Next Article in Journal
Variations in Cold Resistance and Contents of Bioactive Compounds among Dendrobium officinale Kimura et Migo Strains
Previous Article in Journal
Application of CO2-Laser Micro-Perforation Technology to Freeze-Drying Whole Strawberry (Fragaria ananassa Duch.): Effect on Primary Drying Time and Fruit Quality
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 13
Issue 10
10.3390/foods13101466
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Liquid Chromatography–Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat

by
Wenqiong Chai
1,*,
Liyuan Wang
1,
Tong Li
1,
Tianqi Wang
1,
Xinrui Wang
1,
Miao Yan
1,
Mingxia Zhu
1,
Jingrong Gao
3,
Changfa Wang
1,
Qiugang Ma
2 and
Honglei Qu
2,*
1
Liaocheng Research Institute of Donkey High-Efficiency Breeding and Ecological Feeding, Liaocheng University, Liaocheng 252000, China
2
State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
3
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316000, China
*
Authors to whom correspondence should be addressed.
Foods 2024, 13(10), 1466; https://doi.org/10.3390/foods13101466
Submission received: 27 March 2024 / Revised: 29 April 2024 / Accepted: 30 April 2024 / Published: 9 May 2024
(This article belongs to the Section Meat)
Browse Figures
Versions Notes

Abstract

:
Background: Metabolic changes in donkey meat during the early postmortem period have not been previously reported. Methods: The LC–MS-based metabolomics technique was conducted to understand the metabolic profiles and identify the key metabolites of donkey meat in the first 48 h postmortem. Results: The pH values showed a decreasing trend followed by an increasing trend. Shear force was the lowest at 4 h and the highest at 24 h (p < 0.05). For the metabolome, some candidate biomarker metabolites were identified, such as adenine, inosine, n-acetylhistidine, citric acid, isocitrate, and malic acid. Predominant metabolic pathways, such as citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism, and purine metabolism, were affected by aging time. Overabundant n-acetylhistidine was identified in LT, declined at 12 h postmortem aging, and then increased. This may explain the significantly lower pH at 12 h postmortem. Adenine was higher at 4 h postmortem, then declined. Decreased ADP may indicate a fast consumption of ATP and subsequent purine metabolism in donkey meat. Conclusions: The results of this study provided new insights into early postmortem aging of donkey meat quality.

1. Introduction

In China, Dezhou donkeys are known for their tall, muscular bodies and pure black coloring. Female donkeys may weigh up to 270 kg, while males can weigh up to 300 kg. Usually, donkeys are slaughtered at age two, and the slaughter rate could be approximately 55% [1]. Donkey meat has been a traditional Chinese local cuisine for hundreds of years, particularly in the Shandong and Hebei Provinces. In recent years, donkey meat has been more and more favored because of its nutritious and tender meat characteristics, and the price of donkey meat has increased continuously [2]. Our former research comparing different parts of donkey meat (semitendinosus, longissimus thoracis, and gluteus maximus muscles) showed that the longissimus thoracis muscle was the most tender [3]. In the beef industry, tender beef is more popular with consumers, and they are willing to pay higher prices for it; hence, tenderness is acknowledged as the most important economic trait of meat [4]. Meat quality traits like pH value, tenderness, and meat color are affected by both pre- and postharvest factors. What should be noted is that postmortem aging remains a critical process that could ameliorate meat tenderness and ultimately improve eating quality [5].
As muscle converts into meat, its biochemical metabolism undergoes dramatic changes [6,7]. Aging affects the color and pH value of meat. The color of aged beef becomes brighter and slightly red as certain proteins break down due to enzyme changes [8,9]. Aging causes protein hydrolysis, which produces different amino acids that may increase pH levels [8,9]. Boakye and Mittal [10] reported the instrumental color parameters of the longissimus dorsi muscle was caused by the aging time. The vital qualities of raw meat are largely determined by the first 24 h postmortem [7]. Yu et al. [11] have reported that the protein changes in early postmortem periods were associated with glycolysis/gluconeogenesis, oxidative phosphorylation, fatty acid metabolism, the citric acid cycle, and pyruvate metabolism. Throughout all metabolic processes, central carbon metabolism plays an important role in the regeneration of energy, cofactors, substrate degradation, and the supply of biosynthesis precursors [12].
In complex biological processes, metabolomics is widely used to analyze endogenous metabolites [13,14,15]. By identifying and analyzing different metabolites, metabolic pathways can be identified, metabolic networks can be formed, and the functions of metabolites can be determined [16]. Liquid chromatography–mass spectrometry (LC–MS)-based metabolomics allows for the determination of metabolites and metabolic profiles to determine meat quality changes [17,18]. Therefore, in the present study, the LC–MS-based metabolomics technique was conducted to understand the metabolic profiles and identify the key metabolites of donkey meat in the early postmortem aging period. It will be possible to illustrate the variation in metabolism and investigate the main metabolic pathways associated with the aged meat quality of donkeys. In the donkey meat industry, the aging time has been referred to as beef production; this research is expected to reveal the dynamics of the metabolites of donkey meat, which will provide a basis for the application of early postmortem aging in donkey meat production.

2. Materials and Methods

2.1. Samples

Liaocheng University’s Animal Care and Ethics Committee approved this research (No. 2023042602). Donkeys were slaughtered at a commercial slaughterhouse (Dong’e Tianlong Food Co., Ltd., Liaocheng, China). The Longissimus thoracis (LT) were obtained from donkey carcasses as the samples in this research. Eight male donkeys were selected randomly and slaughtered at the age of 24 months with an ante-mortem weight of 220 ± 15 kg. All the donkeys were transported from the same farm, fasted for 12 h before slaughter, and provided free access to water. The animals were stunned and then bled, and the hides were removed based on commercial practices. The LT muscle from the right side of the carcasses was excised within 30 min postmortem, and the samples were designated as 0 h. The LT muscles were cut into 7 × 7 × 5 cm cubes, placed into a sterile culture dish, and then randomly assigned to one of the following aging times: 4 h, 12 h, 24 h, or 48 h (Figure 1). Aging was performed in a refrigerator at 4 ± 1 °C, without illumination. The cooking loss and shear force were measured after 4 h, 12 h, 24 h, and 48 h of aging. Samples were collected (10 g) from the inside of LT muscles after aging for 0 h, 4 h, 12 h, 24 h, and 48 h. The collected samples were placed in a liquid nitrogen tank immediately and then transferred to a refrigerator at −80 °C for metabolomics analysis.

