[0.5h] 16.10 ± 1.82 15.40 ± 0.62 16.15 ± 1.09
b*[0.5h] 4.22 ± 0.72 ^ab 3.80 ± 0.17 ^b 4.70 ± 0.70 ^a
pH[24h] 5.60 ± 0.06 ^b 5.74 ± 0.20 ^ab 5.85 ± 0.32 ^a
L*[24h] 44.61 ± 5.15 ^a 39.36 ± 2.54 ^b 39.60 ± 2.91 ^b
a*[24h] 12.43 ± 5.31 ^b 17.73 ± 2.39 ^a 17.35 ± 1.09 ^a
b*[24h] 4.44 ± 4.39 ^b 7.70 ± 2.79 ^a 10.50 ± 2.00 ^a
Cooking loss (%) 28.80 ± 3.90 30.00 ± 7.23 22.11 ± 8.26
Shear force (N) 122.70 ± 10.36 93.27 ± 13.49 100.13 ± 44.70
Chemical composition
Ash (%) 1.50 ± 0.12 ^a 1.36 ± 0.11 ^b 1.43 ± 0.05 ^ab
Fat (%) 3.95 ± 0.17 ^b 3.64 ± 0.16 ^c 4.25 ± 0.06 ^a
Protein (%) 24.48 ± 4.55 21.72 ± 2.56 24.61 ± 3.89
Water activity (%) 0.95 ± 0.00 ^b 0.98 ± 0.01 ^a 0.97 ± 0.20 ^a
[78]Open in a new tab
a–c Averages sharing the same letter were not significantly different
(p < 0.05).
Furthermore, the ash content of NJYG LD meat was significantly higher
than JTBG. The fat content of LD meat was significantly different from
each other among three goat breeds. Fat content is a pivotal meat
quality parameter and greatly influences the sensory perception of
juiciness, flavor, and texture. Finally, the water activity of NJYG was
significantly lower than JTBG and JZDEG and significantly influenced
the juiciness and tenderness of meat. In our study, we included four
goats of each of the breeds for data analysis. There was a risk that
the sample might not accurately reflect the full range of variation
within each breed. Furthermore, a limited number of samples could
reduce the statistical power of our study. Our study might be less
likely to detect true differences, even if they exist.
3.1.2. Histological and Ultrastructural Analysis
[79]Figure 2A,D revealed significant differences in muscle fiber area
among LD muscles of three goat breeds. Specifically, NJYG LD muscle
exhibited the smallest muscle fiber area, while JZDRG LD muscle
displayed the largest muscle fiber area. Meanwhile, the muscle fiber
diameter of NJYG LD muscle was significantly smaller than JZDEG, as
seen in [80]Figure 2B. Meat with small fiber diameter tends to be much
more tender [[81]26], although no significant differences were found
for shear force among three goat breeds.
Figure 2.
[82]Figure 2
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The muscle fiber area (A), diameter (B), and sarcomere length (C) of
NJYG, JTBG, and JZDEG LD muscle. The histograms are presented as the
average ± SD. The * indicates p < 0.05 and ** indicates p < 0.01. (D):
H&E staining cross sections from LD muscle with a 10 µm scale bar. (E)
Transmission electron micrographs of LD muscle, ×6000 bar = 2 µm and
×25,000 bar = 500 nm. Data are shown as average ± SD, n = 4.
The ultrastructure of LD muscle illustrated in [84]Figure 2E show
parallel myofibrils, intact sarcomeres, and observed mitochondria. The
sarcomere lengths of NJYG, JTBG, and JZDEG range from 1.37 to 2.05 µm,
consistent with report about the LD muscle of the New Zealand goat
[[85]27]. Sarcomere length is a fundamental indicator of muscle
tenderness; longer sarcomeres correspond to increased meat tenderness
[[86]28]. However, no significant variations in sarcomere length were
observed among these three goat breeds, as seen in [87]Figure 2C.
