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Article

Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive

by
Wanjie Zou
1,2,†,
Linglan Deng
1,2,†,
Huadong Wu
2,
Zhiyong Liu
3,
Wei Lu
1,2,* and
Yuyong He
1,2,*
1
Jiangxi Province Key Laboratory of Animal Nutrition/Engineering Research Center of Feed Development, Jiangxi Agricultural University, Nanchang 330045, China
2
College of Animal Science and Technology, Jiangxi Agricultural University, Nanchang 330045, China
3
Key Laboratory of Pharmacology of Traditional Chinese Medicine in Jiangxi, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Animals 2022, 12(20), 2879; https://doi.org/10.3390/ani12202879
Submission received: 11 September 2022 / Revised: 11 October 2022 / Accepted: 17 October 2022 / Published: 21 October 2022
(This article belongs to the Special Issue Increasing Piglet Pre-weaning Survival through Peri-Partum and Lactation Management)
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Abstract

:

Simple Summary

Diarrhea often occurs in suckling piglets and milk secreted by lactating sows with metritis–vaginitis–mastitis is one of the most important contributors. Chinese herbal medicine has antibacterial and anti-inflammatory effects due to its bioactive ingredients, and it is of interest to explore whether maternal feeding with Chinese herbal medicine can increase the level of milk ingredients with anti-infectious and anti-inflammatory properties. The present study found that supplementation of fermented compound Chinese medicine feed additive to sows increased the concentration of functional ingredients, such as quercetin, pinocembrin, chlorogenic acid, methyl succinic acid, L-tryptophan, adenosine, guanine, arteannuin, inosine, guanosine, benzene-1,2,4-triol, hypoxanthine, adenine, ferulic acid, echimidine N-oxide, pogostone, kynurenine and trehalose 6-phosphate in the milk of sows, most of these functional ingredients have anti-infectious, anti-inflammatory, anti-oxidative and immune-enhancing effects. The findings of this study hint that supplementation with fermented compound Chinese medicine feed additive in sows is beneficial for the improvement of milk quality.

Abstract

Different untargeted metabolomics approaches were used to identify the differential metabolites between milk samples collected from two groups. Sows were supplemented with fermented compound Chinese medicine feed additive at levels of 0 g/d/sow (control group, n = 10) and 50 g/d/sow (experimental group, n = 10), respectively, from d 104 of gestation to d 25 of lactation, samples of colostrum and mature milk were collected. Data indicated that supplementing fermented compound Chinese medicine feed additive to sows significantly increased the concentrations of quercetin, pinocembrin, chlorogenic acid, methyl succinic acid, L-tryptophan, adenosine, guanine, arteannuin, ferulic acid, echimidine N-oxide, pogostone and kynurenine in the colostrum and inosine, guanosine, benzene-1,2,4-triol, hypoxanthine, adenine, trehalose 6-phosphate in mature milk, respectively. Seven pathways (flavone and flavanol biosynthesis, galactose metabolism, phenylpropanoid biosynthesis, stilbenoid and gingerol biosynthesis, flavonoid biosynthesis, ABC transporters and purine metabolism) in colostrum and two pathways (sucrose metabolism and retrograde endocannabinoid signaling) in mature milk were significantly enriched in the experimental group compared to control group, respectively. The supplementation of fermented compound Chinese medicine feed additive to sows increased the level of antibacterial and anti-inflammatory ingredients in milk and the findings of this study hint that supplementation with fermented compound Chinese medicine feed additive in sows is beneficial for the improvement of milk quality.

1. Introduction

Sow’s milk is a complex biological fluid and contains macro-chemicals, micro-chemicals and microbes. The composition of sow’s milk often undergoes alterations during the lactation period with the changes in breeds, parities, diets and disease status [1,2,3,4,5,6,7]. It is well known that milk is the main food for piglets during the suckling period, so the quality of sow milk has a vital impact on the survival and growth of suckling piglets [8,9,10]. There are many methods to improve the quality of sow’s milk including the use of different feeding regimens [11] or plant-derived bioactive compounds [12,13,14].
Metritis–vaginitis–mastitis of sows is one of the most prevalent and costly diseases in pig farms, milk secreted by sows with metritis–vaginitis–mastitis often contains harmful microbes and pro-inflammatory mediators, and ingestions of mastitis milk and fecal pathogens from sows are the major contributors to the high mortality of suckling piglets owing to severe diarrhea which is caused by the pathogens, allergic substances and pro-inflammatory mediators in the guts of suckling piglets [3,4,15], this is the reason why sows often have a high number of piglets born alive but a low number of piglets at weaning.
Chinese herbal medicines or extracts are often added to the diet to promote the healthy production of cow’s milk [16,17] and sow’s milk [18] because Chinese herbal medicines and extracts contain bioactive components that possess antibacterial, anti-inflammatory, anti-oxidative and immune-enhancing properties [19,20,21,22]. Some of these bioactive compounds can be transferred directly from Chinese medicine into milk, and some of them can be metabolized to other bioactive compounds by gut microbes and then enter into the milk; providing this functional milk with high levels of Chinese herb medicine-derived bioactive ingredients to suckling piglets is one of the methods to decrease diarrhea and mortality in neonates. In the previous study, we found that fermented compound Chinese medicine feed additive can effectively inhibit the growth of some bacteria associated with metritis–vaginitis–mastitis due to its bioactive metabolites [23]. Chinmedomics strategy is an integration of serum pharmco-chemistry of traditional Chinese medicine and “Omics” technology and is often performed to determine the components of Chinese medicine [24,25]; a metabolomics approach is usually applied to find out the non-Chinese medicine originated compounds [26]. In the present experiment, we applied different untargeted metabolomics approaches to characterize whether maternal feeding with fermented compound Chinese medicine feed additive has an impact on the composition of active ingredients in milk.

2. Materials and Methods

2.1. Animals and Feeding

This feeding experiment started on 20 August 2021, and twenty pregnant crossbred sows (Landrace × Large White) with similar body conditions and parities were randomly assigned to the control group and experimental group with 10 sows (10 replicates) in each group according to a randomized complete block design and fed the same basal diet (Table 1) with supplementation of fermented compound Chinese medicine feed additive from d 104 of gestation to d 25 of lactation at doses of 0 and 50 g/sow, respectively, once a day in the morning. Prior to feeding, the allowance of morning meal of each sow was divided into two parts, one part was mixed with the fermented compound Chinese medicine feed additive and then offered to the sow, and the other part was followed after the sow consumed the mixture of meal and fermented compound Chinese medicine feed additive. The information on ingredients, preparation and chemical compositions of fermented compound Chinese medicine feed additive have been reported in a published paper [23].

2.2. Sample Collection and Preparation

Samples of milk were collected with sterile Eppendorf tubes (Hamburg, Germany), and samples of colostrum and mature milk from each sow were daily collected from d 1 to 5 and d 10 to 20 relative to parturition, respectively, all samples were stored at −20 °C. At the end of sampling, samples of colostrum and mature milk of each sow were mixed, respectively, and the mixture of samples was sub-packed with 5 mL sterile Eppendorf tubes and then stored at −20 °C before analysis.