2.2. Meat Quality Analysis

2.2.1. pH Measurement

The pH value of aged donkey LT muscle was measured in triplicate using a Mettler Toledo testo 205 pH metre (Giessen, Germany). The pH meter was calibrated first at pH 6.86 and pH 4.01 at room temperature (approximately 22 ± 2 °C) before measurement. The pH measurement of each sample was conducted immediately after each aging treatment.

2.2.2. Cooking Loss

Cooking loss was measured based on the method by Kim et al. [19], with slight changes. Small chops (2 cm thickness) were obtained from each sample of donkey meat, weighed, and then cooked at 100 °C in a water bath for 5 min. The cooked donkey meat chop was reweighted after cooling for 30 min. The percentage of loss was calculated as the meat sample weight difference.

2.2.3. Shear Force

Shear force was determined using the cooked meat, and three core parallel samples were cut from the cooked meat. Using a TENOVO C-LM3B muscle tenderness meter to measure the shear force (Tenovo International Co., Ltd., Beijing, China), each donkey meat core (approximate 1.5 cm diameter) was sheared at the maximum force, and the average peak shear force (N) was calculated.

2.3. LC–MS Untargeted Metabolomics Determination

The LC–MS untargeted metabolomics determination was performed at Majorbio Company (Shanghai Majorbio Bio-pharm Technology Co., Ltd.), Shanghai, China.

2.3.1. Sample Preparation

A total of 40 donkey muscle samples from five aging groups were analyzed for the metabolomics study. A total of 400 μL of extraction solution (1:1 acetonitrile in methanol) was added to 50 mg of each sample. The cells were extracted with ultrasonication for 30 min (5 °C, 40 KHz), followed by centrifugation at 4 °C, 13,000× g for 15 min. The supernatant was blown dry with nitrogen and re-solvate with 120 µL of reagent solution (acetonitrile: water, 1:1, v/v) for 5 min (5 °C, 40 KHz), centrifuged (13,000× g 4 °C, 5 min), then transferred to the sample bottle for the next LC–MS analysis. Mixing aliquots into a pooled sample was used to prepare quality control (QC) samples. A repeatability analysis was performed by injecting the pooled sample at regular intervals (every 8 samples) throughout the analytical run.

2.3.2. UHPLC–MS Untargeted Metabolomics Analysis

The analysis was performed using the UHPLC-Q Exactive HF-X platform (Thermo, Ultimate 3000LC). UHPLC–MS conditions and the chromatography parameters were as follows: column, BEH C18 (100 mm × 2.1 mm i.d., 1.8 μm; Waters, Milford, CT, USA); sample injection volume: 2 µL; mobile phase A, water (formic acid containing 0.1%); and mobile phase B, acetonitrile: isopropanol (1:1, v/v) (formic acid containing 0.1%). A gradient for separation is as follows: 0 to 3 min, mobile phase A decreased from 95% to 80%, increased mobile phase B linearity from 5 to 20%; 3–9 min, A decreased from 80% to 5%, B increased from 20% to 95%; 9–13 min, A and B maintained at 5% and 95%, respectively; 13.0–13.1 min, A increased from 5% to 95%, B decreased from 95% to 5%; and 3.1–16 min, A and B maintained 95% and 5%, respectively. The ion source for MS/MS was 500 °C, the declustering potential was 80 V, the collision energy was 5 V, and the collision energy for MS/MS was 20–60 V.

2.4. Data Processing

The raw data were processed using Progenesis QI v3.0 software (WatersCorporation, Milford, CT, USA) for peak identification, retention time correction, and chromatogram alignment. The MS and MS/MS mass spectra were then compared to the metabolic database (http://www.hmdb.ca/, https://metlin.scripps.edu/, accessed on 1 June 2022), with MS mass errors less than 10 ppm, and metabolites were identified using the secondary mass spectra matching scores.
For multivariate statistical analysis, the data matrix was analyzed using the Majorbio Cloud Platform (www.majorbio.com), including a principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and variable importance of projection (VIP). Moreover, the difference multiplier and Student’s t-tests were performed. OPLS-DA analysis was used to identify metabolites that differ significantly based on their VIP and Student’s t-test p-values. Significant metabolites were those which had VIP > 1 and p < 0.05.
A metabolic pathway annotation was performed through the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/kegg/pathway.html, accessed on 1 June 2022) database to determine the pathways involved in differential metabolites. A KEGG pathway enrichment analysis was conducted using Python’s scipy.stats v1.0.0 package, and Fisher’s exact test was used to find the most relevant biological pathways. p-values corrected with Benjamini–Hochberg <0.05 were considered significant.

2.5. Statistical Analysis

The meat quality data were analyzed using Minitab version 16; one-way analysis of variance (ANOVA) and Tukey’s test were conducted to determine significant differences between the means. The replication (8 donkeys) and the interaction of replication × postmortem time were not significant. The results are shown as the mean and standard error of the mean (SEM). The significance level was p < 0.05.

3. Results

3.1. Meat Quality

The quality traits of donkey meat in early postmortem aging time are shown in Figure 2a–d. There was no change in cooking loss with postmortem aging (p > 0.05). The pH value after aging for 12 h was the lowest compared to 4 h, 24 h, and 48 h (p < 0.01). The pH values showed a decreasing trend followed by an increasing trend. Shear force was the lowest after 4 h and highest after 24 h (p < 0.05). The results indicated that donkey meat aging for 24 h presents the best appearance.