3.1.3. Principal Component Analysis (PCA)
PCA was conducted with the phenotype data mentioned above. As shown in
[88]Figure 3, NJYG exhibited a distinct separation from other two
breeds and located in the low part of [89]Figure 3A, while the
divergence between JTBG and JZDEG was less discernible. The first two
principal components (PC) accounted for 43.5% of total variance, with
PC1 explaining 24.8% and PC2 explaining 18.7%, respectively. In the
loading plot shown in [90]Figure 3B, PC1 was characterized by muscle
fiber index (fiber area and diameter), meat color (L*[0.5h] and
b*[0.5h]), and pH[24h]. Meanwhile, meat color (b*[24h], a*[24h], and
L*[24h]), water activity, and protein content were important for PC2.
Figure 3.
[91]Figure 3
[92]Open in a new tab
Principal component analysis of three goat meat quality using
physico-chemical parameters and muscle fiber structure. PC1 vs. PC2
score–score plot (A) and component loading plot (B). Data are shown as
average ± SD, n = 4.
3.2. Proteomics Analysis
3.2.1. Protein Identification and Quantification
For this evaluation, 4D label-free quantitative proteomics was employed
to investigate mechanisms underlying variations in meat quality across
three goat breeds. MS analysis identified a total of 496,633 spectra
and 24,404 peptides. These peptides facilitated a detection of 2535
proteins, with 1984 proteins quantificated.
3.2.2. DEPs Analysis
A comparative assessment between the NJYG and JTBG revealed 244 DEPs,
comprising 239 up-regulated proteins and 5 down-regulated proteins in
the NJYG compared to the JTBG (NJYG vs. JTBG), as shown in [93]Figure
4A,B. This substantial number of DEPs likely stems from their distinct
habitats. The NJYG thrives in the mountainous terrains of northern
Sichuan, primarily subsisting on straw and shrubs, while the JTBG,
situated in the Chengdu Plain, predominantly grazes on pasture grasses
and legumes. In NJYG vs. JZDEG, 71 DEPs were identified, with 63
up-regulated and 8 down-regulated proteins, seen in [94]Figure 4A,C.
Furthermore, for JTBG vs. JZDEG, a total of 31 DEPs were recognized,
featuring 5 up-regulated proteins and 26 down-regulated proteins, shown
in [95]Figure 4A,D.
Figure 4.
[96]Figure 4
[97]Open in a new tab
(A): The number of DEPs identified in this study. Volcano plot of DEPs
in NJYG vs. JTBG (B), in NJYG vs. JZDEG (C), and in JTBG vs. JZDEG (D).
The horizontal dashed line indicated position of −log[10](0.05). The
left and right vertical dashed lines indicated the positions of
log[2](0.83) and log[2](1.2), respectively. The up-regulated or
down-regulated proteins are indicated in red or blue, respectively. n =
3.
3.2.3. Enrichment Analysis
GO analysis assesses proteins regarding their roles in biological
processes (BP), cellular components (CC), and molecular functions (MF).
The GO analysis DEPs from NJYG vs. JTBG, NJYG vs. JZDEG, and JTBG vs.
JZDEG are depicted in [98]Figure 5A–C. Notably, seven of the top ten BP
ontology terms based on p value ranking were congruent across all three
comparisons. These seven GO terms included cellular process, metabolic
process, response to stimulus, biological regulation, regulation of
biological process, multicellular organismal process, and developmental
process. Concurrently, the GO analysis revealed common CC terms such as
cell, cell part, organelle, extracellular region, organelle part,
membrane, protein-containing complex, extracellular region part,
membrane part, and membrane-enclosed lumen. Likewise, GO analysis
highlighted the shared MF terms of binding and catalytic activity.
Figure 5.
[99]Figure 5
[100]Open in a new tab
GO enrichment analysis of DEPs in NJYG vs. JTBG (A), NJYG vs. JZDEG
(B), and JTBG vs. JZDEG (C). KEGG pathway enrichment analysis of DEPs
in NJYG vs. JTBG (D), NJYG vs. JZDEG (E), and JTBG vs. JZDEG (F). n =
3.