2.3. Analysis of Active Ingredients Using UHPLC-QE-MS Based Untargeted Chinmedomics

Samples of milk were processed for the extraction of Chinese medicine active ingredients according to the standardized protocols (Shanghai Biotree Biotech Co., Ltd., Shanghai, China) before analysis. LC-MS/MS analysis was performed using a 1290 UPLC system (Agilent, Santa Clara, CA, USA) with a Waters UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm). The flow rate was set at 0.4 mL/min and the sample injection volume was set at 5 μL. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). The multi-step linear elution gradient program was as follows: 0–3.5 min, 95–85% A; 3.5–6 min, 85–70% A; 6–6.5 min, 70–70% A; 6.5–12 min, 70 -30% A; 12–12.5 min, 30–30% A; 12.5–18 min, 30–0% A; 18–25 min, 0–0% A; 25–26 min, 0–95% A; 26–30 min, 95–95% A.
A Q Exactive Focus mass spectrometer (Vanquish, Thermo Fisher Scientific, Waltham, MA, USA) coupled with Xcalibur TM 3.0 software (Thermo Fisher, Waltham, MA, USA) was employed to obtain the MS and MS/MS data based on the information-dependent acquisition mode. During each acquisition cycle, the mass range was from 100 to 1500, the top three of every cycle were screened and the corresponding MS/MS data were further acquired. The ESI source was applied to analyze the chemical composition in both positive and negative ion modes with full scan/ddMS2. Sheath gas flow rate: 45 Arb, Aux gas flow rate: 15 Arb, Capillary temperature: 400 °C, Full MS resolution: 70,000, MS/MS resolution: 17,500, Collision energy: 15/30/45 in NCE mode, Spray Voltage: 4.0 kV (positive) or −3.6 kV (negative).
Raw data were processed using the XCMS package, and the qualified data were uploaded to SIMCA-P (Version 16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden) for statistical analysis. Significantly altered metabolites were determined by t-test and a p value < 0.05 was considered statistically significant. Identification of active ingredients was performed by searching the Biotree databases and web databases (METLIN, HMDB, PubChem, and ChemSpider).

2.4. Identification of Differential Metabolites Using UHPLC-QE-MS Based Conventional Untargeted Metabolomics

Samples of milk were processed according to the standardized protocols before UHPLC-QE-MS-based conventional untargeted metabolomic analysis. LC-MS/MS analyses were performed using a 3000 UHPLC system (Vanquish, Thermo Fisher Scientific, Waltham, MA, USA) with a UPLC HSS T3 column (1.8 μm, 2.1 mm × 100 mm) coupled to a Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo, Waltham, MA, USA). The mobile phase consisted of 5 mmol/L ammonium acetate and 5 mmol/L acetic acid in water (A) and acetonitrile (B). The auto-sampler temperature was 4 °C, and the injection volume was 2 μL. The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra on information-dependent acquisition mode in the control of the acquisition software (Xcalibur TM 3.0, Thermo, Waltham, MA, USA). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 10 Arb, capillary temperature 350 °C, full MS resolution as 60,000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray Voltage as 4.0 kV (positive) or −3.8 kV (negative), respectively.
Raw data were converted to the mzXML format using ProteoWizard and processed with an in-house program, which was developed using R and based on XCMS for peak detection, extraction, alignment and integration. Significantly altered metabolites were determined by t-test and a p value < 0.05 was considered statistically significant. An in-house MS2 database (Biotree database) was applied in metabolite annotation and the cutoff for annotation was set at 0.3.

3. Results

3.1. Differential Metabolites in Colostrum between Experimental and Control Groups Based on Chinmedomics

Twenty-six metabolites under the negative ion model and twenty-two metabolites under the positive ion model were identified in colostrum between experimental and control groups including eight flavonoids, five phenols, eight alkaloids, two phenylpropanoids, eight terpenoids, two fatty acyls, two fatty acids, one organoheterocyclic compound, two organic acids and derivatives, one organic oxygen compound, six amino acid derivatives, one carbohydrate and derivative, one aliphatic and one organooxygen compound (Table 2). The concentrations of 48 differential metabolites in the colostrum of the experimental group were numerically or significantly higher than that in the colostrum of the control group, and the colostrum of the experimental group had significantly higher concentrations of quercetin (p < 0.05), pinocembrin (p < 0.05), chlorogenic acid (p < 0.01), methyl succinic acid (p < 0.01), L-tryptophan (p < 0.01), adenosine (p < 0.05), guanine (p < 0.05) and arteannuin (p < 0.05) compared to the colostrum of the control group, respectively.

3.2. Differential Metabolites in Mature Milk between Experimental and Control Groups Based on Chinmedomics

Table 3 showed that a total of 32 metabolites were screened under positive and negative ion modes, respectively, in mature milk between the experimental and control groups including four phenols, seven phenylpropanoids, one xanthone, two sesquiterpenoids, three amino acid derivatives, four alkaloids, three flavonoids, one chalcone, four terpenoids, one organooxygen compound, one fatty acid and one carboxylic acid and derivative. The mature milk of the experimental group had numerically or significantly higher concentrations of 32 metabolites compared to the mature milk of the control group, the concentrations of ferulic acid (p < 0.05), echimidine N-oxide (p < 0.05), pogostone (p < 0.05) and kynurenine (p < 0.05) in the mature milk of the experimental group were significantly higher than that of the control group, respectively.

3.3. Differential Metabolites between Colostrum and Mature Milk of Experimental Group Based on Chinmedomics

A total of 23 metabolites were identified under positive and negative ion modes between the colostrum and mature milk in the experimental group including one flavonoid, one phenol, eight alkaloids, seven terpenoids, two phenylpropanoids, one coumarin and derivative, one phospholipid, one organoheterocyclic compound and one fatty acid (Table 4). Colostrum had significantly higher concentrations of bergenin (p < 0.05), 3-furfuryl 2-pyrrolecarboxylate (p < 0.05), guanosine (p < 0.01), guanine (p < 0.05), palmatine (p < 0.01), celastrol (p < 0.05), lindenenol (p < 0.05), artemisinin (p < 0.05), curcumenol (p < 0.05) and aucubin (p < 0.05) than mature milk in the experimental group, respectively.

3.4. Differential Metabolites between Colostrum and Mature Milk of Control Group Based on Chinmedomics

A total of 22 metabolites were found under positive and negative ion modes between colostrum and mature milk in the control group including one organic acid and derivative, one phenol, eight alkaloids, six terpenoids, two phenylpropanoids, one coumarin and derivative, one organooxygen compound, one benzene and substituted derivative and one amino acid derivative (Table 5). Colostrum had significantly higher concentrations of threonic acid (p < 0.05), bergenin (p < 0.01), boldine (p < 0.05), 3-Furfuryl 2-pyrrolecarboxylate (p < 0.05), aucubin (p < 0.05), celastrol (p < 0.01) and eudesmin (p < 0.01) than mature milk in the control group, respectively.

3.5. Differential Metabolites in Colostrum between Experimental and Control Groups Based on Conventional Untargeted Metabolomics

A total of seventy-six differential metabolites have been identified in the colostrum between experimental and control groups including 24 organic acids and derivatives, 8 organoheterocyclic compounds, 1 organooxygen compound, 4 organic oxygen compounds, 1 organic nitrogen compound, 11 nucleosides, nucleotides, and analogs, 21 lipids and lipid-like molecules and 6 benzenoids (Table 6). The colostrum of sows in the experimental group had significantly higher levels of inosine, guanosine, benzene-1,2,4-triol, hypoxanthine and adenine than the colostrum of sows in the control group (p < 0.05), respectively. Other metabolites in the colostrum of the experimental group also had numerically higher concentrations than that in the colostrum of the control group (p > 0.05), respectively.