3.2. Metabolomics Analysis

3.2.1. Multivariate Analysis

UHPLC–MS detected 6738 peaks, including 3475 positive ion peaks and 3263 negative ions, in samples with different aging times after slaughter. A total of 625 metabolites were identified, including 453 positive ion mode metabolites and 172 negative ion mode metabolites. To discriminate the metabolites among different aging times, a PCA analysis was obtained. The PCA results showed that all the samples were within the 95% confidence interval (Figure 3a,d), as well as the obvious differences among the groups. As the time of aging increased, the differences between 0 h and other hours gradually expanded. The PCA results of 4 h were close to 12 h and 24 h, indicating that the difference in metabolites between them was relatively small. In addition, a PLS-DA model was performed. The PSL-DA model has revealed significant differences between these groups (Figure 3b,e), and the slope of the cross-validated straight line is moderate for 200 permutations, indicating that the PLS-DA model does not exceed the fit in this study (Figure 3c,f), thus indicating that the PLS-DA model was reliable. The results indicated the characteristic differences in metabolites among samples of different aging times. Generally, PCA and PLS-DA multivariate statistical analyses produced highly coincident results.

3.2.2. Qualitative and Quantitative Analysis of Different Aging Time

A summary of the HMDB compound metabolites accumulating with the highest frequency in some of these chemical classifications is shown in Figure S1. Most metabolites were categorized as lipids and lipid-like molecules, organic oxygen compounds, and organic acids and derivatives. The metabolites using VIP > 1 in the OPLS-DA model were screened to identify the different metabolites of samples with the change in aging time. In total, 94, 73, 90, and 83 differential metabolites were identified in the comparison between aging 0 h vs. 4 h, 4 h vs. 12 h, 12 h vs. 24 h, and 24 h vs. 48 h, respectively. In the comparison between 0 h and 4 h, 43 metabolites were upregulated and 51 were downregulated. In the 4 h and 12 h group, 27 and 46 metabolites were upregulated and downregulated. In the 12 h and 24 h comparison, 32 and 58 upregulated and downregulated metabolites were observed, and in the comparison between 24 h and 48 h, 25 metabolites were upregulated and 58 were downregulated.
The Venn diagrams showed the common or unique metabolites among different aging times, and the KEGG chemical classification with all the differential expressed metabolites are shown in Figure S2a,b. To further investigate the correlation of metabolites during the aging period, a cluster heatmap was used to present the importance and expression trends with the differential metabolites. The biological activities of key differential metabolites in each comparison group are shown in Figure S2c,d. The heatmaps were plotted for the compositions of aging 0 h, 4 h, 12 h, and 48 h to present the changes in the top 30 metabolite concentrations.

3.2.3. Enrichment of the Differential Metabolic Pathways

To understand metabolic pathway differences in response to different aging times, an analysis was performed based on the KEGG database. The differential metabolites were mapped in the four comparison groups, and we obtained 36, 13, 27, and 23 differential metabolites with KEGG IDs. The KEGG pathway enrichment analysis was performed, and 22, 3, 13, and 1 pathways with significant difference (p < 0.05) diagrams were annotated, respectively (Figure S3). The differential metabolites in the comparison between 0 h and 4 h were mainly annotated in taste transduction, the regulation of lipolysis in adipocytes, purine metabolism, and the phospholipase D signaling pathway, while those in the comparison between 4 h and 12 h were concentrated in the AMPK signaling pathway. In groups 12 h and 24 h, the differential metabolites were concentrated in purine metabolism, lysosome, the phosphatidylinositol signaling system, the glucagon signaling pathway, inositol phosphate metabolism, and oxidative phosphorylation, and in the comparison between 24 h and 48 h, only one pathway identified was significantly enriched (p < 0.05), which was purine metabolism. The main metabolites identified were shown in Table 1, and these metabolites may reveal changes in the aging time that affect donkey meat quality.
The pathways of donkey meat metabolism that affect postmortem aging were determined based on changes in differential endogenous metabolites of donkeys and pathways reported in the KEGG database. These results demonstrate that donkey meat from different aging times reacts to changes in metabolites, which affects its quality. A pathway topology analysis of the different metabolites among the five aging samples was conducted to investigate the most key pathways related to the metabolic response to donkey meat with different aging times. The result can be seen in Figure 4, and the pathway impact and p values can be seen in Table 2. Twenty pathways were predicted, and five pathways had impact values greater than 0.15 and p < 0.05. These pathways included alanine, aspartate, and glutamate metabolism, pantothenate and CoA biosynthesis, the citrate cycle (TCA cycle), purine metabolism, and caffeine metabolism.