In NJYG vs. JTBG, 244 DEPs were notably enriched in 19 KEGG pathways,
shown in [101]Figure 5D. These pathways encompassed energy
production-related processes such as oxidative phosphorylation,
thermogenesis, citrate cycle (TCA cycle), and fatty acid degradation
and metabolism. Additionally, enriched 19 KEGG pathways also included
pathways associated with neurodegenerative diseases, such as
amyotrophic lateral sclerosis, prion disease, Parkinson’s disease, and
Alzheimer’s disease.
In NJYG vs. JZDEG, 71 DEPs were markedly enriched in 16 KEGG pathways,
shown in [102]Figure 5E. Notably, these pathways were linked to energy
metabolism included fatty acid degradation, valine, leucine, and
isoleucine degradation, thermogenesis, glycerolipid metabolism, and
oxidative phosphorylation. Furthermore, enriched KEGG pathways also
included the PPAR signaling pathway and insulin signaling pathway.
In JTBG vs. JZDEG, 31 DEPs were notably enriched in 23 KEGG pathways
involved in disease, metabolism, and environmental information
processing, shown in [103]Figure 5F. These 23 pathways also included
drug metabolism—cytochrome P450—the metabolism of xenobiotics by
cytochrome P450, PI3K-Akt signaling pathway, and fatty acid metabolism.
3.3. WPCNA
WPCNA characterize co-expressed DEPs into several phenotype-related
modules [[104]29]. The soft threshold power of β = 11 was determined
based on the scale-free fit index and mean connectivity to establish
the WPCNA network, shown in [105]Figure 6A,B. Subsequently,
hierarchical clustering of strongly co-expressed proteins facilitated
the creation of a cluster dendrogram, identifying seven distinct module
elements (MEs) represented by different colors, as illustrated in
[106]Figure 6C.
Figure 6.
[107]Figure 6
[108]Open in a new tab
WPCNA visualization. The scale-free topology fitting index with a red
line indicating the soft threshold (A) and mean connectivity (B). (C)
Clustering dendrogram of proteins and module division. (D) The
correlative diagram of modules and goat meat phenotypes. Pearson’s
coefficients for correlations are indicated in the square grid, with p
values in parentheses. n = 3.
The correlations between seven MEs and goat meat quality parameters
were visually represented in a heatmap, shown in [109]Figure 6D.
Specifically, the brown ME (MEbrown) exhibited positive correlations
with b*[0.5h] (r = 0.72, p = 0.029) and fat content (r = 0.87, p =
0.0023). MEturquoise showed a positive correlation with ash content (r
= 0.68, p = 0.044). Furthermore, MEgreen demonstrated negative
correlations with pH*[24h] (r = −0.7, p = 0.036), b*[24h] (r = −0.69, p
= 0.04), and muscle fiber area (r = −0.77, p = 0.015), while exhibiting
a positive correlation with sarcomere length (r = 0.71, p = 0.032).
MEyellow displayed negative correlations with b*[24h] (r = −0.79, p =
0.011) and water activity (r = −0.72, p = 0.029). To delve deeper into
protein networks within each ME associated with goat meat quality, a
protein–protein interaction network (PPI) analysis focused on these
four modules—MEbrown, MEturquoise, MEgreen, and MEyellow.