3.6. Differential Metabolites in Mature Milk between Experimental and Control Groups Based on Conventional Untargeted Metabolomics

Table 7 indicated that 13 differential metabolites under the negative ion model and 10 differential metabolites under the positive ion model had been screened in mature milk between the experimental group and control group including seven organic acids and derivatives, two organoheterocyclic compounds, three organic oxygen compounds, one organic nitrogen compound, three nucleosides, nucleotides and analogs, five lipids and lipid-like molecules and two benzenoids. The concentration of trehalose 6-phosphate in the mature milk of the experimental group was significantly higher than that in the mature milk of the control group (p < 0.05), and other metabolites in the mature milk of the experimental group had numerically higher levels than those in the mature milk of the control group (p > 0.05), respectively.

3.7. Differential Metabolites between Colostrum and Mature Milk of Experimental Group Based on Conventional Untargeted Metabolomics

Nineteen differential metabolites under the negative ion model and thirty-two differential metabolites under the positive ion model had been found in the experimental group between colostrum and mature milk including 11 organic acids and derivatives, 5 organic oxygen compounds, 6 organoheterocyclic compounds, 1 organohalogen compound, 18 lipids and lipid-like molecules, 9 nucleosides, nucleotides and analogs and 1 benzenoid (Table 8). Thirty-five differential metabolites had significantly higher concentrations in colostrum than in mature milk and the other sixteen metabolites in colostrum also had numerically higher levels than in mature milk.

3.8. Differential Metabolites between Colostrum and Mature Milk of Control Group Based on Conventional Untargeted Metabolomics

Thirty-seven differential metabolites had been screened between colostrum and mature milk in the control group including 5 organic acids and derivatives, 12 organic oxygen compounds, 4 organoheterocyclic compounds, 1 organohalogen compound, 8 nucleosides, nucleotides and analogs, and 7 lipids and lipid-like molecules (Table 9). Twenty-seven metabolites in colostrum had significantly higher concentrations than that in mature milk and ten metabolites had numerically higher levels in colostrum than in mature milk.

3.9. Metabolic Pathways Enrichment

In order to identify the changes in the metabolic pathway reflected by differential metabolites, differential abundance analysis of KEGG metabolic pathways for differential metabolites screened by chinmedomics was conducted and results are shown in Figure 1. There were significant differences in the differential abundances of five metabolic pathways of differential metabolites in the colostrum between the experimental group and control group (Figure 1A), differential metabolites enriched in the pathways of flavone and flavanol biosynthesis, galactose metabolism, phenylpropanoid biosynthesis, stilbenoid and gingerol biosynthesis, flavonoid biosynthesis were upregulated in the colostrum of sows supplemented with fermented compound Chinese medicine feed additive. Two pathways of differential metabolites in mature milk had significant differences in differential abundance (Figure 1B), and differential metabolites enriched in ABC transporters and pyrimidine metabolism were downregulated in the mature milk of sows fed with fermented compound Chinese medicine feed additive. Seven pathways with differential abundances were annotated between colostrum and mature milk of sows with fermented compound Chinese medicine feed additive supplementation (Figure 1C), differential metabolites enriched in purine metabolism were significantly upregulated but in phenylpropanoid biosynthesis were significantly downregulated in colostrum. The differential abundances of metabolic pathways, biosynthesis of secondary metabolites and biosynthesis of various plant secondary metabolites were not significantly different when comparing colostrum to mature milk in sows supplemented without fermented compound Chinese medicine feed additive (Figure 1D); differential metabolites enriched in metabolic pathways, biosynthesis of secondary metabolites and biosynthesis of various plant secondary metabolites were downregulated in colostrum.
The differential abundance score of KEGG metabolic pathways for differential metabolites in milk identified by conventional metabolomics is shown in Figure 2. Differential metabolites of colostrum between experimental and control groups were mapped to three pathways, respectively, and metabolites in the colostrum of the experimental group were significantly upregulated in ABC transporters and purine metabolism, respectively, compared to metabolites in the colostrum of the control group (Figure 2A). Differential metabolites in the mature milk between the experimental and control groups were involved in five pathways and metabolites in the mature milk of the experimental group were significantly upregulated in sucrose metabolism and retrograde endocannabinoid signaling, respectively, compared to those metabolites in the mature milk of the control group (Figure 2B). The differential abundances of 15 metabolic pathways of conventional metabolites between the colostrum and mature milk from the experimental group had significant differences (Figure 2C), differential metabolites involved in purine metabolism, biosynthesis of cofactors, thermogenesis, aldosterone synthesis and secretion, glucagon signaling pathway were upregulated in colostrum, respectively. Fifteen metabolic pathways of differential metabolites between the colostrum and mature milk of the control group had significant differences in differential abundance (Figure 2D), and differential metabolites mapped to the biosynthesis of cofactors were upregulated in the colostrum.