4. Discussion

Postmortem aging is an important process in the conversion of muscle to meat. Generally, meat pH affects meat tenderness because it influences proteolytic activity [20]. Polidori et al. [21] have reported that the pH of donkey meat declined and stopped 24 h after slaughtering, but the range of pH value was lower (5.4–5.7) than that of the present study. During the first 48 h post-slaughter, pH decline and its effects on muscle protease activity may contribute to tenderization. It suggested that pH decline is influenced by glycogen content at slaughter, where a lower glycogen content may result in a slower rate of accumulation of glycolysis and lactic acid [22,23]. Hwang and Thompson [24] have reported that an intermediate pH decline (pH 5.9–6.3 at 1.5 h) resulted in tender beef. However, in the present case, the pH of donkey meat dropped dramatically in the first 12 h and then rose, with the ultimate pH value reaching 6.16. The combination of a very rapid pH decline with a slow chilling procedure could cause meat toughness [25]. It is consistent with the results that the pH value of donkey meat declined and the shear force increased within 24 h. The reason could be the earlier depletion of μ-calpain at high temperatures, hence less aging potential during chiller aging [26,27]. Notably, in the present research, the initial pH value was not significantly different than the ultimate pH value. The reason for such a phenomenon may be caused by sampling. Moreover, the slaughterhouse usually rinses carcasses with water several times, which may affect the initial pH value. It is reported that meat pH may be related to the ability of muscle to bind water [28]. The other possible explanation could be free water, which means that during aging, the free water within the muscle cells can be easily mobilized; dehydration creates a region of low water concentration in the surface, resulting in a changed diffusion rate of moisture in the meat, which ultimately affects the pH value [28]. Meat tenderness is a main factor that affects consumer satisfaction. Meat tenderness tends to increase with increasing pH value [28]. Tenderness is also associated with the degradation of the muscle proteins. Moreover, reduced degradation of proteins that tie myofibrils to the cell membrane may result in the shrinkage of muscle cells as a result of shrinking myofibrils [29,30].
Many metabolomics studies have been performed and provided new insights into early postmortem aging [31,32,33]. The process of converting muscle into meat is not only associated with energy-related metabolites but may also affect amino acid and purine metabolism. Based on the metabolomics analysis, several pathways and metabolites were identified in the meat from donkeys in this study, such as the citrate cycle (TCA cycle), pantothenate and CoA biosynthesis, alanine, aspartate and glutamate metabolism, and purine metabolism.
According to previous research, carnosine quantities increased with muscle glycolytic activities [34]. Carnosine is relevant for maintaining the pH homeostasis of muscle cells [35], and higher levels of intramuscular carnosine reveal higher lactate dehydrogenase activity and total buffering capacity in muscle cells [36]. Yu, Tian, Shao, Li, and Dai [31] have reported that N-acetylhistidine and L-histidine were overabundant in longissimus lumborum, and higher contents of carnosine and histidine and their H+ buffering capacity could lead to an increased pH value. In the present study, n-acetylhistidine and carnosine (β-alanyl-L-histidine) were overabundant in LT; however, the former content declined after 12 h of postmortem aging and then increased. Thus, it could explain the significantly lower pH at 12 h postmortem compared to that of other groups.
Early in the postmortem period, muscle myoglobin stores some oxygen [37], and biological processes in a cell could be sustained by aerobic metabolism for some time because pyruvate is converted into acetyl coenzyme A and then passes to the TCA cycle. According to Yu, Tian, Shao, Li, and Dai [32], the content of metabolites involved in the TCA cycle changed during the postmortem period. Antonelo et al. [38] have reported that the TCA cycle pathway was related to the tenderness of meat, specifically when the metabolites adenine, fumarate, glutamine, and valine have higher concentrations. In the present study, citric acid, isocitrate, and malic acid were numerically overabundant within 4 h postmortem. The result is consistent with Yu, Tian, Shao, Li, and Dai [32], who reported that the content of citrate in longissimuss lumborum decreased after 6 h postmortem. Meanwhile, the concentration of adenine was higher at 4 h postmortem and then declined. Those metabolites directly contribute to oxidative metabolism, which forms the substrate for the electron transport chain and ATP synthase [32].
In this study, the contents of inosine and adenine increased with aging time, and ADP was the opposite. Muroya et al. [39] have reported that during postmortem aging, purine metabolism was increased in tender beef. According to Matarneh et al. [40], in the first 24 h postmortem metabolism, the utilization of phosphocreatine is prioritized to maintain a stable concentration of ATP. With the degradation of phosphocreatine, ATP hydrolysis exceeds resynthesis, resulting in excessive ADP formation [40]. Then, ADP goes through purine metabolism, and it produces inosine and hypoxanthine. However, according to Yu, Tian, Shao, Li, and Dai [31], inosine started at a lower value and then increased at 24 h, which was different from the results of the present study. This may indicate that early in the postmortem period, there is fast consumption of ATP and subsequent purine metabolism in donkey meat compared to that of beef. Meanwhile, the accumulation of ADP may also reveal the loss of muscle energy production capability over time. The accumulation of ADP is expected when the rate of ATP hydrolysis exceeds the rate of ATP synthesis [32,41]. Similarly, purine and pyrimidine metabolisms were altered in the postmortem muscle tissue of donkeys based on the identification of other nucleotide degradation metabolites. However, further research is needed to determine whether these changes affect meat color, meat quality, and oxidative stability in postmortem donkey muscle.

5. Conclusions

The present study provided a more in-depth view of the metabolomics of donkey meat with aging time. Some candidate biomarker metabolites were adenine, inosine, n-acetylhistidine, citric acid, isocitrate, and malic acid. Predominant metabolic pathways, such as alanine, the citrate cycle (TCA cycle), aspartate and glutamate metabolism, and purine metabolism were affected by aging time. Overabundant n-acetylhistidine declined after 12 h of postmortem aging and then increased; this may explain the significantly lower pH at 12 h postmortem. Adenine was higher at 4 h postmortem and then declined. Decreased ADP may indicate a fast consumption of ATP and subsequent purine metabolism in donkey meat. However, further research needs to validate and quantify that those metabolites could provide critical insight for understanding and improving donkey meat quality.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/foods13101466/s1: Figure S1: Pie chart exhibiting the HMDB biochemical categories of the metabolites identified in different aging groups.; Figure S2: Metabolite differences in meat between treatments measured using metabolomics; Figure S3: KEGG pathways that the distinguished metabolites enriched in different aging groups.

Author Contributions

Conceptualization, W.C. and C.W.; methodology, L.W.; software, T.L.; formal analysis, T.W.; investigation, M.Y. and X.W.; writing—original draft preparation, W.C.; writing—review and editing, H.Q. and J.G.; visualization, M.Z.; supervision, Q.M.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Youth Science Foundation of China (grant Number: 32102531), the National Key R&D Program of China, (Grant Number. 2023YFD1302000), the Livestock and Poultry Seed Industry Project (Grant Number 19211162), and Shandong Province’s Key Research and Development Project (Grant Number 2021TZXD012).

Institutional Review Board Statement

The animal study protocol was approved by Liaocheng University’s Animal Care and Ethics Committee (No. 2023042602, 10 March 2022).

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.