3.4. PPI Analysis
[110]Figure 7A demonstrates the PPI analysis based on MEbrown,
indicating a significant enrichment with p < 0.033. Within MEbrown,
three proteins—Glutathione S-Transferase Mu 3 (GSTM3), Superoxide
Dismutase 3 (SOD3), and Peroxiredoxin 5 (PRDX5)—exhibited direct
interactions. GSTM3 plays a crucial role in upholding cellular
homeostasis, shielding against external toxins, and safeguarding cells
from oxidative stress. It serves as a potential biomarker for sheep
meat tenderness, displaying a higher expression in tender sheep meat
[[111]30]. Moreover, GSTM1, a member of the GSTM family, demonstrates a
positive association with beef tenderness [[112]31]. Although our
research found no significant difference in the tenderness of goat
meat, we observed a higher abundance of GSTM3 in goat meat with a
higher fat content. Although the significance of fat content in
determining meat tenderness remains debated [[113]32], multiple works
have pointed out the role of fat content in destemming the meat
tenderness [[114]32,[115]33,[116]34,[117]35]. As mentioned above,
MEbrown was also linked with b*[0.5h]. One previous study has shed
lights on GSTM1, GSTM3, and GSTM5 as candidate biomarkers for sheep
meat color due to their involvement in oxidative stress and cell redox
homeostasis [[118]36]. Our study further underscored the impact of
GSTM3 on meat color, although GSTM3 is not traditionally regarded as a
biomarker for meat color. In addition to GSTM3, our research identifies
anti-oxidative stress genes SOD3 and PRDX5 as potential candidate genes
for goat meat color and fat content. SOD3, found exclusively in
extracellular spaces, shields cells and tissues from oxidative stress
by removing superoxide radicals [[119]37]. In contrast, PRDX5 is
present in various subcellular compartments, including mitochondria,
peroxisomes, cytosol, and nucleus, combating peroxide attacks as a
cytoprotective antioxidant enzyme [[120]38]. Several Peroxiredoxin
family members, such as PRDX1, PRDX2, PRDX3, and PRDX6, have been
linked to beef color [[121]39,[122]40]. As shown in [123]Table 2, our
investigation revealed a lower abundance of GSTM3, SOD3, and PRDX5 in
JTBG LD meat characterized by higher a*[24h] and b*[24h], confirming
the influence of cellular redox status and related genes on meat color.
Figure 7.
[124]Figure 7
[125]Open in a new tab
PPI networks for MEbrown (A), MEturquoise (B), sub-network B-I (C), and
its ten hub proteins (D), sub-network B-II (E), sub-network B-III (F),
MEgreen (G), and MEyellow (H). The color coding of the lines in the
STRING was categorized into three sections: known interactions,
predicted interactions, and others. In the known interactions, the sky
blue line represented curated database interactions, while the purple
line signified experimentally determined interactions. For the
predicted interactions, green indicated gene neighborhood associations,
red indicated gene fusions, and dark blue indicated gene co-occurrence.
Other interactions are depicted as medium yellow for text mining, black
for co-expression, and light blue for protein homology.
Table 2.
Relative abundance of proteins in PPI.
Module Protein NJYG JTBG JZDEG FC
NJYG vs. JTBG NJYG vs. JZDEG JTBG vs. JZDEG
MEbrown GSTM3 6.386 5.293 10.106 0.524 *
SOD3 0.545 0.397 0.612 0.649 *
PRDX5 1.548 1.195 1.824 1.295 *
MEturquoise B-I NDUFS3 5.241 3.150 3.944 1.664 **
NDUFA8 3.870 2.421 2.895 1.598 **
NDUFS6 3.030 1.903 2.362 1.592 ***
NDUFS7 8.375 5.761 6.724 1.454 *
NDUFAB1 4.332 2.275 3.282 1.904 **
NDUFV1 3.420 2.215 2.745 1.544 **
NDUFB7 2.797 1.705 2.134 1.641 **
NDUFC2 5.413 3.325 4.033 1.628 **
NDUFB4 4.523 2.750 3.262 1.644 ** 1.386 *
NDUFB3 4.786 3.090 3.697 1.549 **
B-II SUCLG2 3.575 1.654 2.061 2.161 *
SUCLG1 8.394 6.084 6.824 1.380 *
OGDH 5.607 4.253 4.882 1.319 *
ACO2 15.219 8.030 10.182 1.895 * 1.495 *
CS 16.078 10.818 12.348 1.486 *
B-III HADH 4.330 2.654 3.386 1.632 *
ACAT1 12.509 5.618 7.839 2.227 **
ACADS 2.219 0.763 1.647 2.908 *
ACAA2 2.785 1.239 1.609 2.248 *
MEgreen HSPG2 7.333 5.765 5.913 1.272 * 1.240 **
COL4A2 13.659 8.980 10.846 1.521 *
LAMC1 3.253 2.790 2.638 1.233 *
LAMA2 10.567 8.793 8.141 1.298 *
ITGA7 0.532 0.414 0.457 1.285 *
PARVB 0.558 0.370 0.406 1.508 ** 1.374 *
MEyellow ALDH9A1 1.052 0.747 0.650 1.409 * 1.619 *
ADH5 2.931 2.566 2.419 1.211 *
LOC102190016 2.076 1.921 1.294 1.605 *
[126]Open in a new tab
The *, **, and *** represented significance levels p < 0.05, p < 0.01,
and p < 0.001, respectively.