4. Discussion

Milk is the major food source of suckling piglets, and it is one of the crucial factors affecting the survival and growth of suckling piglets [27,28]. Active ingredients in milk are thought to play important roles in the prevention and control of diarrhea during the suckling period [29,30]; however, the level of these active ingredients in milk is generally low and is not enough high for the control of diarrhea, so how to increase the concentration of these active ingredients in milk is an urgent issue to be addressed.
Studies reported that Chinese medicine is a good alternative to in-feed antibiotics because lots of active ingredients in Chinese medicine have antibacterial, anti-inflammatory, antivirus, anti-oxidative and immune-enhancing effects. Our previous study also found that a fermented compound Chinese medicine feed additive had good in vitro effects in inhibiting the growth of Staphylococcus aureus, Salmonella cholerae suis, Escherichia coli and Streptococcus agalactiae, because it contained high levels of active ingredients, such as gallic acid, ellagic acid, kaempferide and adenosine [23]. Results of this feeding experiment further showed that the colostrum and mature milk of sows supplemented with fermented compound Chinese medicine feed additive had higher levels of bioactive ingredients than the milk of sows without supplementation of fermented compound Chinese medicine feed additive, particularly, the milk of sows from the experimental group had significantly higher concentrations of quercetin (p < 0.05), pinocembrin (p < 0.05), chlorogenic acid (p < 0.01), methyl succinic acid (p < 0.01), L-tryptophan (p < 0.01), adenosine (p < 0.05), guanine (p < 0.05), arteannuin (p < 0.05), inosine (p < 0.05), guanosine (p < 0.05), benzene-1,2,4-triol (p < 0.05), hypoxanthine (p < 0.05), adenine (p < 0.05), ferulic acid (p < 0.05), echimidine N-oxide (p < 0.05), pogostone (p < 0.05), kynurenine (p < 0.05) and trehalose 6-phosphate (p < 0.05) than the milk of sows from the control group.
Previous studies evidenced that lots of active ingredients have functions in killing or inhibiting pathogens, alleviating inflammation, enhancing immunity and repairing intestinal barrier function. Quercetin [31], pinocembrin [32,33], pogostone [34], adenosine [35], ferulic acid [36], echimidine-N-oxide [37], purines including adenine, guanine, xanthine and hypoxanthine [38,39,40] have strong antibacterial or antifungal effects on diarrheal pathogens, such as Escherichia coli, Salmonella, Staphylococcus aureus, Dysentery bacilli, Pseudomonas aeruginosa, Streptococcus and Clostridioides difficile, in addition, hypoxanthine can speed the excretion of fecal harmful microbiota and toxic substances via shorting gastrointestinal transit time [41]. Pro-inflammatory substances can also cause diarrhea by impairing the intestinal mucosal barrier with pro-inflammatory cytokines [42], but quercetin [31], arteannuin and kynurenine [43,44], ferulic acid [45], purines [46], inosine [47,48], guanosine [49] and benzene-1,2,4-triol [50] can reduce diarrhea by increasing IFN-γ level or inhibiting the production of pro-inflammatory cytokines [48,51,52,53]. Supplementation with Perilla frutescens leaf to Holstein cows changed the composition of differential metabolites in the milk and many differential metabolites with antibacterial and anti-inflammatory effects had been identified [17], results of our experiment also indicated that maternal supplementation with fermented compound Chinese medicine feed additive accumulated lots of high-level differential metabolites in milk which have antibacterial, anti-inflammatory and immune enhancing properties.
Differential metabolites in colostrum between the experimental group and control group were significantly enriched in the KEGG pathways of flavone and flavanol biosynthesis, galactose metabolism, phenylpropanoid biosynthesis, stilbenoid and gingerol biosynthesis, flavonoid biosynthesis, ABC transporters and purine metabolism, respectively; the colostrum of sows from the experimental group had significantly higher enrichment abundance of differential metabolites than the colostrum of sows from the control group, this means that the colostrum of the experimental group had better antibacterial and anti-inflammatory effects than the colostrum of the control group; differential metabolites in the colostrum of the experimental group can produce other active ingredients in the digestive tract of suckling piglets via these enriched KEGG pathways. It is reported that quercetin can be metabolized into other flavonoids, such as quercitrin, isoquercitrin and myricetin when fermented with some bacteria [54,55]. Adenine, hypoxanthine and guanine can be converted to xanthine and further catabolized to uric acid and allantoin under the action of bacterial fermentation [56,57]. Differential metabolites in mature milk between the experimental group and control group were significantly enriched in the KEGG pathways of starch and sucrose metabolism, and retrograde endocannabinoid signaling; the mature milk of sows from the experimental group had a significantly higher enrichment abundance of differential metabolites than that of sows from the control group. In addition, trehalose-6-phosphate can be mapped onto the pathways of starch and sucrose metabolism and retrograde endocannabinoid signaling. It could be estimated that the mature milk of the experimental group had better functions than the mature milk of the control group in improving the digestion of starter feed and the gut health of suckling piglets; trehalose-6-phosphate can promote the rapid fermentation of carbohydrates, especially glucose and lactose through the pathway of starch and sucrose metabolism to produce volatile fatty acids [58], the rapid fermentation of carbohydrate is particularly important to suckling piglets during the ingestion of starter feed, because mature milk with high trehalose-6-phosphate can quickly lower the intestinal pH of piglets; this is beneficial to the control of gut pathogens and the digestion of nutrients in artificial diets. Retrograde endocannabinoid signaling takes part in the regulation of inflammatory factor release and gut permeability [59], and trehalose-6-phosphate identified in the mature milk of the experimental group is also involved in the pathway of retrograde endocannabinoid signaling, this implies that trehalose-6-phosphate can function in anti-inflammation and intestinal permeability maintenance. Maternal feeding with fermented compound Chinese medicine feed additive had impacts on KEGG pathways and pathway abundances of differential metabolites between colostrum and mature milk. Compared to without supplementation of fermented compound Chinese medicine feed additive, supplementation of fermented compound Chinese medicine feed additive to sows from d 104 of gestation to d 25 of lactation elevated the number of enriched KEGG pathways; increased the enrichment abundance of purine metabolism, the glucagon signaling pathway, aldosterone synthesis and secretion, and the thermogenesis of differential metabolites in colostrum metabolism, but decreased the enrichment abundance of protein digestion and absorption, biosynthesis of amino acid, D-amino acid metabolism and purine metabolism of differential metabolites in mature milk metabolism. Increasing the enrichment abundance of glucagon signaling and the thermogenesis pathways could metabolize nutrients to produce more heat to raise the cold resistance of newborn animals [60,61], it is very important for newborn piglets, because the increased cold resistance can decrease the morbidity of newborn piglets.

5. Conclusions

Maternal feeding with fermented Chinese medicine feed additive elevated the concentrations of functional ingredients in the colostrum and mature milk of sows, and most of these functional ingredients have anti-infectious, anti-inflammatory, anti-oxidative and immune-enhancing effects.

Author Contributions

Design, Y.H. and W.L.; Experiments, W.Z., L.D., H.W. and Z.L.; Data analysis, W.Z.; Manuscript writing, Y.H. and W.Z.; Writing review, W.L. and Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the grants from the Key Research and Development Plan of Jiangxi Province (20192BBFL60021).

Institutional Review Board Statement

The animal study protocol was approved by the Ethics Committee of Jiangxi Agricultural University (JXAULL-202122, 15/08/2021).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in [Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive].