Acknowledgments

We acknowledge the staff members in the commercial slaughterhouse of the National Donkey Breeding Center, Dong’e, Shandong Province.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chai, W.; Qu, H.; Ma, Q.; Zhu, M.; Li, M.; Zhan, Y.; Liu, Z.; Xu, J.; Yao, H.; Li, Z. RNA-seq analysis identifies differentially expressed gene in different types of donkey skeletal muscles. Anim. Biotechnol. 2023, 34, 1786–1795. [Google Scholar] [CrossRef]
  2. Marino, R.; Albenzio, M.; Della Malva, A.; Muscio, A.; Sevi, A. Nutritional properties and consumer evaluation of donkey bresaola and salami: Comparison with conventional products. Meat Sci. 2015, 101, 19–24. [Google Scholar] [CrossRef] [PubMed]
  3. Chai, W.; Xu, J.; Qu, H.; Ma, Q.; Zhu, M.; Li, M.; Zhan, Y.; Wang, T.; Gao, J.; Yao, H. Differential proteomic analysis to identify potential biomarkers associated with quality traits of Dezhou donkey meat using a data-independent acquisition (DIA) strategy. LWT 2022, 166, 113792. [Google Scholar] [CrossRef]
  4. Piao, M.Y.; Jo, C.; Kim, H.J.; Lee, H.J.; Kim, H.J.; Ko, J.-Y.; Baik, M. Comparison of carcass and sensory traits and free amino acid contents among quality grades in loin and rump of Korean cattle steer. Asian-Australas. J. Anim. Sci. 2015, 28, 1629. [Google Scholar] [CrossRef]
  5. Kodani, Y.; Miyakawa, T.; Komatsu, T.; Tanokura, M. NMR-based metabolomics for simultaneously evaluating multiple determinants of primary beef quality in Japanese Black cattle. Sci. Rep. 2017, 7, 1297. [Google Scholar] [CrossRef] [PubMed]
  6. Melody, J.; Lonergan, S.M.; Rowe, L.; Huiatt, T.W.; Mayes, M.S.; Huff-Lonergan, E. Early postmortem biochemical factors influence tenderness and water-holding capacity of three porcine muscles. J. Anim. Sci. 2004, 82, 1195–1205. [Google Scholar] [CrossRef]
  7. Scheffler, T.; Gerrard, D. Mechanisms controlling pork quality development: The biochemistry controlling postmortem energy metabolism. Meat Sci. 2007, 77, 7–16. [Google Scholar] [CrossRef]
  8. Gašperlin, L.; Žlender, B.; Abram, V. Colour of beef heated to different temperatures as related to meat ageing. Meat Sci. 2001, 59, 23–30. [Google Scholar] [CrossRef]
  9. Jayasooriya, S.; Torley, P.; D’arcy, B.; Bhandari, B. Effect of high power ultrasound and ageing on the physical properties of bovine Semitendinosus and Longissimus muscles. Meat Sci. 2007, 75, 628–639. [Google Scholar] [CrossRef] [PubMed]
  10. Boakye, K.; Mittal, G. Changes in colour of beef M. longissimus dorsi muscle during ageing. Meat Sci. 1996, 42, 347–354. [Google Scholar] [CrossRef] [PubMed]
  11. Yu, Q.; Tian, X.; Shao, L.; Xu, L.; Dai, R.; Li, X. Label-free proteomic strategy to compare the proteome differences between longissimus lumborum and psoas major muscles during early postmortem periods. Food Chem. 2018, 269, 427–435. [Google Scholar] [CrossRef]
  12. Luo, B.; Groenke, K.; Takors, R.; Wandrey, C.; Oldiges, M. Simultaneous determination of multiple intracellular metabolites in glycolysis, pentose phosphate pathway and tricarboxylic acid cycle by liquid chromatography–mass spectrometry. J. Chromatogr. A 2007, 1147, 153–164. [Google Scholar] [CrossRef] [PubMed]
  13. Du, H.; Lv, H.; Xu, Z.; Zhao, S.; Huang, T.; Manyande, A.; Xiong, S. The mechanism for improving the flesh quality of grass carp (Ctenopharyngodon idella) following the micro-flowing water treatment using a UPLC-QTOF/MS based metabolomics method. Food Chem. 2020, 327, 126777. [Google Scholar] [CrossRef] [PubMed]
  14. Dannenberger, D.; Nuernberg, G.; Nuernberg, K.; Will, K.; Schauer, N.; Schmicke, M. Effects of diets supplemented with n–3 or n–6 PUFA on pig muscle lipid metabolites measured by non-targeted LC–MS lipidomic profiling. J. Food Compos. Anal. 2017, 56, 47–54. [Google Scholar] [CrossRef]
  15. Tian, Y.; Xu, T.; Li, Y.; Liu, Y.; Liu, J. An untargeted LC-MS metabolomics approach to the metabolic profiles of bottom cultured scallops (Mizuhopecten yessoensis) subjected to mechanical shock in early post-harvest handling. Aquaculture 2021, 533, 736061. [Google Scholar] [CrossRef]
  16. Xiao, Z.; Ge, C.; Zhou, G.; Zhang, W.; Liao, G. 1H NMR-based metabolic characterization of Chinese Wuding chicken meat. Food Chem. 2019, 274, 574–582. [Google Scholar] [CrossRef]
  17. Bi, H.; Cai, D.; Zhang, R.; Zhu, Y.; Zhang, D.; Qiao, L.; Liu, Y. Mass spectrometry-based metabolomics approach to reveal differential compounds in pufferfish soups: Flavor, nutrition, and safety. Food Chem. 2019, 301, 125261. [Google Scholar] [CrossRef]
  18. Jia, W.; Zhang, R.; Shi, L.; Zhang, F.; Chang, J.; Chu, X. Effects of spices on the formation of biogenic amines during the fermentation of dry fermented mutton sausage. Food Chem. 2020, 321, 126723. [Google Scholar] [CrossRef] [PubMed]