The PPI network analysis of DEPs derived from the MEturquoise module
revealed a significant enrichment with p < 1.0 × 10^−16, as depicted in
[127]Figure 7B. To simplify its complexity, we identified three
distinct sub-networks via the MCODE clustering method [[128]41].
Furthermore, cytohubba analyzed the sub-network B-I [[129]42], seen in
[130]Figure 7C, and extracted ten hub proteins, namely NADH:Ubiquinone
Oxidoreductase subunit A8 (NDUFA8), NADH:Ubiquinone Oxidoreductase
subunit AB1 (NDUFAB1), NADH:Ubiquinone Oxidoreductase subunit B3
(NDUFB3), NDUFB4, NDUFB7, NADH:Ubiquinone Oxidoreductase subunit C2
(NDUFC2), NADH:Ubiquinone Oxidoreductase Core subunit S3 (NDUFS3),
NDUFS6, NDUFS7, and NADH:Ubiquinone Oxidoreductase Core subunit V1
(NDUFV1), exhibiting high connectivity, shown in [131]Figure 7D. These
ten genes encode subunits of NADH–ubiquinone oxidoreductase (Complex
I), a pivotal component in the mitochondrial electron transport chain
responsible for electron transfer from NADH to ubiquinone and promoting
ATP production through oxidative phosphorylation [[132]43]. As
indicated in [133]Table 2, Complex I-associated DEPs exhibited higher
levels in NJYG compared to JTBG. Mitochondrial reactive oxygen species
(ROS) primarily stem from the activity of Complex I and Complex III
[[134]44]. Disruption or inhibition of the interaction between Complex
I and Complex III enhances ROS generation [[135]45], which leads to
fragmentation and structural changes in protein structure and impacts
meat quality attributes such as water-holding capacity, tenderness, and
gelation function [[136]46]. The increased L* caused by a low
water-holding capacity might reflect, to some extent, the impacts of
ROS on goat meat quality [[137]47]. Our work indicated that these ten
hub genes could be potential biomarkers for meat color, which show
divergences among the three goat breeds investigated.
As shown in [138]Table 1, the ash content was significantly higher in
NJYG than JTBG and indicates a higher mineral content in NJYG LD meat.
Mitochondrial metabolome studies have revealed the critical role of
mineral homeostasis in mitochondrial activity [[139]48]. Specifically,
mitochondrial accumulation of calcium ions promotes the activity of TCA
cycle enzymes, thereby increasing oxidative phosphorylation and ATP
synthesis [[140]49]. Thus, a higher ash content could support a more
active mitochondrial function.
The sub-network B-II included five DEPs, shown in [141]Figure 7E.
Succinate-CoA Ligase GDP/ADP-Forming Subunit alpha (SUCLG1) and
Succinate-CoA Ligase GDP-Forming Subunit beta (SUCLG2) encode subunits
of succinyl-CoA synthetase crucial for succinyl-CoA and succinate
formation and ATP production in cellular energy metabolism.