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Animals 12 02879 g001 550
Figure 1. Differential abundance score of metabolic pathways of metabolites identified in milk using chinmedomics approach. (A): KEGG pathways enrichment of differential metabolites in colostrum between experimental group and control group. (B): KEGG pathways enrichment of differential metabolites in mature milk between experimental group and control group. (C): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of experimental group. (D): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of control group. * 0.01 < p <0.05, ** 0.001 < p <0.01, *** p < 0.001.
Figure 1. Differential abundance score of metabolic pathways of metabolites identified in milk using chinmedomics approach. (A): KEGG pathways enrichment of differential metabolites in colostrum between experimental group and control group. (B): KEGG pathways enrichment of differential metabolites in mature milk between experimental group and control group. (C): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of experimental group. (D): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of control group. * 0.01 < p <0.05, ** 0.001 < p <0.01, *** p < 0.001.
Animals 12 02879 g001
Animals 12 02879 g002 550
Figure 2. Differential abundance score of metabolic pathways of metabolites identified in milk using conventional metabolomics approach. (A): KEGG pathways enrichment of differential metabolites in colostrum between experimental group and control group. (B): KEGG pathways enrichment of differential metabolites in mature milk between experimental group and control group. (C): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of experimental group. (D): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of control group. * 0.01 < p < 0.05, ** 0.001 < p <0.01, *** p < 0.001.
Figure 2. Differential abundance score of metabolic pathways of metabolites identified in milk using conventional metabolomics approach. (A): KEGG pathways enrichment of differential metabolites in colostrum between experimental group and control group. (B): KEGG pathways enrichment of differential metabolites in mature milk between experimental group and control group. (C): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of experimental group. (D): KEGG pathways enrichment of differential metabolites between colostrum and mature milk of control group. * 0.01 < p < 0.05, ** 0.001 < p <0.01, *** p < 0.001.
Animals 12 02879 g002
Table
Table 1. Composition and nutrient levels of the basal diet (air-dry basis) %.
Table 1. Composition and nutrient levels of the basal diet (air-dry basis) %.
ItemsContent
Ingredients
Corn 60.00
Wheat bran 15.00
Soybean meal15.00
Rapeseed meal4.00
Fish meal2.00
Premix a4.00
Total 100.00
Nutrient levels b
Metabolizable energy (MJ/kg)11.91
Crude protein17.35
Ether extract 4.73
Crude fiber 4.12
Calcium 0.83
Total phosphorus 0.65
Lysine0.94
Methionine + Cystine 0.72
Threonine0.53
a Per kg of premix provided the following: VA 421,000 IU, VD 61,000 IU, VE 940 IU, VK3 125 mg, VB1 65 mg, VB2 230 mg, VB6 75 mg, VB12 1.0 mg, nicotinic acid 1 100 mg, D-pantothenic acid 680 mg, folic acid 45 mg, choline 12 g, Fe 1.5 g, Cu 0.8 g, Zn 1.6 g, Mn 85 g, I 8 mg, Co 30 mg, Se 7 mg, Ca 185 mg, P 18 mg, NaCl 100,000 mg, b Levels of metabolizable energy and amino acids were calculated values, while the others were measured values.
Table
Table 2. Differential metabolites in colostrum between experimental and control groups based on untargeted chinmedomics.
Table 2. Differential metabolites in colostrum between experimental and control groups based on untargeted chinmedomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites
in Colostrum Based on Chinmedomics(×10−3)		VIPp-ValueFold Change (A/B)
Experimental Group (A)Control Group (B)
Negative ion model
Flavonoids
Wogonin0.9962.7440.0351.3220.36378.400
Quercetin-3-O-galactoside0.9690.2860.0351.5520.2328.171
Quercetin0.8540.2570.0353.4440.0397.343
Isorhamnetin0.9990.4160.0631.0580.2126.603
Mulberrin0.8430.2030.0351.8680.3675.800
Pinocembrin0.8552.2840.0342.1880.03367.176
Daidzein0.98619.2799.1521.1370.5182.106
Corylin0.9010.7500.5392.0120.6271.392
Phenols
4-Nitrophenol0.8270.0970.0351.7780.3742.771
P-Anisic acid0.9105.0200.7411.5400.2666.775
6-Gingerol0.933837.143643.3691.9270.3491.301
Alkaloids
Thymidine0.86527.45926.1511.8530.8721.050
Phenylpropanoids
Chlorogenic acid0.8340.4900.0342.5320.00814.412
Methyl chlorogenate0.8358.1726.5471.4830.3551.248
Terpenoids
alpha-Hederin0.9630.3470.0351.6120.1839.914
Fatty Acyls
Citraconic acid0.98247.1620.0351.3210.3631347.486
Methyl hexadecanoate0.98774.30519.1871.8320.3593.873
Fatty acids
Methyl succinic acid0.95821.9300.0332.6860.008664.545
Pimelic acid0.80911.7116.6411.7990.2701.763
Organoheterocyclic compounds
L-Tryptophan0.851578.724232.2781.1350.0022.492
Organic acids and derivatives
Threonic acid0.97930.26417.1621.5250.6471.763
Citrate0.9985601.1155086.0451.8830.9001.101
Organic oxygen compounds
Melibiose1.000133.0840.0352.5580.0993802.400
Amino acid derivatives
Aspartate0.89018.3780.0352.0360.274525.086
Carbohydrates and derivatives
Glyceric acid0.9843.2740.0351.9750.36393.543
Aliphatics
Octyl gallate0.8880.5270.0351.2780.36115.057
Positive ion model
Alkaloids
Adenosine0.88336.1936.1891.0900.0155.848
L-Carnitine0.95945.65023.0092.4910.2361.984
Guanine1.000111.34733.4262.0840.0313.331
Guanosine0.99749.24932.9992.0860.2081.492
Trigonelline HCl0.9963.1302.3341.5780.2751.341
Jatrorrhizine0.9412.5992.3611.0040.5081.101
Palmatine0.9848.6838.0671.5020.2311.076
Phenols
Bergenin0.9362.3101.6541.2960.4291.397
Gallic acid0.90815.72214.6971.0660.9461.070
Terpenoids
Resibufogenin0.9971.6610.0031.4020.363553.667
Estradiol0.8350.1210.0031.3870.36440.333
Lovastatin0.9930.5600.4441.2850.5931.261
Neoandrogsinapixrapholide0.9474.3843.6052.0650.1531.216
Lindenenol0.8341.8341.5832.2090.1001.159
Curcumenol0.8706.6185.9741.8990.1531.108
Arteannuin0.9561.1030.9412.7780.0491.172
Organooxygen compounds
Pogostone0.8081.0570.0133.2450.24281.308
Amino acid derivatives
Isoleucine0.8813.7300.0031.4040.3631243.333
Aspartic acid0.9570.5960.0032.3130.178198.667