  19. Kim, H.W.; Kim, J.H.; Seo, J.K.; Setyabrata, D.; Kim, Y.H.B. Effects of aging/freezing sequence and freezing rate on meat quality and oxidative stability of pork loins. Meat Sci. 2018, 139, 162–170. [Google Scholar] [CrossRef]
  20. Lomiwes, D.; Farouk, M.; Wu, G.; Young, O. The development of meat tenderness is likely to be compartmentalised by ultimate pH. Meat Sci. 2014, 96, 646–651. [Google Scholar] [CrossRef]
  21. Polidori, P.; Santini, G.; Klimanova, Y.; Zhang, J.-J.; Vincenzetti, S. Effects of ageing on donkey meat chemical composition, fatty acid profile and volatile compounds. Foods 2022, 11, 821. [Google Scholar] [CrossRef]
  22. Immonen, K.; Puolanne, E. Variation of residual glycogen-glucose concentration at ultimate pH values below 5.75. Meat Sci. 2000, 55, 279–283. [Google Scholar] [CrossRef]
  23. White, A.; O’sullivan, A.; Troy, D.; O’Neill, E. Manipulation of the pre-rigor glycolytic behaviour of bovine M. longissimus dorsi in order to identify causes of inconsistencies in tenderness. Meat Sci. 2006, 73, 151–156. [Google Scholar] [CrossRef] [PubMed]
  24. Hwang, I.; Thompson, J. The interaction between pH and temperature decline early postmortem on the calpain system and objective tenderness in electrically stimulated beef longissimus dorsi muscle. Meat Sci. 2001, 58, 167–174. [Google Scholar] [CrossRef]
  25. Takahashi, G.; Wang, S.-M.; Lochner, J.; Marsh, B. Effects of 2-Hz and 60-Hz electrical stimulation on the microstructure of beef. Meat Sci. 1987, 19, 65–76. [Google Scholar] [CrossRef]
  26. Dransfield, E. Modelling post-mortem tenderisation—III: Role of calpain I in conditioning. Meat Sci. 1992, 31, 85–94. [Google Scholar] [CrossRef]
  27. Simmons, N.; Singh, K.; Dobbie, P.; Devine, C. The effect of pre-rigor holding temperature on calpain and calpastatin activity and meat tenderness. In Proceedings of the 42nd International Congress of Meat Science and Technology, Lillehammer, Norway, 1–6 September 1996; pp. 414–415. [Google Scholar]
  28. Ribeiro, F.A.; Lau, S.K.; Furbeck, R.A.; Herrera, N.J.; Henriott, M.L.; Bland, N.A.; Fernando, S.C.; Subbiah, J.; Sullivan, G.A.; Calkins, C.R. Ultimate pH effects on dry-aged beef quality. Meat Sci. 2021, 172, 108365. [Google Scholar] [CrossRef]
  29. Kristensen, L.; Purslow, P.P. The effect of ageing on the water-holding capacity of pork: Role of cytoskeletal proteins. Meat Sci. 2001, 58, 17–23. [Google Scholar] [CrossRef]
  30. Rowe, L.; Huff-Lonergan, E.; Lonergan, S. Desmin degradation influences water-holding capacity and tenderness of fresh pork. J. Anim. Sci. 2001, 79, 443. [Google Scholar]
  31. Yu, Q.; Tian, X.; Shao, L.; Li, X.; Dai, R. Mitochondria changes and metabolome differences of bovine longissimus lumborum and psoas major during 24 h postmortem. Meat Sci. 2020, 166, 108112. [Google Scholar] [CrossRef]
  32. Yu, Q.; Tian, X.; Shao, L.; Li, X.; Dai, R. Targeted metabolomics to reveal muscle-specific energy metabolism between bovine longissimus lumborum and psoas major during early postmortem periods. Meat Sci. 2019, 156, 166–173. [Google Scholar] [CrossRef] [PubMed]
  33. Ji, C.; You, L.; Luo, R. Proteomics and metabolomics combined study on endopathic changes of water-soluble precursors in Tan lamb during postmortem aging. Food Sci. Nutr. 2022, 10, 1564–1578. [Google Scholar] [CrossRef] [PubMed]
  34. Cornet, M.; Bousset, J. Free amino acids and dipeptides in porcine muscles: Differences between ‘red’ and ‘white’ muscles. Meat Sci. 1999, 51, 215–219. [Google Scholar] [CrossRef]
  35. Sewell, D.A.; Harris, R.; Marlin, D.; Dunnett, M. Estimation of the carnosine content of different fibre types in the middle gluteal muscle of the thoroughbred horse. J. Physiol. 1992, 455, 447–453. [Google Scholar] [CrossRef]
  36. Abe, H. Role of histidine-related compounds as intracellular proton buffering constituents in vertebrate muscle. Biochem. C/C Biokhimiia 2000, 65, 757–765. [Google Scholar]
  37. Pösö, A.R.; Puolanne, E. Carbohydrate metabolism in meat animals. Meat Sci. 2005, 70, 423–434. [Google Scholar] [CrossRef] [PubMed]
  38. Antonelo, D.; Gómez, J.F.; Cônsolo, N.R.; Beline, M.; Colnago, L.A.; Schilling, W.; Zhang, X.; Suman, S.P.; Gerrard, D.E.; Balieiro, J.C. Metabolites and metabolic pathways correlated with beef tenderness. Meat Muscle Biol. 2020, 4, 1–19. [Google Scholar] [CrossRef]
  39. Muroya, S.; Oe, M.; Ojima, K.; Watanabe, A. Metabolomic approach to key metabolites characterizing postmortem aged loin muscle of Japanese Black (Wagyu) cattle. Asian-Australas. J. Anim. Sci. 2019, 32, 1172. [Google Scholar] [CrossRef]
  40. Matarneh, S.K.; Scheffler, T.L.; Gerrard, D.E. The conversion of muscle to meat. In Lawrie’s Meat Science; Elsevier: Amsterdam, The Netherlands, 2023; pp. 159–194. [Google Scholar]