Oxoglutarate Dehydrogenase (OGDH) encodes a subunit of the
2-oxoglutarate dehydrogenase complex involved in TCA cycle. Citrate
Synthase (CS) synthesizes citrate from oxaloacetate and acetyl-coenzyme
A. Aconitase 2 (ACO2) enables the interconversion from citrate to
isocitrate within TCA cycle and exhibited the highest abundance in
NJYG, shown in [142]Table 2. Mitochondrial metabolism induces a pH
decline and promotes the transformation from muscle to meat and meat
quality development postmortem [[143]50]. These five genes in TCA cycle
had a higher abundance in NJYG than JTBG shown in [144]Table 2 and
might contribute to the fast pH decline, from 6.65 to 5.60, in NJYG
within 24 h postmortem, which could lead to a low protease activity and
high shear force [[145]51]. Furthermore, OGDH has shown an association
with beef tenderness and potentially acts as a meat tenderness
biomarker [[146]52].
As shown in [147]Figure 7F, the sub-network B-III comprised four DEPs,
all associated with fatty acid metabolic enzymes, namely
Hydroxyacyl-CoA Dehydrogenase (HADH), Acyl-CoA Dehydrogenase Short
Chain (ACADS), Acetyl-CoA Acetyltransferase 1 (ACAT1), and Acetyl-CoA
Acyltransferase 2 (ACAA2). These proteins play critical roles in fatty
acid breakdown through beta-oxidation, a key process in energy
production and lipid metabolism. These four genes have been reported to
positively contribute to fat deposit in goat and ACADS is a candidate
gene for intramuscular fat deposits [[148]53]. In line with this
report, NJYG had a higher abundance of these four proteins and a higher
level of fat content compared to JTBG. Thus, HADH, ACAT1, ACADS, and
ACAA2 might serve as candidate biomarkers for fat content in goat meat.
The PPI network analysis based on the MEgreen module revealed a
significant enrichment with p < 2.39 × 10^−12, highlighting 6 DEPs that
exhibited strong interactions: Heparan Sulfate Proteoglycan 2 (HSPG2),
Integrin α7 (ITGA7), Parvin beta (PARVB), Laminin Chain α1 (LAMC1),
Laminin Subunit α2 (LAMA2), and Collagen type IV α2 chain (COL4A2), as
illustrated in [149]Figure 7G. HSPG2 encodes Perlecan, a major heparan
sulfate proteoglycan crucial for basement membrane and extracellular
matrix integrity. ITGA7, a cell surface receptor in skeletal muscle,
mediates connections between the extracellular matrix and the internal
actin cytoskeleton, mainly concentrated at muscle–tendon junctions in
postnatal muscles [[150]54]. Extensive expression of ITGA7 results in
stable adhesion of myofiber to extracellular matrix [[151]55]. PARVB
plays a key role in cytoskeleton organization and cell adhesion. LAMC1,
LAMA2, and COL4A2 are vital basement membrane proteins that indirectly
impact muscle structure and integrity. Basement membranes and the
extracellular matrix constitute the intramuscular connective tissues
critical for meat tenderness. As muscles grow, the heightened integrity
and complexity of intramuscular connective tissues enhance meat
mechanical strength, leading to tougher meat [[152]56]. MEgreen module
was significantly associated with muscle fiber area, sarcomere length,
and meat color. Our findings suggest that up-regulation of cell
adhesion related proteins in NJYG could promote extensive connections
among muscle fibers and their adhesion to extracellular matrix,
potentially enhancing the uniformity and texture of meat in NJYG. In
summary, the interplay and functionality of these genes related to
basement membranes, extracellular matrix components, muscle structure,
and cytoskeleton organization play pivotal roles in defining meat
quality attributes including tenderness, texture, and muscle mechanical
strength.