Lysine0.8561.5070.1301.2700.21611.592
L-Isoleucine0.953913.081209.3781.4020.3154.361
Arginine0.9415.9512.3211.1340.3282.564
Table
Table 3. Differential metabolites in mature milk between experimental and control groups based on untargeted chinmedomics.
Table 3. Differential metabolites in mature milk between experimental and control groups based on untargeted chinmedomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites in Mature milk Based on Chinmedomics (×10−3)VIPp-ValueFold Change
(C/D)
Experimental group (C)Control group (D)
Negative ion model
Phenols
Gallic acid0.990 3.6260.0431.6430.34984.326
4-hydroxybenzaldehyde1.000 1.6730.0431.0560.36338.907
Methyl gallate0.993 1.0870.0431.2020.36325.280
Orcinol0.928 0.2120.0431.2080.3624.930
Phenylpropanoids
Danshensu0.812 0.9910.0431.2020.36323.047
rosmarinic acid1.000 0.3180.0431.2060.3627.395
Sinapic acid0.979 0.1930.0431.9450.1644.488
Ferulic acid0.977 6.3752.0572.0920.0203.099
Xanthones
Gentisic acid0.840 1.2300.0431.2010.36328.605
Sesquiterpenoids
Abscisic acid0.972 0.2830.0431.0810.3606.581
Amino acid derivatives
N-Acetyl-DL-glutamic acid0.908 0.4360.0431.2050.36310.140
Positive ion model
Alkaloids
Boldine0.843 0.1240.0031.2200.36341.333
Securinine0.824 0.0370.0031.2180.36312.333
Actidione0.900 0.0220.0052.1880.0724.400
Echimidine N-oxide0.835 1.0100.7582.1260.0261.333
Flavonoids
Kaempferol0.995 0.0070.0031.1060.3502.333
Isoquercitrin0.822 0.0060.0031.0010.3422.000
Biochanin A0.977 3.3862.5871.1870.2631.309
Chalcones
Loureirin A0.952 0.0170.0031.2150.3645.667
Phenylpropanoids
Ethyl ferulate0.963 0.0700.0031.2370.36323.333
p-Coumaric acid0.976 0.1370.0391.1370.1333.513
Eudesmin0.837 0.9700.4511.3540.5522.151
Terpenoids
Grosheimin0.833 0.0170.0031.2540.3605.667
Reynosin0.855 0.0120.0031.0780.3584.000
Estradiol0.835 0.3120.1371.0680.5932.277
Andrographolide0.932 0.0250.0141.3590.3071.786
Sesquiterpenoids
Germacrone0.947 3.9492.8491.2410.2871.386
Organooxygen compounds
Pogostone0.807 0.4870.0033.3110.021162.333
Fatty acids
Chaulmoogric Acid0.920 1.6861.3511.8560.5491.248
Amino acid derivatives
Isoleucine0.881 1.5880.0031.2220.363529.333
Kynurenine0.928 1.0380.4332.0930.0122.397
Carboxylic acids and derivatives
L-Tyrosine0.914 1.1710.0031.3840.360390.333
Table
Table 4. Differential metabolites between colostrum and mature milk of experimental group based on untargeted chinmedomics.
Table 4. Differential metabolites between colostrum and mature milk of experimental group based on untargeted chinmedomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites in Experimental
Group Based on Chinmedomics (×10−3)		VIPp-ValueFold Change
(A/C)
Colostrum (A)Mature Milk (C)
Negative ion model
Flavonoids
Pinocembrin0.8551.1630.0451.0760.09125.844
Positive ion model
Phenols
Bergenin0.9362.3100.0032.6210.027770.000
Alkaloids
3-Furfuryl 2-pyrrolecarboxylate0.8870.8060.0032.3640.010268.667
Boldine0.84311.8920.1242.5540.05295.903
Adenosine0.88360.9198.4361.8040.2157.221
Guanosine0.99749.24912.5572.3970.0083.922
Guanine1.0000.0830.02522.2810.0173.290
Nicotinamide0.92075.33926.1651.4070.3212.879
Jatrorrhizine0.9412.5992.0111.2200.1371.292
Palmatine0.9848.6836.8612.0290.0061.266
Terpenoids
Beta-Caryophyllene alcohol0.87246.8660.0922.6280.080509.413
Cortodoxone0.9010.2100.0031.0130.36470.000
Celastrol0.8647.8652.2721.9530.0163.462
Lindenenol0.8341.8341.4811.7580.0271.238
Artemisinin0.8732.5942.1201.9060.0121.224
Curcumenol0.8706.6185.4821.7400.0331.207
Aucubin0.8820.2320.0032.6520.02077.333
Phenylpropanoids
Suberosin0.8980.8780.4561.9470.0511.925
Eudesmin0.8371.7680.9701.7290.4121.8230
Coumarins and derivatives
7-Hydroxycoumarin0.9091.1830.8411.0260.1391.407
Phospholipids
Monolinolein0.9570.5480.4411.0020.7611.243
Organoheterocyclic compounds
L-Tryptophan0.8460.2450.0861.0600.2782.846
Fatty acids
Chaulmoogric acid0.9202.0061.6861.3760.8321.190
Table
Table 5. Differential metabolites between colostrum and mature milk of control group based on untargeted chinmedomics.
Table 5. Differential metabolites between colostrum and mature milk of control group based on untargeted chinmedomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites in Control
Group Based on Chinmedomics (×10−3)		VIPp-ValueFold Change (B/D)
Colostrum (B)Mature Milk (D)
Negative Ion Model
Organic acids and derivatives
Threonic acid0.97917.1625.2601.0410.0163.263
Positive ion model
Phenols
Bergenin0.9361.6540.0033.0860.002551.333
Alkaloids
Boldine0.8437.3220.0033.0830.0162440.667
3-Furfuryl 2-pyrrolecarboxylate0.8870.7610.0032.8000.013253.667
L-Phenylalanine0.9802.5460.3641.6550.1376.995
Nicotinamide0.92058.74723.6221.3720.2982.487
Adenosine0.88319.1298.0031.0880.2002.390
Guanosine0.99732.99919.1681.4480.1581.722
Guanine1.00054.50232.3121.4310.1321.687
Echimidine N-oxide0.8351.0460.7581.7850.0741.380
Terpenoids
Beta-Caryophyllene alcohol0.87235.3700.0032.3400.243116.733
Judaicin 0.9430.3040.0031.0620.363101.333
Cortodoxone0.9010.0650.0031.0470.36321.667
Dehydrocostus lactone0.9770.0260.0031.0450.3648.667
Aucubin0.8820.3600.0032.4760.033120.000
Celastrol0.8648.7323.3452.3520.0022.610
Phenylpropanoids
Eudesmin0.8372.2620.4512.5620.0015.016
Suberosin0.8980.7900.5871.5060.1901.346
Coumarins and derivatives
7-Hydroxycoumarin0.9091.0470.7431.3650.2671.409
Organooxygen compounds
Pogostone0.8080.0130.0031.0430.3644.333
Benzene and substituted derivatives
Phenethylacetate0.8680.0220.0031.6510.1827.333
Amino acid derivatives
Kynurenine0.9281.3250.4331.4910.2463.060
Table
Table 6. Differential metabolites in colostrum between experimental and control groups based on conventional untargeted metabolomics.
Table 6. Differential metabolites in colostrum between experimental and control groups based on conventional untargeted metabolomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites
in Colostrum Based on Conventional Untargeted Metabolomics		VIPp-ValueFold Change (A/B)
Experimental Group (A)Control Group (B)
Negative ion model
Organic acids and derivatives
L-Phenylalanine0.9591.7120.3931.0010.2524.356
Succinic acid0.9650.7100.3011.2200.3222.359
Maleic acid0.9950.5180.3721.2030.4311.392