  41. Martinez, H.A.; Arnold, A.N.; Brooks, J.C.; Carr, C.C.; Gehring, K.B.; Griffin, D.B.; Hale, D.S.; Mafi, G.G.; Johnson, D.D.; Lorenzen, C.L. National beef tenderness survey—2015: Palatability and shear force assessments of retail and foodservice beef. Meat Muscle Biol. 2017, 1, 138–148. [Google Scholar] [CrossRef]
Foods 13 01466 g001
Figure 1. Workflow of this study on the early postmortem aging of donkey meat.
Figure 1. Workflow of this study on the early postmortem aging of donkey meat.
Foods 13 01466 g001
Foods 13 01466 g002
Figure 2. Comparison of quality traits during early postmortem aging of donkey meat. (a) pH value. (b) Cooking loss. (c) Shear force. (d) The picture of donkey meat treated with different aging times. Values with different letters (lower a and b) in the bar chat indicate significant differences (p < 0.05).
Figure 2. Comparison of quality traits during early postmortem aging of donkey meat. (a) pH value. (b) Cooking loss. (c) Shear force. (d) The picture of donkey meat treated with different aging times. Values with different letters (lower a and b) in the bar chat indicate significant differences (p < 0.05).
Foods 13 01466 g002
Foods 13 01466 g003
Figure 3. Multivariate statistical analysis of identified metabolites in early postmortem aging. (a) PCA score plot of samples acquired in positive mode. (b) PLS-DA score plots of samples acquired in positive mode. (c) The validation of the PLS-DA model using permutation testing (200 iterations) in positive mode. (d) PCA score plots of samples acquired in negative mode. (e) PLS-DA score plots of samples acquired in negative mode. (f) The validation of the PLS-DA model using permutation testing (200 iterations) in negative mode.
Figure 3. Multivariate statistical analysis of identified metabolites in early postmortem aging. (a) PCA score plot of samples acquired in positive mode. (b) PLS-DA score plots of samples acquired in positive mode. (c) The validation of the PLS-DA model using permutation testing (200 iterations) in positive mode. (d) PCA score plots of samples acquired in negative mode. (e) PLS-DA score plots of samples acquired in negative mode. (f) The validation of the PLS-DA model using permutation testing (200 iterations) in negative mode.
Foods 13 01466 g003
Foods 13 01466 g004
Figure 4. Significantly changed pathways based on the enrichment and topology analysis. The abscissa labels represent five aging times, the red spot represents upregulated metabolites, the green spot represents downregulated metabolites, and the white spot means metabolites were not detected in samples. The colored dashed boxes indicate the levels of significantly up- and down-regulated metabolites in metabolic pathways at different aging times.
Figure 4. Significantly changed pathways based on the enrichment and topology analysis. The abscissa labels represent five aging times, the red spot represents upregulated metabolites, the green spot represents downregulated metabolites, and the white spot means metabolites were not detected in samples. The colored dashed boxes indicate the levels of significantly up- and down-regulated metabolites in metabolic pathways at different aging times.
Foods 13 01466 g004
Table 1. Main metabolites of aged donkey meat identified during early postmortem aging.
Table 1. Main metabolites of aged donkey meat identified during early postmortem aging.
GroupMetaboliteFormulaVIPFCp_Value
0–4 h(R)-S-LactoylglutathioneC13H21N3O8S2.49881.070.0134
1D-Myo-inositol 3,4-bisphosphateC6H14O12P22.45651.090.0472
5-Hydroxyindoleacetic acidC10H9NO31.58480.970.0084
5-Hydroxy-L-tryptophanC11H12N2O31.79721.050.0207
AcetylcarnitineC9H17NO41.37351.020.0178
AdenineC5H5N52.02090.960.0001
Adenosine monophosphateC10H14N5O7P1.56151.030.0220
AdenylosuccinateC14H18N5O11P2.87081.120.0120
ADPC10H15N5O10P21.42561.020.0055
ADP-riboseC15H23N5O14P22.45311.060.0035
Alpha-D-Glucose 1,6-bisphosphateC6H14O12P22.38331.080.0095
ArbutinC12H16O71.19710.980.0493
BiocytinC16H28N4O4S2.24090.890.0230
CreatineC4H9N3O21.89260.980.0000
D-Fructose 2,6-bisphosphateC6H14O12P22.50751.110.0416
DG (8:0/15:0/0:0)C26H50O51.09961.010.0208
D-myo-Inositol 1,4-bisphosphateC6H14O12P22.63241.070.0182
D-XyloseC5H10O51.32850.970.0410
Fructose 1,6-bisphosphateC6H14O12P22.49151.090.0182
Glutathione, oxidizedC20H32N6O12S21.73481.020.0020
InosineC10H12N4O51.69790.970.0304
L-Aspartic AcidC4H7NO42.48540.900.0412
L-GlutamateC5H9NO42.34901.060.0014
LysoSM (d18:1)C23H50N2O5P+1.79291.030.0048
N-Acetyl-alpha-D-glucosamine 1-phosphateC8H16NO9P2.33950.930.0029
Pantothenic AcidC9H17NO51.35551.030.0350
PhosphocreatineC4H10N3O5P2.02660.890.0471
Phosphoric acidH3O4P1.27670.980.0320
PyrophosphateH4O7P21.37790.980.0223
S-LactoylglutathioneC13H21N3O8S2.34091.070.0234
Uric acidC5H4N4O32.23190.940.0137