The PPI network analysis of the MEyellow module revealed an enrichment
with p < 0.108, showcasing three genes: Aldehyde Dehydrogenase 9 Family
Member A1 (ALDH9A1), Alcohol Dehydrogenase 5 (ADH5), and LOC102190016,
as depicted in [153]Figure 7H. ALDH9A1 detoxifies reactive aldehydes
generated during the synthesis of various cellular compounds. It has
been documented that ALDH9A1 overexpression inhibits intracellular
triglyceride synthesis in cattle LD muscle [[154]57]. ADH5 plays a
crucial role in regulating intramuscular fatty acid content and
composition in pork [[155]58]. LOC102190016, also known as Glutathione
S-Transferase Omega-1, possesses glutathione-dependent thiol
transferase activity and is pivotal for maintaining cellular redox
homeostasis [[156]59]. Glutathione S-Transferase and Glutathione
S-Transferase Omega-1-like genes are reported to link with mutton
tenderness [[157]30]. In the current study, these three genes had a
higher abundance in the NJYG compared to JZDEG, as shown in [158]Table
2. The divergence of meat quality between the NJYG and JZDEG mainly lay
in meat color, pH, and fat content. Furthermore, MEyellow was
associated with meat color. Thus, our study suggested ALDH9A1, ADH5,
and LOC102190016 as potential biomarkers for goat meat color.
4. Conclusions
Goat meat is highly favored in Sichuan, particularly during winter.
This study conducted a comprehensive assessment of meat quality of
three representative meat goat breeds, NJYG, JTBG, and JZDEG, in
Sichuan, China. The variations in meat quality of three goat breeds
encompassed parameters such as pH, meat color, water activity, ash, fat
content, and muscle fiber structure. The quality of goat meat is
influenced by both their habitat and genetic background. Utilizing a
proteomic approach, this study identified that the divergence in
quality traits was mainly attributed to proteins related to energy
production and metabolism, fatty acid degradation and metabolism, as
well as valine, leucine, and isoleucine degradation. Multiple
biomarkers were identified for key aspects of goat meat quality,
including meat color, fat content, tenderness, and muscle fiber
structure. These biomarkers can be utilized to predict adult meat
quality by integrating biomarker profiling with early-life tissue
sampling, thereby reducing reliance on post-mortem evaluations and
shortening generational intervals. Moreover, the combination of
biomarker profiling with genetic markers, such as SNPs or haplotypes,
can enhance the accuracy of genomic selection programs aimed at
breeding goats with desired meat quality attributes. This study
provides valuable insights into underlying mechanisms for diversity in
meat quality among Sichuan goat breeds, highlighting potential
biomarkers that could be applied in the meat industry and genetic
selection processes. Nevertheless, given the limited sample size in the
present study, further research with a larger sample size is necessary
to validate these conclusions and discover additional valuable
biomarkers related to the sensory and nutritional attributes of goat
meat.
Author Contributions
Conceptualization, R.Z. and J.Z.; funding acquisition, R.Z., X.W. and
J.Z.; investigation, R.Z., M.X. and R.X.; supervision, R.Z.;
writing—review and editing, R.Z. and J.Z.; data curation, M.X.; formal
analysis, M.X.; methodology, R.X. and T.B.; software, M.X.;
writing—original draft, M.X.; validation, R.X.; visualization, R.X.;
project administration, T.B., D.L., X.W. and Y.Z.; resources, D.L.,
X.W., Y.Z., D.P. L.Z., S.P. and J.Z. All authors have read and agreed
to the published version of the manuscript.
Institutional Review Board Statement
This study was conducted in accordance with the Regulation of
Experimental Animals at Chengdu University (2016-4).
Data Availability Statement
The proteomics data have been archived in the China National GeneBank
([159]https://db.cngb.org, accessed on 8 September 2024) with the
accession number CNP0006223. Other data will be available on request to
corresponding authors.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research was funded by the High Talent Program of Sichuan
Province, grant number 1679; the Open Funding from the Meat Processing
Key Laboratory of Sichuan Province, grant number 22-R-16; the Joint
Fund for Science and Technology Education Project of Sichuan Province
2024NSFSC2067; and the earmarked fund for CARS-43, the National Modern
Agricultural Industrial Technology System, Sichuan Innovation Team,
grant number scsztd-2023-08-07.
Footnotes
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References