Citric acid0.98994.80871.1971.4900.1181.332
Glycine0.9940.0430.0321.2950.2031.344
Malonic acid0.9572.4302.0941.0690.3191.160
Pyruvic acid0.9873.4973.0471.1060.4661.148
Creatinine0.8052.7022.4201.1450.5561.117
Acrylic acid0.9941.5191.3861.0570.4661.096
Organoheterocyclic compounds
2-Hydroxyxanthone0.9700.0350.0291.5430.1441.207
Quinolinic acid0.9130.4930.4041.0390.2121.220
Pyrrole-2-carboxylic acid0.9970.7640.6361.1510.4521.201
Guanine0.9850.8460.3581.2640.1632.363
Organooxygen compounds
D-Ribulose 5-phosphate0.9031.0450.8141.1160.2421.284
Organic oxygen compounds
N-Acetylneuraminic acid0.9880.7620.4091.4920.2091.863
Myo-Inositol0.91120.55816.2711.2080.2291.263
6-Phosphogluconic acid0.8410.5470.2421.3280.1362.260
Nucleosides, nucleotides, and analogues
Uridine diphosphate glucuronic acid0.8422.2571.0361.2090.2582.179
Uridine diphosphategalactose0.8824.9692.3211.3720.1072.141
Uridine 5′-diphosphate0.9030.4230.2041.5750.1582.074
Inosine0.9622.5881.3612.4240.0171.902
Guanosine0.9441.0240.5811.9120.0431.762
Uridine 5′-monophosphate0.86732.04820.5221.0470.2601.562
2-Methylguanosine0.8650.0340.0241.6540.1431.417
S-Adenosylhomocysteine0.8360.0370.0271.9510.1211.370
Lipids and lipid-like molecules
Tetradecanedioic acid0.9770.0190.0131.2980.2161.462
9-Decenoic acid0.9980.0310.0181.9910.1041.722
12-Methyltridecanoic acid0.9850.5070.2941.5620.1391.724
Heptanoic acid0.8560.1710.1341.6380.1371.276
FA(18:2)1.0000.8170.2161.0420.3733.782
LPC(16:0)0.8910.3900.1061.0860.2673.679
LPC(18:1)0.8500.2340.0821.8290.1802.854
Dodecanedioic acid0.9120.0250.0121.5790.1582.083
Benzenoids
benzene-1,2,4-triol0.9280.3620.2192.2410.0371.653
Butylparaben0.9790.1080.0861.5500.1251.256
4-Nitrophenol1.0000.4100.3431.8110.1051.195
N-acetyl-5-aminosalicylic acid0.8910.0760.0241.4230.1233.167
Positive ion model
Organic acids and derivatives
Phenylalanylproline0.9690.1120.0111.4600.18610.182
ACar(18:1)0.8830.6840.0751.2270.3489.120
ACar(14:0)0.9590.1290.0141.2820.3319.214
ACar(6:1)0.9970.1580.0521.9180.0913.038
ACar(6:0)0.9931.0500.6411.2670.2241.638
L-Glutamine0.9950.1080.0141.3890.2477.714
1-Methylhistidine0.9430.0910.0141.4630.2416.500
ACar(16:1)0.9600.0870.0141.4020.3116.214
ACar(8:0)0.9790.0320.0062.4780.0915.333
L-Tryptophan0.9780.3770.0891.0150.2854.236
Pipecolic acid0.9830.1800.1721.0180.5231.047
Elenaic acid0.8710.0420.0221.1800.1981.909
Proline betaine0.9410.3010.2401.1870.4421.254
Betaine0.9992.4902.1441.0660.5731.161
Palmitoylethanolamide0.8970.3090.1051.3420.2592.943
Organic nitrogen compounds
L-Carnitine0.9960.4050.2311.8690.1781.753
Organic oxygen compounds
Adenosine 2’-phosphate0.9431.0020.4441.1700.4482.257
Organoheterocyclic compounds
Pyridoxal0.9960.1430.1171.4320.1851.222
Hypoxanthine1.0001.4260.9072.0270.0351.572
3-Pyridinebutanoic acid0.9731.4160.3311.3240.2674.278
Adenine0.9980.0810.0211.8250.0173.857
Nucleosides, nucleotides, and analogues
5′-Methylthioadenosine0.9880.0230.0101.0060.2452.300
Guanosine diphosphate0.9760.5750.2661.7320.1572.162
Deoxyguanosine1.0000.0980.0591.5340.1911.661
Lipids and lipid-like molecules
L-Palmitoylcarnitine0.9571.8130.1211.1200.34414.983
LysoPE(20:1(11Z)/0:0)0.9310.0190.0051.3820.2253.800
LysoPE(18:1(11Z)/0:0)0.8981.0320.5391.9810.0841.915
LysoPE(16:1(9Z)/0:0)0.9700.0250.0101.3590.2452.500
PE(14:1(9Z)/14:0)0.9120.0250.0091.5080.1722.778
LPC(16:1)0.8880.1250.0561.1450.3402.232
LPC(18:0)0.8790.1450.0691.7900.2102.101
LPE(18:0)0.8912.6161.2511.7360.1302.091
Oleamide0.9920.8140.3961.0270.2782.056
Glycerol tripropanoate0.9990.0340.0191.4220.1551.789
Cohibin C0.8310.2180.1891.1160.4461.153
Stearoylcarnitine0.9250.5980.0821.4560.2767.293
L-Acetylcarnitine0.92914.56711.4101.1090.3371.277
Benzenoids
Dibutyl phthalate0.9980.0940.0891.2880.2871.056
p-Aminobenzoic acid0.9830.1260.0511.2370.3672.471
Table
Table 7. Differential metabolites in mature milk between experimental and control groups based on conventional untargeted metabolomics.
Table 7. Differential metabolites in mature milk between experimental and control groups based on conventional untargeted metabolomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites
in Mature Milk Based on Conventional Untargeted Metabolomics		VIPp-ValueFold Change (C/D)
Experimental Group (C)Control Group (D)
Negative ion model
Organic acids and derivatives
Glycolic acid1.0002.0251.7681.1670.3221.145
3-Sialyl-N-acetyllactosamine0.8950.0810.0161.9300.3375.063
N-Acetylneuraminic acid0.9880.0470.0212.3550.0962.238
Trehalose 6-phosphate0.9480.5520.3452.3770.0471.600
Gluconic acid0.9341.1450.8931.0090.5871.282
Organoheterocyclic compounds
Pyrrole-2-carboxylic acid0.9971.0810.7821.7900.2211.382
Nucleosides, nucleotides, and analogues
Uridine 5′-monophosphate0.86714.5777.3881.2060.2751.973
S-Adenosylhomocysteine0.8360.0150.0101.0940.4901.500
Lipids and lipid-like molecules
SHexCer(d30:3)1.0000.7360.0171.4450.36843.294
FA(20:5)1.0000.0390.0172.2050.1652.294
Dihydrojasmonic acid0.9680.1550.1211.2320.4581.281
Benzenoids
4-Nitrophenol1.0000.5200.4471.6420.3111.163
benzene-1,2,4-triol0.9280.4690.3631.9380.0971.292
Positive ion model
Organic acids and derivatives
O-Acetylserine0.9900.0610.0461.7810.1451.326
Oxypinnatanine0.9950.1250.1121.6410.2551.116
Organic oxygen compounds
Falcarinone0.9920.2310.1771.0660.2831.305
N,O-Didesmethylvenlafaxine0.9970.4860.4411.8260.1221.102
L-Gulose0.9860.4360.3881.1280.6941.124
Organic nitrogen compounds
Choline1.0001.1340.8202.2120.1101.383
Organoheterocyclic compounds
5-Methyl-2(3H)-furanone0.9560.3160.3031.0780.6211.043
Lipids and lipid-like molecules
Montecristin0.8370.0310.0241.5930.4261.292
Ginkgolide J0.9580.0390.0321.2950.5011.219
Nucleosides, nucleotides, and analogues
Guanosine0.9970.1510.1391.0990.7191.086
Table
Table 8. Differential metabolites between colostrum and mature milk of experimental group based on conventional untargeted metabolomics.
Table 8. Differential metabolites between colostrum and mature milk of experimental group based on conventional untargeted metabolomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites in Experimental Group Based on Conventional Untargeted MetabolomicsVIPp-ValueFold Change
(A/C)
Colostrum (A)Mature Milk (C)
Negative ion model
Organic acids and derivatives
Citric acid0.98994.80847.7511.3410.0101.985
D-Alanine0.9992.9262.0861.1620.0241.403
N-Acetylneuraminic acid0.9880.7620.0471.7870.03116.213
6-Phosphogluconic acid0.8410.5470.0351.3260.02815.629
3′-Sialyllactose0.90813.6311.9171.6950.0087.111
3-Sialyl-N-acetyllactosamine0.8950.5320.0811.4330.0446.568
Gluconic acid0.9342.8011.1451.3390.0302.446
Organoheterocyclic compounds
Riboflavin0.9680.1700.0151.6190.07211.333
Lipids and lipid-like molecules
PI(18:2/18:2)0.8732.9032.0971.0270.1381.384
PI(18:1/20:3)0.87623.7023.7811.7130.0006.269
PI(18:0/20:3)0.8894.4291.1151.6960.0013.972
PI(18:0/22:6)0.8250.2090.0631.5510.0433.317
PI(18:0/18:1)0.9152.4681.1891.2690.0852.076
PI(18:0/18:2)0.88625.13312.1841.3070.0782.063
OxPI(18:0/18:1 + 3O)0.9570.4880.0941.7320.0065.191
OxPI(16:0/18:1 + 3O)0.9544.8803.6191.0360.1191.348
FA(19:4)1.0000.0570.0061.1130.3139.500
FA(16:2)1.00020.1750.8941.0570.31522.567
Nucleosides, nucleotides, and analogues
2-Methylguanosine0.8650.0340.0181.2020.0211.889
Positive ion model
Organic acids and derivatives
Elenaic acid0.8710.0420.0071.7130.0436.000
Palmitoylethanolamide0.8970.3090.0841.0890.2183.679
ACar(6:1)0.9970.1580.0151.6970.03710.533
trans-Aconitic acid0.8030.1930.0521.7580.0043.712
Organic oxygen compounds
Picraquassioside A0.9940.1580.0091.8880.03917.556
Pseudouridine 5′-phosphate0.9213.1030.6611.2980.0274.694
L-Gulose0.9861.7440.4361.8770.0144.000
Falcarinone0.9920.8930.2311.6260.0433.866
N-Acetylmannosamine0.9270.3880.1011.6030.0353.842
Organoheterocyclic compounds
Isolinderanolide0.8540.0630.0161.0520.3033.938
Guanine0.9990.9330.3191.2430.1222.925
Hypoxanthine1.0001.4260.7111.4680.0062.006
Thiamine0.9970.6900.1871.4420.0383.690
Safrole0.9743.0802.8591.2840.0221.077
Organohalogen compounds
Chloral hydrate0.9070.0870.0181.7360.0234.833
Lipids and lipid-like molecules
13-HOTE0.8130.2920.0101.1290.30729.200
Caryoptosidic acid0.8330.0140.0011.9110.01214.000
Stearoylcarnitine0.9250.5980.0471.2200.24812.723
LPC(22:4)0.8690.0290.0031.3840.2869.667
LPC(22:5)0.8340.0280.0051.6840.1225.600
L-Acetylcarnitine0.92914.5673.8521.8030.0123.782
Asitrilobin C0.8130.1520.0691.6870.0012.203
Turanose1.0000.8490.7771.0620.9131.093
Nucleosides, nucleotides, and analogues
Cyclic AMP0.8970.0410.0021.2200.06620.500
Cyclic GMP0.9240.1940.0231.6710.0208.435
N6-Methyladenosine0.9990.7300.1581.5490.0204.620
Guanosine0.9970.5410.1331.7870.0024.068
S-Adenosylhomocysteine0.8610.0450.0121.5920.0003.750
1-Methylguanosine0.9920.0510.0231.7170.0012.217
Inosine0.9990.6150.2891.5050.0072.128
7-Methylinosine0.9600.0140.0071.5560.0062.000
Benzenoids
N-cis-Feruloyltyramine0.9920.0240.0141.2580.0411.714
Table
Table 9. Differential metabolites between colostrum and mature milk of control group based on conventional untargeted metabolomics.
Table 9. Differential metabolites between colostrum and mature milk of control group based on conventional untargeted metabolomics.
MS2 NameMS2 ScoreAverage of Differential Metabolites in Control Group Based on Conventional Untargeted MetabolomicsVIPp-ValueFold Change
(B/D)
Colostrum (B)Mature Milk (D)
Negative ion model
Organic acids and derivatives
Indoxyl sulfate0.9820.1390.0841.0780.3391.655
Citric acid0.98971.19747.9041.0240.0141.486
Organic oxygen compounds
3-Sialyl-N-acetyllactosamine0.8950.5200.0161.4810.00932.500
N-Acetylneuraminic acid0.9880.4090.0211.5780.00219.476
6-Phosphogluconic acid0.8410.2420.0431.3230.0235.628
Trehalose 6-phosphate0.9481.6810.3451.2680.0484.872
Gluconic acid0.9342.7440.8931.2050.0033.073
Organoheterocyclic compounds
Dehydroascorbic acid0.9635.5682.9821.2980.0041.867
Lipids and lipid-like molecules
PI(18:1/20:3)0.87622.8684.6061.4790.0014.965
PI(18:0/22:6)0.8250.2400.0811.3650.0212.963
PI(18:0/20:3)0.8894.4121.2851.4220.0083.433
PI(18:0/18:2)0.88624.81313.4221.1690.0511.849
Positive ion model
Organic acids and derivatives
ACar(6:1)0.9970.0520.0231.1250.0552.261
ACar(5:0)1.00041.31522.3191.1470.0671.851
Elenaic acid0.8710.0220.0081.1130.1032.750
Organic oxygen compounds
Picraquassioside A0.9940.1220.0061.6910.00820.333
Pseudouridine 5′-phosphate0.9212.3900.1761.5010.02113.580
Falcarinone0.9921.0170.1771.8180.0005.746
L-Gulose0.9862.1050.3881.0650.0005.425
N-Acetylmannosamine0.9270.4340.0931.7940.0034.667
3′-Sialyllactose0.9240.3810.0351.7460.00210.886
2-Carboxyarabinitol 5-phosphate0.8880.1520.0701.2930.0082.171
Organoheterocyclic compounds
Riboflavin0.9950.8510.0361.6810.01223.639
Guanine0.9990.5400.2681.2350.0582.015
Thiamine0.9970.4670.2401.1130.0471.946
Organohalogen compounds
Chloral hydrate0.9070.1050.0151.6080.0017.000
Nucleosides, nucleotides, and analogues
Cyclic AMP0.8970.0300.0011.0740.09530.000
Cyclic GMP0.9240.1790.0261.6550.0196.885
S-Adenosylhomocysteine0.8610.0370.0091.7170.0004.111
N6-Methyladenosine0.9990.5510.1481.5310.0153.723
Guanosine monophosphate0.9930.1330.0381.2170.0773.500
7-Methylinosine0.9600.0180.0091.7220.0002.000
1-Methylguanosine0.9920.0490.0261.8220.0001.885
Guanosine0.9970.3200.2091.0050.2521.531
Lipids and lipid-like molecules
LPC(22:4)0.8690.0080.0021.1510.0794.000
L-Acetylcarnitine0.92911.4103.5901.6840.0013.178
Asitrilobin C0.8130.1540.0691.5630.0012.232
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Zou, W.; Deng, L.; Wu, H.; Liu, Z.; Lu, W.; He, Y. Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive. Animals 2022, 12, 2879. https://doi.org/10.3390/ani12202879

AMA Style

Zou W, Deng L, Wu H, Liu Z, Lu W, He Y. Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive. Animals. 2022; 12(20):2879. https://doi.org/10.3390/ani12202879

Chicago/Turabian Style

Zou, Wanjie, Linglan Deng, Huadong Wu, Zhiyong Liu, Wei Lu, and Yuyong He. 2022. "Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive" Animals 12, no. 20: 2879. https://doi.org/10.3390/ani12202879

APA Style

Zou, W., Deng, L., Wu, H., Liu, Z., Lu, W., & He, Y. (2022). Untargeted Metabolomics Profiling Reveals Beneficial Changes in Milk of Sows Supplemented with Fermented Compound Chinese Medicine Feed Additive. Animals, 12(20), 2879. https://doi.org/10.3390/ani12202879

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