XanthineC5H4N4O22.60590.940.0012
XanthosineC10H12N4O62.57900.930.0095
4–12 hAdenineC5H5N51.25861.020.0189
Alpha-D-Glucose 1,6-bisphosphateC6H14O12P22.96501.100.0191
ArbutinC12H16O71.31741.020.0071
CalcitriolC27H44O31.93490.970.0093
D-Fructose 2,6-bisphosphateC6H14O12P22.97611.180.0456
DG (8:0/15:0/0:0)C26H50O51.39200.990.0071
Fructose 1,6-bisphosphateC6H14O12P24.48901.210.0016
Glutathione, oxidizedC20H32N6O12S21.35121.020.0495
Glyceric acidC3H6O41.96440.950.0171
Oxidized glutathioneC20H32N6O12S22.28711.040.0113
S-AdenosylhomocysteineC14H20N6O5S1.31350.980.0311
12–24 h(R)-S-LactoylglutathioneC13H21N3O8S3.83221.200.0258
1D-Myo-inositol 3,4-bisphosphateC6H14O12P26.50783.320.0027
9,10,13-TriHOMEC18H34O51.26770.970.0194
AcetylcarnitineC9H17NO41.91201.030.0167
AdenineC5H5N51.37440.970.0194
Adenosine diphosphate riboseC15H23N5O14P25.49470.700.0001
ADPC10H15N5O10P21.48561.030.0101
CalcitriolC27H44O31.68210.970.0280
CinnavalininateC14H8N2O61.00770.990.0009
Citric acidC6H8O72.57841.060.0018
CreatineC4H9N3O21.20220.990.0001
D-Erythrose 4-phosphateC4H9O7P1.92351.040.0132
D-Fructose 2,6-bisphosphateC6H14O12P22.83301.210.0139
D-Mannose 6-phosphateC6H13O9P1.89941.040.0175
D-myo-Inositol 1,4-bisphosphateC6H14O12P23.84931.200.0070
D-Myoinositol 4-phosphateC6H13O9P1.76071.030.0063
D-Pantetheine 4’-phosphateC11H23N2O7PS3.24711.170.0191
D-XyloseC5H10O51.65640.950.0045
Glyceric acidC3H6O42.88080.910.0003
InosineC10H12N4O51.14170.980.0384
IsocitrateC6H8O72.64141.080.0003
PyrophosphateH4O7P21.23760.970.0359
S-AdenosylhomocysteineC14H20N6O5S1.54380.980.0044
S-LactoylglutathioneC13H21N3O8S3.13061.190.0220
Uridine diphosphate-N-acetylglucosamineC17H27N3O17P21.75011.030.0016
Urocanic acidC6H6N2O21.03550.990.0028
XanthineC5H4N4O21.38930.970.0339
Xanthylic acidC10H13N4O9P3.78711.210.0023
24–48 hGuanosineC10H13N5O53.57740.910.0003
GuanineC5H5N5O3.39360.920.0002
3-MethylindoleC9H9N1.36610.980.0229
LysoSM(d18:1)C23H50N2O5P+2.53641.040.0108
DG (14:0/16:1(9Z)/0:0)C33H62O51.23581.010.0144
S-AdenosylhomocysteineC14H20N6O5S1.37070.990.0085
(R)-S-LactoylglutathioneC13H21N3O8S3.54011.160.0353
ADP-riboseC15H23N5O14P23.33051.120.0313
D-myo-Inositol 1,4-bisphosphateC6H14O12P23.39731.140.0240
InosineC10H12N4O52.07810.970.0015
TheobromineC7H8N4O21.44830.980.0014
1,4-beta-D-GlucanC18H32O182.38190.960.0028
Glyceric acidC3H6O43.23650.930.0002
BiocytinC16H28N4O4S2.44940.920.0439
KAPAC9H17NO31.95170.970.0190
Palmitoyl-L-carnitineC23H45NO42.38371.040.0491
9,10,13-TriHOMEC18H34O53.00820.910.0104
AdenylosuccinateC14H18N5O11P3.92321.210.0035
Adenosine diphosphate riboseC15H23N5O14P23.30720.900.0084
D-XyloseC5H10O52.76040.930.0000
Xanthylic acidC10H13N4O9P3.42291.180.0171
Malic acidC4H6O51.93941.030.0069
Table 2. List of metabolic pathways using enriched analysis for the metabolites.
Table 2. List of metabolic pathways using enriched analysis for the metabolites.
Pathway NameTotalHitImpact Valuep Value
Alanine, aspartate and glutamate metabolism2840.4695650.002409
Pantothenate and CoA biosynthesis2730.2462550.011784
Purine metabolism81130.1938120.000000
Citrate cycle (TCA cycle)2030.1666510.006359
Aminoacyl-tRNA biosynthesis5220.1621620.138972
Caffeine metabolism1830.1585900.005423
Biotin metabolism2320.1362850.055364
Amino sugar and nucleotide sugar metabolism10740.1163230.054167
Glyoxylate and dicarboxylate metabolism5450.1078960.001992
Glycolysis/Gluconeogenesis3120.1024510.073197
Fructose and mannose metabolism5230.0995930.043905
Arginine biosynthesis2320.0917070.055364
Pyruvate metabolism2830.0914100.067980
Inositol phosphate metabolism4530.0863980.035429
Tryptophan metabolism5640.0849450.012193
Histidine metabolism3330.0583180.018566
Arginine and proline metabolism7230.0500890.067010
Neomycin, kanamycin and gentamicin biosynthesis7620.0429620.211768
Glycerolipid metabolism3210.0390990.301516
Pentose and glucuronate interconversions5610.0363120.354392
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chai, W.; Wang, L.; Li, T.; Wang, T.; Wang, X.; Yan, M.; Zhu, M.; Gao, J.; Wang, C.; Ma, Q.; et al. Liquid Chromatography–Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat. Foods 2024, 13, 1466. https://doi.org/10.3390/foods13101466

AMA Style

Chai W, Wang L, Li T, Wang T, Wang X, Yan M, Zhu M, Gao J, Wang C, Ma Q, et al. Liquid Chromatography–Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat. Foods. 2024; 13(10):1466. https://doi.org/10.3390/foods13101466

Chicago/Turabian Style

Chai, Wenqiong, Liyuan Wang, Tong Li, Tianqi Wang, Xinrui Wang, Miao Yan, Mingxia Zhu, Jingrong Gao, Changfa Wang, Qiugang Ma, and et al. 2024. "Liquid Chromatography–Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat" Foods 13, no. 10: 1466. https://doi.org/10.3390/foods13101466

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop